diff --git a/ui/qrel-details.jsonl b/ui/qrel-details.jsonl
index 0c46050..573def2 100644
--- a/ui/qrel-details.jsonl
+++ b/ui/qrel-details.jsonl
@@ -1,101 +1,154 @@
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diff --git a/ui/run-details.jsonl b/ui/run-details.jsonl
index 5b805ee..8efc9cf 100644
--- a/ui/run-details.jsonl
+++ b/ui/run-details.jsonl
@@ -1,101 +1,154 @@
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improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and cross-language information retrieval ...", "weights": [[8, 17, 1.0], [59, 68, 1.0], [69, 78, 1.0], [79, 92, 1.0], [193, 202, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.18": {"snippet": "...mage retrieval from multimedia databases, which improves both the effectiveness and efficiency of traditional CBIR by exploring secondary media. We perform retrieval in a two-stage fashion: first rank...", "weights": [[8, 17, 1.0], [69, 82, 1.0], [159, 168, 1.0]]}, "2008.cikm_conference-2008.183": {"snippet": "... the effectiveness of the opinion retrieval and opinion polarity classification\n\n\n ABSTRACTOpinion retrieval is a document retrieving and ranking process. A relevant document must be relevant to the q...", "weights": [[8, 21, 1.0], [37, 46, 1.0], [102, 111, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.2": {"snippet": "...ms \u2192 Retrieval effectiveness; Learning to rank; Information retrieval query processing;Additional Key Words and Phrases: Information systems, information retrieval, learning to rank, retrieval system ...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [63, 72, 1.0], [157, 166, 1.0], [186, 195, 1.0], [196, 202, 1.0]]}, "P92-1014": {"snippet": "...tion retrieval system which uses advanced natural language processing techniques to enhance the effectiveness of traditional key-word based document retrieval. The backbone of our system is a statisti...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [99, 112, 1.0], [152, 161, 1.0], [183, 189, 1.0]]}, "2002.ipm_journal-ir0volumeA38A1.0": {"snippet": "Improving the retrieval effectiveness of very short queries", "weights": [[0, 9, 1.0], [14, 23, 1.0], [24, 37, 1.0]]}, "2017.ipm_journal-ir0volumeA53A4.6": {"snippet": "...arch effectiveness for different kinds of users? We observed four different kinds of information seekers using an experimental information retrieval system: (1) search novices; (2) domain experts; (3)...", "weights": [[8, 21, 1.0], [142, 151, 1.0], [152, 158, 1.0]]}, "2005.trec_conference-2005.26": {"snippet": "... our retrieval system scales up to terabyte-scale collections. We found that our retrieval system can handle 25 million documents, although in terms of indexing time we are approaching the limits of a...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [84, 93, 1.0], [94, 100, 1.0]]}, "2017.sigirconf_conference-2017.144": {"snippet": "... the effectiveness of information retrieval systems by augmenting documents' term probability estimates with those of similar documents, producing higher quality document representations. We propose a...", "weights": [[8, 21, 1.0], [37, 46, 1.0]]}, "2014.cikm_conference-2014.212": {"snippet": "...mean retrieval effectiveness (e.g., MAP) may sacrifice the retrieval stability across queries, implying a tradeoff between effectiveness and stability. The evaluation of both effectiveness and stabili...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [62, 71, 1.0], [126, 139, 1.0], [178, 191, 1.0]]}, "2012.sigirconf_conference-2012.134": {"snippet": "...tion retrieval\n\n\n ABSTRACTMany information retrieval (IR) techniques have been proposed to improve the performance, and some combinations of these techniques has been demonstrated to be effective. How...", "weights": [[8, 17, 1.0], [46, 55, 1.0]]}, "2007.ipm_journal-ir0volumeA43A1.13": {"snippet": "...tion retrieval systems to effectively search for and locate the required information. In this study, we present several improvements over the two main bottlenecks in a distributed information retrieva...", "weights": [[8, 17, 1.0], [195, 204, 1.0]]}, "2008.ipm_journal-ir0volumeA44A3.9": {"snippet": "...tion retrieval systems\n\n\n AbstractNowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval techniques (particularly images retrieval) have beco...", "weights": [[8, 17, 1.0], [141, 150, 1.0], [183, 192, 1.0]]}, "1998.sigirconf_conference-98.33": {"snippet": "...tion retrieval model, in a workable system. First order logic and standard information retrieval techniques are combined together, to the same effect: obtaining an expressive system, able to accuratel...", "weights": [[8, 17, 1.0], [39, 45, 1.0], [90, 99, 1.0], [178, 184, 1.0]]}, "1990.jasis_journal-ir0volumeA41A5.0": {"snippet": "... the effectiveness of information retrieval systems using simulated queries", "weights": [[8, 21, 1.0], [37, 46, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...heir retrieval strategies. To be viable as a laboratory tool, a collection must reliably rank different retrieval variants according to their true effectiveness. In particular, the relative effectiven...", "weights": [[8, 17, 1.0], [107, 116, 1.0], [150, 163, 1.0], [193, 206, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "... the effectiveness of retrieval models from the documents retrievability point of view. We focus particularly on the retrieval bias of different retrieval models, and try to examine to what extent thi...", "weights": [[8, 21, 1.0], [25, 34, 1.0], [120, 129, 1.0], [148, 157, 1.0]]}, "2008.cikm_conference-2008.59": {"snippet": "...tion Retrieval (IR) has long focused on effectiveness and efficiency. However, new and emerging access tasks now demand alternative evaluation measures which go beyond this traditional view. A retriev...", "weights": [[8, 17, 1.0], [43, 56, 1.0], [196, 205, 1.0]]}, "2004.clef_workshop-2004w.28": {"snippet": "...e to Retrieval Effectiveness?\n\n\n Named entities in topics are a major factor contributing to the quality of retrieval results. In this paper, we report on an analysis on the correlation between the nu...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [111, 120, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.12": {"snippet": "...tion retrieval systems offering automated assistance could greatly improve search effectiveness by suggesting or implementing assistance automatically. A critical issue in designing such systems is de...", "weights": [[8, 17, 1.0], [85, 98, 1.0]]}, "1999.cikm_conference-99.43": {"snippet": "...ious retrieval approaches and query representations can improve search effectiveness. Today, many me&search engines exist which combine the results of various search engines in the hopes of improving ...", "weights": [[8, 17, 1.0], [74, 87, 1.0], [193, 202, 1.0]]}, "2012.wwwconf_conference-2012c.84": {"snippet": "...ning system for unrecognized drug side effects discovery based on the text information gathered from the Internet. The system can automatically build a knowledge base for drug side effects by integrat...", "weights": [[8, 14, 1.0], [122, 128, 1.0]]}, "1989.ipm_journal-ir0volumeA25A4.2": {"snippet": "Improving the effectiveness of retrieval systems by information structures", "weights": [[0, 9, 1.0], [14, 27, 1.0], [31, 40, 1.0]]}, "2009.cikm_conference-2009.277": {"snippet": "... for improving retrieval effectiveness, but the noise in the top retrieved documents still can cause topic drift problem that affects the performance of certain topics. By viewing a document as an int...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [28, 41, 1.0]]}, "W05-0704": {"snippet": "...n IR effectiveness and that context sensitive morphology further improves retrieval effectiveness, but the improvement is not statistically significant. Furthermore, the improvement obtained by the us...", "weights": [[8, 21, 1.0], [77, 86, 1.0], [87, 100, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2001.ipm_journal-ir0volumeA37A4.0": {"snippet": "Retrieval effectiveness on the web", "weights": [[0, 9, 1.0], [10, 23, 1.0]]}, "2014.cikm_conference-2014.9": {"snippet": "...ween Retrieval Bias and Retrieval Performance\n\n\n ABSTRACTRetrievability provides an alternative way to assess an Information Retrieval (IR) system by measuring how easily documents can be retrieved. R...", "weights": [[8, 17, 1.0], [27, 36, 1.0], [128, 137, 1.0], [143, 149, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...good retrieval performance is closely related to the use of various retrieval heuristics. We connect the weaknesses and strengths of a retrieval function with its implementations of these retrieval he...", "weights": [[8, 17, 1.0], [71, 80, 1.0], [138, 147, 1.0], [191, 200, 1.0]]}, "2010.irfc_conference-2010.7": {"snippet": "...y in Effectiveness of a Retrieval System", "weights": [[8, 21, 1.0], [27, 36, 1.0], [37, 43, 1.0]]}, "2004.cikm_conference-2004.48": {"snippet": "...hile improving retrieval effectiveness. The algorithm is efficient and scalable, suited for retrieval systems managing large document collections.", "weights": [[8, 17, 1.0], [18, 27, 1.0], [28, 41, 1.0], [95, 104, 1.0]]}, "2017.airs_conference-2017.1": {"snippet": "Improving Retrieval Effectiveness for Temporal-Constrained Top-K Query Processing", "weights": [[0, 9, 1.0], [10, 19, 1.0], [20, 33, 1.0]]}, "O07-2010": {"snippet": "...\u4e4b\u7814\u7a76 (Improving Retrieval Effectiveness by Document Reranking and Local Expansion) [In {C}hinese]", "weights": [[8, 17, 1.0], [18, 27, 1.0], [28, 41, 1.0]]}, "2018.ictir_conference-2018.20": {"snippet": "...d by retrieval systems in terms of Information Theory. The proposed framework allows to formalize: (i) system effectiveness as an information theoretic similarity between system outputs and human asse...", "weights": [[8, 17, 1.0], [106, 112, 1.0], [113, 126, 1.0], [174, 180, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A2.12": {"snippet": "...s of improving the effectiveness of current information retrieval systems. This aim can be achieved by accomplishing numerous supporting objectives.", "weights": [[8, 17, 1.0], [22, 35, 1.0], [59, 68, 1.0]]}, "O01-2005": {"snippet": "Improving the Effectiveness of Information Retrieval with Clustering and Fusion\n\n\n Fusion and clustering are two approaches to improving the effectiveness of information retrieval. In fusion, ranked l...", "weights": [[0, 9, 1.0], [14, 27, 1.0], [43, 52, 1.0], [127, 136, 1.0], [141, 154, 1.0], [170, 179, 1.0]]}, "1998.sigirconf_conference-98.22": {"snippet": "... our system that included a 3-D visualization.The questions we investigated in the context of this work were:Can we build a system for the aspect retrieval task that is more effective than a basic ret...", "weights": [[8, 14, 1.0], [127, 133, 1.0], [149, 158, 1.0], [200, 209, 1.0]]}, "2001.sigirconf_conference-2001.5": {"snippet": "...d in improving the effectiveness of information retrieval systems. Techniques such as relevance feedback, thesaural expansion, and pivoting all provide better quality responses to queries when tested ...", "weights": [[8, 17, 1.0], [22, 35, 1.0], [51, 60, 1.0]]}, "2011.sigirconf_conference-2011.13": {"snippet": "...nked retrieval\n\n\n ABSTRACTThere is a fundamental tradeoff between effectiveness and efficiency when designing retrieval models for large-scale document collections. Effectiveness tends to derive from ...", "weights": [[8, 17, 1.0], [69, 82, 1.0], [113, 122, 1.0], [168, 181, 1.0]]}, "N07-1028": {"snippet": "...tion retrieval systems are frequently required to handle long queries. Simply using all terms in the query or relying on the underlying retrieval model to appropriately weight terms often leads to ine...", "weights": [[8, 17, 1.0], [139, 148, 1.0]]}, "2004.cikm_conference-2004.47": {"snippet": "...tion retrieval systems. Each system demonstrated improved effectiveness with the use of blind feedback, in which the results of a preliminary retrieval step were used to augment the efficacy of a seco...", "weights": [[8, 17, 1.0], [32, 38, 1.0], [61, 74, 1.0], [145, 154, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [196, 202, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.2": {"snippet": "...ment retrieval behaviour. In particular relevance feedback query-expansion methods, which are often effective for improving electronic text retrieval, are observed to be less reliable for retrieval of...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [143, 152, 1.0], [191, 200, 1.0]]}, "1972.ipm_journal-ir0volumeA8A5.1": {"snippet": "... the effectiveness of an on-line, natural language retrieval system", "weights": [[8, 21, 1.0], [54, 63, 1.0], [64, 70, 1.0]]}, "2021.mrqa-1.16": {"snippet": "...ense retrieval has been shown to be effective for Open Domain Question Answering, surpassing sparse retrieval methods like BM25. One such model, REALM, (Guu et al., 2020) is an end-to-end dense retrie...", "weights": [[8, 17, 1.0], [103, 112, 1.0], [197, 206, 1.0]]}, "2007.tois_journal-ir0volumeA26A1.4": {"snippet": "...ment retrieval\n\n\n Due to the existence of large numbers of legacy documents (such as old books and newspapers), improving retrieval effectiveness for OCR'ed documents continues to be an important prob...", "weights": [[8, 17, 1.0], [115, 124, 1.0], [125, 134, 1.0], [135, 148, 1.0]]}, "2011.ecir_conference-2011.27": {"snippet": "...s of Retrieval Effectiveness", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2004.ecir_conference-2004.21": {"snippet": "Improving Retrieval Effectiveness by Reranking Documents Based on Controlled Vocabulary", "weights": [[0, 9, 1.0], [10, 19, 1.0], [20, 33, 1.0]]}, "2008.tois_journal-ir0volumeA27A1.1": {"snippet": "...t of retrieval effectiveness\n\n\n A range of methods for measuring the effectiveness of information retrieval systems has been proposed. These are typically intended to provide a quantitative single-val...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [72, 85, 1.0], [101, 110, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.7": {"snippet": "...tion retrieval\n\n\n a b s t r a c tThe Condorcet fusion is a distinctive fusion method and was found useful in information retrieval. Two basic requirements for the Condorcet fusion to improve retrieval...", "weights": [[8, 17, 1.0], [124, 133, 1.0], [194, 203, 1.0]]}, "1991.ipm_journal-ir0volumeA27A23.1": {"snippet": "... the effectiveness of retrieval algorithms", "weights": [[8, 21, 1.0], [25, 34, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A4.15": {"snippet": "...ated retrieval, that is, the retrieval of blog entries which contain opinions about a topic. There has been a remarkable amount of work towards the improvement of the effectiveness of the opinion retr...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [170, 183, 1.0], [199, 208, 1.0]]}, "2015.ipm_journal-ir0volumeA51A5.7": {"snippet": "... the effectiveness of a non-deterministic system with a deterministic or another non-deterministic system. In addition, we present a statistical best-practice that can be used to safely show how a non...", "weights": [[8, 21, 1.0], [45, 51, 1.0], [102, 108, 1.0]]}, "1973.jasis_journal-ir0volumeA24A2.1": {"snippet": "...e of retrieval effectiveness", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2005.ipm_journal-ir0volumeA41A1.7": {"snippet": "... the retrieval effectiveness of information retrieval systems, however the designing and the implementation of stemmers requires a laborious amount of effort due to the fact that documents and queries...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [47, 56, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ased retrieval.Directions for future work are using these concepts for communication between user and retrieval system, extending upon the translation models and extending CLIR-enhanced concept-based ...", "weights": [[8, 17, 1.0], [105, 114, 1.0], [115, 121, 1.0]]}, "1998.sigirconf_conference-98.15": {"snippet": "... the retrieval effectiveness of distributed information retrieval systems in realistic environments.We find that when a large number of collections are available, the retrieval effectiveness is signif...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [59, 68, 1.0], [170, 179, 1.0], [180, 193, 1.0]]}, "1995.sigirconf_conference-95.30": {"snippet": "... the system, computes a score indicating how good those decisions were.Tune the system so that the expected effectiveness of its decisions will be the best possible.Have the system estimate the effect...", "weights": [[8, 14, 1.0], [83, 89, 1.0], [111, 124, 1.0], [177, 183, 1.0], [197, 210, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...ment retrieval systems is not adversely affected by speech recognition errors. Even with error rates of around 40%, the effectiveness of an IR system falls less than 10%. The paper hypothesizes that t...", "weights": [[8, 17, 1.0], [123, 136, 1.0], [146, 152, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords retrieval model and the retrieval needs of the QA system. Bag-of-words IR retrieves documents matching a query, but the QA system really needs documents that contain answers. Through question ana...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [58, 64, 1.0], [131, 137, 1.0]]}, "2003.trec_conference-2003.21": {"snippet": "...rove Retrieval Effectiveness", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "1978.ipm_journal-ir0volumeA14A1.0": {"snippet": "...y of retrieval effectiveness measures", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2005.trec_conference-2005.11": {"snippet": "...rove Effectiveness and Robustness of Ad Hoc Information Retrieval\n\n\n Our participation in this year's robust track aims to: (1) test how to improve the effectiveness of IR (according to MAP) using dif...", "weights": [[8, 21, 1.0], [59, 68, 1.0], [155, 168, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.0": {"snippet": "...tion retrieval systems using data fusion\n\n\n AbstractMeasuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic...", "weights": [[8, 17, 1.0], [65, 78, 1.0], [94, 103, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.11": {"snippet": "... for improving web retrieval effectiveness\n\n\n AbstractThis paper talks about several schemes for improving retrieval effectiveness that can be used in the named page finding tasks of web information r...", "weights": [[8, 17, 1.0], [22, 31, 1.0], [32, 45, 1.0], [100, 109, 1.0], [110, 119, 1.0], [120, 133, 1.0], [202, 211, 1.0]]}, "2007.sigirconf_conference-2007.212": {"snippet": "...ween system effectiveness and users' performance. People learn to adapt to a system regardless to its effectiveness. This study recommends that a combination of measures (e.g. system effectiveness, us...", "weights": [[8, 14, 1.0], [15, 28, 1.0], [80, 86, 1.0], [105, 118, 1.0], [179, 185, 1.0], [186, 199, 1.0]]}, "2003.sigirconf_conference-2003.49": {"snippet": "... and Retrieval]: Retrieval Models.General Terms: Theory, Experimentation. Keywords: Ranking Retrieval Systems.\nINTRODUCTIONSoboroff, Nicholas and Cahan [2] recently proposed a method for evaluating re...", "weights": [[8, 17, 1.0], [20, 29, 1.0], [95, 104, 1.0], [201, 210, 1.0]]}, "2000.sigirconf_conference-2000.7": {"snippet": "... the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance. The test was run with a best match retrieval system...", "weights": [[8, 21, 1.0], [187, 196, 1.0], [197, 203, 1.0]]}, "1988.jasis_journal-ir0volumeA39A2.0": {"snippet": "... the effectiveness of full-text retrieval. I. On the effectiveness of full-text retrieval", "weights": [[8, 21, 1.0], [35, 44, 1.0], [56, 69, 1.0], [83, 92, 1.0]]}, "2018.wsdm_conference-2018.47": {"snippet": "...zing Retrieval Systems\n\n\n ABSTRACTThe effectiveness of information retrieval systems heavily depends on a large number of hyperparameters that need to be tuned. Hyperparameters range from the choice o...", "weights": [[8, 17, 1.0], [41, 54, 1.0], [70, 79, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A1.9": {"snippet": "... the effectiveness of queries and retrieval systems", "weights": [[8, 21, 1.0], [37, 46, 1.0]]}, "2008.sigirconf_conference-2008.127": {"snippet": "... web retrieval\n\n\n ABSTRACTDocument prior features, such as Pagerank and URL depth, can improve the retrieval effectiveness of Web Information Retrieval (IR) systems. However, not all queries equally b...", "weights": [[8, 17, 1.0], [102, 111, 1.0], [112, 125, 1.0], [145, 154, 1.0]]}, "2016.ntcir_conference-2016.90": {"snippet": "...tent Retrieval (SCR) subtask. We investigated the use of multiple ASR hypotheses (words) and subword units (syllables) for improving retrieval effectiveness. We also compared the retrieval effectivene...", "weights": [[8, 17, 1.0], [126, 135, 1.0], [136, 145, 1.0], [146, 159, 1.0], [182, 191, 1.0], [192, 205, 1.0]]}, "1997.sigirconf_conference-97.36": {"snippet": "...cale retrieval context, or whether inexperienced users can achieve this effectiveness in practice. These experiments address these two questions. A small but significant improvement in potential retri...", "weights": [[8, 17, 1.0], [75, 88, 1.0], [198, 207, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.23": {"snippet": "...tion Retrieval (IR), accurately interpreting the result of a system in terms of user satisfaction is fundamental to push the research in the correct direction. Therefore, measuring systems effectivene...", "weights": [[8, 17, 1.0], [64, 70, 1.0], [192, 205, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.9": {"snippet": "...to a retrieval system and obtains no relevant documents, he or she is bound to feel frustrated. We call these weak queries and retrievals. Improving their effectiveness is an important issue for ad ho...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [142, 151, 1.0], [158, 171, 1.0]]}, "2004.clef_workshop-2004.81": {"snippet": "...e to Retrieval Effectiveness?", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2018.ipm_journal-ir0volumeA54A2.1": {"snippet": "...meta-system. Even simple ensemble techniques, when used in an informed manner, prove to be very effective in improving the overall performance and consistency of summarization systems. A statistically...", "weights": [[8, 14, 1.0], [112, 121, 1.0]]}, "1999.ntcir_workshop-1999.31": {"snippet": "...ndex System at NTCIR1: Information Retrieval using Dependency Relationship between Words\n\n\n It is difficult to improve retrieval effectiveness using only keyword-based retrieval, the major method in d...", "weights": [[8, 14, 1.0], [38, 47, 1.0], [122, 131, 1.0], [132, 145, 1.0], [171, 180, 1.0]]}}}
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We propose a two stage approach for word-level langua...", "weights": [[8, 16, 1.0], [79, 87, 1.0], [197, 205, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.0": {"snippet": "Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments\n\n\n Eliminating the negative effect of non-stationary environmental noise is a long-standing research top...", "weights": [[5, 13, 1.0]]}, "2017.fire_conference-2017w.30": {"snippet": "...tive Language Identification using Support Vector Machines and Ensemble approach\n\n\n This paper describes the systems submitted by our team for Indian Native Language Identification (INLI) task held in...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [160, 168, 1.0], [169, 183, 1.0]]}, "2013.tois_journal-ir0volumeA31A2.1": {"snippet": "...ious machine-learning-based NER systems are domain-specific, which implies that they may only perform well on some specific domains (e.g., Newswire) but tend to adapt poorly to other related but diffe...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "S19-2107": {"snippet": "...sive Language Identification using Traditional Machine Learning and Deep Learning approaches\n\n\n In this paper, the used methods and the results obtained by our team, entitled Emad, on the OffensEval 2...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [50, 57, 1.0], [58, 66, 1.0], [76, 84, 1.0]]}, "L12-1354": {"snippet": "... for Machine Learning Approach to Authorship Identification", "weights": [[8, 15, 1.0], [16, 24, 1.0], [48, 62, 1.0]]}, "W19-1420": {"snippet": "...ting Machine Learning Methods for Language and Dialect Identification of Cuneiform Texts\n\n\n Identification of the languages written using cuneiform symbols is a difficult task due to the lack of resou...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [37, 45, 1.0], [58, 72, 1.0], [95, 109, 1.0]]}, "N19-5005": {"snippet": "Language Learning and Processing in People and Machines", "weights": [[0, 8, 1.0], [9, 17, 1.0]]}, "2020.eamt-1.69": {"snippet": "...ect: Machine Translation impact on language learning", "weights": [[8, 15, 1.0], [38, 46, 1.0], [47, 55, 1.0]]}, "L02-1094": {"snippet": "...n of Machine Learning Methods for Natural Language Processing Tasks", "weights": [[8, 15, 1.0], [16, 24, 1.0], [45, 53, 1.0]]}, "2020.sigirjournals_journal-ir0volumeA54A1.14": {"snippet": "Machine learning applied in natural language processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [36, 44, 1.0]]}, "S19-2130": {"snippet": "...sive Language Identification in Social Media using Traditional and Deep Machine Learning Approaches\n\n\n Offensive language identification (OLI) in user generated text is automatic detection of any prof...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [75, 82, 1.0], [83, 91, 1.0], [116, 124, 1.0], [125, 139, 1.0]]}, "W13-1735": {"snippet": "...tive Language\n\n\n This paper presents our approach to the 2013 Native Language Identification shared task, which is based on machine learning methods that work at the character level. More precisely, w...", "weights": [[8, 16, 1.0], [72, 80, 1.0], [81, 95, 1.0], [127, 134, 1.0], [135, 143, 1.0]]}, "2017.fire_conference-2017w.34": {"snippet": "...tive Language Identification using SVM with SGD Training\n\n\n Native Language Identification (NLI), as a variant of Language Identification task, focuses on determining an author's native language, base...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [70, 78, 1.0], [79, 93, 1.0], [117, 125, 1.0], [126, 140, 1.0], [189, 197, 1.0]]}, "2008.ipm_journal-ir0volumeA44A3.6": {"snippet": "...mmon language modeling approach assumes the data D is generated from a mixture of several language models. The core problem is to find the maximum likelihood estimation of one language model mixture, ...", "weights": [[8, 16, 1.0], [93, 101, 1.0], [179, 187, 1.0]]}, "2009.ipm_journal-ir0volumeA45A1.0": {"snippet": "A language modeling framework for expert finding\n\n\n a b s t r a c tStatistical language models have been successfully applied to many information retrieval tasks, including expert finding: the process...", "weights": [[2, 10, 1.0], [79, 87, 1.0]]}, "2021.wanlp-1.34": {"snippet": "Machine Learning-Based Approach for {A}rabic Dialect Identification\n\n\n This paper describes our systems submitted to the Second Nuanced Arabic Dialect Identification Shared Task (NADI 2021). Dialect i...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [53, 67, 1.0], [151, 165, 1.0], [199, 213, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.7": {"snippet": "...sing machine transliteration. In this paper, we propose a method for improving machine transliteration using an ensemble of three different transliteration models. Because one transliteration model al...", "weights": [[8, 15, 1.0], [82, 89, 1.0]]}, "2018.fire_conference-2018w.10": {"snippet": "...tive Language Identification INLI@FIRE2018\n\n\n Native Language Identification is the process of determining the language native to the author from the written text. We have proposed a system that uses ...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [56, 64, 1.0], [65, 79, 1.0], [114, 122, 1.0]]}, "2020.semeval-1.287": {"snippet": "...sive Language Identification in {E}nglish, {D}anish, {G}reek Using {BERT} and Machine Learning Approach\n\n\n Offensive language identification is to detect the hurtful tweets, derogatory comments, swear...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [81, 88, 1.0], [89, 97, 1.0], [120, 128, 1.0], [129, 143, 1.0]]}, "W14-5151": {"snippet": "...evel Language Identification\n\n\n Language identification is a necessary prerequisite for processing any user generated text, where the language is unknown. It becomes even more challenging when the tex...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [35, 43, 1.0], [44, 58, 1.0], [137, 145, 1.0]]}, "W18-4414": {"snippet": "...sive Language Identification Using Word Embeddings and Sentiment Features\n\n\n This paper describes our participation in the First Shared Task on Aggression Identification. The method proposed relies on...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [158, 172, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.16": {"snippet": "...ural language models Deep learning A B S T R A C T Automatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms. Generally...", "weights": [[8, 16, 1.0], [29, 37, 1.0], [165, 172, 1.0], [173, 181, 1.0]]}, "2014.fire_conference-2014.18": {"snippet": "...sing Machine Learning\n\n\n ABSTRACTThis paper presents the method of Morpheme Extraction and lemmatization for Tamil language in Morpheme Extraction Task (MET) of FIRE-2014. Tamil is a morphologically r...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [118, 126, 1.0]]}, "2014.fire_conference-2014.9": {"snippet": "Machine Learning Approach for Language Identification & Transliteration\n\n\n ABSTRACTIn this paper, we describe the system that we developed as part of our participation to the FIRE-2014 Shared Task on ...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [30, 38, 1.0], [39, 53, 1.0]]}, "W19-6301": {"snippet": "...sing machine learning\n\n\n Predicting the knowledge of language learners is crucial for personalized interactions in any intelligent tutoring system for language learning. This study adopts a machine le...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [56, 64, 1.0], [154, 162, 1.0], [163, 171, 1.0], [193, 200, 1.0], [201, 209, 1.0]]}, "2017.fire_conference-2017w.36": {"snippet": "...tive Language Identification\n\n\n Native Language Identification has played an important role in forensics primarily for author profiling and identification. In this work, we discuss our approach to the...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [42, 50, 1.0], [51, 65, 1.0], [143, 157, 1.0]]}, "W18-1203": {"snippet": "...ural language model (LM), and data bootstrapping to overcome the problem of unbalanced limited resources. As a test, we explore the task of language identification in mixed-language short non-edited t...", "weights": [[8, 16, 1.0], [143, 151, 1.0], [152, 166, 1.0], [176, 184, 1.0]]}, "2018.fire_conference-2018w.11": {"snippet": "A Machine Learning Approach to Indian Native Language Identification\n\n\n NLI (Native Language Identification) determines the native language of the non-native users using their writings in a foreign la...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [45, 53, 1.0], [54, 68, 1.0], [84, 92, 1.0], [93, 107, 1.0], [131, 139, 1.0], [198, 206, 1.0]]}, "1991.riao_conference-1991.48": {"snippet": "Machine learning principles in application to a transportable natural language database interface", "weights": [[0, 7, 1.0], [8, 16, 1.0], [70, 78, 1.0]]}, "I11-1062": {"snippet": "... for Language Identification\n\n\n We show that transductive (cross-domain) learning is an important consideration in building a general-purpose language identification system, and develop a feature sele...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [76, 84, 1.0], [145, 153, 1.0], [154, 168, 1.0]]}, "2015.fire_workshop-2015w.7": {"snippet": "...ased machine learning framework was used for labeling the individual words with their corresponding language labels. We used a dictionary based approach for language identification. We also took into ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [103, 111, 1.0], [160, 168, 1.0], [169, 183, 1.0]]}, "2014.airs_conference-2014.24": {"snippet": "...d on machine learning approaches that extract nationalities, weapons, and crime locations in online crime documents. This study also collected a new corpus of crime and manually labeled them. A machin...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [197, 204, 1.0]]}, "2019.fire_conference-2019w.45": {"snippet": "...9: A Machine Learning Framework for Hate Speech and Offensive Language Detection\n\n\n In this paper, we describe the system submitted by our team for Hate Speech and Offensive Content Identification in ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [65, 73, 1.0], [185, 199, 1.0]]}, "2007.ipm_journal-ir0volumeA43A5.3": {"snippet": "...eral machine learning algorithms for cross-lan7guage patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [144, 151, 1.0], [152, 160, 1.0], [171, 179, 1.0]]}, "W05-0401": {"snippet": "...ovel Machine Learning Approach for the Identification of Named Entity Relations\n\n\n In this paper, a novel machine learning approach for the identification of named entity relations (NERs) called posit...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [42, 56, 1.0], [109, 116, 1.0], [117, 125, 1.0], [143, 157, 1.0]]}, "2020.semeval-1.289": {"snippet": "...d of machine learning and deep learning models for all five languages. Three machine learning and four deep learning models were used in the ensemble. In the OffensEval-2020 competition our model achi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [34, 42, 1.0], [80, 87, 1.0], [88, 96, 1.0], [111, 119, 1.0]]}, "P01-1020": {"snippet": "A Machine Learning Approach to the Automatic Evaluation of Machine Translation\n\n\n We present a machine learning approach to evaluating the wellformedness of output of a machine translation system, usi...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [59, 66, 1.0], [95, 102, 1.0], [103, 111, 1.0], [169, 176, 1.0]]}, "D07-1057": {"snippet": "Identification and Resolution of {C}hinese Zero Pronouns: A Machine Learning Approach\n\n\n In this paper, we present a machine learning approach to the identification and resolution of Chinese anaphoric...", "weights": [[0, 14, 1.0], [60, 67, 1.0], [68, 76, 1.0], [117, 124, 1.0], [125, 133, 1.0], [150, 164, 1.0]]}, "W16-5818": {"snippet": "...hing language identification using Subword Information Enriched Word Vectors\n\n\n Codeswitching is a widely observed phenomenon among bilingual speakers. By combining subword information enriched word v...", "weights": [[8, 16, 1.0], [17, 31, 1.0]]}, "2019.fire_conference-2019w.42": {"snippet": "...019: Identification of Hate Speech using Machine Learning and Deep Learning approaches for social media post\n\n\n This paper describes the research that our team, DA Master, did on the shared task HASOC...", "weights": [[8, 22, 1.0], [44, 51, 1.0], [52, 60, 1.0], [70, 78, 1.0]]}, "2019.fire_conference-2019w.36": {"snippet": "...sive language on Twitter in English, German and Hindi languages. As a part of this task, we (team A3-108) submitted different machine learning and neural network based models for all the languages. Ou...", "weights": [[8, 16, 1.0], [129, 136, 1.0], [137, 145, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.0": {"snippet": "...m of language identification of noisy texts, which could represent the primary step of many natural language processing or information retrieval tasks. Language identification is the task of automatic...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [103, 111, 1.0], [155, 163, 1.0], [164, 178, 1.0]]}, "W17-1301": {"snippet": "Identification of Languages in {A}lgerian {A}rabic Multilingual Documents\n\n\n This paper presents a language identification system designed to detect the language of each word, in its context, in a mul...", "weights": [[0, 14, 1.0], [99, 107, 1.0], [108, 122, 1.0], [153, 161, 1.0]]}, "W13-1734": {"snippet": "...tive Language Identification using large scale lexical features\n\n\n This paper describes an effort to perform Native Language Identification (NLI) using machine learning on a large amount of lexical fe...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [119, 127, 1.0], [128, 142, 1.0], [155, 162, 1.0], [163, 171, 1.0]]}, "R15-1040": {"snippet": "...tive Language Identification Using Language Modeling\n\n\n This paper reports on the task of Native Language Identification (NLI). We developed a machine learning system to identify the native language o...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [38, 46, 1.0], [100, 108, 1.0], [109, 123, 1.0], [146, 153, 1.0], [154, 162, 1.0], [193, 201, 1.0]]}, "U09-1008": {"snippet": "...tive Language Identification\n\n\n Attempts to profile authors based on their characteristics, including native language, have drawn attention in recent years, via several approaches using machine learni...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [112, 120, 1.0], [189, 196, 1.0], [197, 205, 1.0]]}, "2020.coling-main.159": {"snippet": "...tive Language Identification\n\n\n Native language identification (NLI) -identifying the native language (L1) of a person based on his/her writing in the second language (L2) -is useful for a variety of ...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [42, 50, 1.0], [51, 65, 1.0], [96, 104, 1.0], [161, 169, 1.0]]}, "E17-1087": {"snippet": "...gual Language Identification on Character Window\n\n\n In language identification, a common first step in natural language processing, we want to automatically determine the language of some input text. ...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [58, 66, 1.0], [67, 81, 1.0], [114, 122, 1.0], [174, 182, 1.0]]}, "2020.semeval-1.264": {"snippet": "... 12: Machine Learning and Deep Learning Methods for Offensive Language Identification\n\n\n The paper describes systems that our team IRLab DAIICT employed for shared task OffensEval 2020: Multilingual O...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [34, 42, 1.0], [65, 73, 1.0], [74, 88, 1.0]]}, "2014.fire_conference-2014.8": {"snippet": "...ased machine learning model and post-processing heuristics are employed for the WLL identification task. In addition to language identification, two transliteration systems were built to transliterate...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [87, 101, 1.0], [123, 131, 1.0], [132, 146, 1.0]]}}}
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Pervasiv...", "weights": []}, "2020.clef_conference-2020w.135": {"snippet": "...s in social media.", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2017.sigirconf_conference-2017.232": {"snippet": "... and Social Media\n\n\n ABSTRACTPeople regularly use web search and social media to investigate health related issues.is type of Internet data might contain misinformation i.e. incorrect information whic...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [68, 74, 1.0], [75, 80, 1.0]]}, "2015.wwwconf_conference-2015c.131": {"snippet": "...from Social Media Streams\n\n\n ABSTRACTMy proposed research addresses fundamental deficiencies in social media-based event detection by discovering highimpact moments and evaluating their credibility ra...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [99, 105, 1.0], [106, 111, 1.0]]}, "2018.wwwconf_conference-2018c.138": {"snippet": "...sing Social Media Messages in Mass Emergency: Survey Summary\n\n\n ABSTRACTMillions of people use social media to share information during disasters and mass emergencies. Information available on social ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [98, 104, 1.0], [105, 110, 1.0], [196, 202, 1.0]]}, "2020.chiir_conference-2020.49": {"snippet": "Detecting Critical Responses from Deliberate Self-harm Videos on YouTube", "weights": []}, "2015.wwwconf_conference-2015c.125": {"snippet": "...mely social spams. While it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of users s...", "weights": [[8, 14, 1.0]]}, "2020.wwwconf_conference-2020.216": {"snippet": "...s in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2014.sigirconf_conference-2014.185": {"snippet": "...y of social media to automatically detect road hazards. We conduct experiments on a testbed of tweets discussing road conditions and the initial results demonstrate the effectiveness of our approach.", "weights": [[8, 14, 1.0], [15, 20, 1.0], [38, 44, 1.0]]}, "2019.clef_conference-2019w.170": {"snippet": "...ting Social Media users, namely Reddit, who may be under the risk of Anorexia, self-harm, and depression. We participated in both tasks of early risk detection of Anorexia and self-harm. Our predictio...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2018.wwwconf_conference-2018.13": {"snippet": "...s in Social Media\n\n\n ABSTRACTMillions of users share their experiences on social media sites, such as Twitter, which in turn generate valuable data for public health monitoring, digital epidemiology, ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [77, 83, 1.0], [84, 89, 1.0]]}, "2017.wwwconf_conference-2017c.3": {"snippet": "... the social media era. People are willing to share their selfies in various social media platforms such as Facebook, Instagram and Flicker. The popularity of selfie have caught researchers' attention,...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [79, 85, 1.0], [86, 91, 1.0]]}, "2019.clef_conference-2019w.19": {"snippet": "...m on Social Media\n\n\n Three tasks are proposed at CLEF eRisk-2019 for predicting mental disorder using users posts on Reddit. Two tasks (T1 and T2) focus on early risk detection of signs of anorexia an...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2019.clef_conference-2019w.1": {"snippet": "A Baseline Approach for Early Detection of Signs of Anorexia and Self-harm in Reddit Posts\n\n\n This paper describes the systems developed by the BiTeM team for the CLEF eRisk Task 1 and 2, 2019. The go...", "weights": []}, "2020.clef_conference-2020w.60": {"snippet": "...hich social media users are at risk of self-harming, with precision up to 91.3% for Task 1. Given these promising results, it will be interesting to further refine the training regime, classifier and ...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.ipm_journal-ir0volumeA57A3.2": {"snippet": "...n on social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.tweb_journal-ir0volumeA14A4.3": {"snippet": "...n of Social Media Hoaxes at Scale", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2021.wwwconf_conference-2021c.39": {"snippet": "...with Social Media Data", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.1": {"snippet": "...h of social media, massive misinformation is also spreading widely on social media, e.g., Weibo and Twitter, and brings negative effects to human life. Today, automatic misinformation identification h...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [73, 79, 1.0], [80, 85, 1.0]]}, "2015.jasis_journal-ir0volumeA66A6.14": {"snippet": "... for social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.jasis_journal-ir0volumeA71A1.3": {"snippet": "...sing social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2019.wwwconf_conference-2019c.14": {"snippet": "...g in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.clef_conference-2020w.116": {"snippet": "...e to detect these individuals through their content on social media. Early detection is crucial for mental health problems, and for this purpose a shared task named eRisk was proposed. This paper desc...", "weights": [[8, 14, 1.0], [58, 64, 1.0], [65, 70, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A2.11": {"snippet": "... for social user profiling\n\n\n Abstract Traditionally, research about social user profiling assumes that users share some similar interests with their followees. However, it lacks the studies on what t...", "weights": [[8, 14, 1.0], [72, 78, 1.0]]}, "2017.tist_journal-ir0volumeA8A6.11": {"snippet": "...mely social spams. Although it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of user...", "weights": [[8, 14, 1.0]]}, "W17-3108": {"snippet": "...s on social media may reveal themselves to be in various states of crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis from social media text automatically and accurately ca...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [154, 160, 1.0], [161, 166, 1.0]]}, "2016.cikm_workshop-2016cybersafety.1": {"snippet": "...s to detect bad actors in social media. More specifically, we will describe both general methods that are platform independent or valid for any malicious user, and methods specific to a particular soc...", "weights": [[8, 14, 1.0], [29, 35, 1.0], [36, 41, 1.0], [200, 206, 1.0]]}, "W18-4421": {"snippet": "...cale social media monitoring and early detection of undesired situations. This paper presents the Isistanitos's approach for detecting aggressive content in multiple social media sites. The approach i...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [169, 175, 1.0], [176, 181, 1.0]]}, "2018.wwwconf_conference-2018c.181": {"snippet": "...e of social media can be leveraged to identify stress and relaxation. In this PhD research, we introduce an improved method to detect expressions of stress and relaxation in social media content. It u...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [130, 136, 1.0], [177, 183, 1.0], [184, 189, 1.0]]}, "2019.ecir_conference-20192.48": {"snippet": "...d in Social Media platforms and other online communities an easier way to reveal their minds. The textual content produced by such users proved to be handful in evaluating mental health and detecting ...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.clef_conference-2020w.90": {"snippet": "...s in social media.", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2019.clef_conference-2019w.171": {"snippet": "...n in Social Media\n\n\n In this paper we describe the participation of our research group at the CLEF eRisk 2019. The eRisk goal is the early detection of atrisk people by means of machine learning techn...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "R17-1062": {"snippet": "...h in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2021.findings-acl.246": {"snippet": "...n in social media, the use of Internet memes has emerged as a powerful means to convey political, psychological, and socio-cultural opinions. Although memes are typically humorous in nature, recent da...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}}}
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One of the high inflected words in the language...", "weights": [[8, 14, 1.0], [42, 50, 1.0], [195, 204, 1.0]]}, "2012.cikm_conference-2012.349": {"snippet": "... the Arabic microblogs retrieval. We illustrate some of the challenges associated with Arabic microblog retrieval, which mainly stem from the use of different Arabic dialects that vary in lexical sele...", "weights": [[8, 14, 1.0], [90, 96, 1.0], [162, 168, 1.0]]}, "1998.tois_journal-ir0volumeA16A1.2": {"snippet": "...ased Stemming Using Cooccurrence of Word Variants\n\n\n Stemming is used in many information retrieval (IR) systems to reduce variant word forms to common roots. It is one of the simplest applications of...", "weights": [[8, 16, 1.0], [56, 64, 1.0]]}, "C08-2027": {"snippet": "...s\n\n\n Arabic morphological analysers and stemming algorithms have become a popular area of research. Many computational linguists have designed and developed algorithms to solve the problem of morpholo...", "weights": [[8, 14, 1.0], [43, 51, 1.0]]}, "2008.clef_workshop-2008.10": {"snippet": "...sing Stemming and Part of Speech Tagging\n\n\n Abstract. With the emergence of vast resources of information, it is necessary to develop methods that retrieve the most relevant information according to n...", "weights": [[8, 16, 1.0]]}, "2006.bcs-1.6": {"snippet": "... new stemming technique that tries to determine the stem of a word representing the semantic core of this word according to Arabic morphology. This method is compared to a commonly used light stemming...", "weights": [[8, 16, 1.0], [127, 133, 1.0], [195, 203, 1.0]]}, "2013.tois_journal-ir0volumeA31A4.1": {"snippet": "...ased Stemming\n\n\n Stemming is a widely used technique in information retrieval systems to address the vocabulary mismatch problem arising out of morphological phenomena. The major shortcoming of the co...", "weights": [[8, 16, 1.0], [20, 28, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A3.3": {"snippet": "...t of Stemming on Arabic Text Classification: An Empirical Study", "weights": [[8, 16, 1.0], [20, 26, 1.0]]}, "2006.airs_conference-2006.25": {"snippet": "... for stemming for an open class of languages. Since it does not rely on existing large data collections or other linguistic resources than raw text it is especially attractive for low-density language...", "weights": [[8, 16, 1.0], [38, 47, 1.0], [195, 204, 1.0]]}, "P12-2043": {"snippet": "...e to Arabic's morphological complexity, Arabic retrieval benefits greatly from morphological analysis -particularly stemming. However, the best known stemming does not handle linguistic phenomena such...", "weights": [[8, 14, 1.0], [43, 49, 1.0], [119, 127, 1.0], [153, 161, 1.0]]}, "W04-1605": {"snippet": "...g in Arabic requires an efficient method for matching stems with roots and vice versa. Such mapping is complicated by the hundreds of manifestations of the same root. An algorithm based on the generat...", "weights": [[8, 14, 1.0]]}, "2004.sigirconf_conference-2004.112": {"snippet": "... for arabic information retrieval\n\n\n ABSTRACTDue to the high number of inflectional variations of Arabic words, empirical results suggest that stemming is essential for Arabic information retrieval. H...", "weights": [[8, 14, 1.0], [101, 107, 1.0], [146, 154, 1.0], [172, 178, 1.0]]}, "2009.ipm_journal-ir0volumeA45A6.6": {"snippet": "... and stemming approaches for the Czech language\n\n\n a b s t r a c tThis paper describes and evaluates various stemming and indexing strategies for the Czech language. Based on Czech test-collection, we...", "weights": [[8, 16, 1.0], [112, 120, 1.0]]}, "2000.spire_conference-2000.16": {"snippet": "...Word Stemming Algorithm for the Spanish Language", "weights": [[8, 16, 1.0]]}, "2010.airs_conference-2010.27": {"snippet": "...ased Arabic Stemming Using N-Grams\n\n\n Abstract. In languages with high word inflation such as Arabic, stemming improves text retrieval performance by reducing words variants. We propose a change in th...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [54, 63, 1.0], [97, 103, 1.0], [105, 113, 1.0]]}, "Y10-1068": {"snippet": "...s in Arabic language can be attached to a stem or to each other without orthographic marks such as an apostrophe. In this paper we present a statistical study of clitics and its effect in Arabic langu...", "weights": [[8, 14, 1.0], [191, 197, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.3": {"snippet": "...hile stemming is useful normally, with a very large parallel corpus for Arabic-English, stemming hurt performance in our empirical studies with Arabic, a highly inflected language.", "weights": [[8, 16, 1.0], [75, 81, 1.0], [91, 99, 1.0], [147, 153, 1.0]]}, "1999.jasis_journal-ir0volumeA50A6.4": {"snippet": "Stemming Methodologies Over Individual Query Words for an Arabic Information Retrieval System", "weights": [[0, 8, 1.0], [58, 64, 1.0]]}, "C98-1018": {"snippet": "...cale Arabic morphological analyzer which is available for testing on the Internet. introduction Most formal analyses of Semitic languages, including Arabic. deDnd the reality of abstract, unpronouncea...", "weights": [[8, 14, 1.0], [131, 140, 1.0], [152, 158, 1.0]]}, "2005.spire_conference-2005.23": {"snippet": "Stemming Arabic Conjunctions and Prepositions", "weights": [[0, 8, 1.0], [9, 15, 1.0]]}, "W05-0709": {"snippet": "...ical Stemming on {A}rabic Mention Detection and Coreference Resolution\n\n\n Arabic presents an interesting challenge to natural language processing, being a highly inflected and agglutinative language. ...", "weights": [[8, 16, 1.0], [77, 83, 1.0]]}, "2008.cikm_workshop-2008inews.2": {"snippet": "...tive Arabic stemming algorithm that can reduce stemming errors in addition to decreasing computational time and data storage.The novelty of the work arises from the use of neglected Arabic stop-words....", "weights": [[8, 14, 1.0], [15, 23, 1.0], [50, 58, 1.0], [185, 191, 1.0]]}, "2019.ipm_journal-ir0volumeA56A2.1": {"snippet": "... for Arabic dialects (survey)\n\n\n a b s t r a c tArabic dialects also called colloquial Arabic or vernaculars are spoken varieties of Standard Arabic. These dialects have mixed form with many variation...", "weights": [[8, 14, 1.0], [90, 96, 1.0], [145, 151, 1.0]]}, "2002.sigirconf_conference-2002.36": {"snippet": "... for Arabic retrieval\n\n\n ABSTRACTThis work evaluates a few search strategies for Arabic monolingual and cross-lingual retrieval, using the TREC Arabic corpus as the test-bed. The release by NIST in 20...", "weights": [[8, 14, 1.0], [84, 90, 1.0], [147, 153, 1.0]]}, "1990.ipm_journal-ir0volumeA26A2.8": {"snippet": "...n of Arabic text", "weights": [[8, 14, 1.0]]}, "2003.mtsummit-semit.5": {"snippet": "...with Arabic as a source or target language, and focuses on lexical databases. The non-concatenative nature of Arabic morphology, the complex structure of Arabic word-forms, and the general use of vowe...", "weights": [[8, 14, 1.0], [113, 119, 1.0], [157, 163, 1.0]]}, "W04-1616": {"snippet": "...s to Arabic stemming fail to provide effective and accurate equivalence classes when applied to a text like the Qur'an written in Classical Arabic. Therefore, I propose a new stemming approach based o...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [143, 149, 1.0], [178, 186, 1.0]]}, "2016.tois_journal-ir0volumeA35A1.5": {"snippet": "...ther languages, little effort was directed towards QA systems for Arabic. Due to the difficulty of handling why-questions, most Arabic QA systems tend to ignore it. In this article, we specifically ad...", "weights": [[8, 17, 1.0], [69, 75, 1.0], [131, 137, 1.0]]}, "2014.cikm_conference-2014.185": {"snippet": "...mple Arabic Stemmer\n\n\n ABSTRACTWe propose a root stemmer for the Modern Standard Arabic (MSA) language in an attempt to enhance the performance of Arabic Information Retrieval (AIR). The new Simple Ar...", "weights": [[8, 14, 1.0], [84, 90, 1.0], [150, 156, 1.0], [201, 207, 1.0]]}, "W06-1631": {"snippet": "...h as stemming should not be applied indiscriminately to all words in a collection, and so before any stemming, foreign words need to be identified. In this paper, we investigate three approaches for t...", "weights": [[8, 16, 1.0], [104, 112, 1.0]]}, "P03-1050": {"snippet": "...abic Stemming Using a Parallel Corpus\n\n\n This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translatio...", "weights": [[8, 16, 1.0], [125, 131, 1.0], [146, 154, 1.0]]}, "2008.sigirconf_workshop-2008and.16": {"snippet": "... the Arabic Language. The new lemmatizer proposed here is a part of a comprehensive Arabic tokenization system, with a stop words list exceeding 2200 Arabic words. Currently, there are two Arabic lead...", "weights": [[8, 14, 1.0], [87, 93, 1.0], [153, 159, 1.0], [192, 198, 1.0]]}, "2001.spire_conference-2001.18": {"snippet": "A Stemming Algorithmm for the Portuguese Language", "weights": [[2, 10, 1.0]]}, "2003.sigirconf_conference-2003.96": {"snippet": "Stemming in the language modeling framework", "weights": [[0, 8, 1.0]]}, "Y15-2019": {"snippet": "...ion (stemming). Stemmers have improved many NLP tasks without explicit knowledge about its stemming accuracy. In this paper, a study is conducted to evaluate various Arabic stemmers. The study is done...", "weights": [[8, 16, 1.0], [94, 102, 1.0], [169, 175, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.18": {"snippet": "...eral stemming strategies for this language, and also demonstrates that a light stemming approach could be quite effective. Based on searches done on the CLEF test collection, we find that a more aggre...", "weights": [[8, 16, 1.0], [82, 90, 1.0]]}, "2002.cikm_conference-2002.43": {"snippet": "...ight stemming approach\n\n\n ABSTRACTThe inflectional structure of a word impacts the retrieval accuracy of information retrieval systems of Latin-based languages. We present two stemming algorithms for ...", "weights": [[8, 16, 1.0], [153, 162, 1.0], [179, 187, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.8": {"snippet": "...lish/Arabic bilingual dictionary from parallel texts that exist in the Internet archive after using an Arabic light stemmer as a preprocessing step. Before using the Arabic light stemmer, the total sy...", "weights": [[8, 14, 1.0], [106, 112, 1.0], [169, 175, 1.0]]}, "W16-3912": {"snippet": "...Many stemming algorithms have been developed for Modern Standard Arabic (MSA). Although Arabic tweets and MSA are closely related and share many characteristics, there are substantial differences betw...", "weights": [[8, 16, 1.0], [68, 74, 1.0], [91, 97, 1.0]]}, "2014.airs_conference-2014.28": {"snippet": "... two stemming algorithms (i.e., morphologyand syntax-based lemmatization; and morphology-based Information Science Research Institute (ISRI) stemming on clustering Arabic text dataset. We aim to ident...", "weights": [[8, 16, 1.0], [144, 152, 1.0], [167, 173, 1.0]]}, "1993.jasis_journal-ir0volumeA44A1.0": {"snippet": "Stemming of French Words Based on Grammatical Categories", "weights": [[0, 8, 1.0]]}, "2002.sigirconf_conference-2002.37": {"snippet": "...ving stemming for Arabic information retrieval: light stemming and co-occurrence analysis\n\n\n ABSTRACTArabic, a highly inflected language, requires good stemming for effective information retrieval, ye...", "weights": [[8, 16, 1.0], [21, 27, 1.0], [57, 65, 1.0], [155, 163, 1.0]]}}}
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In particular, we introduce a moving object data mining system, MoveMine,...", "weights": [[8, 14, 1.0], [104, 110, 1.0]]}, "2019.clef_conference-2019w.125": {"snippet": "...Bird Recognition in Soundscapes\n\n\n The BirdCLEF challenge-as part of the 2019 LifeCLEF Lab [7]-offers a large-scale proving ground for system-oriented evaluation of bird species identification based o...", "weights": [[8, 19, 1.0], [196, 201, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.7": {"snippet": "...eech recognition technologies, studies of mobile search using voice started to emerge. Many of the studies focused on voice recognition challenges.  defined voice search as \"the technology underlying ...", "weights": [[8, 19, 1.0], [127, 138, 1.0]]}, "N19-2025": {"snippet": "Audio De-identification - a New Entity Recognition Task\n\n\n Named Entity Recognition (NER) has been mostly studied in the context of written text. Specifically, NER is an important step in de-identific...", "weights": [[0, 5, 1.0], [39, 50, 1.0], [72, 83, 1.0]]}, "2020.lrec-1.441": {"snippet": "...eech Recognition\n\n\n We present a corpus of sentence-aligned triples of German audio, German text, and English translation, based on German audio books. The speech translation data consist of 110 hours...", "weights": [[8, 19, 1.0], [81, 86, 1.0], [126, 131, 1.0], [142, 147, 1.0]]}, "2014.clef_conference-2014w.63": {"snippet": "...ance-based classification scheme based on the dense indexing of MFCC features and the pruning of the non-discriminant ones. To make such strategy scalable to the 30M of MFCC features extracted from th...", "weights": [[8, 13, 1.0], [36, 41, 1.0]]}, "2004.wwwconf_conference-2004at.73": {"snippet": "... web-based graphics is that a diagram should be able to directly support interaction.Unfortunately, the current vector-based graphics standard SVG 1.1 does not explicitly support these. Instead, the d...", "weights": [[8, 13, 1.0], [122, 127, 1.0]]}, "W14-5131": {"snippet": "... new audio-visual emotion (AVE) database is developed. The database consists of audio, video and audio-visual clips sourced from TV broadcast like movies and soapoperas in English language. The data c...", "weights": [[8, 13, 1.0], [83, 88, 1.0], [100, 105, 1.0]]}, "2004.jasis_journal-ir0volumeA55A12.7": {"snippet": "Recognition and analysis of audio for copyright protection: The RAA project", "weights": [[0, 11, 1.0], [28, 33, 1.0]]}, "2018.ismir_conference-2018.73": {"snippet": "...n of Audio Features for Recognition of Western and Ethnic Instruments in Polyphonic Mixtures", "weights": [[8, 13, 1.0], [27, 38, 1.0]]}, "2017.clef_conference-2017w.88": {"snippet": "...rine Animal Recognition task, we examined different neural networks to make classification based on weakly-labelled images. 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The goal of the LifeCLEF2019 Bird Recognition is to detect and clas...", "weights": [[8, 13, 1.0], [43, 54, 1.0], [101, 112, 1.0], [113, 118, 1.0], [170, 181, 1.0]]}, "L08-1469": {"snippet": "...ideo-Based Automatic Sign Language Recognition", "weights": [[8, 13, 1.0], [38, 49, 1.0]]}, "1999.sigirconf_conference-99.6": {"snippet": "...eech recognition (ASR) technology, it is now possible to automatically transcribe speech with reasonable accuracy 17]. Once the contents of a speech database or the audio portions of a multimedia data...", "weights": [[8, 19, 1.0], [168, 173, 1.0]]}, "2018.chiir_conference-2018.58": {"snippet": "...eech recognition (ASR) is reported to be on a par with human performance [25; 26], while high quality synthetic voices generated with deep neural networks (WaveNet Model) can sound almost indistinguis...", "weights": [[8, 19, 1.0]]}, "W17-2003": {"snippet": "...gate animal recognition models learned from wildlife video documentaries by using the weak supervision of the textual subtitles. This is a challenging setting, since i) the animals occur in their natu...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2004.ismir_conference-2004.22": {"snippet": "...core-based Gestures In Audio Recordings", "weights": [[8, 13, 1.0], [26, 31, 1.0]]}, "2017.ismir_conference-2017.45": {"snippet": "...king-Based Emotion Recognition for Experimental Music", "weights": [[8, 13, 1.0], [22, 33, 1.0]]}, "2001.sigirconf_conference-2001.57": {"snippet": "...tent-Based Segmentation of Speech Transcripts\n\n\n ABSTRACTAn algorithm for the segmentation of an audio/video source into topically cohesive segments based on automatic speech recognition (ASR) transcr...", "weights": [[8, 13, 1.0], [100, 105, 1.0], [152, 157, 1.0], [178, 189, 1.0]]}, "2014.airs_conference-2014.24": {"snippet": "...tity recognition from the newspaper articles. Thus, this study designs and develops a crime named entity recognition based on machine learning approaches that extract nationalities, weapons, and crime...", "weights": [[8, 19, 1.0], [108, 119, 1.0], [120, 125, 1.0]]}, "2015.clef_conference-2015.39": {"snippet": "...s in Audio Recordings", "weights": [[8, 13, 1.0]]}, "2016.clef_conference-2016w.53": {"snippet": "...s in Audio Recordings using Deep Convolutional Neural Networks\n\n\n This paper summarizes a method for purely audio-based bird species recognition through the application of convolutional neural network...", "weights": [[8, 13, 1.0], [111, 116, 1.0], [117, 122, 1.0], [136, 147, 1.0]]}, "2009.ipm_journal-ir0volumeA45A3.0": {"snippet": "...tion recognition from speech\n\n\n a b s t r a c tEmotional expression and understanding are normal instincts of human beings, but automatical emotion recognition from speech without referring any langua...", "weights": [[8, 19, 1.0], [151, 162, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.21": {"snippet": "...tion recognition with web-scale video data\n\n\n AbstractAction recognition in surveillance video makes our life safer by detecting the criminal events or predicting violent emergencies. However, efficie...", "weights": [[8, 19, 1.0], [64, 75, 1.0]]}, "2018.clef_conference-2018w.16": {"snippet": "Audio-based Bird Species Identification with Deep Convolutional Neural Networks\n\n\n This paper presents deep learning techniques for audio-based bird identification at very large scale. Deep Convolutio...", "weights": [[0, 5, 1.0], [6, 11, 1.0], [132, 137, 1.0], [138, 143, 1.0]]}, "1999.sigirconf_conference-99.68": {"snippet": "...ideo/audio in order to facilitate rapid retrieval, assimilation and browsing. CueVideo employs a combination of automatic indexing and analysis techniques in the domain of video, audio, speech recogni...", "weights": [[8, 13, 1.0], [182, 187, 1.0], [196, 207, 1.0]]}, "W15-5105": {"snippet": "Synthesizing and Evaluating Animations of {A}merican {S}ign {L}anguage Verbs Modeled from Motion-Capture Data\n\n\n Animations of American Sign Language (ASL) can make information accessible for many sig...", "weights": []}, "2020.signlang-1.6": {"snippet": "{PE}2{LGP} Animator: A Tool To Animate A {P}ortuguese {S}ign {L}anguage Avatar\n\n\n Software for the production of sign languages is much less common than for spoken languages. Such software usually rel...", "weights": []}, "2005.ismir_conference-2005.108": {"snippet": "...enre Recognition Systems in Audio Signals", "weights": [[8, 19, 1.0], [31, 36, 1.0]]}, "2019.clef_conference-2019w.34": {"snippet": "... for audio-based bird identification in soundscapes. Deep Convolutional Neural Networks are trained to classify 659 species. Different data augmentation techniques are applied to prevent overfitting a...", "weights": [[8, 13, 1.0], [14, 19, 1.0]]}, "O11-2002": {"snippet": "...cies Recognition Based on Speech Processing Techniques) [In {C}hinese]", "weights": [[8, 19, 1.0], [20, 25, 1.0]]}, "O14-2004": {"snippet": "...sing audio data collected from commercial CDs and websites show that the timbre-based, pitch-based, and the combination thereof systems achieve 71.1%, 72.1%, and 75.0% accuracy of bird sound identific", "weights": [[8, 13, 1.0], [83, 88, 1.0], [96, 101, 1.0]]}, "2013.tois_journal-ir0volumeA31A2.1": {"snippet": "...tity recognition\n\n\n Named Entity Recognition (NER) is a fundamental task in information extraction from unstructured text. Most previous machine-learning-based NER systems are domain-specific, which i...", "weights": [[8, 19, 1.0], [36, 47, 1.0], [157, 162, 1.0]]}, "2014.clef_conference-2014w.69": {"snippet": "... raw audio without segmentation and without using any auxiliary metadata. It successfully classifies among 501 bird categories, and was by far the highest scoring audio-only bird recognition algorithm...", "weights": [[8, 13, 1.0], [166, 171, 1.0], [182, 193, 1.0]]}, "2007.ismir_conference-2007.59": {"snippet": "...hord Recognition in Audio", "weights": [[8, 19, 1.0], [23, 28, 1.0]]}, "2020.lrec-1.555": {"snippet": "...ties recognition which could be integrated into ISTEX resources. This led to testing two detection tools for animal species names in a corpus of 100 documents in zoology. This makes it possible to pro...", "weights": [[8, 19, 1.0], [112, 118, 1.0]]}, "2018.clef_conference-2018w.99": {"snippet": "...l or audio observations of living organism. A picture actually contains only a partial information that is usually not sufficient to determine the right species with certainty. Quantifying this uncert...", "weights": [[8, 13, 1.0]]}, "2020.cikm_conference-2020.223": {"snippet": "...aker recognition models show that adding certain background sounds to clean vocal audio leads to a substantial deterioration of recognition performance. In several noisy settings, our findings reveal ...", "weights": [[8, 19, 1.0], [85, 90, 1.0], [131, 142, 1.0]]}, "2011.sigirconf_conference-2011.210": {"snippet": "...ning audio-visual and textual cues to improve written word recognition and vocabulary skills. JuSe provides an interactive learning environment that allows parents to introduce new words and concepts ...", "weights": [[8, 13, 1.0], [62, 73, 1.0]]}, "2000.riao_conference-2000.9": {"snippet": "...eech Recognition based Audio Indexing System for the Web", "weights": [[8, 19, 1.0], [20, 25, 1.0], [26, 31, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A2.6": {"snippet": "...tion recognition\n\n\n AbstractAs an important task in computer vision, the interaction recognition has attracted extensive attention due to its widely potential applications. The existing methods mainly...", "weights": [[8, 19, 1.0], [88, 99, 1.0]]}, "2021.findings-acl.455": {"snippet": "...e an animal looks like another animal, the more likely it is to survive. Contextual: It's a way of thinking about how organisms adapt to change. The newly found cat is probably part of a separate bran...", "weights": [[8, 14, 1.0], [34, 40, 1.0]]}, "H89-1025": {"snippet": "...tics Based Speech Recognition\n\n\n The objective of this project is to develop a robust and high-performance speech recognitiotl system using a segment-based approach to phonetic recognition. The recogn...", "weights": [[8, 13, 1.0], [21, 32, 1.0], [153, 158, 1.0], [180, 191, 1.0], [197, 208, 1.0]]}, "2018.mir_conference-2018.12": {"snippet": "... for Audio Event Recognition", "weights": [[8, 13, 1.0], [20, 31, 1.0]]}, "2017.clef_conference-2017w.118": {"snippet": "...s in Audio Files Using Transfer Learning\n\n\n In this paper, a method to identify bird species in audio recordings is presented. For this purpose, a pre-trained Inception-v3 convolutional neural network...", "weights": [[8, 13, 1.0], [99, 104, 1.0]]}, "2016.clef_conference-2016w.55": {"snippet": "Audio Based Bird Species Identification using Deep Learning Techniques\n\n\n In this paper we present a new audio classification method for bird species identification. Whereas most approaches apply near...", "weights": [[0, 5, 1.0], [6, 11, 1.0], [105, 110, 1.0]]}, "2019.ismir_conference-2019.4": {"snippet": "...hord Recognition from Audio", "weights": [[8, 19, 1.0], [25, 30, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...eech recognition will never be perfect, so analyses applied to the resulting transcripts must be robust in the face of recognition errors. The ability to capture speech and apply speech recognition on...", "weights": [[8, 19, 1.0], [122, 133, 1.0], [189, 200, 1.0]]}, "2014.ismir_conference-2014.48": {"snippet": "...ical Audio Recordings", "weights": [[8, 13, 1.0]]}, "2014.mir_workshop-2014emr.3": {"snippet": "...oint-based medialness for animal and plant identification", "weights": [[8, 13, 1.0], [29, 35, 1.0]]}, "2013.ismir_conference-2013.55": {"snippet": "Audio Chord Recognition with Recurrent Neural Networks", "weights": [[0, 5, 1.0], [12, 23, 1.0]]}, "S19-1032": {"snippet": "Generating Animations from Screenplays\n\n\n Automatically generating animation from natural language text finds application in a number of areas e.g. movie script writing, instructional videos, and publ...", "weights": []}, "W17-7522": {"snippet": "Tutorial for Deaf {--} Teaching {P}unjabi Alphabet using Synthetic Animations\n\n\n Developing an automatic tool for educating students has become essential in today's world of computerization. For diffe...", "weights": []}, "L18-1680": {"snippet": "...ound Audio Data\n\n\n We present a novel and general approach for fast and efficient non-sequential browsing of sound in large archives that we know little or nothing about, e.g. so called found data -da...", "weights": [[8, 13, 1.0]]}, "R19-2007": {"snippet": "...n of audio transcriptions based on phone-level alignment between the transcriptions and their corresponding audio recordings. Comparing this approach to a more general rule-based verbalization method ...", "weights": [[8, 13, 1.0], [29, 34, 1.0], [111, 116, 1.0], [176, 181, 1.0]]}, "2011.wwwjournals_journal-ir0volumeA14A3.3": {"snippet": "...hing-based skeleton generation method suitable for use in the mobile environment. 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Indeed, it is even difficult, if not impossible, to predict a model's empiri...", "weights": [[8, 17, 1.0], [38, 47, 1.0], [48, 54, 1.0], [104, 113, 1.0]]}, "2017.fire_conference-2017w.24": {"snippet": "...tion Retrieval From Legal Documents\n\n\n This paper details the approach of implementing the Catchphrase Extraction and Precedence Retrieval tasks to be presented at Information Retrieval from Legal Doc...", "weights": [[8, 17, 1.0], [132, 141, 1.0], [179, 188, 1.0]]}, "2011.sigirconf_conference-2011.198": {"snippet": "A comparison of time-aware ranking methods\n\n\n ABSTRACTWhen searching a temporal document collection, e.g., news archives or blogs, the time dimension must be explicitly incorporated into a retrieval m...", "weights": [[2, 12, 1.0], [189, 198, 1.0]]}, "2012.cikm_conference-2012.15": {"snippet": "...tion retrieval evaluation most often involves manually assessing the relevance of particular query-document pairs. In cases where this is difficult (such as personalized search), interleaved compariso...", "weights": [[8, 17, 1.0], [194, 204, 1.0]]}, "2019.ecir_conference-20192.47": {"snippet": "...ased Models for Social Media Retrieval\n\n\n Abstract. The huge amount of information that is available in social media today has resulted in a pressing demand for information retrieval models to support...", "weights": [[8, 14, 1.0], [32, 41, 1.0], [176, 185, 1.0], [186, 192, 1.0]]}, "1992.sigirjournals_journal-ir0volumeA26A2.3": {"snippet": "... for Retrieval of Data on Multimedia Documents\n\n\n AbstractTraditionally, two paradigms are discerned with respect to data retrieval :1. A query mechanism enables the user to express conditions that ch...", "weights": [[8, 17, 1.0], [125, 134, 1.0]]}, "2005.airs_conference-2005.5": {"snippet": "...mage Retrieval Method Using Color Distribution and Common Bitmap\n\n\n Abstract. Image retrieval has emerged as an important problem in multimedia database management. This paper uses the color distribut...", "weights": [[8, 17, 1.0], [87, 96, 1.0]]}, "1986.sigirconf_conference-86.32": {"snippet": "...stic Models for Document Retrieval: A Comparison of Performance on Experimental and Synthetic Databases", "weights": [[8, 14, 1.0], [28, 37, 1.0], [41, 51, 1.0]]}, "1993.sigirconf_conference-93.26": {"snippet": "A Comparison of Indexing Techniques for Japanese Text Retrieval\n\n\n Abstract:A series of Japanese full-text retrieval experiments were conducted using an inference network document retrieval model.", "weights": [[2, 12, 1.0], [54, 63, 1.0], [107, 116, 1.0], [180, 189, 1.0]]}, "2016.ictir_conference-2016.14": {"snippet": "...tion Retrieval Models\n\n\n ABSTRACTDeveloping effective information retrieval models has been a long standing challenge in Information Retrieval (IR), and significant progresses have been made over the ...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [69, 78, 1.0], [79, 85, 1.0], [136, 145, 1.0]]}, "2007.ecir_conference-2007.68": {"snippet": "...rall Comparison at the Standard Levels of Recall of Multiple Retrieval Methods with the Friedman Test", "weights": [[8, 18, 1.0], [64, 73, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.2": {"snippet": "...tion retrieval\n\n\n AbstractTwo probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on...", "weights": [[8, 17, 1.0], [75, 84, 1.0], [137, 143, 1.0]]}, "W09-1320": {"snippet": "...ical Comparison Questions\n\n\n This paper introduces the task of automatically answering clinical comparison questions using MEDLINE \u00ae abstracts. In the beginning, clinical comparison questions and the ...", "weights": [[8, 18, 1.0], [99, 109, 1.0], [174, 184, 1.0]]}, "2004.trec_conference-2004.8": {"snippet": "...tter Retrieval Function From Machine?\n\n\n The quality of an information retrieval system heavily depends on its retrieval function, which returns a similarity measurement between the query and each doc...", "weights": [[8, 17, 1.0], [74, 83, 1.0], [114, 123, 1.0]]}, "2008.airs_conference-2008.76": {"snippet": "A Comparison of Textual Data Mining Methods for Sex Identification in Chat Conversations", "weights": [[2, 12, 1.0]]}, "2009.clef_workshop-2009w.123": {"snippet": "...ment retrieval track (TEL@CLEF) at at the Cross-Language Retrieval Forum (CLEF) Trinity College Dublin and Dublin City University participated in collaboration. Our retrieval experiments focus on i) i...", "weights": [[8, 17, 1.0], [60, 69, 1.0], [168, 177, 1.0]]}, "1980.sigirconf_conference-80.3": {"snippet": "A Comparison of Two Weighting Schemes for Boolean Retrieval", "weights": [[2, 12, 1.0], [50, 59, 1.0]]}, "2006.sigirconf_conference-2006.16": {"snippet": "...tion retrieval\n\n\n ABSTRACTA common limitation of many retrieval models, including the recently proposed axiomatic approaches, is that retrieval scores are solely based on exact (i.e., syntactic) match...", "weights": [[8, 17, 1.0], [57, 66, 1.0], [67, 73, 1.0], [137, 146, 1.0]]}, "2020.clssts-1.6": {"snippet": "A Comparison of Unsupervised Methods for Ad hoc Cross-Lingual Document Retrieval\n\n\n We address the problem of linking related documents across languages in a multilingual collection. We evaluate three...", "weights": [[2, 12, 1.0], [71, 80, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...s of retrieval models from the documents retrievability point of view. We focus particularly on the retrieval bias of different retrieval models, and try to examine to what extent this bias restricts ...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [103, 112, 1.0], [121, 130, 1.0], [131, 140, 1.0], [141, 147, 1.0]]}, "2015.sigirconf_conference-2015.134": {"snippet": "...line Retrieval Evaluation\n\n\n ABSTRACTOnline evaluation methods for information retrieval use implicit signals such as clicks from users to infer preferences between rankers. A highly sensitive way of ...", "weights": [[8, 17, 1.0], [82, 91, 1.0]]}, "1999.sigirconf_conference-99.43": {"snippet": "...uage models of these documents. One obstacle in applying statistical language modeling to information retrieval is the sparse data problem. It is serious in information retrieval for two reasons. Firs...", "weights": [[8, 14, 1.0], [105, 114, 1.0], [172, 181, 1.0]]}, "2017.ictir_conference-2017.45": {"snippet": "...rm a comparison of widely-used click models in terms of the quality of click simulation and analyze this quality for queries with di erent frequencies. We nd that click models fail to accurately simul...", "weights": [[8, 18, 1.0], [40, 46, 1.0], [172, 178, 1.0]]}, "P01-1004": {"snippet": "...uity models (in the form of N-grams). Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word Ngram mod...", "weights": [[8, 14, 1.0], [140, 149, 1.0], [200, 206, 1.0]]}, "2013.tois_journal-ir0volumeA31A4.0": {"snippet": "...aved comparison methods, which compare rankers using click data obtained when interleaving their rankings.In this article, we propose a framework for analyzing interleaved comparison methods. An inter...", "weights": [[8, 18, 1.0], [175, 185, 1.0]]}, "1999.sigirconf_conference-99.30": {"snippet": "...ased retrieval system performs well in comparison to conventional retrieval techniques. This prototype system only begins to tap the full potential of translation-based retrieval.", "weights": [[8, 17, 1.0], [42, 52, 1.0], [69, 78, 1.0], [172, 181, 1.0]]}, "2014.cikm_conference-2014.87": {"snippet": "...port comparison shopping. One key user activity is to compare two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors...", "weights": [[8, 18, 1.0], [134, 144, 1.0], [156, 165, 1.0], [186, 195, 1.0]]}, "2003.clef_workshop-2003.37": {"snippet": "...or a comparison of different domain-specific retrieval systems and techniques. We felt that it was necessary to acquire knowledge on the pros and cons of different retrieval systems through practical ...", "weights": [[8, 18, 1.0], [22, 31, 1.0], [48, 57, 1.0], [157, 166, 1.0], [167, 176, 1.0]]}, "2009.cikm_conference-2009.301": {"snippet": "...tive comparison of retrieval systems based on clickthroughs\n\n\n ABSTRACTThe Cranfield evaluation method has some disadvantages, including its high cost in labor and inadequacy for evaluating interactiv...", "weights": [[8, 18, 1.0], [22, 31, 1.0]]}, "2014.wsdm_conference-2014.18": {"snippet": "...aive comparison can be biased because exposed and unexposed populations typically have different features. One solid methodology for a fair comparison is to apply inverse propensity weighting with dou...", "weights": [[8, 18, 1.0], [90, 99, 1.0], [143, 153, 1.0]]}, "1981.jasis_journal-ir0volumeA32A4.5": {"snippet": "A comparison of two systems of weighted boolean retrieval", "weights": [[2, 12, 1.0], [48, 57, 1.0]]}, "2008.airs_conference-2008.5": {"snippet": "...re's retrieval performance, on different feature spaces? In this paper, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison i...", "weights": [[8, 17, 1.0], [34, 43, 1.0], [191, 201, 1.0]]}, "2015.ecir_conference-2015.22": {"snippet": "... and Retrieval Bias: A Comparison of Inequality Measures", "weights": [[8, 17, 1.0], [26, 36, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "...imal retrieval model, enables analytical comparison of different retrieval models without necessarily requiring empirical evaluation, and has led to the development of multiple more effective retrieva...", "weights": [[8, 17, 1.0], [44, 54, 1.0], [58, 67, 1.0], [68, 77, 1.0], [78, 84, 1.0], [195, 204, 1.0]]}, "1994.trec_conference-1994.7": {"snippet": "Comparison of Fragmentation Schemes for Document Retrieval", "weights": [[0, 10, 1.0], [49, 58, 1.0]]}, "2017.sigirconf_conference-2017.203": {"snippet": "...ined models. In addition, the implementations provided for even the most traditional IR evaluation metrics di er from library to library, thus making the objective evaluation and comparison between tr...", "weights": [[8, 14, 1.0], [182, 192, 1.0]]}, "2020.ictir_conference-2020.24": {"snippet": "Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking\n\n\n Counterfactual evaluation can estimate Click-Through-Rate (CTR) differences between ranking systems based on h...", "weights": []}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...tion Retrieval Models\n\n\n Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is necessary to und...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [49, 58, 1.0], [59, 65, 1.0], [118, 127, 1.0], [173, 179, 1.0]]}, "P06-1076": {"snippet": "A Comparison of Document, Sentence, and Term Event Spaces\n\n\n The trend in information retrieval systems is from document to sub-document retrieval, such as sentences in a summarization system and word...", "weights": [[2, 12, 1.0], [86, 95, 1.0], [137, 146, 1.0]]}, "2017.ictir_conference-2017.17": {"snippet": "...uage models in the language modeling framework to ad hoc retrieval. Since KL is rank equivalent to a speci c weighted geometric mean, we examine alternative weighted means for language-model compariso...", "weights": [[8, 14, 1.0], [60, 69, 1.0], [194, 204, 1.0]]}, "2019.ecir_conference-20191.25": {"snippet": "...king Models in an Online Setting\n\n\n Abstract. Online Learning to Rank (OLTR) methods optimize ranking models by directly interacting with users, which allows them to be very efficient and responsive. ...", "weights": [[8, 14, 1.0], [105, 111, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.13": {"snippet": "...tion retrieval: bridging the theory-effectiveness gap\n\n\n AbstractImproving the effectiveness of general retrieval models has been a long-standing difficult challenge in information retrieval research,...", "weights": [[8, 17, 1.0], [107, 116, 1.0], [117, 123, 1.0], [184, 193, 1.0]]}, "2011.wwwconf_conference-2011.28": {"snippet": "...over different topics of interests that are coherent in geographical regions? (2) how to compare several topics across different geographical locations? To answer these questions, this paper proposes ...", "weights": [[8, 17, 1.0], [122, 131, 1.0]]}, "2016.sigirconf_conference-2016.94": {"snippet": "...tion Retrieval via Topic Modelling\n\n\n ABSTRACTTraditional information retrieval (IR) models, in which a document is normally represented as a bag of words and their frequencies, capture the term-level...", "weights": [[8, 17, 1.0], [73, 82, 1.0], [88, 94, 1.0]]}, "2014.cikm_conference-2014.12": {"snippet": "A Comparison of Retrieval Models using Term Dependencies\n\n\n ABSTRACTA number of retrieval models incorporating term dependencies have recently been introduced. Most of these modify existing \"bag-ofwor...", "weights": [[2, 12, 1.0], [16, 25, 1.0], [26, 32, 1.0], [80, 89, 1.0], [90, 96, 1.0]]}, "1992.jasis_journal-ir0volumeA43A5.2": {"snippet": "Retrieval Testing by the Comparison of Statistically Independent Retrieval Methods", "weights": [[0, 9, 1.0], [25, 35, 1.0], [65, 74, 1.0]]}, "2016.ictir_conference-2016.35": {"snippet": "...s of retrieval accuracy. This paper attempts to fill in this void by reporting the results of an empirical comparison of document expansion methods using translation models estimated based on word co-...", "weights": [[8, 17, 1.0], [110, 120, 1.0], [169, 175, 1.0]]}, "C10-2174": {"snippet": "...ment Retrieval\n\n\n The field of information retrieval still strives to develop models which allow semantic information to be integrated in the ranking process to improve performance in comparison to st...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [81, 87, 1.0], [187, 197, 1.0]]}, "2010.sigirconf_conference-2010.208": {"snippet": "...ring models, making differences hard to appreciate. Seminal efforts on the detection of text reuse  have fostered the composition of standard resources for the accurate evaluation and comparison of me...", "weights": [[8, 14, 1.0], [187, 197, 1.0]]}, "2007.sigirconf_conference-2007.152": {"snippet": "A comparison of sentence retrieval techniques", "weights": [[2, 12, 1.0], [25, 34, 1.0]]}, "2020.sigirconf_conference-2020.110": {"snippet": "...ious retrieval criteria, like different ranking models and values of k. In this paper, we conduct the first extensive comparison between ten effective strategies, many of which were never compared bef...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [51, 57, 1.0], [121, 131, 1.0]]}, "P99-1027": {"snippet": "...tion models for both translation directions using the same training data. We investigate information retrieval between English and French, incorporating both translations directions into both document...", "weights": [[8, 14, 1.0], [104, 113, 1.0]]}, "2008.sigirconf_conference-2008.55": {"snippet": "...stic models for passage based retrieval\n\n\n ABSTRACTThe approach of using passage-level evidence for document retrieval has shown mixed results when it is applied to a variety of test beds with differe...", "weights": [[8, 14, 1.0], [33, 42, 1.0], [112, 121, 1.0], [196, 205, 1.0]]}, "2021.eacl-main.293": {"snippet": "...ased models in real-world ad-hoc retrieval applications by comparison to a \"greener and more sustainable\" alternative, comprising only 620 trainable parameters. We present an analysis of their efficac...", "weights": [[8, 14, 1.0], [36, 45, 1.0], [62, 72, 1.0]]}, "2007.ecir_conference-2007.14": {"snippet": "...ance Comparison of Clustered and Replicated Information Retrieval Systems", "weights": [[8, 18, 1.0], [59, 68, 1.0]]}, "2011.ntcir_workshop-2011.18": {"snippet": "...uage models (LM) representing different semantic roles and scopes of models for documents and a rank aggregation method. The rationale is based on observation that sentence-based language models using...", "weights": [[8, 14, 1.0], [33, 42, 1.0], [72, 78, 1.0], [191, 197, 1.0]]}, "2004.ipm_journal-ir0volumeA40A5.2": {"snippet": "...rove retrieval effectiveness. This paper examines the impact the various types of link-based evidence and their combination with content-based evidence have on the retrieval effectiveness for the topi...", "weights": [[8, 17, 1.0], [167, 176, 1.0]]}, "2020.wsdm_conference-2020.105": {"snippet": "... Hoc Retrieval\n\n\n We present Capreolus, a toolkit designed to facilitate end-to-end ad hoc retrieval experiments with neural networks by providing implementations of prominent neural ranking models wi...", "weights": [[8, 17, 1.0], [94, 103, 1.0], [194, 200, 1.0]]}, "2011.sigirconf_conference-2011.145": {"snippet": "...duct comparison using comparative relations\n\n\n ABSTRACTThis paper proposes a novel Product Comparison approach. The comparative relations between products are first mined from both user reviews on mul...", "weights": [[8, 18, 1.0], [94, 104, 1.0]]}, "2006.airs_conference-2006.60": {"snippet": "...ical Comparison of Translation Disambiguation Techniques for Chinese-English Cross-Language Information Retrieval", "weights": [[8, 18, 1.0], [107, 116, 1.0]]}, "2003.sigirconf_conference-2003.35": {"snippet": "...ting retrieval models in the context of patent retrieval. The relative superiority among existing retrieval models did not significantly differ depending on the document genre, that is, patents and ne...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [50, 59, 1.0], [101, 110, 1.0], [111, 117, 1.0]]}, "2014.sigirconf_conference-2014.219": {"snippet": "...tion retrieval models; retrieval constraints; optimization\nOVERVIEWOne of the most important research problems in Information Retrieval (IR) is to develop optimal general IR models. Since IR models de...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [26, 35, 1.0], [129, 138, 1.0], [177, 183, 1.0], [194, 200, 1.0]]}, "2018.ictir_conference-2018.1": {"snippet": "...ical models (e.g., click models) with user behavior hypothesis to extract and train ranking systems with unbiased relevance signals. Recently, a novel counterfactual learning framework that estimates ...", "weights": [[8, 14, 1.0], [28, 34, 1.0]]}, "2011.sigirconf_conference-2011.45": {"snippet": "...wise comparison between two users as a two-player competition with one winner and one loser. Two competition models are proposed to estimate user expertise from pairwise comparisons. Using the NTCIR-8...", "weights": [[8, 18, 1.0], [112, 118, 1.0]]}, "W19-8504": {"snippet": "...n of comparison on a cognitive and functional basis, 1 investigating how two or more entities could be compared in a language, what patterns are used in these various ways of comparison in Latin, and ...", "weights": [[8, 18, 1.0], [178, 188, 1.0]]}, "2009.cikm_conference-2009.134": {"snippet": "...opic retrieval\n\n\n ABSTRACTTraditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval...", "weights": [[8, 17, 1.0], [41, 47, 1.0], [63, 72, 1.0], [194, 203, 1.0]]}, "O12-2004": {"snippet": "...sive comparison of various topic modeling approaches, including the so-called document topic models (DTM) and word topic models (WTM), for Chinese spoken document retrieval (SDR). Moreover, different ...", "weights": [[8, 18, 1.0], [96, 102, 1.0], [124, 130, 1.0], [166, 175, 1.0], [193, 202, 1.0]]}, "2018.wsdm_conference-2018.38": {"snippet": "...-hoc Retrieval\n\n\n ABSTRACTNeural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals. We argue that the context of these matching signa...", "weights": [[8, 17, 1.0], [39, 45, 1.0]]}, "2017.ictir_conference-2017.36": {"snippet": "...post-retrieval QPP method predicts the query's performance based on the quality of its retrieved result list .A common approach utilized by many state-of-the art post-retrieval QPP methods is the anal...", "weights": [[8, 17, 1.0], [170, 179, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.7": {"snippet": "...tion Retrieval Systems: A Posting-Based Approach\n\n\n Static index pruning methods have been proposed to reduce size of the inverted index of information retrieval systems. The goal is to increase effic...", "weights": [[8, 17, 1.0], [155, 164, 1.0]]}, "2006.sigirconf_conference-2006.7": {"snippet": "...stic models. The first of these directly models an expert's knowledge based on the documents that they are associated with, whilst the second locates documents on topic, and then finds the associated ...", "weights": [[8, 14, 1.0], [44, 50, 1.0]]}, "1981.sigirconf_conference-81.10": {"snippet": "...phic Retrieval Systems\n\n\n Abs t ractThe general model and simulation algorithms for bibliographic retrieval systems presented in an earlier paper I are expanded.The new model integrates the physical a...", "weights": [[8, 17, 1.0], [101, 110, 1.0]]}, "2017.cikm_conference-2017.12": {"snippet": "...aved comparison methods generalize interleaved comparison methods to provide a scalable approach for comparing ranking systems based on regular user interactions. Such methods enable the increasingly ...", "weights": [[8, 18, 1.0], [50, 60, 1.0]]}, "2005.ntcir_workshop-2005.76": {"snippet": "...tion Retrieval task at NTCIR-5. We focused on Chinese monolingual retrieval, and aimed to study the effectiveness of language models and different tokenization methods for Chinese. Our main findings a...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [129, 135, 1.0], [140, 149, 1.0]]}, "2009.clef_workshop-20092.13": {"snippet": "Comparison of Several Combinations of Multimodal and Diversity Seeking Methods for Multimedia Retrieval", "weights": [[0, 10, 1.0], [94, 103, 1.0]]}, "N09-2005": {"snippet": "Comparison of Extended Lexicon Models in Search and Rescoring for {SMT}\n\n\n We show how the integration of an extended lexicon model into the decoder can improve translation performance. The model is b...", "weights": [[0, 10, 1.0], [31, 37, 1.0]]}, "2006.clef_workshop-2006.45": {"snippet": "...sage Retrieval for Question Answering\n\n\n Abstract. In this paper we describe the participation of the Universidad Polit\u00e9cnica of Valencia to the 2006 edition, which was focused on the comparison betwe...", "weights": [[8, 17, 1.0], [187, 197, 1.0]]}, "2017.ictir_workshop-2017learner.7": {"snippet": "...mark comparison of learning-to-rank methods using two evaluation measures: the Normalized Winning Number and the Ideal Winning Number. Evaluation results of 87 learning-to-rank methods on 20 datasets ...", "weights": [[8, 18, 1.0]]}, "2009.sigirconf_conference-2009.92": {"snippet": "A comparison of retrieval-based hierarchical clustering approaches to person name disambiguation", "weights": [[2, 12, 1.0], [16, 25, 1.0]]}, "2010.sigirconf_conference-2010.49": {"snippet": "A comparison of general vs personalised affective models for the prediction of topical relevance\n\n\n ABSTRACTInformation retrieval systems face a number of challenges, originating mainly from the seman...", "weights": [[2, 12, 1.0], [50, 56, 1.0], [120, 129, 1.0]]}, "2016.wsdm_conference-2016.26": {"snippet": "... and Comparison Data\n\n\n ABSTRACTWe present a method for learning potentially intransitive preference relations from pairwise comparison and matchup data. Unlike standard preference-learning models tha...", "weights": [[8, 18, 1.0], [128, 138, 1.0], [193, 199, 1.0]]}, "2008.ir_journal-ir0volumeA11A2.0": {"snippet": "...mage retrieval: an experimental comparison", "weights": [[8, 17, 1.0], [35, 45, 1.0]]}, "1992.ipm_journal-ir0volumeA28A4.5": {"snippet": "...ntal Retrieval Comparisons", "weights": [[8, 17, 1.0]]}, "2000.ismir_conference-2000.1": {"snippet": "A Comparison of Language Modeling and Probabilistic Text Information Retrieval Approaches to Monophonic Music Retrieval", "weights": [[2, 12, 1.0], [69, 78, 1.0], [110, 119, 1.0]]}, "2006.cikm_conference-2006.100": {"snippet": "...rect comparison of commercial and academic retrieval system: an initial study", "weights": [[8, 18, 1.0], [46, 55, 1.0]]}, "2013.cikm_conference-2013.189": {"snippet": "...ning comparison queries. We present a novel snowballing algorithm that \"crawls\" comparison queries from search engines via their query autocompletion services. We propose a novel modeling approach tha...", "weights": [[8, 18, 1.0], [83, 93, 1.0]]}, "2012.cikm_conference-2012.210": {"snippet": "...aved comparison outcomes from historical click data\n\n\n ABSTRACTInterleaved comparison methods, which compare rankers using click data, are a promising alternative to traditional information retrieval ...", "weights": [[8, 18, 1.0], [78, 88, 1.0], [193, 202, 1.0]]}, "2020.sigirconf_conference-2020.42": {"snippet": "...tion retrieval (IR) ranking models in production systems continually evolve in response to user feedback, insights from research, and new developments. Rather than investing all engineering resources ...", "weights": [[8, 17, 1.0], [31, 37, 1.0]]}, "D10-1011": {"snippet": "...uage Models for Clinical Information Retrieval\n\n\n The PECO framework is a knowledge representation for formulating clinical questions. Queries are decomposed into four aspects, which are Patient-Probl...", "weights": [[8, 14, 1.0], [40, 49, 1.0]]}, "2008.sigirconf_conference-2008.62": {"snippet": "Retrieval models for question and answer archives\n\n\n ABSTRACTRetrieval in a question and answer archive involves finding good answers for a user's question. In contrast to typical document retrieval, ...", "weights": [[0, 9, 1.0], [10, 16, 1.0], [189, 198, 1.0]]}, "2012.ecir_conference-2012.24": {"snippet": "...tion Retrieval Model\n\n\n Abstract. We present in this paper well-founded cross-language extensions of the recently introduced models in the information-based family for information retrieval, namely th...", "weights": [[8, 17, 1.0], [128, 134, 1.0], [183, 192, 1.0]]}}}
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The new Web c...", "weights": [[8, 13, 1.0], [54, 59, 1.0], [142, 147, 1.0], [202, 207, 1.0]]}, "2011.wwwconf_workshop-2011ldow.11": {"snippet": "How Caching Improves Efficiency and Result Completeness for Querying Linked Data\n\n\n Link traversal based query execution is a novel query approach which enables applications that exploit the Web of Da...", "weights": []}, "2018.ecir_conference-2018.47": {"snippet": "...eval architecture that intentionally creates skewed postings and access patterns. This work shows that the well-known QtfDf inverted list caching algorithm is as effective with topically-partitioned i...", "weights": [[8, 20, 1.0]]}, "2003.wwwconf_conference-2003.32": {"snippet": "... -an architecture that uses a peer-to-peer approach to cooperatively stream video using patching techniques, while only relying on unicast connections among peers. 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{"snippet": "...eing formula as an expression tree. The other index is a table which stores the parent node and the text string for each node in the expression trees. A hundred thousand documents in the NTCIR-10 Math...", "weights": [[8, 15, 1.0]]}, "2019.sigirconf_workshop-2019birndl.12": {"snippet": "...ne a Formula Concept Retrieval challenge with two subtasks: Formula Concept Discovery (FCD) and Formula Concept Recognition (FCR). While the former aims at the definition and exploration of a Formula ...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [99, 106, 1.0], [195, 202, 1.0]]}, "2014.sigirconf_conference-2014.73": {"snippet": "...ient formula index and retrieval from both webpages and PDF documents. Unlike prior studies, which require users to manually input formula markup language as query, the new system enables users to \"co...", "weights": [[8, 15, 1.0], [134, 141, 1.0]]}, "2013.ntcir_conference-2013.84": {"snippet": "...each document. We employ not only a \"static\" document collection consisting of targeted documents but also a \"dynamic\" document collection including web documents related with queries. We expand the q...", "weights": [[8, 16, 1.0], [48, 56, 1.0], [122, 130, 1.0]]}, "2016.sigirconf_conference-2016.203": {"snippet": "...ased formula retrieval in two different modalities: symbolic for text documents and image-based for videos.We believe that we can achieve high quality formula retrieval results using the visual appear...", "weights": [[8, 15, 1.0], [154, 161, 1.0]]}, "J82-1003": {"snippet": "... the formula for the fragment. However, we have sought to reformulate Gazdar's semantic rules so as to obtain more or less 'conventional' logical translations of English sentences, avoiding the interp...", "weights": [[8, 15, 1.0]]}, "E91-1044": {"snippet": "...ault scoping first. Removing logical redundancy is not only interesting per se, but also drastically reduces the processing time. The input and output formats are described through a few access and co...", "weights": [[8, 15, 1.0]]}, "2009.cikm_conference-2009.275": {"snippet": "Smoothing DCG for learning to rank: a novel approach using smoothed hinge functions\n\n\n ABSTRACTDiscounted cumulative gain (DCG) is widely used for evaluating ranking functions. It is therefore natural...", "weights": []}, "2012.cikm_workshop-2012esair.9": {"snippet": "...ical formula search\n\n\n ABSTRACTIn recent years, growing numbers of scientific papers have been published in XML format generating a large published base of MathML-style formulas. Although these formul...", "weights": [[8, 15, 1.0]]}, "2007.riao_conference-2007.16": {"snippet": "An IE and IR Approach to deal with Geographic Information Scope in Textual Documents", "weights": []}, "2017.ecir_workshop-2017smerp.8": {"snippet": "Extracting Humanitarian Information from Tweets\n\n\n In this paper we describe the application of our methods to humanitarian information extraction from tweets and their performance in the scope of the...", "weights": []}, "2019.tois_journal-ir0volumeA37A3.1": {"snippet": "On Annotation Methodologies for Image Search Evaluation\n\n\n Image search engines differ significantly from general web search engines in the way of presenting search results. The difference leads to di...", "weights": []}, "2018.ictir_conference-2018.36": {"snippet": "...tant document features such as term frequency, document frequency, and document length. We investigate in this paper aggregative and statistical document features for document ranking. Shortly speakin...", "weights": [[8, 16, 1.0], [50, 58, 1.0], [74, 82, 1.0], [148, 156, 1.0], [170, 178, 1.0]]}, "2020.clef_conference-2020w.45": {"snippet": "... (2) Formula Retrieval. Four runs were submitted for Task 1 using systems that take advantage of text and formula embeddings. For Task 2, three runs were submitted: one uses only formula embedding, an...", "weights": [[8, 15, 1.0], [109, 116, 1.0], [182, 189, 1.0]]}, "2006.cikm_conference-2006.60": {"snippet": "...of a document, our model also takes into account domain specific knowledge, i.e., how the domain-specific concepts contained in the document affect the document's readability. Three major readability ...", "weights": [[8, 16, 1.0], [135, 143, 1.0], [155, 163, 1.0]]}, "2011.sigirconf_conference-2011.121": {"snippet": "...tion formula:where |D| represents document length, avdl stands for average document length, c(q, D) is the raw TF of q in D, and b and k1 are two parameters. c (q, D) is the normalized TF by document ...", "weights": [[8, 15, 1.0], [37, 45, 1.0], [78, 86, 1.0], [194, 202, 1.0]]}, "J87-1005": {"snippet": "...ored scoping or generated scopings with mechanisms that have often been inexplicit as to the range of scopings they choose among or profligate in the scopings they allow. This paper presents, along wi...", "weights": [[8, 15, 1.0]]}, "P11-2025": {"snippet": "...s on scoping sentences with only two quantified noun phrases (NPs), where the quantifiers are restricted to a predefined list. It also ignores negation, modal/logical operators, and other sentential a...", "weights": [[8, 15, 1.0]]}, "2012.wsdm_conference-2012.9": {"snippet": "...oral scoping. We show that joint inference is effective compared to doing temporal scoping of individual facts independently. We conduct our experiments on large scale open-domain publicly available t...", "weights": [[8, 15, 1.0], [86, 93, 1.0]]}, "2020.coling-industry.20": {"snippet": "...I}t: Scoping Task Relevant Sentences in Documents\n\n\n A prominent problem faced by conversational agents working with large documents (Eg: emailbased assistants) is the frequent presence of information...", "weights": [[8, 15, 1.0]]}, "2004.spire_conference-2004.15": {"snippet": "Indexing Text Documents Based on Topic Identification\n\n\n Abstract. This work provides algorithms and heuristics to index text documents by determining important topics in the documents. To index text ...", "weights": []}, "2003.spire_conference-2003.15": {"snippet": "...king formula which we evaluate using a medical reference collection, the OHSUMED collection. Our results indicate that this combination of evidences might yield considerable gains in retrieval perform...", "weights": [[8, 15, 1.0]]}, "J90-1001": {"snippet": "... and Scoping\n\n\n Certain restrictions on possible scopings of quantified noun phrases in natural language are usually expressed in terms of formal constraints on binding at a level of logical form. Suc...", "weights": [[8, 15, 1.0]]}, "P92-1031": {"snippet": "Connection Relations and Quantifier Scope\n\n\n A formalism will be presented in this paper which makes it possible to realise the idea of assigning only one scope-ambiguous representation to a sentence ...", "weights": []}, "1997.cikm_workshop-97.53": {"snippet": "...nted Document Search System\n\n\n AbstractWith the increasing numbers of documents available from the Internet and other sources, finding anything is becoming increasingly difficult. This paper presents ...", "weights": [[8, 16, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.6": {"snippet": "Computing, explaining and visualizing shape similarity in content-based image retrieval\n\n\n AbstractAlthough there is a growing need for Content-Based Image Retrieval systems, their use is often hamper...", "weights": []}, "2008.gir_workshop-2008.3": {"snippet": "Geographic scope modeling for web documents", "weights": []}, "J03-1004": {"snippet": "A Machine Learning Approach to Modeling Scope Preferences\n\n\n This article describes a corpus-based investigation of quantifier scope preferences. Following recent work on multimodular grammar framewor...", "weights": []}, "2013.ntcir_conference-2013.75": {"snippet": "...t of formula patterns and a test data corpus. We showcase a simple data analysis program for answering the given queries. We interpret the patterns as regular expressions and assume that matches to th...", "weights": [[8, 15, 1.0]]}, "W14-1612": {"snippet": "...oral Scoping of Relational Facts based on {W}ikipedia Data\n\n\n Most previous work in information extraction from text has focused on named-entity recognition, entity linking, and relation extraction. L...", "weights": [[8, 15, 1.0]]}, "2020.clef_conference-2020w.120": {"snippet": "Named Entity Recognition in Chemical Patents using Ensemble of Contextual Language Models\n\n\n Chemical patent documents describe a broad range of applications holding key reaction and compound informat...", "weights": []}, "2020.lrec-1.269": {"snippet": "...s of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. We also present a novel evaluation dataset for this task...", "weights": [[8, 15, 1.0], [126, 133, 1.0]]}, "2020.sdp-1.16": {"snippet": "... and formula analyses, so-called mathematical language processing, is necessary to enable computers to understand and retrieve information from the documents. However, as we will show in this project,...", "weights": [[8, 15, 1.0]]}, "2004.wwwconf_conference-2004at.37": {"snippet": "Enhancing the SCORM metadata model\n\n\n ABSTRACTNowadays, the leading e-learning platforms are converging towards standardization. This paper presents an extension to the SCORM, today's most well acclai...", "weights": []}, "2016.cikm_workshop-2016fm4m.18": {"snippet": "Augmenting Mathematical Formulae for More Effective Querying & Presentation", "weights": []}, "1999.sigirconf_conference-99.12": {"snippet": "...ical Formula for Chinese Text Segmentation Incorporating Contextual Information\n\n\n ABSTRACTA new statistical formula for identifying 2-character words in Chinese text, called the contextual informatio...", "weights": [[8, 15, 1.0], [112, 119, 1.0]]}, "P89-1019": {"snippet": "... and Scoping\n\n\n Certain restrictions on possible scopings of quantified noun phrases in natural language are usually expressed in terms of formal constraints on binding at a level of logical form. Suc...", "weights": [[8, 15, 1.0]]}, "2015.tois_journal-ir0volumeA33A2.3": {"snippet": "...each document is prenormalized by dividing the term frequency by the verbosity of the document. In scope normalization, an existing retrieval model is applied in a straightforward manner to the prenor...", "weights": [[8, 16, 1.0], [89, 97, 1.0]]}, "2009.cikm_workshop-2009swsm.4": {"snippet": "Automobile, car and BMW: horizontal and hierarchical approach in social tagging systems\n\n\n ABSTRACTSocial tagging systems have recently emerged as an effective way for users to annotate and organize l...", "weights": []}, "P88-1005": {"snippet": "...fier Scoping in the {SRI} Core Language Engine\n\n\n An algorithm for generating the possible quantifier scopings for a sentence, in order of preference, is outlined. The scoping assigned to a quantifier...", "weights": [[8, 15, 1.0], [171, 178, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.5": {"snippet": "...ical formula, all possible (or a significant number of) subformulae of formulae that appear in any document and all possible subterms (e.g., \"methyl\") of chemical names (e.g., \"methylethyl ketone\") mu...", "weights": [[8, 15, 1.0], [102, 110, 1.0]]}, "2015.wsdm_conference-2015.62": {"snippet": "... for Document-Level Sentiment Analysis\n\n\n ABSTRACTIn this paper, we propose a representation learning research framework for document-level sentiment analysis. Given a document as the input, document-...", "weights": [[8, 16, 1.0], [128, 136, 1.0], [171, 179, 1.0], [194, 202, 1.0]]}, "C90-3034": {"snippet": "...fier Scoping Algorithm without A Free Variable Constraint\n\n\n Three recent demands on quantifier scoping algorithms have been that they should be explicitly stated, they should be sound and complete wi...", "weights": [[8, 15, 1.0], [99, 106, 1.0]]}, "2021.newsum-1.14": {"snippet": "...each document and intent (e.g., \"weather\"), a single summary suffices for all users. No datasets exist, however, for subjective intents (e.g., \"interesting places\") where different users will provide ...", "weights": [[8, 16, 1.0]]}, "2002.spire_conference-2002.13": {"snippet": "...Rank formula and the classic formula proposed in . As an example of the versatility of our general formula we derive from it a version of PageRank based on a user personalization. Finally, we discuss ...", "weights": [[8, 15, 1.0], [32, 39, 1.0], [102, 109, 1.0]]}, "2007.wwwconf_conference-2007.26": {"snippet": "...ical formula using a search engine today, she gets articles where the exact keyword string expressing the chemical formula is found. Searching for the exact occurrence of keywords during searching res...", "weights": [[8, 15, 1.0], [118, 125, 1.0]]}, "2020.clef_conference-2020w.14": {"snippet": "...t of formula embedding and term-document matrix, in this paper we have introduced the variable size formula embedding approach where the formula is transformed into the variable size vector. In a vect...", "weights": [[8, 15, 1.0], [35, 43, 1.0], [103, 110, 1.0], [140, 147, 1.0]]}, "2011.sigirconf_conference-2011.20": {"snippet": "...with document length based on a trade-off between the Verbosity hypothesis, which assumes the independence of a document's relevance of its length, and the Scope hypothesis, which assumes the opposite...", "weights": [[8, 16, 1.0], [115, 123, 1.0]]}, "Y10-1055": {"snippet": "A Modular Architecture for the Wide-Coverage Translation of Natural Language Texts into Predicate Logic Formulas\n\n\n We present a new method for translating unrestricted natural language texts into pre...", "weights": []}, "C88-2135": {"snippet": "...lity Formula of {J}apanese Texts for Machine Scoring\n\n\n A readability formula is obtained that can be used by computer programs for style checking of Japanese texts and need not syntactic or semantic ...", "weights": [[8, 15, 1.0], [73, 80, 1.0]]}, "2020.lrec-1.132": {"snippet": "...ting scoping configurations, and is presented specifically as a resource for quantifier scope disambiguation systems, but also more generally as an object of linguistic study.", "weights": [[8, 15, 1.0]]}, "2019.wwwconf_conference-2019c.169": {"snippet": "...oral Scoping of Facts", "weights": [[8, 15, 1.0]]}, "2009.tois_journal-ir0volumeA27A3.4": {"snippet": "Bounded coordinate system indexing for real-time video clip search\n\n\n Recently, video clips have become very popular online. The massive influx of video clips has created an urgent need for video sear...", "weights": []}, "2020.ecir_conference-20202.60": {"snippet": "...tive Formula Editing, Reuse, and Lookup\n\n\n There has been growing interest in math-aware search engines that support retrieval using both formulas and keywords. An important unresolved issue is the de...", "weights": [[8, 15, 1.0]]}, "2006.clef_workshop-2006w.114": {"snippet": "...d in document retrieval through a ranking function that combines BM25 text weighting with a similarity function for geographic scopes. We also tested a topic augmentation method, based on the geograph...", "weights": [[8, 16, 1.0]]}, "2020.clef_conference-2020w.8": {"snippet": "...ntic Formula Search in zbMATH Open?", "weights": [[8, 15, 1.0]]}, "A88-1008": {"snippet": "... the scoping protocol which wcre encountered during the design of the scoping program.", "weights": [[8, 15, 1.0], [73, 80, 1.0]]}, "2014.ntcir_conference-2014.19": {"snippet": "Evaluation of Similarity-Measure Factors for Formulae Based on the NTCIR-11 Math Task\n\n\n In this paper we evaluate the similarity-measure factors proposed by Zhang and Youssef based on the NTCIR-11 go...", "weights": []}, "W19-3306": {"snippet": "Generating Discourse Inferences from Unscoped Episodic Logical Formulas\n\n\n Unscoped episodic logical form (ULF) is a semantic representation capturing the predicateargument structure of English within...", "weights": []}, "Y05-1017": {"snippet": "A study on multiple interpretations of frequency adverbs in {J}apanese\n\n\n This paper is concerned with frequency adverbs in Japanese. Many linguists have pointed out that frequency statements and gene...", "weights": []}, "N15-1126": {"snippet": "...gger Scoping Strategy for Biographical Fact Extraction\n\n\n The rapid growth of information sources brings a unique challenge to biographical information extraction: how to find specific facts without h...", "weights": [[8, 15, 1.0]]}, "2007.cikm_workshop-2007cims.8": {"snippet": "...on a document collection. Each method consists of a combination of term scoring scheme, term scoring formula, and term assignment scheme. Our preliminary experiments evaluate these different slot-base...", "weights": [[8, 16, 1.0], [104, 111, 1.0]]}, "W06-1510": {"snippet": "Quantifier Scope in {G}erman: An {MCTAG} Analysis\n\n\n Relative quantifier scope in German depends, in contrast to English, very much on word order. The scope possibilities of a quantifier are determine...", "weights": []}, "2018.sigirconf_conference-2018.210": {"snippet": "...-idf formula for expanded words in search ranking.", "weights": [[8, 15, 1.0]]}, "2013.clef_conference-2013w.64": {"snippet": "German-Language Content in Biomedical Vocabularies\n\n\n There are hundreds of English-language biomedical vocabularies, but only a minor part has been translated into other languages. We performed a sur...", "weights": []}, "P92-1005": {"snippet": "...fier scoping and reference resolution, are often realised computationally by non-monotonic operations involving loss of information and destructive manipulation of semantic representations. The paper ...", "weights": [[8, 15, 1.0]]}, "2004.spire_conference-2004.6": {"snippet": "...hird document. The current technique for concurrently detecting all co-derivatives in a collection is document fingerprinting, which matches documents based on the hash values of selected document sub...", "weights": [[8, 16, 1.0], [105, 113, 1.0], [191, 199, 1.0]]}, "2016.ntcir_conference-2016.87": {"snippet": "...t of formula importance within a document is introduced into the model. In order to make the ranking results more reasonable, our system (WikiMir) applies the learning to rank algorithm (RankBoost) to...", "weights": [[8, 15, 1.0], [36, 44, 1.0]]}, "2009.wsdm_workshop-2009wscd.10": {"snippet": "...rove document descriptions. A pilot experiment demonstrated that document descriptors extracted from the queries that are associated with documents by clicks provide useful semantic information about ...", "weights": [[8, 16, 1.0], [68, 76, 1.0]]}, "J90-3001": {"snippet": "Resolving Quasi Logical Forms\n\n\n The paper describes intermediate and resolved logical form representations of sentences involving referring expressions and a reference resolution process for mapping ...", "weights": []}, "2007.clef_workshop-2007.101": {"snippet": "... and Document Scopes in Geographic IR", "weights": [[8, 16, 1.0]]}, "2020.sigirconf_conference-2020.307": {"snippet": "KLOOS: KL Divergence-based Out-of-Scope Intent Detection in Human-to-Machine Conversations\n\n\n User intent is not restricted in human-to-machine conversations, and sometimes overshoots the scope of a d...", "weights": []}, "L12-1534": {"snippet": "A methodology for the extraction of information about the usage of formulaic expressions in scientific texts", "weights": []}, "2012.tois_journal-ir0volumeA30A2.5": {"snippet": "Authorship Attribution Based on Specific Vocabulary\n\n\n In this article we propose a technique for computing a standardized Z score capable of defining the specific vocabulary found in a text (or part ...", "weights": []}, "P89-1004": {"snippet": "...s of scoping and reference resolution. The approach has been implemented in the SRI Core Language Engine which handles the English constructions discussed in the paper.", "weights": [[8, 15, 1.0]]}}}
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feedback via tweet selection\n\n\n ABSTRACTQuery expansion methods using pseudo-relevance feedback have been shown effective for microblog search because they can solve vocabulary mismatch problems ...", "weights": [[8, 16, 1.0], [95, 103, 1.0]]}, "2002.ntcir_workshop-2002.18": {"snippet": "...ance Feedback Method Using Taylor Formula at NTCIR-3 Patent Retrieval Task\n\n\n Pseudo relevance feedback is empirically known as a useful method for enhancing retrieval performance. 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Unfortunately, many experiments have shown that although pseudo-relevance feedb...", "weights": [[8, 16, 1.0], [45, 53, 1.0], [198, 206, 1.0]]}, "2008.sigirconf_conference-2008.32": {"snippet": "...ance feedback\n\n\n ABSTRACTTypical pseudo-relevance feedback methods assume the topretrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can...", "weights": [[8, 16, 1.0], [53, 61, 1.0]]}, "W03-2004": {"snippet": "...ance Feedback Method based on {T}aylor Expansion of Retrieval Function in {NTCIR}-3 Patent Retrieval Task\n\n\n Pseudo relevance feedback is empirically known as a useful method for enhancing retrieval p...", "weights": [[8, 16, 1.0], [129, 137, 1.0]]}, "2008.trec_conference-2008.16": {"snippet": "...ers' feedback for an initial result list by the system. With different amount of feedback information available, the optimal feedback strategy might be very different. 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Traditional feedback algorithms use a whole feedback document as a unit to extract words f...", "weights": [[8, 16, 1.0], [45, 53, 1.0], [125, 133, 1.0], [157, 165, 1.0]]}, "2012.cikm_workshop-2012dubmmsm.7": {"snippet": "...icit feedback, high proportion of cold-start tweets and short tweet active time. In this paper, we propose a novel retweet recommendation (RTR) framework which leverages the implicit feedback to help ...", "weights": [[8, 16, 1.0], [186, 194, 1.0]]}, "2017.ecir_conference-2017.63": {"snippet": "...tive feedback from the user in a language modeling framework to compute the relevancy score of new documents. In other words, the negative feedback from the user is considered as the representative of...", "weights": [[8, 16, 1.0], [142, 150, 1.0]]}, "2008.sigirconf_conference-2008.57": {"snippet": "...ance feedback. For disjoint terms, a relationship between probability theory and TF-IDF is established through the integral R 1 x dx = log x. This study uncovers components such as divergence from ran...", "weights": [[8, 16, 1.0]]}, "2012.wsdm_conference-2012.59": {"snippet": "Exploring social influence via posterior effect of word-of-mouth recommendations\n\n\n ABSTRACTWord-of-mouth has proven an effective strategy for promoting products through social relations. Particularly...", "weights": []}, "2008.ecir_conference-2008.44": {"snippet": "...ance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, while improving retrieval in most cases, hurts performance on ...", "weights": [[8, 16, 1.0]]}, "2012.irfc_conference-2012.1": {"snippet": "...ance Feedback for Patent Retrieval", "weights": [[8, 16, 1.0]]}, "2008.sigirconf_conference-2008.31": {"snippet": "...ance feedback\n\n\n ABSTRACTRelevance feedback, which traditionally uses the terms in the relevant documents to enrich the user's initial query, is an effective method for improving retrieval performance...", "weights": [[8, 16, 1.0], [38, 46, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "...ance feedback\nGeneral TermsAlgorithms, Performance, Experimentation\nKeywordsRelevance Feedback, Web Search, Evaluation\n1.INTRODUCTIONRelevance feedback is a classical information retrieval (IR) techni...", "weights": [[8, 16, 1.0], [89, 97, 1.0], [146, 154, 1.0]]}, "2009.cikm_conference-2009.277": {"snippet": "...ance feedback using semantic clustering in relevance language model\n\n\n ABSTRACTPseudo relevance feedback has demonstrated to be in general an effective technique for improving retrieval effectiveness,...", "weights": [[8, 16, 1.0], [99, 107, 1.0]]}, "2013.sigirconf_conference-2013.57": {"snippet": "...ance feedback\n\n\n ABSTRACTPseudo-relevance feedback is an important strategy to improve search accuracy. It is often implemented as a tworound retrieval process: the first round is to retrieve an initi...", "weights": [[8, 16, 1.0], [45, 53, 1.0]]}, "2006.sigirconf_conference-2006.22": {"snippet": "...ance feedback\n\n\n ABSTRACTPseudo-relevance feedback has proven to be an effective strategy for improving retrieval accuracy in all retrieval models. However the performance of existing pseudo feedback ...", "weights": [[8, 16, 1.0], [45, 53, 1.0], [194, 202, 1.0]]}, "2017.ecir_conference-2017.65": {"snippet": "...ance Feedback", "weights": [[8, 16, 1.0]]}, "2008.ecir_conference-2008.54": {"snippet": "...eudo Feedback Document Selection for Query Expansion", "weights": [[8, 16, 1.0]]}, "2015.clef_conference-2015.8": {"snippet": "...ance feedback helps in such a scenario. Therefore we implemented a number of pseudo relevance feedback techniques based on knowledge bases, where the knowledge base is either Wikipedia or a combinatio...", "weights": [[8, 16, 1.0], [97, 105, 1.0]]}, "2009.trec_conference-2009.5": {"snippet": "...eudo feedback strategy which stops pseudo feedback for precision-oriented queries and only uses it for recall-oriented ones. The proposed PRM has shown clear improvements over the relevance model for ...", "weights": [[8, 16, 1.0], [45, 53, 1.0]]}, "2016.sigirconf_conference-2016.129": {"snippet": "...ance Feedback in Content-Based Image Retrieval\n\n\n ABSTRACTThe increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. ...", "weights": [[8, 16, 1.0]]}, "2010.airs_conference-2010.20": {"snippet": "...ance Feedback Based on mRMR Criteria", "weights": [[8, 16, 1.0]]}, "2007.cikm_conference-2007.91": {"snippet": "...ance feedback. To derive this measure, it is necessary to make the assumption that a user is seeking an ideal (usually non-existent) document and the actual relevant documents in the collection will c...", "weights": [[8, 16, 1.0]]}, "2016.tois_journal-ir0volumeA34A4.3": {"snippet": "...ance Feedback\n\n\n Traditional pseudo relevance feedback (PRF) models choose top k feedback documents for query expansion and treat those documents equally. When k is determined, feedback terms are sele...", "weights": [[8, 16, 1.0], [49, 57, 1.0], [84, 92, 1.0], [180, 188, 1.0]]}, "2007.cikm_conference-2007.31": {"snippet": "...icit feedback\n\n\n ABSTRACTImplicit feedback algorithms utilize interaction between searchers and search systems to learn more about users' needs and interests than expressed in query statements alone. ...", "weights": [[8, 16, 1.0], [37, 45, 1.0]]}, "2003.sigirjournals_journal-ir0volumeA37A2.1": {"snippet": "...icit feedback for inferring user preference: a bibliography\n\n\n Relevance feedback has a history in information retrieval that dates back well over thirty years (c.f [SL96]). Relevance feedback is typi...", "weights": [[8, 16, 1.0], [76, 84, 1.0], [187, 195, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.3": {"snippet": "...ance feedback\n\n\n a b s t r a c tTypical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval...", "weights": [[8, 16, 1.0], [60, 68, 1.0]]}, "2009.sigirconf_conference-2009.9": {"snippet": "...ance feedback based on wikipedia", "weights": [[8, 16, 1.0]]}, "2007.clef_workshop-2007w.129": {"snippet": "...ance Feedback to Improve Image Retrieval Results\n\n\n In this paper, we propose a pseudo-relevance feedback method to deal with the photographic retrieval and medical retrieval tasks of ImageCLEF 2007. ...", "weights": [[8, 16, 1.0], [100, 108, 1.0]]}, "2020.ipm_journal-ir0volumeA57A6.55": {"snippet": "...ance feedback framework combining relevance matching and semantic matching for information retrieval", "weights": [[8, 16, 1.0]]}, "2006.sigirconf_conference-2006.110": {"snippet": "...ance feedback results\n\n\n ABSTRACTWe explore interactive methods to further improve the performance of pseudo-relevance feedback. Studies  suggest that new methods for tackling difficult queries are re...", "weights": [[8, 16, 1.0], [122, 130, 1.0]]}, "2018.sigirconf_workshop-2018profs.3": {"snippet": "...ance feedback. Our focus in this paper is on one particular aspect of CAL: refreshing, which is a crucial and recurring event in the CAL process. During a refresh, the machine learning model is traine...", "weights": [[8, 16, 1.0]]}, "2001.clef_workshop-2001.12": {"snippet": "...ance Feedback Using a Comparable Corpus", "weights": [[8, 16, 1.0]]}, "2014.ecir_conference-2014.22": {"snippet": "...ance Feedback Techniques for Informal Text", "weights": [[8, 16, 1.0]]}, "2021.ecir_conference-20212.46": {"snippet": "...ance Feedback Using a Graph-Based Transformer\n\n\n Most research on pseudo relevance feedback (PRF) has been done in vector space and probabilistic retrieval models. This paper shows that Transformer-ba...", "weights": [[8, 16, 1.0], [86, 94, 1.0]]}, "2010.trec_conference-2010.29": {"snippet": "...ance Feedback: QUT E-Discovery Lab at the TREC 2010 Relevance Feedback Track\n\n\n User relevance feedback is usually utilized by Web systems to interpret user information needs and retrieve effective re...", "weights": [[8, 16, 1.0], [65, 73, 1.0], [98, 106, 1.0]]}, "2008.sigirconf_conference-2008.56": {"snippet": "...ance feedback algorithm based on the latent mixture modeling of the Dirichlet compound multinomial distribution to further improve retrieval accuracy. Finally, our experiments show that both the basel...", "weights": [[8, 16, 1.0]]}, "2016.cikm_conference-2016.153": {"snippet": "...ance Feedback Based on Matrix Factorization", "weights": [[8, 16, 1.0]]}, "2001.sigirconf_conference-2001.53": {"snippet": "...ance Feedback Using Optimization Tables", "weights": [[8, 16, 1.0]]}, "2018.ictir_conference-2018.35": {"snippet": "...ance Feedback in Fusion-based Retrieval\n\n\n ABSTRACTThe usage of positive relevance feedback in fusion-based retrieval was previously shown to be very useful. 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Classifying an utterance as sarcastic or not in isolation can be futile since most of the time the sarcastic nature of a sentence heavily relies on i...", "weights": [[8, 21, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.5": {"snippet": "... the natural language has an extremely rich form and structure with different levels of ambiguities. Therefore, we inquire after a potential framework that could bring this challenge within reach by p...", "weights": [[8, 15, 1.0]]}, "2018.wwwconf_conference-2018.162": {"snippet": "...ting natural-sounding sentences with a unified neural encoder-decoder network structure. The end-to-end structure sequentially encodes each word in an input context and generates the response word-by-...", "weights": [[8, 15, 1.0], [143, 147, 1.0], [195, 199, 1.0]]}, "2020.nli-1.4": {"snippet": "...er's natural language questions for questionanswering (QA). 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We accomplish this by reasoning from a grammar which describes gesture declaratively in terms of its discourse function, semantics and synchrony with speech.", "weights": [[8, 15, 1.0]]}, "2005.wwwconf_conference-2005.25": {"snippet": "Web-assisted annotation, semantic indexing and search of television and radio news\n\n\n ABSTRACTThe Rich News system, that can automatically annotate radio and television news with the aid of resources ...", "weights": []}, "2010.sigirconf_conference-2010.107": {"snippet": "Context aware query classification using dynamic query window and relationship net\n\n\n ABSTRACTThe context of the user queries, preceding a given query, is utilized to improve the effectiveness of quer...", "weights": []}, "2015.tois_journal-ir0volumeA34A1.3": {"snippet": "....e., word embeddings) to address text mining, information retrieval, and natural language processing tasks. Most recent efforts have proposed several efficient methods to learn word embeddings from co...", "weights": [[8, 12, 1.0], [76, 83, 1.0], [180, 184, 1.0]]}, "2020.sigirconf_conference-2020.313": {"snippet": "...ging Word Proximity Networks\n\n\n Question answering (QA) over text passages is a problem of longstanding interest in information retrieval. Recently, the conversational setting has attracted attention,...", "weights": [[8, 12, 1.0]]}, "2017.wwwconf_conference-2017.142": {"snippet": "...cale natural setting, in the form of an online team-based geography game, that allows us to disentangle confidence from competence and thus separate their effects.We find that in task-oriented discuss...", "weights": [[8, 15, 1.0]]}, "2021.gwc-1.3": {"snippet": "...ican Word-Nets in OMW using NLTK.", "weights": [[8, 12, 1.0], [13, 17, 1.0]]}, "E87-1030": {"snippet": "Natural and Simulated Pointing\n\n\n Referent identification in human conversation is performed both by describing the objects in question and by pointing at them. Up till now, only the linguistic compon...", "weights": [[0, 7, 1.0]]}, "2012.cikm_conference-2012.197": {"snippet": "...hich represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also t...", "weights": [[8, 17, 1.0]]}, "2018.wwwconf_conference-2018c.101": {"snippet": "...uter Conversations\n\n\n ABSTRACTTo establish an automatic conversation system between human and computer is regarded as one of the most hardcore problems in computer science. It requires interdisciplina...", "weights": [[8, 21, 1.0]]}, "2020.acl-main.184": {"snippet": "...uman conversations naturally evolve around related concepts and scatter to multi-hop concepts. This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowled...", "weights": [[8, 21, 1.0]]}, "1998.tois_journal-ir0volumeA16A4.3": {"snippet": "...ntic nets. The relational inconsistency hinders the creation of domain information models. The structural inconsistency leads to unstable documents, especially when a document is composed by computati...", "weights": [[8, 12, 1.0]]}, "2013.cikm_workshop-2013unlp.10": {"snippet": "Mirroring the real world in social media: twitter, geolocation, and sentiment analysis\n\n\n ABSTRACTIn recent years social media has been used to characterize and predict real world events, and in this ...", "weights": []}, "2003.clef_workshop-2003w.3": {"snippet": "...ying Natural Language Processing techniques for single word and multi-word term conflation. Two different conflation approaches have been tested. The first approach is based on the lemmatization of th...", "weights": [[8, 15, 1.0], [58, 62, 1.0], [73, 77, 1.0]]}, "H01-1013": {"snippet": "... key word search do not allow users to describe their intentions precisely. We have developed a conversational interface to online shopping that provides convenient, personalized access to information...", "weights": [[8, 12, 1.0]]}, "2018.sigirconf_conference-2018.26": {"snippet": "...e to represent a user conversation history as a semi-structured user query with facet-value pairs. This query is generated and updated by belief tracker that analyzes natural language utterances of us...", "weights": [[8, 17, 1.0], [170, 177, 1.0]]}, "W10-1302": {"snippet": "...uses Natural Language Generation (NLG) technology to automatically generate conversational utterances from a domain knowledge base modelled from content suggested by a small AAC user group. Findings f...", "weights": [[8, 15, 1.0]]}, "2019.ipm_journal-ir0volumeA56A1.9": {"snippet": "...line conversations\n\n\n A B S T R A C TWith the explosion of multilingual content on Web, particularly in social media platforms, identification of languages present in the text is becoming an important...", "weights": [[8, 21, 1.0]]}, "2021.emnlp-main.174": {"snippet": "...nted conversations, so as to effectively identify user's intention and generate more accurate responses. Experimental results verify the effectiveness of IR-Net, showing that it achieves the stateof-t...", "weights": [[8, 21, 1.0]]}, "P98-1066": {"snippet": "...th a natural language layer to convert English queries for use by the various search engines. The NLP layer incorporates morphological analysis, noun phrase syntax, and semantic expansion based on Wor", "weights": [[8, 15, 1.0], [200, 204, 1.0]]}, "W11-0112": {"snippet": "...ting natural language semantics. However, it is an open question of how best to integrate it with uncertain, probabilistic knowledge, for example regarding word meaning. This paper describes the first...", "weights": [[8, 15, 1.0], [159, 163, 1.0]]}, "W18-1405": {"snippet": "... use natural language to express spatial relations between and across entities in the world with great facility, natural language systems have a facility that depends on that human facility. This posi...", "weights": [[8, 15, 1.0], [116, 123, 1.0]]}, "2020.peoples-1.7": {"snippet": "...n in Conversations\n\n\n Since language model pretraining to learn contextualized word representations has been proposed, pretrained language models have made success in many natural language processing ...", "weights": [[8, 21, 1.0], [82, 86, 1.0], [175, 182, 1.0]]}, "H91-1064": {"snippet": "...In a natural dialog, a considerable proportion of the utterances actually relate to the maintenance of the dialog itself rather than to furthering the task or goals motivating the conversation. For ex...", "weights": [[8, 15, 1.0]]}, "D19-1186": {"snippet": "...t at word-level or discourse-level with a flat model to alleviate this problem, we propose a hierarchical generation model to capture the different levels of diversity using the conditional variationa...", "weights": [[8, 12, 1.0]]}, "2019.sigirconf_workshop-2019birndl.11": {"snippet": "...ural word embeddings, which have reported good results in the latest years for several NLP tasks. In this work, we evaluate the performance of two popular neural word embeddings (Word2vec and GloVe) i...", "weights": [[8, 12, 1.0], [165, 169, 1.0]]}, "C92-1061": {"snippet": "...roof-Nets which is much related to Dependency Structures similar to those we meet in, for instance (Hudson 1984) . These new Proof-Nets are called Connection Nets. We show that Connection Nets provide...", "weights": [[8, 12, 1.0], [134, 138, 1.0], [161, 165, 1.0], [191, 195, 1.0]]}, "2019.gwc-1.7": {"snippet": "...ng a Word Sense Disambiguation System\n\n\n Arabic WordNet (AWN) represents one of the best-known lexical resources for the Arabic language. However, it contains various issues that affect its use in dif...", "weights": [[8, 12, 1.0]]}, "2018.ecir_conference-2018.12": {"snippet": "...cess natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by ...", "weights": [[8, 15, 1.0]]}, "W10-4211": {"snippet": "...ting Conversations: a User Study\n\n\n In this paper we present a complete system for automatically generating natural language abstracts of meeting conversations. This system is comprised of components ...", "weights": [[8, 21, 1.0], [111, 118, 1.0], [149, 162, 1.0]]}, "2019.gwc-1.34": {"snippet": "...with Word-Net specifications. Besides presenting the representation effort, we show the utilization of OntoLex-Lemon to bridge from WordNet-like resources to full lexical descriptions and extend the c...", "weights": [[8, 12, 1.0]]}, "P14-1115": {"snippet": "...tten Conversations Based on Phrasal Queries\n\n\n We propose a novel abstractive querybased summarization system for conversations, where queries are defined as phrases reflecting a user information need...", "weights": [[8, 21, 1.0], [117, 130, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.23": {"snippet": "... for natural language conversation\n\n\n AbstractAs an important task of artificial intelligence, natural language conversation has attracted wide attention of researchers in natural language processing....", "weights": [[8, 15, 1.0], [98, 105, 1.0], [175, 182, 1.0]]}, "2017.tois_journal-ir0volumeA35A3.10": {"snippet": "...s in natural language processing. Many neural network models such as Convolutional Neural Network (CNN), Recursive Neural Network (RNN), and Long Short-Term Memory (LSTM) have been proposed to learn r...", "weights": [[8, 15, 1.0]]}, "P17-2099": {"snippet": "...s of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of se...", "weights": [[8, 15, 1.0]]}, "2020.sigdial-1.10": {"snippet": "...tive Conversations using Latent Discourse Behaviors\n\n\n In this work, we study collaborative online conversations. Such conversations are rich in content, constructive and motivated by a shared goal. A...", "weights": [[8, 21, 1.0], [102, 115, 1.0], [122, 135, 1.0]]}, "K19-1066": {"snippet": "...ng a natural way of interaction between customers and adaptive intelligent systems. A key requirement in these systems is the ability to efficiently parse user queries, understand the intent behind ea...", "weights": [[8, 15, 1.0]]}, "2022.humeval-1.10": {"snippet": "...y of Word Sense Disambiguation for Naturalistic Social Dialogs in {A}merican {E}nglish\n\n\n Current evaluation practices for social dialog systems, dedicated to human-computer spontaneous conversation, ...", "weights": [[8, 12, 1.0]]}, "W12-4804": {"snippet": "...gram word-based language model, important word-to-word collocation information are lost during this compression and lead to a drop of the conversion accuracies. In order to overcome this problem, we p...", "weights": [[8, 12, 1.0], [45, 49, 1.0], [53, 57, 1.0]]}, "W06-3408": {"snippet": "...-art natural language processing components that have been modified to accommodate the unique nature of conversational data. In addition, we leverage the added richness of conversational data by analy...", "weights": [[8, 15, 1.0]]}, "2003.wwwconf_conference-2003.43": {"snippet": "...s of conversations exhibit unexpected behaviors. For example, there exists a composite e-service based on Mealy peers whose set of conversations is not context free (and not regular). (The set of conv...", "weights": [[8, 21, 1.0], [134, 147, 1.0], [199, 212, 1.0]]}, "2019.gwc-1.32": {"snippet": "...uate word embeddings in general; and (iii) comparing the performance in that task of the two word embeddings, extracted from the two WordNets. A better performance in that evaluation task results from...", "weights": [[8, 12, 1.0], [96, 100, 1.0]]}, "2006.cikm_conference-2006.32": {"snippet": "...g of natural language text. Motivated by the problem of building a noun phrase-level semantic parser and adapting it to various applications, such as machine translation and multilingual question answ...", "weights": [[8, 15, 1.0]]}, "N12-1012": {"snippet": "...e of Conversations?\n\n\n This study aims to infer the social nature of conversations from their content automatically. To place this work in context, our motivation stems from the need to understand how...", "weights": [[8, 21, 1.0], [72, 85, 1.0]]}, "2018.sigirconf_conference-2018.221": {"snippet": "...most natural way.\nTHE TUTORIALIn this tutorial, we start with a brief introduction to the recent progress on DL and RL that is related to natural language processing (NLP), information retrieval (IR) ...", "weights": [[8, 15, 1.0], [141, 148, 1.0]]}, "W10-4318": {"snippet": "...ized conversations. We investigated whether listeners perceive conversations with these improvements as natural (i.e., human-like) as human-human conversations. We also assessed their ability to corre...", "weights": [[8, 21, 1.0], [66, 79, 1.0], [107, 114, 1.0], [149, 162, 1.0]]}, "W93-0103": {"snippet": "...each word from corpus. The concept of a word is defined within the probabilistic framework. A variation of Belief Net named as Collocation Map is used to compute the probabilities. The Belief Net capt...", "weights": [[8, 12, 1.0], [43, 47, 1.0]]}, "R15-1061": {"snippet": "...rate Word Sense Representations\n\n\n Distributed representations of words have boosted the performance of many Natural Language Processing tasks. However, usually only one representation per word is obt...", "weights": [[8, 12, 1.0], [112, 119, 1.0], [192, 196, 1.0]]}, "N15-3017": {"snippet": "Brahmi-Net: A transliteration and script conversion system for languages of the {I}ndian subcontinent\n\n\n We present Brahmi-Net -an online system for transliteration and script conversion for all major...", "weights": []}, "2018.chiir_conference-2018.6": {"snippet": "...g in natural language\u00d0on messaging platforms such as Messenger, Skype, and Sina Weibo. Such conversational agents are attracting more investment, are gaining * Work carried out at Microsoft. Permissio...", "weights": [[8, 15, 1.0]]}, "C14-3002": {"snippet": "...ting Natural Languages\n\n\n Artificial neural networks are powerful statistical models that have been shown to provide excellent results in a number of domains. In the last few years, the computer visio...", "weights": [[8, 15, 1.0]]}, "C98-1063": {"snippet": "...th a natural language layer to convert English queries for use by the various search engines. The NI,P layer incorporates morphological analysis, noun phrase syntax, and semantic expansion based on Wo", "weights": [[8, 15, 1.0], [201, 205, 1.0]]}, "2020.emnlp-main.104": {"snippet": "...s\n\n\n Natural Language Processing (NLP) tasks are usually performed word by word on textual inputs. We can use arbitrary symbols to represent the linguistic meaning of a word and use these symbols as i...", "weights": [[8, 15, 1.0], [70, 74, 1.0], [78, 82, 1.0], [134, 143, 1.0], [172, 176, 1.0]]}, "2022.acl-tutorials.1": {"snippet": "...Deep Nets and Opportunities for the Future\n\n\n The first half of this tutorial will make deep nets more accessible to a broader audience, following \"Deep Nets for Poets\" and \"A Gentle Introduction to F...", "weights": [[8, 12, 1.0], [96, 100, 1.0], [156, 160, 1.0]]}, "2021.blackboxnlp-1.12": {"snippet": "Word Equations: Inherently Interpretable Sparse Word Embeddings through Sparse Coding\n\n\n Word embeddings are a powerful natural language processing technique, but they are extremely difficult to inter...", "weights": [[0, 4, 1.0], [48, 52, 1.0], [89, 93, 1.0], [120, 127, 1.0]]}, "P19-1324": {"snippet": "...ons (word embeddings) which are then put in context in the hidden layers. Since words are often ambiguous, representing the contextually relevant information is not trivial. We investigate how an LSTM...", "weights": [[8, 12, 1.0]]}, "2007.cikm_conference-2007.134": {"snippet": "...ls a word from a bigram connecting two words, and assigns a reasonable probability to words as a whole. It is more powerful than HMM to decode words containing over two characters. We conduct a compre...", "weights": [[8, 12, 1.0]]}, "2007.sigirconf_conference-2007.3": {"snippet": "...uent word in a document is a good indicator of an important topic in the document, so if the query uses the word, the document is likely to be relevant to your information need.One very simple word we...", "weights": [[8, 12, 1.0], [111, 115, 1.0], [196, 200, 1.0]]}, "W18-3016": {"snippet": "...s to represent lexical semantics. While a unified account of the lexical meaning relies on one being able to convert between these representations, in both directions, the conversion direction from se...", "weights": [[8, 17, 1.0]]}, "2014.cikm_conference-2014.122": {"snippet": "...tain word representations enhanced by the knowledge graph. Experiments on popular text mining and natural language processing tasks, including analogical reasoning, word similarity, and topic predicti...", "weights": [[8, 12, 1.0], [101, 108, 1.0], [168, 172, 1.0]]}, "Q16-1033": {"snippet": "...ling Conversations: An Application of Natural Language Processing to Mental Health\n\n\n Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy ca...", "weights": [[8, 21, 1.0], [41, 48, 1.0]]}, "2011.wwwconf_conference-2011.78": {"snippet": "...s in conversations tend to converge to one another's communicative behavior: they coordinate in a variety of dimensions including choice of words, syntax, utterance length, pitch and gestures. In its ...", "weights": [[8, 21, 1.0]]}, "2020.lrec-1.94": {"snippet": "...e in Natural Dialogue\n\n\n The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only tak...", "weights": [[8, 15, 1.0]]}, "2020.jasis_journal-ir0volumeA71A5.4": {"snippet": "Joint Modeling of Characters, Words, and Conversation Contexts for Microblog Keyphrase Extraction", "weights": []}, "C16-1130": {"snippet": "...ised Word Sense Disambiguation with Neural Models\n\n\n Determining the intended sense of words in text -word sense disambiguation (WSD) -is a longstanding problem in natural language processing. Recentl...", "weights": [[8, 12, 1.0], [105, 109, 1.0], [167, 174, 1.0]]}, "2018.sigirconf_workshop-2018birndl.4": {"snippet": "...late word importance scores for each target document using a word-level random walk. Next, we optimize sentence embedding vectors using a Siamese neural network. Here, we utilize localized sentence re...", "weights": [[8, 12, 1.0], [64, 68, 1.0]]}, "D13-1096": {"snippet": "...Text Conversations\n\n\n Natural language conversation is widely regarded as a highly difficult problem, which is usually attacked with either rule-based or learning-based models. In this paper we propos...", "weights": [[8, 21, 1.0], [25, 32, 1.0]]}, "2018.sigirconf_workshop-2018birndl.7": {"snippet": "...date word selection Keyphrase formation Candidate word scoring Suffering overgeneration error (a) Classic Scheme Candidate phrase selection Keyphrase ranking", "weights": [[8, 12, 1.0], [53, 57, 1.0]]}, "S16-2023": {"snippet": "...that represent individual speakers. We show that such 'person vectors' are informative to others, and they influence basic patterns of communication like the choice of one's interlocutor in conversati...", "weights": [[8, 17, 1.0]]}, "J75-4028": {"snippet": "... for Word Concepts\n\n\n V a r i c u s representations have been used to partray the ribmiqs of mrd (mtably action) mncepts. The mst pruninent of these include decanp3sition trees, 1 -repreentations such...", "weights": [[8, 12, 1.0]]}, "2017.sigirconf_workshop-2017birndl2.2": {"snippet": "... 250 word based on the identified sentences in the reference paper from its cited text spans using maximal marginal relevance.", "weights": [[8, 12, 1.0]]}, "2016.cikm_conference-2016.74": {"snippet": "...s in word embedding have shown that semantic representations for words can be efficiently learned by distributional models. A natural generalization is then to represent both queries and documents as ...", "weights": [[8, 12, 1.0], [129, 136, 1.0], [163, 172, 1.0]]}, "2004.clef_workshop-2004.53": {"snippet": "... the Natural Language Processing techniques we had already developed for single word conflation. In addition, the question analysis is based on complex pattern matching either over forms, part-of-spee...", "weights": [[8, 15, 1.0], [83, 87, 1.0]]}, "P18-1091": {"snippet": "... for Natural Language Inference\n\n\n Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important problems in natural language processing. It requir...", "weights": [[8, 15, 1.0], [38, 45, 1.0], [165, 172, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.6": {"snippet": "...pted Word Embeddings using Recurrent Neural Networks", "weights": [[8, 12, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.10": {"snippet": "...d by word embedding models. We present a privacy proof that satises d-privacy where the privacy parameter provides guarantees with respect to a distance metric dened by the word embedding space. We de...", "weights": [[8, 12, 1.0], [176, 180, 1.0]]}, "P93-1034": {"snippet": "...of a word are formed from entries of its near lexical neighbors. A dimensionality reduction creates a space representing the syntactic categories of unambiguous words. A neural net trained on these sp...", "weights": [[8, 12, 1.0]]}, "2011.wsdm_conference-2011.66": {"snippet": "... the natural conversational nature of social messaging systems. Two of the salient features of the proposed approach are (i) an efficient localitybased clustering approach for identifying crowds of us...", "weights": [[8, 15, 1.0]]}, "2017.sigirconf_conference-2017.40": {"snippet": "... the word distributions across the corpus to assign a topic to each word occurrence. ese two paradigms are complementary in how they represent the meaning of word occurrences. While some previous work...", "weights": [[8, 12, 1.0], [71, 75, 1.0], [136, 145, 1.0], [161, 165, 1.0]]}, "2020.acl-main.127": {"snippet": "...uman conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR-Embedding, which uti...", "weights": [[8, 21, 1.0], [152, 156, 1.0]]}, "E83-1021": {"snippet": "... (Sl-Nets) paradigm. The operations are reduced to functions of a fo~al language, thus changing the level of abstraction of the operations to be performed on SI-Nets. In this sense, operations on SI-N...", "weights": [[8, 12, 1.0], [164, 168, 1.0], [202, 206, 1.0]]}, "C80-1002": {"snippet": "Automatic Processing of Written {F}rench Language\n\n\n An automatic processor of written French language is described. This processor uses syntactic and semantic informations about words in order to con...", "weights": []}, "J77-4003": {"snippet": "...ntic Nets\n\n\n This paper presents an analysis in a semantic net formalism of the semantic structure of English sentences containing references to spatial-location. Spatial reference, hereafter -SR, pro...", "weights": [[8, 12, 1.0]]}, "2020.wwwconf_conference-2020c.2": {"snippet": "...duce word category information via the auxiliary word category label predicting task. To train our model, we construct a new Chinese query rewriting dataset and conduct experiments on it. The experime...", "weights": [[8, 12, 1.0], [52, 56, 1.0]]}, "2021.emnlp-main.403": {"snippet": "...t of natural conversation is to tailor one's response such that it accounts for concepts that the speaker and listener may or may not know and the contextual relevance of all prior concepts used in co...", "weights": [[8, 15, 1.0]]}, "W03-1403": {"snippet": "...y to Represent Metaphors in {W}ord{N}ets? Insights from the {H}amburg Metaphor Database\n\n\n This paper addresses the question whether metaphors can be represented in Word-Nets. For this purpose, domain...", "weights": [[8, 17, 1.0], [168, 172, 1.0], [173, 177, 1.0]]}, "W19-7510": {"snippet": "... the word. This allows a thorough understanding of the words, and also the knowledge they embody. Vaijayant\u012bko\u015ba is a popular Sanskrit lexicon that contains words from spoken language as well those us...", "weights": [[8, 12, 1.0]]}, "S19-2056": {"snippet": "...n in conversations through Tweets, {CNN} and {LSTM} deep neural networks\n\n\n Emotion detection from user-generated contents is growing in importance in the area of natural language processing. The appr...", "weights": [[8, 21, 1.0], [166, 173, 1.0]]}, "J18-4008": {"snippet": "...arns word distributions to represent: (1) different roles of conversational discourse, and (2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities o...", "weights": [[8, 12, 1.0], [30, 39, 1.0]]}, "W06-2105": {"snippet": "...atic natural language understanding. We present an approach towards PP interpretation as part of a natural language understanding system which has been successfully employed in various NLP tasks for i...", "weights": [[8, 15, 1.0], [102, 109, 1.0]]}, "S17-1009": {"snippet": "...h in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage. The Paraphrase Database (PPDB) covers 650 times more words, but lacks the semantic str...", "weights": [[8, 15, 1.0]]}, "2020.acl-main.166": {"snippet": "...s to represent \"what to say\" and \"how to say\", and edges to represent natural transition between a message (the last utterance in a dialog context) and its response. We then present a novel CG grounde...", "weights": [[8, 17, 1.0], [63, 72, 1.0], [73, 80, 1.0]]}, "L18-1462": {"snippet": "...el a natural conversation with machines. However, largescale conventional dialogue corpora are mainly built for specified tasks (e.g., task-oriented systems for restaurant or bus information navigatio...", "weights": [[8, 15, 1.0]]}, "2021.naacl-main.246": {"snippet": "...gh a natural conversation. We explore one promising direction for conversational recommendation: mapping a conversational user, for whom there is limited or no data available, to most similar external...", "weights": [[8, 15, 1.0]]}, "2020.coling-main.108": {"snippet": "...tal. Word and Sentence embeddings are one of the most common starting points of any NLP task. A word embedding represents a given word in a predefined vector-space while maintaining vector relations w...", "weights": [[8, 12, 1.0], [99, 103, 1.0], [133, 137, 1.0]]}, "1996.tois_journal-ir0volumeA14A4.1": {"snippet": "...etri nets-namely, the so-called NR/T-nets (Nested-Relation/ Transition Nets). In NR/T-nets, the structural part is modeled by nested relations, and the behavioral part is modeled by a novel Petri net ...", "weights": [[8, 12, 1.0], [40, 44, 1.0], [74, 78, 1.0], [89, 93, 1.0]]}, "W17-5535": {"snippet": "...s in Natural Language Processing that have been shown to be mutually beneficial. In this work, we design and compare two Neural models for jointly learning both tasks. In the proposed approach, we fir...", "weights": [[8, 15, 1.0]]}}}
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Various algorithms have been put up in terms of maxim...", "weights": [[8, 17, 1.0]]}, "2017.mir_conference-2017.25": {"snippet": "Accelerated Nearest Neighbor Search with Quick ADC", "weights": []}, "2001.wwwconf_conference-2001p.85": {"snippet": "Web Acceleration for Electronic Commerce Applications", "weights": [[4, 16, 1.0]]}, "2008.cikm_conference-2008.170": {"snippet": "...Rank algorithm, called two-way reordered PageRank algorithm, which exploits both dangling nodes and reversedangling nodes to reduce the computational complexity of the PageRank vector.", "weights": [[8, 17, 1.0], [53, 62, 1.0]]}, "2018.wwwconf_conference-2018.141": {"snippet": "...ors) algorithm that can be 60x faster than most state-of-the-art unsupervised anomaly detection algorithms. In addition, ACE has appealing privacy properties. Our experiments show that ACE algorithm h...", "weights": [[8, 17, 1.0], [192, 201, 1.0]]}, "H90-1085": {"snippet": "The {PLUS} Accelerator", "weights": []}, "2016.tweb_journal-ir0volumeA10A3.3": {"snippet": "...s an acceleration scenario. We extend to consider heterogeneous multicore hardware for acceleration. This hardware combines processor cores with different microarchitectures such as energy-efficient l...", "weights": [[8, 20, 1.0], [90, 102, 1.0]]}, "2018.cikm_conference-2018.114": {"snippet": "...n, a CUDA parallelizing singlethread-based SNTF (CUSNTF) model is proposed for industrial applications on GPU and multi-GPU (MCUSNTF). Thus, CUSNTF has linear computing and space complexity, and linea...", "weights": [[8, 12, 1.0]]}, "2021.fnp-1.7": {"snippet": "...FSI (NVIDIA Japan Financial Services with AI) system for spanbased causality extraction from financial news documents submitted as part of the Fin-Causal 2021 Workshop. We investigated a list of pretr...", "weights": [[8, 14, 1.0]]}, "2009.wsdm_conference-2009.26": {"snippet": "...tion algorithm.", "weights": [[8, 17, 1.0]]}, "2021.wwwconf_conference-2021.256": {"snippet": "Hashing-Accelerated Graph Neural Networks for Link Prediction", "weights": []}, "C00-2132": {"snippet": "...s an algorithm for accelerating the CFG-parsing process by using dependency or modi er-modi ee relationship information given by, for instance, dependency estimation programs such as stochastic parser...", "weights": [[8, 17, 1.0]]}, "2013.riao_conference-2013.38": {"snippet": "...base acceleration systems", "weights": [[8, 20, 1.0]]}, "2006.cikm_conference-2006.71": {"snippet": "Query optimization using restructured views\n\n\n ABSTRACTWe study optimization of relational queries using materialized views, where views may be regular or restructured. 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A key feature of the proposed algorithm is that it exploits coarse grained parallelism and the components running in paral...", "weights": [[8, 17, 1.0], [111, 120, 1.0]]}, "2019.tist_journal-ir0volumeA10A1.5": {"snippet": "...ard, Nvidia Digits, and Deeplearning4j 1 are able to show some high-level training dynamics, such as loss and the mean of weights in a layer. However, these tools can neither handle industry-level tra...", "weights": [[8, 14, 1.0]]}, "N18-3013": {"snippet": "...ding algorithm for NMT with LMBR n-gram posteriors, showing that LMBR techniques still yield gains on top of the best recently reported results with Transformers. 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Second, we propose a flexible CUDA memory manager to reduce the memory footp...", "weights": [[8, 17, 1.0], [157, 161, 1.0]]}, "2012.cikm_conference-2012.93": {"snippet": "GPU acceleration of probabilistic frequent itemset mining from uncertain databases\n\n\n ABSTRACTUncertain databases have been widely developed to deal with the vast amount of data that contain uncertain...", "weights": [[4, 16, 1.0]]}, "2013.sigirconf_conference-2013.72": {"snippet": "...best algorithm for the exact intersection, which runs in O((|A|+|B|)/ \u221a w +|A\u2229B|) expected time. Our performance studies show that our implementations of Cardinality Filters are from 2 to 10 times fas...", "weights": [[8, 17, 1.0]]}, "2018.clef_conference-2018w.118": {"snippet": "...ates CUDA from MATLAB. The results obtained are promising for a first participation to such a task, with F1-measure@10=0.424 which placed us at third behind AILabGTi Team with 0.545 and HCMUS Team wit", "weights": [[8, 12, 1.0]]}, "D18-2023": {"snippet": "... and NVIDIA's GPU-accelerated libraries. The toolkit features training efficiency, code simplicity and translation quality. Benchmarks show that CytonMT accelerates the training speed by 64.5% to 110....", "weights": [[8, 14, 1.0]]}, "2000.cikm_conference-2000.40": {"snippet": "... KDD algorithm: Consider a KDD algorithm that performs a range query (with range \u03b5) in a large database of points P i (0<i<n) for a large set of query points Q j (0<j<m). During the processing of such...", "weights": [[8, 17, 1.0], [34, 43, 1.0]]}, "2017.sigirconf_conference-2017.140": {"snippet": "...eedy algorithm. We also combine several strategies to accelerate the algorithm. Experimental results show that after the acceleration, our algorithm significantly outperforms other heuristics, and it ...", "weights": [[8, 17, 1.0], [72, 81, 1.0], [124, 136, 1.0], [142, 151, 1.0]]}, "2011.cikm_workshop-2011books.9": {"snippet": "...d by NVIDIA. Ville has authored books and research papers on real-time graphics, gaming technologies, and crowdsourcing. Ville is the chairman of ACM SIGGRAPH Helsinki and on the advisory board of IGD...", "weights": [[8, 14, 1.0]]}, "2009.cikm_conference-2009.70": {"snippet": "...pose CUDA-DClust, a massively parallel algorithm for density-based clustering for the use of a Graphics Processing Unit (GPU). While the result of this algorithm is guaranteed to be equivalent to that...", "weights": [[8, 12, 1.0], [42, 51, 1.0], [155, 164, 1.0]]}, "2019.sigirconf_conference-2019.193": {"snippet": "Accelerating Exact Inner Product Retrieval by CPU-GPU Systems", "weights": []}, "2019.cikm_conference-2019.242": {"snippet": "GPU-Accelerated Decoding of Integer Lists", "weights": []}, "P18-1251": {"snippet": "Composing Finite State Transducers on {GPU}s\n\n\n Weighted finite state transducers (FSTs) are frequently used in language processing to handle tasks such as part-of-speech tagging and speech recognitio...", "weights": []}, "2003.wwwconf_conference-2003at.6": {"snippet": "Acceleration of Web Service Workflow Execution through Edge Computing", "weights": [[0, 12, 1.0]]}, "2015.cikm_conference-2015.117": {"snippet": "F1: Accelerating the Optimization of Aggregate Continuous Queries", "weights": []}, "2015.tois_journal-ir0volumeA33A3.5": {"snippet": "A General SIMD-Based Approach to Accelerating Compression Algorithms\n\n\n Compression algorithms are important for data-oriented tasks, especially in the era of \"Big Data.\" Modern processors equipped wi...", "weights": []}, "2011.sigirconf_conference-2011.194": {"snippet": "...ized algorithm towards efficient approximate string searches\n\n\n ABSTRACTWe investigated the problem of finding from a collection of strings those similar to a given query string based on edit distance...", "weights": [[8, 17, 1.0]]}, "1996.tois_journal-ir0volumeA14A1.3": {"snippet": "... ASM Algorithm\n\n\n We describe an improvement on Ukkonen's Enhanced Dynamic Programming (EHD) approximate string-matching algorithm for unit-penalty four-edit comparisons. The new algorithm has an asym...", "weights": [[8, 17, 1.0], [124, 133, 1.0], [182, 191, 1.0]]}, "W11-2921": {"snippet": "... CKY algorithm. In this paper, we explore the design space of parallelizing the dynamic programming computations carried out by the CKY algorithm. We use the Compute Unified Device Architecture (CUDA)...", "weights": [[8, 17, 1.0], [139, 148, 1.0], [198, 202, 1.0]]}, "2009.ismir_conference-2009.44": {"snippet": "Accelerating Query-by-Humming on GPU", "weights": []}, "2018.tweb_journal-ir0volumeA12A3.5": {"snippet": "Faster Base64 Encoding and Decoding Using AVX2 Instructions\n\n\n Web developers use base64 formats to include images, fonts, sounds, and other resources directly inside HTML, JavaScript, JSON, and XML f...", "weights": []}, "2006.wwwjournals_journal-ir0volumeA9A3.2": {"snippet": "Answering XML Queries Using Path-Based Indexes: A Survey\n\n\n Abstract The problem of answering XML queries using path-based indexes is to find efficient methods for accelerating the XML query with pre-...", "weights": []}, "2006.cikm_conference-2006.56": {"snippet": "Vector and matrix operations programmed with UDFs in a relational DBMS\n\n\n ABSTRACTIn general, a relational DBMS provides limited capabilities to perform multidimensional statistical analysis, which re...", "weights": []}, "2009.wwwconf_conference-2009.43": {"snippet": "Using graphics processors for high performance IR query processing\n\n\n ABSTRACTWeb search engines are facing formidable performance challenges due to data sizes and query loads. The major engines have ...", "weights": []}, "2014.cikm_conference-2014.198": {"snippet": ".... An acceleration scheme is proposed to accelerate this method further. Our proposed method is based on our claim that PCC is a special case of Bayesian network. This claim may inspire more inference ...", "weights": [[8, 20, 1.0]]}, "2017.cikm_conference-2017.232": {"snippet": "...eans algorithm has been widely used in machine learning and data mining due to its simplicity and good performance. However, the standard k-means algorithm would be quite slow for clustering millions ...", "weights": [[8, 17, 1.0], [149, 158, 1.0]]}, "2021.wmt-1.76": {"snippet": "...n an NVIDIA A100, being 3\u00d7 faster than our last year's system. Our system is the fastest and has the lowest memory consumption on the GPU-throughput track. The code, model, and pipeline will be availa...", "weights": [[8, 14, 1.0]]}, "2021.wmt-1.18": {"snippet": "{NVIDIA} {N}e{M}o{'}s Neural Machine Translation Systems for {E}nglish-{G}erman and {E}nglish-{R}ussian News and Biomedical Tasks at {WMT}21\n\n\n This paper provides an overview of NVIDIA NeMo's neural ...", "weights": [[1, 7, 1.0], [179, 185, 1.0]]}, "2020.wsdm_conference-2020.44": {"snippet": "Incremental Filter Pruning via Random Walk for Accelerating Deep Convolutional Neural Networks\n\n\n Accelerating Deep Convolutional Neural Networks (CNNs) has recently received ever-increasing research ...", "weights": []}, "2003.wwwconf_conference-2003.28": {"snippet": "...ovel algorithm for the fast computation of PageRank, a hyperlink-based estimate of the \"importance\" of Web pages. The original PageRank algorithm uses the Power Method to compute successive iterates t...", "weights": [[8, 17, 1.0], [139, 148, 1.0]]}, "P02-1002": {"snippet": "... the algorithm trains them sequentially. The algorithm is easy to implement, typically uses only slightly more memory, and will lead to improvements for most maximum entropy problems.", "weights": [[8, 17, 1.0], [48, 57, 1.0]]}, "2012.cikm_conference-2012.161": {"snippet": "...with Nvidia CUDA compatible GPUs. We compare the effectiveness of TERESA with that of LDA by introducing a new measure called diversity plus focusedness (D+F). We also present topic evolution examples", "weights": [[8, 14, 1.0], [15, 19, 1.0]]}, "2001.wwwconf_conference-2001.4": {"snippet": "...tion algorithm based on a learning automaton. The simulation of the scheme demonstrates substantial benefits for the end user.", "weights": [[8, 17, 1.0]]}, "2022.iwslt-1.18": {"snippet": "{NVIDIA} {N}e{M}o Offline Speech Translation Systems for {IWSLT} 2022\n\n\n This paper provides an overview of NVIDIA NeMo's speech translation systems for the IWSLT 2022 Offline Speech Translation Task....", "weights": [[1, 7, 1.0], [108, 114, 1.0]]}, "P19-1626": {"snippet": "Accelerating Sparse Matrix Operations in Neural Networks on Graphics Processing Units\n\n\n Graphics Processing Units (GPUs) are commonly used to train and evaluate neural networks efficiently. While pre...", "weights": []}, "2004.wwwconf_conference-2004.30": {"snippet": "... Web acceleration solutions. Consequently, although many web acceleration solutions have shown promising performance improvement and scalability, architecting and engineering distributed enterprise We...", "weights": [[8, 20, 1.0], [64, 76, 1.0]]}, "2011.cikm_conference-2011.384": {"snippet": "A data mining system based on SQL queries and UDFs for relational databases\n\n\n ABSTRACTMost research on data mining has proposed algorithms and optimizations that work on flat files, outside a DBMS, m...", "weights": []}, "1989.sigirjournals_journal-ir0volumeA23A34.8": {"snippet": "...oved Algorithm to Find the Length of the Longest Common Subsequence of Two Strings\n\n\n (n21ogn).We present an improvement to this algorithm which changes the time complexity to O(r + n(LLCS ). Some exp...", "weights": [[8, 17, 1.0], [132, 141, 1.0]]}, "2019.wwwconf_conference-2019.201": {"snippet": "...ding Acceleration of Web Apps with Nondeterministic JavaScript Execution", "weights": [[8, 20, 1.0]]}, "2005.cikm_conference-2005.26": {"snippet": "...DAWA algorithm, standing for a hybrid algorithm of Dct for Data and discrete WAvelet transform, is proposed to approximate the cube streams. The DAWA algorithm combines the advantage of high compressi...", "weights": [[8, 17, 1.0], [41, 50, 1.0], [153, 162, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.6": {"snippet": "...ated algorithm for such architectures. We present a fast sorting algorithm implementing an efficient bitonic sorting network. This algorithm is highly suitable for information retrieval applications. ...", "weights": [[8, 17, 1.0], [68, 77, 1.0], [134, 143, 1.0]]}, "2017.cikm_conference-2017.61": {"snippet": "... the acceleration of graph clustering is an important issue for handling large-scale graphs. 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We then propose a new clustering algorithm that uses citations' context as a way to cluster the extracted mentions into coherent c...", "weights": [[8, 17, 1.0], [106, 115, 1.0]]}, "2017.tist_journal-ir0volumeA8A5.3": {"snippet": "... ToR algorithm is the first ranking-oriented solution, which can effectively generate recommendations in a more direct manner than those regression-oriented methods. Extensive empirical studies show t...", "weights": [[8, 17, 1.0]]}, "P94-1017": {"snippet": "...sing Algorithm\n\n\n In this paper we relate a number of parsing algorithms which have been developed in very different areas of parsing theory, and which include deterministic algorithms, tabular algori...", "weights": [[8, 17, 1.0]]}, "I13-1012": {"snippet": "...gual Mention Detection for Coreference Resolution\n\n\n This paper proposes a novel algorithm for multilingual mention detection: we extract mentions from parse trees via kernelbased SVM learning. Our ap...", "weights": [[8, 15, 1.0], [84, 93, 1.0], [111, 118, 1.0]]}, "C14-1061": {"snippet": "...of a mention based on the most informative mention pair relation between the given mention entity pair. We introduce an informativeness score for determining the most precise relation of a mention ent...", "weights": [[8, 15, 1.0], [46, 53, 1.0], [86, 93, 1.0], [192, 199, 1.0]]}, "D09-1101": {"snippet": "...tity-mention classifier to determine whether a preceding cluster is coreferent with a given mention. We propose a cluster-ranking approach to coreference resolution that combines the strengths of ment...", "weights": [[8, 15, 1.0], [95, 102, 1.0], [199, 206, 1.0]]}, "S14-2052": {"snippet": "...rder Mention Detection and Disambiguation\n\n\n In this paper we briefly describe our supervised machine learning approach for disorder mention detection system that we submitted as part of our participa...", "weights": [[8, 15, 1.0], [136, 143, 1.0]]}, "P17-2085": {"snippet": "List-only Entity Linking\n\n\n Traditional Entity Linking (EL) technologies rely on rich structures and properties in the target knowledge base (KB). However, in many applications, the KB may be as simpl...", "weights": []}, "Y03-1026": {"snippet": "...ased Algorithm for {C}hinese Text Classification\n\n\n In this paper, vector-distance-weighted algorithm and representative-vector-distance algorithm are described and used to implement the process of au...", "weights": [[8, 17, 1.0], [95, 104, 1.0], [140, 149, 1.0]]}, "2015.tois_journal-ir0volumeA33A3.5": {"snippet": "A General SIMD-Based Approach to Accelerating Compression Algorithms\n\n\n Compression algorithms are important for data-oriented tasks, especially in the era of \"Big Data.\" Modern processors equipped wi...", "weights": []}, "2017.wwwconf_conference-2017c.103": {"snippet": "Media Attention to Science\n\n\n ABSTRACTHow has media attention to science changed over the past decade? Does media attention bring scientific attention too? To answer these questions, we collected medi...", "weights": []}, "W12-1503": {"snippet": "...ntal Algorithm and the more recent Graph algorithm. Both rely on a notion of preferred attributes that can be learned from human descriptions. In our experiments, preferences are learned from training...", "weights": [[8, 17, 1.0], [44, 53, 1.0]]}, "P17-1149": {"snippet": "...same mention phrase usually refers to various entities. In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple ...", "weights": [[8, 15, 1.0], [156, 163, 1.0]]}, "2018.sigirconf_conference-2018.173": {"snippet": "...rent Mention Retrieval\n\n\n ABSTRACTThis paper introduces the coreferent mention retrieval task, in which the goal is to retrieve sentences that mention a specific entity based on a query by example in ...", "weights": [[8, 15, 1.0], [74, 81, 1.0], [146, 153, 1.0]]}, "L18-1060": {"snippet": "...s of mention features, increased number of negative examples, Siamese network architecture and a global mention clustering algorithm. The highest results are achieved by the system combining our best ...", "weights": [[8, 15, 1.0], [107, 114, 1.0], [126, 135, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.8": {"snippet": "...oved Algorithm to Find the Length of the Longest Common Subsequence of Two Strings\n\n\n (n21ogn).We present an improvement to this algorithm which changes the time complexity to O(r + n(LLCS ). Some exp...", "weights": [[8, 17, 1.0], [132, 141, 1.0]]}, "2016.wsdm_conference-2016.15": {"snippet": "To Suggest, or Not to Suggest for Queries with Diverse Intents: Optimizing Search Result Presentation\n\n\n ABSTRACTWe propose a method of optimizing search result presentation for queries with diverse i...", "weights": []}, "S15-1028": {"snippet": "... our algorithm outperforms the most closely related unsupervised solution. The similarity value calculated by our algorithm proved to be an effective feature in a supervised learning setting, helping ...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.12": {"snippet": "Seeking and implementing automated assistance during the search process\n\n\n AbstractSearchers seldom make use of the advanced searching features that could improve the quality of the search process bec...", "weights": []}, "2009.ipm_journal-ir0volumeA45A5.5": {"snippet": "... SMO algorithm in support vector machine\n\n\n a b s t r a c tSequential minimal optimization (SMO) is quite an efficient algorithm for training the support vector machine. The most important step of thi...", "weights": [[8, 17, 1.0], [122, 131, 1.0]]}, "C08-2029": {"snippet": "... new algorithm for attribute selection based on observations from a corpus, which outperformed a simple base algorithm by a significant margin. We then carried out a detailed comparison between our al...", "weights": [[8, 17, 1.0], [112, 121, 1.0], [201, 210, 1.0]]}, "P16-1061": {"snippet": "...d of mention pairs. We present a neural network based coreference system that produces high-dimensional vector representations for pairs of coreference clusters. Using these representations, our syste...", "weights": [[8, 15, 1.0]]}, "2004.wwwconf_conference-2004.67": {"snippet": "...ring algorithm to build a topic hierarchy for a collection of search results retrieved in response to a query. At every level of the hierarchy, the new algorithm progressively identifies topics in a w...", "weights": [[8, 17, 1.0], [155, 164, 1.0]]}, "2016.tois_journal-ir0volumeA34A4.1": {"snippet": "... LtR algorithm. The algorithm directly optimizes an information retrieval metric of choice (in our case, NDCG) in a greedy manner. Direct optimization of the listwise objective functions is computatio...", "weights": [[8, 17, 1.0], [23, 32, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A1.4": {"snippet": "...tion algorithm. As traditional recommendation algorithms are based on popularity and download, they inadvertently fail to recommend the desirable applications. At the same time, many users tend to pay...", "weights": [[8, 17, 1.0]]}, "2010.cikm_conference-2010.243": {"snippet": "...cept mention identification is performed by training a sequential tagging model. Each identified concept mention is then associated with a set of candidate ontology concepts along with a feature vecto...", "weights": [[8, 15, 1.0], [108, 115, 1.0]]}, "2017.ipm_journal-ir0volumeA53A2.5": {"snippet": "... new algorithm presents some improvements over the previous ones. Several simulations were completed to analyze the connectivity and query propagation patterns of the emergent logical networks. The re...", "weights": [[8, 17, 1.0]]}, "2014.tist_journal-ir0volumeA5A4.14": {"snippet": "... our algorithm.", "weights": [[8, 17, 1.0]]}, "2014.wwwconf_conference-2014c.78": {"snippet": "...tion algorithm based on a node similarity measurement. Using the proposed algorithm, we evaluated the privacy risk of several \"light\" anonymization algorithms on real datasets.", "weights": [[8, 17, 1.0], [77, 86, 1.0]]}, "W06-1633": {"snippet": "...raph Algorithm for Coreference Resolution\n\n\n In this paper we describe a coreference resolution method that employs a classification and a clusterization phase. In a novel way, the clusterization is p...", "weights": [[8, 17, 1.0]]}, "P14-2006": {"snippet": "...that mention manipulation for scoring predicted mentions is unnecessary, and potentially harmful as it could produce unintuitive results; (ii) illustrate the application of all these measures to scori...", "weights": [[8, 15, 1.0]]}, "W16-2904": {"snippet": "...Gene Mention Tagging\n\n\n The rapidly growing biomedical literature has been a challenging target for natural language processing algorithms. One of the tasks these algorithms focus on is called named e...", "weights": [[8, 15, 1.0]]}, "J04-2005": {"snippet": "...ther algorithm in the older article-the \"algorithm for sorted data\"-can be generalized in a similar way. The new algorithm is faster than the algorithm for addition of strings presented in Carrasco an...", "weights": [[8, 17, 1.0], [44, 53, 1.0], [116, 125, 1.0], [145, 154, 1.0]]}, "1996.tois_journal-ir0volumeA14A1.3": {"snippet": "... ASM Algorithm\n\n\n We describe an improvement on Ukkonen's Enhanced Dynamic Programming (EHD) approximate string-matching algorithm for unit-penalty four-edit comparisons. The new algorithm has an asym...", "weights": [[8, 17, 1.0], [124, 133, 1.0], [182, 191, 1.0]]}, "2003.wwwconf_conference-2003.54": {"snippet": "... Our algorithm looks to exploit this. We have defined matrix density, and our algorithm basically uses matrix density considerations in its working.The algorithm is a partitional-agglomerative algorit...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [155, 164, 1.0], [196, 205, 1.0]]}, "2010.wwwconf_conference-2010.112": {"snippet": "...This algorithm specific information is then utilized for algorithm ranking in response to user queries. Experimental results show the superiority of our system on other popular search engines.", "weights": [[8, 17, 1.0], [60, 69, 1.0]]}, "P19-1511": {"snippet": "...tity Mention Detection via Anchor-Region Networks\n\n\n Sequential labeling-based NER approaches restrict each word belonging to at most one entity mention, which will face a serious problem when recogni...", "weights": [[8, 15, 1.0], [148, 155, 1.0]]}, "C18-1003": {"snippet": "...tion algorithm that selectively creates clusters to handle both singular and plural mentions, and also a deep learning-based entity linking model that jointly handles both types of mentions through mu...", "weights": [[8, 17, 1.0]]}, "2014.sigirconf_workshop-2014erd.11": {"snippet": "...eedy algorithm for finding sets of entity linking interpretations in queries\n\n\n ABSTRACTWe describe our participation in the short text track of the Entity Recognition and Disambiguation (ERD) challen...", "weights": [[8, 17, 1.0]]}, "2019.wsdm_conference-2019.9": {"snippet": "MSA: Jointly Detecting Drug Name and Adverse Drug Reaction Mentioning Tweets with Multi-Head Self-Attention\n\n\n ABSTRACTTwitter is a popular social media platform for information sharing and disseminat...", "weights": []}, "2017.cikm_conference-2017.170": {"snippet": "...e of mention's context to disambiguate (i.e., local objective), and promoting coherence of all the linked entities (i.e., global objective). In this paper, we propose a deep neural network model to ef...", "weights": [[8, 15, 1.0]]}, "1998.tois_journal-ir0volumeA16A2.0": {"snippet": "...ning algorithm applied to a set of relevance judgments obtained from a single human would be expected to perform on new material at a level somewhat below that human. However, we examine two learning ...", "weights": [[8, 17, 1.0]]}, "2012.cikm_conference-2012.103": {"snippet": "...osed algorithm, GraphOrder, is a novel and scalable graph-based label propagation algorithm that takes transitivity of temporal order into account, as well as these statistics on narrative order of ve...", "weights": [[8, 17, 1.0], [85, 94, 1.0]]}, "2007.sigirconf_conference-2007.84": {"snippet": "...ask. Mention detection takes advantages of machine learning approaches and character-based models. It manipulates different types of entities being mentioned and different constitution units (i.e. ext...", "weights": [[8, 15, 1.0]]}, "2014.wwwconf_workshop-2014ldow.1": {"snippet": "...ping algorithm. It first identifies a set of \"easy\" mentions with low ambiguity and links them to entities in a very efficient manner. This stage also determines the thematic domain of the input text ...", "weights": [[8, 17, 1.0]]}, "C04-1091": {"snippet": "...irst algorithm is a provably linear time search algorithm. We use this algorithm as a subroutine in the other algorithms. We believe that decoding algorithms derived from our framework can be of pract", "weights": [[8, 17, 1.0], [51, 60, 1.0], [74, 83, 1.0]]}, "S15-1034": {"snippet": "...ular mention-pair models. This study advocates a novel approach that keeps the simplicity of a mention-pair framework, while showing state-of-the-art results. Apart from being very efficient and strai...", "weights": [[8, 15, 1.0], [98, 105, 1.0]]}, "W10-4305": {"snippet": "Evaluation Metrics For End-to-End Coreference Resolution Systems\n\n\n Commonly used coreference resolution evaluation metrics can only be applied to key mentions, i.e. already annotated mentions. We her...", "weights": []}, "2018.wwwjournals_journal-ir0volumeA21A4.12": {"snippet": "...tion algorithm based on integrating social network information such as trust relationships, rating information of users and users' own knowledge. Specifically, since we cannot directly measure a user'...", "weights": [[8, 17, 1.0]]}, "J94-4002": {"snippet": "An Algorithm for Pronominal Anaphora Resolution\n\n\n This paper presents an algorithm for identifying the noun phrase antecedents of third person pronouns and lexical anaphors (reflexives and reciprocal...", "weights": [[3, 12, 1.0], [74, 83, 1.0]]}, "J88-2013": {"snippet": "... the algorithm described \"is more efficient than any existing general parsing algorithms in terms of recognition time as far as practical natural language grammars are concerned\" (p. 73). The crucial ...", "weights": [[8, 17, 1.0]]}, "D16-1245": {"snippet": "...ural mention-ranking model for coreference evaluation metrics. We experiment with two approaches: the REINFORCE policy gradient algorithm and a rewardrescaled max-margin objective. We find the latter ...", "weights": [[8, 15, 1.0], [131, 140, 1.0]]}, "2013.clef_conference-2013w.79": {"snippet": "...cept mention detection is based on Conditional Random Fields (CRF) and the concept mention normalization is based on a greedy dictionary lookup algorithm. A distinctive feature in MedTagger compared t...", "weights": [[8, 15, 1.0], [86, 93, 1.0], [147, 156, 1.0]]}, "2016.wwwconf_conference-2016.52": {"snippet": "...tity Mention Detection and Linking in Web Queries\n\n\n ABSTRACTIn this paper we study the problem of linking open-domain web-search queries towards entities drawn from the full entity inventory of Wikip...", "weights": [[8, 15, 1.0]]}, "2015.tois_journal-ir0volumeA33A3.2": {"snippet": "...mory algorithm, called disk SA-IS (DSA-IS), to exactly emulate the induced sorting algorithm SA-IS previously proposed for sorting suffixes in RAM. DSA-IS is a new diskfriendly method for sequentially...", "weights": [[8, 17, 1.0], [86, 95, 1.0]]}, "P18-2017": {"snippet": "...ible mention, and (2) jointly optimizing the mention detection accuracy and the mention clustering log-likelihood given the mention cluster labels. Our model achieves the stateof-the-art performance o...", "weights": [[8, 15, 1.0], [48, 55, 1.0], [83, 90, 1.0], [127, 134, 1.0]]}, "P16-1179": {"snippet": "Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation\n\n\n Named Entity Disambiguation (NED) algorithms disambiguate mentions of named entities with respect to a knowledge-b...", "weights": []}, "2019.ecir_workshop-2019amir.2": {"snippet": "...ated Algorithm Selection - Predict which algorithm to use!\n\n\n To achieve state-of-the-art performance, it is often crucial to select a suitable algorithm for a given problem instance. For example, wha...", "weights": [[8, 17, 1.0], [44, 53, 1.0], [147, 156, 1.0]]}, "P08-1096": {"snippet": "...tity-Mention Model for Coreference Resolution with Inductive Logic Programming\n\n\n The traditional mention-pair model for coreference resolution cannot capture information beyond mention pairs for both...", "weights": [[8, 15, 1.0], [101, 108, 1.0], [181, 188, 1.0]]}, "2017.cikm_conference-2017.137": {"snippet": "...hase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.). In the rst phase of our algorithm we propose PhraseType, a probabilistic genera...", "weights": [[8, 17, 1.0], [148, 157, 1.0]]}, "W15-0807": {"snippet": "...tion algorithm and results during the Event Mention Evaluation pilot study. We analyze the problems of evaluating multiple event mention attributes and discontinuous event mention spans. In addition, ...", "weights": [[8, 17, 1.0], [47, 54, 1.0], [132, 139, 1.0], [175, 182, 1.0]]}, "2013.wsdm_conference-2013.42": {"snippet": "App recommendation: a contest between satisfaction and temptation\n\n\n ABSTRACTDue to the huge and still rapidly growing number of mobile applications (apps), it becomes necessary to provide users an ap...", "weights": []}, "2018.sigirconf_conference-2018.22": {"snippet": "...n to mention. It is a significant role in promoting the user experience and information propagation. To help users easily find the usernames they want to mention, the mention recommendation task has r...", "weights": [[8, 15, 1.0], [157, 164, 1.0], [170, 177, 1.0]]}, "2004.ipm_journal-ir0volumeA40A2.4": {"snippet": "...ding algorithm", "weights": [[8, 17, 1.0]]}, "2013.wwwconf_conference-2013.115": {"snippet": "...s to mention, we propose in this paper a novel recommendation scheme named as whom-to-mention. Specifically, we present an in-depth study of mention mechanism and propose a recommendation scheme to so...", "weights": [[8, 15, 1.0], [89, 96, 1.0], [144, 151, 1.0]]}, "2020.wsdm_conference-2020.49": {"snippet": "...zing mention clustering and imbalanced mention detection, which enables it to detect more gold mentions in a document to make more accurate coreference decisions. Experimental results on the CoNLL-201...", "weights": [[8, 15, 1.0], [42, 49, 1.0]]}, "C12-2086": {"snippet": "...ring algorithm to rerank the output of coreference hypotheses. The filter is based on the relational structures between mentions and their corresponding relationships. The second approach is to use a ...", "weights": [[8, 17, 1.0]]}, "H90-1003": {"snippet": "...Best algorithm is an improved algorithm for finding the top N sentence hypotheses. The new algorithm is shown to perform as well as the Exact Sentence-Dependent algorithm presented previously but with...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [94, 103, 1.0], [164, 173, 1.0]]}, "2021.acl-short.37": {"snippet": "...AN}: Mention-Only Linking of Entities with a Mention Annotation Network\n\n\n We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems ...", "weights": [[8, 15, 1.0], [48, 55, 1.0]]}, "2013.clef_conference-2013w.158": {"snippet": "ShARe/CLEF eHealth 2013 Named Entity Recognition and Normalization of Disorders Challenge\n\n\n Objective: There are abundant mentions of clinical conditions, anatomical sites, medications and procedures...", "weights": []}, "2012.tist_journal-ir0volumeA3A4.16": {"snippet": "Evaluation of Folksonomy Induction Algorithms\n\n\n Algorithms for constructing hierarchical structures from user-generated metadata have caught the interest of the academic community in recent years. In...", "weights": []}, "P04-1018": {"snippet": "A Mention-Synchronous Coreference Resolution Algorithm Based On the Bell Tree\n\n\n This paper proposes a new approach for coreference resolution which uses the Bell tree to represent the search space an...", "weights": [[2, 9, 1.0], [45, 54, 1.0]]}, "2015.wwwconf_conference-2015c.35": {"snippet": "...tion algorithm to generate sub-queries online which preserve the intent of the original query the best. Experimental results over a very large real-world data set demonstrate the superb performance of...", "weights": [[8, 17, 1.0]]}, "2016.wwwconf_conference-2016.112": {"snippet": "...ased algorithm. Based on the concept of multi-view learning, we further enable the algorithm to learn from unlabeled data to alleviate the dearth of labeled data. Extensive experiments are conducted t...", "weights": [[8, 17, 1.0], [86, 95, 1.0]]}, "K15-1002": {"snippet": "... and Mention Head Detection\n\n\n In coreference resolution, a fair amount of research treats mention detection as a preprocessed step and focuses on developing algorithms for clustering coreferred menti...", "weights": [[8, 15, 1.0], [94, 101, 1.0]]}}}
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Our quantitative and qualitative analyses further show that our model has...", "weights": [[8, 13, 1.0], [23, 30, 1.0]]}, "2015.clef_conference-2015w.112": {"snippet": "Overview of the Author Identification Task at PAN 2015\n\n\n This paper presents an overview of the author identification task at PAN-2015 evaluation lab. Similar to previous editions of PAN, this shared...", "weights": []}, "W98-1313": {"snippet": "Author Index", "weights": []}, "P14-2057": {"snippet": "...ers (authors) only write a few reviews, which are not enough to serve as the training data for accurate classification. In this paper, we present a novel three-view tritraining method to iteratively i...", "weights": [[8, 15, 1.0], [157, 162, 1.0]]}, "W91-0117": {"snippet": "Author Index", "weights": []}, "2015.jasis_journal-ir0volumeA66A9.6": {"snippet": "Who reads research articles? An altmetrics analysis of Mendeley user categories", "weights": []}, "C08-1065": {"snippet": "...rent authors as negative examples. In this paper, we show, on the basis of a new corpus with 145 authors, what the effect is of many authors on feature selection and learning, and show robustness of a...", "weights": [[8, 15, 1.0], [100, 107, 1.0], [136, 143, 1.0]]}, "2018.cikm_conference-2018.214": {"snippet": "A Network-embedding Based Method for Author Disambiguation\n\n\n ABSTRACTMost existing author disambiguation work relies heavily on feature engineering or cannot use multiple paper relationships. In this...", "weights": []}, "2020.jasis_journal-ir0volumeA71A8.4": {"snippet": "\"Mind the five\": Guidelines for data privacy and security in humanitarian work with undocumented migrants and other vulnerable populations", "weights": []}, "J88-1012": {"snippet": "Author Index: Volumes 6-13", "weights": []}, "W15-1813": {"snippet": "...date authors is gradually increasing: i.e. starting from 3, going up to 5, 10, 20, 50, and 100. 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To further boost author profiling, we integrate hierarchical features learned by different models. Concret...", "weights": [[8, 13, 1.0]]}, "2017.ipm_journal-ir0volumeA53A5.5": {"snippet": "..., at least in the context of an author profiling task. Moreover, our experiments indicate that having a training set closely related to the test set (e.g., the same collection) has a clear impact on t...", "weights": [[8, 13, 1.0]]}, "2016.jasis_journal-ir0volumeA67A10.16": {"snippet": "...ther authors, institutions, and journals", "weights": [[8, 15, 1.0]]}, "L16-1716": {"snippet": "... not least, provisions have been made towards linked open data (LOD) aspects, by exposing an RDF access point allowing to query on the authors, papers and resources. Finally, a complete technological ...", "weights": [[8, 13, 1.0], [138, 145, 1.0]]}, "L18-1523": {"snippet": "..., at least in our setting, both methods require relatively long texts and are furthermore highly sensitive to the choice of authors and texts in the comparison corpus.", "weights": [[8, 13, 1.0], [127, 134, 1.0]]}, "2018.ecir_workshop-2018text2story.3": {"snippet": "... not least, on the basis of pilot analysis, the excellent opportunities of text analysis are demonstrated. Furthermore, the research aims the future application of sentiment and topic analysis in orde...", "weights": [[8, 13, 1.0]]}, "2017.ictir_conference-2017.18": {"snippet": "...d on Least Information Measurement\n\n\n ABSTRACTWe developed a new information retrieval framework based on the Least Information (LI) metric. We derived multiple term weighting schemes and combined the...", "weights": [[8, 13, 1.0], [113, 118, 1.0]]}, "2000.jasis_journal-ir0volumeA51A9.11": {"snippet": "Authors of information science", "weights": [[0, 7, 1.0]]}, "J88-3017": {"snippet": "...rent authors. Anthologies, in a discipline such as philosophy of language, are better in that they (usually) contain primary sources. My own preference when teaching philosophy of language is not to u...", "weights": [[8, 15, 1.0]]}, "2006.ipm_journal-ir0volumeA42A6.14": {"snippet": "...rk's authors, and on the other hand, a simplified approach to all-author co-citation counting that takes into account the first five authors of a cited work. Results indicate that the picture produced...", "weights": [[8, 15, 1.0], [136, 143, 1.0]]}, "W98-1513": {"snippet": "Author Index", "weights": []}, "C14-1037": {"snippet": "...s of three well-known Russian authors, Ivan Turgenev, Fyodor Dosteyevsky and Anton Chekhov. This analysis seeks to identify common linguistic patterns which hold for all of the translations from the s...", "weights": [[8, 13, 1.0], [33, 40, 1.0]]}, "2001.cikm_conference-2001.23": {"snippet": "...t at least some notion of the structure, a user cannot meaningfully query the data. This problem is compounded when one considers that heterogeneous data adhering to different schema are likely to exi...", "weights": [[8, 13, 1.0]]}, "2016.wwwconf_conference-2016c.30": {"snippet": "...ween authors for the first time from a social networks perspective. We make many observations -we find that the citations follow a heavy-tailed distribution and they are created in a way that follows ...", "weights": [[8, 15, 1.0]]}, "2016.clef_conference-2016w.90": {"snippet": "...d of three different Sub-tasks: authorship clustering, authorship links and author diarization. We developed a machine learning approaches for two of three of these tasks. For the two authorship relat...", "weights": [[8, 13, 1.0], [153, 158, 1.0]]}, "2017.jasis_journal-ir0volumeA68A8.13": {"snippet": "...ited authors according to citation location and content", "weights": [[8, 15, 1.0]]}, "1979.jasis_journal-ir0volumeA30A3.10": {"snippet": "...r of authors", "weights": [[8, 15, 1.0]]}, "2016.airs_conference-2016.25": {"snippet": "... the Authors of Online Books in Digital Libraries Using Users Affinity", "weights": [[8, 15, 1.0]]}, "1971.ipm_journal-ir0volumeA7A3.1": {"snippet": "Least-cost decision rules for dynamic information management", "weights": [[0, 5, 1.0]]}, "2018.tweb_journal-ir0volumeA12A3.2": {"snippet": "...ets. Authors' addresses: K. Rudra, N. Ganguly, P. Goyal, and S. Ghosh, Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur 721302, India; emails: koustav.rudra@cse...", "weights": [[8, 15, 1.0]]}, "1972.jasis_journal-ir0volumeA23A2.2": {"snippet": "Who cited what? a citation analysis of the four basic cataloging texts", "weights": []}, "1985.jasis_journal-ir0volumeA36A5.11": {"snippet": "Authors' Reply", "weights": [[0, 7, 1.0]]}, "2013.clef_conference-2013w.116": {"snippet": "...ring three languages and the evaluation results of the 18 participant teams that submitted their software. Moreover, we survey the characteristics of the submitted approaches and show that a very effe...", "weights": [[8, 13, 1.0]]}, "W96-0515": {"snippet": "Author Index", "weights": []}, "P12-2052": {"snippet": "...many authors. In addition, we define a model that combines LDA and AT by representing authors and documents over two disjoint topic sets, and show that our model outperforms LDA, AT and support vector...", "weights": [[8, 15, 1.0], [89, 96, 1.0]]}, "O06-5006": {"snippet": "Author and Subject Index\n\n\n The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the coauthors' names, the title of paper or othe...", "weights": []}, "2013.wwwconf_conference-2013c.274": {"snippet": "...r at least two reasons. First, as a decentralized data publishing platform in which anyone can share nearly anything, the Web has no inherent quality control mechanisms to ensure that content publishe...", "weights": [[8, 13, 1.0]]}, "2018.clef_conference-2018.25": {"snippet": "Overview of PAN 2018 - Author Identification, Author Profiling, and Author Obfuscation\n\n\n Abstract. PAN 2018 explores several authorship analysis tasks enabling a systematic comparison of competitive ...", "weights": []}, "2016.tist_journal-ir0volumeA7A4.3": {"snippet": "...f at least 92% while creating a character that was at least 75% as accurate as its reference counterparts. The commitment reduction and accuracy achieved by our framework represents a first step towar...", "weights": [[8, 13, 1.0], [57, 62, 1.0]]}, "Y02-1045": {"snippet": "Author Index", "weights": []}, "2010.tois_journal-ir0volumeA28A1.3": {"snippet": "...g of authors by topic and topics by author, parsing of abstracts by topics and authors, and detection of unusual papers by specific authors. Experiments based on perplexity scores for test documents a...", "weights": [[8, 15, 1.0], [82, 89, 1.0], [135, 142, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A6.1": {"snippet": "... the Least Recently Used algorithm (LRU for short) to evict the least recently watched video when Memcached runs out of space. 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In this paper, we discuss the challenges of providing recommendation in the domains where no sufficient hi...", "weights": [[8, 14, 1.0]]}, "2016.wwwconf_conference-2016c.120": {"snippet": "...nary domain. It has been recently defined as innovation, not necessarily but mainly through information and communications technologies (ICT), which enhance urban life in terms of people, living, econ...", "weights": [[8, 14, 1.0]]}, "2020.amta-research.14": {"snippet": "...r in-domain robustness in terms of their effectiveness at improving domain robustness. In experiments on German\u2192English OPUS data, and German\u2192Romansh (a low-resource setting) we find that several meth...", "weights": [[8, 14, 1.0], [71, 77, 1.0], [108, 114, 1.0], [138, 144, 1.0]]}, "2006.clef_workshop-2006w.137": {"snippet": "...ies (German-English, English-German, German-Russian, English-Russian) were available. Monolingual and cross-language tasks were offered. Topics were prepared in English, German and Russian. Participan...", "weights": [[8, 14, 1.0], [32, 38, 1.0], [40, 46, 1.0], [173, 179, 1.0]]}, "2004.ir_journal-ir0volumeA7A34.3": {"snippet": "... for German Text Retrieval?", "weights": [[8, 14, 1.0]]}, "2003.clef_workshop-2003.37": {"snippet": "... the German scientific information domain. One reason for this was that the main ad hoc retrieval tracks in TREC used English news collections for IR system testing. General-purpose news documents req...", "weights": [[8, 14, 1.0], [38, 44, 1.0]]}, "2021.mtsummit-research.15": {"snippet": "...o in-domain parallel corpora, and only very limited in-domain monolingual corpora. Here, we use the domain classifier to reweight the sentences of our out-of-domain training corpus. This leads to impr...", "weights": [[8, 14, 1.0], [58, 64, 1.0], [103, 109, 1.0], [161, 167, 1.0]]}, "2020.cmlc-1.2": {"snippet": "...rary German. The dependency parser is trained on the German dataset from the SPMRL 2014 Shared Task which contains text from the news domain, whereas DeReKo also covers other domains including fiction...", "weights": [[8, 14, 1.0], [56, 62, 1.0], [137, 143, 1.0]]}, "W13-5630": {"snippet": "...eral-domain parallel corpora such that (a) the number of out-of-vocabulary words (OOV) is reduced while (b) the in-domain terminology is preserved. In our experiments, a German-French and a German-Ita...", "weights": [[8, 14, 1.0], [118, 124, 1.0], [173, 179, 1.0], [193, 199, 1.0]]}, "S18-2003": {"snippet": "...s in German complex verbs, which frequently undergo meaning shifts. We use a novel strategy to obtain source and target domain characterisations via sentence generation rather than sentence annotation...", "weights": [[8, 14, 1.0], [123, 129, 1.0]]}, "D17-1156": {"snippet": "...ised domain adaptation for neural machine translation where an existing model trained on a large out-of-domain dataset is adapted to a small in-domain dataset. In this scenario, overfitting is a major...", "weights": [[8, 14, 1.0], [107, 113, 1.0], [147, 153, 1.0]]}, "2021.konvens-1.8": {"snippet": "... for German texts. We have set up a comprehensive compilation of 19 sentiment lexicon resources and 20 sentiment-annotated corpora available for German across multiple domains. In addition to the eval...", "weights": [[8, 14, 1.0], [148, 154, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "... the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copy...", "weights": [[8, 14, 1.0], [192, 198, 1.0]]}, "2001.clef_workshop-2001.44": {"snippet": "...gual German Runs", "weights": [[8, 14, 1.0]]}, "2004.clef_workshop-2004.44": {"snippet": "...open-domain QA system to perform in a cross-lingual environment, using off-the-shelf machine translation software. In our experiments we use French and German as source language, and English as target...", "weights": [[8, 14, 1.0], [155, 161, 1.0]]}, "2004.sigirconf_conference-2004.36": {"snippet": "Web-a-where: geotagging web content\n\n\n ABSTRACTWe describe Web-a-Where, a system for associating geography with Web pages. Web-a-Where locates mentions of places and determines the place each name ref...", "weights": []}, "E12-1039": {"snippet": "...ng a domain-independent senseannotated corpus harvested from the web. As a proof of concept, this method has been applied to German, a language for which sense-annotated corpora are still in short sup...", "weights": [[8, 14, 1.0], [128, 134, 1.0]]}, "E03-2012": {"snippet": "... and German medical scientific abstracts relevant to a German query document (electronic patient record). The modularity of the system allows for deployment in other domains, given appropriate linguis...", "weights": [[8, 14, 1.0], [58, 64, 1.0]]}, "2013.clef_conference-2013w.64": {"snippet": "...ough German terms occasionally appear as synonyms in an unsystematic way. The most important German terminology systems are made available by DIMDI, the German Institute for Medical Documentation and ...", "weights": [[8, 14, 1.0], [96, 102, 1.0], [156, 162, 1.0]]}, "2003.clef_workshop-2003.54": {"snippet": "... for German and English", "weights": [[8, 14, 1.0]]}, "2006.clef_workshop-2006w.67": {"snippet": "...th a Domain-Specific Document Corpus at the Chemnitz Technical University\n\n\n This article describes the first participation of the Media Informatics Section of the Chemnitz Technical University at the...", "weights": [[8, 14, 1.0]]}, "C02-1036": {"snippet": "...n in German that must be addressed in German sentence realization. We present two different approaches to the modeling of extraposition, both based on machine learned decision tree classifiers. The tw...", "weights": [[8, 14, 1.0], [41, 47, 1.0]]}, "P95-1024": {"snippet": "...plex Domain Formation\n\n\n We propose a novel approach to extraposition in German within an alternative conception of syntax in which syntactic structure and linear order are mediated not via encodings ...", "weights": [[8, 14, 1.0], [76, 82, 1.0]]}, "2007.clef_workshop-2007w.28": {"snippet": "... the German-German run we achieved an overall accuracy (ACC) of 30%; for the English-German run 18.5% (ACC); for the German-English run 7% (ACC), for the Spanish-English run 10% (ACC) and for the Port...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [88, 94, 1.0], [120, 126, 1.0]]}, "2019.wwwconf_conference-2019.14": {"snippet": "...Open-Domain Entity Descriptions from Facts", "weights": [[8, 14, 1.0]]}, "W13-2902": {"snippet": "...sing German/Simple German documents from the web. We require parallel data to build a statistical machine translation (SMT) system that translates from German into Simple German. Parallel data for SMT...", "weights": [[8, 14, 1.0], [22, 28, 1.0], [155, 161, 1.0], [174, 180, 1.0]]}, "2003.clef_workshop-2003w.56": {"snippet": "... and Domain-Specific Cross-Language Retrieval\n\n\n As in the previous years, Berkeley's group 1 experimented with the domain-specific CLEF collection GIRT as well as with Russian as query and document l...", "weights": [[8, 14, 1.0], [119, 125, 1.0]]}, "2004.clef_workshop-2004.27": {"snippet": "...in a domain-specific context 1 using the GIRT4 German/English social science database. GIRT4 data is offered as pseudo-parallel German and English corpora. In addition multilingual controlled vocabula...", "weights": [[8, 14, 1.0], [50, 56, 1.0], [131, 137, 1.0]]}, "2013.wwwconf_conference-2013c.180": {"snippet": "... for german text sentiment classification", "weights": [[8, 14, 1.0]]}, "2005.clef_workshop-2005.29": {"snippet": "Domain Specific Mono- and Bilingual English to German Retrieval Experiments with a Social Science Document Corpus", "weights": [[0, 6, 1.0], [47, 53, 1.0]]}, "2007.clef_workshop-2007w.31": {"snippet": "Domain-Specific IR for German, English and Russian Languages\n\n\n In participating in this CLEF evaluation campaign, our first objective is to propose and evaluate various indexing and search strategies...", "weights": [[0, 6, 1.0], [23, 29, 1.0]]}, "K17-1038": {"snippet": "... for German compared with several baseline domain adaptation techniques. As a by-product of this approach, our models that are trained on a combination of English and German utterances perform reasona...", "weights": [[8, 14, 1.0], [46, 52, 1.0], [170, 176, 1.0]]}, "2017.wsdm_conference-2017.35": {"snippet": "German Typographers vs. German Grammar: Decomposition of Wikipedia Category Labels into Attribute-Value Pairs", "weights": [[0, 6, 1.0], [24, 30, 1.0]]}, "2020.lrec-1.537": {"snippet": "A Domain-Specific Dataset of Difficulty Ratings for {G}erman Noun Compounds in the Domains {DIY}, Cooking and Automotive\n\n\n We present a dataset with difficulty ratings for 1,030 German closed noun co...", "weights": [[2, 8, 1.0], [179, 185, 1.0]]}, "2006.clef_workshop-2006.40": {"snippet": "...gual German-English Framework for Open-Domain Question Answering", "weights": [[8, 14, 1.0], [42, 48, 1.0]]}, "2021.konvens-1.15": {"snippet": "...n to German, surpassing the previous state-of-theart performance by a wide margin of 10-30 points F1 across three established datasets for German. This is achieved by a neural end-to-end approach, tra...", "weights": [[8, 14, 1.0], [142, 148, 1.0]]}, "1999.jasis_journal-ir0volumeA50A3.3": {"snippet": "...cted German Literature", "weights": [[8, 14, 1.0]]}, "2021.konvens-1.9": {"snippet": "...oth, German and English, from the sub-domain of graph theory. Mathematical texts differ from other domains because sentences which appear as definitions from a linguistic perspective are not necessari...", "weights": [[8, 14, 1.0], [41, 47, 1.0]]}, "W12-4210": {"snippet": "...sing Domain-specific and Collaborative Resources for Term Translation\n\n\n In this article we investigate the translation of terms from English into German and vice versa in the isolation of an ontology...", "weights": [[8, 14, 1.0], [150, 156, 1.0]]}, "W17-7002": {"snippet": "...n of German terminology in an inhomogeneous domain. The text basis is thematically broad and contains various registers, from expert text to user-generated data taken from an online discussion forum. ...", "weights": [[8, 14, 1.0], [47, 53, 1.0]]}, "2006.clef_workshop-2006w.31": {"snippet": "...best German-German run we achieved an overall accuracy (ACC) of 42.33% and a mean reciprocal rank (MRR) of 0.45; for the best English-German run 32.98% (ACC) and 0.35 (MRR); for the German-English run...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [137, 143, 1.0], [185, 191, 1.0]]}, "2008.wwwconf_conference-2008.102": {"snippet": "Towards a global schema for web entities\n\n\n ABSTRACTPopular entities often have thousands of instances on the Web. In this paper, we focus on the case where they are presented in table-like format, na...", "weights": []}, "W14-5310": {"snippet": "...dard German, the official language of the German part of Switzerland. However, dealing with Swiss German in natural language processing, usually the detour through Standard German is taken. As writing...", "weights": [[8, 14, 1.0], [45, 51, 1.0], [101, 107, 1.0], [176, 182, 1.0]]}, "2005.wwwconf_conference-2005si.24": {"snippet": "Automatic text processing to enhance product search for on-line shopping\n\n\n AbstractThe growing eCommerce business requires an advanced way of searching for products. Buyers today are not only using t...", "weights": []}, "2008.clef_workshop-2008w.41": {"snippet": "...a to Domain-Specific Track in CLEF 2008\n\n\n We participate in 2008 to our first Domain-Specific Track, with the aim to establish a baseline for our Information Retrieval engine in an unknown domain for...", "weights": [[8, 14, 1.0], [82, 88, 1.0], [193, 199, 1.0]]}, "2020.vardial-1.10": {"snippet": "...from German should be further investigated systematically. For translating user reviews, creating synthetic in-domain parallel data through back-and forward-translation and adding them to a small out-...", "weights": [[8, 14, 1.0], [114, 120, 1.0]]}, "2019.gwc-1.4": {"snippet": "...dard German, into GermaNet. This shall give a more inclusive perspective on the German language. We will argue that Swiss Standard German words, Helvetisms, are best included into the already existing...", "weights": [[8, 14, 1.0], [83, 89, 1.0], [134, 140, 1.0]]}, "2020.lrec-1.331": {"snippet": "...wiss German dialects normalized into High German. As Swiss German is, for now, a predominantly spoken language, there is a significant variation in the written forms, even between speakers of the same...", "weights": [[8, 14, 1.0], [45, 51, 1.0], [62, 68, 1.0]]}, "2008.clef_workshop-2008w.146": {"snippet": "...E at Domain-Specific IR - CLEF 2008: Scientific Data Retrieval: Various Query Expansion Approaches\n\n\n Our first objective in participating in this domain-specific evaluation campaign is to propose and...", "weights": [[8, 14, 1.0], [150, 156, 1.0]]}, "2010.tweb_journal-ir0volumeA4A2.0": {"snippet": "...rtal domain refers to a Web domain that shows only advertisements, served by a thirdparty advertisement syndication service, in the form of ads listing. We develop a machine-learningbased classifier t...", "weights": [[8, 14, 1.0], [31, 37, 1.0]]}, "2018.cikm_workshop-2018w.14": {"snippet": "...from German literature in the legal domain which are then integrated into a knowledge base with multiple clusters, allowing for different perspectives and efficient lookups. Having references to regul...", "weights": [[8, 14, 1.0], [39, 45, 1.0]]}, "2017.wwwconf_conference-2017c.34": {"snippet": "...Open Domain Entity Search\n\n\n ABSTRACTThe paper introduces an open domain entity search system called ESearch, which aims at finding a list of relevant entities to an open domain entity search query (a...", "weights": [[8, 14, 1.0], [69, 75, 1.0], [174, 180, 1.0]]}, "P83-1016": {"snippet": "... The German data are well attested and thoroughly discussed in the descriptive literature The fact that English and German are closely related makes it easier to assess these data and to draw parallel...", "weights": [[8, 14, 1.0], [119, 125, 1.0]]}, "W16-2333": {"snippet": "...}: A Domain Adaptive {MT} System\n\n\n This paper presents the JU-USAAR English-German domain adaptive machine translation (MT) system submitted to the IT domain translation task organized in WMT-2016 . ...", "weights": [[8, 14, 1.0], [80, 86, 1.0], [87, 93, 1.0], [155, 161, 1.0]]}, "2018.sigirconf_conference-2018.222": {"snippet": "Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances\n\n\n ABSTRACTGenerative adversarial nets (GANs) have been widely studied during the recent development of deep learning a...", "weights": []}, "2007.clef_workshop-2007.26": {"snippet": "Domain-Specific IR for German, English and Russian Languages", "weights": [[0, 6, 1.0], [23, 29, 1.0]]}, "C10-2146": {"snippet": "... new domain. We integrate our algorithm into the MXPOST POS tagger (Ratnaparkhi, 1996) and experiment with three languages (English, German and Chinese) in seven in-domain and domain adaptation scenar...", "weights": [[8, 14, 1.0], [136, 142, 1.0], [168, 174, 1.0], [179, 185, 1.0]]}, "2020.lt4gov-1.5": {"snippet": "...tion domain, a language guide, named entities based on open public data, translation memories, etc.). German is a pluricentric language that considerably varies in the domain of public administration ...", "weights": [[8, 14, 1.0], [105, 111, 1.0], [171, 177, 1.0]]}, "2021.nllp-1.19": {"snippet": "...ets. German court rulings contain much structural information, so we create a pre-processing pipeline tailored explicitly to the German legal domain. Additionally, we implement multiple extractive as ...", "weights": [[8, 14, 1.0], [132, 138, 1.0], [145, 151, 1.0]]}, "1995.ipm_journal-ir0volumeA31A5.7": {"snippet": "...s in German Medical Journals: A Linguistic Analysis", "weights": [[8, 14, 1.0]]}, "2014.sigirconf_conference-2014.192": {"snippet": "...\"the German chancellor met the Ukrainian opposition leader and former heavy-weight champion\", even if these texts never mention the strings \"Angela Merkel\" and \"Vitali Klitschko\".\nRELATED WORKPrior wo...", "weights": [[8, 14, 1.0]]}, "2004.clef_workshop-2004w.74": {"snippet": "... the German Indexing and Retrieval Test (GIRT) task of the CLEF 2004 evaluation campaign. We conducted both monolingual and bilingual information retrieval experiments. For monolingual experiments wit...", "weights": [[8, 14, 1.0]]}, "L18-1498": {"snippet": "...wiss German speakers, annotated with a standard German transcription. The desired output of the speech recognizer is again standard German, since there is no other official or standardized way to writ...", "weights": [[8, 14, 1.0], [51, 57, 1.0], [135, 141, 1.0]]}}}
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The toolkit prioritizes efficiency, modularity, and extensibility wi...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [77, 81, 1.0], [82, 88, 1.0]]}, "2020.nlposs-1.13": {"snippet": "Open-Source Morphology for Endangered Mordvinic Languages\n\n\n This document describes shared development of finite-state description of two closely related but endangered minority languages, Erzya and ...", "weights": [[0, 4, 1.0], [5, 11, 1.0]]}, "W13-5631": {"snippet": "...g an Open-Source Development Infrastructure for Language Technology Projects\n\n\n The article presents the Giellatekno & Divvun language technology resources, more specifically the effort to utilise ope...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [200, 204, 1.0]]}, "2016.cikm_conference-2016.136": {"snippet": "Geotagging Named Entities in News and Online Documents\n\n\n ABSTRACTNews sources generate constant streams of text with many references to real world entities; understanding the content from such source...", "weights": []}, "2018.cikm_conference-2018.304": {"snippet": "...ased open sources for the startup success prediction task and model the task using a very rich set of signals from such sources. In particular, we enrich structured data about the startup ecosystem wi...", "weights": [[8, 12, 1.0]]}, "2019.sigirconf_conference-2019osirrc.1": {"snippet": "...2019 Open-Source IR Replicability Challenge (OSIRRC 2019)", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2015.cikm_workshop-2015esair.2": {"snippet": "...sing open information extraction techniques. In this talk we'll discuss some of the differences between open/closed schema knowledge bases including the ideas of objective vs subjective content as wel...", "weights": [[8, 12, 1.0], [107, 111, 1.0]]}, "2008.wwwconf_workshop-2008ldow.14": {"snippet": "Open Data Commons, a License for Open Data\n\n\n Attendees at the WWW2007 panel session on Open Data [1, 2] will remember a wide-ranging discussion of the role that easily accessible data could play in e...", "weights": [[0, 4, 1.0], [33, 37, 1.0], [88, 92, 1.0]]}, "2003.wwwconf_conference-2003at.5": {"snippet": "...g to Open Standards and Open Source", "weights": [[8, 12, 1.0], [27, 31, 1.0], [32, 38, 1.0]]}, "2017.wsdm_conference-2017.98": {"snippet": "...s of source code for the purposes of indexing and querying.Indexing Source Code. Source code indexing is hard for several reasons. 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Companies see the chance to use Moses to l...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [35, 39, 1.0], [40, 46, 1.0]]}, "2021.dash-1.6": {"snippet": ".... An open-source and readyto-use implementation based on the text annotation platform INCEpTION 1 is made available 2 .", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2012.coria_conference-2012.15": {"snippet": "...orme open-source de recherche d'information s\u00e9mantique", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2012.sigirconf_workshop-2012osir.11": {"snippet": "Yet Another Comparison of Lucene and Indri Performance", "weights": []}, "2010.cikm_conference-2010.243": {"snippet": "...cept mention identification is performed by training a sequential tagging model. Each identified concept mention is then associated with a set of candidate ontology concepts along with a feature vecto...", "weights": [[8, 15, 1.0], [108, 115, 1.0]]}, "2010.amta-tutorials.5": {"snippet": "...with Open source Software", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "L16-1118": {"snippet": "... and Open Source Performance\n\n\n Current state-of-the-art speech synthesizers for domain-independent systems still struggle with the challenge of generating understandable and natural-sounding speech. ...", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "L14-1413": {"snippet": "...mong Open Source Software Communication Messages", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "L12-1614": {"snippet": "An Open Source {P}ersian Computational Grammar", "weights": [[3, 7, 1.0], [8, 14, 1.0]]}, "2019.sigirconf_conference-2019osirrc.2": {"snippet": "STELLA: Towards a Framework for the Reproducibility of Online Search Experiments", "weights": []}, "2017.cikm_conference-2017.320": {"snippet": "... -an open source data lake service -which offers researchers and developers a single REST API to organize, index and query their data and metadata. CoreDB manages multiple database technologies and of...", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2009.trec_conference-2009.21": {"snippet": "...s. 1 Open Text eDOCS SearchServer and Open Text eDOCS Suite are trademarks or registered trademarks of Open Text Corporation in the United States of America, Canada, the European Union and/or other co...", "weights": [[8, 12, 1.0], [41, 45, 1.0], [106, 110, 1.0]]}, "P13-4003": {"snippet": "An Open Source Toolkit for Quantitative Historical Linguistics\n\n\n Given the increasing interest and development of computational and quantitative methods in historical linguistics, it is important tha...", "weights": [[3, 7, 1.0], [8, 14, 1.0]]}, "2020.acl-main.447": {"snippet": "...olar Open Research Corpus\n\n\n We introduce S2ORC, 1 a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, r...", "weights": [[8, 12, 1.0]]}, "2002.wwwconf_conference-2002.69": {"snippet": "...able open source based toolkit that interprets the VoiceXML dialog markup language. It is designed to serve as a framework for system integrators and platform vendors who want to incorporate VoiceXML ...", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2017.wsdm_conference-2017.89": {"snippet": "...om a mention, i.e., a text segment which refers to an entity. Most (if not all) KGs share this notion of entities and, historically, information retrieval methods have used such information as a sourc...", "weights": [[8, 15, 1.0], [198, 204, 1.0]]}, "2012.sigirconf_workshop-2012osir.3": {"snippet": "Apache Lucene 4", "weights": []}, "2021.conll-1.39": {"snippet": "...t an open-access dataset for cross-document event coreference, CDEC-WN, collected from English Wikinews and open-source our annotation toolkit to encourage further research on cross-document tasks. 1", "weights": [[8, 12, 1.0], [111, 115, 1.0], [116, 122, 1.0]]}, "P19-1511": {"snippet": "...tity Mention Detection via Anchor-Region Networks\n\n\n Sequential labeling-based NER approaches restrict each word belonging to at most one entity mention, which will face a serious problem when recogni...", "weights": [[8, 15, 1.0], [148, 155, 1.0]]}, "C18-1003": {"snippet": "They Exist! 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However, ...", "weights": [[8, 12, 1.0], [130, 134, 1.0]]}, "2007.sigirconf_conference-2007.84": {"snippet": "...data source and evaluation benchmark from the ACE program. The Chinese Entity Detection and Recognition (EDR) task is of particular interest to us. This task presents us several language-independent a...", "weights": [[8, 14, 1.0]]}, "2013.cikm_workshop-2013esair.6": {"snippet": "...: an open source framework for entity linking\n\n\n ABSTRACTWe introduce Dexter, an open source framework for entity linking. The entity linking task aims at identifying all the small text fragments in a...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [84, 88, 1.0], [89, 95, 1.0]]}, "W01-1512": {"snippet": "...g an Open-Source Unification-Based System for {CL}/{NLP} Teaching\n\n\n We demonstrate the open-source LKB system which has been used to teach the fundamentals of constraint-based grammar development to ...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [91, 95, 1.0], [96, 102, 1.0]]}, "2012.sigirconf_workshop-2012osir.7": {"snippet": "...: An Open Source Framework for Searching the Physical World", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2012.cikm_conference-2012.238": {"snippet": "...Most open-domain question answering systems achieve better performances with large corpora, such as Web, by taking advantage of information redundancy. However, explicit answers are not always mention...", "weights": [[8, 12, 1.0]]}, "2012.sigirconf_workshop-2012osir.2": {"snippet": "ezDL: An Interactive Search and Evaluation System", "weights": []}, "2017.clef_conference-2017w.113": {"snippet": "...best source of expansion terms compared to the aliases or combination of both features. Finally, we also found that Relevance Feedback and Pseudo Relevance Feedback are effective to further improve th...", "weights": [[8, 14, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A1.0": {"snippet": "Open source search: a data mining platform", "weights": [[0, 4, 1.0], [5, 11, 1.0]]}, "1993.sigirconf_conference-93.7": {"snippet": "...both source texts and abstracts. This has led Paice to suggest the use of a so-called ubstractframe to guide the extraction of sentences from a source document, thus improving the balance and coverage...", "weights": [[8, 14, 1.0], [147, 153, 1.0]]}, "2019.sigirconf_conference-2019osirrc.5": {"snippet": "...ity. Open-Source IR Replicability Challenge (OSIRRC) has addressed this problem by introducing a unified framework for dockerizing a variety of retrieval tasks. In this paper, a Docker image is built ...", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "R11-1010": {"snippet": "An Open Source {P}unjabi Resource Grammar\n\n\n We describe an open source computational grammar for Punjabi; a resource-poor language. The grammar is developed in GF (Grammatical framework), which is a ...", "weights": [[3, 7, 1.0], [8, 14, 1.0], [60, 64, 1.0], [65, 71, 1.0]]}, "2014.sigirconf_workshop-2014erd.1": {"snippet": "...es a mention of \"Milan\" much more likely to refer to the soccer team than the city), and many more features that are strong indicators of the kinds of strategies that should be relied on.Aside from th...", "weights": [[8, 15, 1.0]]}, "2014.cikm_conference-2014.259": {"snippet": "...y an open source framework, which provides a user friendly and reactive Web interface to support both EL and SE labelling tasks, through a guided two-step process.", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2019.sigirconf_conference-2019osirrc.14": {"snippet": "BM25 Pseudo Relevance Feedback Using Anserini at Waseda University", "weights": []}, "2016.wwwconf_conference-2016.52": {"snippet": "...tity Mention Detection and Linking in Web Queries\n\n\n ABSTRACTIn this paper we study the problem of linking open-domain web-search queries towards entities drawn from the full entity inventory of Wikip...", "weights": [[8, 15, 1.0], [110, 114, 1.0]]}, "2020.chiir_conference-2020.66": {"snippet": "...: An Open Source Document Viewer", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.9": {"snippet": "Open source information petrieval: a report on the SIGIR 2012 workshop\n\n\n AbstractOn August 16, 2012 the SIGIR 2012 Workshop on Open Source Information Retrieval was held as part of the SIGIR 2012 con...", "weights": [[0, 4, 1.0], [5, 11, 1.0], [128, 132, 1.0], [133, 139, 1.0]]}, "2018.wsdm_conference-2018.81": {"snippet": "..., an open and modular Python framework that supports extensible and adaptable research in recommender systems. Each recommender is modeled as a computational graph that consists of a structured ensemb...", "weights": [[8, 12, 1.0]]}, "P18-2017": {"snippet": "...ible mention, and (2) jointly optimizing the mention detection accuracy and the mention clustering log-likelihood given the mention cluster labels. Our model achieves the stateof-the-art performance o...", "weights": [[8, 15, 1.0], [48, 55, 1.0], [83, 90, 1.0], [127, 134, 1.0]]}, "2012.sigirconf_workshop-2012osir.5": {"snippet": "...ween Open Source Search Tools", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "W12-4304": {"snippet": "Open-domain Anatomical Entity Mention Detection\n\n\n Anatomical entities such as kidney, muscle and blood are central to much of biomedical scientific discourse, and the detection of mentions of anatomi...", "weights": [[0, 4, 1.0], [30, 37, 1.0]]}, "L10-1469": {"snippet": "... and Source Information", "weights": [[8, 14, 1.0]]}, "L04-1136": {"snippet": "Open-source Tools for Creation, Maintenance, and Storage of Lexical Resources for Language Generation from Ontologies", "weights": [[0, 4, 1.0], [5, 11, 1.0]]}, "L06-1340": {"snippet": "An Open Source Prosodic Feature Extraction Tool", "weights": [[3, 7, 1.0], [8, 14, 1.0]]}, "W17-7552": {"snippet": "...tter Mention Network\n\n\n Users in social network either unicast or broadcast their messages. At mention is the popular way of unicasting for Twitter whereas, general tweeting could be considered as bro...", "weights": [[8, 15, 1.0], [98, 105, 1.0]]}, "2020.emnlp-main.592": {"snippet": "...high mention coverage and sufficient context diversity. Unfortunately, when scaling NER to open situations, these advantages may no longer exist. And therefore it raises a critical question of whether...", "weights": [[8, 15, 1.0], [94, 98, 1.0]]}, "W12-5018": {"snippet": "...sing open source framework\n\n\n This paper presents a comparison of open source search engine development frameworks in the context of their malleability for constructing multilingual search index. The ...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [69, 73, 1.0], [74, 80, 1.0]]}, "R19-1083": {"snippet": "...e of source codes. Now the system is available on the GitHub repository (https://github.com/ CLARIN-PL/Inforex) as an open source project. The system can be easily setup and run in a Docker container ...", "weights": [[8, 14, 1.0], [121, 125, 1.0], [126, 132, 1.0]]}, "2021.acl-short.37": {"snippet": "...AN}: Mention-Only Linking of Entities with a Mention Annotation Network\n\n\n We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems ...", "weights": [[8, 15, 1.0], [48, 55, 1.0]]}, "L12-1092": {"snippet": "Open-Source Boundary-Annotated Corpus for {A}rabic Speech and Language Processing", "weights": [[0, 4, 1.0], [5, 11, 1.0]]}, "2019.sigirconf_conference-2019osirrc.8": {"snippet": "University of Waterloo Docker Images for OSIRRC at SIGIR 2019", "weights": []}, "2012.sigirconf_workshop-2012osir.6": {"snippet": "Towards an Efficient and Effective Search Engine", "weights": []}, "2012.amta-tutorials.5": {"snippet": "Open Source Statistical Machine Translation", "weights": [[0, 4, 1.0], [5, 11, 1.0]]}, "2014.wwwconf_conference-2014c.274": {"snippet": "...s in open source communities: an ecological approach applied to github\n\n\n ABSTRACTOpen Source Software (OSS) has gained high amount of popularity during the last few years. It is becoming used by publ...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [90, 96, 1.0]]}, "2014.wwwconf_conference-2014c.289": {"snippet": "...m in open source education: a case study\n\n\n ABSTRACTIn this paper, we describe the details of an interactive online web-based degree program in the area of Computer Science with specialization in Free...", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2005.mtsummit-invited.3": {"snippet": "...e of Open Source", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "K15-1002": {"snippet": "... and Mention Head Detection\n\n\n In coreference resolution, a fair amount of research treats mention detection as a preprocessed step and focuses on developing algorithms for clustering coreferred menti...", "weights": [[8, 15, 1.0], [94, 101, 1.0]]}}}
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This paper presents our analysis of news-article summaries generated by sentence selection. Sentences are ranked for potential inclusion in the summary using a weighted co...", "weights": [[8, 17, 1.0], [159, 168, 1.0]]}, "2012.wwwconf_conference-2012c.185": {"snippet": "...ding inclusion dependencies on the web\n\n\n ABSTRACTThis paper addresses the problem of finding inclusion dependencies on the Web. In our approach, we enumerate pairs of HTML/XML elements that possibly ...", "weights": [[8, 17, 1.0], [97, 106, 1.0]]}, "2015.cikm_workshop-2015dtmbio.12": {"snippet": "Evaluation of Disease-Associated Text-Mining Databases\n\n\n ABSTRACTThere are about 20 million scientific articles in PubMed and this is a great source of knowledge. 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In this paper, we descri...", "weights": []}, "2008.cikm_conference-2008.106": {"snippet": "A two-stage text mining model for information filtering\n\n\n ABSTRACTMismatch and overload are the two fundamental issues regarding the effectiveness of information filtering. Both termbased and pattern...", "weights": []}, "2011.sigirconf_conference-2011.27": {"snippet": "DOM based content extraction via text density\n\n\n ABSTRACTIn addition to the main content, most web pages also contain navigation panels, advertisements and copyright and disclaimer notices. This addit...", "weights": []}, "2014.cikm_workshop-2014dtmbio.15": {"snippet": "Inferring Undiscovered Public Knowledge by Using Text Mining-driven Graph Model\n\n\n ABSTRACTDue to the recent development of Information Technology, the number of publications is increasing exponential...", "weights": []}, "2012.cikm_conference-2012.416": {"snippet": "InCaToMi: integrative causal topic miner between textual and non-textual time series data\n\n\n ABSTRACTTopic modeling is popular for text mining tasks. Recently, topic modeling has been combined with ti...", "weights": []}, "2001.cikm_conference-2001.89": {"snippet": "A Domain Independent Environment for Creating Information Extraction Modules\n\n\n ABSTRACTText-Mining is a growing area of interest within the field of Data Mining and Knowledge Discovery. Given a colle...", "weights": []}, "2005.sigirconf_conference-2005.126": {"snippet": "...d an inclusion rate of 95% and overall translation accuracy of 90%, outperforming state-of-the-art OOV translation techniques.", "weights": [[8, 17, 1.0]]}, "2001.jasis_journal-ir0volumeA52A13.6": {"snippet": "Citation mining: Integrating text mining and bibliometrics for research user profiling", "weights": []}, "P04-3015": {"snippet": "...d on Inclusion of Appearance\n\n\n In this paper, we propose a method of automatically extracting word hierarchies based on the inclusion relation of appearance patterns from corpora. We apply a compleme...", "weights": [[8, 17, 1.0], [128, 137, 1.0]]}, "L04-1126": {"snippet": "An Evaluation Protocol for Text Mining Tools : {ALCESTE}, {SAS} Text Miner, {SPAD}-{CRM} and Temis Text Mining Solutions Testing", "weights": []}, "2007.ipm_journal-ir0volumeA43A2.15": {"snippet": "Integrating textual and visual information for cross-language image retrieval: A trans-media dictionary approach\n\n\n AbstractThis paper explores the integration of textual and visual information for cr...", "weights": []}, "2012.cikm_workshop-2012dtmbio.7": {"snippet": "... for inclusion and exclusion criteria) than randomly choosing from a set of candidates obtained from relevant documents. In user simulation experiments, we were able to automatically construct eligibi...", "weights": [[8, 17, 1.0]]}, "2013.cikm_conference-2013.84": {"snippet": "...ding inclusion dependencies on the Web. The problem is important because (1) applications of inclusion dependencies, such as data quality management, are beneficial in the Web context, and (2) such de...", "weights": [[8, 17, 1.0], [96, 105, 1.0]]}, "2009.wsdm_conference-2009.23": {"snippet": "Mining common topics from multiple asynchronous text streams\n\n\n ABSTRACTText streams are becoming more and more ubiquitous, in the forms of news feeds, weblog archives and so on, which result in a lar...", "weights": []}, "2017.sigirconf_conference-2017.243": {"snippet": "Probabilistic Topic Models for Text Data Retrieval and Analysis\n\n\n ABSTRACTText data include all kinds of natural language text such as web pages, news articles, scienti c literature, emails, enterpri...", "weights": []}, "2018.iir_workshop-2018.17": {"snippet": "The Malware Text Collection and Mining Project", "weights": []}, "S17-2170": {"snippet": "{WING}-{NUS} at {S}em{E}val-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling\n\n\n We describe an end-to-end pipeline processing approach for SemEval 2017's Task 10 to ext...", "weights": []}, "2018.wsdm_conference-2018.89": {"snippet": "Event Mining over Distributed Text Streams\n\n\n ABSTRACTThis research presents a new set of techniques to deal with event mining from different text sources, a complex set of NLP tasks which aim to extr...", "weights": []}, "2009.cikm_workshop-2009dtmbio.0": {"snippet": "Proceeding of the 3rd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO 2009, Hong Kong, China, November 6, 2009", "weights": []}, "2014.cikm_conference-2014.164": {"snippet": "... the inclusion criteria by adapting techniques from recent research into coverage-based diversification. We propose a novel approach for modelling the coverage of the query inclusion criteria within t...", "weights": [[8, 17, 1.0], [176, 185, 1.0]]}, "2007.cikm_conference-2007.25": {"snippet": "Discovering interesting usage patterns in text collections: integrating text mining with visualization\n\n\n ABSTRACT", "weights": []}, "2011.clef_workshop-2011w.84": {"snippet": "Text Mining in Biograph\n\n\n The Biograph project is a biomedical knowledge discovery project combining graph data mining with structured biomedical information and with text mining on medline abstracts...", "weights": []}, "C14-1097": {"snippet": "...onal Inclusion Hypothesis, which states that hypernyms tend to occur in a superset of contexts in which their hyponyms are found. We find that this hypothesis only holds when it is applied to relevant...", "weights": [[8, 17, 1.0]]}, "W04-3107": {"snippet": "Text Mining - Next Steps for Drug Discovery\n\n\n The text mining community has shown significant impact for use of text mining in drug discovery and basic research. So far, there have been a lot of poin...", "weights": []}, "2020.peoples-1.5": {"snippet": "... and Inclusion\n\n\n Over the past few years, systems have been developed to control online content and eliminate abusive, offensive or hate speech content. However, people in power sometimes misuse this...", "weights": [[8, 17, 1.0]]}, "2004.sigirconf_conference-2004.118": {"snippet": "Context-based methods for text categorisation\n\n\n ABSTRACTWe propose several context-based methods for text categorization. One method, a small modification to the PPM compression-based model which is ...", "weights": []}, "P99-1068": {"snippet": "Mining the Web for Bilingual Text\n\n\n STRAND (Resnik, 1998 ) is a languageindependent system for automatic discovery of text in parallel translation on the World Wide Web. This paper extends the prelim...", "weights": []}, "P05-2023": {"snippet": "An Unsupervised System for Identifying {E}nglish Inclusions in {G}erman Text\n\n\n We present an unsupervised system that exploits linguistic knowledge resources, namely English and German lexical databa...", "weights": []}, "2009.cikm_conference-2009.17": {"snippet": "...near inclusion for XML regular expression types\n\n\n ABSTRACTType inclusion is a fundamental operation in every typechecking compiler, but it is quite expensive for XML manipulation languages. We recent...", "weights": [[8, 17, 1.0], [67, 76, 1.0]]}, "2018.jasis_journal-ir0volumeA69A12.3": {"snippet": "Text Mining for Personalized Knowledge Extraction From Online Support Groups", "weights": []}, "J07-1007": {"snippet": "Book Reviews: Text Mining for Biology and Biomedicine, edited by Sophia Ananiadou and John {M}c{N}aught\n\n\n Text mining is defined by Hearst (1999) as the automatic discovery of new, previously unknown...", "weights": []}, "2005.ipm_journal-ir0volumeA41A4.0": {"snippet": "A hybrid approach to protein name identification in biomedical texts\n\n\n AbstractThis paper presents a hybrid approach to identifying protein names in biomedical texts, which is regarded as a crucial s...", "weights": []}, "2017.wsdm_conference-2017.94": {"snippet": "WSDM 2017 Workshop on Mining Online Health Reports: MOHRS 2017\n\n\n ABSTRACTThe workshop on Mining Online Health Reports (MOHRS) draws upon the rapidly developing field of Computational Health, focusing...", "weights": []}, "2001.cikm_conference-2001.15": {"snippet": "... the inclusion of the background text, and compare our work to other efforts that can incorporate unlabeled data and other background text in the classification process.", "weights": [[8, 17, 1.0]]}, "2001.cikm_conference-2001.84": {"snippet": "... the inclusion relation, i.e., the inclusion of the meaning of a block of text in the meaning of another block. In this paper, we consider the general problem of clustering of asymmetrically related d...", "weights": [[8, 17, 1.0], [38, 47, 1.0]]}, "2011.trec_conference-2011.34": {"snippet": "...hing inclusion/exclusion criteria using plain text from unstructured EMR.", "weights": [[8, 17, 1.0]]}, "2015.cikm_conference-2015.138": {"snippet": "Inclusion Dependencies Reloaded", "weights": [[0, 9, 1.0]]}, "W14-3107": {"snippet": "Interactive Exploration of Asynchronous Conversations: Applying a User-centered Approach to Design a Visual Text Analytic System\n\n\n Exploring an online conversation can be very difficult for a user, e...", "weights": []}, "2009.ecir_conference-2009.24": {"snippet": "...aded-Inclusion-Based Information Retrieval Systems\n\n\n Abstract. This paper investigates the use of fuzzy logic mechanisms coming from the database community, namely graded inclusions, to model the inf...", "weights": [[8, 17, 1.0]]}, "2015.wsdm_conference-2015.60": {"snippet": "Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation\n\n\n ABSTRACTPrevious research on Recommender Systems (RS), especially the continuously popular approach ...", "weights": []}, "2013.sigirconf_conference-2013.3": {"snippet": "... for inclusion in the result summary.Our extensive experiments and analysis demonstrate that our method improves the quality of result summaries compared to existing state-of-the-art methods. We belie...", "weights": [[8, 17, 1.0]]}, "2008.cikm_workshop-2008dtmbio.13": {"snippet": "Predicting protein-protein relationships from literature using collapsed variational latent dirichlet allocation\n\n\n ABSTRACTThis paper investigates applying statistical topic models to extract and pre...", "weights": []}, "2013.ijirr_journal-ir0volumeA3A2.1": {"snippet": "Text Mining in Bioinformatics: Research and Application", "weights": []}, "2006.ipm_journal-ir0volumeA42A4.15": {"snippet": "Online Course: Text Mining by Statistics.com (http://www.statistics.com/)", "weights": []}, "2008.cikm_conference-2008.230": {"snippet": "Effective pattern taxonomy mining in text documents\n\n\n ABSTRACTMany data mining techniques have been proposed for mining useful patterns in databases. However, how to effectively utilize discovered pa...", "weights": []}, "2010.cikm_conference-2010.91": {"snippet": "... the inclusion of some patterns can improve the classification performance. Currently, most methods adopt a two-phases approach by generating all frequent patterns in the first phase and selecting the...", "weights": [[8, 17, 1.0]]}, "C16-1268": {"snippet": "...onal Inclusion Hypothesis for Tensor-based Composition\n\n\n According to the distributional inclusion hypothesis, entailment between words can be measured via the feature inclusions of their distributio...", "weights": [[8, 17, 1.0], [93, 102, 1.0]]}, "2012.sigirconf_conference-2012.211": {"snippet": "Methods for mining and summarizing text conversations", "weights": []}, "C10-2074": {"snippet": "Text Mining for Automatic Image Tagging\n\n\n This paper introduces several extractive approaches for automatic image tagging, relying exclusively on information mined from texts. Through evaluations on ...", "weights": []}, "2021.ecir_conference-20212.27": {"snippet": "...e as inclusion or exclusion respectively. Further, our inclusion/exclusion classifier provides an F1 of \u223c98 and \u223c97 for 11-class inclusion and exclusion classification respectively. We believe that ou...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [132, 141, 1.0]]}, "2011.ipm_journal-ir0volumeA47A5.3": {"snippet": "Multilingual sentence categorization and novelty mining\n\n\n a b s t r a c tA challenge for sentence categorization and novelty mining is to detect not only when text is relevant to the user's informati...", "weights": []}, "2015.cikm_workshop-2015dtmbio.0": {"snippet": "Proceedings of the ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2015, Melbourne, Australia, October 23, 2015", "weights": []}, "D07-1016": {"snippet": "... our inclusion entity model achieves a performance gain of 4.3 points in F-score over a baseline of no inclusion detection, and even outperforms a parser with access to gold standard part-of-speech ta", "weights": [[8, 17, 1.0], [106, 115, 1.0]]}, "L06-1368": {"snippet": "Text Mining for Semantic Relations as a Support Base of a Scientific Portal Generator", "weights": []}, "N16-1165": {"snippet": "Cross-Domain Mining of Argumentative Text through Distant Supervision\n\n\n Argumentation mining is considered as a key technology for future search engines and automated decision making. In such applica...", "weights": []}, "R11-1072": {"snippet": "... the inclusion of lexical resources. The results obtained show a similarity with the ones obtained with the JRC Quotes corpus demonstrating the EmotiBlog validity as a resource for the SA task.", "weights": [[8, 17, 1.0]]}, "2004.jasis_journal-ir0volumeA55A5.2": {"snippet": "Text mining: Generating hypotheses from MEDLINE", "weights": []}, "2015.sigirjournals_journal-ir0volumeA49A2.20": {"snippet": "Utilising Wikipedia for Text Mining Applications", "weights": []}, "2012.tist_journal-ir0volumeA3A4.1": {"snippet": "Bootstrapping a Game with a Purpose for Commonsense Collection\n\n\n Text mining has been very successful in extracting huge amounts of commonsense knowledge from data, but the extracted knowledge tends ...", "weights": []}, "2013.tist_journal-ir0volumeA4A4.14": {"snippet": "...,760 inclusion and 4,871 exclusion eligibility criteria. This corpus is well represented by a relatively small number of 126 inclusion clusters and 175 exclusion clusters, each of which corresponds to...", "weights": [[8, 17, 1.0], [128, 137, 1.0]]}, "1999.cikm_conference-99.31": {"snippet": "Efficient Mining of Association Rules in Text Databases\n\n\n AbstractIn this paper, we propose two new algorithms for mining association rules between words in text databases. The characteristics of tex...", "weights": []}, "2012.cikm_workshop-2012dtmbio.0": {"snippet": "Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics, DTMBIO@CIKM 2012, Maui, Hawaii, USA, October 29, 2012", "weights": []}, "2011.ir_journal-ir0volumeA14A2.5": {"snippet": "Michael W. 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Although some basic knowledge of machine learning may be necessary to follow a few topics, the book includes clear illustrations and explanations, which make it accessibl...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2015.tist_journal-ir0volumeA6A4.9": {"snippet": "Analyzing Activity Recognition Uncertainties in Smart Home Environments\n\n\n In spite of the importance of activity recognition (AR) for intelligent human-computer interaction in emerging smart space ap...", "weights": []}, "W19-8403": {"snippet": "...able Artificial Intelligence attempts to solve the problem of algorithmic opacity. Many terms and notions have been introduced recently to define Explainable AI, however, these terms seem to be used i...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2021.wwwconf_conference-2021c.33": {"snippet": "...ncy, Ethics and Society on the Web", "weights": [[8, 14, 1.0]]}, "2017.wwwconf_conference-2017c.159": {"snippet": "...ars, artificial intelligence has achieved great success in many important applications. Both novel machine learning algorithms (e.g., deep neural networks), and their distributed implementations play ...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2022.trustnlp-1.4": {"snippet": "...}\n\n\n Artificial Intelligence (AI) and Machine Learning (ML) are rapidly becoming musthave capabilities. According to a 2019 Forbes Insights Report, \"seventy-nine percent [of executives] agree that AI ...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2015.wwwconf_conference-2015.15": {"snippet": "...arch ethics review. We describe the creation of a new ethics framework and a research ethics submission system (RESS) within Microsoft Research (MSR). This RESS is customized to the needs of web resea...", "weights": [[8, 14, 1.0], [57, 63, 1.0], [89, 95, 1.0]]}, "W19-8404": {"snippet": "...able Artificial Intelligence\n\n\n Among challenges for eXplainable Artificial Intelligence (XAI) is explanation generation. In this paper we put the stress on this issue by focusing on a semantic repres...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [68, 78, 1.0], [79, 91, 1.0]]}, "2020.nl4xai-1.10": {"snippet": "...able Artificial Intelligence\n\n\n This paper discusses four major argumentation theoretical frameworks with respect to their use in support of explainable artificial intelligence (XAI). We consider thes...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [156, 166, 1.0], [167, 179, 1.0]]}, "2021.chiir_conference-2021.41": {"snippet": "... and Ethics in Studies Involving Participants with Disabilities: A Tutorial", "weights": [[8, 14, 1.0]]}, "W19-8401": {"snippet": "...able Artificial Intelligence and its potential within Industry\n\n\n The age of Big Data has enabled the creation of artificial intelligence solutions that has allowed systems to better respond to their ...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [117, 127, 1.0], [128, 140, 1.0]]}, "2022.nlp4convai-1.13": {"snippet": "...t of artificial intelligence and natural language processing research. 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Existing works in this field mainly focus on consistency of neur...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2016.tist_journal-ir0volumeA7A4.0": {"snippet": "...ween artificial intelligence and human intelligence with the wisdom of a crowd.This special issue contributes to addressing key research questions centered on the role of the crowd in intelligent syst...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [42, 54, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.4": {"snippet": "Artificial intelligence: Underlying assumptions and basic objectives", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2020.wwwconf_conference-2020.240": {"snippet": "IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems\n\n\n Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Corta...", "weights": []}, "2020.ccl-1.50": {"snippet": "... for Artificial Intelligence Ethical Computing)\n\n\n The construction of the moral dictionary is based on artificial intelligence ethical computing. Moral behavior is complex and varied. The existing mo...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [107, 117, 1.0], [118, 130, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.2": {"snippet": "Artificial intelligence: Concepts, techniques, applications, promise", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2018.cikm_workshop-2018w.29": {"snippet": "...iven artificial intelligence-based technologies is of highest importance to maximize its progressive influence to human life and human society. Life style diseases, social network affinity, impulsive ...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2019.fire_conference-2019w.4": {"snippet": "...xts. Artificial Intelligence is now applied in this area to understand and retrieve legal documents. This paper explains a working model developed for the track Artificial Intelligence for Legal Assis...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [164, 174, 1.0], [175, 187, 1.0]]}, "2019.wsdm_conference-2019.114": {"snippet": "Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned\n\n\n ABSTRACTResearchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by...", "weights": []}, "2021.teachingnlp-1.6": {"snippet": "...e on Ethics for Natural Language Processing\n\n\n It is generally agreed upon in the natural language processing (NLP) community that ethics should be integrated into any curriculum. 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Transfer lear...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [59, 69, 1.0], [70, 82, 1.0]]}, "2007.jasis_journal-ir0volumeA58A2.14": {"snippet": "...tion ethics: Privacy, property, and power", "weights": [[8, 14, 1.0]]}, "2021.jasis_journal-ir0volumeA72A1.8": {"snippet": "Artificial intelligence and the world of work, a co-constitutive relationship", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2011.jasis_journal-ir0volumeA62A12.13": {"snippet": "...t of Artificial Intelligence Fellowship Award? Evaluating researchers by mining bibliographic data", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2004.sigirconf_conference-2004.140": {"snippet": "An artificial intelligence approach to information retrieval (abstract only)", "weights": [[3, 13, 1.0], [14, 26, 1.0]]}, "2021.naacl-main.295": {"snippet": "...ects ethics in NLP research. We enumerate common scenarios where crowdworkers performing NLP tasks are at risk of harm. We thus recommend that researchers evaluate these risks by considering the three...", "weights": [[8, 14, 1.0]]}, "1997.jasis_journal-ir0volumeA48A7.14": {"snippet": "Ethics and Computing: Living Responsibly in a Computerized World, by Kevin W. Bowyer", "weights": [[0, 6, 1.0]]}}}
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Information Retrieval systems can benefit from advanced linguistic resources when carrying out tasks such as word-stemming or query translation. The main goal of our experiments has been the deve...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2021.ecir_conference-20212.26": {"snippet": "...tage Retrieval Pipeline\n\n\n Pre-trained deep language models (LM) have advanced the state-of-the-art of text retrieval. Rerankers fine-tuned from deep LM estimates candidate relevance based on rich con...", "weights": [[8, 17, 1.0], [111, 120, 1.0]]}, "2011.ecir_conference-2011.0": {"snippet": "...s in Information Retrieval - 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2010.wwwconf_conference-2010.11": {"snippet": "...dard information retrieval (IR). Recently, there has been a great deal of research into developing advanced match ranking algorithms. 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The reliable evaluation of these technologies has been recognized for decades as central to the advancement of the field. As information retri...", "weights": [[8, 19, 1.0], [38, 49, 1.0], [186, 197, 1.0], [198, 207, 1.0]]}, "2016.ecir_conference-2016.0": {"snippet": "...s in Information Retrieval - 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20-23, 2016. Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2010.ecir_conference-2010.0": {"snippet": "...s in Information Retrieval, 32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28-31, 2010. Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2010.jasis_journal-ir0volumeA61A9.0": {"snippet": "...s in Information Science", "weights": [[8, 19, 1.0]]}, "2018.sigirconf_conference-2018.222": {"snippet": "...s in information retrieval. Finally, we conclude this tutorial with a comprehensive summarization and a prospect of further research directions for GANs in information retrieval. \nKEYWORDS\nBACKGROUND ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [159, 170, 1.0], [171, 180, 1.0]]}, "2009.ecir_conference-2009.92": {"snippet": "...s in Information Retrieval Evaluation", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2007.sigirconf_conference-2007.35": {"snippet": "...vant information and learn improved searching strategies.", "weights": [[8, 19, 1.0]]}, "2011.ictir_conference-2011.0": {"snippet": "...s in Information Retrieval Theory - Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2010.irfc_conference-2010.0": {"snippet": "...nary Retrieval, First Information Retrieval Facility Conference, IRFC 2010, Vienna, Austria, May 31, 2010. 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1.0], [195, 202, 1.0]]}, "1992.trec_conference-1992.31": {"snippet": "Machine Learning and Relevance Feedback", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2010.sigirconf_conference-2010.89": {"snippet": "...arch results. Vertical selection is the subtask of deciding, given a query, which verticals, if any, are relevant. State of the art approaches use machine learned models to predict which verticals are...", "weights": [[8, 15, 1.0], [108, 116, 1.0], [150, 157, 1.0]]}, "2012.sigirconf_conference-2012.192": {"snippet": "...anic results such as ads, embedded image results, news or local results, etc. -thereby recreating a realistic search experience for the subjects. Paid annotators rated every search result for relevanc...", "weights": [[8, 15, 1.0], [44, 51, 1.0], [67, 74, 1.0]]}, "W00-1316": {"snippet": "A Machine Learning Approach to Answering Questions for Reading Comprehension Tests\n\n\n In this paper, we report results on answering questions for the reading comprehension task, using a machine learni...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [111, 118, 1.0], [186, 193, 1.0], [194, 202, 1.0]]}, "2010.wsdm_conference-2010.16": {"snippet": "...rch, machine learning, and information retrieval. Recent work on ranking focused on a number of different paradigms, namely, pointwise, pairwise, and list-wise approaches. Each of those paradigms focu...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2016.jasis_journal-ir0volumeA67A9.5": {"snippet": "A machine-learning approach to negation and speculation detection for sentiment analysis", "weights": [[2, 9, 1.0], [10, 18, 1.0]]}, "2007.ntcir_workshop-2007.5": {"snippet": "...able machine-learning framework to model the subtasks as classification problems and report on my system's performance. Additionally, I show that blind relevance feedback improves results sentenceleve...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [183, 190, 1.0]]}, "2010.cikm_conference-2010.163": {"snippet": "...arch results. We propose a machine learning approach to adaptively optimizing the diversification of search results for each query so as to optimize the overall utility in an entire session. Experimen...", "weights": [[8, 15, 1.0], [30, 37, 1.0], [38, 46, 1.0], [111, 118, 1.0]]}, "P19-1488": {"snippet": "...y of machine learning (ML) models becomes more relevant with their increasing adoption. In this work, we address the interpretability of ML based question answering (QA) models on a combination of kno...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [50, 58, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.1": {"snippet": "...arch results to users. Instead, they rely on the user of the system to estimate which items are relevant, partially relevant, or not relevant. The user of the system is given the task of distinguishin...", "weights": [[8, 15, 1.0], [99, 107, 1.0], [119, 127, 1.0], [136, 144, 1.0]]}, "P01-1020": {"snippet": "A Machine Learning Approach to the Automatic Evaluation of Machine Translation\n\n\n We present a machine learning approach to evaluating the wellformedness of output of a machine translation system, usi...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [59, 66, 1.0], [95, 102, 1.0], [103, 111, 1.0], [169, 176, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "... are relevant; the coverage of a system is measured by recall, the proportion of relevant documents that are retrieved.) A second system configuration is used to produce a second set of retrieval resu...", "weights": [[8, 16, 1.0], [84, 92, 1.0], [199, 206, 1.0]]}, "2012.tois_journal-ir0volumeA30A4.0": {"snippet": "...line Learning Framework for Refining Recency Search Results with User Click Feedback\n\n\n Traditional machine-learned ranking systems for Web search are often trained to capture stationary relevance of ...", "weights": [[8, 16, 1.0], [55, 62, 1.0], [103, 110, 1.0]]}, "2008.sigirconf_conference-2008.37": {"snippet": "...arch results. The problem has been the focus of much of the research at the intersection of Web search and machine learning, and learning ranking functions from preference data in particular has recen...", "weights": [[8, 15, 1.0], [110, 117, 1.0], [118, 126, 1.0], [132, 140, 1.0]]}, "2011.tist_journal-ir0volumeA2A2.1": {"snippet": "...tive learning in multimedia annotation and retrieval: A survey\n\n\n Active learning is a machine learning technique that selects the most informative samples for labeling and uses them as training data....", "weights": [[8, 16, 1.0], [76, 84, 1.0], [90, 97, 1.0], [98, 106, 1.0]]}, "2008.tois_journal-ir0volumeA26A4.1": {"snippet": "...arch results that will be returned. These expectations can be based on the current information need, but are also influenced by how the searcher believes the search engine works, where relevant result...", "weights": [[8, 15, 1.0], [188, 196, 1.0], [197, 204, 1.0]]}, "2021.semeval-1.157": {"snippet": "...onal Machine Learning for Humorous and Offensive Tasks enough in 2021?\n\n\n This paper describes the approach that was developed for SemEval 2021 Task 7 Hahackathon: Incorporating Demographic Factors in...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "W05-0406": {"snippet": "...nt a machine learning system for identifying non-referential it. Types of non-referential it are examined to determine relevant linguistic patterns. The patterns are incorporated as features in a mach...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [122, 130, 1.0], [199, 206, 1.0]]}, "2019.cikm_conference-2019.385": {"snippet": "Machine Learning on Graphs with Kernels", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2008.sigirconf_conference-2008.32": {"snippet": "... are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a clusterbased resampling method...", "weights": [[8, 16, 1.0], [38, 46, 1.0]]}, "L00-1159": {"snippet": "...sing Machine Learning Methods to Improve Quality of Tagged Corpora and Learning Models", "weights": [[8, 15, 1.0], [16, 24, 1.0], [74, 82, 1.0]]}, "2017.wwwconf_conference-2017c.159": {"snippet": "...uted Machine Learning: Foundations, Trends, and Practices\n\n\n ABSTRACTIn recent years, artificial intelligence has achieved great success in many important applications. Both novel machine learning alg...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [183, 190, 1.0], [191, 199, 1.0]]}, "2019.tist_journal-ir0volumeA10A2.1": {"snippet": "...shot learning. First of all, we provide an overview of zero-shot learning. According to the data utilized in model optimization, we classify zero-shot learning into three learning settings. Second, we...", "weights": [[8, 16, 1.0], [68, 76, 1.0], [154, 162, 1.0], [174, 182, 1.0]]}, "N06-1051": {"snippet": "... how machine learning techniques can fit this task. This leads to smaller pools than traditional round robin pooling, thus reduces significantly the manual assessment workload. Experimental results on...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [193, 200, 1.0]]}, "2001.trec_conference-2001.32": {"snippet": "... Our results show that about 84% of the testing queries had the correct homepage returned within the top 10 pages. This shows that our machine learning approaches are effective since without any machi...", "weights": [[8, 15, 1.0], [138, 145, 1.0], [146, 154, 1.0], [198, 205, 1.0]]}, "2012.amta-papers.12": {"snippet": "...ated Results on Translingual Information Retrieval\n\n\n In an ideal cross-lingual information retrieval (CLIR) system, a user query would generate a search over documents in a different language and the...", "weights": [[8, 15, 1.0]]}, "W97-1002": {"snippet": "...t of relevant items. Given the recent success of empirical or corpusbased approaches in other areas of natural language processing, machine learning has the potential to significantly aid the developm...", "weights": [[8, 16, 1.0], [135, 142, 1.0], [143, 151, 1.0]]}, "2016.cikm_conference-2016.93": {"snippet": "...aged machine learning to ameliorate these weaknesses, and in the process created novel experiences. In this talk, I will give three examples of machine learning improving the email experience.The firs...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [147, 154, 1.0], [155, 163, 1.0]]}, "2010.wsdm_conference-2010.19": {"snippet": "...This results in a model based on intrinsic relevance, as opposed to perceived relevance. We use the model to predict document relevance and then use this as feature for a \"Learning to Rank\" machine le...", "weights": [[8, 15, 1.0], [175, 183, 1.0], [193, 200, 1.0], [201, 209, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.7": {"snippet": "...s to machine learning", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2007.ipm_journal-ir0volumeA43A4.3": {"snippet": "...r of relevant documents. The proposed method achieved 131%, 34%, 12% improvements in micro-averaged F 1 for 25, 46, and 58 topics with less than 10, 30, and 50 relevant documents in learning, respecti...", "weights": [[8, 16, 1.0], [163, 171, 1.0], [185, 193, 1.0]]}, "1986.sigirconf_conference-86.28": {"snippet": "A Machine Learning Approach to Information Retrieval", "weights": [[2, 9, 1.0], [10, 18, 1.0]]}, "2018.cikm_conference-2018.299": {"snippet": "...se a machine learned model with words as features and target clicks or relevance labels. Contrary to these approaches, we build a parameterized model to determine the weights of popular title terms fo...", "weights": [[8, 15, 1.0]]}, "2016.sigirconf_workshop-2016sal.9": {"snippet": "...arch Results for Educational Goals: Incorporating Keyword Density as a Retrieval Objective\n\n\n While past research has shown that learning outcomes can be influenced by the amount of e\u21b5ort students inv...", "weights": [[8, 15, 1.0], [132, 140, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.10": {"snippet": "...onal machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing import...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [149, 156, 1.0], [157, 165, 1.0]]}, "2020.wsdm_conference-2020.110": {"snippet": "...ible Machine Learning and Knowledge Discovery Boosted by Visual Means\n\n\n Intelligible machine learning and knowledge discovery are important for modeling individual and social behavior, user activity,...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [89, 96, 1.0], [97, 105, 1.0]]}, "2005.cikm_conference-2005.3": {"snippet": "...e of machine learning and statistical modeling techniques. This talk will provide an overview of recent progress and new trends in machine learning and discuss their relevance for developing intellige...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [134, 141, 1.0], [142, 150, 1.0]]}, "2010.wsdm_conference-2010.37": {"snippet": "...be a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model to learn from past user clicks ...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2019.wsdm_conference-2019.114": {"snippet": "...s in machine learning systems. We will motivate the need for adopting a \"fairness-first\" approach (as opposed to viewing algorithmic bias / fairness considerations as an afterthought), when developing...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2014.trec_conference-2014.18": {"snippet": "...ding relevant inforamtion using pseudorelevance feedback and Google search engine results) to retrieve more relevant tweets, then extracted several effective semantic features (e.g., Jansen-Shannon Di...", "weights": [[8, 16, 1.0], [85, 92, 1.0], [111, 119, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "...arch results. Even with state-of-the-art search engines users are often dissatisfied with the returned results and have to manually alter their query in order to retrieve relevant documents. Despite t...", "weights": [[8, 15, 1.0], [106, 113, 1.0], [174, 182, 1.0]]}, "2013.wsdm_conference-2013.53": {"snippet": "...long machine learning and transfer learning", "weights": [[8, 15, 1.0], [16, 24, 1.0], [38, 46, 1.0]]}, "2012.sigirconf_conference-2012.5": {"snippet": "...se a machine learning approach to learn the probability that a user may find a follow-up query both useful and relevant, given his initial query. Our approach is based on a machine learning model whic...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [114, 122, 1.0], [176, 183, 1.0], [184, 192, 1.0]]}, "2019.wsdm_conference-2019.5": {"snippet": "...dard machine learning has helped us a lot in the singleturn paradigm, where we use it to predict: intent, relevance, user satisfaction, etc. When we think of search or recommendation as a series of ex...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "W01-1603": {"snippet": "...of a Machine Learnable Discourse Tagging Tool\n\n\n We have developed a discourse level tagging tool for spoken dialogue corpus using machine learning methods. As discourse level information, we focused ...", "weights": [[8, 15, 1.0], [134, 141, 1.0], [142, 150, 1.0]]}, "2017.ipm_journal-ir0volumeA53A4.5": {"snippet": "...nt a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [187, 194, 1.0], [195, 203, 1.0]]}, "2011.tist_journal-ir0volumeA2A3.7": {"snippet": "...cale machine learning\n\n\n With the dramatic increase in computing power, the field of machine learning has undergone tremendous changes in recent years. This change can be attributed to a deeper unders...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [88, 95, 1.0], [96, 104, 1.0]]}, "2009.trec_conference-2009.35": {"snippet": "Machine Learning for Information Retrieval: TREC 2009 Web, Relevance Feedback and Legal Tracks", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2021.ipm_journal-ir0volumeA58A4.9": {"snippet": "A machine learning, bias-free approach for predicting business success using Crunchbase data", "weights": [[2, 9, 1.0], [10, 18, 1.0]]}, "2005.cikm_conference-2005.105": {"snippet": "...some relevant pages are not retrieved. More relevant pages can be obtained by adding the active ingredient of the drug to the query. In order to eliminate irrelevant pages, we propose a machine learni...", "weights": [[8, 16, 1.0], [47, 55, 1.0], [189, 196, 1.0], [197, 205, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A5.3": {"snippet": "...ised learning to rank with pseudo labels\n\n\n Abstract Semi-supervised learning is a machine learning paradigm that can be applied to create pseudo labels from unlabeled data for learning a ranking mode...", "weights": [[8, 16, 1.0], [72, 80, 1.0], [86, 93, 1.0], [94, 102, 1.0], [180, 188, 1.0]]}, "2015.tist_journal-ir0volumeA7A1.0": {"snippet": "Learning to Rank from Noisy Data\n\n\n Learning to rank, which learns the ranking function from training data, has become an emerging research area in information retrieval and machine learning. Most exi...", "weights": [[0, 8, 1.0], [36, 44, 1.0], [174, 181, 1.0], [182, 190, 1.0]]}, "2018.wwwconf_conference-2018.184": {"snippet": "...s to relevant products and services, search advertising systems use sophisticated machine learning algorithms to estimate the revenue expectations of thousands of matching ad listings per query. These...", "weights": [[8, 16, 1.0], [85, 92, 1.0], [93, 101, 1.0]]}, "2009.wwwconf_conference-2009.1": {"snippet": "...n of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can also provide an important source of implic...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2018.sigirconf_workshop-2018profs.3": {"snippet": "...tive Learning (CAL) is a technique where a human reviewer works in loop with a machine learning model; the model presents a set of documents likely to be relevant and the reviewer provides relevance f...", "weights": [[8, 16, 1.0], [82, 89, 1.0], [90, 98, 1.0], [157, 165, 1.0]]}, "2007.sigirconf_conference-2007.13": {"snippet": "...ndem learning using an \"oracle\" results in much better performance than learning on only features or only instances. We find that humans can emulate the oracle to an extent that results in performance...", "weights": [[8, 16, 1.0], [35, 42, 1.0], [75, 83, 1.0], [181, 188, 1.0]]}, "2018.ecir_conference-2018.17": {"snippet": "...arch results by either the explicit relevance annotations from external assessors or implicit behavior signals from users. Because the mobile search is different from the desktop search in terms of th...", "weights": [[8, 15, 1.0]]}, "2018.sigirconf_conference-2018.80": {"snippet": "...arch results and the probability of user satisfaction, from mobile click logs. With this information, we can estimate the quality of different vertical results and improve the ranking of heterogeneous...", "weights": [[8, 15, 1.0], [155, 162, 1.0]]}, "2017.ictir_workshop-2017learner.8": {"snippet": "...s on Learning to Rank\n\n\n Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of documents an...", "weights": [[8, 16, 1.0], [28, 36, 1.0], [71, 78, 1.0], [79, 87, 1.0]]}, "2011.wsdm_conference-2011.79": {"snippet": "...ical results within blocks of textual web results challenging: how do we represent, assess, and compare the relevance of these heterogeneous entities?In this paper we present a machine-learning framew...", "weights": [[8, 15, 1.0], [45, 52, 1.0], [180, 187, 1.0], [188, 196, 1.0]]}, "2011.cikm_conference-2011.248": {"snippet": "...o be relevant; if the top-ranked document is deemed most relevant, similar documents are also likely to be relevant, discouraging diversity. At the other extreme, n = 1 encourages diversity since only...", "weights": [[8, 16, 1.0], [60, 68, 1.0], [110, 118, 1.0]]}, "2019.tist_journal-ir0volumeA10A1.6": {"snippet": "Learning Facial Expressions with 3D Mesh Convolutional Neural Network\n\n\n Making machines understand human expressions enables various useful applications in human-machine interaction. In this article,...", "weights": [[0, 8, 1.0], [163, 170, 1.0]]}, "2009.cikm_conference-2009.190": {"snippet": "...arch results play a crucial role in guiding a user's search process. We present a machine-learned algorithm that trains a boosted tree to pick the most relevant title for a Web search result. We compa...", "weights": [[8, 15, 1.0], [85, 92, 1.0], [155, 163, 1.0]]}, "2018.sigirconf_conference-2018.219": {"snippet": "...sly, machine learning methods have been exploited to address the problem, which learns a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning h...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [150, 158, 1.0], [193, 201, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "...asic machine learning concepts and tools; in the second session, we will introduce more advanced topics in machine learning, and will present recent developments in machine learning and its applicatio...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [110, 117, 1.0], [118, 126, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "2011.sigirconf_conference-2011.12": {"snippet": "Learning to rank for freshness and relevance\n\n\n ABSTRACTFreshness of results is important in modern web search. Failing to recognize the temporal aspect of a query can negatively affect the user exper...", "weights": [[0, 8, 1.0], [69, 76, 1.0]]}, "2015.wwwconf_conference-2015c.287": {"snippet": "...sing results with regards to the baseline according to all used metrics. By this paper, we wish to provide a novel viewpoint to address the BR relevance problem, different from the dominating machine ", "weights": [[8, 15, 1.0], [195, 202, 1.0]]}, "I08-1068": {"snippet": "...that learning these user profiles using snippets surrounding the results for a query gives better performance than learning from entire document collection.", "weights": [[8, 16, 1.0], [68, 75, 1.0], [118, 126, 1.0]]}, "2016.airs_conference-2016.27": {"snippet": "...ovel machine learning framework for ranking, which depends on two new samples. The first sample is one-group constructed of one document with higher relevance judgment and a group of documents with lo...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2012.sigirconf_conference-2012.180": {"snippet": "...emed relevant iff its subtopic ti matches query subtopic t:We next define R k = P k i=1 ri, where R k is the number of relevant items from the first k selections. Reading R k \u2265 n as I[R k \u2265 n], we exp...", "weights": [[8, 16, 1.0], [122, 130, 1.0]]}, "2007.sigirconf_conference-2007.39": {"snippet": "... for learning ranking functions for improving relevance of search engines serving diverse streams of user queries. We explore supervised learning methodology from machine learning, and we distinguish ...", "weights": [[8, 16, 1.0], [140, 148, 1.0], [166, 173, 1.0], [174, 182, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.3": {"snippet": "... are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling metho...", "weights": [[8, 16, 1.0], [38, 46, 1.0]]}, "W07-2412": {"snippet": "...oved results on training machine-learning classifiers compared to previous experiments (Granfeldt et al., 2006) . We also introduce a method to select attributes in order to identify the most relevant...", "weights": [[8, 15, 1.0], [28, 35, 1.0], [36, 44, 1.0], [195, 203, 1.0]]}, "2014.sigirconf_conference-2014.17": {"snippet": "...tive learning, require substantially and significantly less human review effort (P < 0.01) to achieve any given level of recall, than passive learning, in which the machine-learning algorithm plays no...", "weights": [[8, 16, 1.0], [145, 153, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "D14-1068": {"snippet": "...ffer relevant results. We present a machine learning approach to correcting these errors, based largely on character-level n-gram features. We demonstrate superior performance over rule-based methods,...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [39, 46, 1.0], [47, 55, 1.0]]}, "1997.sigirconf_conference-97.4": {"snippet": "... non-relevant corpus. Results show that simple strategies to select non-relevant articles for learning the feedback queries yield significantly better feedback queries. The rest of this study ia organ...", "weights": [[8, 16, 1.0], [25, 32, 1.0], [75, 83, 1.0], [97, 105, 1.0]]}, "2012.wsdm_conference-2012.45": {"snippet": "...arch results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms using either direct human relevance judgments or indirect judgments obtai...", "weights": [[8, 15, 1.0], [102, 109, 1.0], [110, 118, 1.0]]}, "2020.emnlp-main.692": {"snippet": "...from Machine Learning Papers\n\n\n Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. 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In either method, crawling results in more information being harvested than just the websites intended for preservat...", "weights": [[8, 16, 1.0], [39, 47, 1.0], [105, 113, 1.0], [172, 180, 1.0]]}, "2011.eamt-1.40": {"snippet": "...ards Using Web-Crawled Data for Domain Adaptation in Statistical Machine Translation\n\n\n This paper reports on the ongoing work focused on domain adaptation of statistical machine translation using dom...", "weights": [[8, 13, 1.0], [68, 75, 1.0], [174, 181, 1.0], [194, 199, 1.0]]}, "2014.wwwconf_conference-2014c.198": {"snippet": "...site using a fuzzy tree difference algorithm, and (ii) using a machine-learning algorithm to derive a model of relevant website changes that can be used to cluster similar modifications to reduce the ...", "weights": [[8, 13, 1.0], [58, 63, 1.0], [66, 73, 1.0], [74, 82, 1.0]]}, "2020.wwwconf_conference-2020.16": {"snippet": "...ffic websites and actual browsing behaviour measured from an opt-in sample of over 50,000 users of the Firefox Web browser. Our analysis reveals differences between the treatment of stateless crawling...", "weights": [[8, 16, 1.0], [195, 203, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A1.12": {"snippet": "... web crawling", "weights": [[8, 16, 1.0]]}, "2021.wmt-1.118": {"snippet": "Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora\n\n\n Large web-crawled corpora represent an excellent resource for improving the performance of Neural Machine Translation (...", "weights": [[0, 8, 1.0], [25, 30, 1.0], [179, 186, 1.0]]}, "2006.clef_workshop-2006.49": {"snippet": "Using Machine Learning and Text Mining in Question Answering", "weights": [[0, 5, 1.0], [6, 13, 1.0], [14, 22, 1.0]]}, "L00-1159": {"snippet": "Using Machine Learning Methods to Improve Quality of Tagged Corpora and Learning Models", "weights": [[0, 5, 1.0], [6, 13, 1.0], [14, 22, 1.0], [72, 80, 1.0]]}, "2013.wwwconf_conference-2013.41": {"snippet": "... al. using on-page and anchor text features . Tang, et al. studied homepage acquisition from search engine results using researcher names as queries . 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We present netSifter, a crawler system which ...", "weights": [[8, 16, 1.0], [127, 135, 1.0], [148, 156, 1.0]]}, "2015.wwwconf_conference-2015c.314": {"snippet": "...hese websites can influence search engines results or introduce an extra burden for crawling systems. In addition, spam websites represent a cost for ad bidders that are obliged to pay for impressions...", "weights": [[8, 16, 1.0], [87, 95, 1.0], [123, 131, 1.0]]}, "2015.sigirconf_conference-2015.17": {"snippet": "...re a machine learning approach that combines different frontier prioritization approaches. 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Idea...", "weights": [[8, 16, 1.0], [25, 33, 1.0]]}, "2010.cikm_conference-2010.192": {"snippet": "...used crawling using navigational rank\n\n\n ABSTRACTThe goal of focused crawling is to use limited resources to effectively discover web pages related to a specific topic rather than downloading all acce...", "weights": [[8, 16, 1.0], [17, 22, 1.0], [72, 80, 1.0]]}, "2020.lrec-1.348": {"snippet": "...ated using a crowd-sourcing policy. LDA sampling, which is an efficient Active Learning strategy using Topic Modeling, is proposed in this research. Active Learning Strategies have shown promising res...", "weights": [[8, 13, 1.0], [82, 90, 1.0], [100, 105, 1.0], [159, 167, 1.0]]}, "2007.wwwconf_conference-2007.134": {"snippet": "...used crawling\n\n\n ABSTRACTThis paper reports a new general framework of focused web crawling based on \"relational subgroup discovery\". Predicates are used explicitly to represent the relevance clues of...", "weights": [[8, 16, 1.0], [86, 94, 1.0]]}, "2020.acl-main.417": {"snippet": "...a by crawling the web, using open source software. We empirically compare alternative methods and publish benchmark data sets for sentence alignment and sentence pair filtering. We also describe the p...", "weights": [[8, 16, 1.0], [26, 31, 1.0]]}, "2019.tweb_journal-ir0volumeA13A1.0": {"snippet": "...ing, machine learning, and natural language processing to interpret privacy policies at scale, thus producing annotations for use in interfaces that inform Internet users of salient policy details. Ho...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.7": {"snippet": "...s to machine learning", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2008.cikm_workshop-2008inews.13": {"snippet": "...best machine learning algorithm, outperforming the dictionary-based approach and other machine learning algorithms. Our main contribution is to propose a new hybrid approach, LearnLexTo, which further...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [90, 97, 1.0], [98, 106, 1.0]]}, "L18-1599": {"snippet": "... Web crawling is an efficient way for compiling the monolingual, parallel and/or domain-specific corpora needed for machine translation and other HLT applications. These corpora can be automatically p...", "weights": [[8, 16, 1.0], [119, 126, 1.0]]}, "2013.clef_workshop-2013bioasq.4": {"snippet": "...ypes Using Machine Learning", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 30, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A5.3": {"snippet": "...ised learning to rank with pseudo labels\n\n\n Abstract Semi-supervised learning is a machine learning paradigm that can be applied to create pseudo labels from unlabeled data for learning a ranking mode...", "weights": [[8, 16, 1.0], [72, 80, 1.0], [86, 93, 1.0], [94, 102, 1.0], [180, 188, 1.0]]}, "2020.wwwconf_conference-2020.37": {"snippet": "... for Crawling Commercial Web Content\n\n\n The rapid growth of commercial web content has driven the development of shopping search services to help users find product offers. Due to the dynamic nature o...", "weights": [[8, 16, 1.0]]}, "L16-1312": {"snippet": "A Machine Learning based Music Retrieval and Recommendation System\n\n\n In this paper, we present a music retrieval and recommendation system using machine learning techniques. We propose a query by hum...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [140, 145, 1.0], [146, 153, 1.0], [154, 162, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.25": {"snippet": "... web crawling and retrieval remains as a challenging issue. Recently, several link based ranking algorithms like PageRank, HITS and OPIC have been proposed. In this paper, we propose a novel recursive...", "weights": [[8, 16, 1.0]]}, "2005.wwwconf_conference-2005si.108": {"snippet": "...d on learning automata\n\n\n ABSTRACTAutonomics or self-reorganization becomes pertinent for websites serving a large number of users with highly varying workloads. An important component of self-adaptat...", "weights": [[8, 16, 1.0], [93, 101, 1.0]]}, "1995.jasis_journal-ir0volumeA46A3.2": {"snippet": "Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms", "weights": [[0, 7, 1.0], [8, 16, 1.0], [70, 78, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A4.8": {"snippet": "... Web crawling: a survey\n\n\n Abstract Deep Web crawling refers to the problem of traversing the collection of pages in a deep Web site, which are dynamically generated in response to a particular query ...", "weights": [[8, 16, 1.0], [48, 56, 1.0]]}, "2005.wwwconf_conference-2005si.172": {"snippet": "...nd a machine learning classifier trained by active learning. The analyst first configures the search queries and then labels training and testing sets using an active learning process that creates bot...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [54, 62, 1.0], [154, 159, 1.0], [170, 178, 1.0]]}, "2013.cikm_conference-2013.177": {"snippet": "...date websites from initial seed objects. Based on the objects extracted from these websites it learns a set of URL patterns which covers the greatest number of target objects with little redundancy. F...", "weights": [[8, 16, 1.0], [86, 94, 1.0]]}, "2010.wsdm_conference-2010.39": {"snippet": "Learning URL patterns for webpage de-duplication\n\n\n ABSTRACTPresence of duplicate documents in the World Wide Web adversely affects crawling, indexing and relevance, which are the core building blocks...", "weights": [[0, 8, 1.0], [132, 140, 1.0]]}, "2009.ipm_journal-ir0volumeA45A3.2": {"snippet": "...in a machine learning model (in our case, Neural Network). 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In order to get the ranks of websites, researchers used to describe the inter-connectivity among websites wi...", "weights": [[8, 16, 1.0], [85, 93, 1.0], [124, 132, 1.0], [192, 200, 1.0]]}, "1999.tois_journal-ir0volumeA17A2.1": {"snippet": "...tive Learning Methods for Text Categorization\n\n\n Two recently implemented machine-learning algorithms, RIPPER and sleeping-experts for phrases, are evaluated on a number of large text categorization p...", "weights": [[8, 16, 1.0], [77, 84, 1.0], [85, 93, 1.0]]}, "2008.sigirjournals_journal-ir0volumeA42A2.4": {"snippet": "...ated crawling", "weights": [[8, 16, 1.0]]}, "2014.wwwconf_conference-2014.55": {"snippet": "...able crawling capability that enables us to gather the details of display ads including creatives and landing pages. Our crawling strategy is focused on maximizing the number of unique ads harvested. ...", "weights": [[8, 16, 1.0], [124, 132, 1.0]]}}}
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For example, users are linked to other users via friendship links, and movies are 941 linked to actors/actress...", "weights": [[8, 13, 1.0], [106, 111, 1.0], [132, 137, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A1.3": {"snippet": "...y of users opinions, we apply the uncertain set to acquire users' sentiment polarity, and the uncertain variable to determine users' sentiment strength through sentiment analysis. To more accurately i...", "weights": [[8, 13, 1.0], [62, 67, 1.0], [129, 134, 1.0]]}, "2014.ir_journal-ir0volumeA17A2.1": {"snippet": "...s by influence analysis", "weights": [[8, 17, 1.0]]}, "2016.wwwconf_conference-2016.102": {"snippet": "...work recommenders, the system relies on a \"friend-of-friend\"-style algorithm, which we show generally results in users being recommended proportional to their degree. Second, we find that the baseline...", "weights": [[8, 20, 1.0], [116, 121, 1.0]]}, "W12-0608": {"snippet": "...from users' reviews about products, in order to improve a recommendation system. While typical recommender systems compare a user profile with some reference characteristics to rate unseen items, they...", "weights": [[8, 13, 1.0]]}, "2018.sigirconf_workshop-2018birndl.3": {"snippet": "... new users and items into the system. In this work, we present a time-aware recommender system that accounts for the concept-drift in user interest. By computing user-specific conceptdrift score, our ...", "weights": [[8, 13, 1.0]]}, "2013.wsdm_conference-2013.85": {"snippet": "...cial influence analysis\n\n\n ABSTRACTSocial influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence...", "weights": [[8, 17, 1.0], [45, 54, 1.0], [194, 203, 1.0]]}, "2018.tweb_journal-ir0volumeA12A1.3": {"snippet": "...atch users' interests, but also have high chances to be selected by the users, because the users can find suitable partners to attend the corresponding activities together. We conduct experiments on r...", "weights": [[8, 13, 1.0], [75, 80, 1.0], [94, 99, 1.0]]}, "2020.wwwconf_conference-2020.301": {"snippet": "Influence Function based Data Poisoning Attacks to Top-N Recommender Systems", "weights": [[0, 9, 1.0]]}, "2020.wwwconf_conference-2020.249": {"snippet": "...cial Influence in Recommender Networks\n\n\n People's ability to influence others' opinion on matters of taste varies greatly-both offline and in recommender systems. What are the mechanisms underlying t...", "weights": [[8, 17, 1.0], [65, 74, 1.0]]}, "2012.wsdm_conference-2012.59": {"snippet": "... can influence users' posterior evaluation on the products or services recommended to them, the answer to which is critical to estimating user satisfaction when proposing a word-of-mouth marketing str...", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "2013.sigirconf_conference-2013.129": {"snippet": "... can influence users to select events that result in tighter and more compact routes, thus allowing users to spend less time travelling and more time visiting events.", "weights": [[8, 17, 1.0], [18, 23, 1.0], [103, 108, 1.0]]}, "2016.cikm_conference-2016.202": {"snippet": "...cial influence, an implicit source of information in social networks, is seldomly considered, even though it often drives recommendations in social networks. In this paper, we introduce a new global a...", "weights": [[8, 17, 1.0]]}, "2018.sigirconf_conference-2018.21": {"snippet": "...ween users' internal interests and the social influence from the social network drives the evolution of users' preferences over time. Nevertheless, traditional approaches either neglected the social n...", "weights": [[8, 13, 1.0], [49, 58, 1.0], [107, 112, 1.0]]}, "2020.ipm_journal-ir0volumeA57A2.10": {"snippet": "...cial influence in recommender systems: A study of bribery in collaborative hybrid algorithms", "weights": [[8, 17, 1.0]]}, "2013.riao_conference-2013.23": {"snippet": "Exploiting the diversity of user preferences for recommendation", "weights": []}, "2012.tist_journal-ir0volumeA3A4.15": {"snippet": "...tive influence can be beneficial while negative influence may cause-negative impact on other users of the same community. In this article, a research framework was proposed to study user influence wit...", "weights": [[8, 17, 1.0], [51, 60, 1.0], [96, 101, 1.0], [190, 199, 1.0]]}, "2008.tweb_journal-ir0volumeA2A3.1": {"snippet": "...many users over an extended time period, and their collective behavior provides a basis for computing source authority. They are drawn from the end of users' postquery browse trails where users may ce...", "weights": [[8, 13, 1.0], [154, 159, 1.0], [191, 196, 1.0]]}, "2018.tist_journal-ir0volumeA9A6.0": {"snippet": "...fies users' personal interests, temporal cyclic patterns, and the sequential influence of recently visited locations in a principled manner.In light of the above challenges, we propose a Temporal Pers...", "weights": [[8, 13, 1.0], [80, 89, 1.0]]}, "2017.ipm_journal-ir0volumeA53A5.11": {"snippet": "...sing users' community membership as a source of personalized recommendations for individual users. The first part of the paper focuses on feasibility. It attempts to assess to what extent the interest...", "weights": [[8, 13, 1.0], [95, 100, 1.0]]}, "2019.wwwconf_conference-2019.264": {"snippet": "...ds): Influence Neighborhoods for Social Recommendations", "weights": [[8, 17, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A4.0": {"snippet": "...cial influence diffusion in the network to find the global and local influence nodes and then embed this dual influence data into a", "weights": [[8, 17, 1.0], [72, 81, 1.0], [113, 122, 1.0]]}, "2011.wsdm_conference-2011.57": {"snippet": "...cial influence. However, we also discover that this does not hold true across all trusted users. A trust relation does not guarantee that two users have similar preferences, implying that personalised...", "weights": [[8, 17, 1.0], [93, 98, 1.0], [145, 150, 1.0]]}, "2020.wsdm_conference-2020.60": {"snippet": "... the users' preference to some extent, the lack of such clicks does not necessarily indicate a negative response from the users, as it is possible that the users were not exposed to the items (positiv...", "weights": [[8, 13, 1.0], [125, 130, 1.0], [159, 164, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.11": {"snippet": "...Ns), users are able to share the Point-of-Interests (POIs) they visited by check-ins. By analyzing the users' historical check-in records, POI recommendation can help users get better visiting experie...", "weights": [[8, 13, 1.0], [106, 111, 1.0], [170, 175, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A4.12": {"snippet": "...work users equally, which assume that the effect of recommendation on a user is decided by the user's own preferences and social influence. However, a user's own knowledge in a field has not been cons...", "weights": [[8, 13, 1.0], [132, 141, 1.0]]}, "2020.sigirconf_conference-2020.263": {"snippet": "...s to users, then the further recommendation model is trained with the data that users' feedback to the items. Such self-reinforcing pattern can cause data bias problems. There are several debiasing me...", "weights": [[8, 13, 1.0], [83, 88, 1.0]]}, "2014.wwwconf_conference-2014c.235": {"snippet": "...ased recommenders\n\n\n ABSTRACTCritiquing based recommenders are very commonly used to help users navigate through the product space to find the required product by tweaking/critiquing one or more featu...", "weights": [[8, 20, 1.0], [49, 61, 1.0], [93, 98, 1.0]]}, "2013.cikm_workshop-2013ueo.2": {"snippet": "...ween users and books, and a user-follower network. We first investigate in general, if one's recommendations have impacts on her followers' decisions. We then analyze the correlation between one's inf...", "weights": [[8, 13, 1.0], [200, 209, 1.0]]}, "2016.wwwconf_conference-2016.120": {"snippet": "...lect users' interaction with previously seen recommendations (up to 15% enhancement on all users and 34% improvement on heavy users).", "weights": [[8, 13, 1.0], [94, 99, 1.0], [129, 134, 1.0]]}, "2011.sigirconf_conference-2011.135": {"snippet": "...cial influence for item recommendation\n\n\n ABSTRACTSocial networking services possess two features: (1) capturing the social relationships among people, represented by the social network, and (2) allow...", "weights": [[8, 17, 1.0]]}, "2002.cikm_conference-2002.7": {"snippet": "...help users find and evaluate items of interest. They do so by connecting users with information regarding the content of recommended items or the opinions of other individuals. Such systems have becom...", "weights": [[8, 13, 1.0], [76, 81, 1.0]]}, "2011.tist_journal-ir0volumeA2A3.11": {"snippet": "... the users are independent and identically distributed; this assumption ignores the connections among users, which is not consistent with the real-world observations where we always turn to our truste...", "weights": [[8, 13, 1.0], [105, 110, 1.0]]}, "2013.sigirconf_conference-2013.39": {"snippet": "...mong users over time. The results of experiments on a real world dataset demonstrate that RTM-based recommendation outperforms the state-ofthe-art recommendation methods. Case studies also show that R...", "weights": [[8, 13, 1.0]]}, "2016.tist_journal-ir0volumeA7A4.15": {"snippet": "...text influence. STCAPLRS can make accurate recommendation and facilitate people's local visiting and new location exploration by exploiting the context information of user behavior, associations betwe...", "weights": [[8, 17, 1.0]]}, "2016.tist_journal-ir0volumeA7A3.16": {"snippet": "...s of users. It is found that in contradiction to common assumptions, not all users suffer as expected from the data sparsity problem. In fact, the group of users that receive the most accurate recomme...", "weights": [[8, 13, 1.0], [80, 85, 1.0], [159, 164, 1.0]]}, "2019.ir_journal-ir0volumeA22A12.2": {"snippet": "Influence me! Predicting links to influential users", "weights": [[0, 9, 1.0], [46, 51, 1.0]]}, "2011.sigirconf_conference-2011.35": {"snippet": "...cial influence and geographical influence for POI recommendations. In addition to deriving user preference based on user-based collaborative filtering and exploring social influence from friends, we p...", "weights": [[8, 17, 1.0], [35, 44, 1.0], [175, 184, 1.0]]}, "2016.wwwconf_conference-2016.11": {"snippet": "...ting influence on recommenders in generating conversion.We first confirm that the use of recommenders increases the baseline conversion rate by 5.9%. We find that the recommenders act as substitutes f...", "weights": [[8, 17, 1.0], [21, 33, 1.0], [92, 104, 1.0], [170, 182, 1.0]]}, "2014.tois_journal-ir0volumeA32A4.1": {"snippet": "...ween users. For instance, in Epinions, the concepts of personal \"web of trust\" and personal \"block list\" allow users to categorize their friends based on the quality of reviews into trusted and distru...", "weights": [[8, 13, 1.0], [114, 119, 1.0]]}, "2018.wwwconf_conference-2018.80": {"snippet": "...TWeb users are increasingly turning to ad blockers to avoid ads, which are often perceived as annoying or an invasion of privacy. While there has been significant research into the factors driving ad ...", "weights": [[8, 13, 1.0]]}, "2020.wwwconf_conference-2020c.114": {"snippet": "...work Influence Ranking via Embedding Network Interactions for User Recommendation\n\n\n Within social networks user influence may be modelled based on user interactions. Further, it is typical to recomme...", "weights": [[8, 17, 1.0], [116, 125, 1.0]]}, "2017.jasis_journal-ir0volumeA68A3.1": {"snippet": "...cial influence", "weights": [[8, 17, 1.0]]}, "2015.tist_journal-ir0volumeA7A1.10": {"snippet": "...tial Influence Modeling for Location Recommendations: A Gravity-based Approach\n\n\n Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which ...", "weights": [[8, 17, 1.0], [101, 106, 1.0]]}, "2017.wwwconf_conference-2017.53": {"snippet": "...Food recommenders have the potential to positively influence the eating habits of users. To achieve this, however, we need to understand how healthy recommendations are and the factors which influence...", "weights": [[8, 20, 1.0], [54, 63, 1.0], [85, 90, 1.0], [194, 203, 1.0]]}, "2018.cikm_conference-2018.98": {"snippet": "...cial influence on users' exposure, i.e. social knowledge influence and social consumption influence, into basic EXMF model for better recommendation performance. Furthermore, SoEXBMF uses Bernoulli di...", "weights": [[8, 17, 1.0], [21, 26, 1.0], [60, 69, 1.0], [93, 102, 1.0]]}, "2017.cikm_conference-2017.302": {"snippet": "...mous users. In this paper, we propose a list-wise deep neural network based architecture to model the limited user behaviors within each session. To train the model efficiently, we first design a sess...", "weights": [[8, 13, 1.0]]}, "2012.sigirconf_conference-2012.107": {"snippet": "...l of influence. The opinion influence can be modeled and inferred from the time-stamped reading and writing behaviors between users. The opinion diffusion can be observed by a serial of opinion influe...", "weights": [[8, 17, 1.0], [31, 40, 1.0], [129, 134, 1.0], [197, 206, 1.0]]}, "2013.airs_conference-2013.37": {"snippet": "...d on Users' Preference", "weights": [[8, 13, 1.0]]}, "2013.cikm_conference-2013.194": {"snippet": "...meet users' individualities, especially for experienced users. Moreover, for cold start users, the interpersonal interest similarity and interpersonal influence can enhance the intrinsic link among fe...", "weights": [[8, 13, 1.0], [59, 64, 1.0], [91, 96, 1.0], [154, 163, 1.0]]}, "2009.sigirconf_conference-2009.27": {"snippet": "...with users who have made very few ratings.(2) The traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections among u...", "weights": [[8, 13, 1.0], [105, 110, 1.0], [202, 207, 1.0]]}, "2011.wwwconf_conference-2011c.33": {"snippet": "...zing users' context. However, incorporating context into recommender systems is not a simple task in that context can influence users' item preferences in various ways depending on the application. In...", "weights": [[8, 13, 1.0], [121, 130, 1.0], [131, 136, 1.0]]}, "2014.wwwconf_conference-2014.62": {"snippet": "...t on users in these terms. This paper examines the longitudinal impacts of a collaborative filtering-based recommender system on users. To the best of our knowledge, it is the first paper to measure t...", "weights": [[8, 13, 1.0], [132, 137, 1.0]]}, "2004.tois_journal-ir0volumeA22A1.0": {"snippet": "... the users of the system. And, indeed, these systems generated substantial enthusiasm and support from their users. In 1996, at the first of a series of workshops on collaborative filtering, it first ...", "weights": [[8, 13, 1.0], [112, 117, 1.0]]}, "2015.ecir_conference-2015.37": {"snippet": "... the Influence of User Characteristics on Music Recommendation Algorithms", "weights": [[8, 17, 1.0]]}, "2017.wwwconf_conference-2017c.241": {"snippet": "...lly, users' desktop context has been modeled on data about the users' browsing and searching activities. With the shift towards mobile information access, recommendations need to be delivered to users...", "weights": [[8, 13, 1.0], [66, 71, 1.0], [198, 203, 1.0]]}, "2018.tois_journal-ir0volumeA36A4.8": {"snippet": "...ware Influence Maximization over Dynamic Social Streams\n\n\n Influence maximization (IM), which selects a set of k seed users (a.k.a., a seed set) to maximize the influence spread over a social network,...", "weights": [[8, 17, 1.0], [62, 71, 1.0], [121, 126, 1.0], [164, 173, 1.0]]}}}
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In this work, we propose a mechanism that propagates the expiration decision...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [75, 81, 1.0], [82, 88, 1.0]]}, "2010.jasis_journal-ir0volumeA61A3.1": {"snippet": "Effects of granularity of search results on the relevance judgment behavior of engineers: Building systems for retrieval and understanding of context", "weights": [[0, 7, 1.0], [26, 32, 1.0]]}, "2010.sigirconf_conference-2010.12": {"snippet": "Caching search engine results over incremental indices", "weights": [[0, 7, 1.0], [8, 14, 1.0], [15, 21, 1.0]]}, "2012.spire_conference-2012.17": {"snippet": "... Web Search Engine Result Caches", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2008.wwwconf_conference-2008.179": {"snippet": "...sult caching revisited\n\n\n ABSTRACTQuery result caching is an important mechanism for search engine efficiency. In this study, we first review several query features that are used to determine the cont...", "weights": [[8, 15, 1.0], [50, 57, 1.0], [88, 94, 1.0], [95, 101, 1.0]]}, "2019.ipm_journal-ir0volumeA56A1.7": {"snippet": "... web search. Such impact is reflected both in users' subjective perception of the usability of a search engine and in their interaction with the search engine in terms of the number of search results ...", "weights": [[8, 14, 1.0], [100, 106, 1.0], [107, 113, 1.0], [148, 154, 1.0], [155, 161, 1.0], [188, 194, 1.0]]}, "2013.tweb_journal-ir0volumeA8A1.2": {"snippet": "...sult Caching and Prefetching in Search Engines\n\n\n Web search engines are known to cache the results of previously issued queries. The stored results typically contain the document summaries and some d...", "weights": [[8, 15, 1.0], [35, 41, 1.0], [57, 63, 1.0]]}, "2012.wwwconf_conference-2012.18": {"snippet": "...ting search engine overload, and implement the two-level cache scheme on a very busy web server. Our experimental results show that the proposed caching framework can effectively reduce the impact of ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [148, 155, 1.0]]}, "2011.tweb_journal-ir0volumeA5A2.3": {"snippet": "...sult Caching in Web Search Engines\n\n\n Search engines and large-scale IR systems need to cache query results for efficiency and scalability purposes. Static and dynamic caching techniques (as well as t...", "weights": [[8, 15, 1.0], [23, 29, 1.0], [41, 47, 1.0], [171, 178, 1.0]]}, "2013.cikm_conference-2013.248": {"snippet": "...cial search engine, we explore the idea of a caching scheme based on pairs of terms, rather than individual terms (which is the typical approach used by search engines today). We propose an inverted l...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [48, 55, 1.0], [156, 162, 1.0]]}, "2017.jasis_journal-ir0volumeA68A8.3": {"snippet": "The effects of credibility cues on the selection of search engine results", "weights": [[4, 11, 1.0], [52, 58, 1.0], [59, 65, 1.0]]}, "2015.cikm_conference-2015.183": {"snippet": "...pid\" Search Components on User Interaction with Search Engines", "weights": [[8, 14, 1.0], [51, 57, 1.0]]}, "2015.wwwconf_conference-2015.65": {"snippet": "...ing, caching has been adopted as a key mechanism. However, the existing passive measurement studies cannot comprehensively characterize the performance of mobile Web caching. For example, most of thes...", "weights": [[8, 15, 1.0], [169, 176, 1.0]]}, "2011.jasis_journal-ir0volumeA62A4.7": {"snippet": "... and caching in Web search engines", "weights": [[8, 15, 1.0], [23, 29, 1.0]]}, "2011.cikm_conference-2011.222": {"snippet": "...e to search providers, the relationship between search success and search engine re-use is poorly understood. In this paper, we present a longitudinal log-based study with a large cohort of search eng...", "weights": [[8, 14, 1.0], [51, 57, 1.0], [70, 76, 1.0], [77, 83, 1.0], [193, 199, 1.0], [200, 206, 1.0]]}, "2005.wwwconf_conference-2005.28": {"snippet": "...evel caching for efficient query processing in large Web search engines\n\n\n ABSTRACTLarge web search engines have to answer thousands of queries per second with interactive response times. Due to the s...", "weights": [[8, 15, 1.0], [60, 66, 1.0], [96, 102, 1.0]]}, "2009.jasis_journal-ir0volumeA60A8.3": {"snippet": "...n of search engine performance", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2015.sigirconf_conference-2015.149": {"snippet": "... for search engine storage, the bottleneck of snippet generation shifts from I/O to computation. We propose a simple, but effective method for exploiting this trend, which we call fragment caching: in...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [192, 199, 1.0]]}, "2017.ipm_journal-ir0volumeA53A4.5": {"snippet": "...sult caching in web search engines\n\n\n a b s t r a c tA commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippet...", "weights": [[8, 15, 1.0], [23, 29, 1.0], [96, 102, 1.0], [103, 109, 1.0], [132, 139, 1.0], [151, 158, 1.0]]}, "2007.wwwconf_conference-MenasceA07.0": {"snippet": "...ased Caching\n\n\n Abstract Online auction sites have very specific workloads and user behavior characteristics. Previous studies on workload characterization conducted by the authors showed that (1) bid...", "weights": [[8, 15, 1.0]]}, "2010.wwwconf_conference-2010.19": {"snippet": "...e of search engine caching\n\n\n ABSTRACTCommercial Web search engines have to process user queries over huge Web indexes under tight latency constraints. In practice, to achieve low latency, large resul...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 29, 1.0], [56, 62, 1.0]]}, "2009.ecir_conference-2009.68": {"snippet": "... and Caching Effects", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2014.tweb_journal-ir0volumeA8A2.1": {"snippet": "...in a search engine's ranking function, provide guidelines for SEOs and Web masters to optimize their Web pages, validate or disprove new ranking features, and evaluate search engine ranking results fo...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [171, 177, 1.0], [178, 184, 1.0]]}, "2012.sigirconf_conference-2012.66": {"snippet": "... web search\n\n\n ABSTRACTCaches of results are critical components of modern Web search engines, since they enable lower response time to frequent queries and reduce the load to the search engine backen...", "weights": [[8, 14, 1.0], [82, 88, 1.0], [183, 189, 1.0], [190, 196, 1.0]]}, "2008.sigirconf_conference-2008.19": {"snippet": "...ults caching and index pruning for high-performance web search engines\n\n\n ABSTRACTResults caching is an efficient technique for reducing the query processing load, hence it is commonly used in real se...", "weights": [[8, 15, 1.0], [59, 65, 1.0], [93, 100, 1.0], [201, 207, 1.0]]}, "2006.tois_journal-ir0volumeA24A1.1": {"snippet": "... Web search engines: Caching and prefetching query results by exploiting historical usage data\n\n\n This article discusses efficiency and effectiveness issues in caching the results of queries submitted...", "weights": [[8, 14, 1.0], [24, 31, 1.0], [163, 170, 1.0]]}, "2008.tweb_journal-ir0volumeA2A4.2": {"snippet": "... for search engine caching\n\n\n In this article we study the trade-offs in designing efficient caching systems for Web search engines. We explore the impact of different approaches, such as static vs. d...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 29, 1.0], [96, 103, 1.0], [120, 126, 1.0]]}, "2008.cikm_workshop-2008lsdsir.7": {"snippet": "...osed caching techniques can have a significant impact on performance, like caching on text queries has been proved effective for traditional Web search engines.", "weights": [[8, 15, 1.0], [78, 85, 1.0], [148, 154, 1.0]]}, "2012.ipm_journal-ir0volumeA48A5.2": {"snippet": "... web search engines\n\n\n a b s t r a c tCaching is a crucial performance component of large-scale web search engines, as it greatly helps reducing average query response times and query processing workl...", "weights": [[8, 14, 1.0], [103, 109, 1.0]]}, "2011.ecir_conference-2011.51": {"snippet": "...sult Caching in Search Engines\n\n\n Abstract. Result caches are vital for efficiency of search engines. In this work, we propose a novel caching strategy in which a dynamic result cache is split into tw...", "weights": [[8, 15, 1.0], [19, 25, 1.0], [89, 95, 1.0], [138, 145, 1.0]]}, "2019.ecir_conference-20192.20": {"snippet": "... for Search Engine Result Caching\n\n\n Abstract.We investigate the impact of the size of query frequency history and its compact representation in memory for search result caching.", "weights": [[8, 14, 1.0], [15, 21, 1.0], [29, 36, 1.0], [159, 165, 1.0], [173, 180, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.8": {"snippet": "...uted search engines\n\n\n AbstractWeb search engines have to deal with a rapidly increasing amount of information, high query loads and tight performance constraints. The success of a search engine depen...", "weights": [[8, 14, 1.0], [38, 44, 1.0], [184, 190, 1.0], [191, 197, 1.0]]}, "2007.sigirconf_conference-2007.19": {"snippet": "...d by search engines has become an expectation of search engine users. In this paper we explore the algorithms and data structures required as part of a search engine to allow efficient generation of q...", "weights": [[8, 14, 1.0], [52, 58, 1.0], [59, 65, 1.0], [155, 161, 1.0], [162, 168, 1.0]]}, "2011.ecir_conference-2011.12": {"snippet": "Caching for Realtime Search\n\n\n Abstract. Modern search engines feature real-time indices, which incorporate changes to content within seconds. As search engines also cache search results for reducing ...", "weights": [[0, 7, 1.0], [21, 27, 1.0], [48, 54, 1.0], [146, 152, 1.0], [172, 178, 1.0]]}, "2011.wwwconf_workshop-2011ldow.11": {"snippet": "...n of caching strategies based on these assumptions. In this paper we evaluate the potential of caching to improve efficiency and result completeness in link traversal based query execution systems. We...", "weights": [[8, 15, 1.0], [98, 105, 1.0]]}, "2011.jasis_journal-ir0volumeA62A7.5": {"snippet": "...y of search engine results", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2007.sigirconf_conference-2007.26": {"snippet": "...ient caching systems for Web search engines. We explore the impact of different approaches, such as static vs. dynamic caching, and caching query results vs. caching posting lists. Using a query log s...", "weights": [[8, 15, 1.0], [32, 38, 1.0], [122, 129, 1.0], [135, 142, 1.0], [161, 168, 1.0]]}, "2014.tois_journal-ir0volumeA32A4.5": {"snippet": "...ased Search Engine Architectures: An Experimental Study\n\n\n Caching is an important optimization in search engine architectures. Existing caching techniques for search engine optimization are mostly bi...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [62, 69, 1.0], [102, 108, 1.0], [109, 115, 1.0], [140, 147, 1.0], [163, 169, 1.0], [170, 176, 1.0]]}, "2008.wwwconf_conference-2008.40": {"snippet": "...list caching in search engines\n\n\n ABSTRACTDue to the rapid growth in the size of the web, web search engines are facing enormous performance challenges. The larger engines in particular have to be abl...", "weights": [[8, 15, 1.0], [19, 25, 1.0], [97, 103, 1.0]]}, "2006.wwwjournals_journal-ir0volumeA9A4.1": {"snippet": "...ther caching levels. We also observe that a careful selection of cache admission and eviction policies is crucial for best overall performance.Keywords web search . search engine architecture . search...", "weights": [[8, 15, 1.0], [159, 165, 1.0], [168, 174, 1.0], [175, 181, 1.0], [197, 203, 1.0]]}, "2008.cikm_conference-2008.225": {"snippet": "... and caching in search engines\n\n\n ABSTRACTWe propose result page models with varying granularities for navigational queries and show that this approach provides a better utilization of cache space and...", "weights": [[8, 15, 1.0], [19, 25, 1.0]]}, "2013.sigirconf_conference-2013.71": {"snippet": "...e on search engine cache management\n\n\n ABSTRACTCaching is an important optimization in search engine architectures. Existing caching techniques for search engine optimization are mostly biased towards...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [90, 96, 1.0], [97, 103, 1.0], [128, 135, 1.0], [151, 157, 1.0], [158, 164, 1.0]]}, "2010.wwwconf_conference-2010.114": {"snippet": "Caching search engine results over incremental indices", "weights": [[0, 7, 1.0], [8, 14, 1.0], [15, 21, 1.0]]}, "2012.sigirconf_conference-2012.203": {"snippet": "...ware caching and prefetching query results in web search engines\n\n\n ABSTRACTQuery results caching is an efficient technique for Web search engines. In this paper we present User-Aware Cache, a novel a...", "weights": [[8, 15, 1.0], [53, 59, 1.0], [93, 100, 1.0], [135, 141, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.4": {"snippet": "...n of search engine query logs, and estimate the effect of statistical characteristics of search engine queries on new topic identification. By applying multiple linear regression and multi-factor ANOV...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [92, 98, 1.0], [99, 105, 1.0]]}, "2013.sigirconf_conference-2013.184": {"snippet": "... web search engines . In particular, the tutorial provides an in-depth architectural overview of a web search engine, mainly focusing on the web crawling, indexing, query processing, and caching compo...", "weights": [[8, 14, 1.0], [106, 112, 1.0], [113, 119, 1.0], [190, 197, 1.0]]}, "2004.wwwconf_conference-2004.3": {"snippet": "...t of search engines on page popularity\n\n\n ABSTRACTRecent studies show that a majority of Web page accesses are referred by search engines. In this paper we study the widespread use of Web search engin...", "weights": [[8, 14, 1.0], [126, 132, 1.0], [191, 197, 1.0]]}, "2015.sigirconf_conference-2015.80": {"snippet": "...sult Caching for Meta-Search Engines\n\n\n ABSTRACTOur goal in this paper is to design cost-aware result caching approaches for meta-search engines. We introduce different levels of eviction, namely, que...", "weights": [[8, 15, 1.0], [25, 31, 1.0], [105, 112, 1.0], [133, 139, 1.0]]}}}
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Reviewers share their experiences of using a product and provide...", "weights": [[8, 15, 1.0], [40, 47, 1.0], [48, 55, 1.0], [59, 66, 1.0], [67, 74, 1.0], [184, 191, 1.0]]}, "2012.tist_journal-ir0volumeA3A4.3": {"snippet": "...esty reviews; a store is more reliable if it has more positive reviews from trustworthy reviewers; and a review is more honest if many other honest reviews support it. This is the first time such intr...", "weights": [[8, 15, 1.0], [66, 73, 1.0], [151, 158, 1.0]]}, "2010.jasis_journal-ir0volumeA61A1.1": {"snippet": "Consumer health information on the Web: The relationship of visual design and perceptions of credibility", "weights": [[0, 8, 1.0]]}, "2014.wwwconf_conference-2014c.66": {"snippet": "...n of product recommendations\n\n\n ABSTRACTBecause of the important role of product reviews during users' decision process, we propose a novel explanation interface that particularly fuses the feature se...", "weights": [[8, 15, 1.0], [76, 83, 1.0], [84, 91, 1.0]]}, "I17-1040": {"snippet": "...s in Consumer Product Reviews\n\n\n In this paper we introduce the problem of identifying usage expression sentences in a consumer product review. We create a human-annotated gold standard dataset of 565...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [25, 32, 1.0], [122, 130, 1.0], [131, 138, 1.0]]}, "2017.ir_journal-ir0volumeA20A6.1": {"snippet": "Product review summarization through question retrieval and diversification", "weights": [[0, 7, 1.0]]}, "W13-4205": {"snippet": "...line Consumer Reviews as a Classification Problem\n\n\n The paper aims to address an opinion mining problem: to find the helpful reviews from online consumer reviews before mining the detail. This task c...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [129, 136, 1.0], [149, 157, 1.0], [158, 165, 1.0]]}, "D12-1036": {"snippet": "...n of Consumer Reviews\n\n\n This paper proposes to generate appropriate answers for opinion questions about products by exploiting the hierarchical organization of consumer reviews. The hierarchy organiz...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [164, 172, 1.0], [173, 180, 1.0]]}, "2010.riao_conference-2010.34": {"snippet": "...pful product reviews", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2020.ipm_journal-ir0volumeA57A6.89": {"snippet": "Consumer health information needs: A systematic review of instrument development", "weights": [[0, 8, 1.0]]}, "2020.ipm_journal-ir0volumeA57A6.19": {"snippet": "...line Consumer Reviews on Perceived Helpfulness", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "2018.jasis_journal-ir0volumeA69A11.1": {"snippet": "...ful\" reviews? The interaction effect of review valence and review usefulness on consumers' purchase decisions", "weights": [[8, 15, 1.0]]}, "2020.ipm_journal-ir0volumeA57A2.15": {"snippet": "Consumer health information needs: A systematic review of measures", "weights": [[0, 8, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A2.8": {"snippet": "...ding reviews. However, such extracted summaries are lack of immediacy, and could not keep the reviews' narrative structures. Aiming at enhancing the diversity of reviews and eliminating the above meth...", "weights": [[8, 15, 1.0], [97, 104, 1.0], [165, 172, 1.0]]}, "2005.wwwconf_conference-2005.38": {"snippet": "...n of consumer opinions on these products, which helps him/her to decide which product to buy. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product ...", "weights": [[8, 16, 1.0], [81, 88, 1.0], [103, 110, 1.0], [195, 202, 1.0]]}, "L14-1003": {"snippet": "...s in Product Reviews", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2018.jasis_journal-ir0volumeA69A3.9": {"snippet": "...line consumer reviews and sales: Examining the chicken-egg relationships", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "2016.ipm_journal-ir0volumeA52A6.8": {"snippet": "...rent product information to find matches for the customer's preferences. Some studies have also shown that sellers can use specifically designed techniques to alter consumer behavior. This study propo...", "weights": [[8, 15, 1.0], [168, 176, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.5": {"snippet": "...etic reviews\n\n\n A B S T R A C TConsumers evaluate products through online reviews, in addition to sharing their product experiences. Online reviews affect product marketing, and companies use online r...", "weights": [[8, 15, 1.0], [77, 84, 1.0], [115, 122, 1.0], [143, 150, 1.0], [158, 165, 1.0], [202, 209, 1.0]]}, "2010.tweb_journal-ir0volumeA4A2.1": {"snippet": "...line reviews have become increasingly popular as a way to judge the quality of various products and services. However, recent work demonstrates that the absence of reporting incentives leads to a bias...", "weights": [[8, 15, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A6.3": {"snippet": "...line reviews based on topic frequency and order\n\n\n Abstract Large volume of online review data can reveal consumers' major interests on domain product, which attracts great research interests from the...", "weights": [[8, 15, 1.0], [146, 153, 1.0]]}, "O17-2003": {"snippet": "...ract Product Features from {C}hinese Consumer Reviews and Establish Product Feature Structure Tree\n\n\n With the progress of e-commerce and web technology, a large volume of consumer reviews for product...", "weights": [[8, 15, 1.0], [40, 48, 1.0], [49, 56, 1.0], [71, 78, 1.0], [175, 183, 1.0], [184, 191, 1.0]]}, "2008.wwwconf_conference-2008.153": {"snippet": "...ring reviews, sampling, or both), our study shows that online markets behavior as communication markets, and consumers learn product quality information both passively (reading online reviews) and act...", "weights": [[8, 15, 1.0], [128, 135, 1.0], [187, 194, 1.0]]}, "2001.jasis_journal-ir0volumeA52A4.7": {"snippet": "... for consumer products, Eric Bergman, editor", "weights": [[8, 16, 1.0]]}, "2015.sigirconf_conference-2015.41": {"snippet": "... the reviews are all written by consumers who have bought and used a product, there are generally very few or even no reviews available for a new product or an unpopular product. We study the novel pr...", "weights": [[8, 15, 1.0], [72, 79, 1.0], [121, 128, 1.0], [149, 156, 1.0], [173, 180, 1.0]]}, "H05-1043": {"snippet": "...ting Product Features and Opinions from Reviews\n\n\n Consumers are often forced to wade through many on-line reviews in order to make an informed product choice. This paper introduces OPINE, an unsuperv...", "weights": [[8, 15, 1.0], [43, 50, 1.0], [110, 117, 1.0], [147, 154, 1.0]]}, "2019.ipm_journal-ir0volumeA56A4.3": {"snippet": "...fake reviews that has been evaluated in the consumer electronics domain. The contributions are fourfold: (i) Construction of a dataset for classifying fake reviews in the consumer electronics domain i...", "weights": [[8, 15, 1.0], [47, 55, 1.0], [159, 166, 1.0], [174, 182, 1.0]]}, "2015.ipm_journal-ir0volumeA51A1.3": {"snippet": "... for consumer decision making. Product aspect extraction is fundamental to online review mining. This research is aimed to improve the performance of aspect extraction from online consumer reviews. To...", "weights": [[8, 16, 1.0], [34, 41, 1.0], [183, 191, 1.0], [192, 199, 1.0]]}, "2016.tois_journal-ir0volumeA35A2.3": {"snippet": "...ion, product recommendation under a given unexplored category, and product recommendation under generic unexplored categories.", "weights": [[8, 15, 1.0], [70, 77, 1.0]]}, "2016.sigirconf_conference-2016.41": {"snippet": "...ze a Product Review\n\n\n ABSTRACTProduct reviews have become an important resource for customers before they make purchase decisions. However, the abundance of reviews makes it difficult for customers t...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [161, 168, 1.0]]}, "2012.ecir_conference-2012.71": {"snippet": "...n of Product Reviews", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2019.wwwconf_conference-2019.125": {"snippet": "...ting Product Descriptions from User Reviews", "weights": [[8, 15, 1.0], [39, 46, 1.0]]}, "2012.wwwconf_conference-2012.20": {"snippet": "...s in consumer reviews\n\n\n ABSTRACTOpinionated social media such as product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit o...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [69, 76, 1.0], [77, 84, 1.0]]}, "2016.wwwconf_conference-2016.57": {"snippet": "...tive Product-Related Queries with Customer Reviews\n\n\n ABSTRACTOnline reviews are often our first port of call when considering products and purchases online. When evaluating a potential purchase, we m...", "weights": [[8, 15, 1.0], [46, 53, 1.0], [72, 79, 1.0]]}, "2011.wwwconf_conference-2011c.86": {"snippet": "...l as consumer reviews. Based on the derived hierarchy, we generate a hierarchical organization of the consumer reviews based on various aspects of the product, and aggregate consumer opinions on the a...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [105, 113, 1.0], [114, 121, 1.0], [154, 161, 1.0], [177, 185, 1.0]]}, "2009.ecir_conference-2009.41": {"snippet": "...g of Product Reviews\n\n\n Abstract. Online product reviews are becoming increasingly available, and are being used more and more frequently by consumers in order to choose among competing products. Tool...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [44, 51, 1.0], [52, 59, 1.0]]}, "2019.ipm_journal-ir0volumeA56A4.16": {"snippet": "...line reviews need to be investigated. Although the criteria and viewpoints for judging the value of these reviews may differ from one person to another, the existing studies on online reviews have not...", "weights": [[8, 15, 1.0], [109, 116, 1.0], [187, 194, 1.0]]}, "2009.airs_conference-2009.9": {"snippet": "...y of Product Reviews", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2016.ecir_conference-2016.53": {"snippet": "...hase Consumer Reviews", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "2021.ipm_journal-ir0volumeA58A3.52": {"snippet": "...line consumer reviews: A multi-perspective approach", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "R15-1019": {"snippet": "...l of Product Properties, Aspects and Ratings for Online Reviews\n\n\n Product review mining is an important task that can benefit both businesses and consumers. Lately a number of models combining collab...", "weights": [[8, 15, 1.0], [59, 66, 1.0], [70, 77, 1.0]]}, "2016.wwwconf_conference-2016.11": {"snippet": "...s of Product Attributes and Consumer Reviews on Recommender Performance\n\n\n ABSTRACTWe investigate the moderating effect of product attributes and consumer reviews on the efficacy of a collaborative fi...", "weights": [[8, 15, 1.0], [31, 39, 1.0], [40, 47, 1.0], [126, 133, 1.0], [149, 157, 1.0], [158, 165, 1.0]]}, "2010.wwwconf_conference-2010.191": {"snippet": "...s of consumer reviews is more and more desirable with the evolutionary development of online review systems. Existing helpfulness assessment methodologies make use of the positive vote fraction as a b...", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "2012.wwwjournals_journal-ir0volumeA15A3.2": {"snippet": "...uy a product. In real life, when obtaining a service, many consumers depend on the user reviews. User reviews-presumably written by other consumers-provide details on the consumers' experiences and th...", "weights": [[8, 15, 1.0], [91, 98, 1.0], [105, 112, 1.0]]}, "2017.sigirconf_workshop-2017ecom.6": {"snippet": "...into consumer attitudes. When studying consumer behavior, the existing literature is primarily concerned with product reviews. In this paper we posit that textual product descriptions are also importa...", "weights": [[8, 16, 1.0], [42, 50, 1.0], [113, 120, 1.0], [121, 128, 1.0], [166, 173, 1.0]]}, "H05-2017": {"snippet": "...line reviews in order to make an informed product choice. We introduce OPINE, an unsupervised, high-precision information extraction system which mines product reviews in order to build a model of pro...", "weights": [[8, 15, 1.0], [45, 52, 1.0], [155, 162, 1.0], [163, 170, 1.0], [200, 207, 1.0]]}, "2012.wwwconf_conference-2012.21": {"snippet": "...line reviews based on economic signaling theory , in which consumer reviews diminish the inherent information asymmetry between consumers and producers, by acting as a signal to a product's true, unkn...", "weights": [[8, 15, 1.0], [62, 70, 1.0], [71, 78, 1.0], [183, 190, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A2.0": {"snippet": "...ning Product Reviews in Web Forums", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "D11-1013": {"snippet": "... the product specifications), as well as consumer reviews. Based on the derived hierarchy, we generate a hierarchical organization of consumer reviews on various product aspects and aggregate consumer...", "weights": [[8, 15, 1.0], [44, 52, 1.0], [53, 60, 1.0], [137, 145, 1.0], [146, 153, 1.0], [165, 172, 1.0], [195, 203, 1.0]]}, "O14-3002": {"snippet": "...line Consumer Reviews as a Classification Problem\n\n\n The paper addresses an opinion mining problem: how to find the helpful reviews from online consumer reviews via the quality of the content. Since t...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [127, 134, 1.0], [147, 155, 1.0], [156, 163, 1.0]]}, "P11-1150": {"snippet": "...tant Product Aspects from Online Consumer Reviews\n\n\n In this paper, we dedicate to the topic of aspect ranking, which aims to automatically identify important product aspects from online consumer revi...", "weights": [[8, 15, 1.0], [36, 44, 1.0], [45, 52, 1.0], [162, 169, 1.0], [190, 198, 1.0], [199, 206, 1.0]]}, "2013.wwwconf_conference-2013.77": {"snippet": "...line reviews\n\n\n ABSTRACTRecommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend ...", "weights": [[8, 15, 1.0]]}}}
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Unb...", "weights": []}, "2020.sigirconf_conference-2020.4": {"snippet": "...s of Machine Learning: A Meta Theory\n\n\n Machine learning (ML) has been run and applied by premising a series of presuppositions, which contributes both the great success of AI and the bottleneck of fu...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [43, 50, 1.0], [51, 59, 1.0]]}, "2007.cikm_conference-2007.3": {"snippet": "...ised machine learning recognize mentions of entities and relationships of predefined types in different kinds of sources, which can then be used to answer some useful types of queries. 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In this talk, I will give three examples of machine learning improving the email experience.The firs...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [147, 154, 1.0], [155, 163, 1.0]]}, "2011.tist_journal-ir0volumeA2A3.0": {"snippet": "...e on machine learning for business applications", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "W08-0305": {"snippet": "Learning Performance of a Machine Translation System: a Statistical and Computational Analysis\n\n\n We present an extensive experimental study of a Statistical Machine Translation system, Moses (Koehn e...", "weights": [[0, 8, 1.0], [26, 33, 1.0], [158, 165, 1.0]]}, "2021.wsdm_conference-2021.78": {"snippet": "...ther machine learning models, GNNs may make predictions biased on protected sensitive attributes, e.g., skin color and gender. Because machine learning algorithms including GNNs are trained to reflect...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [138, 145, 1.0], [146, 154, 1.0]]}, "2007.cikm_conference-2007.109": {"snippet": "...ture learning on limited data\n\n\n ABSTRACTIn recent years, Bagging method has been applied to learn Bayesian networks (BNs), especially on limited datasets. However, the BNs learned using Bagging metho...", "weights": [[8, 16, 1.0]]}, "2020.cikm_conference-2020.93": {"snippet": "...lain machine learning models and interpret its results have been proposed. These include shadow model approaches, like LIME and SHAP; model inspection approaches like Grad-CAM and data-based approache...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2018.wsdm_conference-2018.3": {"snippet": "...s to Machine Learning With Seven Sparks from the Causal Revolution\n\n\n ABSTRACTCurrent machine learning systems operate, almost exclusively, in a statistical, or model-blind mode, which entails severe ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [89, 96, 1.0], [97, 105, 1.0]]}, "W01-0501": {"snippet": "Limitations of Co-Training for Natural Language Learning from Large Datasets", "weights": [[0, 11, 1.0], [48, 56, 1.0]]}, "2021.acl-tutorials.6": {"snippet": "...e in machine learning.", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2021.paclic-1.37": {"snippet": "Machine Learning Approach for Depression Detection in {J}apanese\n\n\n In this paper, we describe the results of our research aimed at detecting depression in Japanese sentences. 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This change can be attributed to a deeper unders...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [88, 95, 1.0], [96, 104, 1.0]]}, "2020.scil-1.55": {"snippet": "Limitations in learning an interpreted language with recurrent models", "weights": [[0, 11, 1.0], [15, 23, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.7": {"snippet": "...s to machine learning", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2021.ipm_journal-ir0volumeA58A4.9": {"snippet": "A machine learning, bias-free approach for predicting business success using Crunchbase data", "weights": [[2, 9, 1.0], [10, 18, 1.0]]}, "1991.riao_conference-1991.48": {"snippet": "Machine learning principles in application to a transportable natural language database interface", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A5.3": {"snippet": "...ised learning to rank with pseudo labels\n\n\n Abstract Semi-supervised learning is a machine learning paradigm that can be applied to create pseudo labels from unlabeled data for learning a ranking mode...", "weights": [[8, 16, 1.0], [72, 80, 1.0], [86, 93, 1.0], [94, 102, 1.0], [180, 188, 1.0]]}, "2015.wwwconf_conference-2015.79": {"snippet": "...onal machine learning (RML) is an excellent framework for these problems as it jointly models the user labels given their attributes and the relational structure. Further, semi-supervised learning met...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [191, 199, 1.0]]}, "2020.wsdm_conference-2020.42": {"snippet": "...shot learning-based speaker identification framework (AFEASI) to develop robust speaker identification models with only a limited number of training instances. We first employ metric learning-based fe...", "weights": [[8, 16, 1.0], [186, 194, 1.0]]}, "W15-3802": {"snippet": "...ised machine learning techniques have proved to be the most effective approach to this problem. However, they require labelled training data which may not be available in sufficient quantity (or at al...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2017.sigirconf_conference-2017.229": {"snippet": "Machine Learning Powered A/B Testing\n\n\n ABSTRACTOnline search evaluation, and A/B testing in particular, is an irreplaceable tool for modern search engines. Typically, online experiments last for seve...", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2014.wwwconf_conference-2014c.288": {"snippet": "...line learning platforms\n\n\n ABSTRACTThe main objectives of massive open online courses (MOOC) are to foster knowledge through free high quality learning materials procurement; to create new knowledge t...", "weights": [[8, 16, 1.0], [146, 154, 1.0]]}, "2017.tist_journal-ir0volumeA8A2.15": {"snippet": "...sual Learning: Image Recognition via Dissipative Learning Model\n\n\n According to consciousness involvement, human's learning can be roughly classified into explicit learning and implicit learning. Cont...", "weights": [[8, 16, 1.0], [52, 60, 1.0], [118, 126, 1.0], [167, 175, 1.0], [189, 197, 1.0]]}, "W13-5103": {"snippet": "...ts a machine learning deidentification system for clinical free text in Dutch, relying on best practices from the state of the art in de-identification of English-language texts. We combine string and...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2016.cikm_conference-2016.306": {"snippet": "...s of Machine Learning with Dataflow", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "N07-2043": {"snippet": "...ised machine learning approaches to IE customization pose too high a burden on the user. Semisupervised learning approaches may in principle offer a more resource effective solution but are still insu...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [107, 115, 1.0]]}, "2018.cikm_conference-2018.112": {"snippet": "...ming machine learning tasks with large data within the devices is challenging because of their limited memory resources. For this reason, diskbased machine learning methods have been actively research...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [151, 158, 1.0], [159, 167, 1.0]]}, "2020.acl-srw.11": {"snippet": "...ural Machine Translation by Character Tokenization and Phoneme Decomposition\n\n\n The primary limitation of North Korean to English translation is the lack of a parallel corpus; therefore, high translat...", "weights": [[8, 15, 1.0]]}, "2010.cikm_conference-2010.189": {"snippet": "...line learning for recency search ranking using real-time user feedback\n\n\n ABSTRACTTraditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance...", "weights": [[8, 16, 1.0], [97, 104, 1.0]]}, "2012.sigirconf_conference-2012.105": {"snippet": "...sfer learning provides solutions to the problem and has explored applications in the area of text mining , whereas direct application to KBE or IF remains absent.Transfer learning is a research area i...", "weights": [[8, 16, 1.0], [174, 182, 1.0]]}, "2013.wwwconf_conference-2013c.237": {"snippet": "...cial machine properties such as semantic, intentional and navigational properties across different systems and applications including -but not limited to -social media. 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In the system, we applied a hybrid m...", "weights": [[8, 16, 1.0], [96, 104, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.25": {"snippet": "...test research findings in clinical practice. In this dissertation, I propose several methods to overcome these challenges, thus improving search outcomes.For queries issued by lay users, I introduce q...", "weights": [[8, 16, 1.0]]}, "2017.clef_conference-2017w.59": {"snippet": "...ical Medicine\n\n\n Observational evidence in clinical practice is critical in healthcare and policy making. Researchers spend a lot of time searching for relevant published articles to write a systemati...", "weights": [[8, 16, 1.0]]}, "1983.sigirconf_conference-83.27": {"snippet": "Information Retrieval: New Directions, Old Solutions", "weights": []}, "2018.sigirconf_conference-2018.226": {"snippet": ".... IR research has much to offer here by developing new tools and techniques specific to this domain .The range of health information available (primary research sources, secondary research sources, pa...", "weights": [[8, 16, 1.0], [155, 163, 1.0], [183, 191, 1.0]]}, "1983.sigirconf_conference-83.24": {"snippet": "An Approach to Enhancement of Statistical Survey Databases", "weights": []}, "2017.trec_conference-2017.60": {"snippet": "...sion Medicine Track\n\n\n In this paper, we describe the system designed for the TREC 2017 Precision Medicine track by the University of Texas at Dallas (UTD) Human Language Technology Research Institute...", "weights": [[8, 16, 1.0], [101, 109, 1.0], [185, 193, 1.0]]}, "2014.sigirconf_workshop-2014medir.9": {"snippet": "Exploring Clustering Based Knowledge Discovery towards Improved Medical Diagnosis\n\n\n We propose to develop a framework for an intelligent reasoner with capabilities that support complex decision makin...", "weights": []}, "2012.jasis_journal-ir0volumeA63A8.15": {"snippet": "... the research in the research literature?", "weights": [[8, 16, 1.0], [24, 32, 1.0]]}, "1983.sigirconf_conference-83.4": {"snippet": "Intelligent Information Systems\n\n\n ABSTRACTNatural language processing techniques developed for Artificial Intelligence programs can aid in constructing powerful information retrieval systems in at le...", "weights": []}, "2020.sigirconf_conference-2020.179": {"snippet": "...sion Medicine Literature Search\n\n\n In professional search tasks such as precision medicine literature search, queries often involve multiple aspects. To assess the relevance of a document, a searcher ...", "weights": [[8, 16, 1.0], [85, 93, 1.0]]}, "1991.jasis_journal-ir0volumeA42A4.5": {"snippet": "...text research project", "weights": [[8, 16, 1.0]]}, "1977.jasis_journal-ir0volumeA28A1.8": {"snippet": "...ions Research Models to Libraries: A Case Study of the Use of Monographs in the Francis A. Countway Library of Medicine, Harvard University. Chen, Ching-chih Cambridge, MA: The MIT Press (1976) (ISBN ", "weights": [[8, 16, 1.0], [114, 122, 1.0]]}, "W12-2422": {"snippet": "...ased Medicine (EBM) is the practice of using the knowledge gained from the best medical evidence to make decisions in the effective care of patients. This medical evidence is extracted from medical do...", "weights": [[8, 16, 1.0]]}, "2003.trec_conference-2003.8": {"snippet": "Recognizing Gene and Protein Function in MEDLINE Abstracts\n\n\n Identification of genes and proteins that affect biological function in humans and other organisms is a critical step in the discovery of ...", "weights": []}, "1976.jasis_journal-ir0volumeA27A5.12": {"snippet": "...ions research models to libraries: A Case Study of the Use of Monographs in the Francis A. Countway Library of Medicine, Harvard University. Ching-chih Chen. Cambridge, MA: MIT Press (1976)", "weights": [[8, 16, 1.0], [114, 122, 1.0]]}, "2007.wwwconf_conference-2007.14": {"snippet": "Scaling up all pairs similarity search\n\n\n ABSTRACTGiven a large collection of sparse vector data in a high dimensional space, we investigate the problem of finding all pairs of vectors whose similarit...", "weights": []}, "2020.trec_conference-2020.3": {"snippet": "...sion Medicine: The IMS Unipd at TREC 2020 Precision Medicine", "weights": [[8, 16, 1.0], [55, 63, 1.0]]}, "2019.wwwconf_conference-2019c.213": {"snippet": "...edia medicine-related articles", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "U11-1012": {"snippet": "...ased Medicine Summarisation\n\n\n In this paper we introduce some of the key NLP-related problems related to the practice of Evidence Based Medicine and propose the task of multi-document query-focused s...", "weights": [[8, 16, 1.0], [81, 88, 1.0], [98, 105, 1.0], [140, 148, 1.0]]}, "2018.trec_conference-2018.44": {"snippet": "... IMS Research Group at TREC 2018 Precision Medicine Track\n\n\n We report on the participation of the Information Management System (IMS) Research Group of the University of Padua in the second task of t...", "weights": [[8, 16, 1.0], [46, 54, 1.0], [138, 146, 1.0]]}, "J80-1004": {"snippet": "...s in Medicine", "weights": [[8, 16, 1.0]]}, "2021.naacl-main.51": {"snippet": "...NLP} research\n\n\n Preregistration refers to the practice of specifying what you are going to do, and what you expect to find in your study, before carrying out the study. This practice is increasingly ...", "weights": [[8, 16, 1.0]]}, "2009.cikm_conference-2009.57": {"snippet": "...nese medicine book search engine\n\n\n ABSTRACTLearning traditional Chinese medicine knowledge from the digital library is becoming more and more important these days in China. In medicine learning, many...", "weights": [[8, 16, 1.0], [76, 84, 1.0], [180, 188, 1.0]]}, "2014.sigirconf_conference-2014.16": {"snippet": "Interactions between health searchers and search engines\n\n\n ABSTRACTThe Web is an important resource for understanding and diagnosing medical conditions. Based on exposure to online content, people ma...", "weights": []}, "2019.trec_conference-2019.17": {"snippet": "...sion Medicine Track\n\n\n he paper describes the system presented by the University of L'Aquila in collaboration with the University of Havana-team named UNIVAQ-to the TREC 2019 Precision Medicine Track....", "weights": [[8, 16, 1.0], [188, 196, 1.0]]}, "P17-5001": {"snippet": "...sion Medicine\n\n\n We will introduce precision medicine and showcase the vast opportunities for NLP in this burgeoning field with great societal impact. We will review pressing NLP problems, state-of-th...", "weights": [[8, 16, 1.0], [48, 56, 1.0]]}, "2020.wsdm_conference-2020.30": {"snippet": "...This research direction draws increasing attention in research areas such as economics, healthcare, and education. For example, we can study how a medicine (the treatment) causally affects the health ...", "weights": [[8, 16, 1.0], [57, 65, 1.0], [150, 158, 1.0]]}, "2020.sigirconf_conference-2020.382": {"snippet": "...tern Medicine in Medical Text Mining\n\n\n Traditional Chinese Medicine (TCM) has been used by practitioners for millennia to prevent and treat disease, but has struggled to gain broad acceptance in the ...", "weights": [[8, 16, 1.0], [63, 71, 1.0]]}, "2008.cikm_conference-2008.16": {"snippet": "...ests related medical phrases to help the user quickly digest search results and refine the query. We evaluated MedSearch using medical questions posted on medical discussion forums. The results show t...", "weights": [[8, 15, 1.0]]}, "2020.findings-emnlp.78": {"snippet": "... and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83 % for AI papers and 95 % in the field of medicine. It is open source 1 and available for t...", "weights": [[8, 16, 1.0], [154, 162, 1.0]]}, "1972.ipm_journal-ir0volumeA8A6.0": {"snippet": "...y of medicine and international medlars cooperation", "weights": [[8, 16, 1.0]]}, "1983.sigirconf_conference-83.13": {"snippet": "... and Related Measures\n\n\n AbstractThe normalized recall is one of the most popular evaluation measures for information retrieval systems. In this paper an overview of its development is given. It is th...", "weights": [[8, 15, 1.0]]}, "D12-1053": {"snippet": "...ased Medicine Categories\n\n\n Evidence-based medicine is an approach whereby clinical decisions are supported by the best available findings gained from scientific research. This requires efficient acce...", "weights": [[8, 16, 1.0], [46, 54, 1.0], [165, 173, 1.0]]}, "W11-0225": {"snippet": "...rbal Medicine Mining and Visualizing Tool\n\n\n Thai Traditional Medicine (TTM) has a long history in Thailand and is nowadays considered an effective alternative approach to the modern medicine. One of ...", "weights": [[8, 16, 1.0], [65, 73, 1.0], [186, 194, 1.0]]}, "1983.sigirconf_conference-83.20": {"snippet": "Reseda, An Information Retrieval System Using Artificial Intelligence and Knowledge Representation Techniques\n\n\n ABSTRACT.The RESEDA project is concerned with the construction of AI Information Retrie...", "weights": []}, "2014.cikm_workshop-2014dtmbio.17": {"snippet": "...tive medicine (CAM) and conventional medicine are composites of multiple chemical compounds. These chemical compounds potentially offer an extensive source for drug discovery with accumulated knowledg...", "weights": [[8, 16, 1.0], [40, 48, 1.0]]}, "W17-2321": {"snippet": "...sion Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations\n\n\n The Precision Medicine Track in BioCreative VI aims to bring together the BioNLP commun...", "weights": [[8, 16, 1.0], [130, 138, 1.0]]}, "1983.sigirconf_conference-83.9": {"snippet": "Evaluation of The 2-Poisson Model as a Basis for Using Term Frequency Data in Searching\n\n\n ABSTRACTThe early work on the probabilistic", "weights": []}, "W14-1101": {"snippet": "...ased medicine using text mining\n\n\n Evidence-based medicine uses systematic reviews to identify relevant studies to answer specific research questions. An underlying principle of the approach is the im...", "weights": [[8, 16, 1.0], [53, 61, 1.0], [134, 142, 1.0]]}}}
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The Natural Language Processing Project in the AI Program at MCC\n\n\n We discuss a recently launched, long-term project in natural language processing, the primary concern of which is that natural lang...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [125, 132, 1.0], [133, 141, 1.0], [142, 152, 1.0], [191, 198, 1.0], [199, 207, 1.0]]}, "1992.trec_conference-1992.13": {"snippet": "Natural Language Processing in Large-Scale Text Retrieval Tasks", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "1990.ipm_journal-ir0volumeA26A1.3": {"snippet": "Natural language processing and information retrieval", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2015.fire_workshop-2015w.20": {"snippet": "...s\n\n\n Natural Language processing (NLP) in its pure sense, is a platform that provides the ability for transforming natural language text to useful information. 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Bender\n\n\n The phenomenal success of machine learning in engineering natural language applications has led to a...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [161, 168, 1.0], [169, 177, 1.0]]}, "2020.rocling-1.36": {"snippet": "Natural Language Processing for Digital Humanities", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "W04-0609": {"snippet": "...ased Natural Language Processing\n\n\n Conceptualising a domain has long been recognised as a prerequisite for understanding that domain and processing information about it. Ontologies are explicit speci...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [141, 151, 1.0]]}, "1986.jasis_journal-ir0volumeA37A4.2": {"snippet": "Natural language processing in information retrieval", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2020.wsdm_conference-2020.120": {"snippet": "...p on Natural Language Processing for Recommendations\n\n\n Natural language processing is becoming more and more important in recommender systems. This half day workshop explores challenges and potential...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [59, 66, 1.0], [67, 75, 1.0], [76, 86, 1.0]]}, "2020.sigirconf_conference-2020.365": {"snippet": "...ires processing and understanding such information effectively and efficiently, where natural language processing (NLP) technologies are widely deployed. Natural language data are represented as a seq...", "weights": [[8, 18, 1.0], [89, 96, 1.0], [97, 105, 1.0], [106, 116, 1.0], [157, 164, 1.0], [165, 173, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.12": {"snippet": "...atic processing of temporal expressions and events in natural language\n\n\n a b s t r a c tThis paper addresses the problem of the automatic recognition and classification of temporal expressions and ev...", "weights": [[8, 18, 1.0], [57, 64, 1.0], [65, 73, 1.0]]}, "J95-3006": {"snippet": "...ews: Natural Language Processing: A Paninian Perspective\n\n\n This book is an elementary introduction to natural language processing, but it is a very unusual one for two reasons. First, its subject lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [106, 113, 1.0], [114, 122, 1.0], [123, 133, 1.0]]}, "2016.coria_conference-2016.23": {"snippet": "Processing Natural Language Queries to Disambiguate Named Entities and Extract Users' Goals : Application to e-Tourism", "weights": [[0, 10, 1.0], [11, 18, 1.0], [19, 27, 1.0]]}, "1985.tmi-1.17": {"snippet": "... and Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2016.sigirconf_conference-2016.1": {"snippet": "...uman Language: Can NLP and Deep Learning Help?\n\n\n ABSTRACTThere is a lot of overlap between the core problems of information retrieval (IR) and natural language processing (NLP). An IR system gains fr...", "weights": [[8, 16, 1.0], [147, 154, 1.0], [155, 163, 1.0], [164, 174, 1.0]]}, "J81-4005": {"snippet": "Natural Language Processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2001.jasis_journal-ir0volumeA52A13.20": {"snippet": "Natural language processing: Word recognition without segmentation", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "C80-1059": {"snippet": "Processing of Syntax and Semantics of Natural Language by Predicate Logic of Predicate Logic\n\n\n The syntax and semantic analyses of natural language are described from the standpoint of manmachine com...", "weights": [[0, 10, 1.0], [38, 45, 1.0], [46, 54, 1.0], [132, 139, 1.0], [140, 148, 1.0]]}, "N03-5001": {"snippet": "...ical Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "C86-1103": {"snippet": "... for Natural Language Processing\n\n\n The paper describes the design of a fair sized lexical database that is to be used with a natural language based expert system with German as the language of intera...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [129, 136, 1.0], [137, 145, 1.0], [185, 193, 1.0]]}, "L06-1315": {"snippet": "Natural Language Processing: A Terminological and Statistical Approach", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2013.tist_journal-ir0volumeA4A3.2": {"snippet": "...ious natural language processing tasks, including statistical machine translation. A commonly used method for paraphrase generation is pivoting , which benefits from linguistic knowledge implicit in t...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2021.teachingnlp-1.22": {"snippet": "...k on Natural Language Processing\n\n\n There are thousands of papers about natural language processing and computational linguistics, but very few textbooks. I describe the motivation and process for wri...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [75, 82, 1.0], [83, 91, 1.0], [92, 102, 1.0]]}, "2016.fire_conference-2016w.55": {"snippet": "...sing Natural Language Processing Techniques & Semantic Similarity Computations\n\n\n The paper reports the approaches utilized and results achieved for our system in the shared task (in FIRE-2016) for pa...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "1982.sigirjournals_journal-ir0volumeA17A3.3": {"snippet": "...3\n\n\n Natural language processing techniques developed for Artificial Intelligence programs can aid in constructing powerful information retrieval systems in at least two areas. Automatic construction ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "R17-1039": {"snippet": "Natural Language Processing Technologies for Document Profiling", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...t of natural language processing techniques. Different groups focused on different aspects of the overall problem, ranging from the development of language-independent tools such as stemmers to much w...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [150, 158, 1.0]]}, "H94-1119": {"snippet": "...h in Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2001.ipm_journal-ir0volumeA37A6.0": {"snippet": "Natural language processing in support of decision-making: phrases and part-of-speech tagging", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "1995.sigirjournals_journal-ir0volumeA29A2.2": {"snippet": "...ion: Natural Language Processing and Information Retrieval Group Information Access and User Interfaces Division National Institute of Standards and Technology\n\n\n OverviewThe Natural Language Processi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [178, 185, 1.0], [186, 194, 1.0], [195, 205, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.23": {"snippet": "... for natural language conversation\n\n\n AbstractAs an important task of artificial intelligence, natural language conversation has attracted wide attention of researchers in natural language processing....", "weights": [[8, 15, 1.0], [16, 24, 1.0], [98, 105, 1.0], [106, 114, 1.0], [175, 182, 1.0], [183, 191, 1.0], [192, 202, 1.0]]}, "H93-1101": {"snippet": "...h in Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2018.sigirconf_workshop-2018birndl.0": {"snippet": "... and Natural Language Processing for Digital Libraries (BIRNDL 2018) co-located with the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), Ann ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "H86-1007": {"snippet": "...y of natural language processing outside of a laboratory setting. The paper will be most valuable to those engaged in applying state-of-the-art techniques to deliver natural language interfaces and to...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [169, 176, 1.0], [177, 185, 1.0]]}, "J75-4040": {"snippet": "A Natural Language Processing Package\n\n\n A set of SAIL programs has been implemented for analyzing large bodies of natural language data in which associations exist between strings and sets of strings...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [19, 29, 1.0], [115, 122, 1.0], [123, 131, 1.0]]}, "2017.wwwconf_conference-2017c.152": {"snippet": "...uent natural language questions.\nBACKGROUND AND MOTIVATIONAs more and more RDF Question/Answering (Q/A) systems over RDF knowledge graphs are designed, which allow users to express queries in natural ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [195, 202, 1.0]]}, "W17-8106": {"snippet": "Natural Language Processing in Political Campaigns", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2010.wwwjournals_journal-ir0volumeA13A3.3": {"snippet": "...work Language Generation based on a Semantic Networks Serialization Grammar\n\n\n Abstract This paper studies the Semantic Network Language Generation (SNLG), which is used to generate natural language f...", "weights": [[8, 16, 1.0], [131, 139, 1.0], [185, 192, 1.0], [193, 201, 1.0]]}, "C10-2128": {"snippet": "...s of natural languages and formal languages for knowledge representation, and discuss how domain specialists can be supported writing specifications in controlled natural language.", "weights": [[8, 15, 1.0], [166, 173, 1.0], [174, 182, 1.0]]}, "M95-1021": {"snippet": "...UNO} Natural Language Processing System as Used for {MUC}-6\n\n\n The UNO natural language processing (NLP) system implements a Boolean algebra computational model o f natural language [Iwadska, 1992 ] [...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [74, 81, 1.0], [82, 90, 1.0], [91, 101, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "Y12-1005": {"snippet": "...e of Natural Language Processing?\n\n\n In this talk, I will outline some of the myriad of challenges and opportunities that social media offer for natural language processing. I will present analysis of...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [148, 155, 1.0], [156, 164, 1.0], [165, 175, 1.0]]}, "2005.clef_workshop-2005w.8": {"snippet": "...ight Natural Language Processing to Ad-Hoc Cross Language Information Retrieval\n\n\n In the CLEF 2005 Ad-Hoc Track we experimented with language-specific morphosyntactic processing and light Natural Lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [52, 60, 1.0], [137, 145, 1.0], [171, 181, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "L18-1025": {"snippet": "...s in natural language processing papers, can account for a variety of types of research reports. These dimensions are reproducibility of a conclusion, of a finding, and of a value. Three biomedical na...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [201, 208, 1.0]]}, "H89-2078": {"snippet": "...r on Natural Language Processing\n\n\n I. Scope Major Challenges We take the ultimate goal of natural language processing (NLP) to be the ability to use natural languages as effectively as humans do. Nat...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [94, 101, 1.0], [102, 110, 1.0], [111, 121, 1.0], [153, 160, 1.0], [200, 207, 1.0]]}, "Y09-1039": {"snippet": "...nal) Natural Language Processing: A Cognitive Perspective\n\n\n In this paper I aim at sketching out in bare outline a new model/framework of language processing with its implications for natural languag...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [142, 150, 1.0], [151, 161, 1.0], [188, 195, 1.0], [196, 204, 1.0]]}, "C88-1008": {"snippet": "... for Natural Language Processing\n\n\n This paper presents the most important characteristic of the new formalism used in Eurotra, the E-Framework. It is a formalism for natural language processing withi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [170, 177, 1.0], [178, 186, 1.0], [187, 197, 1.0]]}, "C69-4101": {"snippet": "...ated Language Processing\n\n\n This paper is concerned with natural language, computers, and two groups of people interested in natural language: ]inguists, and persons engaged in computer processing of ...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [60, 67, 1.0], [68, 76, 1.0], [128, 135, 1.0], [136, 144, 1.0], [189, 199, 1.0]]}, "2007.sigirconf_conference-2007.3": {"snippet": "...NLP -natural language processing -or NLIP -processing information conveyed in natural language. At that time, most researchers thought primarily of the efficiency gains that could be Copyright is held...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [46, 56, 1.0], [81, 88, 1.0], [89, 97, 1.0]]}, "J75-4013": {"snippet": "...nd a Natural-Language Interface for Interactive Data Management\n\n\n We describe a natural-language recognition system having both applied and theoretical relevance. A t the applications level, the prwr...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [84, 91, 1.0], [92, 100, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.5": {"snippet": "... for natural language generation\n\n\n a b s t r a c tReferring expression generation is the part of natural language generation that decides how to refer to the entities appearing in an automatically ge...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [101, 108, 1.0], [109, 117, 1.0]]}, "2020.acl-tutorials.4": {"snippet": "...wing Natural Language Processing Research", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "1985.tois_journal-ir0volumeA3A2.1": {"snippet": "...LSP) natural language processing system has been developed as a domain-independent natural language processing system. Initially utilized for processing sets of medical messages and other texts in the...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [86, 93, 1.0], [94, 102, 1.0], [103, 113, 1.0], [145, 155, 1.0]]}, "2020.cikm_conference-2020.67": {"snippet": "...d in natural language and convert it into forms that can be more easily and precisely processed by computers. Knowledge Graphs (KGs) offer offer a good solution for such processing. Natural Language P...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [173, 183, 1.0], [185, 192, 1.0], [193, 201, 1.0], [202, 212, 1.0]]}, "J89-3012": {"snippet": "...ews: Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2007.cikm_workshop-2007pikm.1": {"snippet": "Natural language processing for information retrieval: the time is ripe (again)", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "J06-1006": {"snippet": "... and Language Processing by John Coleman represents a bold effort to educate students in speech science about some of the important methods used in speech and natural language processing (NLP). This b...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [162, 169, 1.0], [170, 178, 1.0], [179, 189, 1.0]]}, "H94-1109": {"snippet": "...h in Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2004.ipm_journal-ir0volumeA40A6.4": {"snippet": "... the natural language processing (NLP) system for improving the system. Therefore, the necessity of co-occurrence word information in the natural language processing system increases further and vario...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [141, 148, 1.0], [149, 157, 1.0], [158, 168, 1.0]]}, "J86-2001": {"snippet": "...y, a natural language interface should be constructed in accord with principles of integrated processing: processing syntax and semantics at the same time, processing syntax and semantics using the sa...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [97, 107, 1.0], [109, 119, 1.0], [159, 169, 1.0]]}, "2001.ir_journal-ir0volumeA4A1.4": {"snippet": "...ical Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2007.ecir_conference-2007.46": {"snippet": "Natural Language Processing for Usage Based Indexing of Web Resources", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2018.cikm_conference-2018.119": {"snippet": "... for Natural Language Queries to Alleviate Lexical Chasm Problem\n\n\n ABSTRACTIn recent years, the task of reformulating natural language queries has received considerable attention from both industry a...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [122, 129, 1.0], [130, 138, 1.0]]}, "W05-0705": {"snippet": "...ng a Natural Language Processing System for {E}uropean Languages to Treat {A}rabic in Information Processing and Information Retrieval Applications\n\n\n The goal of many natural language processing plat...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [101, 111, 1.0], [171, 178, 1.0], [179, 187, 1.0], [188, 198, 1.0]]}, "1984.tois_journal-ir0volumeA2A1.0": {"snippet": "...ased Natural Language Processor for Office Environments\n\n\n During the 1970s, a number of systems providing limited English-language processing capabilities were developed to permit computer access by ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [126, 134, 1.0], [135, 145, 1.0]]}, "1971.sigirconf_conference-71.2": {"snippet": "...ated processing of natural language is a realistic goal. It should not be neglected, despite the fact that it is difficult to predict when this goal will be achieved.\nKEY WORDS AND PHRASESnatural lang...", "weights": [[8, 18, 1.0], [22, 29, 1.0], [30, 38, 1.0], [199, 207, 1.0]]}, "T87-1023": {"snippet": "Natural Language Processing: What{'}s Really Involved?", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2019.sigirconf_workshop-2019birndl.0": {"snippet": "... and Natural Language Processing for Digital Libraries (BIRNDL 2019) co-located with the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), Pari...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "W15-5955": {"snippet": "Natural Language Processing for Solving Simple Word Problems\n\n\n This paper describes our system which solves simple arithmetic word problems. The system takes a word problem described in natural langu...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [187, 194, 1.0], [195, 203, 1.0]]}, "P14-6001": {"snippet": "... for Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2018.sigirconf_conference-2018.88": {"snippet": "...ces. Natural language interfaces, exemplified by virtual assistants like Apple Siri and Microsoft Cortana, are widely believed to be a promising direction. However, current natural language interfaces...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [176, 183, 1.0], [184, 192, 1.0]]}, "2022.acl-tutorials.8": {"snippet": "Natural Language Processing for Multilingual Task-Oriented Dialogue", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "E91-1043": {"snippet": "... for Natural Language Processing\n\n\n In this paper* I will argue for a model of grammatical processing that is based on uniform processing and knowledge sources. The main feature of this model is to vi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [94, 104, 1.0], [130, 140, 1.0]]}, "J88-3020": {"snippet": "... for natural language processing and the fact that one must consider what control structures or procedures must be in place to process language. These issues are never related to language as a process...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [138, 146, 1.0], [182, 190, 1.0]]}}}
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In this paper, we propose a variant of SALSA to give sNorm(p) for the effic...", "weights": [[8, 15, 1.0], [26, 31, 1.0], [78, 85, 1.0]]}, "2007.wwwconf_conference-2007.194": {"snippet": "...hted graph model k-UCM graph to reveal the implicit relationship between authors hidden behind the citations among their articles. Furthermore, we design a graph-based ranking algorithm, the Credit Au...", "weights": [[8, 13, 1.0], [26, 31, 1.0], [159, 164, 1.0], [165, 170, 1.0], [171, 178, 1.0]]}, "2021.wwwconf_conference-2021.223": {"snippet": "Graph Neural Networks for Friend Ranking in Large-scale Social Platforms", "weights": [[0, 5, 1.0], [33, 40, 1.0]]}, "2018.sigirconf_workshop-2018profs.6": {"snippet": "...edge graph. Graphs are a natural and transparent means of representing knowledge. A graph-based search paradigm enables and stimulates the exploratory search behaviour for complex information needs th...", "weights": [[8, 13, 1.0], [87, 92, 1.0], [93, 98, 1.0]]}, "2018.sigirconf_conference-2018.16": {"snippet": "... for Ranking Refinement\n\n\n ABSTRACTLearning to rank has been intensively studied and widely applied in information retrieval. Typically, a global ranking function is learned from a set of labeled data...", "weights": [[8, 15, 1.0], [149, 156, 1.0]]}, "2019.ipm_journal-ir0volumeA56A4.5": {"snippet": "...ised graph-based rank aggregation for improved retrieval\n\n\n A R T I C L E I N F O\nKeywords:Rank aggregation Content-based retrieval Multimodal retreival Graph-based fusion A B S T R A C T This paper p...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [119, 124, 1.0], [156, 161, 1.0], [162, 167, 1.0]]}, "I13-1062": {"snippet": "...k, a graph-based keyphrase extraction method that relies on a topical representation of the document. Candidate keyphrases are clustered into topics and used as vertices in a complete graph. A graph-b...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [187, 192, 1.0], [196, 201, 1.0], [202, 207, 1.0]]}, "2008.sigirconf_conference-2008.173": {"snippet": "...sage-based approach to re-ranking documents in an initially retrieved list so as to improve precision at top ranks. While most work on passage-based document retrieval ranks a document based on the qu...", "weights": [[8, 13, 1.0], [29, 36, 1.0], [146, 151, 1.0], [188, 193, 1.0]]}, "2011.sigirconf_conference-2011.55": {"snippet": "...fold ranking for image retrieval\n\n\n ABSTRACTManifold Ranking (MR), a graph-based ranking algorithm, has been widely applied in information retrieval and shown to have excellent performance and feasibi...", "weights": [[8, 15, 1.0], [56, 63, 1.0], [72, 77, 1.0], [78, 83, 1.0], [84, 91, 1.0]]}, "2008.wwwconf_conference-2008.42": {"snippet": "...n in ranking of the documents. This paper addresses the issue and formulates it as a novel learning problem, referred to as, 'learning to rank relational objects'. In the new learning task, the rankin...", "weights": [[8, 15, 1.0], [197, 204, 1.0]]}, "2015.sigirconf_conference-2015.45": {"snippet": "...M: A Ranking based Geographical Factorization Method for Point of Interest Recommendation\n\n\n ABSTRACTWith the rapid growth of location-based social networks, Point of Interest (POI) recommendation has...", "weights": [[8, 15, 1.0], [16, 21, 1.0], [138, 143, 1.0]]}, "2014.trec_conference-2014.12": {"snippet": "...lity based Document Ranking\n\n\n In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place ed...", "weights": [[8, 13, 1.0], [23, 30, 1.0], [75, 82, 1.0], [119, 124, 1.0], [185, 190, 1.0]]}, "P04-3020": {"snippet": "Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization\n\n\n This paper presents an innovative unsupervised method for automatic sentence extraction using graphbased rankin...", "weights": [[0, 5, 1.0], [6, 11, 1.0], [12, 19, 1.0], [194, 201, 1.0]]}, "2017.ictir_conference-2017.31": {"snippet": "...best graph-based vertical ranking methods substantially and statistically signi cantly outperform a competitive baseline that ranks verticals based on click counts.", "weights": [[8, 13, 1.0], [14, 19, 1.0], [29, 36, 1.0], [145, 150, 1.0]]}, "W04-3252": {"snippet": "...k -a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications. In particular, we propose two innovative unsupervised methods for...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0]]}, "2008.ecir_conference-2008.70": {"snippet": "Graph-Based Profile Similarity Calculation Method and Evaluation", "weights": [[0, 5, 1.0], [6, 11, 1.0]]}, "2011.wwwconf_conference-2011c.118": {"snippet": "Ranking on large-scale graphs with rich metadata\n\n\n ABSTRACTFor many Web applications, one needs to deal with the ranking problem on large-scale graphs with rich metadata. However, it is non-trivial t...", "weights": [[0, 7, 1.0], [114, 121, 1.0]]}, "2009.cikm_conference-2009.103": {"snippet": "...odel-based ranking for queries on RDF-graphs", "weights": [[8, 13, 1.0], [14, 21, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A3.5": {"snippet": "...g of graph similarity search\n\n\n Abstract A graph similarity search is to find a set of graphs from a graph database that are similar to a given query graph. Existing works solve this problem by first ...", "weights": [[8, 13, 1.0], [46, 51, 1.0], [104, 109, 1.0], [153, 158, 1.0]]}, "2015.sigirconf_conference-2015.119": {"snippet": "...ment Ranking with Rank-based Features\n\n\n ABSTRACTLearning to Rank (LtR) is an effective machine learning methodology for inducing high-quality document ranking functions. Given a query and a candidate...", "weights": [[8, 15, 1.0], [26, 31, 1.0], [155, 162, 1.0]]}, "2006.sigirconf_conference-2006.12": {"snippet": "...ment ranking wherein we utilize cluster information within a graph-based framework. The main idea is to perform re-ranking based on centrality within bipartite graphs of documents (on one side) and cl...", "weights": [[8, 15, 1.0], [64, 69, 1.0], [70, 75, 1.0], [118, 125, 1.0], [126, 131, 1.0]]}, "W06-3315": {"snippet": "A Graph-Search Framework for {G}ene{I}d Ranking", "weights": [[2, 7, 1.0], [40, 47, 1.0]]}, "2020.cikm_conference-2020.329": {"snippet": "Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation", "weights": [[0, 5, 1.0], [29, 36, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.26": {"snippet": "...ntic Ranking approach that soft matches the query and documents with continuous knowledge graph embeddings. The entity-based text representations are then combined with word-based representations in a...", "weights": [[8, 15, 1.0], [93, 98, 1.0], [122, 127, 1.0], [177, 182, 1.0]]}, "2020.wsdm_conference-2020.70": {"snippet": "...item ranking under learned neural network based ranking measures is largely still an open question. In this paper, we formulate ranking under neural network based measures as a generic ranking task, O...", "weights": [[8, 15, 1.0], [45, 50, 1.0], [51, 58, 1.0], [131, 138, 1.0], [160, 165, 1.0], [188, 195, 1.0]]}, "2018.sigirconf_workshop-2018profs.8": {"snippet": "...edge graph (KG) and propose a general approach for increasing the interpretability of recommendation systems. We demonstrate the usefulness of applying a variational autoencoder to the Entity Set Expa...", "weights": [[8, 13, 1.0]]}, "2013.cikm_workshop-2013ueo.4": {"snippet": "...ts a graph-based ranking model to the context of the problem of improving the process of serving advertisements to users. We transform the ad-based clickstream data into a heterogeneous graph model wh...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0], [145, 150, 1.0], [189, 194, 1.0]]}, "2004.wwwconf_conference-2004at.94": {"snippet": "...Link-based ranking algorithms are used for ordering results of search queries passed to search engines. More precisely, each document is assigned some ranking score which is computed by a ranking algo...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [154, 161, 1.0], [191, 198, 1.0]]}, "P14-2013": {"snippet": "Graph Ranking for Collective Named Entity Disambiguation\n\n\n Named Entity Disambiguation (NED) refers to the task of mapping different named entity mentions in running text to their correct interpretat...", "weights": [[0, 5, 1.0], [6, 13, 1.0]]}, "2012.cikm_conference-2012.75": {"snippet": "...ues. Graph-based algorithms have been proposed to rank papers based on networks formed by citation and co-author relationships. This paper proposes a new graph-based ranking framework MutualRank that ...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [65, 70, 1.0], [157, 162, 1.0], [163, 168, 1.0], [169, 176, 1.0]]}, "2018.cikm_conference-2018.276": {"snippet": "Deep Graph Embedding for Ranking Optimization in E-commerce", "weights": [[5, 10, 1.0], [25, 32, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.34": {"snippet": "... the graph. In this paper, we focus on the impact of pairwise preferences on the diversity of recommendations. We propose a novel graph-based ranking oriented recommendation algorithm that exploits bo...", "weights": [[8, 13, 1.0], [133, 138, 1.0], [139, 144, 1.0], [145, 152, 1.0]]}, "P18-1223": {"snippet": "...ural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval\n\n\n This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs ...", "weights": [[8, 15, 1.0], [53, 58, 1.0], [147, 154, 1.0]]}, "2009.cikm_conference-2009.121": {"snippet": "... and ranking, feature weighting methods developed for classification cannot be readily applied to feature weighting for ranking. A state of the art feature selection method for ranking, called GAS, ha...", "weights": [[8, 15, 1.0], [123, 130, 1.0], [180, 187, 1.0]]}, "2008.cikm_conference-2008.190": {"snippet": "Ranking in folksonomy systems: can context help?\n\n\n ABSTRACTFolksonomy systems have shown to contribute to the quality of Web search ranking strategies. In this paper, we analyze and compare different...", "weights": [[0, 7, 1.0], [133, 140, 1.0]]}, "N19-1104": {"snippet": "...lize graph patterns in a knowledge graph to overcome such problems. Our proposed model, the graph pattern entity ranking model (GRank), constructs an entity ranking system for each graph pattern and e...", "weights": [[8, 13, 1.0], [38, 43, 1.0], [95, 100, 1.0], [116, 123, 1.0], [160, 167, 1.0], [184, 189, 1.0]]}, "2015.mir_conference-2015.27": {"snippet": "... and Graph-based Ranking Algorithms", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0]]}, "2017.sigirconf_workshop-2017kg4ir.6": {"snippet": "Enhancing Categorization of Computer Science Research Papers using Knowledge Bases\n\n\n Automatic categorization of computer science research papers using just the abstracts, is a hard problem to solve....", "weights": []}, "2017.sigirconf_workshop-2017kg4ir.3": {"snippet": "... the ranking process. The ranking model identifies and ranks the most important attributes (facts) of an entity. In order to experiment with our approach, we initially collected a dataset by exploitin...", "weights": [[8, 15, 1.0], [29, 36, 1.0]]}, "2016.tist_journal-ir0volumeA7A4.20": {"snippet": "...ting graph-based ranking algorithms mostly focus on ranking the current importance of literature. For researchers who enter an emerging research area, they might be more interested in new papers and y...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0], [55, 62, 1.0]]}, "2016.tist_journal-ir0volumeA7A4.21": {"snippet": "...nity Graph for Image Segmentation and Image Annotation\n\n\n This article investigates a low-rank representation-based graph, which can used in graph-based vision tasks including image segmentation and i...", "weights": [[8, 13, 1.0], [113, 118, 1.0], [119, 124, 1.0], [144, 149, 1.0], [150, 155, 1.0]]}, "2020.ecir_conference-20202.79": {"snippet": "...ploy graph database management systems to implement existing and novel graph-based models for information retrieval. For this a unifying mapping from a graph query language to graph based retrieval mo...", "weights": [[8, 13, 1.0], [74, 79, 1.0], [80, 85, 1.0], [155, 160, 1.0], [179, 184, 1.0], [185, 190, 1.0]]}, "2018.cikm_workshop-2018w.24": {"snippet": "... the ranking qualities, this paper explores possibilities of graph analytical methods. LD is regarded as a large graph, graph analytical approaches are therefore appropriate for this purpose. Since qu...", "weights": [[8, 15, 1.0], [64, 69, 1.0], [116, 121, 1.0], [123, 128, 1.0]]}, "2017.wwwconf_conference-2017.131": {"snippet": "...ntic Ranking for Academic Search via Knowledge Graph Embedding\n\n\n ABSTRACTThis paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analy...", "weights": [[8, 15, 1.0], [50, 55, 1.0], [117, 124, 1.0], [138, 145, 1.0], [181, 186, 1.0]]}, "2010.wwwconf_conference-2010.87": {"snippet": "...t\" a graph model to a real social graph, extract a set of model parameters, and use them to generate multiple synthetic graphs statistically similar to the original graph. While numerous graph models ...", "weights": [[8, 13, 1.0], [37, 42, 1.0], [168, 173, 1.0], [190, 195, 1.0]]}, "2021.ipm_journal-ir0volumeA58A2.4": {"snippet": "Rank-based self-training for graph convolutional networks", "weights": [[5, 10, 1.0], [29, 34, 1.0]]}, "D08-1079": {"snippet": "...t on Graph-Based Multi-Document Summarization\n\n\n The graph-based ranking algorithm has been recently exploited for multi-document summarization by making only use of the sentence-to-sentence relations...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [56, 61, 1.0], [62, 67, 1.0], [68, 75, 1.0]]}, "S15-1036": {"snippet": "...ovel graph-based coherence features based on frequent subgraphs and compare their ability to assess the readability of Wall Street Journal articles. In contrast to Pitler and Nenkova (2008) some of ou...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [39, 44, 1.0]]}, "2005.ir_journal-ir0volumeA8A2.4": {"snippet": "...Link-Based Web Ranking Algorithms in Authority-Connected Graphs", "weights": [[8, 13, 1.0], [18, 25, 1.0]]}, "2012.sigirconf_conference-2012.77": {"snippet": "...ing, ranking and evaluation\n\n\n ABSTRACTIn this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes ...", "weights": [[8, 15, 1.0], [144, 151, 1.0]]}, "2014.cikm_conference-2014.106": {"snippet": "...raph-based ranking plays a key role in many applications, such as web search and social computing. 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The ranking leverages the graph-structure of the ontology to incorporate not explicitly mentioned concepts. I...", "weights": [[8, 15, 1.0], [98, 105, 1.0], [120, 125, 1.0]]}, "2018.tweb_journal-ir0volumeA12A2.0": {"snippet": "...edge Graph Embedding: A Locally and Temporally Adaptive Translation-Based Approach\n\n\n A knowledge graph is a graph with entities of different types as nodes and various relations among them as edges. ...", "weights": [[8, 13, 1.0], [71, 76, 1.0], [101, 106, 1.0], [112, 117, 1.0]]}, "2017.sigirconf_conference-2017.48": {"snippet": "...ovel graph-based multi-channel ranking scheme for social indicator computation by exploring the rich multi-channel Web data. For each channel, this scheme presents the semi-structured and unstructured...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [34, 41, 1.0]]}, "2017.wsdm_conference-2017.83": {"snippet": "...ised Ranking using Graph Structures and Node Attributes\n\n\n ABSTRACTPageRank has been the signature unsupervised ranking model for ranking node importance in a graph. One potential drawback of PageRank...", "weights": [[8, 15, 1.0], [22, 27, 1.0], [115, 122, 1.0], [133, 140, 1.0], [162, 167, 1.0]]}, "2017.tist_journal-ir0volumeA8A2.11": {"snippet": "...ngle graph such that the graph structure with the maximum clustering coefficient is obtained from the single graph. It has two distinct features compared with existing approaches. First, dealing with ...", "weights": [[8, 13, 1.0], [28, 33, 1.0], [112, 117, 1.0]]}, "2008.airs_conference-2008.25": {"snippet": "...ised Graph-Ranking for Text Retrieval", "weights": [[8, 13, 1.0], [14, 21, 1.0]]}, "2009.sigirconf_conference-2009.70": {"snippet": "...sing graph-based ranking on multi-type interrelated objects", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0]]}, "2008.cikm_conference-2008.116": {"snippet": "...this graph using a simple union of the Web's hyperlink and click graphs. The hyperlink graph expresses link structure among Web pages, while the click graph is a bipartite graph of queries and documen...", "weights": [[8, 13, 1.0], [90, 95, 1.0], [154, 159, 1.0], [175, 180, 1.0]]}, "2011.cikm_workshop-2011pikm.3": {"snippet": "...ship graph, it can incorporate diverse semantics existing in the graph. Utilizing this kind of graph-based data models has been recognized as a reasonable way for dealing with heterogeneous data. Howe...", "weights": [[8, 13, 1.0], [68, 73, 1.0], [98, 103, 1.0], [104, 109, 1.0]]}, "2013.cikm_conference-2013.93": {"snippet": "... for graph classification? (2) how many sub-graph features are sufficient for good classification results? (3) does the length of the sub-graph features play major roles for classification? and (4) wh...", "weights": [[8, 13, 1.0], [47, 52, 1.0], [141, 146, 1.0]]}, "I11-1113": {"snippet": "...m of ranking these entities by their \"importance\", we introduce three degree-based measures of graph connectivity. Experimental results on the TAC-KBP benchmark data sets show that our graph-based met...", "weights": [[8, 15, 1.0], [80, 85, 1.0], [98, 103, 1.0], [188, 193, 1.0], [194, 199, 1.0]]}, "2016.ntcir_conference-2016.92": {"snippet": "...Link-based Ranking on iUnit-Page Bipartite Graph\n\n\n We participated in the iUnit ranking subtask and the iUnit summarization subtask of the NTCIR-12 MobileClick for the Japanese and English languages....", "weights": [[8, 13, 1.0], [14, 21, 1.0], [46, 51, 1.0], [84, 91, 1.0]]}, "2014.cikm_conference-2014.13": {"snippet": "...path-based ranking features to derive the final ranking scores for candidate cited papers. 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How...", "weights": [[8, 13, 1.0], [19, 26, 1.0]]}, "2004.cikm_conference-2004.88": {"snippet": "...m of ranking hyperlinked documents for a given query. Given an arbitrary directed graph with edge and node labels, we present a new flow-based model and an efficient method to dynamically rank the nod...", "weights": [[8, 15, 1.0], [85, 90, 1.0], [140, 145, 1.0]]}, "2015.sigirconf_workshop-2015gsb.1": {"snippet": "...onal graph. This graph essentially unifies both the searcher and the information resources that played a fundamentally different role in traditional IR, and \"Graph Search\" offers major new ways to acc...", "weights": [[8, 13, 1.0], [20, 25, 1.0], [161, 166, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...link-based ranking features can be implemented in large-scale search engines; and we identify promising avenues for future research.\nKeywords:Web graph, link analysis, ranking, PageRank, HITS, SALSA.\n...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [149, 154, 1.0], [171, 178, 1.0]]}, "2009.wsdm_conference-2009.25": {"snippet": "...d by graph-based machine learning algorithms, we propose a novel and general framework to model the re-ranking algorithm, by regularizing the smoothness of ranking scores over the graph, along with a ...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [106, 113, 1.0], [159, 166, 1.0], [183, 188, 1.0]]}, "2010.tweb_journal-ir0volumeA4A4.3": {"snippet": "... Web Graph Representations\n\n\n Compressed graph representations, in particular for Web graphs, have become an attractive research topic because of their applications in the manipulation of huge graphs ...", "weights": [[8, 13, 1.0], [44, 49, 1.0]]}, "2018.sigirconf_workshop-2018profs.1": {"snippet": "...ion, based on feedback about whether the candidate was SELECTED or REJECTED. The TEAC system is deployed in a large multinational IT services organization.", "weights": [[8, 13, 1.0]]}, "2009.cikm_conference-2009.258": {"snippet": "...raph based semi-supervised sentence ranking for query-oriented summarization\n\n\n ABSTRACTGraph based sentence ranking algorithms such as PageRank and HITS have been successfully used in query-oriented ...", "weights": [[8, 13, 1.0], [39, 46, 1.0], [97, 102, 1.0], [112, 119, 1.0]]}, "2018.wwwconf_conference-2018c.293": {"snippet": "...oned graph data models through the foundational RDF standard, the recent hype around industry-driven initiativessuch as Google's Knowledge Graph, Facebook's Graph API or the Open Graph Protocol -indic...", "weights": [[8, 13, 1.0], [142, 147, 1.0], [160, 165, 1.0], [182, 187, 1.0]]}, "2001.jasis_journal-ir0volumeA52A9.4": {"snippet": "...page ranking applied to different topological web graph structures", "weights": [[8, 15, 1.0], [53, 58, 1.0]]}}}
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For example, 'cancer', 'tumour', and 'neoplasms', which are synonyms, may...", "weights": [[8, 15, 1.0], [60, 67, 1.0], [113, 120, 1.0]]}, "2013.sigirconf_conference-2013.205": {"snippet": "...ver, medical records search is challenging, due to the implicit knowledge inherent within the medical records -such knowledge may be known by medical practitioners, but hidden to an information retrie...", "weights": [[8, 15, 1.0], [97, 104, 1.0], [145, 152, 1.0], [185, 196, 1.0], [197, 206, 1.0]]}, "2008.clef_workshop-2008.87": {"snippet": "...s in Medical Image Retrieval", "weights": [[8, 15, 1.0], [22, 31, 1.0]]}, "2009.clef_workshop-2009w.82": {"snippet": "Medical Image Retrieval: ISSR at CLEF 2009\n\n\n This paper represents the first participation of the Institute of Statistical Studies and Research at Cairo University group in CLEF 2009-Medical image re...", "weights": [[0, 7, 1.0], [14, 23, 1.0], [124, 131, 1.0], [184, 191, 1.0], [198, 207, 1.0]]}, "2012.trec_conference-2012.83": {"snippet": "... for Medical Records Search: An Experiment in Identifying Cohorts for Comparative Effectiveness Research\n\n\n This paper describes an experiment performed on a medical record data set, using an informat...", "weights": [[8, 15, 1.0], [161, 168, 1.0], [195, 206, 1.0]]}, "2012.sigirconf_conference-2012.219": {"snippet": "Medical information retrieval: an instance of domain-specific search\n\n\n ABSTRACTDue to an explosion in the amount of medical information available, search techniques are gaining importance in the medi...", "weights": [[0, 7, 1.0], [8, 19, 1.0], [20, 29, 1.0], [117, 124, 1.0], [125, 136, 1.0], [196, 203, 1.0]]}, "2014.sigirconf_workshop-2014medir.2": {"snippet": "...here Information Retrieval Meets Data Mining", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2014.clef_conference-2014.8": {"snippet": "...ised Medical Literature Search", "weights": [[8, 15, 1.0]]}, "2015.wsdm_conference-2015.53": {"snippet": "...uire medical information. People seek this information using both traditional search engines and via social media. The information created using the search process offers an unprecedented opportunity ...", "weights": [[8, 15, 1.0], [16, 27, 1.0], [46, 57, 1.0], [122, 133, 1.0]]}, "2014.jasis_journal-ir0volumeA65A3.4": {"snippet": "... the retrieval effectiveness of collaborative tags and author keywords in different retrieval environments: An experimental study on medical collections", "weights": [[8, 17, 1.0], [87, 96, 1.0], [136, 143, 1.0]]}, "2014.cikm_conference-2014.164": {"snippet": "... the medical domain, information retrieval systems can be used for identifying cohorts (i.e. patients) required for clinical studies. However, a challenge faced by such search systems is to retrieve t...", "weights": [[8, 15, 1.0], [24, 35, 1.0], [36, 45, 1.0], [128, 135, 1.0]]}, "2014.clef_conference-2014w.17": {"snippet": "...s of Information Retrieval systems to support patients in accessing easily relevant information. We propose a method which exploits external resources for improving information retrieval in the biomed...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [87, 98, 1.0], [168, 179, 1.0], [180, 189, 1.0]]}, "2015.clef_conference-2015w.148": {"snippet": "...y of information retrieval systems when searching for web medical documents. In this task, our approach is to integrate a retrieval of medical concepts into the preprocessing of corpora. This means th...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [61, 68, 1.0], [125, 134, 1.0], [138, 145, 1.0]]}, "W02-0310": {"snippet": "...ture retrieval\n\n\n We describe the use of clinical data present in the medical record to determine the relevance of research evidence from literature databases. We studied the effect of using automated...", "weights": [[8, 17, 1.0], [37, 40, 1.0], [73, 80, 1.0]]}, "2012.trec_conference-2012.78": {"snippet": "...2012 Medical Records track, which fosters research in the retrieval of electronic health records using free text fields. Our contributions to this track investigate several problem areas in the retrie...", "weights": [[8, 15, 1.0], [61, 70, 1.0], [197, 206, 1.0]]}, "2003.spire_conference-2003.15": {"snippet": "...Text Retrieval in Medical Collections Through Automatic Categorization\n\n\n Abstract.A current and important research issue is the retrieval of relevant medical information. In fact, while the medical k...", "weights": [[8, 17, 1.0], [21, 28, 1.0], [132, 141, 1.0], [154, 161, 1.0], [162, 173, 1.0], [194, 201, 1.0]]}, "W17-6943": {"snippet": "...tory medical guidelines; the system uses a combination of text analysis and information retrieval modules. We also report positive evaluation results on a small corpus of contradictory medical recomme", "weights": [[8, 15, 1.0], [79, 90, 1.0], [91, 100, 1.0], [188, 195, 1.0]]}, "2011.trec_conference-2011.60": {"snippet": "Medical-Miner at TREC 2011 Medical Records Track\n\n\n This paper presents work done at Medical Minner Project on the TREC-2011 Medical Records Track. The paper proposes four models for medical informati...", "weights": [[0, 7, 1.0], [27, 34, 1.0], [85, 92, 1.0], [125, 132, 1.0], [183, 190, 1.0], [191, 202, 1.0]]}, "2014.ipm_journal-ir0volumeA50A1.1": {"snippet": "...alth information retrieval systems: An empirical user study\n\n\n a b s t r a c tWhen consumers search for health information, a major obstacle is their unfamiliarity with the medical terminology. Even t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [114, 125, 1.0], [176, 183, 1.0]]}, "2017.fire_conference-2017w.43": {"snippet": "... The medical knowledge have the potential to impact considerably on the quality of care provided by clinicians. Medical field has various types of queries: short questions, medical case reports, medic...", "weights": [[8, 15, 1.0], [115, 122, 1.0], [176, 183, 1.0], [198, 205, 1.0]]}, "2007.riao_conference-2007.59": {"snippet": "...y in Studies of Information Retrieval Evaluation", "weights": [[8, 15, 1.0], [19, 30, 1.0], [31, 40, 1.0]]}, "2013.clef_conference-2013w.44": {"snippet": "...ased Medical Document Retrieval: THCIB at CLEF eHealth Lab 2013 Task 3\n\n\n We describe our participation in the task 3 of ShARe/CLEF eHealth Lab 2013: information retrieval to address questions patient...", "weights": [[8, 15, 1.0], [25, 34, 1.0], [153, 164, 1.0], [165, 174, 1.0]]}, "2006.clef_workshop-2006.72": {"snippet": "...2006 Medical Retrieval and Medical Annotation Tasks", "weights": [[8, 15, 1.0], [16, 25, 1.0], [30, 37, 1.0]]}, "1989.sigirconf_conference-89.24": {"snippet": "...onic information systems but at the potential risk of becoming lost in the network of interconnected pieces of information. A study was conducted on information retrieval using a commercial hypertextb...", "weights": [[8, 19, 1.0], [114, 125, 1.0], [152, 163, 1.0], [164, 173, 1.0]]}, "2010.clef_workshop-2010w.49": {"snippet": "...CLEF medical retrieval task, namely ImageCLEFmed. In 2010, we are particularly interested in the case-based retrieval task. Our information retrieval approach integrates a hybrid method of concept ext...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [111, 120, 1.0], [131, 142, 1.0], [143, 152, 1.0]]}, "2012.sigirconf_conference-2012.29": {"snippet": "Studies of the onset and persistence of medical concerns in search logs\n\n\n ABSTRACTThe Web provides a wealth of information about medical symptoms and disorders. Although this content is often valuabl...", "weights": [[0, 7, 1.0], [40, 47, 1.0], [112, 123, 1.0], [130, 137, 1.0]]}, "2014.clef_conference-2014w.23": {"snippet": "...h in Medical Information Retrieval\n\n\n In our participation in the CLEF 2014 eHealth task 3a, we investigate the effectiveness of concept-based retrieval techniques on medical IR. Concepts are determin...", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0], [146, 155, 1.0], [170, 177, 1.0]]}, "2013.cikm_conference-2013.176": {"snippet": "...e in medical records search\n\n\n ABSTRACTNegated language is frequently used by medical practitioners to indicate that a patient does not have a given medical condition. Traditionally, information retri...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [152, 159, 1.0], [186, 197, 1.0], [198, 207, 1.0]]}, "2007.clef_workshop-2007.76": {"snippet": "...rove Medical Information Retrieval\n\n\n Abstract. This paper describes the SINAI team participation in the ImageCLEFmed campaign. The SINAI research group has participated in the multilingual image retr...", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0], [199, 208, 1.0]]}, "2013.sigirconf_conference-2013.147": {"snippet": "... for medical record search\n\n\n ABSTRACTIn this paper, we present a medical record search system which is useful for identifying cohorts required in clinical studies. In particular, we propose a query-a...", "weights": [[8, 15, 1.0], [69, 76, 1.0], [159, 166, 1.0]]}, "2020.wsdm_workshop-2020hsdm.2": {"snippet": "...king retrieval and other uses challenging. This talk will describe recent research applying and evaluating information retrieval techniques to two use cases: discovering cohorts for clinical research ...", "weights": [[8, 17, 1.0], [110, 121, 1.0], [122, 131, 1.0], [150, 153, 1.0]]}, "2008.sigirconf_conference-2008.3": {"snippet": "...gent medical search\n\n\n ABSTRACTSearching for medical information on the Web has become highly popular, but it remains a challenging task because searchers are often uncertain about their exact medical...", "weights": [[8, 15, 1.0], [48, 55, 1.0], [56, 67, 1.0], [196, 203, 1.0]]}, "2012.clef_workshop-2012w.1": {"snippet": "...ased Medical Image Retrieval", "weights": [[8, 15, 1.0], [22, 31, 1.0]]}, "2011.clef_workshop-2011w.42": {"snippet": "...011: Medical Retrieval Task\n\n\n In this paper, we reported some experiments conducted by our members in the SIG team at the IRIT laboratory in the University of Toulouse within the context of the medic...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [198, 205, 1.0]]}, "1995.sigirconf_conference-95.20": {"snippet": "...s of Information Retrieval Evaluation\n\n\n AbstractAll of the methods cumently used to evaluate information retrieval (Ill) systems have limitations in their ability to measure how well users are able t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "1995.ipm_journal-ir0volumeA31A4.3": {"snippet": "...s of Medical Librarianship", "weights": [[8, 15, 1.0]]}, "P14-1057": {"snippet": "... for Medical Records Search\n\n\n An important search task in the biomedical domain is to find medical records of patients who are qualified for a clinical trial. One commonly used approach is to apply N...", "weights": [[8, 15, 1.0], [95, 102, 1.0]]}, "2014.sigirconf_conference-2014.131": {"snippet": "... for information retrieval in the medical domain", "weights": [[8, 19, 1.0], [20, 29, 1.0], [37, 44, 1.0]]}, "2013.clef_conference-2013w.195": {"snippet": "... the retrieval of medical information from the web: 1) to what degree retrieval techniques effective in searching Electronic Medical Records (EMRs) could aid in finding medical web documents; 2) to wh...", "weights": [[8, 17, 1.0], [21, 28, 1.0], [29, 40, 1.0], [73, 82, 1.0], [128, 135, 1.0], [172, 179, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A2.12": {"snippet": "...stic information retrieval study: an investigation of methods for enhanced searching of distributed data objects exploiting bidirectional relevance feedback\n\n\n The primary aim of this research is to i...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2014.sigirconf_workshop-2014medir.7": {"snippet": "... for Medical Image Retrieval\n\n\n Medical image retrieval can assist physicians in finding information supporting their diagnosis. Systems that allow searching for medical images need to provide tools f...", "weights": [[8, 15, 1.0], [22, 31, 1.0], [35, 42, 1.0], [49, 58, 1.0], [92, 103, 1.0], [165, 172, 1.0]]}, "2005.clef_workshop-2005w.57": {"snippet": "... for Medical Image Retrieval\n\n\n This paper presents the 2005 MIRACLE's team participation in the ImageCLEFmed task of ImageCLEF 2005. This task certainly requires the use of image retrieval techniques...", "weights": [[8, 15, 1.0], [22, 31, 1.0], [170, 173, 1.0], [183, 192, 1.0]]}, "2018.tist_journal-ir0volumeA9A2.0": {"snippet": "... for Medical Question Answering\n\n\n Answering medical questions related to complex medical cases, as required in modern Clinical Decision Support (CDS) systems, imposes (1) access to vast medical knowl...", "weights": [[8, 15, 1.0], [48, 55, 1.0], [85, 92, 1.0], [190, 197, 1.0]]}, "2005.clef_workshop-2005w.4": {"snippet": "... for Medical Image Indexing and Retrieval\n\n\n Medical images are critical assets for medical diagnosis, research, and teaching. To facilitate automatic indexing and retrieval of large medical image dat...", "weights": [[8, 15, 1.0], [35, 44, 1.0], [48, 55, 1.0], [87, 94, 1.0], [167, 176, 1.0], [186, 193, 1.0]]}, "1994.ipm_journal-ir0volumeA30A2.3": {"snippet": "Information Retrieval Evaluation in Practice: A Case Study Approach", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2016.ir_journal-ir0volumeA19A12.1": {"snippet": "Information retrieval as semantic inference: a Graph Inference model applied to medical search", "weights": [[0, 11, 1.0], [12, 21, 1.0], [80, 87, 1.0]]}, "2006.airs_conference-2006.36": {"snippet": "... for Medical Image Retrieval Using the Insight Toolkit (ITK)\n\n\n Abstract. Visual information retrieval is an emerging domain in the medical field as it has been in computer vision for more than ten ye...", "weights": [[8, 15, 1.0], [22, 31, 1.0], [84, 95, 1.0], [96, 105, 1.0], [135, 142, 1.0]]}, "2010.clef_workshop-2010w.65": {"snippet": "Medical Case-based Retrieval by Leveraging Medical Ontology and Physician Feedback: UIUC-IBM at ImageCLEF 2010\n\n\n This paper reports the experiment results of the UIUC-IBM team in participating in the...", "weights": [[0, 7, 1.0], [19, 28, 1.0], [43, 50, 1.0]]}, "2014.cikm_workshop-2014esair.13": {"snippet": "...into retrieval (in particular for medical information retrieval) as well as previous attempts that have been made so far with mixed success. Medical information retrieval is a fertile ground for testi...", "weights": [[8, 17, 1.0], [37, 44, 1.0], [45, 56, 1.0], [57, 66, 1.0], [144, 151, 1.0], [152, 163, 1.0], [164, 173, 1.0]]}, "2022.findings-acl.110": {"snippet": "...d on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge\n\n\n Medical code prediction from clinical notes aims at automatically associating medical codes with...", "weights": [[8, 15, 1.0], [57, 64, 1.0], [107, 114, 1.0], [185, 192, 1.0]]}, "2015.clef_conference-2015.12": {"snippet": "...cing Medical Information Retrieval by Exploiting a Content-Based Recommender Method", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0]]}, "2008.ipm_journal-ir0volumeA44A3.9": {"snippet": "...sing information gain to improve multi-modal information retrieval systems\n\n\n AbstractNowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval ...", "weights": [[8, 19, 1.0], [48, 59, 1.0], [60, 69, 1.0], [109, 120, 1.0], [193, 202, 1.0]]}, "2011.trec_conference-2011.6": {"snippet": "...2011 Medical Track: A Concept-Based Information Retrieval Approach\n\n\n recently participated in the TREC 2011 Medical Records Track. This paper reports on our methods, results and experience using a co...", "weights": [[8, 15, 1.0], [39, 50, 1.0], [51, 60, 1.0], [112, 119, 1.0]]}, "2012.wwwconf_conference-2012c.128": {"snippet": "...y on medical searches\n\n\n ABSTRACTIn this paper, we analyze medical searching behavior performed by a typical medical searcher. We broadly classify a typical medical searcher as: non-medical profession...", "weights": [[8, 15, 1.0], [62, 69, 1.0], [112, 119, 1.0], [160, 167, 1.0], [185, 192, 1.0]]}, "2018.sigirconf_conference-2018.243": {"snippet": "...PTS\u2022 Information systems \u2192 Document representation; Information extraction; Specialized information retrieval; \nKEYWORDS\nRESEARCH DIRECTIONClinical Decision Support (CDS) systems are designed to assis...", "weights": [[8, 19, 1.0], [55, 66, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "2014.sigirconf_workshop-2014medir.6": {"snippet": "...ical retrieval task is cognitively demanding. Previous research has highlighted the challenges and issues information retrieval systems are faced with when determining the relevance of documents in th...", "weights": [[8, 17, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "2007.clef_workshop-2007.59": {"snippet": "...2007 Medical Retrieval and Medical Annotation Tasks", "weights": [[8, 15, 1.0], [16, 25, 1.0], [30, 37, 1.0]]}, "2018.trec_conference-2018.14": {"snippet": "...e of information useful to tailor medical decisions to a specific clinical case. With this respect, Information Retrieval tools play an essential role in enabling physicians to automatically analyze h...", "weights": [[8, 19, 1.0], [37, 44, 1.0], [103, 114, 1.0], [115, 124, 1.0]]}, "2014.sigirconf_workshop-2014medir.1": {"snippet": "...p on Medical Information Retrieval (MedIR)\n\n\n The workshop on Medical Information Retrieval took place at SIGIR 2014 in Gold Coast, Australia on July 11. The workshop included eight oral presentations...", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0], [65, 72, 1.0], [73, 84, 1.0], [85, 94, 1.0]]}, "2010.clef_workshop-2010w.66": {"snippet": "Medical Image Retrieval: ISSR at CLEF 2010\n\n\n This is the second participation of Institute of Statistical Studies and Research (ISSR) group in CLEF 2010-Medical image retrieval track. This paper desc...", "weights": [[0, 7, 1.0], [14, 23, 1.0], [107, 114, 1.0], [154, 161, 1.0], [168, 177, 1.0]]}, "W19-2606": {"snippet": "...lth: Information Extraction to Support New Interfaces for Accessing Medical Evidence\n\n\n Standard paradigms for search do not work well in the medical context. Typical information needs, such as retrie...", "weights": [[8, 19, 1.0], [71, 78, 1.0], [145, 152, 1.0], [170, 181, 1.0]]}, "2013.cikm_conference-2013.34": {"snippet": "...what retrieval model best suits the information need behind the query. The main challenge in medical image retrieval is to cope the semantic gap between user information needs and retrieval models. In...", "weights": [[8, 17, 1.0], [39, 50, 1.0], [96, 103, 1.0], [110, 119, 1.0], [161, 172, 1.0], [183, 192, 1.0]]}, "2006.jasis_journal-ir0volumeA57A8.8": {"snippet": "...mall medical journal: A case study of the Croatian Medical Journal", "weights": [[8, 15, 1.0], [54, 61, 1.0]]}, "2011.sigirconf_conference-2011.139": {"snippet": "...ting medical information retrieval\n\n\n ABSTRACTThis paper presents a framework for evaluating information retrieval of medical records. We use the BLULab corpus, a large collection of real-world de-ide...", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0], [96, 107, 1.0], [108, 117, 1.0], [121, 128, 1.0], [141, 144, 1.0]]}, "2017.ijmir_journal-ir0volumeA6A4.0": {"snippet": "...ased medical image retrieval", "weights": [[8, 15, 1.0], [22, 31, 1.0]]}, "2011.trec_conference-2011.7": {"snippet": "... for Medical Record Tracks\n\n\n We report our system and experiments at the 2011 Medical Record Track. Our goal is to return most relevant visits according to a query. In particular, we start with an ax...", "weights": [[8, 15, 1.0], [82, 89, 1.0]]}}}
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Translation retrieval aims at retrieving a list of target-language translati...", "weights": [[8, 17, 1.0], [55, 64, 1.0], [108, 117, 1.0], [139, 148, 1.0], [185, 193, 1.0]]}, "2007.tois_journal-ir0volumeA25A1.3": {"snippet": "...ross-language information retrieval\n\n\n We present a method for creating a comparable text corpus from two document collections in different languages. The collections can be very different in origin. ...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [133, 142, 1.0], [182, 191, 1.0]]}, "2013.tweb_journal-ir0volumeA7A2.6": {"snippet": "...lity sources and ranking the results from the selected sources. Though there are existing methods for both the steps, they assess the relevance of the sources and the results using the query-result si...", "weights": [[8, 15, 1.0], [58, 65, 1.0], [154, 161, 1.0]]}, "2013.clef_conference-2013w.193": {"snippet": "...urce Retrieval Notebook for PAN at CLEF 2013\n\n\n The source retrieval task for plagiarism detection involves the use of a search engine to retrieve candidate sources of plagiarism for a suspicious docu...", "weights": [[8, 17, 1.0], [62, 71, 1.0], [160, 167, 1.0]]}, "2004.clef_workshop-2004.47": {"snippet": "...urce Language in Bilingual Question Answering\n\n\n Abstract. This paper presents a bilingual question answering system that has Finnish as its source language and English as its target language. The sys...", "weights": [[8, 16, 1.0], [151, 159, 1.0], [186, 194, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.16": {"snippet": "... and information pathways. The former denotes the ways information sources are mapped in preference order in an imaginary field, while information pathways refers to the sequences in which sources pla...", "weights": [[8, 19, 1.0], [58, 69, 1.0], [70, 77, 1.0], [138, 149, 1.0], [192, 199, 1.0]]}, "2007.wwwconf_conference-2007.9": {"snippet": "...bute information extracted from different sources, we may not be able to achieve satisfactory retrieval performance. In this paper, we propose several language models for Web object retrieval, namely ...", "weights": [[8, 19, 1.0], [35, 44, 1.0], [45, 52, 1.0], [97, 106, 1.0], [154, 162, 1.0], [185, 194, 1.0]]}, "2019.ecir_conference-20192.54": {"snippet": "... the different elements of narratives which are often spread among different sources. In the second edition of the workshop, we foster the discussion of recent advances in the link between Information...", "weights": [[8, 17, 1.0], [70, 79, 1.0], [80, 87, 1.0], [192, 203, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [43, 51, 1.0], [91, 102, 1.0], [103, 112, 1.0], [148, 156, 1.0]]}, "2000.riao_conference-2000.60": {"snippet": "Language-Based Multimedia Information Retrieval", "weights": [[0, 8, 1.0], [26, 37, 1.0], [38, 47, 1.0]]}, "2001.clef_workshop-2000w.8": {"snippet": "...ross-Language Information Retrieval using Dutch Query Translation\n\n\n This paper describes a hastily carried out, rather limited bilingual information retrieval experiment. The experiment takes Dutch t...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [141, 152, 1.0], [153, 162, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.6": {"snippet": "...d by different language models (i.e., Dirichlet, Jelinek-Mercer and two-stage models). These language models are generally used in information retrieval to represent the relationship between documents...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [96, 104, 1.0], [134, 145, 1.0], [146, 155, 1.0]]}, "2001.ir_journal-ir0volumeA4A34.2": {"snippet": "...ross-Language Information Retrieval: Problems, Methods, and Research Findings", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.5": {"snippet": "...tive language as one index unit. For Cross-Language Information Retrieval (CLIR), one of the major hindrances to achieving retrieval performance at the level of Mono-Lingual Information Retrieval (MLI...", "weights": [[8, 16, 1.0], [46, 54, 1.0], [55, 66, 1.0], [67, 76, 1.0], [126, 135, 1.0], [177, 188, 1.0], [189, 198, 1.0]]}, "2015.fire_conference-2015.9": {"snippet": "... the language of reused document differs from the source document. In this paper we present a comparative study of different translation approaches used to map the language barrier for cross-lingual r...", "weights": [[8, 16, 1.0], [118, 127, 1.0], [167, 175, 1.0], [202, 211, 1.0]]}, "I05-1003": {"snippet": "...ross-Language Information Retrieval\n\n\n For cross-language text retrieval systems that rely on bilingual dictionaries for bridging the language gap between the source query language and the target docu...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [52, 60, 1.0], [66, 75, 1.0], [137, 145, 1.0], [175, 183, 1.0]]}, "1995.ipm_journal-ir0volumeA31A3.9": {"snippet": "...ural Language Information Retrieval", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0]]}, "2004.clef_workshop-2004.60": {"snippet": "...ross-Language Image Retrieval\n\n\n Abstract. For many cross-language retrieval tasks, the predominant approach is to translate the query into the language of the document collection (target language). T...", "weights": [[8, 16, 1.0], [23, 32, 1.0], [61, 69, 1.0], [70, 79, 1.0], [147, 155, 1.0], [191, 199, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.17": {"snippet": "A Language Modeling Approach to Information Retrieval", "weights": [[2, 10, 1.0], [32, 43, 1.0], [44, 53, 1.0]]}, "2009.cikm_conference-2009.261": {"snippet": "...ting information sources by analyzing static information of available information sources that is sampled in the offline manner. On the other hand, most prior research ignored a large amount of valuab...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [48, 59, 1.0], [73, 84, 1.0], [85, 92, 1.0]]}, "2004.ir_journal-ir0volumeA7A12.7": {"snippet": "...gual Information Retrieval Using Machine Translation, Relevance Feedback and Decompounding", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2001.ntcir_workshop-2001.12": {"snippet": "...lish retrieval and in cross-language retrieval using each language as a source language for the other. We found that 6-grams performed comparably with English words and that 2-grams and 3-grams perfor...", "weights": [[8, 17, 1.0], [31, 39, 1.0], [40, 49, 1.0], [61, 69, 1.0], [82, 90, 1.0]]}, "2015.fire_workshop-2015w.4": {"snippet": "...ross Language Source Code Reuse Detection Using Intermediate Language\n\n\n In today's digital era information access is just a click away. so computer science students also have easy access to all the s...", "weights": [[8, 16, 1.0], [64, 72, 1.0], [99, 110, 1.0]]}, "1990.ipm_journal-ir0volumeA26A1.3": {"snippet": "...ural language processing and information retrieval", "weights": [[8, 16, 1.0], [32, 43, 1.0], [44, 53, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A1.16": {"snippet": "...s on language modeling for information retrieval", "weights": [[8, 16, 1.0], [30, 41, 1.0], [42, 51, 1.0]]}, "2015.fire_workshop-2015w.1": {"snippet": "...ross-Language SOurce COde Re-use\n\n\n The detection of source code re-use is an important research field for both software industry and academia fields. This paper summarizes the goals, organization and...", "weights": [[8, 16, 1.0]]}, "2016.sigirconf_conference-2016.27": {"snippet": "...rget language, and the classification performance is far from satisfactory because of the language gap between the source language and the translated target language.In this paper, to address the abov...", "weights": [[8, 16, 1.0], [93, 101, 1.0], [125, 133, 1.0], [160, 168, 1.0]]}, "2012.wsdm_conference-2012.46": {"snippet": "...iple information sources\n\n\n ABSTRACTMost standard information retrieval models use a single source of information (e.g., the retrieval corpus) for query formulation tasks such as term and phrase weigh...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [53, 64, 1.0], [65, 74, 1.0], [105, 116, 1.0], [128, 137, 1.0]]}, "2006.clef_workshop-2006.4": {"snippet": "...ross Language Information Retrieval (CLIR) experiments of Language Technologies Research Centre (LTRC, IIIT-Hyderabad) as part of our participation in the ad-hoc track of CLEF 2006. This is our first ...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [61, 69, 1.0]]}, "1986.jasis_journal-ir0volumeA37A4.2": {"snippet": "...ural language processing in information retrieval", "weights": [[8, 16, 1.0], [31, 42, 1.0], [43, 52, 1.0]]}, "2017.wsdm_conference-2017.98": {"snippet": "...port Retrieval-Based Applications\n\n\n ABSTRACTAdvances in text retrieval do not apply directly to source code retrieval because of the difference in characteristics of source code when compared to text...", "weights": [[8, 17, 1.0], [65, 74, 1.0], [112, 121, 1.0]]}, "1999.trec_conference-1999.18": {"snippet": "...many different languages. Compared to the usual definition of cross-language information retrieval, where systems work with a single language pair, retrieving documents in a language L1 using queries ...", "weights": [[8, 17, 1.0], [71, 79, 1.0], [80, 91, 1.0], [92, 101, 1.0], [136, 144, 1.0], [177, 185, 1.0]]}, "2008.sigirconf_conference-2008.190": {"snippet": "...gual information retrieval, which can handle tens of languages and millions of documents. Functioning of the system is demonstrated on corpus of European Legislation (22 languages, more than 400,000 d...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2002.clef_workshop-2002.29": {"snippet": "Information Retrieval with Language Knowledge", "weights": [[0, 11, 1.0], [12, 21, 1.0], [27, 35, 1.0]]}, "2007.clef_workshop-2007.9": {"snippet": "...ross-Language Retrieval with Wikipedia", "weights": [[8, 16, 1.0], [17, 26, 1.0]]}, "2019.icon-1.7": {"snippet": "...ross-Language Information Retrieval\n\n\n This paper describes our first experiment on Neural Machine Translation (NMT) based query translation for Amharic-Arabic Cross-Language Information Retrieval (CL...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [169, 177, 1.0], [178, 189, 1.0], [190, 199, 1.0]]}, "O12-1015": {"snippet": "...ment retrieval model for cross-language document retrieval. Given a certain document in the source language, it will be translated into the target language of statistical machine translation model. Th...", "weights": [[8, 17, 1.0], [34, 42, 1.0], [52, 61, 1.0], [102, 110, 1.0], [150, 158, 1.0]]}, "2008.cikm_workshop-2008inews.5": {"snippet": "...ross-language information retrieval using compressed word format mapping algorithm\n\n\n ABSTRACTTransliteration of named entities in user queries is a vital step in any Cross-Language Information Retrie...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [176, 184, 1.0], [185, 196, 1.0], [197, 206, 1.0]]}, "2002.cikm_conference-2002.1": {"snippet": "...able information retrieval systems\n\n\n AbstractImplementing scalable information retrieval systems requires the design and development of efficient methods to ingest data from multiple sources, search ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [71, 82, 1.0], [83, 92, 1.0], [187, 194, 1.0]]}, "2015.clef_conference-2015.10": {"snippet": "...e in different languages. The lack of material in individual languages means that cross-language information retrieval (CLIR) within these collections is required to satisfy the user's information nee...", "weights": [[8, 17, 1.0], [91, 99, 1.0], [100, 111, 1.0], [112, 121, 1.0], [188, 199, 1.0]]}, "1988.riao_conference-1988.72": {"snippet": "...gual Information Retrieval Mechanism Using VLSI. Requirements and Approaches for Information Retrieval Systems in the Computer-Aided Software Engineering and Document Processing Environment", "weights": [[8, 19, 1.0], [20, 29, 1.0], [84, 95, 1.0], [96, 105, 1.0]]}, "2018.wwwconf_conference-2018c.22": {"snippet": "...ving Information from Multiple Sources\n\n\n ABSTRACTThe Web has several information sources on which an ongoing event is discussed. To get a complete picture of the event, it is important to retrieve in...", "weights": [[8, 19, 1.0], [34, 41, 1.0], [73, 84, 1.0], [85, 92, 1.0], [201, 212, 1.0]]}, "W16-3716": {"snippet": "...ross-Language Information Retrieval using Multilingual Word Clusters\n\n\n In Cross-Language Information Retrieval, finding the appropriate translation of the source language query has always been a diff...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [84, 92, 1.0], [93, 104, 1.0], [105, 114, 1.0], [166, 174, 1.0]]}, "2022.findings-acl.255": {"snippet": "... and retrieval from the knowledge in the source language. The retrieved knowledge is then translated into the target language and integrated into a pre-trained multilingual language model via visible ...", "weights": [[8, 17, 1.0], [51, 59, 1.0], [120, 128, 1.0], [176, 184, 1.0]]}, "2011.wwwconf_conference-2011.26": {"snippet": "... the sources. We compute the agreement between the sources as the agreement of the answers returned. While computing the agreement, we also measure and compensate for possible collusion between the so...", "weights": [[8, 15, 1.0], [54, 61, 1.0], [201, 208, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [42, 53, 1.0], [54, 63, 1.0], [92, 103, 1.0], [104, 113, 1.0], [122, 130, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "2005.sigirconf_conference-2005.70": {"snippet": "...ross-language information retrieval\n\n\n ABSTRACTThe bottleneck for dictionary-based cross-language information retrieval is the lack of comprehensive dictionaries, in particular for many different lang...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [92, 100, 1.0], [101, 112, 1.0], [113, 122, 1.0], [189, 198, 1.0]]}, "W03-1120": {"snippet": "...ross-Language Information Retrieval Based on Category Matching Between Language Versions of a Web Directory\n\n\n Since the Web consists of documents in various domains or genres, the method for Cross-La...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [74, 82, 1.0], [201, 209, 1.0]]}, "2011.cikm_conference-2011.235": {"snippet": "... the language barrier in cross language information retrieval (CLIR) in general is machine translation (MT). High quality MT systems are becoming widely available for many language pairs and generally...", "weights": [[8, 16, 1.0], [34, 42, 1.0], [43, 54, 1.0], [55, 64, 1.0], [175, 183, 1.0]]}, "2010.sigirconf_conference-2010.208": {"snippet": "...ross-language information retrieval are not enough. In fact, if the analysed source and target languages are related in some way (common linguistic ancestors or technical vocabulary), a simple compari...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0]]}, "2017.chiir_conference-2017.28": {"snippet": "...ixed Language Queries in Different Search Intents\n\n\n ABSTRACTWith the increasing number of multilingual resources on the Internet, cross-language information retrieval has become an important research...", "weights": [[8, 16, 1.0], [28, 37, 1.0], [140, 148, 1.0], [149, 160, 1.0], [161, 170, 1.0]]}, "1998.amta-tutorials.5": {"snippet": "...ross language information retrieval", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0]]}, "2016.sigirconf_conference-2016.230": {"snippet": "...ical information search refers to methodologies and technologies that seek to improve access to medical information archives via a process of information retrieval (IR). Such information is now potent...", "weights": [[8, 19, 1.0], [107, 118, 1.0], [145, 156, 1.0], [157, 166, 1.0], [178, 189, 1.0]]}, "2003.cikm_conference-2003.21": {"snippet": "...ross-language retrieval\n\n\n ABSTRACTMost cross language information retrieval research concentrates on language pairs for which direct, rich, and often multiple translation resources already exist. How...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [49, 57, 1.0], [58, 69, 1.0], [70, 79, 1.0], [105, 113, 1.0]]}, "2002.clef_workshop-2002w.32": {"snippet": "...gual Information Access\n\n\n The third Cross-Language Evaluation Forum workshop (CLEF-2002) provides the unprecedented opportunity to evaluate retrieval in eight different languages using a uniform set ...", "weights": [[8, 19, 1.0], [46, 54, 1.0], [144, 153, 1.0], [163, 172, 1.0]]}, "2011.fire_workshop-2011.30": {"snippet": "Information Retrieval with Hindi, Bengali, and Marathi Languages: Evaluation and Analysis\n\n\n Abstract. Our first objective in participating in FIRE evaluation campaigns is to analyze the retrieval eff...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [187, 196, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.9": {"snippet": "...ross-language information retrieval (CLIR). However, technical terms and proper names in different languages often share the same Latin or Greek origin, being thus spelling variants of each other. In ...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [92, 101, 1.0]]}, "A97-1049": {"snippet": "...tic) information browsing and retrieval system, which is aimed at monolingual users who are interested in information from multiple language sources. The system takes advantage of information extracti...", "weights": [[8, 19, 1.0], [33, 42, 1.0], [109, 120, 1.0], [135, 143, 1.0], [144, 151, 1.0], [183, 194, 1.0]]}, "2008.clef_workshop-2008w.21": {"snippet": "... XML retrieval system, to evaluate image retrieval based on textual evidence in the context of the wikipediaMM task at ImageCLEF 2008. We employed a language modelling framework and found that the tex...", "weights": [[8, 17, 1.0], [44, 53, 1.0], [152, 160, 1.0]]}, "1999.ipm_journal-ir0volumeA35A4.0": {"snippet": "Information retrieval with Asian languages: an introduction", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "U05-1011": {"snippet": "...erse Information Sources to Retrieve Samples of Low Density Languages\n\n\n Language samples are useful as an object of study for a diverse range of people. Samples of low-density languages in particular...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [76, 84, 1.0]]}, "1999.cikm_conference-99.41": {"snippet": "... for Information Retrieval\n\n\n ABSTRACTStatistical language modeling has been successfully used for speech recognition, part-of-speech tagging, and syntactic parsing. Recently, it has also been applied...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 61, 1.0]]}, "I05-1075": {"snippet": "...ngle-language cases, but in fact, mixed-case performance always exceeds the maximum of the single-language cases. We observed this effect in all six experiments we ran, involving three different sourc...", "weights": [[8, 16, 1.0], [101, 109, 1.0], [188, 197, 1.0]]}, "2005.sigirconf_conference-2005.101": {"snippet": "...gual information retrieval using on-line translation systems\n\n\n ABSTRACTTypical cross language retrieval requires special linguistic resources, such as bilingual dictionaries and parallel corpus. In t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [89, 97, 1.0], [98, 107, 1.0]]}, "1999.trec_conference-1999.12": {"snippet": "...ross-language information retrieval (CLIR) track, we adopted the approach of using machine translation to prepare a source-language query for use in a target-language retrieval task. We empirically ev...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [126, 134, 1.0], [161, 169, 1.0], [170, 179, 1.0]]}, "I08-6012": {"snippet": "...urce language into an equivalent query in the language of the target document collection. The relevant documents are then retrieved using a Language Modeling based retrieval algorithm. On CLEF 2007 da...", "weights": [[8, 16, 1.0], [49, 57, 1.0], [143, 151, 1.0], [167, 176, 1.0]]}, "2002.trec_conference-2002.13": {"snippet": "...uery-language term weights from document-language evidence were tried. A new technique that exploits translation probability information was found to outperform a comparable technique in which that in...", "weights": [[8, 16, 1.0], [44, 52, 1.0], [128, 139, 1.0], [201, 212, 1.0]]}, "2001.clef_workshop-2001.4": {"snippet": "...gual Information Retrieval Using English and Chinese Queries", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2002.clef_workshop-2002w.29": {"snippet": "...ross-language information retrieval, this problem of choice is mitigated since more than one translation alternative can be retained in the target query. Between choosing just one word as a translatio...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0]]}, "2007.clef_workshop-2007w.69": {"snippet": "...ical retrieval task (i.e. ImageCLEFmed). The main idea in this paper is to incorporate medical knowledge in the language modeling approach to information retrieval (IR). Our model makes use of the tex...", "weights": [[8, 17, 1.0], [115, 123, 1.0], [145, 156, 1.0], [157, 166, 1.0]]}, "2016.clef_conference-2016w.4": {"snippet": "...ding Sources for Medical Information Retrieval\n\n\n This paper describes the participation of MRIM team in Task 3: Patient-Centered Information Retrieval-IRTask 1: Ad-hoc search of CLEF eHealth Evaluati...", "weights": [[8, 15, 1.0], [28, 39, 1.0], [40, 49, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2005.clef_workshop-2005w.82": {"snippet": "...GIRT Information Retrieval\n\n\n For our fifth participation in the CLEF evaluation campaigns, the first objective was to propose an effective and general stopword list along with a light stemming proced...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2020.ecir_conference-20202.78": {"snippet": "...ured information sources such as knowledge bases. One opportunity that remains open is the research of unified frameworks for the representation and retrieval of heterogeneous information sources. The...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [152, 161, 1.0], [179, 190, 1.0], [191, 198, 1.0]]}, "2008.cikm_conference-2008.178": {"snippet": "...iple sources.Example 1. Consider a system with four information sources and a mediator. Query Q = \u03a0B,C \u03c3P (r 1 s) involves relations r(A, B) and s(A, C) in the mediator schema. Assume XML data sources...", "weights": [[8, 15, 1.0], [55, 66, 1.0], [67, 74, 1.0], [196, 203, 1.0]]}, "C12-1057": {"snippet": "... use information from documents in the source language. We overcome this limitation by using information from retrieved documents in the source language to improve the retrieval quality of the target ...", "weights": [[8, 19, 1.0], [49, 57, 1.0], [96, 107, 1.0], [147, 155, 1.0], [171, 180, 1.0]]}, "2017.cikm_conference-2017.230": {"snippet": "...urce Retrieval for Web-Scale Text Reuse Detection\n\n\n ABSTRACT e rst step of text reuse detection addresses the source retrieval problem: given a suspicious document, a set of candidate sources from wh...", "weights": [[8, 17, 1.0], [121, 130, 1.0], [188, 195, 1.0]]}, "2004.riao_conference-2004.5": {"snippet": "...gual Information Retrieval with Asian Languages", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2014.fire_conference-2014.5": {"snippet": "...: An Information Retrieval Approach for Source Code Plagiarism Detection\n\n\n ABSTRACTWe investigate an information retrieval (IR) based approach to source code plagiarism detection. The standard method...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [105, 116, 1.0], [117, 126, 1.0]]}, "2016.iwslt-1.8": {"snippet": "...urce language affect the efficiency of the MLAN schemes. We experiment with three different scenarios using, i) French, as a source language uncorrelated to the target language, ii) Ukrainian, as a so...", "weights": [[8, 16, 1.0], [85, 94, 1.0], [135, 143, 1.0], [171, 179, 1.0]]}, "2003.clef_workshop-2003w.21": {"snippet": "...gual Information Retrieval\n\n\n This paper reports on the participation of ITC-irst in the Cross Language Evaluation Forum 2003; in particular, in the monolingual, bilingual, small multilingual, and spo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [98, 106, 1.0]]}, "2009.clef_workshop-2009w.33": {"snippet": "... for different languages which is combined with a conceptbased retrieval approach based on Explicit Semantic Analysis. In order to create the language-specific indices for each language, a language de...", "weights": [[8, 17, 1.0], [66, 75, 1.0], [145, 153, 1.0], [180, 188, 1.0], [192, 200, 1.0]]}, "O08-2009": {"snippet": "...urce language to the target language. Currently, this system provides the functions of the monolingual and cross-language patent retrieval in English and Japanese. The users can input the queries and ...", "weights": [[8, 16, 1.0], [31, 39, 1.0], [116, 124, 1.0], [132, 141, 1.0]]}, "2014.clef_conference-2014w.104": {"snippet": "...urce Retrieval\n\n\n Source retrieval involves making use of a search engine to retrieve candidate sources of plagiarism for a given suspicious document so that more accurate comparisons can be made. We ...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [99, 106, 1.0]]}, "2010.airs_conference-2010.17": {"snippet": "...gual Information Retrieval\n\n\n Abstract. The performance of transliteration from a source language to a target language builds the ground work in support of proper name Cross Lingual Information Retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [92, 100, 1.0], [113, 121, 1.0], [185, 196, 1.0], [197, 206, 1.0]]}, "2007.clef_workshop-2007w.23": {"snippet": "...urce language into an equivalent query in the language of the target document collection. The relevant documents are then retrieved using a Language Modeling based retrieval algorithm. On CLEF 2007 da...", "weights": [[8, 16, 1.0], [49, 57, 1.0], [143, 151, 1.0], [167, 176, 1.0]]}}}
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Machine Translation (MT) systems employed to translate queries for Cross-Lingual Information Retrieval typically produce a single translation with maximum trans...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [124, 135, 1.0], [136, 145, 1.0]]}, "2001.sigirconf_conference-2001.52": {"snippet": "...s of Multiple-Evidence Combinations for Retrieval Strategies", "weights": [[8, 16, 1.0], [43, 52, 1.0]]}, "2009.tois_journal-ir0volumeA27A3.1": {"snippet": "...ated information retrieval, a query is routed to multiple collections and a single answer list is constructed by combining the results. Such metasearch provides a mechanism for locating documents on t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [52, 60, 1.0]]}, "2001.clef_workshop-2000w.9": {"snippet": "...uage Retrieval for the CLEF Collections - Comparing Multiple Methods of Retrieval\n\n\n For our participation in CLEF, the Berkeley group participated in the monolingual, multilingual and GIRT tasks. To ...", "weights": [[8, 17, 1.0], [55, 63, 1.0], [64, 71, 1.0], [75, 84, 1.0]]}, "1985.sigirconf_conference-85.5": {"snippet": "...ling Multiple Databases in Document Retrieval", "weights": [[8, 16, 1.0], [39, 48, 1.0]]}, "2016.airs_conference-2016.10": {"snippet": "...tent Retrieval Based on Multiple Information Resources\n\n\n Abstract. Query expansion methods have been proven to be effective to improve the average performance of patent retrieval, and most of query e...", "weights": [[8, 17, 1.0], [27, 35, 1.0], [36, 47, 1.0], [87, 94, 1.0], [173, 182, 1.0]]}, "2008.clef_workshop-2008w.23": {"snippet": "...r to compare retrieval based on text description, to image-only retrieval, and to merged retrieval, and to compare results for the different clustering methods.", "weights": [[8, 15, 1.0], [16, 25, 1.0], [67, 76, 1.0], [92, 101, 1.0], [110, 117, 1.0], [155, 162, 1.0]]}, "2008.clef_workshop-2008.87": {"snippet": "Methods for Combining Content-Based and Textual-Based Approaches in Medical Image Retrieval", "weights": [[0, 7, 1.0], [82, 91, 1.0]]}, "I08-1024": {"snippet": "...ning multiple resources use simple methods such as linear combination. In this paper, we drew up a comparative study between linear combination and confidence measures to combine multiple translation ...", "weights": [[8, 16, 1.0], [38, 45, 1.0], [182, 190, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords Information Retrieval (IR) solutions to retrieve passages matching a set of terms extracted from the question.There is a fundamental disconnect between the capabilities of the bag-of-words retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [197, 206, 1.0]]}, "2017.ipm_journal-ir0volumeA53A1.4": {"snippet": "...emic papers. Given a tested paper from a given corpus of 10,099 peer-reviewed scientific papers, a two-stage process was activated. During the first stage, most of the papers were filtered out using a...", "weights": [[8, 14, 1.0], [92, 98, 1.0], [171, 177, 1.0]]}, "2013.cikm_conference-2013.342": {"snippet": "...ally multiple relevant domains where knowledge can be transferred, Multiple Source Transfer Learning (MSTL) has recently attracted much attention. Most existing MSTL methods work in an offline fashion...", "weights": [[8, 16, 1.0], [70, 78, 1.0], [169, 176, 1.0]]}, "2015.clef_conference-2015w.87": {"snippet": "Information Retrieval Features for Personality Traits\n\n\n This paper describes the methods employed to solve the Author Profiling task at PAN-2015. The main goal was to test the use of features derived...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [82, 89, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.7": {"snippet": "...ated information retrieval system generally contains multiple databases that are inconsistent in terms of their content and indexing. This paper proposes a rough set-based transfer (RST) model for int...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [56, 64, 1.0]]}, "2012.cikm_conference-2012.269": {"snippet": "...op-k retrieval using conditional preference networks\n\n\n ABSTRACTThis paper considers top-k retrieval using Conditional Preference Network (CP-Net). As a model for expressing user preferences on multip...", "weights": [[8, 17, 1.0], [94, 103, 1.0], [197, 205, 1.0]]}, "2004.ntcir_workshop-2004.67": {"snippet": "... for Information Retrieval\n\n\n This paper presents a genre-independent method of generating a single summary from a set of the retrieved documents for information retrieval. The proposed method generat...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [153, 164, 1.0], [165, 174, 1.0]]}, "2005.ntcir_workshop-2005.23": {"snippet": "...sion Methods for Text Retrieval\n\n\n This paper describes our work at the fifth NTCIR workshop on the subtasks of single language information retrieval (SLIR). Several automatic global query expansion s...", "weights": [[8, 15, 1.0], [25, 34, 1.0], [131, 142, 1.0], [143, 152, 1.0]]}, "2002.spire_conference-2002.20": {"snippet": "Multiple Example Queries in Content-Based Image Retrieval", "weights": [[0, 8, 1.0], [48, 57, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.18": {"snippet": "...y of information retrieval metrics based on graded relevance\n\n\n AbstractThis paper compares 14 information retrieval metrics based on graded relevance, together with 10 traditional metrics based on bi...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "1995.ipm_journal-ir0volumeA31A3.11": {"snippet": "...e of Multiple Query Representations for Information Retrieval", "weights": [[8, 16, 1.0], [43, 54, 1.0], [55, 64, 1.0]]}, "2002.ntcir_workshop-2002.58": {"snippet": "...ging Multiple Results from Information Retrieval System\n\n\n We participated in Web retrieval tasks in order to investigate the integration of multiple retrieval results. We also examined the possibilit...", "weights": [[8, 16, 1.0], [30, 41, 1.0], [42, 51, 1.0], [85, 94, 1.0], [144, 152, 1.0], [153, 162, 1.0]]}, "2018.wwwconf_conference-2018c.22": {"snippet": "...ving Information from Multiple Sources\n\n\n ABSTRACTThe Web has several information sources on which an ongoing event is discussed. To get a complete picture of the event, it is important to retrieve in...", "weights": [[8, 19, 1.0], [25, 33, 1.0], [73, 84, 1.0], [201, 212, 1.0]]}, "2002.tois_journal-ir0volumeA20A3.0": {"snippet": "...ving retrieval feedback with multiple term-ranking function combination\n\n\n In this article we consider methods for automatic query expansion from top retrieved documents (i.e., retrieval feedback) tha...", "weights": [[8, 17, 1.0], [32, 40, 1.0], [106, 113, 1.0], [180, 189, 1.0]]}, "2016.cikm_conference-2016.73": {"snippet": "...ring Information Retrieval Systems", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2018.clef_conference-2018w.86": {"snippet": "...d by Multiple Query Variants Using Data Fusion\n\n\n In this paper, we describe the methods deployed in the different runs submitted for our participation to the CLEF eHealth2018 Task 3: Consumer Health ...", "weights": [[8, 16, 1.0], [84, 91, 1.0]]}, "2008.airs_conference-2008.56": {"snippet": "...ting Multiple News Sites", "weights": [[8, 16, 1.0]]}, "2016.ictir_conference-2016.17": {"snippet": "... for information retrieval evaluation with multiple test collections. Given a topic-by-run raw score matrix in terms of some evaluation measure, each score can be standardised using the topic's sample...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 54, 1.0]]}, "2021.emnlp-main.322": {"snippet": "...tain multiple comparisons. The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences. To address these issues, in this work we first introduce a...", "weights": [[8, 16, 1.0]]}, "2007.ecir_conference-2007.68": {"snippet": "...l of Multiple Retrieval Methods with the Friedman Test", "weights": [[8, 16, 1.0], [17, 26, 1.0], [27, 34, 1.0]]}, "A97-1049": {"snippet": "...gual Information Browsing and Retrieval System Using Information Extraction\n\n\n In this paper, we describe our multilingual (or cross-linguistic) information browsing and retrieval system, which is aim...", "weights": [[8, 19, 1.0], [33, 42, 1.0], [56, 67, 1.0], [148, 159, 1.0], [173, 182, 1.0]]}, "2002.jasis_journal-ir0volumeA53A1.3": {"snippet": "...sual information retrieval methodologies case study: Comparative analysis of textual, icon, graphical, and spring displays", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2008.cikm_workshop-2008dtmbio.9": {"snippet": "...e of multiple parameter sets. Preliminary results for a knowledge discovery application such as homology detection reveal that using multiple parameter sets for pairwise statistical significance estim...", "weights": [[8, 16, 1.0], [136, 144, 1.0]]}, "2016.ntcir_workshop-2016evia.1": {"snippet": "...n of Information Retrieval Systems (IRSs) using publically available test collections has become an established practice in Information Retrieval (IR). By means of the popular Cranfield evaluation par...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [127, 138, 1.0], [139, 148, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.5": {"snippet": "...uage information retrieval\n\n\n AbstractQuery translation is a viable method for cross-language information retrieval (CLIR), but it suffers from translation ambiguities caused by multiple translations ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [97, 108, 1.0], [109, 118, 1.0], [181, 189, 1.0]]}, "1998.jasis_journal-ir0volumeA49A13.3": {"snippet": "...from Multiple Collection Information Retrieval Systems", "weights": [[8, 16, 1.0], [28, 39, 1.0], [40, 49, 1.0]]}, "2007.sigirconf_conference-2007.38": {"snippet": "...with multiple hyperplanes\n\n\n ABSTRACTThe central problem for many applications in Information Retrieval is ranking and learning to rank is considered as a promising approach for addressing the issue. ...", "weights": [[8, 16, 1.0], [85, 96, 1.0], [97, 106, 1.0]]}, "2012.sigirconf_conference-2012.84": {"snippet": "...zing multiple centralized retrieval algorithms. One simple approach is to learn a set of combination weights for multiple centralized retrieval algorithms (e.g., logistic regression) to compute compar...", "weights": [[8, 16, 1.0], [29, 38, 1.0], [116, 124, 1.0], [137, 146, 1.0]]}, "2014.trec_conference-2014.41": {"snippet": "...e of multiple information retrieval techniques: retrieval functions, re-ranking, query expansion and classification of medical articles into question categories. The best performing run was based on a...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [51, 60, 1.0]]}, "2012.ipm_journal-ir0volumeA48A2.11": {"snippet": "...opic retrieval methods: Clustering versus diversification of search results\n\n\n a b s t r a c tTo address the inability of current ranking systems to support subtopic retrieval, two main post-processin...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [169, 178, 1.0]]}, "2013.cikm_conference-2013.200": {"snippet": "... the multiple views. An advantage of the proposed method over existing methods is that the tuning parameters can be determined effectively from the given data. Through experiments on synthetic and ben...", "weights": [[8, 16, 1.0], [74, 81, 1.0]]}, "2014.clef_conference-2014w.20": {"snippet": "A Multiple-stage Approach to Re-ranking Clinical Documents\n\n\n This paper presents our approach to medical information retrieval and experimental results of participating in eHealth Task 3-A of CLEF 20...", "weights": [[2, 10, 1.0], [106, 117, 1.0], [118, 127, 1.0]]}, "2002.trec_conference-2002.13": {"snippet": "...lish Retrieval\n\n\n The focus of the experiments reported in this paper was techniques for combining evidence for crosslanguage retrieval, searching Arabic documents using English queries. Evidence from...", "weights": [[8, 17, 1.0], [129, 138, 1.0]]}, "2015.tois_journal-ir0volumeA33A2.4": {"snippet": "...ased methods for multimodal information fusion in content-based multimedia information retrieval. Additional Key Words and Phrases: Content-based multimedia information retrieval, information fusion, ...", "weights": [[8, 15, 1.0], [31, 42, 1.0], [78, 89, 1.0], [90, 99, 1.0], [160, 171, 1.0], [172, 181, 1.0], [183, 194, 1.0]]}, "2015.clef_conference-2015.33": {"snippet": "...ixed-Methods for Evaluating Information Access Systems\n\n\n Abstract. The evaluation of information access systems is increasingly making use of multiple evaluation methods. While such studies represent...", "weights": [[8, 15, 1.0], [31, 42, 1.0], [89, 100, 1.0], [146, 154, 1.0], [166, 173, 1.0]]}, "2020.ictir_conference-2020.7": {"snippet": "...ment Retrieval with Multiple Queries\n\n\n The merits of using multiple queries representing the same information need to improve retrieval effectiveness have recently been demonstrated in several studie...", "weights": [[8, 17, 1.0], [23, 31, 1.0], [63, 71, 1.0], [102, 113, 1.0], [130, 139, 1.0]]}, "2008.clef_workshop-2008.72": {"snippet": "...sity Methods for Hybrid Text and Image Retrieval Approaches", "weights": [[8, 15, 1.0], [42, 51, 1.0]]}, "2000.clef_workshop-2000.11": {"snippet": "...uage Retrieval for the CLEF Collections - Comparing Multiple Methods of Retrieval", "weights": [[8, 17, 1.0], [55, 63, 1.0], [64, 71, 1.0], [75, 84, 1.0]]}, "2009.clef_workshop-2009w.33": {"snippet": "...gual Information Retrieval based on Multiple Indexes\n\n\n In this paper we present the technical details of the retrieval system with which we participated at the CLEF09 Ad-hoc TEL task. We present a re...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [39, 47, 1.0], [113, 122, 1.0], [201, 210, 1.0]]}, "2004.ntcir_workshop-2004.7": {"snippet": "...ying Multiple Characteristics and Techniques to Obtain High Levels of Performance in Information Retrieval at NTCIR-4\n\n\n Our information retrieval system takes advantage of numerous characteristics of...", "weights": [[8, 16, 1.0], [88, 99, 1.0], [100, 109, 1.0], [128, 139, 1.0], [140, 149, 1.0]]}, "O12-2004": {"snippet": "...y of Methods for Topic Modeling in Spoken Document Retrieval\n\n\n Topic modeling for information retrieval (IR) has attracted significant attention and demonstrated good performance in a wide variety of...", "weights": [[8, 15, 1.0], [54, 63, 1.0], [86, 97, 1.0], [98, 107, 1.0]]}, "1999.ntcir_workshop-1999.3": {"snippet": "...ring Multiple Methods for Japanese and Japanese-English Text Retrieval", "weights": [[8, 16, 1.0], [17, 24, 1.0], [64, 73, 1.0]]}, "2013.cikm_conference-2013.86": {"snippet": "...tial methods. Our extensive experimental evaluation shows the scalability exhibited by our proposed methods under various workload settings.", "weights": [[8, 15, 1.0], [103, 110, 1.0]]}, "2005.ntcir_workshop-2005.13": {"snippet": "...ying Multiple Characteristics and Techniques in the NICT Information Retrieval System in NTCIR-5\n\n\n Our information retrieval system takes advantage of numerous characteristics of information and uses...", "weights": [[8, 16, 1.0], [60, 71, 1.0], [72, 81, 1.0], [107, 118, 1.0], [119, 128, 1.0], [183, 194, 1.0]]}, "C16-1134": {"snippet": "...ring methods as long as they output multiple preordering candidates, and it is trivial to employ existing preordering methods into our system. In our experiments for translating diverse 11 languages i...", "weights": [[8, 15, 1.0], [39, 47, 1.0], [121, 128, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2005.ir_journal-ir0volumeA8A3.3": {"snippet": "...tion Methods for Data Fusion in Information Retrieval", "weights": [[8, 15, 1.0], [35, 46, 1.0], [47, 56, 1.0]]}, "W03-1120": {"snippet": "...uage Information Retrieval Based on Category Matching Between Language Versions of a Web Directory\n\n\n Since the Web consists of documents in various domains or genres, the method for Cross-Language In...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [201, 212, 1.0]]}, "2006.sigirconf_conference-2006.43": {"snippet": "...ning multiple retrieval sources\n\n\n ABSTRACTCombining the output from multiple retrieval sources over the same document collection is of great importance to a number of retrieval tasks such as multimed...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [72, 80, 1.0], [81, 90, 1.0], [171, 180, 1.0]]}, "2006.trec_conference-2006.13": {"snippet": "...omic Information Retrieval\n\n\n We participated in the passage retrieval and aspect retrieval subtasks of the TREC 2006 Genomics Track. This paper describes the methods developed for these two subtasks....", "weights": [[8, 19, 1.0], [20, 29, 1.0], [64, 73, 1.0], [85, 94, 1.0], [162, 169, 1.0]]}, "1997.sigirconf_conference-97.30": {"snippet": "...s in retrieval performance can be achieved by combining multiple representations of an information need.However, little effort has been made to understand the reason why combining multiple sources of ...", "weights": [[8, 17, 1.0], [59, 67, 1.0], [90, 101, 1.0], [183, 191, 1.0]]}, "2002.ntcir_workshop-2002.6": {"snippet": "...ying Multiple Characteristics and Techniques to Obtain High Levels of Performance in Information Retrieval\n\n\n Our information retrieval system which achieves its goals by taking advantage of numerous ...", "weights": [[8, 16, 1.0], [88, 99, 1.0], [100, 109, 1.0], [117, 128, 1.0], [129, 138, 1.0]]}, "2018.cikm_conference-2018.74": {"snippet": "... for Retrieval Experiments Involving Multiple Test Collections\n\n\n ABSTRACTTraditional practice recommends that information retrieval experiments be run over multiple test collections, to support, if n...", "weights": [[8, 17, 1.0], [40, 48, 1.0], [114, 125, 1.0], [126, 135, 1.0], [160, 168, 1.0]]}, "2007.sigirconf_conference-2007.34": {"snippet": "...ting multiple information-seeking strategies in a single system framework\n\n\n ABSTRACTThis paper reports on an experiment comparing the retrieval effectiveness of an integrated interactive information ...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [138, 147, 1.0], [191, 202, 1.0]]}, "2022.naacl-srw.7": {"snippet": "Retrieval-augmented Generation across Heterogeneous Knowledge\n\n\n Retrieval-augmented generation (RAG) methods have been receiving increasing attention from the NLP community and achieved stateof-the-a...", "weights": [[0, 9, 1.0], [65, 74, 1.0], [102, 109, 1.0]]}, "2007.sigirconf_conference-2007.77": {"snippet": "...n in information retrieval\n\n\n ABSTRACTResearch in Information Retrieval usually shows performance improvement when many sources of evidence are combined to produce a ranking of documents (e.g., texts,...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 64, 1.0], [65, 74, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ased retrieval problem. Similarly to the distribution of future wins, the retrieval framework considers multiple possible concept-based document representations for each document resulting in multiple...", "weights": [[8, 17, 1.0], [77, 86, 1.0], [107, 115, 1.0], [195, 203, 1.0]]}, "2016.tist_journal-ir0volumeA7A4.21": {"snippet": "...uses multiple types of image features in a framework named multitask low-rank affinity pursuit. Given the image patches described with multiple types of features, we aim at inferring a unified affinit...", "weights": [[8, 16, 1.0], [138, 146, 1.0]]}, "2006.sigirconf_conference-2006.58": {"snippet": "...racy retrieval with multiple nested ranker\n\n\n ABSTRACTHigh precision at the top ranks has become a new focus of research in information retrieval. This paper presents the multiple nested ranker approa...", "weights": [[8, 17, 1.0], [23, 31, 1.0], [127, 138, 1.0], [139, 148, 1.0], [174, 182, 1.0]]}, "2018.sigirconf_conference-2018.224": {"snippet": "...t in Information Retrieval. The goal of fusion methods is to merge different sources of information so as to address a retrieval task. For example, in the adhoc retrieval setting, fusion methods have ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 57, 1.0], [91, 102, 1.0], [122, 131, 1.0], [164, 173, 1.0], [190, 197, 1.0]]}, "2006.spire_conference-2006.20": {"snippet": "A Multiple Criteria Approach for Information Retrieval", "weights": [[2, 10, 1.0], [33, 44, 1.0], [45, 54, 1.0]]}, "2008.clef_workshop-2008w.72": {"snippet": "Methods for combining Content-based and Textual-based Approaches in Medical Image Retrieval\n\n\n This paper describes our participation in the Medical Image Retrieval task of Image CLEF 2008. Our aim wa...", "weights": [[0, 7, 1.0], [82, 91, 1.0], [155, 164, 1.0]]}, "2018.cikm_conference-2018.203": {"snippet": "...ning multiple retrieval functions can lead to notable gains in retrieval performance. Learning to Rank (LETOR) techniques achieve outstanding retrieval results, by learning models with no bounds on mo...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [66, 75, 1.0], [145, 154, 1.0]]}}}
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Nowadays, social media users react in real-time to local and global events. Therefore, social media can be used to measure the impact of ...", "weights": [[8, 17, 1.0], [76, 82, 1.0], [83, 88, 1.0], [153, 159, 1.0], [160, 165, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A4.17": {"snippet": "...tric information shown in medical records. However, methods of systematically summarizing medical records into risk factors have not been evaluated to support such a patient-centric focus in healthcar...", "weights": [[8, 19, 1.0]]}, "2019.tois_journal-ir0volumeA37A2.10": {"snippet": "...s in Information Retrieval. Risk-sensitive L2R is a subarea of L2R that tries to learn models that are good on average while at the same time reducing the risk of performing poorly in a few but import...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2022.acl-short.70": {"snippet": "...t on Social Media\n\n\n Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings. With advances in Natur...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [55, 61, 1.0], [62, 67, 1.0]]}, "1989.sigirconf_conference-89.24": {"snippet": "...onic information systems but at the potential risk of becoming lost in the network of interconnected pieces of information. 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In this paper, we investigate the problem of information retrieval in a mobile peer-to-peer network. The prevailing approach to information...", "weights": [[8, 19, 1.0], [109, 120, 1.0], [121, 130, 1.0], [192, 203, 1.0]]}, "2016.sigirconf_conference-2016.54": {"snippet": "...that retrieval performance highly varies among IR systems across topics. Thus, a single system would in general fail in providing enough information about the real baseline performance for every topic...", "weights": [[8, 17, 1.0], [140, 151, 1.0]]}, "2018.clef_conference-2018w.7": {"snippet": "...a on Social Media Text\n\n\n In recent years, people actively write text messages on social media platforms like Twitter and Reddit. The text shared on social media drives various applications including ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [85, 91, 1.0], [92, 97, 1.0], [152, 158, 1.0], [159, 164, 1.0]]}, "S19-2113": {"snippet": "...ning social media language to discover offensive language\n\n\n User's content share through social media has reached huge proportions nowadays. However, along with the free expression of thoughts on soc...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [93, 99, 1.0], [100, 105, 1.0], [200, 206, 1.0]]}, "2017.clef_conference-2017w.64": {"snippet": "...s in social media relying on various features that we detail in this paper. We submitted 4 models which differences are also detailed in this paper. Best results were obtained when using a combination...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "W19-3021": {"snippet": "...t on Social Media: {USI}-{UPF} at the {CLP}sych 2019 Shared Task\n\n\n This paper describes the participation of the USI-UPF team at the shared task of the 2019 Computational Linguistics and Clinical Psy...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2021.wsdm_conference-2021.6": {"snippet": "...n on Social Media\n\n\n The rising ubiquity of social media presents a platform for individuals to express suicide ideation, instead of traditional, formal clinical settings. While neural methods for ass...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [47, 53, 1.0], [54, 59, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A2.7": {"snippet": "...s on social media data\n\n\n Abstract Social media sites contain a considerable amount of data for natural calamities events, such as earthquakes, snowstorms, mud-rock flows. With the increasing amount o...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [38, 44, 1.0], [45, 50, 1.0]]}, "2016.sigirconf_conference-2016.232": {"snippet": "...ving Information Retrieval\nMOTIVATIONInformation retrieval and information privacy/security are two fast-growing computer science disciplines. Information retrieval provides a set of information seeki...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [52, 61, 1.0], [66, 77, 1.0], [146, 157, 1.0], [158, 167, 1.0], [186, 197, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.2": {"snippet": "... for information retrieval\n\n\n AbstractThis paper presents a probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [77, 88, 1.0], [89, 98, 1.0], [122, 131, 1.0]]}, "2020.acl-main.723": {"snippet": "...from social media as a ranking problem whose goal is maximizing detection of severely at-risk individuals given the time available. Building on measures developed for resource-bounded document retriev...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [196, 205, 1.0]]}, "W10-0504": {"snippet": "An Information-Retrieval Approach to Language Modeling: Applications to Social Data\n\n\n In this paper we propose the IR-LM (Information Retrieval Language Model) which is an approach to carrying out la...", "weights": [[3, 14, 1.0], [15, 24, 1.0], [72, 78, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2012.tois_journal-ir0volumeA30A2.2": {"snippet": "...Peer Information Retrieval: An Overview\n\n\n Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been develo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [157, 168, 1.0], [169, 178, 1.0]]}, "2010.jasis_journal-ir0volumeA61A12.18": {"snippet": "Social Information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively", "weights": [[0, 6, 1.0], [7, 18, 1.0], [19, 28, 1.0]]}, "2010.sigirconf_conference-2010.212": {"snippet": "...e of information available in social media, ways of accessing this information intelligently are needed; this is the scope of my research.Why should we care about information in social media? Here are...", "weights": [[8, 19, 1.0], [33, 39, 1.0], [40, 45, 1.0], [70, 81, 1.0], [166, 177, 1.0], [181, 187, 1.0], [188, 193, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.15": {"snippet": "...s in social media\n\n\n a b s t r a c tSocial media is increasingly becoming a significant fraction of the content retrieved daily by Web users. However, the potential lack of quality of user generated c...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [46, 51, 1.0]]}, "2014.sigirconf_conference-2014.226": {"snippet": "...when information retrieval meets privacy and security\n\n\n ABSTRACTInformation retrieval (IR) and information privacy/security are two fast-growing computer science disciplines. There are many synergies...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 89, 1.0], [99, 110, 1.0]]}, "2012.ir_journal-ir0volumeA15A34.0": {"snippet": "...e on information retrieval for social media", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 40, 1.0], [41, 46, 1.0]]}, "2014.cikm_workshop-2014psbd.5": {"snippet": "...vacy Risks in User Search Histories\n\n\n ABSTRACTThis paper proposes a new model of user-centric, global, probabilistic privacy, geared for today's challenges of helping users to manage their privacy-se...", "weights": [[8, 13, 1.0]]}, "2017.wwwconf_conference-2017c.190": {"snippet": "...mic, social and medical burden coupled with increased morbidity of the aforementioned diseases makes their prevention an active research area. Currently, the traditional method of MetS diagnosis is ba...", "weights": [[8, 14, 1.0]]}, "2013.mir_conference-2013.21": {"snippet": "...sual information for event-related social media retrieval", "weights": [[8, 19, 1.0], [38, 44, 1.0], [45, 50, 1.0], [51, 60, 1.0]]}, "2017.sigirconf_conference-2017.239": {"snippet": "...line Social Media (OSM) in providing actionable situational information. However, in such media, the crucial information is typically obscured by a lot of insignificant information (e.g., personal opi...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [63, 74, 1.0], [93, 98, 1.0], [112, 123, 1.0], [172, 183, 1.0]]}, "2019.sigirconf_conference-2019.260": {"snippet": "...Text Retrieval", "weights": [[8, 17, 1.0]]}, "N18-1110": {"snippet": "...n of risks in social media sources. The goal is to effectively identify the potential risk using as little evidence as possible and with as much anticipation as possible. MulR allows us to generate mu...", "weights": [[8, 13, 1.0], [17, 23, 1.0], [24, 29, 1.0]]}, "2012.ecir_conference-2012.53": {"snippet": "...ople Retrieval in Social Media with Multiple Roles", "weights": [[8, 17, 1.0], [21, 27, 1.0], [28, 33, 1.0]]}, "2012.wwwconf_conference-2012c.121": {"snippet": "...y in social networks\n\n\n ABSTRACTCurrent online social networking (OSN) sites pose severe risks to their users' privacy. Facebook in particular is capturing more and more of a user's past activities, s...", "weights": [[8, 14, 1.0], [50, 56, 1.0], [92, 97, 1.0]]}, "2016.tist_journal-ir0volumeA8A1.3": {"snippet": "...nown risks. In order to be proactive for handling the unknown risks, the above workforce must be scheduled dynamically so the system is adaptive to meet the day-to-day stochastic demands on its workfo...", "weights": [[8, 13, 1.0], [65, 70, 1.0]]}, "2018.wwwconf_conference-2018c.305": {"snippet": "... for social media applications. In essence it is completely at the discretion of the social network provider how the user's data is used and what content is delivered. However, even though social medi...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [88, 94, 1.0], [192, 198, 1.0], [199, 204, 1.0]]}}}
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We however argue that the conventiona...", "weights": [[8, 17, 1.0], [86, 95, 1.0]]}, "2021.blackboxnlp-1.2": {"snippet": "...stic knowledge has focused on interpolation tasks. In this paper, we instead analyse probes in an extrapolation setting, where the inputs at test time are deliberately chosen to be 'harder' than the t...", "weights": [[8, 17, 1.0]]}, "Y03-1001": {"snippet": "...ical Knowledge Base for Causality Reasoning\n\n\n The A virtually linked knowledge base is designed to utilize a pre-constructed knowledge base in a dynamic mode when it is in actual use. An algorithm is...", "weights": [[8, 17, 1.0], [73, 82, 1.0], [129, 138, 1.0], [176, 182, 1.0]]}, "C18-1277": {"snippet": "...over Knowledge Bases\n\n\n Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most impo...", "weights": [[8, 17, 1.0], [51, 60, 1.0], [164, 173, 1.0]]}, "1981.jasis_journal-ir0volumeA32A3.3": {"snippet": "... and experiments. I. Basic theoretical and conceptual development", "weights": [[8, 19, 1.0], [30, 41, 1.0]]}, "2020.sigirconf_conference-2020.32": {"snippet": "...over Knowledge Graphs\n\n\n Knowledge graphs have been widely adopted to improve recommendation accuracy. The multi-hop user-item connections on knowledge graphs also endow reasoning about why an item is...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [145, 154, 1.0]]}, "2020.sigirconf_conference-2020.2": {"snippet": "Proof by Experimentation? Towards Better IR Research", "weights": []}, "L08-1425": {"snippet": "Strengthening the {E}stonian Language Technology", "weights": []}, "2008.jasis_journal-ir0volumeA59A1.17": {"snippet": "Evaluations of individual scientists and research institutions", "weights": []}, "2021.trustnlp-1.3": {"snippet": "...rong theoretical guarantees on its utility. We support our theoretical proofs with empirical experiments on multiple word embedding models and NLP datasets, achieving in some cases more than 10% gains...", "weights": [[8, 19, 1.0], [62, 73, 1.0], [96, 107, 1.0]]}, "2021.adaptnlp-1.3": {"snippet": "...ther experiments demonstrate that CAN has a good ability to generalize learned knowledge to unseen domains.", "weights": [[8, 19, 1.0], [82, 91, 1.0]]}, "2021.findings-acl.140": {"snippet": "...ring knowledge from three aspects, i.e., domain, language and task, and strengthening connections among them. Specifically, we propose four practical guidelines to guide knowledge transfer and task fi...", "weights": [[8, 17, 1.0], [173, 182, 1.0]]}, "2008.jasis_journal-ir0volumeA59A5.4": {"snippet": "The Effect of training on biologists acceptance of bioinformatics tools: A field experiment", "weights": []}, "2014.wwwconf_conference-2014.28": {"snippet": "...ield experiments\n\n\n ABSTRACTOnline experiments are widely used to compare specific design alternatives, but they can also be used to produce generalizable knowledge and inform strategic decision makin...", "weights": [[8, 19, 1.0], [38, 49, 1.0], [158, 167, 1.0]]}, "2006.sigirconf_conference-2006.14": {"snippet": "...s of knowledge that are important for information seeking. We instantiate this general framework in the domain of clinical medicine based on the principles of evidence-based medicine (EBM). Experiment...", "weights": [[8, 17, 1.0], [193, 204, 1.0]]}, "2004.airs_conference-2004.22": {"snippet": "...gh a theoretical analysis, and then verify the above claim through experiments by actually ranking the systems submitted to the NTCIR-3 CLIR Task. Our experiments confirm that the Q-measure ranking is...", "weights": [[8, 19, 1.0], [70, 81, 1.0], [154, 165, 1.0]]}, "2018.cikm_conference-2018.45": {"snippet": "... for knowledge-graph-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating...", "weights": [[8, 17, 1.0], [124, 133, 1.0], [177, 183, 1.0]]}, "2013.sigirconf_conference-2013.122": {"snippet": "...that actual error rates seem to be lower than the theoretically expected 5%, further confirming that we may actually be underestimating significance.", "weights": [[8, 14, 1.0]]}, "2022.acl-long.407": {"snippet": "...duct experiments to demonstrate the existence of each logic trap. Through both theoretical and experimental analysis, we hope to increase attention on the inaccurate evaluation of attribution scores. ...", "weights": [[8, 19, 1.0], [82, 93, 1.0]]}, "2020.conll-1.10": {"snippet": "...y in theoretical linguistic literature, but computational linguistics research on the phenomenon is sparse. Not only that, the long standing linguistic controversy between the determinative and the no...", "weights": [[8, 19, 1.0]]}, "W12-0603": {"snippet": "... the knowledge about unlabeled data. The efficacy of semi-supervised learning is established by our extensive experiments, which also show that transductive SVM is more suitable than graph-based label...", "weights": [[8, 17, 1.0], [113, 124, 1.0]]}, "2015.wwwconf_conference-2015.6": {"snippet": "... Our experiments reinforce the theoretical findings by demonstrating that our algorithms generally outperform previous approaches, both in terms of solution cost and reconstruction of an underlying gr...", "weights": [[8, 19, 1.0], [34, 45, 1.0]]}, "J93-2006": {"snippet": "...mall experiments to test the effectiveness of supplementing knowledge-based techniques with probabilistic models. This paper reports our experiments in predicting parts of speech of highly ambiguous w...", "weights": [[8, 19, 1.0], [63, 72, 1.0], [140, 151, 1.0]]}, "2018.ictir_conference-2018.20": {"snippet": "...ical experiments suggest that it captures quality aspects from traditional metrics, while the reverse is not true. Our work also advances the understanding of theoretical foundations of the empiricall...", "weights": [[8, 19, 1.0], [162, 173, 1.0]]}, "W00-1406": {"snippet": "... its actual effect, determined on a model of the system's beliefs. We consider three main rebuttal strategies: refute the user's rejoinder, strengthen the argument goal, and dismiss the user's line of", "weights": [[8, 14, 1.0], [143, 153, 1.0]]}, "P86-1036": {"snippet": "...oded knowledge and the limited inference-making capability of a taxonomic knowledge representation system to reduce the constituent structure of logical expressions. The specific application is to the...", "weights": [[8, 17, 1.0], [77, 86, 1.0]]}, "O02-2006": {"snippet": "...t on Knowledge Extraction from an Encyclopedia Based on Lexicon Semantics) [In {C}hinese]\n\n\n The typical approaches to extracting text knowledge are sentential parsing and pattern matching. Theoretica...", "weights": [[8, 17, 1.0], [138, 147, 1.0]]}, "2010.jasis_journal-ir0volumeA61A6.8": {"snippet": "...tive theoretical model of knowledge-sharing behavior in the context of Wikipedia", "weights": [[8, 19, 1.0], [29, 38, 1.0]]}, "2013.ntcir_workshop-2013evia.10": {"snippet": "The Unreusability of Diversified Search Test Collections\n\n\n Traditional \"ad hoc\" test collections, typically built based on depth-100 pools, are often used a posteriori by non-contributors, i.e., rese...", "weights": []}, "L16-1243": {"snippet": "...ion, theoretical linguistics, etc. To strengthen the automatic processing of the Vietnamese language, a Vietnamese treebank has been built. However, the quality of this treebank is not satisfactory an...", "weights": [[8, 19, 1.0], [41, 51, 1.0]]}, "2011.wsdm_conference-2011.80": {"snippet": "...ires knowledge of the structure of the queries and the results space, as well as knowledge of the input query distribution. We formulate the problem under the framework of stochastic optimization; the...", "weights": [[8, 17, 1.0], [84, 93, 1.0]]}, "1997.jasis_journal-ir0volumeA48A6.4": {"snippet": "...ng a Theoretical Framework", "weights": [[8, 19, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.6": {"snippet": "...lude theoretical and empirical results, as well as the actual code of each algorithm. An extensive bibliography is also included.", "weights": [[8, 19, 1.0], [58, 64, 1.0]]}, "1981.sigirconf_conference-81.18": {"snippet": "...s of Knowledge: Validity and Applications to Information Science\n\n\n AbstractThis research examined the relationship between subjects' knowledge of an area and their similarity ratings of pairs of conc...", "weights": [[8, 17, 1.0], [137, 146, 1.0]]}, "2020.semeval-1.214": {"snippet": "... the actual meaning of the message to the readers. Emphasis selection is the task of choosing candidate words for emphasis, it helps in automatically designing posters and other media contents with wr...", "weights": [[8, 14, 1.0]]}, "2021.acl-long.429": {"snippet": "... for Knowledge Graph Embedding\n\n\n In knowledge graph embedding, the theoretical relationship between the softmax crossentropy and negative sampling loss functions has not been investigated. This makes...", "weights": [[8, 17, 1.0], [40, 49, 1.0], [71, 82, 1.0]]}, "2001.tois_journal-ir0volumeA19A4.0": {"snippet": "....e., theoretical) evaluation of IR systems has been proposed to circumvent the controversies of the experimental methods. Several studies have adopted the inductive approach, but they mostly focus on ...", "weights": [[8, 19, 1.0]]}, "2020.emnlp-main.11": {"snippet": "... the actual task. This work proposes Knowledge Triplet Learning (KTL), a self-supervised task over knowledge graphs. We propose heuristics to create synthetic graphs for commonsense and scientific kno...", "weights": [[8, 14, 1.0], [40, 49, 1.0], [102, 111, 1.0], [200, 209, 1.0]]}, "P19-1101": {"snippet": "...mple Theoretical Model of Importance for Summarization\n\n\n Research on summarization has mainly been driven by empirical approaches, crafting systems to perform well on standard datasets with the notio...", "weights": [[8, 19, 1.0]]}, "2001.wwwconf_conference-2001.33": {"snippet": "...logy experiments\n\n\n ABSTRACTThe virtual laboratories enhance learning experiences by providing the student with a supplement to the physical lab. The laboratories allow students to perform exercises a...", "weights": [[8, 19, 1.0]]}, "2020.emnlp-main.17": {"snippet": "...ides theoretical inspirations on the not-well-understood questions: why and how one uses multi-head attention. Extensive experiments on various attention models and applications demonstrate that the p...", "weights": [[8, 19, 1.0], [124, 135, 1.0]]}, "2021.eacl-main.212": {"snippet": "...udes theoretical and empirical evidence as well as practical experiments to support the effectiveness of our Annealing-KD method. We did a comprehensive set of experiments on different tasks such as i...", "weights": [[8, 19, 1.0], [64, 75, 1.0], [163, 174, 1.0]]}, "2021.wsdm_conference-2021.32": {"snippet": "... the theoretical discoveries, we conduct exploratory experiments that supports our theoretical insights for the product embeddings.", "weights": [[8, 19, 1.0], [56, 67, 1.0], [86, 97, 1.0]]}, "2019.sigirconf_conference-2019.88": {"snippet": "Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval", "weights": []}, "P12-2053": {"snippet": "...s to strengthen the views' consistency for target data with source training data by identifying the correlations of domain-specific features from different domains. We present an Information-theoretic...", "weights": [[8, 18, 1.0]]}, "W18-5819": {"snippet": "...ast, theoretical linguistics usually works with deterministic generalizations. The goal of this paper is to explore possible interactions between information-theoretic methods and deterministic lingui...", "weights": [[8, 19, 1.0]]}, "1984.ipm_journal-ir0volumeA20A12.19": {"snippet": "...ning experiments in information science", "weights": [[8, 19, 1.0]]}, "A00-1036": {"snippet": "...stic Knowledge can Improve Information Retrieval\n\n\n This paper describes the results of some experiments using a new approach to information access that combines techniques from natural language proce...", "weights": [[8, 17, 1.0], [96, 107, 1.0]]}, "2020.sigirconf_conference-2020.19": {"snippet": "...sive experiments in three real-world datasets, Chorus gains significant improvements compared to state-of-the-art baseline methods. Furthermore, the time-related parameters are highly interpretable an...", "weights": [[8, 19, 1.0]]}, "2013.ipm_journal-ir0volumeA49A5.6": {"snippet": "...that knowledge was correlated with users' perceptions of task attributes and accomplishment. In addition, task type was found to affect several aspects of knowledge levels and knowledge change. These ...", "weights": [[8, 17, 1.0], [158, 167, 1.0], [179, 188, 1.0]]}, "2011.sigirconf_conference-2011.96": {"snippet": "...sfer knowledge across languages. With these recognitions, we attempt to transfer knowledge from the auxiliary language, in which abundant labeled Web pages are available, to target languages, in which...", "weights": [[8, 17, 1.0], [84, 93, 1.0]]}, "2017.cikm_conference-2017.110": {"snippet": "...hich strengthen their influences. We incorporate the concept of hyper questions into existing answer aggregation methods. The results of our experiments conducted using both synthetic datasets and rea...", "weights": [[8, 18, 1.0], [144, 155, 1.0]]}, "2014.wwwconf_conference-2014c.9": {"snippet": "...ough theoretical resource to formalize such phenomena, economics and social sciences lack adequate technology to properly operationalize their theoretical insights, thereby demanding an integrative so...", "weights": [[8, 19, 1.0], [146, 157, 1.0]]}, "1975.jasis_journal-ir0volumeA26A6.1": {"snippet": "...s on theoretical research in information science", "weights": [[8, 19, 1.0]]}, "2013.wsdm_conference-2013.1": {"snippet": "...oral experiments on a scale and speed that would have been hard to imagine just a decade ago. To illustrate the point, I describe some recent experiments that showcase the advantages of virtual over t...", "weights": [[8, 19, 1.0], [145, 156, 1.0]]}, "2021.cinlp-1.1": {"snippet": "Causal Augmentation for Causal Sentence Classification\n\n\n Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification....", "weights": []}, "2010.clef_conference-2010.1": {"snippet": "IR between Science and Engineering, and the Role of Experimentation\n\n\n Abstract. Evaluation has always played a major role in IR research, as a means for judging about the quality of competing models....", "weights": []}, "D17-1037": {"snippet": "...ough theoretical analysis to demonstrate its validity. Empirical experiments demonstrated the correctness of the theoretical analysis as well as the practical usefulness of the incremental algorithm.", "weights": [[8, 19, 1.0], [68, 79, 1.0], [116, 127, 1.0]]}, "C92-3146": {"snippet": "...ical Knowledge Base\n\n\n This paper describes a project to construct a terminological knowledge base, called COGNITERM. First, we position our research framework in relationship to recent developments i...", "weights": [[8, 17, 1.0], [87, 96, 1.0]]}, "P02-1012": {"snippet": "...been theoretical rather than applied in nature. Frequently, such methods are based on Centering Theory, which deals with the resolution of anaphoric pronouns. But it is not clear that complex theoreti...", "weights": [[8, 19, 1.0], [195, 206, 1.0]]}, "W14-5418": {"snippet": "... Our experiments show that the concept topologies in the two modalities share similarities, results that strengthen our conjecture.", "weights": [[8, 19, 1.0], [108, 118, 1.0]]}, "2015.wwwconf_conference-2015c.338": {"snippet": "...line experiments. First, we review basic concepts in causal inference and motivate the need for experiments. Then we will discuss basic statistical tools to help plan experiments: exploratory analysis...", "weights": [[8, 19, 1.0], [99, 110, 1.0], [170, 181, 1.0]]}, "2020.coling-main.489": {"snippet": "Knowledge Base Embedding By Cooperative Knowledge Distillation\n\n\n Knowledge bases are increasingly exploited as gold standard data sources which benefit various knowledge-driven NLP tasks. In this pap...", "weights": [[0, 9, 1.0], [40, 49, 1.0], [66, 75, 1.0], [161, 170, 1.0]]}, "2018.sigirconf_conference-2018.191": {"snippet": "...here theoretical advances are empirically demonstrated and validated, often across a large set of collections, tasks and domains. Experiments often consist of glueing together a number of tools for in...", "weights": [[8, 19, 1.0], [133, 144, 1.0]]}, "H86-1008": {"snippet": "...oded knowledge and the limited inference-making capability of a taxonomic knowledge representation system to reduce the constituent structure of logical expressions. The specific application is to the...", "weights": [[8, 17, 1.0], [77, 86, 1.0]]}, "2016.ecir_conference-2016.88": {"snippet": "Living Labs for Online Evaluation: From Theory to Practice", "weights": []}, "2017.jasis_journal-ir0volumeA68A6.5": {"snippet": "...g on theoretical models and crowdsourced knowledge", "weights": [[8, 19, 1.0], [44, 53, 1.0]]}, "2020.sigirconf_conference-2020.258": {"snippet": "...se a knowledge-enhanced recommendation model ACAM, which incorporates item attributes distilled from knowledge graphs (KGs) as side information, and is built with a co-attention mechanism on attribute...", "weights": [[8, 17, 1.0], [104, 113, 1.0]]}, "2010.tois_journal-ir0volumeA28A3.1": {"snippet": "...on a theoretical and numerical comparison of the two approaches. Numerical experiments illustrate the effectiveness of our theoretical results.", "weights": [[8, 19, 1.0], [78, 89, 1.0], [126, 137, 1.0]]}, "2017.wwwconf_conference-2017.48": {"snippet": "...this theoretical algorithm, and test it on a large class of test graphs. In all cases, Tur\u00e1n-shadow has less than 2% error, and runs in a fraction of the time used by well-tuned exact algorithms. We d...", "weights": [[8, 19, 1.0]]}, "2019.wsdm_conference-2019.112": {"snippet": "...tory Experiments Properly with Statistical Tools: An Easy Hands-On Tutorial", "weights": [[8, 19, 1.0]]}, "2004.wwwconf_conference-2004at.71": {"snippet": "...t to strengthen this spartan theory of meaning.", "weights": [[8, 18, 1.0]]}, "2017.wwwconf_conference-2017c.74": {"snippet": "...ized Experiments\n\n\n ABSTRACTWe propose a nonparametric sequential test that aims to address two practical problems pertinent to online randomized experiments: (i) how to do a hypothesis test for compl...", "weights": [[8, 19, 1.0], [149, 160, 1.0]]}, "2018.iwslt-1.4": {"snippet": "...s of knowledge distillation, few have discussed the actual reasons why it works, especially in the context of neural MT. In this paper, we conduct several experiments aimed at understanding why and ho...", "weights": [[8, 17, 1.0], [55, 61, 1.0], [158, 169, 1.0]]}, "2014.wwwconf_conference-2014c.276": {"snippet": "Strengthening collaborative data analysis and decision making in web communities", "weights": []}, "2021.eacl-main.133": {"snippet": "... our theoretical relationship. We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. Using the derived objective, accurate KGEs are learnt f...", "weights": [[8, 19, 1.0], [83, 94, 1.0], [131, 140, 1.0]]}, "P14-1001": {"snippet": "...with theoretical guarantees for learning accurate ensembles of several structured prediction rules for which no prior knowledge is assumed. This includes a number of randomized and deterministic algor...", "weights": [[8, 19, 1.0], [121, 130, 1.0]]}, "2009.wsdm_workshop-2009wscd.7": {"snippet": "...s to strengthen the original formulation by introducing a new global objective function, and thus formalising the problem as a combinatorial optimisation one. Such a reformulation defines a common fra...", "weights": [[8, 18, 1.0]]}, "D19-1590": {"snippet": "...ound knowledge to our classifiers. Since BERT models are pre-trained with a large corpus, some sort of background knowledge for event causality may be learned during pre-training. Our experiments with...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [187, 198, 1.0]]}, "2014.wwwconf_conference-2014.57": {"snippet": "...lled experiments of human participants in a continuous sequence of ad auctions, similar to those used by Internet companies. The goal of the research was to understand users' strategies in making bids...", "weights": [[8, 19, 1.0]]}, "2018.sigirconf_conference-2018.183": {"snippet": "Theoretical Analysis of Interdependent Constraints in Pseudo-Relevance Feedback\n\n\n ABSTRACTAxiomatic analysis is a well-defined theoretical framework for analytical evaluation of information retrieval...", "weights": [[0, 11, 1.0], [128, 139, 1.0]]}, "2015.tois_journal-ir0volumeA33A3.1": {"snippet": "... our theoretical findings, we experimentally show the versatility of our approach by considering two important cases in the context of Web search. In the first case, we favor the retrieval of document...", "weights": [[8, 19, 1.0]]}, "2015.wwwconf_conference-2015.58": {"snippet": "Events and Controversies: Influences of a Shocking News Event on Information Seeking\n\n\n ABSTRACTIt has been suggested that online search and retrieval contributes to the intellectual isolation of user...", "weights": []}, "2020.cikm_conference-2020.260": {"snippet": "...ting knowledge graph entities in a continuous vector space. In this paper, we investigate the effect of materializing implicit A-box axioms induced by subproperties, as well as symmetric and transitiv...", "weights": [[8, 17, 1.0]]}, "2006.jasis_journal-ir0volumeA57A1.6": {"snippet": "...h: A theoretical framework", "weights": [[8, 19, 1.0]]}, "1986.sigirconf_conference-86.23": {"snippet": "... New Theoretical Framework for Information Retrieval", "weights": [[8, 19, 1.0]]}, "2005.jasis_journal-ir0volumeA56A14.6": {"snippet": "...s of Knowledge hypothesis in a real-life problem situation: A study of history and psychology undergraduates seeking information for a course essay", "weights": [[8, 17, 1.0]]}, "2017.wwwconf_conference-2017.58": {"snippet": "...ogs. Experiments on real-world logs demonstrate the effectiveness of the proposed model on both applications.", "weights": [[8, 19, 1.0]]}}}
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Many countries have been seriously affected by fake news....", "weights": [[8, 17, 1.0], [41, 45, 1.0], [113, 117, 1.0], [118, 122, 1.0], [193, 197, 1.0], [198, 202, 1.0]]}, "2022.acl-long.311": {"snippet": "...rve: News Environment Perception for Fake News Detection\n\n\n Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones...", "weights": [[8, 12, 1.0], [40, 44, 1.0], [45, 49, 1.0], [50, 59, 1.0], [63, 67, 1.0], [68, 72, 1.0], [73, 82, 1.0], [179, 183, 1.0], [184, 188, 1.0]]}, "2020.cikm_conference-2020.206": {"snippet": "... for Fake News Detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2022.acl-long.97": {"snippet": "...ging fake news detection tasks show that using inference operators leads to a better understanding of the social media framework enabling fake news spread, resulting in improved performance.", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [141, 145, 1.0], [146, 150, 1.0]]}, "2020.cikm_conference-2020.118": {"snippet": "... for Fake News Detection Using Graph Representation", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2020.rdsm-1.2": {"snippet": "...h to Fake News Detection Through Interpretable Features and {BRNN}\n\n\n Fake news' -succinctly defined as false or misleading information masquerading as legitimate news -is a ubiquitous phenomenon and ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [73, 77, 1.0], [78, 82, 1.0], [166, 170, 1.0]]}, "2020.fire_conference-2020w.62": {"snippet": "Urdu Fake News Detection using Generalized Autoregressors", "weights": [[5, 9, 1.0], [10, 14, 1.0], [15, 24, 1.0]]}, "2020.fire_conference-2020w.66": {"snippet": "...Urdu Fake News Detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "N19-1347": {"snippet": "... for Fake News Detection\n\n\n On the one hand, nowadays, fake news articles are easily propagated through various online media platforms and have become a grand threat to the trustworthiness of informat...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [58, 62, 1.0], [63, 67, 1.0]]}, "2019.wsdm_conference-2019.40": {"snippet": "...yond News Contents: The Role of Social Context for Fake News Detection", "weights": [[8, 12, 1.0], [54, 58, 1.0], [59, 63, 1.0], [64, 73, 1.0]]}, "2021.ipm_journal-ir0volumeA58A1.24": {"snippet": "... for fake news detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2020.clef_conference-2020w.200": {"snippet": "...Such news is termed as fake news and detecting the fake news spreader is drawing more attention these days as fake news can manipulate communities' minds and also social trust. Until date, many studie...", "weights": [[8, 12, 1.0], [26, 30, 1.0], [31, 35, 1.0], [54, 58, 1.0], [59, 63, 1.0], [113, 117, 1.0], [118, 122, 1.0]]}, "I17-2043": {"snippet": "Fake News Detection Through Multi-Perspective Speaker Profiles\n\n\n Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very l...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0], [76, 80, 1.0], [81, 85, 1.0], [86, 95, 1.0]]}, "2020.clef_conference-2020w.102": {"snippet": "...s of Fake News Spreaders on Twitter\n\n\n Fake news detection on social medial has attracted a huge body of research as one of the most important tasks of social analysis in recent years. In this task, g...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [42, 46, 1.0], [47, 51, 1.0], [52, 61, 1.0]]}, "2018.sigirconf_conference-2018.30": {"snippet": "...mbat Fake News\n\n\n ABSTRACTA large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. De...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [102, 106, 1.0], [107, 111, 1.0], [170, 174, 1.0], [175, 179, 1.0]]}, "2019.wwwconf_conference-2019.283": {"snippet": "... for Fake News Detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2020.fire_conference-2020w.65": {"snippet": "..._NLP@Fake news detection in the Urdu language (UrduFake) 2020", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "C18-1287": {"snippet": "...n of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collec...", "weights": [[8, 12, 1.0], [31, 35, 1.0], [121, 125, 1.0], [126, 130, 1.0], [131, 140, 1.0], [167, 171, 1.0]]}, "2020.clef_conference-2020w.20": {"snippet": "...t of fake news on politics, economy and even public health is yet to be specified. Internet users must face a high amount of false information in digital media such as rumours, fake news, and extremel...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [180, 184, 1.0], [185, 189, 1.0]]}, "2018.wwwconf_conference-2018c.140": {"snippet": "Fake News Detection in Social Networks via Crowd Signals\n\n\n ABSTRACTOur work considers leveraging crowd signals for detecting fake news and is motivated by tools recently introduced by Facebook that e...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0], [126, 130, 1.0], [131, 135, 1.0]]}, "2021.eacl-main.287": {"snippet": "...ased Fake News Detection using a Summarization Technique\n\n\n Nowadays, fake news is spreading in various ways, and this fake information is causing a lot of social damages. Thus the need to detect fake...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [73, 77, 1.0], [78, 82, 1.0], [122, 126, 1.0], [199, 203, 1.0]]}, "2020.wwwconf_conference-2020c.117": {"snippet": "...able Detection of Fake News and Cyberbullying on Social Media", "weights": [[8, 17, 1.0], [21, 25, 1.0], [26, 30, 1.0]]}, "2019.icon-1.27": {"snippet": "...ving fake news detection problem in online news contents of multiple domains. We evaluate our techniques on the two recently released datasets, namely Fake-News AMT and Celebrity for fake news detecti...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [46, 50, 1.0], [154, 158, 1.0], [159, 163, 1.0], [186, 190, 1.0], [191, 195, 1.0], [196, 205, 1.0]]}, "2021.wsdm_conference-2021.62": {"snippet": "...city detection, i.e. explaining why a particular piece of text is factually correct or incorrect. In this work, we seek to bridge this gap by proposing a technique, FACE-KEG, to automatically perform ...", "weights": [[8, 17, 1.0]]}, "2020.clef_conference-2020w.54": {"snippet": "...ting Fake News Spreaders in Social Networks via Linguistic and Personality Features\n\n\n This paper addresses the problem of automatically detecting fake news spreaders in social networks such as Twitte...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [150, 154, 1.0], [155, 159, 1.0]]}, "2017.cikm_conference-2017.84": {"snippet": "... for Fake News Detection\n\n\n ABSTRACT e topic of fake news has drawn a ention both from the public and the academic communities. Such misinformation has the potential of a ecting public opinion, provid...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [51, 55, 1.0], [56, 60, 1.0]]}, "2020.tist_journal-ir0volumeA11A1.6": {"snippet": "...bust Fake News Detection Over Time and Attack", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "N19-1141": {"snippet": "Fake News Detection using Deep {M}arkov Random Fields\n\n\n Deep-learning-based models have been successfully applied to the problem of detecting fake news on social media. While the correlations among n...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0], [143, 147, 1.0], [148, 152, 1.0], [199, 203, 1.0]]}, "2020.clef_conference-2020w.161": {"snippet": "...ling Fake News Spreaders on Twitter\n\n\n Automatic detection of fake news in social media has become a prominent research topic due to its widespread, adverse effect on not only the society and public h...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [52, 61, 1.0], [65, 69, 1.0], [70, 74, 1.0]]}, "2018.wsdm_conference-2018.78": {"snippet": "...like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features from content. For example, intentional spreaders of fake news may manipulate the content to m...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [28, 37, 1.0], [162, 166, 1.0], [167, 171, 1.0]]}, "2020.lrec-1.309": {"snippet": "... for Fake News Detection in the {U}rdu Language\n\n\n The task of fake news detection is to distinguish legitimate news articles that describe real facts from those which convey deceiving and fictitious ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [66, 70, 1.0], [71, 75, 1.0], [76, 85, 1.0], [115, 119, 1.0]]}, "2021.acl-srw.32": {"snippet": "... for fake news detection is confirmed, firstly, by manual experiment based on a set of known true and fake news. Besides, we compared our fake news classification system based on the proposed feature ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [105, 109, 1.0], [110, 114, 1.0], [141, 145, 1.0], [146, 150, 1.0]]}, "2020.clef_conference-2020w.74": {"snippet": "Fake News Spreader Detection on Twitter using Character N-Grams\n\n\n The authors of fake news often use facts from verified news sources and mix them with misinformation to create confusion and provoke ...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [19, 28, 1.0], [82, 86, 1.0], [87, 91, 1.0], [122, 126, 1.0]]}, "2021.internlp-1.7": {"snippet": "...n of fake news, text that is published with an intent to spread misinformation and sway beliefs. Detecting fake news is important to prevent misinformation and maintain a healthy society. While prior ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [110, 114, 1.0], [115, 119, 1.0]]}, "2020.clef_conference-2020w.66": {"snippet": "...in a Fake News Detection Approach\n\n\n Misinformation is a growing problem around the web. The spread of such a phenomenon may impact public opinions. Hence fake news detection is indispensable. The fir...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [158, 162, 1.0], [163, 167, 1.0], [168, 177, 1.0]]}, "2018.wwwconf_conference-2018c.270": {"snippet": "... for Fake News Stance Identification\n\n\n ABSTRACTIdentifying the veracity of a news article is an interesting problem while automating this process can be a challenging task. Detection of a news articl...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [81, 85, 1.0], [177, 186, 1.0], [192, 196, 1.0]]}, "2020.fire_conference-2020w.61": {"snippet": "Fake News Detection in the Urdu Language using CharCNN-RoBERTa", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0]]}, "2021.wwwconf_conference-2021.305": {"snippet": "... for Fake News Detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "W18-5510": {"snippet": "...ance Detection in Fake News A Combined Feature Representation\n\n\n With the uncontrolled increasing of fake news and rumors over the Web, different approaches have been proposed to address the problem. ...", "weights": [[8, 17, 1.0], [21, 25, 1.0], [26, 30, 1.0], [104, 108, 1.0], [109, 113, 1.0]]}, "2012.wwwconf_conference-2012.20": {"snippet": "...ting fake reviews and individual fake reviewers. However, a fake reviewer group (a group of reviewers who work collaboratively to write fake reviews) is even more damaging as they can take total contr...", "weights": [[8, 12, 1.0], [36, 40, 1.0], [63, 67, 1.0], [139, 143, 1.0]]}, "2020.clef_conference-2020w.111": {"snippet": "...gual Detection of Fake News Spreaders via Sparse Matrix Factorization\n\n\n Fake news is an emerging problem in online news and social media. Efficient detection of fake news spreaders and spurious accou...", "weights": [[8, 17, 1.0], [21, 25, 1.0], [26, 30, 1.0], [76, 80, 1.0], [81, 85, 1.0], [119, 123, 1.0], [152, 161, 1.0], [165, 169, 1.0], [170, 174, 1.0]]}, "C18-1131": {"snippet": "...lass Fake News Detection\n\n\n Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent yea...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [31, 35, 1.0], [36, 40, 1.0], [145, 149, 1.0], [150, 154, 1.0]]}, "2020.clef_conference-2020w.52": {"snippet": "...read fake news or not. We have built models for two languages-English and Spanish. We have achieved 72% accuracy in this fake news spreader detection task.", "weights": [[8, 12, 1.0], [13, 17, 1.0], [124, 128, 1.0], [129, 133, 1.0], [143, 152, 1.0]]}, "2020.clef_conference-2020w.24": {"snippet": "... for Detection of Fake Profiles\n\n\n The explosive growth of fake news on social networks has aroused great interest from researchers in different disciplines. To achieve efficient and effective detecti...", "weights": [[8, 17, 1.0], [21, 25, 1.0], [62, 66, 1.0], [67, 71, 1.0], [196, 205, 1.0]]}, "2021.jasis_journal-ir0volumeA72A1.0": {"snippet": "...ting fake news stories via multimodal analysis", "weights": [[8, 12, 1.0], [13, 17, 1.0]]}, "2020.coling-main.165": {"snippet": "...shot fake news detection, alleviating the need of large scale training data to train fake news detection models. Experimental results on FakenewsNet, a benchmark dataset for fake news detection, demon...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [88, 92, 1.0], [93, 97, 1.0], [98, 107, 1.0], [177, 181, 1.0], [182, 186, 1.0], [187, 196, 1.0]]}, "2021.ecir_conference-20212.9": {"snippet": "...s of News Title and Body to Detect Fake News: A Reproducibility Study\n\n\n Fake news, a deliberately designed news to mislead others, is becoming a big societal threat with its fast dissemination over t...", "weights": [[8, 12, 1.0], [38, 42, 1.0], [43, 47, 1.0], [76, 80, 1.0], [81, 85, 1.0], [111, 115, 1.0]]}, "W17-4214": {"snippet": "Fake news stance detection using stacked ensemble of classifiers\n\n\n Fake news has become a hotly debated topic in journalism. In this paper, we present our entry to the 2017 Fake News Challenge which ...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [17, 26, 1.0], [68, 72, 1.0], [73, 77, 1.0], [174, 178, 1.0], [179, 183, 1.0]]}, "2020.ipm_journal-ir0volumeA57A2.1": {"snippet": "...line fake news: Characterization, detection, and discussion", "weights": [[8, 12, 1.0], [13, 17, 1.0], [37, 46, 1.0]]}, "2019.wsdm_conference-2019.115": {"snippet": "Fake News: Fundamental Theories, Detection Strategies and Challenges\n\n\n ABSTRACTThe explosive growth of fake news and its erosion to democracy, justice, and public trust increased the demand for fake ...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [33, 42, 1.0], [104, 108, 1.0], [109, 113, 1.0], [195, 199, 1.0]]}, "2021.triton-1.16": {"snippet": "Fake News Detection for {P}ortuguese with Deep Learning\n\n\n The exponential growth of the internet and social media in the past decade gave way to the increase in dissemination of false or misleading i...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0]]}, "2020.wwwconf_conference-2020c.36": {"snippet": "...tter Fake News Detection on Mobile", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2019.wwwconf_conference-2019c.232": {"snippet": "Fake News Detection: An Interdisciplinary Research", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0]]}, "2020.lrec-1.747": {"snippet": "...d of fake news. Thus, the effect of fake news has been growing, sometimes extending to the offline world and threatening public safety. Given the massive amount of Web content, automatic fake news det...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [39, 43, 1.0], [44, 48, 1.0], [190, 194, 1.0], [195, 199, 1.0], [200, 209, 1.0]]}, "2020.rdsm-1.5": {"snippet": "...with fake news detection better. We investigated the nature of fake news by dividing it into two non-overlapping classes: satire and fake news. As a result, we obtained the set of models for fake news...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [66, 70, 1.0], [71, 75, 1.0], [136, 140, 1.0], [141, 145, 1.0], [194, 198, 1.0], [199, 203, 1.0]]}, "2020.clef_conference-2020w.169": {"snippet": "...utes fake news, as in the PAN @ CLEF 2020 competition Profiling Fake News Spreaders on Twitter. This competition asks for systems that identify possible fake news spreaders on social media as a first ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [67, 71, 1.0], [72, 76, 1.0], [156, 160, 1.0], [161, 165, 1.0]]}, "2014.wwwconf_conference-2014c.229": {"snippet": "...t as fake or real. We created a data corpus of tweets around big events focusing on the ones linking to images (fake or real) of which the reliability could be verified by independent online sources. ...", "weights": [[8, 12, 1.0], [115, 119, 1.0]]}, "2020.mir_conference-2020.82": {"snippet": "Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0]]}, "2021.case-1.10": {"snippet": "...s of news articles and tweets on those articles. Therefore, it is extremely important to stop the spread of fake news before it reaches a mass level, which is a big challenge. We propose a novel fake ...", "weights": [[8, 12, 1.0], [111, 115, 1.0], [116, 120, 1.0], [198, 202, 1.0]]}, "2022.fever-1.4": {"snippet": "...atic Fake News Detection: Are current models {``}fact-checking{''} or{``}gut-checking{''}?\n\n\n Automatic fake news detection models are ostensibly based on logic, where the truth of a claim made in a h...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [107, 111, 1.0], [112, 116, 1.0], [117, 126, 1.0]]}, "2020.coling-main.475": {"snippet": "...arly Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning\n\n\n The dissemination of fake news significantly affects personal reputation ...", "weights": [[8, 17, 1.0], [21, 25, 1.0], [26, 30, 1.0], [63, 67, 1.0], [151, 155, 1.0], [156, 160, 1.0]]}, "2021.acl-long.62": {"snippet": "...ural Fake News Detection with External Knowledge\n\n\n Nowadays, fake news detection, which aims to verify whether a news document is trusted or fake, has become urgent and important. Most existing metho...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [65, 69, 1.0], [70, 74, 1.0], [75, 84, 1.0], [117, 121, 1.0], [145, 149, 1.0]]}, "P17-2067": {"snippet": "... for Fake News Detection\n\n\n Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approache...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [41, 45, 1.0], [46, 50, 1.0], [51, 60, 1.0], [99, 108, 1.0]]}, "2019.cikm_conference-2019.376": {"snippet": "...able Fake News Detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "W17-4215": {"snippet": "...t to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles\n\n\n We present a system for the detection of the stance of headlines with regard to their corresponding arti...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [127, 136, 1.0]]}, "2019.wwwconf_conference-2019.387": {"snippet": "...able Fake News Detector with Visualizations", "weights": [[8, 12, 1.0], [13, 17, 1.0]]}}}
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However, without proper tools for retrieving specific moments, the la...", "weights": [[2, 12, 1.0], [48, 57, 1.0]]}, "2016.ijmir_journal-ir0volumeA5A3.0": {"snippet": "Top multimedia information retrieval papers", "weights": [[4, 14, 1.0], [27, 36, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.20": {"snippet": "...t in multimedia Web search, those few Web search engines offering multimedia Web search, provide limited multimedia search functionality. Keywords are still the only means of multimedia retrieval, whi...", "weights": [[8, 18, 1.0], [69, 79, 1.0], [108, 118, 1.0], [178, 188, 1.0], [189, 198, 1.0]]}, "2012.mir_conference-2012.69": {"snippet": "...tive multimedia retrieval system", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2002.sigirconf_conference-2002.91": {"snippet": "...stic multimedia retrieval\n\n\n ABSTRACTWe present a framework in which probabilistic models for textual and visual information retrieval can be integrated seamlessly. 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Additional Key Words and Phrases: Content-based multimedia information retrieval, information fusion, graph-based methods, cross-media similarity, random walk, V...", "weights": [[8, 18, 1.0], [31, 40, 1.0], [90, 100, 1.0], [113, 122, 1.0]]}, "2007.sigirjournals_journal-ir0volumeA41A1.4": {"snippet": "Multimedia retrieval at INEX 2006", "weights": [[0, 10, 1.0], [11, 20, 1.0]]}, "2013.wwwconf_conference-2013c.137": {"snippet": "Multimedia information retrieval on the social web", "weights": [[0, 10, 1.0], [23, 32, 1.0]]}, "2014.mir_workshop-2014emr.0": {"snippet": "...ntal Multimedia Retrieval co-located with ACM International Conference on Multimedia Retrieval, EMR@ICMR 2014, Glasgow, UK, April 1, 2014", "weights": [[8, 18, 1.0], [19, 28, 1.0], [77, 87, 1.0], [88, 97, 1.0]]}, "2000.sigirconf_conference-2000.53": {"snippet": "...tion retrieval from multimedia data streams which occur in the presentation recording scenario.\nAOF as an Example for Presentation RecordingWith the Authoring on the Fly (AOF) approach multimedia pres...", "weights": [[8, 17, 1.0], [23, 33, 1.0], [188, 198, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A1.6": {"snippet": "...n of multimedia retrieval", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2014.mir_workshop-2014somus.0": {"snippet": "...cial Multimedia and Storytelling co-located with ACM International Conference on Multimedia Retrieval (ICMR 2014), Glasgow, Scotland, UK, April 1, 2014", "weights": [[8, 18, 1.0], [84, 94, 1.0], [95, 104, 1.0]]}, "2014.mir_conference-2014.58": {"snippet": "...mage Retrieval via Adaptive Weighting", "weights": [[8, 17, 1.0]]}, "2014.mir_conference-2014.28": {"snippet": "...sage Retrieval in Audio-Visual Documents", "weights": [[8, 17, 1.0]]}, "2012.sigirconf_conference-2012.50": {"snippet": "...s of multimedia items for making personalized recommendations\n\n\n ABSTRACTExisting multimedia recommenders suggest a specific type of multimedia items rather than items of different types personalized ...", "weights": [[8, 18, 1.0], [85, 95, 1.0], [136, 146, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.16": {"snippet": "...ideo retrieval: application of implicit feedback and semantic user profiles\n\n\n AbstractA challenging problem in the user profiling domain is to create profiles of users of retrieval systems. This prob...", "weights": [[8, 17, 1.0], [175, 184, 1.0]]}, "2020.clef_conference-2020.22": {"snippet": "...020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2017.chiir_conference-2017.72": {"snippet": "...ideo retrieval tools and methods, which allow for automatic search in videos, still the performance of automatic video retrieval is far from optimal.At the same time, the organization of personal data...", "weights": [[8, 17, 1.0], [122, 131, 1.0]]}, "1986.tois_journal-ir0volumeA4A4.3": {"snippet": "...nted multimedia information system that provides integrated facilities for creating and managing complex multimedia objects. In this paper the model for multimedia documents supported by MINOS and its...", "weights": [[8, 18, 1.0], [108, 118, 1.0], [156, 166, 1.0]]}, "2014.mir_conference-2014.90": {"snippet": "...ased Multimedia Retrieval", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2010.sigirconf_conference-2010.82": {"snippet": "...tion retrieval (MIR) holds great promise as a technology for managing large music archives. One of the key components of MIR that has been actively researched into is music tagging. While significant ...", "weights": [[8, 17, 1.0]]}, "2013.sigirconf_conference-2013.1": {"snippet": "... and retrieval, including IBM Multimedia Analysis and Retrieval System (IMARS), and show recent results for semanticbased classification and retrieval. We conclude with future directions for improving...", "weights": [[8, 17, 1.0], [33, 43, 1.0], [57, 66, 1.0], [144, 153, 1.0]]}, "2010.sigirconf_conference-2010.200": {"snippet": "Multimedia information retrieval\n\n\n Abstract:This tutorial is concerned with creating the best possible multimedia search experience. The intriguing bit here is that the query itself can be a multimed...", "weights": [[0, 10, 1.0], [23, 32, 1.0], [104, 114, 1.0], [192, 202, 1.0]]}, "1995.ipm_journal-ir0volumeA31A1.9": {"snippet": "Multimedia Document Retrieval", "weights": [[0, 10, 1.0], [20, 29, 1.0]]}, "2013.mir_conference-2013.49": {"snippet": "...mage retrieval", "weights": [[8, 17, 1.0]]}, "2013.mir_conference-2013.16": {"snippet": "...d 3D retrieval using modal space transform", "weights": [[8, 17, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ased multimedia retrieval\n\n\n AbstractRepresenting multimedia documents by means of concepts labels attached to parts of these documents has great potential for improving retrieval performance. The rea...", "weights": [[8, 18, 1.0], [19, 28, 1.0], [53, 63, 1.0], [173, 182, 1.0]]}, "2014.mir_conference-2014.34": {"snippet": "... for multimedia retrieval", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "1997.cikm_workshop-97.38": {"snippet": "...ctIn multimedia information retrieval applications, contentbased image retrieval is essential for retrieving relevant mnltimedia documents. The purpose of our paper is to provide both effective repres...", "weights": [[8, 18, 1.0], [31, 40, 1.0], [74, 83, 1.0]]}, "1999.cikm_conference-99.67": {"snippet": "...n of Multimedia Data\n\n\n AbstractThe management of multimedia information poses special requirements for multimedia information systems. Both representation and retrieval of the complex and multifacete...", "weights": [[8, 18, 1.0], [53, 63, 1.0], [107, 117, 1.0], [163, 172, 1.0]]}, "2013.mir_conference-2013.21": {"snippet": "...edia retrieval", "weights": [[8, 17, 1.0]]}}}
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However, high performance parsers are ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [128, 139, 1.0]]}, "1988.sigirconf_conference-88.5": {"snippet": "Natural Language Techniques for Intelligent Information Retrieval", "weights": [[0, 7, 1.0], [8, 16, 1.0], [44, 55, 1.0], [56, 65, 1.0]]}, "J01-2007": {"snippet": "...ews: Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language\n\n\n The strong connection between knowledge representation and reasoning and natural lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [66, 74, 1.0], [107, 115, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [35, 42, 1.0], [43, 51, 1.0], [52, 62, 1.0], [91, 102, 1.0], [103, 112, 1.0], [140, 147, 1.0], [148, 156, 1.0], [157, 167, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.5": {"snippet": "Natural language processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2003.jasis_journal-ir0volumeA54A2.1": {"snippet": "...ying Natural Language Processing to Information Retrieval", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [39, 50, 1.0], [51, 60, 1.0]]}, "2000.riao_conference-2000.38": {"snippet": "Natural Language Processing for Text Mining and Decision Making", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "1991.riao_conference-1991.40": {"snippet": "Processing natural language for an expert system using a sub-language approach", "weights": [[0, 10, 1.0], [11, 18, 1.0], [19, 27, 1.0], [61, 69, 1.0]]}, "1985.tois_journal-ir0volumeA3A2.5": {"snippet": "... The Natural Language Processing Project in the AI Program at MCC\n\n\n We discuss a recently launched, long-term project in natural language processing, the primary concern of which is that natural lang...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [125, 132, 1.0], [133, 141, 1.0], [142, 152, 1.0], [191, 198, 1.0], [199, 207, 1.0]]}, "1995.ipm_journal-ir0volumeA31A3.9": {"snippet": "Natural Language Information Retrieval", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0]]}, "H92-1115": {"snippet": "...h in Natural Language Processing\n\n\n Our general objective has been the enhancement of techniques for extracting information and retrieving documents from natural language text. Our focus has been on m...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [115, 126, 1.0], [157, 164, 1.0], [165, 173, 1.0]]}, "1990.ipm_journal-ir0volumeA26A1.3": {"snippet": "Natural language processing and information retrieval", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [32, 43, 1.0], [44, 53, 1.0]]}, "2018.fire_conference-2018.12": {"snippet": "...krit language by embedding character level features\n\n\n ABSTRACTPart-of-Speech (POS) tagging is an important task in Natural Language Processing and numerous taggers have been developed for POS tagging...", "weights": [[8, 16, 1.0], [119, 126, 1.0], [127, 135, 1.0], [136, 146, 1.0]]}, "1973.sigirconf_conference-73.4": {"snippet": "... for Information Retrieval\n\n\n AbstractNatural language query formulations exhibit advantages over artificial language statements since they permit the user to approach the retrieval environment withou...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [49, 57, 1.0], [112, 120, 1.0], [175, 184, 1.0]]}, "N06-5003": {"snippet": "... for Natural Language Processing and Information Retrieval\n\n\n Graph theory is a well studied discipline, and so are the fields of natural language processing and information retrieval. However, most o...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [40, 51, 1.0], [52, 61, 1.0], [133, 140, 1.0], [141, 149, 1.0], [150, 160, 1.0], [165, 176, 1.0], [177, 186, 1.0]]}, "2015.fire_workshop-2015w.20": {"snippet": "...s\n\n\n Natural Language processing (NLP) in its pure sense, is a platform that provides the ability for transforming natural language text to useful information. Named Entity Recognition (NER) is a key ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [118, 125, 1.0], [126, 134, 1.0], [150, 161, 1.0]]}, "C92-2121": {"snippet": "...cale Natural Language Processing\n\n\n This paper demonstrates the utility of the Semantic Network Array Processor (SNAP) as a massively parallel platform for high performance and large-scale natural lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "2018.spire_conference-2018.27": {"snippet": "...ting Natural Language into Denotations", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2008.cikm_conference-2008.27": {"snippet": "...k on Natural Language Processing for Information Retrieval has shown the inadequateness of semantic and syntactic structures for both document retrieval and categorization. The main reason is the high...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [40, 51, 1.0], [52, 61, 1.0], [146, 155, 1.0]]}, "1996.tois_journal-ir0volumeA14A3.0": {"snippet": "Natural-Language Retrieval of Images Based on Descriptive Captions\n\n\n We describe a prototype intelligent information retrieval system that uses natural-language understanding to efficiently locate ca...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 26, 1.0], [106, 117, 1.0], [118, 127, 1.0], [145, 152, 1.0], [153, 161, 1.0]]}, "1986.jasis_journal-ir0volumeA37A4.2": {"snippet": "Natural language processing in information retrieval", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [31, 42, 1.0], [43, 52, 1.0]]}, "P96-1003": {"snippet": "... for Information Retrieval\n\n\n Information retrieval is an important application area of natural-language processing where one encounters the genuine challenge of processing large quantities of unrestr...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [33, 44, 1.0], [45, 54, 1.0], [91, 98, 1.0], [99, 107, 1.0], [108, 118, 1.0], [165, 175, 1.0]]}, "A92-1033": {"snippet": "...w of Natural Language Processing of Captions for Retrieving Multimedia Data\n\n\n This paper briefly describes the current implementation status of an intelligent information retrieval system, MARIE, tha...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [163, 174, 1.0], [175, 184, 1.0]]}, "2020.wsdm_conference-2020.120": {"snippet": "...p on Natural Language Processing for Recommendations\n\n\n Natural language processing is becoming more and more important in recommender systems. This half day workshop explores challenges and potential...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [59, 66, 1.0], [67, 75, 1.0], [76, 86, 1.0]]}, "2020.sigirconf_conference-2020.365": {"snippet": "...ires processing and understanding such information effectively and efficiently, where natural language processing (NLP) technologies are widely deployed. Natural language data are represented as a seq...", "weights": [[8, 18, 1.0], [42, 53, 1.0], [89, 96, 1.0], [97, 105, 1.0], [106, 116, 1.0], [157, 164, 1.0], [165, 173, 1.0]]}, "2016.sigirconf_conference-2016.1": {"snippet": "...uman Language: Can NLP and Deep Learning Help?\n\n\n ABSTRACTThere is a lot of overlap between the core problems of information retrieval (IR) and natural language processing (NLP). An IR system gains fr...", "weights": [[8, 16, 1.0], [116, 127, 1.0], [128, 137, 1.0], [147, 154, 1.0], [155, 163, 1.0], [164, 174, 1.0]]}, "J81-4005": {"snippet": "Natural Language Processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "1999.sigirconf_conference-99.6": {"snippet": "...te), retrieval e ectiveness with document expansion is indistinguishable from retrieval from human transcriptions. This makes speech retrieval from automatic transcriptions, even poor ones, competitiv...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [136, 145, 1.0]]}, "2001.sigirconf_workshop-2001w1.2": {"snippet": "...text processing techniques rely on proper capitalization, and people can more easily read mixed case text. Proper capitalization, however, is often absent in a number of text sources, including automa...", "weights": [[8, 18, 1.0]]}, "U06-1026": {"snippet": "Natural Language Processing and {XML} Retrieval\n\n\n XML information retrieval (XML-IR) systems respond to user queries with results more specific than documents. XML-IR queries contain both content and...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [38, 47, 1.0], [55, 66, 1.0], [67, 76, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.13": {"snippet": "...e in information retrieval research, yet is also a fundamentally important task, because an improved general retrieval model would benefit every search engine. The language modeling approach to inform...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [112, 121, 1.0], [167, 175, 1.0], [197, 208, 1.0]]}, "2016.fire_conference-2016w.55": {"snippet": "...sing Natural Language Processing Techniques & Semantic Similarity Computations\n\n\n The paper reports the approaches utilized and results achieved for our system in the shared task (in FIRE-2016) for pa...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "1982.sigirjournals_journal-ir0volumeA17A3.3": {"snippet": "...3\n\n\n Natural language processing techniques developed for Artificial Intelligence programs can aid in constructing powerful information retrieval systems in at least two areas. Automatic construction ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [127, 138, 1.0], [139, 148, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [42, 53, 1.0], [54, 63, 1.0], [92, 103, 1.0], [104, 113, 1.0], [122, 130, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "X96-1030": {"snippet": "Natural Language Information Retrieval: {TIPSTER}-2 Final Report\n\n\n We report on the joint GE/NYU natural language information retrieval project as related to the Tipster Phase 2 research conducted in...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [98, 105, 1.0], [106, 114, 1.0], [115, 126, 1.0], [127, 136, 1.0]]}, "1995.sigirjournals_journal-ir0volumeA29A2.2": {"snippet": "...ion: Natural Language Processing and Information Retrieval Group Information Access and User Interfaces Division National Institute of Standards and Technology\n\n\n OverviewThe Natural Language Processi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [40, 51, 1.0], [52, 61, 1.0], [68, 79, 1.0], [178, 185, 1.0], [186, 194, 1.0], [195, 205, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.0": {"snippet": "...m of language identification of noisy texts, which could represent the primary step of many natural language processing or information retrieval tasks. Language identification is the task of automatic...", "weights": [[8, 16, 1.0], [95, 102, 1.0], [103, 111, 1.0], [112, 122, 1.0], [126, 137, 1.0], [138, 147, 1.0], [155, 163, 1.0]]}, "H86-1007": {"snippet": "...y of natural language processing outside of a laboratory setting. The paper will be most valuable to those engaged in applying state-of-the-art techniques to deliver natural language interfaces and to...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [169, 176, 1.0], [177, 185, 1.0]]}, "2011.cikm_workshop-2011esair.9": {"snippet": "... the natural language processing literature. Will semantic annotation of word senses improve information retrieval? This paper provides some thoughts on this question, which lies at the intersection o...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [96, 107, 1.0], [108, 117, 1.0]]}, "1991.riao_conference-1991.42": {"snippet": "Natural language processing of the texts of chemical patent abstracts", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2017.wwwconf_conference-2017c.152": {"snippet": "...uent natural language questions.\nBACKGROUND AND MOTIVATIONAs more and more RDF Question/Answering (Q/A) systems over RDF knowledge graphs are designed, which allow users to express queries in natural ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [195, 202, 1.0]]}, "1994.riao_conference-1994.11": {"snippet": "... and Retrieval using Natural Language Processing", "weights": [[8, 17, 1.0], [24, 31, 1.0], [32, 40, 1.0], [41, 51, 1.0]]}, "2017.sigirconf_workshop-2017birndl.0": {"snippet": "...nced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017) co-located with the 40th International ACM SIGIR Conference on Research and Development in Information Re...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 41, 1.0], [42, 50, 1.0], [51, 61, 1.0], [189, 200, 1.0], [201, 210, 1.0]]}, "2001.sigirconf_workshop-2001w1.9": {"snippet": "...Text Retrieval: Using Target IR Collections for Statistical Language Model Adaptation in Speech Recognition", "weights": [[8, 17, 1.0], [63, 71, 1.0]]}, "A94-1028": {"snippet": "...Text Processing in Automated Information Retrieval\n\n\n We report on the results of a series of experiments with a prototype text retrieval system which uses relatively advanced natural language process...", "weights": [[8, 18, 1.0], [32, 43, 1.0], [44, 53, 1.0], [131, 140, 1.0], [179, 186, 1.0], [187, 195, 1.0], [196, 206, 1.0]]}, "F13-4002": {"snippet": "Information Retrieval and Natural Language Processing (Recherche d{'}Information et Traitement Automatique des Langues Naturelles) [in {F}rench]", "weights": [[0, 11, 1.0], [12, 21, 1.0], [26, 33, 1.0], [34, 42, 1.0], [43, 53, 1.0], [69, 80, 1.0]]}, "H91-1068": {"snippet": "...Text Processing for Information Retrieval\n\n\n We describe an advanced text processing system for information retrieval from natural language document collections. We use both syntactic processing as we...", "weights": [[8, 18, 1.0], [23, 34, 1.0], [35, 44, 1.0], [77, 87, 1.0], [99, 110, 1.0], [111, 120, 1.0], [126, 133, 1.0], [134, 142, 1.0], [187, 197, 1.0]]}, "1996.trec_conference-1996.17": {"snippet": "Information Retrieval and Trainable Natural Language Processing", "weights": [[0, 11, 1.0], [12, 21, 1.0], [36, 43, 1.0], [44, 52, 1.0], [53, 63, 1.0]]}, "W93-0302": {"snippet": "...Text Processing in Automated Information Retrieval\n\n\n This paper outlines a prototype text retrieval system which uses relatively advanced natural language processing techniques in order to enhance th...", "weights": [[8, 18, 1.0], [32, 43, 1.0], [44, 53, 1.0], [94, 103, 1.0], [142, 149, 1.0], [150, 158, 1.0], [159, 169, 1.0]]}, "2005.clef_workshop-2005w.8": {"snippet": "...ight Natural Language Processing to Ad-Hoc Cross Language Information Retrieval\n\n\n In the CLEF 2005 Ad-Hoc Track we experimented with language-specific morphosyntactic processing and light Natural Lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [52, 60, 1.0], [61, 72, 1.0], [73, 82, 1.0], [137, 145, 1.0], [171, 181, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "H89-2078": {"snippet": "...r on Natural Language Processing\n\n\n I. Scope Major Challenges We take the ultimate goal of natural language processing (NLP) to be the ability to use natural languages as effectively as humans do. Nat...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [94, 101, 1.0], [102, 110, 1.0], [111, 121, 1.0], [153, 160, 1.0], [200, 207, 1.0]]}, "Y09-1039": {"snippet": "...nal) Natural Language Processing: A Cognitive Perspective\n\n\n In this paper I aim at sketching out in bare outline a new model/framework of language processing with its implications for natural languag...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [142, 150, 1.0], [151, 161, 1.0], [188, 195, 1.0], [196, 204, 1.0]]}, "A00-1036": {"snippet": "...from natural language processing and knowledge representation with a penaltybased technique for relevance estimation and passage retrieval. Unlike many attempts to combine natural language processing ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [132, 141, 1.0], [175, 182, 1.0], [183, 191, 1.0], [192, 202, 1.0]]}, "1990.ipm_journal-ir0volumeA26A1.5": {"snippet": "...a in natural language processing and information retrieval", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [40, 51, 1.0], [52, 61, 1.0]]}, "2021.rocling-1.22": {"snippet": "... the natural language processing tasks have made great progresses by leveraging the bidirectional encoder representations from Transformers (BERT). The goal of information retrieval is to search the m...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [163, 174, 1.0], [175, 184, 1.0]]}, "2010.ecir_conference-2010.4": {"snippet": "... and Natural Language Processing for Multimedia Information Retrieval", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [51, 62, 1.0], [63, 72, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.5": {"snippet": "... for natural language generation\n\n\n a b s t r a c tReferring expression generation is the part of natural language generation that decides how to refer to the entities appearing in an automatically ge...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [101, 108, 1.0], [109, 117, 1.0]]}, "H93-1071": {"snippet": "...uery Processing for Retrieval From Large Text Bases\n\n\n Natural language experiments in information retrieval have often been inconclusive due to the lack of large text bases with associated queries an...", "weights": [[8, 18, 1.0], [23, 32, 1.0], [58, 65, 1.0], [66, 74, 1.0], [90, 101, 1.0], [102, 111, 1.0]]}, "1985.tois_journal-ir0volumeA3A2.1": {"snippet": "...LSP) natural language processing system has been developed as a domain-independent natural language processing system. Initially utilized for processing sets of medical messages and other texts in the...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [86, 93, 1.0], [94, 102, 1.0], [103, 113, 1.0], [145, 155, 1.0]]}, "2020.cikm_conference-2020.67": {"snippet": "...d in natural language and convert it into forms that can be more easily and precisely processed by computers. Knowledge Graphs (KGs) offer offer a good solution for such processing. Natural Language P...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [173, 183, 1.0], [185, 192, 1.0], [193, 201, 1.0], [202, 212, 1.0]]}, "H92-1040": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a fully automated Information Retrieval System which uses advanced natural language processing techniques to enhance the ...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [35, 42, 1.0], [43, 51, 1.0], [52, 62, 1.0], [97, 108, 1.0], [109, 118, 1.0], [146, 153, 1.0], [154, 162, 1.0], [163, 173, 1.0]]}, "2007.cikm_workshop-2007pikm.1": {"snippet": "Natural language processing for information retrieval: the time is ripe (again)", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [32, 43, 1.0], [44, 53, 1.0]]}, "A97-1046": {"snippet": "...g\n\n\n Information Retrieval (IR) is an important application area of Natural Language Processing (NLP) where one encounters the genuine challenge of processing large quantities of unrestricted natural ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [71, 78, 1.0], [79, 87, 1.0], [88, 98, 1.0], [151, 161, 1.0], [195, 202, 1.0]]}, "J06-1006": {"snippet": "... and Language Processing by John Coleman represents a bold effort to educate students in speech science about some of the important methods used in speech and natural language processing (NLP). This b...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [162, 169, 1.0], [170, 178, 1.0], [179, 189, 1.0]]}, "2004.clef_workshop-2004.6": {"snippet": "...act. Information Retrieval systems can benefit from advanced linguistic resources when carrying out tasks such as word-stemming or query translation. The main goal of our experiments has been the deve...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2004.ipm_journal-ir0volumeA40A6.4": {"snippet": "...word information in the natural language processing system increases further and various researches using co-occurrence word information are done. Moreover, in the natural language processing, diction...", "weights": [[8, 19, 1.0], [27, 34, 1.0], [35, 43, 1.0], [44, 54, 1.0], [128, 139, 1.0], [167, 174, 1.0], [175, 183, 1.0], [184, 194, 1.0]]}, "2017.sigirconf_workshop-2017birndl.1": {"snippet": "...nced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL) at SIGIR 2017", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 41, 1.0], [42, 50, 1.0], [51, 61, 1.0]]}, "2016.sigirconf_conference-2016.219": {"snippet": "... for Information Retrieval\n\n\n ABSTRACTRecent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being successfully appl...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [94, 105, 1.0], [106, 115, 1.0], [120, 127, 1.0], [128, 136, 1.0], [137, 147, 1.0]]}, "1983.sigirconf_conference-83.4": {"snippet": "...gent Information Systems\n\n\n ABSTRACTNatural language processing techniques developed for Artificial Intelligence programs can aid in constructing powerful information retrieval systems in at least, tw...", "weights": [[8, 19, 1.0], [47, 55, 1.0], [56, 66, 1.0], [158, 169, 1.0], [170, 179, 1.0]]}, "2018.cikm_conference-2018.119": {"snippet": "...ween natural language queries and web documents, if we directly use natural language queries as inputs for retrieval, the results are usually unsatisfactory. In this work, we formulated the task as a ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [71, 78, 1.0], [79, 87, 1.0], [110, 119, 1.0]]}, "2007.spire_conference-2007.2": {"snippet": "...ided Natural Language Text Compression\n\n\n Abstract. We describe a novel compression technique for natural language text collections which takes advantage of the information provided by edges when a gr...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [101, 108, 1.0], [109, 117, 1.0], [164, 175, 1.0]]}, "W05-0705": {"snippet": "...ng a Natural Language Processing System for {E}uropean Languages to Treat {A}rabic in Information Processing and Information Retrieval Applications\n\n\n The goal of many natural language processing plat...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [89, 100, 1.0], [101, 111, 1.0], [116, 127, 1.0], [128, 137, 1.0], [171, 178, 1.0], [179, 187, 1.0], [188, 198, 1.0]]}, "1971.sigirconf_conference-71.2": {"snippet": "...ated processing of natural language is a realistic goal. It should not be neglected, despite the fact that it is difficult to predict when this goal will be achieved.\nKEY WORDS AND PHRASESnatural lang...", "weights": [[8, 18, 1.0], [22, 29, 1.0], [30, 38, 1.0], [199, 207, 1.0]]}, "2005.clef_workshop-2005.19": {"snippet": "...ight Natural Language Processing to Ad-Hoc Cross Language Information Retrieval", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [52, 60, 1.0], [61, 72, 1.0], [73, 82, 1.0]]}, "2019.sigirconf_workshop-2019birndl.0": {"snippet": "...nced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) co-located with the 42nd International ACM SIGIR Conference on Research and Development in Information Re...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 41, 1.0], [42, 50, 1.0], [51, 61, 1.0], [189, 200, 1.0], [201, 210, 1.0]]}, "W15-5955": {"snippet": "...r in natural language. The emphasis of this paper is on the natural language processing(NLP) techniques used to retrieve the relevant information from the English word problem. The system shows improv...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [63, 70, 1.0], [71, 79, 1.0], [80, 90, 1.0], [137, 148, 1.0]]}, "2018.sigirconf_conference-2018.88": {"snippet": "...ces. Natural language interfaces, exemplified by virtual assistants like Apple Siri and Microsoft Cortana, are widely believed to be a promising direction. However, current natural language interfaces...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [176, 183, 1.0], [184, 192, 1.0]]}}}
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Colla...", "weights": [[8, 22, 1.0], [70, 77, 1.0], [81, 95, 1.0], [96, 103, 1.0]]}, "2020.wwwjournals_journal-ir0volumeA23A3.1": {"snippet": "Recommender system for marketing optimization", "weights": []}, "2017.ecir_workshop-2017bir.2": {"snippet": "...nder Systems for Academia: The Pain and Gain in Building, Operating, and Researching them\n\n\n Research on recommender systems is a challenging task, as is building and operating such systems. Major cha...", "weights": [[8, 15, 1.0], [120, 127, 1.0], [185, 192, 1.0]]}, "2015.wsdm_conference-2015.2": {"snippet": "...nder Systems\n\n\n ABSTRACTRecommender systems are an integral part of how we experience the Web today and they have become so ubiquitous that we do not even notice them anymore. However, today's recomme...", "weights": [[8, 15, 1.0], [39, 46, 1.0]]}, "2007.ismir_conference-2007.47": {"snippet": "...usic Recommendation Systems", "weights": [[8, 22, 1.0], [23, 30, 1.0]]}, "2020.sigirconf_conference-2020.355": {"snippet": "...nder systems. CCS CONCEPTS \u2022 Information systems \u2192 Users and interactive retrieval; Recommender systems; Personalization; \u2022 Human-centered computing \u2192 Interactive systems and tools.", "weights": [[8, 15, 1.0], [44, 51, 1.0], [99, 106, 1.0], [166, 173, 1.0]]}, "2018.ipm_journal-ir0volumeA54A4.13": {"snippet": "...nder systems. Selecting suitable neighbors set for the users leads to improve the accuracy of ratings prediction in recommendation process. In this paper, a novel social recommendation method is propo...", "weights": [[8, 15, 1.0], [119, 133, 1.0], [173, 187, 1.0]]}, "2017.clef_conference-2017w.8": {"snippet": "...nder systems have been developed for supporting users in finding the most interesting items in vast collections of available items. In contrast to traditional recommender systems, news recommender sys...", "weights": [[8, 15, 1.0], [174, 181, 1.0], [200, 207, 1.0]]}, "2018.tweb_journal-ir0volumeA12A1.3": {"snippet": "...y of recommendation significantly.\nCCS Concepts: \u2022 Information systems \u2192 Social recommendation;Additional Key Words and Phrases: Recommendation system, Location-based social network Permission to make...", "weights": [[8, 22, 1.0], [66, 73, 1.0], [83, 97, 1.0], [132, 146, 1.0]]}, "2016.ismir_conference-2016.11": {"snippet": "...usic Recommendation Systems: Do Demographic, Profiling, and Contextual Features Improve Their Performance?", "weights": [[8, 22, 1.0], [23, 30, 1.0]]}, "2019.tois_journal-ir0volumeA37A3.3": {"snippet": "... for recommendation than historical ones.\nCCS Concepts: \u2022 Information systems \u2192 Recommender systems;X. Li is now at the National University of Defense Technology, Changsha, China. This research was pa...", "weights": [[8, 22, 1.0], [73, 80, 1.0], [95, 102, 1.0]]}, "2021.wsdm_conference-2021.147": {"snippet": "...onal systems will have a major impact on human-computer interaction, and specifically, the IR/DM/RecSys communities have begun to explore Conversational Recommendation Systems. Conversational recommen...", "weights": [[8, 15, 1.0], [156, 170, 1.0], [171, 178, 1.0], [195, 209, 1.0]]}, "2017.chiir_conference-2017.68": {"snippet": "...uery recommendation, using an approximation of need.\nCCS Concepts\u2022Information systems \u2192 Query suggestion; Personalization; Document topic models; Retrieval effectiveness;\nKeywordsSearch session analys...", "weights": [[8, 22, 1.0], [81, 88, 1.0]]}, "2011.wsdm_conference-2011.36": {"snippet": "...nder systems with social regularization\n\n\n ABSTRACTAlthough Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In th...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [176, 183, 1.0]]}, "2016.wwwconf_conference-2016.11": {"snippet": "...nder Systems Work the Best?: The Moderating Effects of Product Attributes and Consumer Reviews on Recommender Performance\n\n\n ABSTRACTWe investigate the moderating effect of product attributes and cons...", "weights": [[8, 15, 1.0]]}, "2017.tweb_journal-ir0volumeA11A3.1": {"snippet": "...onal Recommendation in Heterogeneous Networks\n\n\n Recommender systems are key components in information-seeking contexts where personalization is sought. However, the dominant framework for recommendat...", "weights": [[8, 22, 1.0], [64, 71, 1.0], [192, 206, 1.0]]}, "2014.sigirconf_conference-2014.212": {"snippet": "...nder systems and information retrieval\n\n\n ABSTRACTThe development and evaluation of Information Retrieval and Recommender Systems has traditionally focused on the relevance and accuracy of retrieved d...", "weights": [[8, 15, 1.0], [125, 132, 1.0]]}, "2009.sigirconf_conference-2009.27": {"snippet": "...nder systems suffer the following problems: (1) The large-scale and sparse data of the user-item matrix seriously affect the recommendation quality. As a result, most of the recommender systems cannot...", "weights": [[8, 15, 1.0], [128, 142, 1.0], [189, 196, 1.0]]}, "2018.sigirconf_conference-2018.55": {"snippet": "...nder systems today are designed in the context of an offline setting. Compared with the traditional recommender systems, large-volume and high-velocity are posing severe challenges for streaming recom...", "weights": [[8, 15, 1.0], [115, 122, 1.0]]}, "2005.cikm_conference-2005.115": {"snippet": "...ased recommendation system\n\n\n ABSTRACTThe explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems-a personalized information filtering ...", "weights": [[8, 22, 1.0], [158, 165, 1.0]]}, "2011.tweb_journal-ir0volumeA5A1.0": {"snippet": "...nder systems", "weights": [[8, 15, 1.0]]}, "2021.wsdm_conference-2021.138": {"snippet": "...nder Systems Utilizing Side Information\n\n\n Recommendation Systems (RS) are designed to assist users in decision making by recommending the most appropriate information or products for them. Nonetheles...", "weights": [[8, 15, 1.0], [46, 60, 1.0], [61, 68, 1.0]]}, "2013.sigirconf_conference-2013.168": {"snippet": "...term recommendation systems", "weights": [[8, 22, 1.0], [23, 30, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A6.3": {"snippet": "...ased recommendation system for online social networks\n\n\n Abstract Recommendation systems are popular both commercially and in the research community. For example, Online in Social Networks (OSNs) like...", "weights": [[8, 22, 1.0], [69, 83, 1.0], [84, 91, 1.0]]}, "2013.ecir_conference-2013.35": {"snippet": "...nder Systems\n\n\n Abstract.Recommender systems suggest new items to users to try or buy based on their previous preferences or behavior. Many times the information used to recommend these items is limit...", "weights": [[8, 15, 1.0], [40, 47, 1.0]]}, "2008.wwwconf_conference-2008.21": {"snippet": "...ased recommendation systems: an axiomatic approach\n\n\n ABSTRACTHigh-quality, personalized recommendations are a key feature in many online systems. Since these systems often have explicit knowledge of ...", "weights": [[8, 22, 1.0], [23, 30, 1.0], [141, 148, 1.0], [162, 169, 1.0]]}, "2012.wwwconf_conference-2012c.20": {"snippet": "...s of recommendation requests are very important. In this paper, we take a graph-based approach to accomplishing such requirements in recommender systems and present a generic graph-based multidimensio...", "weights": [[8, 22, 1.0], [148, 155, 1.0]]}, "2011.tois_journal-ir0volumeA29A4.3": {"snippet": "Recommendation systems with complex constraints: A course recommendation perspective\n\n\n We study the problem of making recommendations when the objects to be recommended must also satisfy constraints ...", "weights": [[0, 14, 1.0], [15, 22, 1.0], [58, 72, 1.0]]}, "2013.cikm_workshop-2013livinglab.3": {"snippet": "...nder systems have become a ubiquitous feature in the online world. Research on systems and algorithms in this area has flourished, leading to novel techniques for personalization and recommendation. T...", "weights": [[8, 15, 1.0], [82, 89, 1.0], [186, 200, 1.0]]}, "2016.ipm_journal-ir0volumeA52A3.6": {"snippet": "... the recommendation for a group of size one (i.e. single user recommendation). Obtained results, support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation...", "weights": [[8, 22, 1.0], [65, 79, 1.0], [189, 203, 1.0]]}, "2017.wsdm_conference-2017.97": {"snippet": "...nder Systems: Research Direction\n\n\n ABSTRACTRecommender systems are omnipresent on the web. They aim to address the problem of information overload. Recommender systems personalizes the content to eac...", "weights": [[8, 15, 1.0], [59, 66, 1.0], [164, 171, 1.0]]}, "2015.clef_conference-2015w.55": {"snippet": "...nder systems, able to deliver recommendations in real time, is a challenging task. In contrast to traditional recommender systems, recommending news entails additional requirements. These requirements...", "weights": [[8, 15, 1.0], [125, 132, 1.0]]}, "2017.iir_workshop-2017.13": {"snippet": "...nder Systems", "weights": [[8, 15, 1.0]]}, "2020.wsdm_conference-2020.1": {"snippet": "...s of Recommendation\n\n\n The challenge of building a good recommendation system is deeply connected to missing data-unknown features and labels to suggest the most \"valuable\" items to the user. The myst...", "weights": [[8, 22, 1.0], [59, 73, 1.0]]}, "D19-1189": {"snippet": "... the recommendation system by introducing knowledge-grounded information about users' preferences, and the recommender system can improve that of the dialog generation system by providing recommendati...", "weights": [[8, 22, 1.0], [191, 205, 1.0]]}, "2019.sigirjournals_journal-ir0volumeA53A1.7": {"snippet": "...nder systems", "weights": [[8, 15, 1.0]]}, "2015.cikm_conference-2015.150": {"snippet": "...nder systems by exploiting the anticipated social-network information diffusion and withholding recommendation of items that are expected to reach a user through sharing/reposting. We devise a novel r...", "weights": [[8, 15, 1.0], [99, 113, 1.0]]}, "2019.tois_journal-ir0volumeA37A3.4": {"snippet": "...e of recommendation. In this article, we consider the knowledge graph (KG) as the source of side information. To address the limitations of existing embedding-based and path-based methods for KG-aware...", "weights": [[8, 22, 1.0]]}, "2014.wwwconf_conference-2014.11": {"snippet": "...ized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering approaches to recommendation systems ...", "weights": [[8, 22, 1.0], [23, 30, 1.0], [180, 194, 1.0], [195, 202, 1.0]]}, "2020.wsdm_conference-2020.75": {"snippet": "...s in Recommendation Systems: Opinion Elicitation and Diffusion\n\n\n Recommendation systems typically rely on the interactions between a crowd of ordinary users and items, ignoring the fact that many rea...", "weights": [[8, 22, 1.0], [23, 30, 1.0], [69, 83, 1.0], [84, 91, 1.0]]}, "2019.wsdm_conference-2019.31": {"snippet": "...nder Systems\n\n\n ABSTRACTThe increasing role of recommender systems in many aspects of society makes it essential to consider how such systems may impact social good. Various modifications to recommend...", "weights": [[8, 15, 1.0], [62, 69, 1.0], [137, 144, 1.0], [194, 208, 1.0]]}, "2018.cikm_workshop-2018w.4": {"snippet": "...nder Systems\n\n\n Chat-based recommender systems are getting more and more attention in recent time given their natural interaction with the user. Indeed, chat-based recommender systems implement a para...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [179, 186, 1.0]]}, "2017.wwwconf_conference-2017c.165": {"snippet": "...rich recommendation.\nKeywordsRecommender Systems; Information Network Mining; contexrich recommendation; Spatio-temporal recommendation\nORGANIZERSYizhou Sun (Ph.D., UIUC), is an assistant professor at...", "weights": [[8, 22, 1.0], [44, 51, 1.0], [92, 106, 1.0], [124, 138, 1.0]]}, "2014.ecir_conference-2014.6": {"snippet": "...ised Recommendation System for Context-Aware Suggestions", "weights": [[8, 22, 1.0]]}, "2007.wwwjournals_journal-ir0volumeA10A4.3": {"snippet": "...nder systems that engage some MCDM method. Such systems, which we refer to as multi-criteria recommender systems, have early demonstrated the potential of applying MCDM methods to facilitate recommend...", "weights": [[8, 15, 1.0], [51, 58, 1.0], [108, 115, 1.0], [194, 208, 1.0]]}, "2014.wwwconf_conference-2014.32": {"snippet": "...tart recommendation\n\n\n ABSTRACTRecommendation systems have been widely used in E-commerce sites, social networks, etc. One of the core tasks in recommendation systems is to predict the users' ratings ...", "weights": [[8, 22, 1.0], [49, 56, 1.0], [147, 161, 1.0], [162, 169, 1.0]]}, "2017.ecir_conference-2017.73": {"snippet": "... Set Recommendation in Group Recommender Systems", "weights": [[8, 22, 1.0], [44, 51, 1.0]]}, "2008.cikm_workshop-2008wicow.4": {"snippet": "...ring recommendation method for use with recently popular online social tagging systems. Combining the information provided by tagging systems with the effective recommendation abilities given by colla...", "weights": [[8, 22, 1.0], [82, 89, 1.0], [137, 144, 1.0], [164, 178, 1.0]]}, "2016.fire_conference-2016.2": {"snippet": "...ware Recommendation System\n\n\n ABSTRACTThe effectiveness of recommendation systems is improving with the incorporation of richer context. The frequentist recommendation methods such as Markov models ar...", "weights": [[8, 22, 1.0], [62, 76, 1.0], [77, 84, 1.0], [156, 170, 1.0]]}, "2004.wwwconf_conference-2004.40": {"snippet": "...nder systems for fun and profit\n\n\n ABSTRACTRecommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application ...", "weights": [[8, 15, 1.0], [58, 65, 1.0]]}, "2013.sigirconf_conference-2013.127": {"snippet": "...nder systems\n\n\n ABSTRACTPersonalized recommender systems aim to push only the relevant items and information directly to the users without requiring them to browse through millions of web resources. T...", "weights": [[8, 15, 1.0], [52, 59, 1.0]]}, "2002.cikm_conference-2002.7": {"snippet": "Meta-recommendation systems: user-controlled integration of diverse recommendations\n\n\n ABSTRACTIn a world where the number of choices can be overwhelming, recommender systems help users find and evalu...", "weights": [[5, 19, 1.0], [20, 27, 1.0], [167, 174, 1.0]]}, "2020.wwwconf_conference-2020c.22": {"snippet": "...-art recommendation algorithms. A focus of this repository is on best practices in development of recommendation systems. We have also incorporated learnings from our experience with recommendation sy...", "weights": [[8, 22, 1.0], [101, 115, 1.0], [116, 123, 1.0], [186, 200, 1.0], [201, 208, 1.0]]}, "2017.tois_journal-ir0volumeA35A4.8": {"snippet": "...nder systems have been proposed and deployed for the App domain. However, existing work on App recommendation has largely focused on one single platform (e.g., smartphones), while it ignores the rich ...", "weights": [[8, 15, 1.0], [98, 112, 1.0]]}, "2005.tois_journal-ir0volumeA23A3.0": {"snippet": "...nder systems", "weights": [[8, 15, 1.0]]}, "R19-2009": {"snippet": "...nder Systems for Books\n\n\n Recommender systems are an essential part of today's largest websites. Without them, it would be hard for users to find the right products and content. One of the most popula...", "weights": [[8, 15, 1.0], [41, 48, 1.0]]}, "2004.tois_journal-ir0volumeA22A1.0": {"snippet": "...hese systems, originally referred to as collaborative filtering systems, were developed to address two challenges that could not be addressed by existing keyword-based information filtering systems. F...", "weights": [[8, 15, 1.0], [67, 74, 1.0], [193, 200, 1.0]]}, "2012.wwwconf_conference-2012.2": {"snippet": "...nder systems\n\n\n ABSTRACTThe aim of a web-based recommender system is to provide highly accurate and up-to-date recommendations to its users; in practice, it will hope to retain its users over time. Ho...", "weights": [[8, 15, 1.0]]}, "2013.sigirconf_conference-2013.32": {"snippet": "...nder systems in various scenarios, including the zero-query pull-based recommendation scenario (e.g. recommendation on an e-commerce web site) and a proactive push-based promotion scenario (e.g. email...", "weights": [[8, 15, 1.0], [74, 88, 1.0], [104, 118, 1.0]]}, "2011.wwwconf_conference-2011c.117": {"snippet": "...nder systems\n\n\n ABSTRACTThe goal of this tutorial is to expose participants to the current research on social recommender systems (i.e., recommender systems for the social web). Participants will beco...", "weights": [[8, 15, 1.0], [125, 132, 1.0], [152, 159, 1.0]]}, "2001.wwwconf_conference-2001p.25": {"snippet": "...nder systems are new types of internet-based software tools, designed to help users find their way through today's complex on-line shops and entertainment websites. Here we provide an overview of curr...", "weights": [[8, 15, 1.0]]}, "2020.emnlp-main.654": {"snippet": "...able Recommendation Dialog Systems\n\n\n In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. However, this is a challenge in developing a s...", "weights": [[8, 22, 1.0], [30, 37, 1.0], [44, 58, 1.0]]}, "2017.sigirconf_workshop-2017ecom.12": {"snippet": "...nder systems (RSs) are useful in libraries, schools and e-commerce applications. To our knowledge, no book RS exploits social networks other than book-cataloguing websites. We propose a recommendation...", "weights": [[8, 15, 1.0], [189, 203, 1.0]]}, "2018.wwwconf_conference-2018c.51": {"snippet": "...nder Systems\n\n\n ABSTRACTRecent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-proces...", "weights": [[8, 15, 1.0], [43, 57, 1.0], [58, 65, 1.0], [137, 151, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.6": {"snippet": "...ideo Recommendation\n\n\n With the availability of abundant online multi-relational video information, recommender systems that can effectively exploit these sorts of data and suggest creatively interest...", "weights": [[8, 22, 1.0], [115, 122, 1.0]]}, "2021.wsdm_conference-2021.141": {"snippet": "...ware Recommendation in Multi-Sided Platforms\n\n\n Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important ...", "weights": [[8, 22, 1.0], [112, 119, 1.0]]}, "2016.ipm_journal-ir0volumeA52A6.8": {"snippet": "...ice, recommendation systems are widely used by ecommerce practitioners and have become an important research topic in the field of information sciences and decision support systems (Liang et al., 2008...", "weights": [[8, 22, 1.0], [23, 30, 1.0], [176, 183, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A4.5": {"snippet": "...nder systems in tackling cold start problem and dealing with sparse rating datasets. Our system also enjoys improved recommendation accuracy compared with several major existing recommendation approac", "weights": [[8, 15, 1.0], [120, 134, 1.0], [181, 195, 1.0]]}, "2012.cikm_conference-2012.302": {"snippet": "...brid recommendation\n\n\n ABSTRACTMost existing recommender systems can be classified into two categories: collaborative filtering and content-based filtering. Hybrid recommender systems combine the adva...", "weights": [[8, 22, 1.0], [60, 67, 1.0], [179, 186, 1.0]]}, "2016.sigirconf_conference-2016.34": {"snippet": "...onal recommendation systems (RSs) aim to recommend items that are relevant to the user's interest. Unfortunately, the recommended items will soon become too familiar to the user and hence fail to arou...", "weights": [[8, 22, 1.0], [23, 30, 1.0]]}, "2014.tist_journal-ir0volumeA5A4.1": {"snippet": "...nder Systems\n\n\n Recommender systems are quickly becoming ubiquitous in applications such as e-commerce, social media channels, and content providers, among others, acting as an enabling mechanism desi...", "weights": [[8, 15, 1.0], [31, 38, 1.0]]}, "2004.tois_journal-ir0volumeA22A1.5": {"snippet": "...op-N recommendation algorithms\n\n\n The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems-a personalized information filtering tech...", "weights": [[8, 22, 1.0], [154, 161, 1.0]]}, "2020.ir_journal-ir0volumeA23A4.0": {"snippet": "...nder systems", "weights": [[8, 15, 1.0]]}, "2018.sigirconf_conference-2018.231": {"snippet": "...able Recommendation and Search, under the background of Explainable AI in a more general sense. Early recommendation and search systems adopted intuitive yet easily explainable models to generate reco...", "weights": [[8, 22, 1.0], [105, 119, 1.0], [131, 138, 1.0], [199, 213, 1.0]]}, "2017.wwwconf_conference-2017.42": {"snippet": "...nder Systems\n\n\n ABSTRACTThe increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming s...", "weights": [[8, 15, 1.0], [79, 86, 1.0], [178, 185, 1.0]]}, "2017.wsdm_workshop-2017swm.4": {"snippet": "...nder systems for scholarly information\n\n\n ABSTRACT e depth and breadth of research now being published is overwhelming for an individual researcher to keep track of, let alone consume. Recommender sys...", "weights": [[8, 15, 1.0], [200, 207, 1.0]]}, "2018.cikm_conference-2018.138": {"snippet": "...nder systems and deep neural network. And deep learning methods have been shown to achieve state-of-the-art on many recommendation tasks. For example, a recent model, NeuMF, first projects users and i...", "weights": [[8, 15, 1.0], [119, 133, 1.0]]}, "2019.cikm_conference-2019.156": {"snippet": "Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling", "weights": []}, "2007.wwwconf_conference-2007.195": {"snippet": "...nder systems based on maximum entropy\n\n\n ABSTRACTWe propose a model for user purchase behavior in online stores that provide recommendation services. We model the purchase probability given recommenda...", "weights": [[8, 15, 1.0], [128, 142, 1.0]]}, "2014.tist_journal-ir0volumeA5A4.4": {"snippet": "...d of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and no...", "weights": [[8, 22, 1.0], [23, 30, 1.0], [112, 126, 1.0]]}}}
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Neverthe...", "weights": []}, "2004.ir_journal-ir0volumeA7A34.6": {"snippet": "OSGS - A Personalized Online Store for E-Commerce Environments", "weights": []}, "2016.clef_conference-2016.14": {"snippet": "...erce Search\n\n\n Abstract. During the online shopping process, users search for interesting products in order to quickly access those that fit with their needs among a long tail of similar or closely re...", "weights": [[8, 14, 1.0], [70, 76, 1.0]]}, "2011.wsdm_conference-2011.9": {"snippet": "... web search using long term browsing history\n\n\n ABSTRACTPersonalizing web search results has long been recognized as an avenue to greatly improve the search experience. We present a personalization ap...", "weights": [[8, 14, 1.0], [77, 83, 1.0], [153, 159, 1.0]]}, "2012.wwwconf_conference-2012c.137": {"snippet": "...ajor search engines develop different types of personalisation of search results. Personalisation includes deriving user's long-term preferences, query disambiguation etc. User sessions provide very p...", "weights": [[8, 14, 1.0], [69, 75, 1.0]]}, "2012.airs_conference-2012.25": {"snippet": "... and Search in E-Commerce", "weights": [[8, 14, 1.0]]}, "2018.sigirconf_workshop-2018ecom.31": {"snippet": "...erce Search\n\n\n Query Understanding is a semantic search method that can classify tokens in a customer's search query to entities such as Product, Brand, etc. This method can overcome the limitations o...", "weights": [[8, 14, 1.0], [52, 58, 1.0], [107, 113, 1.0]]}, "2017.sigirconf_conference-2017.67": {"snippet": "...duct Search\n\n\n ABSTRACTProduct search is an important part of online shopping. In contrast to many search tasks, the objectives of product search are not con ned to retrieving relevant products. Inste...", "weights": [[8, 14, 1.0], [34, 40, 1.0], [102, 108, 1.0], [142, 148, 1.0]]}, "2018.cikm_conference-2018.21": {"snippet": "...onal Search and Recommendation: System Ask, User Respond\n\n\n ABSTRACTConversational search and recommendation based on user-system dialogs exhibit major differences from conventional search and recomme...", "weights": [[8, 14, 1.0], [86, 92, 1.0], [185, 191, 1.0]]}, "2009.wwwconf_conference-2009.130": {"snippet": "Rare item detection in e-commerce site", "weights": []}, "2020.wsdm_conference-2020.122": {"snippet": "... Web Search and Conversational Agents\n\n\n User intent understanding is a crucial step in designing both conversational agents and search engines. Detecting or inferring user intent is challenging, sinc...", "weights": [[8, 14, 1.0], [132, 138, 1.0]]}, "2020.ecir_conference-20201.43": {"snippet": "...erce search engines consists of infrequent queries and identifying user intent in such tail queries is critical in retrieving relevant products. The intent of a query is defined as a labelling of its ...", "weights": [[8, 14, 1.0]]}, "2020.wwwconf_conference-2020.244": {"snippet": "Review-guided Helpful Answer Identification in E-commerce", "weights": []}, "2017.ictir_conference-2017.34": {"snippet": "Personalised Search Time Prediction using Markov Chains\n\n\n ABSTRACTFor improving the effectiveness of Interactive Information Retrieval (IIR), a system should minimise the search time by guiding the u...", "weights": [[0, 12, 1.0], [13, 19, 1.0], [172, 178, 1.0]]}, "2019.sigirconf_workshop-2019ecom.4": {"snippet": "... web search, query classification (QC) is used to map a query to a user's search intent. In the e-commerce domain, user's product search queries can be broadly categorised into product specific querie...", "weights": [[8, 14, 1.0], [77, 83, 1.0], [133, 139, 1.0]]}, "2019.tois_journal-ir0volumeA37A2.5": {"snippet": "...duct search. In fact, personalized search has been widely studied in literature over the past few years. Thereinto, the study of personalized web search incorporating the user preference is the most r...", "weights": [[8, 14, 1.0], [38, 44, 1.0], [149, 155, 1.0]]}, "2020.wsdm_conference-2020.5": {"snippet": "...duct Search on E-Commerce Platforms\n\n\n Product search forms an indispensable component of any e-commerce service, and helps customers find products of their interest from a large catalog on these webs...", "weights": [[8, 14, 1.0], [50, 56, 1.0]]}, "2014.sigirconf_workshop-2014pir.6": {"snippet": "... Web Search: Exploring Search Query Parameters and User Information Privacy Implications-The Case of Google\n\n\n Personalised search adapts search results to the needs and interests of users. This is do...", "weights": [[8, 14, 1.0], [26, 32, 1.0], [114, 126, 1.0], [127, 133, 1.0], [141, 147, 1.0]]}, "2018.sigirconf_conference-2018.182": {"snippet": "...erce Search\n\n\n ABSTRACTUnderstanding the search tasks and search behavior of users is necessary for optimizing search engine results. While much work has been done on understanding the users in Web se...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [61, 67, 1.0], [114, 120, 1.0], [201, 207, 1.0]]}, "2012.sigirconf_conference-2012.118": {"snippet": "...rent search services and their associated pipelines for their search interface. Students can then select and customise the set of search services that they would like within their search interface. As...", "weights": [[8, 14, 1.0], [65, 71, 1.0], [133, 139, 1.0], [183, 189, 1.0]]}, "2010.airs_conference-2010.3": {"snippet": "...duct Search - Exemplified in a Chinese E-commerce Portal", "weights": [[8, 14, 1.0]]}, "2007.wwwconf_conference-2007.59": {"snippet": "...ized search has significant improvement over common web search on some queries but it has little effect on other queries (e.g., queries with small click entropy). It even harms search accuracy under s...", "weights": [[8, 14, 1.0], [59, 65, 1.0], [180, 186, 1.0]]}, "2010.wsdm_conference-2010.36": {"snippet": "...ored search\n\n\n ABSTRACTSponsored search is a multi-billion dollar business that generates most of the revenue for search engines. Predicting the probability that users click on ads is crucial to spons...", "weights": [[8, 14, 1.0], [36, 42, 1.0], [117, 123, 1.0]]}, "2014.clef_conference-2014.8": {"snippet": "...y of Personalised Medical Literature Search", "weights": [[8, 20, 1.0], [40, 46, 1.0]]}, "2020.sigirconf_conference-2020.161": {"snippet": "...duct search logs has shown that personalization does not always improve product search quality. Most existing product search techniques, however, conduct undifferentiated personalization across search...", "weights": [[8, 14, 1.0], [83, 89, 1.0], [121, 127, 1.0], [197, 203, 1.0]]}, "2022.ecnlp-1.9": {"snippet": "...erce Search\n\n\n In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries. However, resolving phonetic errors is a...", "weights": [[8, 14, 1.0], [32, 38, 1.0], [148, 154, 1.0]]}, "2018.sigirconf_conference-2018.223": {"snippet": "...s of search (exploratory search vs. lookup tasks), recommender systems, and natural language processing in e-commerce portals. Recently, the explosive popularity of e-commerce sites has made research ...", "weights": [[8, 14, 1.0], [28, 34, 1.0]]}, "2013.cikm_conference-2013.157": {"snippet": "...erce search\n\n\n ABSTRACTCommerce search engines allow users to discover products, learn about them, and, importantly, make purchases. Commerce search is a challenging problem -one that is very differen...", "weights": [[8, 14, 1.0], [35, 41, 1.0], [145, 151, 1.0]]}, "2007.wwwconf_conference-2007.180": {"snippet": "A novel collaborative filtering-based framework for personalized services in m-commerce\n\n\n ABSTRACTWith the rapid growth of wireless technologies and handheld devices, m-commerce is becoming a promisi...", "weights": []}, "2018.chiir_conference-2018.70": {"snippet": "...n of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmi...", "weights": [[8, 20, 1.0], [108, 114, 1.0], [148, 160, 1.0]]}, "2021.wsdm_conference-2021.144": {"snippet": "Personalization in Practice: Methods and Applications\n\n\n Personalization is one of the key applications in machine learning with widespread usage across e-commerce, entertainment, production, healthca...", "weights": []}, "2015.wwwconf_conference-2015c.65": {"snippet": "...ware Search Tasks for Search Personalisation\n\n\n ABSTRACTRecent research has shown that mining and modelling search tasks helps improve the performance of search personalisation. Some approaches have b...", "weights": [[8, 14, 1.0], [25, 31, 1.0], [111, 117, 1.0], [157, 163, 1.0]]}, "2021.chiir_workshop-2021birds.9": {"snippet": "Location-based Reminder for Memorizing What Visitors Learn at a Museum", "weights": []}, "2012.cikm_conference-2012.362": {"snippet": "...duct search by best-selling prediction in e-commerce\n\n\n ABSTRACTWith the rapid growth of E-Commerce on the Internet, online product search service has emerged as a popular and effective paradigm for c...", "weights": [[8, 14, 1.0], [135, 141, 1.0]]}, "2015.ecir_conference-2015.67": {"snippet": "... for Search Personalisation", "weights": [[8, 14, 1.0]]}, "2014.wwwconf_conference-2014.11": {"snippet": "... web search, and more. Collaborative filtering approaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers) is lowrank. In this paper, we examine an al...", "weights": [[8, 14, 1.0]]}, "2007.sigirconf_conference-2007.105": {"snippet": "... for personalised search systems is the ability to disambiguate queries based on some knowledge of a user's interests. An analysis of log files from three search providers, covering a range of scenari...", "weights": [[8, 20, 1.0], [21, 27, 1.0], [158, 164, 1.0]]}, "2019.ir_journal-ir0volumeA22A5.0": {"snippet": "... and search", "weights": [[8, 14, 1.0]]}, "2002.cikm_conference-2002.71": {"snippet": "... web search by mapping user queries to categories\n\n\n ABSTRACTCurrent web search engines are built to serve all users, independent of the needs of any individual user. Personalization of web search is ...", "weights": [[8, 14, 1.0], [76, 82, 1.0], [193, 199, 1.0]]}, "2018.sigirconf_workshop-2018ecom.19": {"snippet": "...uent search, recommendations and related personalized services. E-commerce product classification is challenging due to the large scale and complexity of the product information and categories. In the...", "weights": [[8, 14, 1.0]]}, "2015.sigirconf_workshop-2015sps.3": {"snippet": "Improving Contextual Suggestions using Open Web Domain Knowledge\n\n\n Contextual suggestion aims at recommending items to users given their current context, such as location-based tourist recommendation...", "weights": []}, "2021.chiir_workshop-2021birds.11": {"snippet": "Quiz Generation on the Electronic Guide Application for Improving Learning Experience in the Museum", "weights": []}, "2018.sigirconf_workshop-2018ecom.8": {"snippet": "...uage Search Query in E-Commerce\n\n\n This paper is a preliminary work, which studies the problem of finding central intention of natural language queries with multiple intention terms in e-commerce sear...", "weights": [[8, 14, 1.0], [199, 205, 1.0]]}, "2003.ecir_conference-2003.9": {"snippet": "Attaining Fast and Successful Searches in E-commerce Environments", "weights": []}, "2014.wsdm_conference-2014.80": {"snippet": "... for search and for other domains. This is increasingly the case as devices enable more forms of data to be gathered, are always on/connected and are always with users. This workshop brings researcher...", "weights": [[8, 14, 1.0]]}, "2018.wwwconf_conference-2018.189": {"snippet": "...ored Search\n\n\n ABSTRACTOn most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding ...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [135, 141, 1.0]]}, "2009.wsdm_conference-2009.5": {"snippet": "...ized search\n\n\n ABSTRACTPersonalized Web search takes advantage of information about an individual to identify the most relevant results for that person. A challenge for personalization lies in collect...", "weights": [[8, 14, 1.0], [43, 49, 1.0]]}, "2008.wwwconf_conference-2008.49": {"snippet": "...eted search\n\n\n ABSTRACTFaceted search is becoming a popular method to allow users to interactively search and navigate complex information spaces. A faceted search system presents users with keyvalue ...", "weights": [[8, 14, 1.0], [34, 40, 1.0], [102, 108, 1.0], [160, 166, 1.0]]}, "2015.sigirconf_workshop-2015sps.7": {"snippet": "... web search logs to present a large-scale study of such \"on behalf of\" searches. We develop an exploratory technique for recognizing such searches, and present information to describe and understand t...", "weights": [[8, 14, 1.0]]}, "2019.chiir_conference-2019.55": {"snippet": "...n of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmi...", "weights": [[8, 20, 1.0], [108, 114, 1.0], [148, 160, 1.0]]}, "2020.wwwconf_conference-2020c.44": {"snippet": "...erce Search\n\n\n We address the problem of personalization in the context of eCommerce search. Specifically, we develop personalization ranking features that use in-session context to augment a generic ...", "weights": [[8, 14, 1.0], [88, 94, 1.0]]}, "2011.wsdm_conference-2011.56": {"snippet": "...mark search using personalised latent variable language models\n\n\n ABSTRACTSocial tagging systems have recently become very popular as a method of categorising information online and have been used to ...", "weights": [[8, 14, 1.0], [21, 33, 1.0]]}, "2016.wsdm_conference-2016.87": {"snippet": "...duct search system similar to Amazon. The system hosts real skin care products from Amazon. The experimental subjects are hired from Amazon Mechanical Turk. Each subject is instructed to go through mu...", "weights": [[8, 14, 1.0]]}, "2020.cikm_conference-2020.95": {"snippet": "Personalized Re-ranking with Item Relationships for E-commerce", "weights": []}, "2009.cikm_conference-2009.278": {"snippet": "...ween search sessions to support multi-session search tasks. This approach to supporting users with their multi-session search tasks is evaluated in a between-subjects multiple time-series study with t...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [122, 128, 1.0]]}, "2019.sigirconf_conference-2019.200": {"snippet": "...tive Search Experience in E-commerce Platforms", "weights": [[8, 14, 1.0]]}, "2018.sigirconf_workshop-2018ecom.29": {"snippet": "...erce Search and Discovery\n\n\n E-Commerce (E-Com) search is an emerging problem with multiple new challenges. One of the primary challenges constitutes optimizing multiple objectives involving business ...", "weights": [[8, 14, 1.0], [51, 57, 1.0]]}, "2011.wsdm_conference-2011.84": {"snippet": "...very search engine. It helps search engine users narrow or broaden their searches. Published work on query suggestion methods has mainly focused on the web domain. But, the module is also popular in t...", "weights": [[8, 14, 1.0], [32, 38, 1.0]]}, "2020.sigirconf_conference-2020.143": {"snippet": "...ized Search\n\n\n Personalized search is a task to tailor the general document ranking list based on user interests to better satisfy the user's information need. Many personalized search models have bee...", "weights": [[8, 14, 1.0], [31, 37, 1.0], [181, 187, 1.0]]}, "2021.chiir_workshop-2021birds.10": {"snippet": "Information Retrieval Evaluation in Knowledge Acquisition Tasks", "weights": []}, "2015.sigirconf_workshop-2015sps.5": {"snippet": "Analysis of Sentiment Communities in Online Networks\n\n\n This article reports our experience in developing a recommender system (RS) able to suggest relevant people to the target user. Such a RS relies...", "weights": []}, "2010.sigirconf_conference-2010.49": {"snippet": "... the search experience to the user-specific needs has become evident. In this study we examine ways of personalising affective models trained on facial expression data. Using personalised data we adap...", "weights": [[8, 14, 1.0], [178, 190, 1.0]]}, "2017.sigirconf_workshop-2017ecom.1": {"snippet": "A Content-based Recommender System for E-commerce Offers and Coupons", "weights": []}, "2017.chiir_conference-2017.32": {"snippet": "...ng a personalised query suggestion framework for Intranet search. For each search session, we construct two temporal user profiles: a click user profile using the user's clicked documents and a query ...", "weights": [[8, 20, 1.0], [61, 67, 1.0], [78, 84, 1.0]]}, "2015.sigirconf_workshop-2015sps.2": {"snippet": "... the search or recent actions of the searcher such as queries issued, results clicked, and pages viewed. In this talk, I will describe a recent framework for personalized search which improves the qua...", "weights": [[8, 14, 1.0], [174, 180, 1.0]]}, "2014.wwwconf_conference-2014c.42": {"snippet": "...s in search and ranking ranges from traditional relational databases to document systems to web search and, more recently, vertical applications like product search in e-commerce or vertical search ap...", "weights": [[8, 14, 1.0], [99, 105, 1.0], [161, 167, 1.0], [194, 200, 1.0]]}, "2014.wsdm_conference-2014.26": {"snippet": "Taxonomy discovery for personalized recommendation\n\n\n ABSTRACTPersonalized recommender systems based on latent factor models are widely used to increase sales in e-commerce. Such systems use the past ...", "weights": []}, "2020.sigirconf_conference-2020.352": {"snippet": "...erce Search via Embedding Learning\n\n\n Nowadays e-commerce search has become an integral part of many people's shopping routines. Two critical challenges stay in today's e-commerce search: how to retri...", "weights": [[8, 14, 1.0], [61, 67, 1.0], [183, 189, 1.0]]}, "2015.sigirconf_workshop-2015sps.1": {"snippet": "Wisdom of Crowds or Wisdom of a Few?\n\n\n In this keynote we give an introduction to wisdom of crowds in the Web, the long tail of web content, and the bias involved in the generation of user generated ...", "weights": []}, "2013.wwwconf_conference-2013c.143": {"snippet": "Context mining and integration into predictive web analytics\n\n\n ABSTRACTPredictive Web Analytics is aimed at understanding behavioural patterns of users of various web-based applications: e-commerce, ...", "weights": []}, "2021.naacl-industry.19": {"snippet": "...erce Search\n\n\n Nowadays, with many e-commerce platforms conducting global business, e-commerce search systems are required to handle product retrieval under multilingual scenarios. Moreover, comparing...", "weights": [[8, 14, 1.0], [98, 104, 1.0]]}, "2012.wsdm_conference-2012.45": {"snippet": "... web search\n\n\n ABSTRACTWe present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms ...", "weights": [[8, 14, 1.0], [74, 80, 1.0], [135, 141, 1.0]]}, "2016.wwwconf_conference-2016.10": {"snippet": "...n or search in practice. The performance metrics of recommender and search systems currently tend to focus on just one side of the match, in some cases the consumers (e.g. satisfaction) and in other c...", "weights": [[8, 14, 1.0], [71, 77, 1.0]]}, "2013.cikm_conference-2013.323": {"snippet": "...vide personalised ranked lists of documents which improve significantly over a non-personalised baseline. Further examination shows that the performance of the personalised system is particularly good...", "weights": [[8, 20, 1.0], [86, 98, 1.0], [163, 175, 1.0]]}, "2018.sigirconf_workshop-2018ecom.18": {"snippet": "...s to search and buy products. Identifying attributes in a query is a critical component in connecting users to relevant items. However, in many cases, the queries have multiple attributes, and some of...", "weights": [[8, 14, 1.0]]}}}
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"W17-7515"}]}], "docs": {"2018.sigirconf_conference-2018.20": {"snippet": "Sentiment Analysis of Peer Review Texts for Scholarly Papers\n\n\n ABSTRACTSentiment analysis has been widely explored in many text domains, including product reviews, movie reviews, tweets, and so on. H...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [82, 90, 1.0]]}, "C16-3002": {"snippet": "...ring sentiment and opinions are one of the major categories of user generated data in social media. Sentiment analysis is the main technology to quickly capture what people think from these text data,...", "weights": [[8, 17, 1.0], [103, 112, 1.0], [113, 121, 1.0]]}, "2018.wwwconf_conference-2018c.379": {"snippet": "...cial Sentiment Analysis using Deep Learning\n\n\n ABSTRACTAspect based sentiment analysis aims to detect an aspect (i.e. features) in a given text and then perform sentiment analysis of the text with res...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [71, 80, 1.0], [81, 89, 1.0], [164, 173, 1.0], [174, 182, 1.0]]}, "2022.acl-srw.39": {"snippet": "...eted sentiment analysis. The results demonstrate that the proposed models outperform the traditional sentiment analysis models for the targeted sentiment analysis task.", "weights": [[8, 17, 1.0], [18, 26, 1.0], [104, 113, 1.0], [114, 122, 1.0], [147, 156, 1.0], [157, 165, 1.0]]}, "2021.emnlp-main.22": {"snippet": "...icit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training\n\n\n Aspect-based sentiment analysis aims to identify the sentiment polarity of a specific aspect in product re...", "weights": [[8, 17, 1.0], [34, 43, 1.0], [44, 52, 1.0], [110, 119, 1.0], [120, 128, 1.0], [150, 159, 1.0]]}, "2016.tois_journal-ir0volumeA35A1.6": {"snippet": "...s in Sentiment Analysis\n\n\n This article introduces a sentiment analysis approach that adopts the way humans read, interpret, and extract sentiment from text. Our motivation builds on the assumption th...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [56, 65, 1.0], [66, 74, 1.0], [140, 149, 1.0]]}, "2015.ipm_journal-ir0volumeA51A2.12": {"snippet": "...form sentiment analysis, there is no publicly available interactive on-line platform for dynamic adaptive sentiment analysis, which would be able to handle changes in data streams and adapt its behavi...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [109, 118, 1.0], [119, 127, 1.0]]}, "2012.coria_conference-2012.23": {"snippet": "...n in Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2017.ipm_journal-ir0volumeA53A4.1": {"snippet": "...tter sentiment analysis using hybrid cuckoo search method\n\n\n a b s t r a c tSentiment analysis is one of the prominent fields of data mining that deals with the identification and analysis of sentimen...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [89, 97, 1.0], [183, 191, 1.0]]}, "2016.wsdm_conference-2016.2": {"snippet": "...like sentiment, self-disclosure, and responsiveness) to address these dynamics. In the prediction phase, a tweet posted by a user is deemed a query and the predicted repliers/retweeters are retrieved ...", "weights": [[8, 17, 1.0]]}, "L12-1239": {"snippet": "...zing Sentiment Analysis on a User Forum", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2013.cikm_conference-2013.122": {"snippet": "...tent sentiment explanations for aspect-level review analysis\n\n\n ABSTRACTIn sentiment analysis, aspect-level review analysis has been an important task because it can catalogue, aggregate, or summarize...", "weights": [[8, 17, 1.0], [55, 63, 1.0], [78, 87, 1.0], [88, 96, 1.0], [118, 126, 1.0]]}, "J15-2004": {"snippet": "... for sentiment analysis, we develop a statistical parser to directly analyze the sentiment structure of a sentence. We show that complicated phenomena in sentiment analysis (e.g., negation, intensific...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [84, 93, 1.0], [157, 166, 1.0], [167, 175, 1.0]]}, "E17-3019": {"snippet": "...tif: Sentiment Analysis for the Digital Humanities\n\n\n Lingmotif is a lexicon-based, linguistically-motivated, user-friendly, GUI-enabled, multi-platform, Sentiment Analysis desktop application. Lingmo...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [157, 166, 1.0], [167, 175, 1.0]]}, "2020.ccl-1.61": {"snippet": "...ased sentiment analysis. This work discussed and analyzed the appearance, characteristics, and sentiment marking of repetition in text, with a focus on the analysis of the repetition of words and repe...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [98, 107, 1.0], [159, 167, 1.0]]}, "L18-1100": {"snippet": "...ated Sentiment Analysis in {T}elugu (a low resource language) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction\n\n\n Understanding the polarity or sentiment of a text is an importa...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [116, 125, 1.0], [170, 179, 1.0]]}, "2014.sigirconf_workshop-2014somera.9": {"snippet": "...usic analysis and creation within the giantsteps project\n\n\n ABSTRACTGiantSteps is an EU-funded project that aims at developing the next generation of music composition tools for the creative industrie...", "weights": [[8, 16, 1.0]]}, "W14-3010": {"snippet": "...s in Sentiment Analysis\n\n\n Sentiment Analysis, an important area of Natural Language Understanding, often relies on the assumption that lexemes carry inherent sentiment values, as reflected in special...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [30, 39, 1.0], [40, 48, 1.0], [162, 171, 1.0]]}, "P12-4004": {"snippet": "... and Sentiment Analysis\n\n\n Subjectivity and sentiment analysis focuses on the automatic identification of private states, such as opinions, emotions, sentiments, evaluations, beliefs, and speculations...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [47, 56, 1.0], [57, 65, 1.0]]}, "W13-5502": {"snippet": "... for Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "W11-0705": {"snippet": "Sentiment Analysis of {T}witter Data\n\n\n We examine sentiment analysis on Twitter data. The contributions of this paper are: (1) We introduce POS-specific prior polarity features. (2) We explore the us...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [51, 60, 1.0], [61, 69, 1.0]]}, "2017.ijirr_journal-ir0volumeA7A4.0": {"snippet": "...line Sentiment Analysis: Issues With Sentiment Analysis of Online Micro-Texts", "weights": [[8, 17, 1.0], [18, 26, 1.0], [40, 49, 1.0], [50, 58, 1.0]]}, "L08-1619": {"snippet": "Sentiment Analysis Based on Probabilistic Models Using Inter-Sentence Information", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2014.cikm_workshop-2014dtmbio.21": {"snippet": "...sion analysis or network analysis. Meanwhile, the activity in dryexperiment rather than expression in wet-experiment have been proposed to effectively estimate the actual regulation power of such impo...", "weights": [[8, 16, 1.0], [28, 36, 1.0]]}, "2020.ecir_workshop-2020text2story.7": {"snippet": "...dict sentiment of narrative text however despite much work being carried out to date on sentiment analysis of social media and user feedback. In this paper, we present an approach to sentiment analysi...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [101, 109, 1.0], [186, 195, 1.0], [196, 204, 1.0]]}, "J16-3008": {"snippet": "...dly. Sentiment analysis, the topic studying such subjective feelings expressed in text, has attracted significant attention from both the research community and industry. Although we have known sentim...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [197, 206, 1.0]]}, "2017.ipm_journal-ir0volumeA53A4.4": {"snippet": "... for sentiment classification\n\n\n a b s t r a c tSentiment analysis is a critical task of extracting subjective information from online text documents. Ensemble learning can be employed to obtain more ...", "weights": [[8, 17, 1.0], [61, 69, 1.0]]}, "2016.mir_workshop-2016marmi.3": {"snippet": "...tive Analysis in the Health-Care Domain", "weights": [[8, 16, 1.0]]}, "D17-1115": {"snippet": "...odal Sentiment Analysis\n\n\n Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [41, 50, 1.0], [51, 59, 1.0], [162, 171, 1.0], [172, 180, 1.0]]}, "W10-3111": {"snippet": "...n in sentiment analysis. We will present various computational approaches modeling negation in sentiment analysis. We will, in particular, focus on aspects, such as level of representation used for se...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [98, 107, 1.0], [108, 116, 1.0], [201, 210, 1.0]]}, "W19-3717": {"snippet": "Sentiment Analysis for Multilingual Corpora\n\n\n The paper presents a generic approach to the supervised sentiment analysis of social media content in foreign languages. The method proposes translating ...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [103, 112, 1.0], [113, 121, 1.0]]}, "2011.ipm_journal-ir0volumeA47A4.10": {"snippet": "... for sentiment analysis\n\n\n a b s t r a c tSentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [55, 63, 1.0], [105, 114, 1.0]]}, "2011.airs_conference-2011.33": {"snippet": "Sentiment Analysis for Online Reviews Using an Author-Review-Object Model", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "O16-1020": {"snippet": "...ting Sentiment Scores of Verb Phrases for {V}ietnamese\n\n\n Sentiment analysis is an emerging research field. One of the major tasks of sentiment analysis is building sentiment lexicons and calculating ...", "weights": [[8, 17, 1.0], [61, 70, 1.0], [71, 79, 1.0], [137, 146, 1.0], [147, 155, 1.0], [168, 177, 1.0]]}, "O12-5003": {"snippet": "...rity Analysis for Sentiment Classification) [In {C}hinese]\n\n\n The researches of sentiment analysis aim at exploring the emotional state of writers. The analysis highly depends on the application domai...", "weights": [[8, 16, 1.0], [21, 30, 1.0], [83, 92, 1.0], [93, 101, 1.0], [155, 163, 1.0]]}, "2020.acl-main.374": {"snippet": "...EP}: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis\n\n\n Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge...", "weights": [[8, 17, 1.0], [54, 63, 1.0], [64, 72, 1.0], [86, 95, 1.0], [96, 104, 1.0], [184, 193, 1.0]]}, "2012.wwwconf_conference-2012c.82": {"snippet": "...s of sentiment analysis in text require an understanding of what kinds of sentiment-bearing language are generally used to describe specific topics. Thus, fine-grained sentiment analysis requires both...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [77, 86, 1.0], [171, 180, 1.0], [181, 189, 1.0]]}, "R17-1084": {"snippet": "...n of Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2009.cikm_conference-2009.41": {"snippet": "...oint sentiment/topic model for sentiment analysis\n\n\n ABSTRACTSentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings...", "weights": [[8, 17, 1.0], [34, 43, 1.0], [44, 52, 1.0], [74, 82, 1.0]]}, "2018.mir_workshop-2018mmart.0": {"snippet": "...orks Analysis and Attractiveness Computing in Multimedia, MMArt&ACM@ICMR 2018, Yokohama, Japan, June 11, 2018", "weights": [[8, 16, 1.0]]}, "2013.cikm_workshop-2013unlp.7": {"snippet": "Sentiment analysis of sentences with modalities\n\n\n ABSTRACTThis paper is concerned with sentiment analysis of sentences with modality. Modality is a commonly occuring linguistic phenomenon. Due to its...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [88, 97, 1.0], [98, 106, 1.0]]}, "P09-1029": {"snippet": "... for Sentiment Analysis\n\n\n This paper describes an approach to utilizing term weights for sentiment analysis tasks and shows how various term weighting schemes improve the performance of sentiment ana...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [93, 102, 1.0], [103, 111, 1.0], [190, 199, 1.0], [200, 208, 1.0]]}, "2015.cikm_workshop-2015dtmbio.14": {"snippet": "... and Analysis of Medical Entity Co-occurrences in Twitter\n\n\n ABSTRACTTwitter is an attractive source of data for public health surveillance, as it is less hindered by the legal and technical obstacles...", "weights": [[8, 16, 1.0]]}, "L16-1008": {"snippet": "Sentiment Analysis in Social Networks through Topic modeling\n\n\n In this paper, we analyze the sentiments derived from the conversations that occur in social networks. Our goal is to identify the senti...", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2016.sigirconf_conference-2016.170": {"snippet": "... for sentiment prediction or to detect the possible reasons of a sentiment change. Understanding topic and sentiment evolution allows enterprises or government to capture negative sentiment and act pr...", "weights": [[8, 17, 1.0], [68, 77, 1.0], [110, 119, 1.0], [183, 192, 1.0]]}, "2020.sigirconf_conference-2020.131": {"snippet": "...pect-Sentiment Analysis with Minimal User Guidance\n\n\n Aspect-based sentiment analysis is a substantial step towards text understanding which benefits numerous applications. Since most existing algorit...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [70, 79, 1.0], [80, 88, 1.0]]}, "2020.rocling-1.35": {"snippet": "...base sentiment analysis (ABSA) has been proposed to explore the sentiment categories of a target in different aspects. In this paper, we explore the idea of ABSA for sentiment analysis of singers on s...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [67, 76, 1.0], [169, 178, 1.0], [179, 187, 1.0]]}, "2018.wwwconf_conference-2018c.330": {"snippet": "...just sentiment analysis techniques to automatically detect customer emotion in on-line service interactions of multiple business domains. Then we use the adjusted sentiment analysis tool to report ins...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [166, 175, 1.0], [176, 184, 1.0]]}, "2014.sigirconf_workshop-2014somera.10": {"snippet": "...irst analysis within the PHENICX project: \"performances as highly enriched aNd interactive concert eXperiences\"\n\n\n ABSTRACTIn the ongoing EU-FP7 project \"Performances as Highly Enriched aNd Interactiv...", "weights": [[8, 16, 1.0]]}, "2018.ecir_workshop-2018brodyn.2": {"snippet": "... the sentiment analysis around specific aspects of a resource. Firstly, certain aspects are identified, according to the distributions of the emotional terms in the vicinity of the most frequent names...", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2018.sigirconf_conference-2018.103": {"snippet": "...nts, sentiment analysis as a core area of social media analytics has gone beyond traditional text-based analysis. Multimodal sentiment analysis has become an important research topic in recent years. ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [107, 115, 1.0], [128, 137, 1.0], [138, 146, 1.0]]}, "C18-1085": {"snippet": "...e of sentiment analysis. Our proposed method is based on a convolutional neural network and an external sentiment lexicon. Experiments on four popular sentiment analysis datasets show that this method...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [107, 116, 1.0], [154, 163, 1.0], [164, 172, 1.0]]}, "2020.rocling-1.26": {"snippet": "...ars. Sentiment analysis is a useful technique to accomplish the investment psychology. In sentiment analysis, there are two main approaches to emotion state representation: categorical and dimensional...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [93, 102, 1.0], [103, 111, 1.0]]}, "2006.sigirconf_conference-2006.144": {"snippet": "... of 'sentiment analysis' seeks to characterize such aspects of natural language text, as opposed to just the bare facts. We suggest that 'appraisal expression extraction' should be viewed as a fundame...", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "P11-4009": {"snippet": "Dr Sentiment Knows Everything!\n\n\n Sentiment analysis is one of the hot demanding research areas since last few decades. Although a formidable amount of research have been done, the existing reported s...", "weights": [[3, 12, 1.0], [34, 43, 1.0], [44, 52, 1.0]]}, "S13-2078": {"snippet": "... for Sentiment Analysis in {T}witter\n\n\n Sentiment analysis refers to automatically extracting the sentiment present in a given natural language text. We present our participation to the SemEval2013 co...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [43, 52, 1.0], [53, 61, 1.0], [101, 110, 1.0]]}, "2020.aacl-srw.12": {"snippet": "...tes, sentiment analysis has gained more importance. Aspect Based Sentiment Analysis (ABSA) is a task of identifying the sentiment at aspect level. It has more importance than sentiment analysis from c...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [68, 77, 1.0], [78, 86, 1.0], [123, 132, 1.0], [178, 187, 1.0], [188, 196, 1.0]]}, "W12-3701": {"snippet": "...odal Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "W14-2619": {"snippet": "... for Sentiment Analysis\n\n\n This paper illustrates the use of deep semantic processing for sentiment analysis. Existing methods for sentiment analysis use supervised approaches which take into account ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [93, 102, 1.0], [103, 111, 1.0], [134, 143, 1.0], [144, 152, 1.0]]}, "2015.ipm_journal-ir0volumeA51A4.6": {"snippet": "Sentiment analysis meets social media - Challenges and solutions of the field in view of the current information sharing context", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "L08-1395": {"snippet": "Sentiment Analysis and the Use of Extrinsic Datasets in Evaluation", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2017.cikm_conference-2017.307": {"snippet": "...odal Sentiment Analysis\n\n\n ABSTRACTWith the prevalence of more diverse and multiform user-generated content in social networking sites, multimodal sentiment analysis has become an increasingly importa...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [150, 159, 1.0], [160, 168, 1.0]]}, "D14-1053": {"snippet": "Sentiment Analysis on the People{'}s Daily\n\n\n We propose a semi-supervised bootstrapping algorithm for analyzing China's foreign relations from the People's Daily. Our approach addresses sentiment tar...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [187, 196, 1.0]]}, "2016.wsdm_conference-2016.3": {"snippet": "...tual sentiment analysis techniques. We first fine-tune a convolutional neural network (CNN) for image sentiment analysis and train a paragraph vector model for textual sentiment analysis. On top of th...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [105, 114, 1.0], [115, 123, 1.0], [171, 180, 1.0], [181, 189, 1.0]]}, "2016.ipm_journal-ir0volumeA52A1.1": {"snippet": "... for sentiment analysis of Twitter\n\n\n a b s t r a c tSentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this pa...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [66, 74, 1.0]]}, "2009.jasis_journal-ir0volumeA60A12.7": {"snippet": "Sentiment analysis of Chinese documents: From sentence to document level", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2020.mir_workshop-2020mmart.7": {"snippet": "...orks Analysis", "weights": [[8, 16, 1.0]]}, "2014.sigirconf_conference-2014.136": {"snippet": "...evel sentiment labeling with review-level sentiment classification\n\n\n ABSTRACTCurrent approaches for contextual sentiment lexicon construction in phrase-level sentiment analysis assume that the numeri...", "weights": [[8, 17, 1.0], [45, 54, 1.0], [115, 124, 1.0], [162, 171, 1.0], [172, 180, 1.0]]}, "L10-1623": {"snippet": "Sentiment Analysis in the News", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2012.jasis_journal-ir0volumeA63A12.12": {"snippet": "Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "W19-6120": {"snippet": "...ased Sentiment Analysis using {BERT}\n\n\n Sentiment analysis has become very popular in both research and business due to the vast amount of opinionated text currently produced by Internet users. Standa...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [43, 52, 1.0], [53, 61, 1.0]]}, "N18-1171": {"snippet": "Sentiment Analysis: It{'}s Complicated!\n\n\n Sentiment analysis is used as a proxy to measure human emotion, where the objective is to categorize text according to some predefined notion of sentiment. S...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [43, 52, 1.0], [53, 61, 1.0], [188, 197, 1.0], [199, 208, 1.0]]}, "2020.ecnlp-1.4": {"snippet": "... for Sentiment Analysis of Service Calls\n\n\n Sentiment analysis is crucial for the advancement of artificial intelligence (AI). Sentiment understanding can help AI to replicate human language and disco...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [47, 56, 1.0], [57, 65, 1.0], [130, 139, 1.0]]}, "R15-1002": {"snippet": "...ased Sentiment Analysis for {P}ersian Text", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2015.wwwconf_conference-2015c.41": {"snippet": "... the sentiment of posts in online health communities enables the investigation of various factors such as what affects the sentiment change and discovery of sentiment change patterns. Since each write...", "weights": [[8, 17, 1.0], [126, 135, 1.0], [160, 169, 1.0]]}, "2020.sigirconf_conference-2020.212": {"snippet": "...ring Sentiment Analysis\n\n\n Question-answering sentiment analysis (QASA) is a novel but meaningful sentiment analysis task based on question-answering online reviews. Existing neural network-based mode...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [49, 58, 1.0], [59, 67, 1.0], [101, 110, 1.0], [111, 119, 1.0]]}, "2011.cikm_conference-2011.128": {"snippet": "...ment analysis has applications in many areas and the exploration of its potential has only just begun. We propose Pathos, a framework which performs document sentiment analysis (partly) based on a doc...", "weights": [[8, 16, 1.0], [161, 170, 1.0], [171, 179, 1.0]]}, "2015.sigirconf_workshop-2015sps.5": {"snippet": "Analysis of Sentiment Communities in Online Networks\n\n\n This article reports our experience in developing a recommender system (RS) able to suggest relevant people to the target user. Such a RS relies...", "weights": [[0, 8, 1.0], [12, 21, 1.0]]}, "2012.sigirconf_conference-2012.101": {"snippet": "...a of Sentiment Analysis . An important task is the classification of documents according to the overall sentiment, i.e. whether positive or negative. A common behaviour among reviewers is to summarise...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [107, 116, 1.0]]}, "W17-7515": {"snippet": "...ets) sentiment text when sentiment classes are limited to two/three. Furthermore, when the sentiment analysis task turn into a kind of multi-class classifiction instead of binary on large corpora, dee...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [94, 103, 1.0], [104, 112, 1.0]]}, "W16-0411": {"snippet": "...efit Sentiment Analysis? A Holistic Approach to Sentiment Analysis\n\n\n Existing opinion analysis techniques rely on the clues within the sentence that focus on the sentiment analysis task itself. Howev...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [51, 60, 1.0], [61, 69, 1.0], [90, 98, 1.0], [166, 175, 1.0], [176, 184, 1.0]]}, "2015.wsdm_conference-2015.62": {"snippet": "...evel Sentiment Analysis\n\n\n ABSTRACTIn this paper, we propose a representation learning research framework for document-level sentiment analysis. Given a document as the input, document-level sentiment...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [128, 137, 1.0], [138, 146, 1.0], [194, 203, 1.0]]}, "2022.findings-acl.109": {"snippet": "Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with {ASR} Errors\n\n\n Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed...", "weights": [[0, 9, 1.0], [58, 67, 1.0], [68, 76, 1.0], [109, 118, 1.0], [119, 127, 1.0]]}, "2020.lrec-1.617": {"snippet": "...ased Sentiment Analysis (ABSA) is an area of sentiment analysis which deals with sentiment at a finer level. ABSA classifies sentiment with respect to each aspect to gain greater insights into the sen...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [48, 57, 1.0], [58, 66, 1.0], [84, 93, 1.0], [128, 137, 1.0], [200, 209, 1.0]]}, "2019.ipm_journal-ir0volumeA56A2.5": {"snippet": "...abic sentiment analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A4.11": {"snippet": "... and sentiment at message level, is addressed by proposing a novel fully unsupervised generative model. The experimental results show that the proposes sentiment analysis models grounded on Approval N...", "weights": [[8, 17, 1.0], [155, 164, 1.0], [165, 173, 1.0]]}, "W18-3306": {"snippet": "...s of Sentiment Analysis\n\n\n Current multimodal sentiment analysis frames sentiment score prediction as a general Machine Learning task. However, what the sentiment score actually represents has often b...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [49, 58, 1.0], [59, 67, 1.0], [75, 84, 1.0], [156, 165, 1.0]]}, "2021.emnlp-main.551": {"snippet": "...icit Sentiment Analysis with Event-centered Text Representation\n\n\n Implicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive resear...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [79, 88, 1.0], [89, 97, 1.0], [123, 132, 1.0], [155, 164, 1.0]]}, "2018.sigirconf_conference-2018.170": {"snippet": "... for Sentiment Analysis\n\n\n ABSTRACTAnalyzing public opinions towards products, services and social events is an important but challenging task. Despite the remarkable successes of deep neural networks...", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2012.ecir_conference-2012.36": {"snippet": "...s of Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2018.ecir_workshop-2018brodyn.4": {"snippet": "...ased Analysis of Refugees in Turkey\n\n\n In this paper, we propose a method to find out public social media accounts of refugees, and trace them back to infer temporal and spatial events.These data will...", "weights": [[8, 16, 1.0]]}, "2014.cikm_conference-2014.109": {"snippet": "...pect/Sentiment Model for Sentiment Analysis\n\n\n ABSTRACTSentiment analysis in various languages has been a research hotspot with many applications. However, sentiment resources (e.g., labeled corpora, ...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [38, 46, 1.0], [68, 76, 1.0], [159, 168, 1.0]]}, "2021.findings-acl.320": {"snippet": "...in a Sentiment Lexicon\n\n\n Sentiment lexicons are instrumental for sentiment analysis. One can use a set of sentiment words provided in a sentiment lexicon and a lexicon-based classifier to perform sen...", "weights": [[8, 17, 1.0], [29, 38, 1.0], [69, 78, 1.0], [79, 87, 1.0], [110, 119, 1.0], [140, 149, 1.0], [200, 209, 1.0]]}, "W15-1829": {"snippet": "Sentiment analysis on conversational texts\n\n\n This paper describes ongoing work related to the analysis of spoken utterance transcripts and estimating the speaker's attitude towards the whole dialogue...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [95, 103, 1.0]]}}}
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The...", "weights": [[8, 14, 1.0], [169, 174, 1.0]]}, "1991.sigirconf_conference-91.21": {"snippet": "Using the Cosine Measure in a Neural Network for Document\n\n\n AbstractThe task of document retrieval systems is to match one natural language query against a large number of natural language documents....", "weights": [[0, 5, 1.0], [30, 36, 1.0], [90, 99, 1.0]]}, "2014.wsdm_conference-2014.10": {"snippet": "... the neural network in RankNet: one of these methods regularize the error back-propagation via a truncated gradient approach, while the other method limits the depth of the back propagation when adapt...", "weights": [[8, 14, 1.0]]}, "2019.wsdm_conference-2019.78": {"snippet": "Neural Demographic Prediction using Search Query\n\n\n ABSTRACTDemographics of online users such as age and gender play an important role in personalized web applications. However, it is difficult to dir...", "weights": [[0, 6, 1.0], [30, 35, 1.0]]}, "2019.sigirconf_conference-2019.125": {"snippet": "...y of Neural Retrieval Models using DeepSHAP", "weights": [[8, 14, 1.0], [15, 24, 1.0], [32, 37, 1.0]]}, "W15-2915": {"snippet": "...onal Neural Network\n\n\n Sentiment lexicons and other linguistic knowledge proved to be beneficial in polarity classification. This paper introduces a linguistically informed Convolutional Neural Networ...", "weights": [[8, 14, 1.0], [190, 196, 1.0]]}, "2015.tois_journal-ir0volumeA34A1.3": {"snippet": "...ding Using Morphological Knowledge\n\n\n Neural network techniques are widely applied to obtain high-quality distributed representations of words (i.e., word embeddings) to address text mining, informati...", "weights": [[8, 13, 1.0], [41, 47, 1.0]]}, "P18-1123": {"snippet": "...eved using a traditional TF-IDF based retrieval model. The retrieved responses are used to create exemplar vectors that are used by the decoder to generate the response. The contribution of each retri...", "weights": [[8, 13, 1.0], [41, 50, 1.0]]}, "1997.trec_conference-1997.27": {"snippet": "...tion Retrieval Using Term Relationship Networks", "weights": [[8, 17, 1.0], [18, 23, 1.0], [42, 50, 1.0]]}, "2019.rocling-1.5": {"snippet": "...ment Retrieval based on Neural Network and Document Concept Graph)\n\n\n If the search results can consider topics or similar situations, we can find results that are more in line with user's expectation...", "weights": [[8, 17, 1.0], [27, 33, 1.0]]}, "2015.wwwconf_conference-2015c.329": {"snippet": "...s of neural networks, and their structures. Then we explain the training algorithm via backpropagation, which is a common method of training artificial neural networks including deep neural networks. ...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [155, 161, 1.0], [162, 170, 1.0], [186, 192, 1.0], [193, 201, 1.0]]}, "2017.ijirr_journal-ir0volumeA7A2.2": {"snippet": "...tion Retrieval Model using Uncertain Confidence's Network", "weights": [[8, 17, 1.0], [24, 29, 1.0]]}, "1996.cikm_conference-96.18": {"snippet": "...to a Neural Network Model for Information Retrieval", "weights": [[8, 14, 1.0], [45, 54, 1.0]]}, "D08-1108": {"snippet": "... web using a search engine, generate hypotheses of formal equivalents via this data, and rank the hypotheses using a conditional log-linear model. In the log-linear model, we incorporate as feature fu...", "weights": [[8, 13, 1.0], [112, 117, 1.0]]}, "2017.sigirconf_conference-2017.244": {"snippet": "...rch, neural networks have resurfaced as a popular machine learning paradigm in many other directions of research as well, including information retrieval. Recent years have seen neural networks being ...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [147, 156, 1.0], [181, 187, 1.0], [188, 196, 1.0]]}, "2012.cikm_conference-2012.221": {"snippet": "...blog retrieval. The results based on the TREC Microblog track show that the proposed quality model, combined with a variety of retrieval models, can improve retrieval performance and is competitive wi...", "weights": [[8, 17, 1.0], [130, 139, 1.0], [160, 169, 1.0]]}, "2021.ecir_conference-20211.19": {"snippet": "...rned using deep neural networks, for figures. We learn representations using both text and image data and compare different model architectures and loss functions for the task. Furthermore, to overcom...", "weights": [[8, 13, 1.0], [19, 25, 1.0], [26, 34, 1.0], [74, 79, 1.0]]}, "2021.ecir_conference-20211.34": {"snippet": "...h to Neural Multi-label Hierarchical Patent Classification Using Transformers\n\n\n With the aim of facilitating internal processes as well as search applications, patent offices categorize documents int...", "weights": [[8, 14, 1.0], [62, 67, 1.0]]}, "2019.chiir_conference-2019.21": {"snippet": "...ased Neural Network Model for Click Modeling", "weights": [[8, 14, 1.0]]}, "2017.sigirconf_conference-2017.187": {"snippet": "...onal Neural Network Models for Answer Selection\n\n\n ABSTRACTIn recent years, neural networks have been applied to many text processing problems. One example is learning a similarity function between pa...", "weights": [[8, 14, 1.0], [79, 85, 1.0], [86, 94, 1.0]]}, "Q19-1028": {"snippet": "...rent Neural Network Architecture for Multilingual Readability Assessment\n\n\n We present a multiattentive recurrent neural network architecture for automatic multilingual readability assessment. This ar...", "weights": [[8, 14, 1.0], [117, 123, 1.0]]}, "2017.tois_journal-ir0volumeA35A3.10": {"snippet": "...e in Neural Networks for Sentiment Classification\n\n\n Phrase/Sentence representation is one of the most important problems in natural language processing. Many neural network models such as Convolution...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [162, 168, 1.0]]}, "2020.wsdm_conference-2020.126": {"snippet": "...Deep Neural Networks: Challenges and Opportunities\n\n\n One crucial aspect that yet remains fairly unknown while can inform us about the behavior of deep neural networks is their decision boundaries. Tr...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [155, 161, 1.0], [162, 170, 1.0]]}, "2021.ecir_conference-20211.5": {"snippet": "... and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits\n\n\n We study the utility of the lexical translation model (IBM Model 1) for En...", "weights": [[8, 14, 1.0], [58, 67, 1.0]]}, "2017.ecir_workshop-2017bir.7": {"snippet": "...thor networks, citation networks and text models. The three types of information are mixed by building network embeddings and sentence embeddings, which rely on the network topology and context-driven...", "weights": [[8, 16, 1.0], [27, 35, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.4": {"snippet": "...deep neural network-based approach that relies on coarse-grained sentence modelling using a convolutional neural network (CNN) and a recurrent neural network (RNN) model, combined with a specific fine...", "weights": [[8, 14, 1.0], [87, 92, 1.0], [109, 115, 1.0], [146, 152, 1.0]]}, "2013.sigirconf_conference-2013.152": {"snippet": "... the informational utility, which stands for the degree to which a friend satisfies the target user's unfulfilled informational need, called informational friend recommendation. In order to capture us...", "weights": [[8, 21, 1.0], [117, 130, 1.0], [144, 157, 1.0]]}, "2012.cikm_workshop-2012webkr.1": {"snippet": "...edge retrieval\n\n\n ABSTRACTWeb-scale knowledge retrieval can be enabled by distributed information retrieval; clustering Web clients to a large-scale computing infrastructure for knowledge discovery fr...", "weights": [[8, 17, 1.0], [49, 58, 1.0], [101, 110, 1.0]]}, "2020.mir_conference-2020.25": {"snippet": "...n of Neural Network", "weights": [[8, 14, 1.0]]}, "2018.chiir_conference-2018.2": {"snippet": "...s in informational search sessions. Using crowdsourcing, we recruited 500 distinct users and orchestrated real-world search sessions spanning 10 different topics and information needs. By using scient...", "weights": [[8, 21, 1.0], [39, 44, 1.0], [191, 196, 1.0]]}, "2020.wsdm_conference-2020.62": {"snippet": "...cial networks; \u2022 Computing methodologies \u2192 Neural networks;", "weights": [[8, 16, 1.0], [46, 52, 1.0], [53, 61, 1.0]]}, "2020.emnlp-main.535": {"snippet": "...able neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. The model is first pretrained on the huge general-domain data, then finetuned on our corpus. We show ...", "weights": [[8, 14, 1.0], [91, 100, 1.0]]}, "2017.wwwconf_conference-2017c.234": {"snippet": "...rent Neural Networks (RNN) with selfdeveloped association rules in getting the most out of the data for training and evaluation. We aim to test the performance of our approach using human-induced terr...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [179, 184, 1.0]]}, "2020.cikm_conference-2020.432": {"snippet": "...tors using Semantically Informed Neural Networks", "weights": [[8, 13, 1.0], [36, 42, 1.0], [43, 51, 1.0]]}, "2020.wsdm_conference-2020.105": {"snippet": "...-End Neural Ad Hoc Retrieval\n\n\n We present Capreolus, a toolkit designed to facilitate end-to-end ad hoc retrieval experiments with neural networks by providing implementations of prominent neural ran...", "weights": [[8, 14, 1.0], [22, 31, 1.0], [108, 117, 1.0], [135, 141, 1.0], [142, 150, 1.0], [193, 199, 1.0]]}, "W10-3505": {"snippet": "... for using crowd-sourcing techniques to label informative and non-informative information regarding the sentiment expressed in a sentence. We also show that pruning non-informative information using n...", "weights": [[8, 13, 1.0], [196, 201, 1.0]]}, "1989.sigirconf_conference-89.4": {"snippet": "A Neural Network for Probabilistic Information Retrieval", "weights": [[2, 8, 1.0], [47, 56, 1.0]]}, "2021.ecir_conference-20212.82": {"snippet": "...mely using neural networks algorithms. However, the incorporation of semantics is still scarce. This project proposes that using external semantic sources of knowledge along with the latest state-of-t...", "weights": [[8, 13, 1.0], [14, 20, 1.0], [21, 29, 1.0], [126, 131, 1.0]]}, "P15-2033": {"snippet": "...onal Neural Networks for Automatic Resolution of Crossword Puzzles\n\n\n Automatic resolution of Crossword Puzzles (CPs) heavily depends on the quality of the answer candidate lists produced by a retriev...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [196, 205, 1.0]]}, "2016.mir_conference-2016.44": {"snippet": "...mage Retrieval with Top-Weighted Convolutional Neural Networks", "weights": [[8, 17, 1.0], [50, 56, 1.0], [57, 65, 1.0]]}, "2017.ismir_conference-2017.47": {"snippet": "...onal Neural Networks", "weights": [[8, 14, 1.0], [15, 23, 1.0]]}, "2015.sigirconf_conference-2015.39": {"snippet": "...Deep Neural Networks\n\n\n ABSTRACTLearning a similarity function between pairs of objects is at the core of learning to rank approaches. In information retrieval tasks we typically deal with query-docum...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [153, 162, 1.0]]}, "2005.sigirconf_conference-2005.47": {"snippet": "Using term informativeness for named entity detection\n\n\n ABSTRACTInformal communication (e-mail, bulletin boards) poses a difficult learning environment because traditional grammatical and lexical inf...", "weights": [[0, 5, 1.0]]}, "2017.ictir_conference-2017.48": {"snippet": "...tion retrieval methods (e.g., TF-IDF) to complex systems that adopt query expansion, knowledge bases and deep neural networks. e goal is to o er an overview of some promising approaches to tackle this", "weights": [[8, 17, 1.0], [113, 119, 1.0], [120, 128, 1.0]]}, "1995.ipm_journal-ir0volumeA31A6.0": {"snippet": "...nd a Neural Network for Intelligent Information Retrieval", "weights": [[8, 14, 1.0], [51, 60, 1.0]]}, "2020.wwwconf_conference-2020c.182": {"snippet": "...onal Neural Network Models for Retrieval-based Question Answering System in E-commerce\n\n\n A key solution to the Information Retrieval (IR)-based question answering (QA) models is to retrieve the most ...", "weights": [[8, 14, 1.0], [34, 43, 1.0], [127, 136, 1.0]]}, "2018.fire_conference-2018w.9": {"snippet": "...logy using convolutional neural networks. Convolutional neural networks are a class of neural networks that have proven very effective in areas such as pattern recognition and classification. They are...", "weights": [[8, 13, 1.0], [28, 34, 1.0], [35, 43, 1.0], [59, 65, 1.0], [66, 74, 1.0], [90, 96, 1.0], [97, 105, 1.0]]}, "2018.ictir_conference-2018.13": {"snippet": "...r of Neural Retrieval Models Using NLP Tasks\n\n\n ABSTRACTThe ease of constructing effective neural networks has resulted in a large number of varying architectures iteratively improving performance on ...", "weights": [[8, 14, 1.0], [15, 24, 1.0], [32, 37, 1.0], [94, 100, 1.0], [101, 109, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.19": {"snippet": "...onal neural network is an effective method to extract high-level semantic features for image database retrieval. To overcome the problem of over-fitting when the number of training samples in dataset ...", "weights": [[8, 14, 1.0], [105, 114, 1.0]]}, "N19-1330": {"snippet": "Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models\n\n\n Recurrent neural network language models (RNNLM) form a valuable foundation for many NLP systems, but training the m...", "weights": [[0, 5, 1.0], [58, 64, 1.0], [94, 100, 1.0]]}, "1992.sigirconf_conference-92.3": {"snippet": "...of a Retrieval Process", "weights": [[8, 17, 1.0]]}, "X96-1031": {"snippet": "...tion Retrieval Technology\n\n\n Over the past few years, HNC has developed a neural network based, vector space approach to text retrieval. This approach, embodied in a system called MatchPlus, allows th...", "weights": [[8, 17, 1.0], [77, 83, 1.0], [129, 138, 1.0]]}, "2018.ictir_conference-2018.23": {"snippet": "...tion Retrieval\n\n\n ABSTRACTNeural network approaches have recently shown to be effective in several information retrieval (IR) tasks. However, neural approaches often require large volumes of training ...", "weights": [[8, 17, 1.0], [114, 123, 1.0], [145, 151, 1.0]]}, "2016.sigirconf_conference-2016.219": {"snippet": "...tion retrieval, such as word embedding, recurrent neural networks, and convolutional neural networks. In the second part, we explain how deep learning, particularly representation learning techniques,...", "weights": [[8, 17, 1.0], [53, 59, 1.0], [60, 68, 1.0], [88, 94, 1.0], [95, 103, 1.0]]}, "2018.sigirconf_conference-2018.13": {"snippet": "Neural Query Performance Prediction using Weak Supervision from Multiple Signals\n\n\n ABSTRACTPredicting the performance of a search engine for a given query is a fundamental and challenging task in inf...", "weights": [[0, 6, 1.0], [36, 41, 1.0]]}, "I05-2024": {"snippet": "...tion Retrieval Capable of Visualization and High Precision\n\n\n We present a neural-network based selforganizing approach that enables visualization of the information retrieval while at the same time i...", "weights": [[8, 17, 1.0], [78, 84, 1.0], [169, 178, 1.0]]}, "2018.wwwconf_conference-2018c.187": {"snippet": "...cial Networks\n\n\n ABSTRACTSocial networks have been a popular choice of study, given the surge of online data on friendship networks, communication networks, collaboration networks etc. This popularity...", "weights": [[8, 16, 1.0], [35, 43, 1.0], [126, 134, 1.0], [150, 158, 1.0], [174, 182, 1.0]]}, "2017.ismir_conference-2017.5": {"snippet": "...usic Using Convolutional Neural Networks", "weights": [[8, 13, 1.0], [28, 34, 1.0], [35, 43, 1.0]]}, "2020.sigirconf_conference-2020.249": {"snippet": "...raph neural network to capture the user-news, user-user and news-news relatedness in the user-news bipartite graphs by modeling the interactions between different users and news. In addition, we propo...", "weights": [[8, 14, 1.0]]}, "2018.wsdm_conference-2018.14": {"snippet": "Neural Graph Learning: Training Neural Networks Using Graphs\n\n\n ABSTRACTLabel propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, h...", "weights": [[0, 6, 1.0], [32, 38, 1.0], [39, 47, 1.0], [48, 53, 1.0], [163, 169, 1.0], [170, 178, 1.0]]}, "N16-1007": {"snippet": "Neural Network-Based Abstract Generation for Opinions and Arguments\n\n\n We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model...", "weights": [[0, 6, 1.0], [180, 186, 1.0]]}, "2018.cikm_conference-2018.120": {"snippet": "...tity retrieval from knowledge graphs indicates superior retrieval accuracy of the proposed neural architecture relative to both state-of-the-art neural architectures for ad-hoc document retrieval and ...", "weights": [[8, 17, 1.0], [59, 68, 1.0], [94, 100, 1.0], [148, 154, 1.0], [189, 198, 1.0]]}, "2003.ismir_conference-2003.13": {"snippet": "... and retrieval of melodies using a self-organizing neural network", "weights": [[8, 17, 1.0], [30, 35, 1.0], [54, 60, 1.0]]}, "1995.jasis_journal-ir0volumeA46A3.2": {"snippet": "...tion Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms", "weights": [[8, 17, 1.0], [19, 25, 1.0], [26, 34, 1.0]]}, "2017.fire_conference-2017.1": {"snippet": "...tion Retrieval using a Novel Tripartite Neural Network based Approach\n\n\n ABSTRACTCollective intelligence of the crowds is distilled together in various Community estion Answering (CQA) Services such a...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [43, 49, 1.0]]}, "P19-1028": {"snippet": "...ting Neural Networks with First-order Logic\n\n\n Today, the dominant paradigm for training neural networks involves minimizing task loss on a large dataset. Using world knowledge to inform a model, and ...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [92, 98, 1.0], [99, 107, 1.0], [158, 163, 1.0]]}, "2018.wsdm_conference-2018.102": {"snippet": "Neural Networks for Information Retrieval\n\n\n AbstractMachine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day resear...", "weights": [[0, 6, 1.0], [7, 15, 1.0], [32, 41, 1.0]]}, "2016.sigirconf_conference-2016.231": {"snippet": "...p on Neural Information Retrieval\n\n\n KeywordsNeural networks; deep learning; information retrieval Website: http://research.microsoft.com/neuir2016\nMOTIVATIONIn recent years, deep neural networks have...", "weights": [[8, 14, 1.0], [27, 36, 1.0], [55, 63, 1.0], [92, 101, 1.0], [183, 189, 1.0], [190, 198, 1.0]]}, "2000.sigirconf_conference-2000.40": {"snippet": "...n on neural networks using only positive examples\n\n\n AbstractIn this paper, we show how a simple feedforward neural network can be trained to filter documents when only positive information is availab...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [24, 29, 1.0], [112, 118, 1.0]]}, "2017.wwwconf_conference-2017.133": {"snippet": "...deep neural networks, one that matches the query and the document using a local representation, and another that matches the query and the document using learned distributed representations. The two n...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [69, 74, 1.0], [151, 156, 1.0], [202, 210, 1.0]]}}}
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Yet, despite the deluge of information, query log analysis often suffers from the sparsity of the query space. Based on the ...", "weights": [[8, 16, 1.0], [119, 124, 1.0], [125, 128, 1.0], [129, 137, 1.0], [177, 182, 1.0]]}, "2012.wwwconf_conference-2012c.210": {"snippet": "...ting query refinements from another subset (test set) of the user search logs. This process implies learning from or reusing previous users' querying experience to recommend queries for a new but simi...", "weights": [[8, 13, 1.0]]}, "2011.wsdm_conference-2011.46": {"snippet": "... the analysis of log data. To handle a huge amount of log data, we develop distributed algorithms for learning model parameters efficiently. We also devise approaches to computing a topic-concept cube...", "weights": [[8, 16, 1.0], [20, 23, 1.0], [57, 60, 1.0]]}, "2011.wwwconf_conference-2011c.32": {"snippet": "...from query logs instead of web corpora has several advantages, in view of query-log-based application . 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Therefore, properly characterizing the...", "weights": [[8, 11, 1.0]]}, "1998.sigirjournals_journal-ir0volumeA32A1.1": {"snippet": "Real Life Information Retrieval: A Study of User Queries on the Web\n\n\n ABSTRACTWe analyzed transaction logs of a set of 51,473 queries posed by 18,113 users of Excite, a major Internet search service....", "weights": []}, "2005.jasis_journal-ir0volumeA56A13.1": {"snippet": "Analysis of the query logs of a Web site search engine", "weights": [[0, 8, 1.0], [16, 21, 1.0]]}, "2005.ecir_conference-2005.24": {"snippet": "An Analysis of Query Similarity in Collaborative Web Search\n\n\n Abstract. Web search logs provide an invaluable source of information regarding the search behaviour of users. This information can be re...", "weights": [[3, 11, 1.0], [15, 20, 1.0]]}, "2016.ipm_journal-ir0volumeA52A6.12": {"snippet": "...e of analysis allows comparisons of user search behaviours across systems of different nature and design. This analysis provides insights not achievable with traditional log analysis.", "weights": [[8, 16, 1.0], [114, 122, 1.0], [173, 176, 1.0], [177, 185, 1.0]]}, "2009.clef_workshop-2009w.6": {"snippet": "...e on Query Log Analysis\n\n\n We present our views on the CLEF log file analysis task. We argue for a task definition that focuses on the semantic enrichment of query logs. In addition, we discuss how ad...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0], [63, 66, 1.0], [72, 80, 1.0], [161, 166, 1.0]]}, "1999.sigirjournals_journal-ir0volumeA33A1.1": {"snippet": "Analysis of a Very Large Web Search Engine Query Log\n\n\n AbstractIn this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search re...", "weights": [[0, 8, 1.0], [43, 48, 1.0], [49, 52, 1.0], [92, 100, 1.0], [131, 136, 1.0], [137, 140, 1.0]]}, "2003.sigirconf_conference-2003.86": {"snippet": "...om a query can help a user to refine an overly specific query to one which has more coverage. Most query reformulation research focuses on query expansion (eg. ). Our research on query term deletion p...", "weights": [[8, 13, 1.0], [59, 64, 1.0], [102, 107, 1.0], [142, 147, 1.0], [182, 187, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.14": {"snippet": "...cale log analysis of their search actions at a national audiovisual broadcast archive. Our analysis characterizes not only the searches of media professionals, but also their purchasing behavior. In o...", "weights": [[8, 11, 1.0], [12, 20, 1.0], [94, 102, 1.0]]}, "2011.cikm_conference-2011.246": {"snippet": "... for query expansion, considering the information of topics and the global relationships of queries concurrently, with a query log collected by a practical search engine.Experimental results on two TR...", "weights": [[8, 13, 1.0], [124, 129, 1.0], [130, 133, 1.0]]}, "2008.tweb_journal-ir0volumeA2A4.0": {"snippet": "...e on query log analysis: Technology and ethics", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0]]}, "E12-2018": {"snippet": "Query log analysis with {L}ang{L}og\n\n\n This article describes GALATEAS LangLog, a system performing Search Log Analysis. LangLog illustrates how NLP technologies can be a powerful support tool for mar...", "weights": [[0, 5, 1.0], [6, 9, 1.0], [10, 18, 1.0], [107, 110, 1.0], [111, 119, 1.0]]}, "2020.sigirconf_conference-2020.290": {"snippet": "...nes. Query logs record users' activities along with timestamps and additional search-related information. The analysis of those chronological query logs enables the modeling of search tasks from user ...", "weights": [[8, 13, 1.0], [113, 121, 1.0], [145, 150, 1.0]]}, "2009.clef_workshop-2009.64": {"snippet": "...d on Query Logs, and Search Log Analysis by Automatic Language Identification", "weights": [[8, 13, 1.0], [31, 34, 1.0], [35, 43, 1.0]]}, "2010.sigirconf_conference-2010.169": {"snippet": "Query log analysis in the context of information retrieval for children\n\n\n ABSTRACTIn this paper we analyze queries and sessions intended to satisfy children's information needs using a large-scale qu...", "weights": [[0, 5, 1.0], [6, 9, 1.0], [10, 18, 1.0], [198, 203, 1.0]]}, "2006.spire_conference-2006.18": {"snippet": "...l of Query Log Generation", "weights": [[8, 13, 1.0], [14, 17, 1.0]]}, "2018.wwwconf_conference-2018c.78": {"snippet": "...Deep Analysis of SPARQL Queries\n\n\n ABSTRACTIn this demonstration, we showcase DARQL, the first tool for deep, large-scale analysis of SPARQL queries. We have harvested a large corpus of query logs wit...", "weights": [[8, 16, 1.0], [125, 133, 1.0], [189, 194, 1.0]]}, "2009.clef_workshop-2009w.5": {"snippet": "...d on Query Logs and Search Log Analysis at the University of Sunderland\n\n\n This work describes a variation on the traditional Information Retrieval paradigm, where instead of text documents being inde...", "weights": [[8, 13, 1.0], [30, 33, 1.0], [34, 42, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.7": {"snippet": "...sive analysis of a web search engine voice query log. -We combine a semantic analysis using novel methods, such as analyzing a broad set of triggered cards, with an in-depth syntactic analysis, to she...", "weights": [[8, 16, 1.0], [46, 51, 1.0], [52, 55, 1.0], [80, 88, 1.0], [187, 195, 1.0]]}, "2002.wwwconf_conference-2002.32": {"snippet": "... web query logs. In this study, we propose a new method for query expansion based on query logs. The central idea is to extract probabilistic correlations between query terms and document terms by ana...", "weights": [[8, 13, 1.0], [63, 68, 1.0], [88, 93, 1.0], [166, 171, 1.0]]}, "2014.sigirconf_conference-2014.114": {"snippet": "...ng a query log via crowdsourcing\n\n\n ABSTRACTA query log is a key asset in a commercial search engine. Everyday millions of users rely on search engines to find information on the Web by entering a few...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [49, 54, 1.0], [55, 58, 1.0]]}, "2013.wsdm_conference-2013.91": {"snippet": "...arch log datasets. Research relating to search logs has been hampered by the limited availability of click datasets. This workshop comes with a new dataset based on logged user search behaviour and an...", "weights": [[8, 11, 1.0]]}, "2008.sigirconf_conference-2008.121": {"snippet": "Web query translation via web log mining", "weights": [[4, 9, 1.0], [30, 33, 1.0]]}, "2013.wsdm_conference-2013.82": {"snippet": "... the query covering approach. The general idea is to populate the cache with those documents that contribute to the result pages of a large number of queries, as opposed to caching the top documents o...", "weights": [[8, 13, 1.0]]}, "2009.sigirconf_conference-2009.157": {"snippet": "...a: a query log analysis", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0]]}, "2017.chiir_conference-2017.68": {"snippet": "...k to Query Recommendation and Evaluation\n\n\n ABSTRACTIn practice, query recommenders in Web search typically recommend queries directly from a query log or iteratively refine a user's current context t...", "weights": [[8, 13, 1.0], [68, 73, 1.0], [145, 150, 1.0], [151, 154, 1.0]]}, "2013.wsdm_conference-2013.51": {"snippet": "...tent analysis of those documents depends primarily on the text. Although automated discovery of semantically related words from text improves free text query understanding, translating videos into tex...", "weights": [[8, 16, 1.0], [155, 160, 1.0]]}, "2010.wsdm_conference-2010.20": {"snippet": "...cale query log analysis of re-finding\n\n\n ABSTRACTAlthough Web search engines are targeted towards helping people find new information, people regularly use them to re-find Web pages they have seen bef...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0]]}, "2006.wwwconf_conference-2006.194": {"snippet": "...gine query logs for query recommendation\n\n\n ABSTRACTThis paper presents a simple and intuitive method for mining search engine query logs to get fast query recommendations on a large scale industrial-...", "weights": [[8, 13, 1.0], [23, 28, 1.0], [130, 135, 1.0], [153, 158, 1.0]]}, "2017.wsdm_conference-2017.96": {"snippet": "...om a query log or 2) make incremental changes to queries in a user's current session. While such approaches have been effective in improving retrieval, they either are limited to suggesting queries in...", "weights": [[8, 13, 1.0], [14, 17, 1.0]]}, "2007.wwwconf_conference-2007.166": {"snippet": "...from query logs\n\n\n ABSTRACTWe present a method for acquiring ontological knowledge using search query logs. We first use query logs to identify important contexts associated with terms belonging to a ...", "weights": [[8, 13, 1.0], [99, 104, 1.0], [124, 129, 1.0]]}, "2011.clef_workshop-2011w.99": {"snippet": "...ying Query Refinement\n\n\n This paper describes the analysis we performed for the query success task of LogCLEF 2011. In particular, we address the issue of query refinement. The motivating assumption o...", "weights": [[8, 13, 1.0], [53, 61, 1.0], [83, 88, 1.0], [158, 163, 1.0]]}, "2008.cikm_workshop-2008inews.12": {"snippet": "...?: a query log analysis study\n\n\n ABSTRACTThis study investigates how Greeks search the web through the analysis of search logs from the AltaVista search engine. The analysis looks at the data as a who...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0], [106, 114, 1.0], [168, 176, 1.0]]}, "2010.wsdm_conference-2010.5": {"snippet": "...tary query logs and selected samples of queries. In this paper, we suggest that anchor text, which is readily available, can be an effective substitute for a query log and study the effectiveness of a...", "weights": [[8, 13, 1.0], [161, 166, 1.0], [167, 170, 1.0]]}, "2008.spire_conference-2008.19": {"snippet": "...ique Analysis of Query Log Graphs", "weights": [[8, 16, 1.0], [20, 25, 1.0], [26, 29, 1.0]]}, "2009.ecir_conference-2009.94": {"snippet": "...ning Query Logs", "weights": [[8, 13, 1.0]]}, "2016.wsdm_conference-2016.55": {"snippet": "...ence analysis is an important problem in social network analysis due to its impact on viral marketing and targeted advertisements. Most of the existing influence analysis methods determine the influen...", "weights": [[8, 16, 1.0], [59, 67, 1.0], [165, 173, 1.0]]}, "2009.clef_workshop-2009w.75": {"snippet": "...ned: Log Analysis and Geographic Query Identification (LAGI) and Log Analysis for Digital Societies (LADS). Five groups using a variety of approaches submitted experiments. The data for the track, the...", "weights": [[8, 11, 1.0], [12, 20, 1.0], [36, 41, 1.0], [68, 71, 1.0], [72, 80, 1.0]]}, "2008.airweb_workshop-2008.3": {"snippet": "Query-log mining for detecting spam", "weights": [[0, 5, 1.0], [6, 9, 1.0]]}, "2009.sigirconf_workshop-2009uiir.7": {"snippet": "...e of log analysis has grown, log data constitute a relevant aspect in the evaluation process of the quality of a digital library system. In this paper, we address the problem of log analysis for compl...", "weights": [[8, 11, 1.0], [12, 20, 1.0], [32, 35, 1.0], [181, 184, 1.0], [185, 193, 1.0]]}, "2016.iir_workshop-2016.2": {"snippet": "...h on Query Log Privacy", "weights": [[8, 13, 1.0], [14, 17, 1.0]]}, "2016.wsdm_conference-2016.3": {"snippet": "...ment Analysis of Social Multimedia\n\n\n ABSTRACTSentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentime...", "weights": [[8, 16, 1.0], [59, 67, 1.0]]}, "2018.chiir_conference-2018.55": {"snippet": "... log analysis is an unobtrusive technique used to better understand user behavior in search systems . It can be used to evaluate search algorithms or user interfaces, or to (re-)design systems.Traditi...", "weights": [[8, 16, 1.0]]}, "2013.wsdm_conference-2013.89": {"snippet": "...ets. Query logs have also revolutionized query assistance tools such as related queries (recommended queries after the search was issued) or query completions (suggested queries as the query is being ...", "weights": [[8, 13, 1.0], [44, 49, 1.0], [144, 149, 1.0], [188, 193, 1.0]]}, "2013.ir_journal-ir0volumeA16A6.3": {"snippet": "... use query suggestion? A query suggestion log analysis", "weights": [[8, 13, 1.0], [28, 33, 1.0], [45, 48, 1.0], [49, 57, 1.0]]}, "2008.cikm_conference-2008.64": {"snippet": "... the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more.In this paper we introduce the query-flow ...", "weights": [[8, 13, 1.0], [61, 66, 1.0], [67, 70, 1.0], [71, 79, 1.0], [130, 135, 1.0], [192, 197, 1.0]]}, "2016.tois_journal-ir0volumeA35A2.0": {"snippet": "...of a query log and make the first attempt to cross the language barrier in query logs. We propose a novel model named Cross-Lingual Query Log Topic Model (CL-QLTM) to analyze query logs from a cross-l...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [78, 83, 1.0], [135, 140, 1.0], [141, 144, 1.0], [178, 183, 1.0]]}, "2011.sigirconf_conference-2011.7": {"snippet": "...ple: analysis of a people search engine log\n\n\n ABSTRACTRecent years show an increasing interest in vertical search: searching within a particular type of information. Understanding what people search ...", "weights": [[8, 16, 1.0], [43, 46, 1.0]]}, "2011.sigirconf_conference-2011.164": {"snippet": "...duct analysis of an expert search query log. The aim is to understand the special characteristics of expert search usage. To the best of our knowledge, this is one of the earliest work on expert searc...", "weights": [[8, 16, 1.0], [37, 42, 1.0], [43, 46, 1.0]]}, "2010.wsdm_conference-2010.33": {"snippet": "... the query, url and position, neglecting some other important attributes in click log data, such as the local time. Obviously, the click through rate is different between daytime and midnight. In this...", "weights": [[8, 13, 1.0], [85, 88, 1.0]]}, "2017.wwwconf_conference-2017.58": {"snippet": "... and Query Suggestion\n\n\n ABSTRACTContextual data plays an important role in modeling search engine users' behaviors on both query auto-completion (QAC) log and normal query (click) log. User's recent ...", "weights": [[8, 13, 1.0], [127, 132, 1.0], [155, 158, 1.0], [170, 175, 1.0], [184, 187, 1.0]]}}}
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Recognition Using Cost-Sensitive Structured Perceptron for NTCIR-10 MedNLP\n\n\n This paper reports on our approach to the NTCIR-10 MedNLP task, which aims at identifying personal and medical...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2021.emnlp-demo.35": {"snippet": "...amed Entity Recognition\n\n\n Named Entity Recognition is a fundamental task in information extraction and is an essential element for various Natural Language Processing pipelines. Adversarial attacks h...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [36, 42, 1.0], [43, 54, 1.0]]}, "2015.clef_conference-2015w.167": {"snippet": "...amed Entity Recognition Based on CRF\n\n\n Named entity recognition of biomedical text is the shared task 1b of the 2015 CLEF eHealth evaluation lab, which focuses on making biomedical text easier to und...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [49, 55, 1.0], [56, 67, 1.0]]}, "2015.clef_conference-2015w.168": {"snippet": "...amed Entity Recognition Based on CRF\n\n\n Named entity recognition of biomedical text is the shared task 1b of the 2015 CLEF eHealth evaluation lab, which focuses on making biomedical text easier to und...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [49, 55, 1.0], [56, 67, 1.0]]}, "2007.sigirconf_conference-2007.145": {"snippet": "...amed entity recognition and relation extraction for question answering", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2016.fire_conference-2016w.68": {"snippet": "...ixed Entity Recognition\n\n\n Entity Recognition is an essential part of Information Extraction, where explicitly available information and relations are extracted from the entities within the text. Plet...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [30, 36, 1.0], [37, 48, 1.0]]}, "2020.clef_conference-2020w.210": {"snippet": "...amed Entity Recognition in Chemical Reactions from Patents\n\n\n This paper describes our VinAI system for the ChEMU task 1 of named entity recognition (NER) in chemical reactions. Our system employs a B...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [133, 139, 1.0], [140, 151, 1.0]]}, "2014.sigirconf_workshop-2014erd.9": {"snippet": "...h to entity recognition: magnetic and IISAS team at ERD challenge\n\n\n ABSTRACTERD 2014 was a research challenge focused on the task of recognition and disambiguation of knowledge base entities in short...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [137, 148, 1.0]]}, "2016.clef_conference-2016w.3": {"snippet": "... For entity recognition, the highest performance was achieved on the EMEA corpus, with an overall F-measure of 0.702 for plain entities recognition and 0.529 for normalized entity recognition. For ent...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [139, 150, 1.0], [176, 182, 1.0], [183, 194, 1.0], [200, 206, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.16": {"snippet": "...amed entity recognition with multiple segment representations\n\n\n a b s t r a c tNamed entity recognition (NER) is mostly formalized as a sequence labeling problem in which segments of named entities a...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [89, 95, 1.0], [96, 107, 1.0]]}, "2011.trec_conference-2011.79": {"snippet": "...2011 Entity Track: Related Entity Finding and Entity List Completion", "weights": [[8, 14, 1.0], [30, 36, 1.0], [49, 55, 1.0]]}, "2021.wwwconf_workshop-2021cleopatra.2": {"snippet": "...amed-Entity Recognition English corpus", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "W02-2035": {"snippet": "...amed Entity Recognition", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "D09-1015": {"snippet": "...amed Entity Recognition\n\n\n Many named entities contain other named entities inside them. Despite this fact, the field of named entity recognition has almost entirely ignored nested named entity recogn...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [130, 136, 1.0], [137, 148, 1.0], [190, 196, 1.0], [197, 208, 1.0]]}, "2019.cikm_conference-2019.256": {"snippet": "...tive Entity Recognition in Low Resource Settings", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "L10-1310": {"snippet": "...amed Entity Recognition", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2020.acl-main.722": {"snippet": "...amed Entity Recognition\n\n\n Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [48, 54, 1.0], [55, 66, 1.0]]}, "2009.eamt-1.13": {"snippet": "...amed Entity Recognition and Translation\n\n\n We present an environment for the recognition and translation of Named Entities (NEs). The environment consists of a new formalism for the Named Entity Recog...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [80, 91, 1.0], [191, 197, 1.0], [198, 209, 1.0]]}, "W07-0803": {"snippet": "...Name Entity Recognition for {A}rabic\n\n\n Named entity recognition (NER) is nowadays an important task, which is responsible for the identification of proper names in text and their classification as di...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [49, 55, 1.0], [56, 67, 1.0]]}, "O05-5010": {"snippet": "...amed Entity Categorization Based on the People{'}s Daily Corpus) [In {C}hinese]\n\n\n Named entity recognition is a very important part of information retrieval and information extraction. Classification...", "weights": [[8, 14, 1.0], [92, 98, 1.0], [99, 110, 1.0]]}, "2006.clef_workshop-2006w.105": {"snippet": "...t of Entity Recognition in Answer Validation\n\n\n The Answer Validation Exercise (AVE) 2006 is aimed at developing systems able to decide whether the answer of a Question Answering (QA) system is correc...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2004.sigirconf_conference-2004.37": {"snippet": "...amed entity recognition using machine learning\n\n\n ABSTRACTIn this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2006.clef_workshop-2006.57": {"snippet": "...t of Entity Recognition on Answer Validation", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "W19-3644": {"snippet": "...amed Entity Recognition with Commonsense Knowledge", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2006.airs_conference-2006.49": {"snippet": "...amed entity recognizer and fine-grained named entity dictionary. In this paper, we describe a fine-grained Named Entity Recognition using Conditional Random Fields (CRFs) for question answering. We us...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [116, 122, 1.0], [123, 134, 1.0]]}, "S15-1028": {"snippet": "...icit entity mentions and introduce the problem of implicit entity recognition (IER) in clinical documents. We propose a solution to IER that leverages entity definitions from a knowledge base to creat...", "weights": [[8, 14, 1.0], [62, 68, 1.0], [69, 80, 1.0], [154, 160, 1.0]]}, "C18-1168": {"snippet": "... for Entity Recognition of Novel Classes\n\n\n In this reproduction paper, we replicate and extend several past studies on transfer learning for entity recognition. In particular, we are interested in en...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [145, 151, 1.0], [152, 163, 1.0], [201, 207, 1.0]]}, "2009.sigirconf_conference-2009.35": {"snippet": "...amed entity recognition in query\n\n\n ABSTRACTThis paper addresses the problem of Named Entity Recognition in Query (NERQ), which involves detection of the named entity in a given query and classificati...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [89, 95, 1.0], [96, 107, 1.0], [163, 169, 1.0]]}, "L14-1328": {"snippet": "...amed Entity Recognition on {T}urkish Tweets", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "W18-3215": {"snippet": "...amed Entity Recognition on Code-Switched Data Using Conditional Random Fields\n\n\n Named Entity Recognition is an important information extraction task that identifies proper names in unstructured texts...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [90, 96, 1.0], [97, 108, 1.0]]}, "D19-1652": {"snippet": "... for Entity Recognition\n\n\n The task of entity recognition has traditionally been modelled as a sequence labelling task. However, this usually requires a large amount of fine-grained data annotated at ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [42, 48, 1.0], [49, 60, 1.0]]}, "2013.clef_conference-2013.32": {"snippet": "Entity Recognition in Parallel Multi-lingual Biomedical Corpora: The CLEF-ER Laboratory Overview", "weights": [[0, 6, 1.0], [7, 18, 1.0]]}, "W14-1309": {"snippet": "...amed entity recognition on Turkish tweets, using two different annotated data sets. In these experiments, starting with a baseline named entity recognition system, we adapt its recognition rules and r...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [140, 146, 1.0], [147, 158, 1.0], [180, 191, 1.0]]}, "2014.sigirconf_conference-2014.223": {"snippet": "...'14: entity recognition and disambiguation challenge", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2014.airs_conference-2014.24": {"snippet": "...amed entity recognition from the newspaper articles. Thus, this study designs and develops a crime named entity recognition based on machine learning approaches that extract nationalities, weapons, an...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [108, 114, 1.0], [115, 126, 1.0]]}, "2020.clef_conference-2020w.119": {"snippet": "...amed entity recognition, entity mention detection and entity linking. For the task of named entity recognition we used an ensemble of fine-tuned BERT models; entity linking was approached by three dif...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [28, 34, 1.0], [57, 63, 1.0], [95, 101, 1.0], [102, 113, 1.0], [161, 167, 1.0]]}, "2018.fire_conference-2018w.19": {"snippet": "...amed Entity Recognition System for Indian Languages\n\n\n Named Entity Recognition is the process of classifying the elementary units in a text document into meaningful categories such as person, locatio...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [64, 70, 1.0], [71, 82, 1.0]]}, "2021.acl-long.216": {"snippet": "...amed Entity Recognition\n\n\n Named entity recognition (NER) is a wellstudied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [36, 42, 1.0], [43, 54, 1.0]]}, "2019.sigirconf_conference-2019.185": {"snippet": "Name Entity Recognition with Policy-Value Networks", "weights": [[5, 11, 1.0], [12, 23, 1.0]]}, "2021.triton-1.10": {"snippet": "...amed Entity Recognition via {F}ast{A}lign: a Case Study\n\n\n Named Entity Recognition is an essential task in natural language processing to detect entities and classify them into predetermined categori...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [68, 74, 1.0], [75, 86, 1.0]]}, "L08-1253": {"snippet": "...amed Entity Recognition for Digitised Historical Texts", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2019.wwwconf_conference-2019.343": {"snippet": "...amed Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "W03-1121": {"snippet": "...amed entity type. Therefore we focus on named entity recognition model in Korean. Korean named entity recognition is difficult since each word of named entity has not specific features such as the cap...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [56, 67, 1.0], [98, 104, 1.0], [105, 116, 1.0], [155, 161, 1.0]]}, "2017.fire_conference-2017.4": {"snippet": "...amed Entity Recognition for Telugu\n\n\n ABSTRACTIn this paper, we apply three classification learning algorithms to Telugu Named Entity Recognition (NER) task and we present a comparative study between ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [130, 136, 1.0], [137, 148, 1.0]]}, "2020.clef_conference-2020.18": {"snippet": "...amed Entity Recognition and Event Extraction of Chemical Reactions from Patents", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2022.semeval-1.217": {"snippet": "...amed Entity Recognition\n\n\n Named Entity Recognition (NER), an essential subtask in NLP that identifies text belonging to predefined semantics such as a person, location, organization, drug, time, clin...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [36, 42, 1.0], [43, 54, 1.0]]}, "2020.jasis_journal-ir0volumeA71A5.5": {"snippet": "...amed Entity Recognition in User Comments via Parameterized Label Propagation", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2022.findings-acl.329": {"snippet": "...shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework\n\n\n Entity recognition is a fundamental task in understanding document images. Traditional sequence labeling fra...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [95, 101, 1.0], [102, 113, 1.0]]}, "2012.cikm_workshop-2012dtmbio.6": {"snippet": "...ical entity recognition using structural support vector machines with rich features\n\n\n ABSTRACTNamed entity recognition (NER) is an important task for natural language processing (NLP) of clinical tex...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [104, 110, 1.0], [111, 122, 1.0]]}, "W03-0423": {"snippet": "...amed Entity Recognition with a Maximum Entropy Approach", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2015.wwwconf_conference-2015.49": {"snippet": "... for Entity Recognition\n\n\n ABSTRACTRecognizing entity instances in documents according to a knowledge base is a fundamental problem in many data mining applications. The problem is extremely challengi...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [50, 56, 1.0]]}, "2020.acl-main.519": {"snippet": "...amed Entity Recognition\n\n\n The task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not. Models are usually separat...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [48, 54, 1.0], [55, 66, 1.0]]}, "2020.sigirconf_conference-2020.184": {"snippet": "...ting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants\n\n\n We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken quer...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [51, 62, 1.0]]}, "2017.cikm_conference-2017.242": {"snippet": "...amed-Entity Recognition System Using Syllable Bigram Embedding with Eojeol Prefix Information\n\n\n ABSTRACTKorean named-entity recognition (NER) systems have been developed mainly on the morphological-l...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [121, 127, 1.0], [128, 139, 1.0]]}, "W17-1413": {"snippet": "...amed Entity Recognition\n\n\n In the paper we present an adaptation of Liner2 framework to solve the BSNLP 2017 shared task on multilingual named entity recognition. The tool is tuned to recognize and le...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [146, 152, 1.0], [153, 164, 1.0]]}, "L00-1128": {"snippet": "...amed Entity Recognition in {G}reek Texts", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2014.wwwconf_conference-2014.38": {"snippet": "...amed entity recognition for idiosyncratic web collections\n\n\n ABSTRACTNamed Entity Recognition (NER) plays an important role in a variety of online information management tasks including text categoriz...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [78, 84, 1.0], [85, 96, 1.0]]}, "W06-1660": {"snippet": "...amed Entity Recognition Model across Domains\n\n\n When a machine learning-based named entity recognition system is employed in a new domain, its performance usually degrades. In this paper, we provide a...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [87, 93, 1.0], [94, 105, 1.0]]}, "2020.aacl-main.22": {"snippet": "...amed Entity Recognition in Multi-level Contexts\n\n\n Named entity recognition is a critical task in the natural language processing field. Most existing methods for this task can only exploit contextual...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [60, 66, 1.0], [67, 78, 1.0]]}, "W04-1221": {"snippet": "...amed Entity Recognition using Conditional Random Fields and Rich Feature Sets", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "W02-2010": {"snippet": "...amed Entity Recognition as a House of Cards: Classifier Stacking", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2011.cikm_conference-2011.338": {"snippet": "...amed entity recognition using a modified Pegasos algorithm\n\n\n ABSTRACTIn this paper, we describe a named entity recognition using a modified Pegasos algorithm for structural SVMs. We show the modified...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [108, 114, 1.0], [115, 126, 1.0]]}, "2013.tist_journal-ir0volumeA4A1.2": {"snippet": "...amed entity recognition for tweets\n\n\n Two main challenges of Named Entity Recognition (NER) for tweets are the insufficient information in a tweet and the lack of training data. We propose a novel met...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [70, 76, 1.0], [77, 88, 1.0]]}, "2013.fire_conference-2013.14": {"snippet": "...amed-Entity Recognition in Bengali\n\n\n ABSTRACTThis paper 1 describes two systems for Named Entity Recognition (NER) and performance of two systems has been compared. The first system is a rule-based o...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [94, 100, 1.0], [101, 112, 1.0]]}, "2018.ecir_workshop-2018newsir.8": {"snippet": "...amed Entity Recognition for Telugu News Articles using Na\u00efve Bayes Classifier\n\n\n The Named Entity Recognition (NER) is identifying name of Person, Location, Organization etc. in a given sentence or a ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [94, 100, 1.0], [101, 112, 1.0]]}, "L18-1705": {"snippet": "...amed Entity Recognition using Conditional Random Fields and Local Grammars\n\n\n Named Entity Recognition involves automatically identifying and classifying entities such as persons, places, and organiza...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [87, 93, 1.0], [94, 105, 1.0]]}, "W17-4423": {"snippet": "...tive Recognition for Emerging and Rare Entities\n\n\n We present a novel named entity recognition (NER) system, and its participation in the Emerging and Rare Entity Recognition shared task, hosted at th...", "weights": [[8, 19, 1.0], [79, 85, 1.0], [86, 97, 1.0], [159, 165, 1.0], [166, 177, 1.0]]}, "R17-1021": {"snippet": "...amed Entity Recognition", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "I13-1101": {"snippet": "...amed Entity Recognizer for Open-Domain Search Queries\n\n\n Named entity recognition in queries is the task of identifying sequences of terms in search queries that refer to a unique concept. This proble...", "weights": [[8, 14, 1.0], [66, 72, 1.0], [73, 84, 1.0]]}, "2014.cikm_workshop-2014dtmbio.3": {"snippet": "...amed Entity Recognition Based on the Combination of Regional and Global Text Features\n\n\n ABSTRACTThe biomedical information extraction, especially Named Entity Recognition (NER), is a primary task in ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [156, 162, 1.0], [163, 174, 1.0]]}, "N09-1037": {"snippet": "...amed Entity Recognition\n\n\n For many language technology applications, such as question answering, the overall system runs several independent processors over the data (such as a named entity recognize...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [187, 193, 1.0]]}, "W07-1712": {"snippet": "...amed Entity Recognition for {U}krainian: A Resource-Light Approach\n\n\n Named entity recognition (NER) is a subtask of information extraction (IE) which can be used further on for different purposes. In...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [79, 85, 1.0], [86, 97, 1.0]]}, "2014.sigirconf_workshop-2014erd.17": {"snippet": "... for entity recognition and disambiguation\n\n\n ABSTRACTWe present a system for entity recognition and disambiguation (ERD) in short text, aiming at identifying all text fragments referring to an entity...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [81, 87, 1.0], [88, 99, 1.0], [197, 203, 1.0]]}, "2015.clef_conference-2015w.42": {"snippet": "...lain entity recognition, 0.711 for normalized entity recognition and 0.872 for entity normalization.", "weights": [[8, 14, 1.0], [15, 26, 1.0], [49, 55, 1.0], [56, 67, 1.0], [82, 88, 1.0]]}, "2017.clef_conference-2017w.49": {"snippet": "Entity Recognition and Language Identification with FELTS", "weights": [[0, 6, 1.0], [7, 18, 1.0]]}, "W03-0420": {"snippet": "...amed Entity Recognition\n\n\n In this paper, we describe a system that applies maximum entropy (ME) models to the task of named entity recognition (NER). Starting with an annotated corpus and a set of fe...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [128, 134, 1.0], [135, 146, 1.0]]}, "W18-3221": {"snippet": "...tity Recognition with Embedding Attention\n\n\n We describe our work for the CALCS 2018 shared task on named entity recognition on code-switched data. Our system ranked first place for MS Arabic-Egyptian...", "weights": [[8, 19, 1.0], [109, 115, 1.0], [116, 127, 1.0]]}, "W12-6327": {"snippet": "...amed Entity Recognition and Disambiguation System\n\n\n In this paper we describe an integrated approach for named entity recognition and disambiguation in Chinese. The proposed method relies on named en...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [115, 121, 1.0], [122, 133, 1.0], [201, 207, 1.0]]}, "2020.clef_conference-2020w.120": {"snippet": "...amed Entity Recognition in Chemical Patents using Ensemble of Contextual Language Models\n\n\n Chemical patent documents describe a broad range of applications holding key reaction and compound informati...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2009.jasis_journal-ir0volumeA60A8.9": {"snippet": "...amed Entity Recognition for Arabic", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2020.ecir_conference-20202.48": {"snippet": "...amed-Entity Recognition: An Empirical Study on Generalization\n\n\n Contextualized embeddings use unsupervised language model pretraining to compute word representations depending on their context. This ...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2011.trec_conference-2011.54": {"snippet": "...2011 Entity Track: Entity List Completion Using Contextual Unsupervised Scores for Candidate Entities Ranking", "weights": [[8, 14, 1.0], [22, 28, 1.0]]}, "2014.fire_conference-2014.17": {"snippet": "...amed Entity Recognition for Indian Languages using Rich Features\n\n\n ABSTRACTThis paper aims at implementing Named Entity Recognition (NER) for four languages such as English, Tamil, Hindi and Malayala...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [117, 123, 1.0], [124, 135, 1.0]]}, "2008.cikm_conference-2008.14": {"snippet": "...amed entity recognition\n\n\n ABSTRACTAutomatic recognition of named entities such as people, places, organizations, books, and movies across the entire web presents a number of challenges, both of scale...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [48, 59, 1.0]]}, "2022.acl-long.67": {"snippet": "...amed Entity Recognition\n\n\n Named entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructin...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [36, 42, 1.0], [43, 54, 1.0], [140, 146, 1.0], [147, 158, 1.0]]}, "P01-1041": {"snippet": "...amed Entity Recognition based on a Simple Rule Generator and Decision Tree Learning\n\n\n Named entity (NE) recognition is a task in which proper nouns and numerical information in a document are detecte...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [96, 102, 1.0], [108, 119, 1.0]]}, "2015.wwwconf_conference-2015c.71": {"snippet": "...ards Entity Correctness, Completeness and Emergence for Entity Recognition\n\n\n ABSTRACTLinking words or phrases in unstructured text to entities in knowledge bases is the problem of entity recognition ...", "weights": [[8, 14, 1.0], [59, 65, 1.0], [66, 77, 1.0], [184, 190, 1.0], [191, 202, 1.0]]}, "E06-3004": {"snippet": "...amed Entity Recognition with Automatically Generated Gazetteer Lists\n\n\n Current Named Entity Recognition systems suffer from the lack of hand-tagged data as well as degradation when moving to other do...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [89, 95, 1.0], [96, 107, 1.0]]}, "2021.acl-long.275": {"snippet": "...amed Entity Recognition via Explicitly Excluding the Influence of the Best Path\n\n\n This paper presents a novel method for nested named entity recognition. As a layered method, our method extends the p...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [138, 144, 1.0], [145, 156, 1.0]]}}}
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Obtaining co...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [168, 177, 1.0], [178, 189, 1.0]]}, "1983.jasis_journal-ir0volumeA34A1.7": {"snippet": "Validity of citation criteria for assessing the influence of scientific publications: New evidence with peer assessment", "weights": []}, "2020.birws_workshop-2020.10": {"snippet": "...ween Relevance Assessments, Bibliometrics and Altmetrics", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2013.cikm_workshop-2013compsci.4": {"snippet": "...lly, relevance assessments for expert search have been gathered through self-assessment or based on the opinions of coworkers. We introduce three benchmark datasets 1 for expert search that use confer...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2017.trec_conference-2017.29": {"snippet": "...t of relevance assessments (\"qrels\") comparable to the official Common Core Track qrels (cf. [12, 2]). To this end, we adapted for live, human-in-the-loop use, the AutoTAR CAL implementation, 1 which ...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2010.sigirconf_conference-2010.126": {"snippet": "...icit relevance judgments which are expensive to collect. Relevance assessments inferred from implicit feedback such as click-through data can be collected inexpensively, but may be less reliable. We c...", "weights": [[8, 17, 1.0], [60, 69, 1.0], [70, 81, 1.0]]}, "2012.sigirconf_conference-2012.89": {"snippet": "...rced relevance assessments\n\n\n ABSTRACTCrowdsourcing is a market of steadily-growing importance upon which both academia and industry increasingly rely. However, this market appears to be inherently in...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2003.ipm_journal-ir0volumeA39A3.3": {"snippet": "Relevance thresholds: a multi-stage predictive model of how users evaluate information", "weights": [[0, 9, 1.0]]}, "2008.jasis_journal-ir0volumeA59A3.10": {"snippet": "...eudo-relevance feedback using graded relevance assessments", "weights": [[8, 17, 1.0], [40, 49, 1.0], [50, 61, 1.0]]}, "2009.sigirconf_conference-2009.59": {"snippet": "...l of relevance assessments\n\n\n ABSTRACTGrowing interest in online collections of digital books and video content motivates the development and optimization of adequate retrieval systems. However, tradi...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2016.clef_conference-2016.20": {"snippet": "... the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content bet...", "weights": [[8, 17, 1.0], [74, 83, 1.0]]}, "2017.sigirconf_conference-2017.155": {"snippet": "...y of Relevance Assessments using Inter-Rater Agreement", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2016.chiir_conference-2016.45": {"snippet": "...aded Relevance Assessments", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2010.ntcir_workshop-2010evia.5": {"snippet": "...hout Relevance Assessments: Revisited\n\n\n We reexamine the problem of ranking retrieval systems without relevance assessments in the context of collaborative evaluation forums such as TREC and NTCIR. T...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [106, 115, 1.0], [116, 127, 1.0]]}, "2006.ir_journal-ir0volumeA9A4.2": {"snippet": "...aded relevance assessments: Improving effectiveness by pseudo-relevance feedback", "weights": [[8, 17, 1.0], [18, 29, 1.0], [65, 74, 1.0]]}, "1968.ipm_journal-ir0volumeA4A4.2": {"snippet": "Relevance assessments and retrieval system evaluation", "weights": [[0, 9, 1.0], [10, 21, 1.0]]}, "2013.tweb_journal-ir0volumeA7A2.6": {"snippet": "...sing relevance and trust of the deep web sources and results based on inter-source agreement\n\n\n Deep web search engines face the formidable challenge of retrieving high-quality results from the vast c...", "weights": [[8, 17, 1.0]]}, "2017.trec_conference-2017.30": {"snippet": "...t of relevance assessments (\"qrels\") comparable to the official Common Core Track qrels (cf. [12, 2]). To this end, we adapted for live, human-in-the-loop use, the AutoTAR CAL implementation, 1 which ...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2020.ipm_journal-ir0volumeA57A5.6": {"snippet": "Value assessment of companies by using an enterprise value assessment system based on their public transfer specification", "weights": []}, "2009.cikm_conference-2009.270": {"snippet": "...from relevance assessments has been proposed as an effective means for improving retrieval. We introduce a novel language modeling method which uses information from each assessed document and their a...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2006.wwwconf_conference-2006.34": {"snippet": "Addressing the testing challenge with a web-based e-assessment system that tutors as it assesses\n\n\n ABSTRACTSecondary teachers across the country are being asked to use formative assessment data to in...", "weights": []}, "2007.ecir_conference-2007.26": {"snippet": "...lete Relevance Assessments", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2012.trec_conference-2012.49": {"snippet": "...text relevance assessing task (TRAT) and an image relevance assessing task (IRAT). This track overview describes the track and provides analysis of the track's results.", "weights": [[8, 17, 1.0], [53, 62, 1.0]]}, "2016.ir_journal-ir0volumeA19A4.0": {"snippet": "...sing relevance judgments", "weights": [[8, 17, 1.0]]}, "2017.chiir_conference-2017.44": {"snippet": "...ment Relevance in Exploratory Search User Studies\n\n\n ABSTRACTEvaluation is crucial in assessing the e\u21b5ectiveness of new information retrieval and human computer interaction techniques and systems. Rel...", "weights": [[8, 17, 1.0], [200, 209, 1.0]]}, "2012.clef_conference-2012.14": {"snippet": "...y of Relevance Assessments with Students", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2008.tois_journal-ir0volumeA27A1.0": {"snippet": "... and relevance assessments. Obtaining relevance assessments that are as sound and complete as possible is crucial for the comparison of retrieval approaches. In XML retrieval, the problem of obtaining...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [41, 50, 1.0], [51, 62, 1.0]]}, "2017.sigirconf_conference-2017.233": {"snippet": "Relevance Judgments: Preferences, Scores and Ties", "weights": [[0, 9, 1.0]]}, "2002.jasis_journal-ir0volumeA53A13.5": {"snippet": "...aded relevance assessments in IR evaluation", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2017.ipm_journal-ir0volumeA53A2.1": {"snippet": "...ion, relevance assessment and retrieval performance in clinical settings\n\n\n a b s t r a c tThe large volumes of medical information available on the web may provide answers for a wide range of users a...", "weights": [[8, 17, 1.0]]}, "2014.sigirconf_conference-2014.224": {"snippet": "...ient assessments of relevance (GEAR)\n\n\n ABSTRACTEvaluation is a fundamental part of Information Retrieval, and in the conventional Cranfield evaluation paradigm, sets of relevance assessments are a fu...", "weights": [[8, 19, 1.0], [23, 32, 1.0], [173, 182, 1.0], [183, 194, 1.0]]}, "2002.sigirconf_conference-2002.44": {"snippet": "...eral relevance criteria of TREC -: counting on negligible documents?\n\n\n ABSTRACTMost test collections (like TREC and CLEF) for experimental research in information retrieval apply binary relevance ass...", "weights": [[8, 17, 1.0], [190, 199, 1.0], [200, 211, 1.0]]}, "2010.wwwconf_conference-2010.109": {"snippet": "...ank: relevance and trust assessment for deep web sources based on inter-source agreement\n\n\n ABSTRACTWe consider the problem of deep web source selection and argue that existing source selection method...", "weights": [[8, 17, 1.0]]}, "2006.cikm_conference-2006.13": {"snippet": "...ning relevance assessments is a costly task. In XML retrieval this problem is exacerbated as the elements of the document must also be assessed with respect to the exhaustivity and specificity dimensi...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2012.trec_conference-2012.84": {"snippet": "... for relevance on the same document set, and use this training set to build strategies. We apply machine approaches, including clustering, to order documents for each topic, and then ask crowdworkers ...", "weights": [[8, 17, 1.0]]}, "2014.sigirjournals_journal-ir0volumeA48A2.0": {"snippet": "... for Relevance Assessments", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2012.wsdm_conference-2012.41": {"snippet": "...ment relevance assessments. As this is no longer practical for modern corpora, numerous problems arise, including scalability, reusability, and applicability. We propose a new method for relevance ass...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [190, 199, 1.0]]}, "2012.sigirconf_conference-2012.115": {"snippet": "...rced relevance assessments with terrier\n\n\n ABSTRACTInformation retrieval (IR) systems rely on document relevance assessments for queries to gauge their effectiveness for a variety of tasks, e.g. Web r...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [106, 115, 1.0], [116, 127, 1.0]]}, "2000.ir_journal-ir0volumeA2A1.6": {"snippet": "...iple Relevance Assessments", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2011.ecir_conference-2011.16": {"snippet": "...n of Relevance Assessments Using Crowdsourcing\n\n\n Abstract. In the last years crowdsourcing has emerged as a viable platform for conducting relevance assessments. The main reason behind this trend is ...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [143, 152, 1.0], [153, 164, 1.0]]}, "2018.wwwconf_conference-2018c.59": {"snippet": "...lect assessments, including comments and grades provided by students during the peer assessment process and then each feedback-and-grade pair is labeled with its credibility level by Mechanical Turk e...", "weights": [[8, 19, 1.0]]}, "2011.wwwconf_conference-2011.26": {"snippet": "...ased relevance assessment. When applied to the deep web these methods have two deficiencies. First is that the methods are agnostic to the correctness (trustworthiness) of the sources. Secondly, the q...", "weights": [[8, 17, 1.0]]}, "2001.wwwconf_conference-2001p.73": {"snippet": "Relevance Evaluation of Search Engines' Query Results", "weights": [[0, 9, 1.0]]}, "1996.ipm_journal-ir0volumeA32A3.1": {"snippet": "Relevance Judgments for Assessing Recall", "weights": [[0, 9, 1.0]]}, "2003.sigirconf_conference-2003.49": {"snippet": "...e of relevance judgments\n\n\n ABSTRACTSoboroff, Nicholas and Cahan recently proposed a method for evaluating the performance of retrieval systems without relevance judgments. They demonstrated that the ...", "weights": [[8, 17, 1.0], [155, 164, 1.0]]}, "1975.ipm_journal-ir0volumeA11A12.4": {"snippet": "... the relevance assessment of documents", "weights": [[8, 17, 1.0]]}, "2015.sigirconf_conference-2015.140": {"snippet": "Relevance-aware Filtering of Tuples Sorted by an Attribute Value via Direct Optimization of Search Quality Metrics", "weights": [[0, 9, 1.0]]}, "2004.ecir_conference-2004.22": {"snippet": "...t of Relevance for the INEX'02 Test Collection", "weights": [[8, 17, 1.0]]}, "2014.sigirconf_conference-2014.101": {"snippet": "...d in relevance assessments for images\n\n\n ABSTRACTHow assessors and end users judge the relevance of images has been studied in information science and information retrieval for a considerable time. Th...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [90, 99, 1.0]]}, "2012.ipm_journal-ir0volumeA48A6.2": {"snippet": "...TREC relevance assessment\n\n\n a b s t r a c tCrowdsourcing has recently gained a lot of attention as a tool for conducting different kinds of relevance evaluations. At a very high level, crowdsourcing ...", "weights": [[8, 17, 1.0], [144, 153, 1.0]]}, "2018.sigirconf_conference-2018.163": {"snippet": "... for relevance assessment based on the results of prior assessments. Unlike static and dynamic pooling methods that are commonly used to compile relevance assessments for the creation of information r...", "weights": [[8, 17, 1.0], [59, 70, 1.0], [148, 157, 1.0], [158, 169, 1.0]]}, "1996.jasis_journal-ir0volumeA47A1.3": {"snippet": "...s in Relevance Assessments and the Measurement of Retrieval Effectiveness", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2014.sigirconf_workshop-2014medir.6": {"snippet": "...sing Relevance in Medical IR is Demanding\n\n\n This study investigates if and why assessing relevance of clinical records for a clinical retrieval task is cognitively demanding. Previous research has hi...", "weights": [[8, 17, 1.0], [93, 102, 1.0]]}, "2020.ecir_conference-20201.30": {"snippet": "...h to relevance. To consider semantic matching between query and document, we propose an unsupervised semantic matching model by simulating a user who makes relevance decisions. The primary goal of the...", "weights": [[8, 17, 1.0], [159, 168, 1.0]]}, "2014.sigirconf_conference-2014.190": {"snippet": "...eval relevance assessment}\n\n\n ABSTRACTRelevation! is a system for performing relevance judgements for information retrieval evaluation. Relevation! is web-based, fully configurable and expandable; it ...", "weights": [[8, 17, 1.0], [80, 89, 1.0]]}, "2019.ecir_conference-20191.50": {"snippet": "...stic Relevance for Crowdsourcing\n\n\n Abstract. It has been recently proposed to consider relevance assessment as a stochastic process where relevance judgements are modeled as binomial random variables...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [142, 151, 1.0]]}, "1979.jasis_journal-ir0volumeA30A5.3": {"snippet": "Relevance", "weights": [[0, 9, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...s in relevance assessments across system types is unknown.To verify that the TREC collections are reliable laboratory tools, NIST investigated the effect changes in the relevance assessments have on t...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [172, 181, 1.0], [182, 193, 1.0]]}, "2008.sigirconf_conference-2008.86": {"snippet": "...t in relevance judgements between these three groups. We report on experiments to determine if this is sufficient to invalidate the use of a test collection for measuring system performance when relev...", "weights": [[8, 17, 1.0], [198, 207, 1.0]]}, "2013.sigirconf_conference-2013.82": {"snippet": "Is relevance hard work?: evaluating the effort of making relevant assessments\n\n\n ABSTRACTThe judging of relevance has been a subject of study in information retrieval for a long time, especially in th...", "weights": [[3, 12, 1.0], [66, 77, 1.0], [104, 113, 1.0]]}, "2018.cikm_conference-2018.66": {"snippet": "Relevance Estimation with Multiple Information Sources on Search Engine Result Pages\n\n\n ABSTRACTRelevance estimation is among the most important tasks in the ranking of search results. Current relevan...", "weights": [[0, 9, 1.0], [193, 202, 1.0]]}, "2007.cikm_workshop-2007pikm.21": {"snippet": "... for relevance assessment\n\n\n ABSTRACTThe major question raised in information retrieval on semistructured documents relates to the manner of effectively handling the structure and the contents of the ...", "weights": [[8, 17, 1.0]]}, "2002.clef_workshop-2002w.5": {"snippet": "...uage Relevance Assessment and Task Context\n\n\n An experiment on how users assess relevance in a foreign language they know well is reported. Results show that relevance assessment in a foreign language...", "weights": [[8, 17, 1.0], [83, 92, 1.0], [161, 170, 1.0]]}, "2010.iir_workshop-2010.9": {"snippet": "...onal Relevance Assessment", "weights": [[8, 17, 1.0]]}, "2003.clef_workshop-2003.45": {"snippet": "...uage Relevance Assessment", "weights": [[8, 17, 1.0]]}, "1985.jasis_journal-ir0volumeA36A5.14": {"snippet": "Relevance?", "weights": [[0, 9, 1.0]]}, "2008.ir_journal-ir0volumeA11A5.3": {"snippet": "...lete relevance assessments", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}}}
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Deep Neural architectures like Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRU) were applied and compared agai...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [49, 55, 1.0], [56, 64, 1.0], [118, 124, 1.0], [125, 133, 1.0]]}, "2016.mir_conference-2016.43": {"snippet": "...odal Deep Convolutional Neural Network for Audio-Visual Emotion Recognition", "weights": [[8, 12, 1.0], [27, 33, 1.0]]}, "2016.ismir_conference-2016.111": {"snippet": "...sing Deep Convolutional Neural Networks", "weights": [[8, 12, 1.0], [27, 33, 1.0], [34, 42, 1.0]]}, "O15-1011": {"snippet": "...\u795e\u7d93\u7db2\u8def(deep neural networks, DNN)\u8207\u647a\u7a4d\u985e\u795e\u7d93\u7db2\u8def(convolutional neuron networks, CNN)\u4e4b\u5148\u9032\u7684\u8072\u5b78\u6a21\u578b\u4ee5\u7522\u751f \u66f4\u5177\u9451\u5225\u6027\u767c\u97f3\u6aa2\u6e2c\u7279\u5fb5\uff1b2)\u6211\u5011\u6bd4\u8f03\u4e26\u7d50\u5408\u4e0d\u540c\u5206\u985e\u65b9\u6cd5\uff0c\u4ee5\u671f\u80fd\u9054\u5230\u66f4\u4f73\u7684\u767c\u97f3\u6aa2 \u6e2c\u8868\u73fe\uff1b3)\u91dd\u5c0d\u932f\u8aa4\u767c\u97f3\u6aa2\u6e2c\u6240\u5305\u62ec\u7684\u6a21\u7d44\uff0c\u9032\u884c\u4e00\u7cfb\u5217\u5ee3\u6cdb\u4e14\u6df1\u5165\u7684\u5be6\u9a57\u5206\u6790\u8207\u8a0e\u8ad6\u3002 \u5f9e\u4e00\u5957\u4ee5\u83ef\u8a9e\u505a\u70ba\u7b2c\u4e8c\u8a9e\u5b78\u7fd2\u76ee\u6a19\u8a9e\u8a00\u7684\u5927\u91cf\u8a9e\u6599\u5eab\u4e4b\u5be6\u9a57\u7d50\u679c\u986f\u793a\uff0c\u6211...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [64, 72, 1.0]]}, "C16-1138": {"snippet": "...n by deep recurrent neural networks with data augmentation\n\n\n Nowadays, neural networks play an important role in the task of relation classification. 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The general framework...", "weights": [[0, 4, 1.0], [14, 20, 1.0]]}, "E17-1104": {"snippet": "Very Deep Convolutional Networks for Text Classification\n\n\n The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, the...", "weights": [[5, 9, 1.0], [24, 32, 1.0], [115, 121, 1.0], [122, 130, 1.0], [171, 177, 1.0], [178, 186, 1.0]]}, "L16-1330": {"snippet": "...dard Neural Networks look like, limiting the possibility to use standard Neural Networks frameworks. This work presents NNBlocks, a new framework written in Python to build and train Neural Networks t...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [76, 82, 1.0], [83, 91, 1.0], [186, 192, 1.0], [193, 201, 1.0]]}, "2018.wwwconf_conference-2018c.381": {"snippet": "...onal Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with a ridge regression and a voting strategy for sentiment and aspect predictions, and therefore, does not rely on any handcrafted fea...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [45, 51, 1.0], [52, 60, 1.0]]}, "2018.ismir_conference-2018.37": {"snippet": "Deep Neural Networks with Voice Entry Estimation Heuristics for Voice Separation in Symbolic Music Representations", "weights": [[0, 4, 1.0], [5, 11, 1.0], [12, 20, 1.0]]}, "2017.clef_conference-2017w.68": {"snippet": "...with Deep Convolutional Neural Networks\n\n\n This paper presents deep learning techniques for image-based plant identification at very large scale. State-of-the-art Deep Convolutional Neural Networks (D...", "weights": [[8, 12, 1.0], [27, 33, 1.0], [34, 42, 1.0], [66, 70, 1.0], [166, 170, 1.0], [185, 191, 1.0], [192, 200, 1.0]]}, "2021.scil-1.51": {"snippet": "Deep neural networks easily learn unnatural infixation and reduplication patterns", "weights": [[0, 4, 1.0], [5, 11, 1.0], [12, 20, 1.0]]}, "D18-1092": {"snippet": "...ning Neural Networks for On-Device Short Text Classification\n\n\n Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applic...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [67, 71, 1.0], [72, 78, 1.0], [79, 87, 1.0]]}, "2018.tist_journal-ir0volumeA9A6.8": {"snippet": "...ired Neural Networks\n\n\n Neural networks have become very popular in recent years, because of the astonishing success of deep learning in various domains such as image and speech recognition. In many o...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [27, 33, 1.0], [34, 42, 1.0], [123, 127, 1.0]]}, "2020.wsdm_conference-2020.4": {"snippet": "Can Deep Learning Only Be Neural Networks?\n\n\n The word \"deep learning\" is generally regarded as a synonym of \"deep neural networks (DNNs)\". In this talk, we will discuss on essentials in deep learning...", "weights": [[4, 8, 1.0], [26, 32, 1.0], [33, 41, 1.0], [56, 60, 1.0], [110, 114, 1.0], [115, 121, 1.0], [122, 130, 1.0], [187, 191, 1.0]]}, "L18-1063": {"snippet": "...sing deep neural networks. In this paper, we describe Sanaphor++, which is an improvement of a top-level deep neural network system for coreference resolution-namely Stanford deep-coref-through the ad...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [108, 112, 1.0], [113, 119, 1.0], [178, 182, 1.0]]}, "2020.cikm_workshop-2020w.2": {"snippet": "... for Deep Neural Networks", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "2017.clef_conference-2017w.45": {"snippet": "Deep Neural Architecture for News Recommendation\n\n\n Deep neural networks have yielded immense success in speech recognition, computer vision and natural language processing. However, the exploration o...", "weights": [[0, 4, 1.0], [5, 11, 1.0], [52, 56, 1.0], [57, 63, 1.0], [64, 72, 1.0]]}, "D14-1080": {"snippet": "...with Deep Recurrent Neural Networks\n\n\n Recurrent neural networks (RNNs) are connectionist models of sequential data that are naturally applicable to the analysis of natural language. Recently, \"depth ...", "weights": [[8, 12, 1.0], [23, 29, 1.0], [30, 38, 1.0], [52, 58, 1.0], [59, 67, 1.0]]}, "2017.wsdm_conference-2017.46": {"snippet": "...nt a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks co...", "weights": [[8, 12, 1.0], [115, 119, 1.0], [132, 138, 1.0], [139, 147, 1.0], [185, 191, 1.0], [192, 200, 1.0]]}, "2018.wwwconf_conference-2018c.377": {"snippet": "...with Deep Neural Networks\n\n\n ABSTRACTAspect-based financial sentiment analysis, which aims to classify the text instance into a pre-defined aspect class and predict the sentiment score for the mention...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.8": {"snippet": "...cale deep context convolutional neural networks for semantic segmentation\n\n\n Abstract Recent years have witnessed the great progress for semantic segmentation using deep convolutional neural networks ...", "weights": [[8, 12, 1.0], [35, 41, 1.0], [42, 50, 1.0], [168, 172, 1.0], [187, 193, 1.0], [194, 202, 1.0]]}, "S17-1006": {"snippet": "...rent neural network, and convolutional neural networks. In experimental results we show that convolutional neural networks are able to outperform the previous state-of-the-art for MWE identification, ...", "weights": [[8, 14, 1.0], [42, 48, 1.0], [49, 57, 1.0], [110, 116, 1.0], [117, 125, 1.0]]}, "2019.wsdm_conference-2019.119": {"snippet": "...ity, deep learning has also attracted much attention, and researchers have proposed a large number of deep matching models to tackle the matching problem for different IR applications. Despite the fac...", "weights": [[8, 12, 1.0], [105, 109, 1.0]]}, "2017.cikm_conference-2017.151": {"snippet": "...king Deep Neural Network\n\n\n ABSTRACTFriend recommendation is a critical task in social networks. In this paper, we propose a Bayesian Personalized Ranking Deep Neural Network (BayDNN) model for friend...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [90, 98, 1.0], [158, 162, 1.0], [163, 169, 1.0]]}, "2018.wwwconf_conference-2018.180": {"snippet": "...se a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate predictio...", "weights": [[8, 12, 1.0], [141, 145, 1.0]]}, "2017.wwwconf_conference-2017.21": {"snippet": "...ars, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems h...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [158, 162, 1.0], [163, 169, 1.0], [170, 178, 1.0]]}, "2020.ipm_journal-ir0volumeA57A6.78": {"snippet": "A deep neural network model for speakers coreference resolution in legal texts", "weights": [[2, 6, 1.0], [7, 13, 1.0]]}, "2018.wwwconf_conference-2018.46": {"snippet": "...rent networks to have different sizes, to be (un)weighted and (un)directed. It leverages multiple networks via cross-network relationships between nodes in different networks, which may form many-to-m...", "weights": [[8, 16, 1.0], [101, 109, 1.0], [169, 177, 1.0]]}, "D16-1173": {"snippet": "Deep Neural Networks with Massive Learned Knowledge\n\n\n Regulating deep neural networks (DNNs) with human structured knowledge has shown to be of great benefit for improved accuracy and interpretabilit...", "weights": [[0, 4, 1.0], [5, 11, 1.0], [12, 20, 1.0], [66, 70, 1.0], [71, 77, 1.0], [78, 86, 1.0]]}, "2018.wsdm_conference-2018.102": {"snippet": "Neural Networks for Information Retrieval\n\n\n AbstractMachine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day resear...", "weights": [[0, 6, 1.0], [7, 15, 1.0], [125, 129, 1.0]]}, "2018.ijmir_journal-ir0volumeA7A2.0": {"snippet": "...sing deep neural networks", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "2018.mir_conference-2018.42": {"snippet": "...with Deep Neural Network", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2016.sigirconf_conference-2016.231": {"snippet": "...p on Neural Information Retrieval\n\n\n KeywordsNeural networks; deep learning; information retrieval Website: http://research.microsoft.com/neuir2016\nMOTIVATIONIn recent years, deep neural networks have...", "weights": [[8, 14, 1.0], [55, 63, 1.0], [65, 69, 1.0], [178, 182, 1.0], [183, 189, 1.0], [190, 198, 1.0]]}, "2019.tois_journal-ir0volumeA37A2.13": {"snippet": "...s of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction.However, most existing deep learning solutions are not optimized toward the targ...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [58, 62, 1.0], [146, 150, 1.0]]}, "2020.wwwconf_conference-2020c.107": {"snippet": "...with Deep Neural Networks", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "R17-2007": {"snippet": "...ying Deep Neural Network to Retrieve Relevant Civil Law Articles", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2017.mir_conference-2017.50": {"snippet": "...task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "P17-2059": {"snippet": "A Deep Network with Visual Text Composition Behavior\n\n\n While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep netwo...", "weights": [[2, 6, 1.0], [120, 126, 1.0], [190, 194, 1.0]]}, "L18-1060": {"snippet": "Deep Neural Networks for Coreference Resolution for {P}olish\n\n\n The paper presents several configurations of deep neural networks aimed at the task of coreference resolution for Polish. Starting with ...", "weights": [[0, 4, 1.0], [5, 11, 1.0], [12, 20, 1.0], [109, 113, 1.0], [114, 120, 1.0], [121, 129, 1.0]]}, "2018.sigirconf_workshop-2018ecom.33": {"snippet": "...ding Deep Neural Networks in CTR Prediction\n\n\n Although deep learning techniques have been successfully applied to many tasks, interpreting deep neural network models is still a big challenge to us. 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This exploratory study investigates this issue by submitting the same...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [113, 124, 1.0]]}, "2010.jasis_journal-ir0volumeA61A11.15": {"snippet": "Information Retrieval: Searching in the 21st Century; Human Information Retrieval", "weights": [[0, 11, 1.0], [12, 21, 1.0], [60, 71, 1.0], [72, 81, 1.0]]}, "1998.ecir_conference-1998.4": {"snippet": "...ning Information Retrieval with Information Extraction for Efficient Retrieval of Calls for Papers", "weights": [[8, 19, 1.0], [20, 29, 1.0], [35, 46, 1.0], [72, 81, 1.0]]}, "1991.sigirconf_conference-91.24": {"snippet": "...ssor Information Retrieval Systems\n\n\n Abstract.Information retrieval is the selection of documents that are potentially relevant to a user's information need. Given the vast volume of data stored in m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0], [144, 155, 1.0]]}, "C94-2169": {"snippet": "...mple Retrieval by Generating Retrieval Queries from Similarities\n\n\n In example-based NLP, the problem of eoml)utational cost of example retrieval is severe, since the retrieval time increases in propo...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [139, 148, 1.0], [170, 179, 1.0]]}, "2002.clef_workshop-2002.29": {"snippet": "Information Retrieval with Language Knowledge", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "1991.jasis_journal-ir0volumeA42A1.7": {"snippet": "...ased information retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2014.sigirconf_conference-2014.222": {"snippet": "... for Information Retrieval, where his research interests include: (i) Models for the retrieval of documents, sentences, experts and other information objects ; (ii) Probabilistic models of user intera...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [88, 97, 1.0], [141, 152, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2014.cikm_conference-2014.9": {"snippet": "...ween Retrieval Bias and Retrieval Performance\n\n\n ABSTRACTRetrievability provides an alternative way to assess an Information Retrieval (IR) system by measuring how easily documents can be retrieved. R...", "weights": [[8, 17, 1.0], [27, 36, 1.0], [116, 127, 1.0], [128, 137, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...n of Information Retrieval Models\n\n\n Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is nece...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [61, 70, 1.0], [118, 129, 1.0], [130, 139, 1.0]]}, "2001.ntcir_workshop-2001.29": {"snippet": "On a Retrieval Support System by Suggesting Terms to a User\n\n\n We propose a support method for information retrieval. This method automatically suggests terms relevant to a query, to a user, and when ...", "weights": [[5, 14, 1.0], [95, 106, 1.0], [107, 116, 1.0]]}, "1986.sigirjournals_journal-ir0volumeA20A14.4": {"snippet": "... the retrieval processes of document retrieval systems\u00b0 Two basic categories of models that have been employed in information retrieval are the vector processing models and the Boolean retrieval model", "weights": [[8, 17, 1.0], [40, 49, 1.0], [117, 128, 1.0], [129, 138, 1.0], [188, 197, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "1987.sigirconf_conference-87.21": {"snippet": "...near Information Retrieval Models\n\n\n AbstractLinear decision (retrieval)functions have been widely adopted in information retrieval systems such as in Boolean, vector, and probabilistic", "weights": [[8, 19, 1.0], [20, 29, 1.0], [65, 74, 1.0], [113, 124, 1.0], [125, 134, 1.0]]}, "2010.jasis_journal-ir0volumeA61A4.16": {"snippet": "...n to Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1977.sigirjournals_journal-ir0volumeA12A2.0": {"snippet": "Information Storage and Retrieval, A Survey and Functional Description", "weights": [[0, 11, 1.0], [24, 33, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "1999.jasis_journal-ir0volumeA50A11.6": {"snippet": "Information Retrieval and the Virtual Document", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2002.cikm_conference-2002.59": {"snippet": "Information retrieval on the semantic web", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2019.sigirjournals_journal-ir0volumeA53A2.10": {"snippet": "Information retrieval and its sister disciplines", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2010.ntcir_workshop-2010.21": {"snippet": "... for information retrieval for question answering (IR4QA) of NTCIR-8 tasks. For improving information retrieval performance, we focus mostly on the document re-ranking technique, which locates between...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [93, 104, 1.0], [105, 114, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.12": {"snippet": "...line retrieval", "weights": [[8, 17, 1.0]]}, "2008.sigirconf_conference-2008.170": {"snippet": "...ment retrieval has shown to be a competitive approach in XML element retrieval, which is counter-intuitive since the element retrieval task requests all and only relevant document parts to be retrieve...", "weights": [[8, 17, 1.0], [72, 81, 1.0], [128, 137, 1.0]]}, "2003.sigirconf_conference-2003.73": {"snippet": "XML retrieval: what to retrieve?\n\n\n ABSTRACTThe fundamental difference between standard information retrieval and XML retrieval is the unit of retrieval. In traditional IR, the unit of retrieval is fi...", "weights": [[4, 13, 1.0], [88, 99, 1.0], [100, 109, 1.0], [118, 127, 1.0], [143, 152, 1.0], [185, 194, 1.0]]}, "2017.trec_conference-2017.21": {"snippet": "...ular information retrieval application is web search engine such as Google [2] , Baidu [3] , Bing [4] and Sogou [5]. These application will return top-N best retrieval result to users. Information Ret...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [161, 170, 1.0], [188, 199, 1.0], [200, 209, 1.0]]}, "1987.jasis_journal-ir0volumeA38A2.2": {"snippet": "...ment retrieval: II. The retrieval process", "weights": [[8, 17, 1.0], [27, 36, 1.0]]}, "2009.cikm_conference-2009.134": {"snippet": "...opic retrieval\n\n\n ABSTRACTTraditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [63, 72, 1.0], [167, 178, 1.0], [194, 203, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.2": {"snippet": "... for information retrieval\n\n\n AbstractThis paper presents a probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [77, 88, 1.0], [89, 98, 1.0], [122, 131, 1.0]]}, "1990.jasis_journal-ir0volumeA41A2.10": {"snippet": "...s of information retrieval and automatic text processing: The transformation analysis, and retrieval of information by computer", "weights": [[8, 19, 1.0], [20, 29, 1.0], [94, 103, 1.0], [107, 118, 1.0]]}, "2004.jasis_journal-ir0volumeA55A7.1": {"snippet": "Information retrieval by metabrowsing", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ical information retrieval\n\n\n AbstractIn this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 153, 1.0], [154, 163, 1.0]]}, "2005.spire_conference-2005.25": {"snippet": "Retrieval Status Values in Information Retrieval Evaluation", "weights": [[0, 9, 1.0], [27, 38, 1.0], [39, 48, 1.0]]}, "2018.ictir_conference-2018.10": {"snippet": "...ment Retrieval\n\n\n ABSTRACTIn this paper, we study jointly query reformulation and document relevance estimation, the two essential aspects of information retrieval (IR). Their interactions are modelle...", "weights": [[8, 17, 1.0], [145, 156, 1.0], [157, 166, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ment retrieval. The shot retrieval and the video segment model re-use the probability of relevance and a language modeling ranking function respectively, basing themselves on decades of text retrieval...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [194, 203, 1.0]]}, "2010.clef_workshop-2010w.79": {"snippet": "...edia Retrieval Task at ImageCLEF 2010\n\n\n ImageCLEF's Wikipedia Retrieval task provides a testbed for the system-oriented evaluation of multimedia information retrieval from a collection of Wikipedia i...", "weights": [[8, 17, 1.0], [66, 75, 1.0], [149, 160, 1.0], [161, 170, 1.0]]}, "N07-1012": {"snippet": "Information Retrieval On Empty Fields\n\n\n We explore the problem of retrieving semi-structured documents from a realworld collection using a structured query. We formally develop Structured Relevance M...", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "1992.jasis_journal-ir0volumeA43A2.6": {"snippet": "...gent Information Retrieval: An Introduction", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2015.sigirconf_conference-2015.71": {"snippet": "Information Retrieval as Card Playing: A Formal Model for Optimizing Interactive Retrieval Interface", "weights": [[0, 11, 1.0], [12, 21, 1.0], [81, 90, 1.0]]}, "2014.clef_conference-2014.5": {"snippet": "An Information Retrieval Ontology for Information Retrieval Nanopublications", "weights": [[3, 14, 1.0], [15, 24, 1.0], [38, 49, 1.0], [50, 59, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A2.6": {"snippet": "Information retrieval in context: IRiX", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords Information Retrieval (IR) solutions to retrieve passages matching a set of terms extracted from the question.There is a fundamental disconnect between the capabilities of the bag-of-words retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [197, 206, 1.0]]}, "1976.sigirjournals_journal-ir0volumeA11A3.3": {"snippet": "Information Retrieval at Cornell", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2012.tist_journal-ir0volumeA3A4.10": {"snippet": "...edia retrieval. The first three studies deal primarily with comment-targeting retrieval, while the last one deals with commentexploiting retrieval. Throughout the article, connections to information r...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [140, 149, 1.0], [190, 201, 1.0], [202, 211, 1.0]]}, "2015.ictir_conference-2015.2": {"snippet": "...y of Retrieval: The Retrievability of Information\n\n\n ABSTRACTRetrievability is an important and interesting indicator that can be used in a number of ways to analyse Information Retrieval systems and ...", "weights": [[8, 17, 1.0], [41, 52, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A3.1": {"snippet": "...bout Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.20": {"snippet": "Information Retrieval as Statistical Translation", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "I05-2024": {"snippet": "Information Retrieval Capable of Visualization and High Precision\n\n\n We present a neural-network based selforganizing approach that enables visualization of the information retrieval while at the same...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [161, 172, 1.0], [173, 182, 1.0]]}, "2005.clef_workshop-2005w.119": {"snippet": "... All retrieval results rely heavily on two retrieval systems: for visual retrieval we use the GNU Image Finding Tool (GIFT), and for textual retrieval the EasyIR retrieval system. For the ad-hoc retri...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [76, 85, 1.0], [144, 153, 1.0], [165, 174, 1.0], [198, 207, 1.0]]}, "2012.ipm_journal-ir0volumeA48A1.9": {"snippet": "... and information science, the same cannot be said about the subfield of information retrieval. We address that in this work by sketching the information retrieval intellectual landscape through visual...", "weights": [[8, 19, 1.0], [75, 86, 1.0], [87, 96, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "1999.sigirconf_conference-99.73": {"snippet": "Information Retrieval Library (IRLIB) (demonstration abstract)\n\n\n ABSTRACTThe IRLIB is a full-text web searchable digital library, whose contents are publications and documents relative to the field o...", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "W09-4632": {"snippet": "...e\n\n\n Information retrieval is a field of research reaching from computer and information science to lingusitics. As a linguist in the information retrieval field, I leave the quest for effective searc...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [137, 148, 1.0], [149, 158, 1.0]]}, "1988.riao_conference-1988.41": {"snippet": "...Text Retrieval for Legal Information Retrieval Systems", "weights": [[8, 17, 1.0], [28, 39, 1.0], [40, 49, 1.0]]}, "1995.jasis_journal-ir0volumeA46A9.12": {"snippet": "Information Retrieval: New Systems and Current Research. Proceedings of the 15th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, edited by Ruben Leon", "weights": [[0, 11, 1.0], [12, 21, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "1990.sigirjournals_journal-ir0volumeA24A3.10": {"snippet": "Information Retrieval Abstracts", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2017.clef_conference-2017w.74": {"snippet": "...ered Information Retrieval Task-1: Improving Medical Document Retrieval with Query Expansion\n\n\n In this report, we describe our retrieval framework for participating in CLEF eHealth 2017 Patient-Cente...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [65, 74, 1.0], [131, 140, 1.0]]}, "2004.sigirconf_conference-2004.8": {"snippet": "...y of information retrieval heuristics\n\n\n ABSTRACTEmpirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [73, 84, 1.0], [85, 94, 1.0], [118, 127, 1.0], [181, 190, 1.0]]}}}
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Each item in a catalog is characterized by several attribut...", "weights": [[0, 9, 1.0], [10, 17, 1.0]]}, "2019.cikm_conference-2019.314": {"snippet": "...time Ranking with Reinforcement Learning in E-commerce Sponsored Search Advertising", "weights": [[8, 15, 1.0], [68, 74, 1.0]]}, "2021.wwwconf_conference-2021.223": {"snippet": "...raph Neural Networks for Friend Ranking in Large-scale Social Platforms", "weights": [[8, 14, 1.0], [35, 42, 1.0]]}, "2018.sigirconf_workshop-2018ecom.5": {"snippet": "... for eCommerce-scale Product Catalogs, we propose an approach based on deep convolutional neural networks to predict product taxonomies using their descriptions. The classification performance of the ...", "weights": [[8, 17, 1.0], [24, 31, 1.0], [93, 99, 1.0], [120, 127, 1.0]]}, "E17-2101": {"snippet": "...erce Product Listings.\n\n\n In this paper we study the impact of using images to machine-translate user-generated ecommerce product listings. 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They guide customers to explore a massive product catalog and assist customers to find th...", "weights": [[8, 15, 1.0], [93, 102, 1.0], [156, 163, 1.0]]}, "2012.airs_conference-2012.25": {"snippet": "... and Search in E-Commerce", "weights": [[8, 14, 1.0]]}, "2017.cikm_conference-2017.229": {"snippet": "...Item Ranking\n\n\n ABSTRACTPersonalized item ranking for recommending top-N items of interest to a user is an interesting and challenging problem in ecommerce. Researchers and practitioner are continuous...", "weights": [[8, 15, 1.0], [45, 52, 1.0], [149, 158, 1.0]]}, "2018.sigirconf_workshop-2018ecom.6": {"snippet": "...Deep Neural Network (pMDNN) model is developed later on. In the second step, an existing Deep Structured Semantic Model (DSSM) is utilized for homogeneous feed ranking. A/B test on Alibaba product env...", "weights": [[8, 14, 1.0], [163, 170, 1.0], [192, 199, 1.0]]}, "2011.wwwconf_workshop-2011ldow.14": {"snippet": "...ured eCommerce data benefit more due to structured data being more readily search engine indexable, but the lack of available product ontologies and product master datasheets is impeding the creation ...", "weights": [[8, 17, 1.0], [78, 84, 1.0], [129, 136, 1.0], [152, 159, 1.0]]}, "2016.sigirconf_conference-2016.50": {"snippet": "...ance ranking both positively influence the shopping experience of millions of customers and significantly impact revenue. In the past, Amazon's product search engine consisted of several handtuned ran...", "weights": [[8, 15, 1.0], [147, 154, 1.0], [155, 161, 1.0], [200, 207, 1.0]]}, "2020.sigirconf_conference-2020.65": {"snippet": "...that product reviews usually provide useful information for a given question, and thus can assist the ranking process. In this paper, we investigate the answer ranking problem for product-related ques...", "weights": [[8, 15, 1.0], [105, 112, 1.0], [163, 170, 1.0], [183, 190, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.12": {"snippet": "...s of search engines in response to ecommerce queries by comparing the engines\u00d5 quality of ecommerce links using topical relevancy ratings. This research employs 100 ecommerce queries, five major searc...", "weights": [[8, 14, 1.0], [38, 47, 1.0], [93, 102, 1.0], [168, 177, 1.0], [198, 204, 1.0]]}, "2011.cikm_conference-2011.281": {"snippet": "...y in eCommerce search. Our work focuses on bridging this gap. As little work has been done around diversity in eCommerce search, we study and describe the role of relevance and diversity in eCommerce ...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [114, 123, 1.0], [124, 130, 1.0], [193, 202, 1.0]]}, "2008.wwwconf_workshop-2008l.7": {"snippet": "...oved search for local businesses. As a result, major search companies have invested significant resources into geographic search technologies, also often called local search.This paper studies geograp...", "weights": [[8, 14, 1.0], [56, 62, 1.0], [125, 131, 1.0], [170, 176, 1.0]]}, "2006.cikm_conference-2006.64": {"snippet": "...inal ranking score for each tuple. Tuples with the top ranking scores are presented to the user first. Our ranking method is domain independent and requires no user feedback. Experimental results demo...", "weights": [[8, 15, 1.0], [58, 65, 1.0], [110, 117, 1.0]]}, "2022.ecnlp-1.6": {"snippet": "...y in Product Search using High-Precision Query-Product Semantic Similarity\n\n\n Ensuring relevance quality in product search is a critical task as it impacts the customer's ability to find intended prod...", "weights": [[8, 15, 1.0], [16, 22, 1.0], [50, 57, 1.0], [111, 118, 1.0], [119, 125, 1.0]]}, "N18-3002": {"snippet": "... for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advant...", "weights": [[8, 15, 1.0], [69, 75, 1.0], [87, 93, 1.0], [125, 132, 1.0], [145, 152, 1.0]]}, "2013.sigirconf_conference-2013.164": {"snippet": "...ased product retrieval for e-commerce\n\n\n ABSTRACTThe retrieval results of online products information in ecommerce web sites are often difficult for users to use because of different descriptions for ...", "weights": [[8, 15, 1.0], [108, 117, 1.0]]}, "2011.sigirconf_conference-2011.10": {"snippet": "...call ecommerce queries\n\n\n ABSTRACTUser expectation and experience for web search and eCommerce (product) search are quite different. Product descriptions are concise as compared to typical web documen...", "weights": [[8, 17, 1.0], [77, 83, 1.0], [88, 97, 1.0], [99, 106, 1.0], [108, 114, 1.0], [136, 143, 1.0]]}, "2019.spire_conference-2019.4": {"snippet": "...k in eCommerce Search", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "2018.sigirconf_workshop-2018ecom.10": {"snippet": "...erce Search\n\n\n In this paper, we address the problem of evaluating whether results served by an e-commerce search engine for a query are good or not. is is a critical question in evaluating any e-comm...", "weights": [[8, 14, 1.0], [110, 116, 1.0]]}, "2019.ictir_conference-2019.18": {"snippet": "...wise Neural Ranking Models", "weights": [[8, 14, 1.0], [15, 22, 1.0]]}, "2019.cikm_conference-2019.288": {"snippet": "Neural Review Summarization Leveraging User and Product Information", "weights": [[0, 6, 1.0], [48, 55, 1.0]]}, "2014.wwwconf_conference-2014c.42": {"snippet": "... web search and contrasts them with approaches specifically designed for e-commerce product search.\u2022 e-commerce product search: More specifically in commerce search top-k plays an important role as pr...", "weights": [[8, 14, 1.0], [87, 94, 1.0], [95, 101, 1.0], [115, 122, 1.0], [123, 129, 1.0], [161, 167, 1.0], [201, 208, 1.0]]}, "2017.sigirconf_conference-2017.251": {"snippet": "...opic.eCommerce IR is ripe for research and has a unique set of problems. For example, in eCommerce search there may be no hypertext links between documents (products); there is a click stream, but mor...", "weights": [[8, 17, 1.0], [92, 101, 1.0], [102, 108, 1.0]]}, "2020.wwwconf_conference-2020c.44": {"snippet": "...ized Ranking in eCommerce Search\n\n\n We address the problem of personalization in the context of eCommerce search. Specifically, we develop personalization ranking features that use in-session context ...", "weights": [[8, 15, 1.0], [19, 28, 1.0], [29, 35, 1.0], [99, 108, 1.0], [109, 115, 1.0], [158, 165, 1.0]]}, "2019.wwwconf_conference-2019.217": {"snippet": "Neural IR Meets Graph Embedding: A Ranking Model for Product Search", "weights": [[0, 6, 1.0], [35, 42, 1.0], [53, 60, 1.0], [61, 67, 1.0]]}, "2018.sigirconf_workshop-2018ecom.14": {"snippet": "...-End Neural Ranking for eCommerce Product Search: an Application of Task Models and Textual Embeddings\n\n\n Yan. 2018. End-to-End Neural Ranking for eCommerce Product Search: An application of task mode...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [27, 36, 1.0], [37, 44, 1.0], [45, 51, 1.0], [131, 137, 1.0], [138, 145, 1.0], [150, 159, 1.0], [160, 167, 1.0], [168, 174, 1.0]]}, "2020.acl-main.614": {"snippet": "...erce Product Search\n\n\n Showing items that do not match search query intent degrades customer experience in ecommerce. These mismatches result from counterfactual biases of the ranking algorithms towar...", "weights": [[8, 15, 1.0], [16, 22, 1.0], [58, 64, 1.0], [110, 119, 1.0], [179, 186, 1.0]]}, "2018.sigirconf_conference-2018.230": {"snippet": "...opic.eCommerce IR is ripe for research and has a unique set of problems. For example, in eCommerce search there may be no hypertext links between documents (products); there is a click stream, but mor...", "weights": [[8, 17, 1.0], [92, 101, 1.0], [102, 108, 1.0]]}, "2019.sigirconf_conference-2019.33": {"snippet": "...ment Ranking with Neural Signals", "weights": [[8, 15, 1.0], [21, 27, 1.0]]}, "N18-2095": {"snippet": "...onal Neural Networks with Auxiliary Domain Discriminators\n\n\n With the growing amount of reviews in ecommerce websites, it is critical to assess the helpfulness of reviews and recommend them accordingl...", "weights": [[8, 14, 1.0], [102, 111, 1.0]]}, "2021.emnlp-main.191": {"snippet": "...ient Neural Architecture Search by Pairwise Ranking\n\n\n This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem. Previous methods ...", "weights": [[8, 14, 1.0], [28, 34, 1.0], [47, 54, 1.0], [107, 113, 1.0], [127, 133, 1.0], [169, 176, 1.0]]}, "2017.cikm_conference-2017.302": {"snippet": "...user search behaviors such as clicks and views. Based on the learnt session representation, we further propose a list-wise ranking model to generate the recommendation result for each anonymous user s...", "weights": [[8, 14, 1.0], [126, 133, 1.0]]}, "2019.cikm_conference-2019.276": {"snippet": "Neural Review Rating Prediction with User and Product Memory", "weights": [[0, 6, 1.0], [46, 53, 1.0]]}, "2017.sigirconf_conference-2017.50": {"snippet": "...zing search engines, and is thus also a key technology for E-Com search. While the use of LETOR for web search has been well studied, its use for E-Com search has not yet been well explored. In this p...", "weights": [[8, 14, 1.0], [68, 74, 1.0], [107, 113, 1.0], [155, 161, 1.0]]}, "2012.cikm_conference-2012.362": {"snippet": "...each product item based on its transaction history. By incorporating such bestselling prediction with relevance, we propose a new ranking model for product search, in which we rank higher the product ...", "weights": [[8, 15, 1.0], [133, 140, 1.0], [151, 158, 1.0], [159, 165, 1.0], [195, 202, 1.0]]}, "2018.sigirconf_workshop-2018ecom.23": {"snippet": "...rent Neural Networks\n\n\n We present a neural network for predicting purchasing intent in an Ecommerce setting. Our main contribution is to address the significant investment in feature engineering that...", "weights": [[8, 14, 1.0], [40, 46, 1.0], [94, 103, 1.0]]}, "2019.tois_journal-ir0volumeA37A2.5": {"snippet": "...ized product search. To this end, we propose a novel Attentive Long Short-Term Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to lear...", "weights": [[8, 15, 1.0], [16, 22, 1.0], [134, 141, 1.0], [142, 148, 1.0], [171, 177, 1.0]]}, "2018.sigirconf_conference-2018.39": {"snippet": "...d to product search. There are several intrinsic diferences between web search and product search that make the direct application of traditional search ranking algorithms to EC search platforms diicu...", "weights": [[8, 15, 1.0], [16, 22, 1.0], [75, 81, 1.0], [86, 93, 1.0], [94, 100, 1.0], [149, 155, 1.0], [156, 163, 1.0], [181, 187, 1.0]]}}}
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We demonstrate by experiments the impact of our rewriting approach on query evaluation costs and we prove formally its correctness.", "weights": [[8, 20, 1.0], [132, 137, 1.0]]}, "1993.cikm_conference-93.47": {"snippet": "...iple Query Optimization with Depth-First Branch-and-Bound and Dynamic Query Ordering\n\n\n AbstractIn certain database applications such as deductive databases, batch query processing, and recursive quer...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [73, 78, 1.0], [167, 172, 1.0], [199, 204, 1.0]]}, "2001.cikm_conference-2001.29": {"snippet": "...ased optimization of cost and coverage can unfortunately lead to expensive planning as well as highly inefficient plans. In this paper we present techniques for joint optimization of cost and coverage...", "weights": [[8, 20, 1.0], [170, 182, 1.0]]}, "2013.cikm_workshop-2013ueo.7": {"snippet": "...ific query expansion\n\n\n ABSTRACTQueries submitted to a search engine are usually short and ambiguous. Query expansion is an effective way to resolve the ambiguity of a query. We attempt to make a cons...", "weights": [[8, 13, 1.0], [105, 110, 1.0], [171, 176, 1.0]]}, "2000.cikm_conference-2000.49": {"snippet": "Query Optimization Using An Improved Genetic Algorithm", "weights": [[0, 5, 1.0], [6, 18, 1.0]]}, "2009.cikm_conference-2009.88": {"snippet": "...k of query expansion via robust constrained optimization\n\n\n ABSTRACTWe introduce a new theoretical derivation, evaluation methods, and extensive empirical analysis for an automatic query expansion fra...", "weights": [[8, 13, 1.0], [47, 59, 1.0], [184, 189, 1.0]]}, "2014.cikm_conference-2014.186": {"snippet": "...rase Query Optimization on Inverted Indexes\n\n\n ABSTRACTPhrase queries are a key functionality of modern search engines. Beyond that, they increasingly serve as an important building block for applicat...", "weights": [[8, 13, 1.0], [14, 26, 1.0]]}, "2016.ictir_conference-2016.37": {"snippet": "...ires query analysis to identify the most important query concepts as well as query transformation by adding new concepts to a query, which can be extracted from the top retrieved documents or medical ...", "weights": [[8, 13, 1.0], [54, 59, 1.0], [80, 85, 1.0], [129, 134, 1.0]]}, "2007.cikm_conference-2007.41": {"snippet": "...oute query classes. We prove the correctness of the unified algorithm. We then turn our attention to the optimization of the proposed algorithm. Several pruning and optimization techniques are derived...", "weights": [[8, 13, 1.0], [108, 120, 1.0], [168, 180, 1.0]]}, "1993.cikm_conference-93.78": {"snippet": "Query Optimization and Processing in Federated Database Systems", "weights": [[0, 5, 1.0], [6, 18, 1.0]]}, "2019.wsdm_conference-2019.33": {"snippet": "Federated Online Learning to Rank with Evolution Strategies\n\n\n ABSTRACTOnline Learning to Rank is a powerful paradigm that allows to train a ranking model using only online feedback from its users. In...", "weights": []}, "2017.wsdm_conference-2017.72": {"snippet": "...atic optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeated...", "weights": [[8, 20, 1.0]]}, "2018.wsdm_conference-2018.104": {"snippet": "... and Optimization\n\n\n In defining the essence of professional teamwork, Hackman and Katz  stated that teams function as 'purposive social systems', defined as people who are readily identifiable to eac...", "weights": [[8, 20, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A3.4": {"snippet": "...ting query performance. In order to compare our approach with an approach which also can support the optimization of every query on the schema, we implemented an improvement of a state-of-the-art appr...", "weights": [[8, 13, 1.0], [104, 116, 1.0], [126, 131, 1.0]]}, "1993.cikm_conference-93.23": {"snippet": "...tive Query Optimization in a Deductive Database System", "weights": [[8, 13, 1.0], [14, 26, 1.0]]}, "2006.cikm_conference-2006.71": {"snippet": "...ng a query, and (2) algorithms to rewrite a query using usable views.Semantic information, such as knowledge of the key of a view, can be used to further optimize a rewritten query. Within our general...", "weights": [[8, 13, 1.0], [47, 52, 1.0], [178, 183, 1.0]]}, "2017.wwwconf_conference-2017.83": {"snippet": "...ased query optimization for RDF graph pattern queries. The approach comprises of (i) a novel type system for RDF data induced from data and ontologies and (ii) a query optimization and evaluation fram...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [165, 170, 1.0], [171, 183, 1.0]]}, "1995.sigirconf_conference-95.5": {"snippet": "...osed optimization techniques. Optimizing execution, however, is even more important in the context of large document collections. We present a new structured query optimization technique which we have...", "weights": [[8, 20, 1.0], [161, 166, 1.0], [167, 179, 1.0]]}, "1995.ipm_journal-ir0volumeA31A6.2": {"snippet": "Query Evaluation: Strategies and Optimizations", "weights": [[0, 5, 1.0]]}, "1999.cikm_conference-99.35": {"snippet": "...ased Query Optimization, Revisited\n\n\n ABSTRACTWe present the architecture and a performance assessment of an extensible query optimizer written in Venus. Venus is a generalpurpose active-database rule...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [123, 128, 1.0]]}, "1993.cikm_conference-93.5": {"snippet": "...onal query optimization usually minimizes the total time for complete evaluation of a query. We argue that this criterion may not be the only or even necessary requirement. In some interactive environ...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [89, 94, 1.0]]}, "2012.cikm_workshop-2012clouddb.10": {"snippet": "...port query optimization, and histograms are of particular interest. Naturally, histograms could be used to support query optimization and efficient utilization of computing resources in the cloud. His...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [118, 123, 1.0], [124, 136, 1.0]]}, "2019.wsdm_conference-2019.83": {"snippet": "... for optimization of the TL model, and the performance of the TL model can provide \"rewards\" in turn to update the selector. We build the reinforced data selector based on the actor-critic framework a...", "weights": [[8, 20, 1.0]]}, "2004.ipm_journal-ir0volumeA40A6.1": {"snippet": "Optimization of some factors affecting the performance of query expansion\n\n\n AbstractThis paper examines the factors affecting the performance of global query expansion based on term cooccurrence data...", "weights": [[0, 12, 1.0], [58, 63, 1.0], [153, 158, 1.0]]}, "2018.cikm_conference-2018.168": {"snippet": "...tive Query Expansion for Web Search\n\n\n ABSTRACTQuery Expansion (QE) techniques expand the user queries with additional terms, e.g., synonyms and acronyms, to enhance the system recall. State-of-the-ar...", "weights": [[8, 13, 1.0]]}, "2008.tweb_journal-ir0volumeA2A1.5": {"snippet": "...vice query algebra and optimization\n\n\n We present a query algebra that supports optimized access of Web services through service-oriented queries. The service query algebra is defined based on a forma...", "weights": [[8, 13, 1.0], [26, 38, 1.0], [55, 60, 1.0], [162, 167, 1.0]]}, "2013.cikm_conference-2013.311": {"snippet": "... two query optimization modes: offline periodic optimization and online optimization. We devise efficient algorithms for both offline and online cases followed by a competitive analysis to show the po...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [51, 63, 1.0], [75, 87, 1.0]]}, "2011.cikm_conference-2011.391": {"snippet": "...tive query languages, views and multi-query optimization. Users create personalized feeds by defining and composing content-based filtering and aggregation queries on collections of RSS feeds. These q...", "weights": [[8, 13, 1.0], [41, 46, 1.0], [47, 59, 1.0]]}, "2001.sigirconf_conference-2001.63": {"snippet": "Query Optimization for Vector Space Problems\n\n\n ABSTRACTWe present performance measurement results for a parallel SQL based information retrieval system implemented on a PC cluster system. We used the...", "weights": [[0, 5, 1.0], [6, 18, 1.0]]}, "2000.ipm_journal-ir0volumeA36A5.2": {"snippet": "...s to query optimization in document retrieval", "weights": [[8, 13, 1.0], [14, 26, 1.0]]}, "2008.cikm_conference-2008.178": {"snippet": "...e . \nQUERY-PROCESSING ALGORITHMSThis section outlines our basic query-processing algorithms for information integration. (Please see [1] for the details.) All our algorithms, with the exception of sem...", "weights": [[8, 13, 1.0], [67, 72, 1.0]]}, "2018.wsdm_conference-2018.21": {"snippet": "...hing query and document n-grams, Conv-KNRM uses Convolutional Neural Networks to represent ngrams of various lengths and so matches them in a uni ed embedding space. e n-gram so matches are then utili...", "weights": [[8, 13, 1.0]]}, "1987.sigirconf_conference-87.4": {"snippet": "...imal query has been defined as one which will recover all the known relevant", "weights": [[8, 13, 1.0]]}, "1993.cikm_conference-93.49": {"snippet": "Optimization of Object Queries Containing Encapsulated Methods\n\n\n AbstractOptimization of object queries containing encapsulated methodsis still an open problem.", "weights": [[0, 12, 1.0]]}, "2019.wsdm_conference-2019.47": {"snippet": "...m an optimization (objective function) perspective. We demonstrate that matrix factorization is equivalent to optimizing two objectives: one is for bringing together the embeddings of similar nodes; t...", "weights": [[8, 20, 1.0]]}, "1991.jasis_journal-ir0volumeA42A4.2": {"snippet": "... the optimization of search logic for the MEDLINE database", "weights": [[8, 20, 1.0]]}, "1999.cikm_conference-99.48": {"snippet": "... the optimization of techniques for query processing in order to exploit these new alternatives. Accordingly, this paper introduces an improved query optimization technique. which is capable of assess...", "weights": [[8, 20, 1.0], [39, 44, 1.0], [147, 152, 1.0], [153, 165, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...t of query operators on Web search engine results\n\n\n Research has reported that about 10% of Web searchers utilize advanced query operators, with the other 90% using extremely simple queries. It is of...", "weights": [[8, 13, 1.0], [127, 132, 1.0]]}, "2011.cikm_conference-2011.397": {"snippet": "... are query optimization methods specifically conceived in the database theory community to efficiently answer (near-)acyclic queries. We propose to demonstrate H-DB, an SQL query optimizer that combin...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [176, 181, 1.0]]}, "2015.wwwconf_conference-2015c.137": {"snippet": "...ized query optimization capabilities of the data warehouse-based approaches with the result freshness and explorative data source discovery capabilities of link-traversal approaches. This is achieved ...", "weights": [[8, 13, 1.0], [14, 26, 1.0]]}, "2011.cikm_conference-2011.182": {"snippet": "...uery optimization based on program slicing\n\n\n ABSTRACTXQuery has become the standard query language for XML. The efforts put on this language have produced mature and efficient implementations of XQue...", "weights": [[8, 20, 1.0], [88, 93, 1.0]]}, "1996.cikm_conference-96.15": {"snippet": "...amic query optimization technique for a multidatabaae system, namely MIND, implement ed on a DOM environment.Dynamic query optimization, which schedules intersite operations at runtime, fits better to...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [120, 125, 1.0], [126, 138, 1.0]]}, "2008.wwwconf_conference-2008.134": {"snippet": "...or a query can be organized into a few subtopics about the query, each subtopic representing a specific aspect of the query. We believe that the subtopics in the search results for a query can reflect...", "weights": [[8, 13, 1.0], [62, 67, 1.0], [121, 126, 1.0], [186, 191, 1.0]]}, "1985.tois_journal-ir0volumeA3A1.2": {"snippet": "...uted query optimization algorithms are presented here for two types of local area networks: address ring networks and broadcast networks. Optimal algorithms are designed for simple queries. Optimizati...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [193, 205, 1.0]]}, "2016.cikm_conference-2016.262": {"snippet": "...ring Query Optimization", "weights": [[8, 13, 1.0], [14, 26, 1.0]]}, "2010.wwwconf_conference-2010.91": {"snippet": "... the query logs, it is much more difficult to efficiently suggest relevant queries to a rare query. In this paper, we propose an optimal rare query suggestion framework by leveraging implicit feedback...", "weights": [[8, 13, 1.0], [96, 101, 1.0], [145, 150, 1.0]]}, "2010.wsdm_conference-2010.17": {"snippet": "An optimization framework for query recommendation\n\n\n ABSTRACTQuery recommendation is an integral part of modern search engines. The goal of query recommendation is to facilitate users while searching...", "weights": [[3, 15, 1.0], [30, 35, 1.0], [141, 146, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.8": {"snippet": "...ined query processing, which as we show can improve the query processing performance and/or the quality of results. Then, we extend one of these methods with intra-query parallelism, which as we show ...", "weights": [[8, 13, 1.0], [59, 64, 1.0], [167, 172, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.23": {"snippet": "...sing query-level loss functions in learning of ranking functions. We discuss the basic properties that a query-level loss function should have and propose a query-level loss function based on the cosi...", "weights": [[8, 13, 1.0], [108, 113, 1.0], [160, 165, 1.0]]}, "2005.cikm_conference-2005.150": {"snippet": "...tive query optimization\n\n\n ABSTRACTModern query optimizers select an efficient join ordering for a physical execution plan based essentially on the average join selectivity factors among the reference...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [45, 50, 1.0]]}, "2008.ipm_journal-ir0volumeA44A6.3": {"snippet": "...good query terms can be seen as an optimization problem where the objective function to be optimized is based on the effectiveness of a query to retrieve relevant material. Some characteristics of thi...", "weights": [[8, 13, 1.0], [38, 50, 1.0], [139, 144, 1.0]]}, "2014.cikm_conference-2014.105": {"snippet": "...king Optimization with Constraints", "weights": [[8, 20, 1.0]]}, "2019.wsdm_conference-2019.37": {"snippet": "Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs\n\n\n ABSTRACTWe tackle Attitude Detection, which we define as the task of extracting the replier's attitude, i.e.,...", "weights": []}, "2013.cikm_workshop-2013ueo.0": {"snippet": "...ment optimization, UEO@CIKM 2013, San Francisco, California, USA, November 1, 2013", "weights": [[8, 20, 1.0]]}, "2010.airs_conference-2010.49": {"snippet": "...tive Optimization Algorithm for Query Model and Ranking Refinement", "weights": [[8, 20, 1.0], [35, 40, 1.0]]}, "2010.cikm_conference-2010.156": {"snippet": "Query optimization for ontology-based information integration\n\n\n ABSTRACTIn recent years, there has been an explosion of publicly available RDF and OWL data sources. In order to effectively and quickl...", "weights": [[0, 5, 1.0], [6, 18, 1.0]]}, "2003.tois_journal-ir0volumeA21A3.0": {"snippet": "...mal. Query optimization based on a relevance feedback or genetic algorithm using dynamic query contexts can help casual users search the Internet. These algorithms can draw on implicit user feedback b...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [92, 97, 1.0]]}, "2016.wsdm_conference-2016.15": {"snippet": "...ting query suggestions for leading users to more relevant search results. 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One general class of optimizations called early termination techniques is used in all major engines, and essentially involves computing top results without an exhaust...", "weights": [[8, 13, 1.0]]}, "2019.wsdm_conference-2019.8": {"snippet": "...same query in the training data are used as the input. We propose a novel ranking method, referred to as WassRank, under which the problem of listwise document ranking boils down to the task of learni...", "weights": [[8, 13, 1.0]]}}}
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Similarity (DV-ngrams-cosine) proposed in their paper and the Bag-of-Ngrams (BON) vectors scaled by Naive Bayesian weights. While large pre-trained Transformerbased models have shown SOTA ...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [37, 43, 1.0]]}, "W18-4507": {"snippet": "...tual similarity (cosine similarity on tf-idf vectors or topic vectors) are also compared to new strategies that strive to anchor textual measurement in a social context.", "weights": [[8, 18, 1.0], [20, 26, 1.0], [27, 37, 1.0]]}, "2013.ntcir_conference-2013.74": {"snippet": "...k by cosine similarity and the Yahoo! Japan Web search results, and 0.86 in the Japanese task by cosine similarity and the Bing search results.", "weights": [[8, 14, 1.0], [15, 25, 1.0], [100, 106, 1.0], [107, 117, 1.0]]}, "D09-1083": {"snippet": "... for Similarity Measures\n\n\n Measuring the similarity between two texts is a fundamental problem in many NLP and IR applications. Among the existing approaches, the cosine measure of the term vectors r...", "weights": [[8, 18, 1.0], [45, 55, 1.0], [167, 173, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.0": {"snippet": "...rent similarity search techniques and real-life datasets demonstrate that the secondary filtering can speed-up similarity search several times. \nINTRODUCTIONAn efficient similarity search presents a s...", "weights": [[8, 18, 1.0], [114, 124, 1.0], [173, 183, 1.0]]}, "2004.cikm_conference-2004.55": {"snippet": "...ning similarity measures in non-orthogonal space\n\n\n ABSTRACTMany machine learning and data mining algorithms crucially rely on the similarity metrics. The Cosine similarity, which calculates the inner...", "weights": [[8, 18, 1.0], [134, 144, 1.0], [158, 164, 1.0], [165, 175, 1.0]]}, "2013.clef_conference-2013w.34": {"snippet": "...mage vector, such that the resulting vector contain the maximal similarity to each concept of the intermediate space. The 96/116 concepts to identify are represented in the same space and their simila...", "weights": [[8, 14, 1.0], [40, 46, 1.0], [67, 77, 1.0], [197, 207, 1.0]]}, "W16-2503": {"snippet": "... for vector-space semantic models: it is considered desirable for a space to represent semantic relations as consistent vector offsets. We show that the method's reliance on cosine similarity conflate...", "weights": [[8, 14, 1.0], [123, 129, 1.0], [177, 183, 1.0], [184, 194, 1.0]]}, "2015.airs_conference-2015.33": {"snippet": ".... In vector space model, different types of term weighting schemes are used to adjust bag-of-words document vectors in order to improve the performance of the most widely used cosine distance. Even th...", "weights": [[8, 14, 1.0], [179, 185, 1.0]]}, "P16-1214": {"snippet": "....g., vector normalization achieves an efficient search with cosine similarity.", "weights": [[8, 14, 1.0], [63, 69, 1.0], [70, 80, 1.0]]}, "2004.ntcir_workshop-2004.61": {"snippet": "...as a vector that is composed of vectors associated with words included in the document. The word vectors and document vectors are positioned in an identical vector space and the relevant degree of sim...", "weights": [[8, 14, 1.0], [160, 166, 1.0], [200, 210, 1.0]]}, "2015.cikm_conference-2015.100": {"snippet": "...uery similarity functions. In this paper, we propose new algorithms that facilitate and increase the effectiveness of this propagation. Given a query similarity function and a query-document relevance...", "weights": [[8, 18, 1.0], [153, 163, 1.0]]}, "2004.cikm_conference-2004.52": {"snippet": "...m of similarity-search in PDNs, and propose a family of distributed access methods, termed Small-World Access Methods (SWAM), for efficient execution of various similarity-search queries, namely exact...", "weights": [[8, 18, 1.0], [164, 174, 1.0]]}, "2010.cikm_workshop-2010smuc.11": {"snippet": "...ased similarity measures. Based on the weighted similarity model, we present weighted variations of several existing measures like Dice and Cosine similarity measures. We evaluate the proposed similar...", "weights": [[8, 18, 1.0], [51, 61, 1.0], [143, 149, 1.0], [150, 160, 1.0], [196, 206, 1.0]]}, "2017.ijmir_journal-ir0volumeA6A2.2": {"snippet": "...NOVA Cosine Similarity Image Recommendation in vertical search", "weights": [[8, 14, 1.0], [15, 25, 1.0]]}, "2016.tweb_journal-ir0volumeA10A2.0": {"snippet": "...User similarity is traditionally calculated by cosine similarity or the Pearson correlation coefficient. However, both of these measures consider only the direction of rating vectors, and suffer from ...", "weights": [[8, 18, 1.0], [50, 56, 1.0], [57, 67, 1.0]]}, "2002.cikm_conference-2002.16": {"snippet": "...d to similarity search in databases. We investigate the vulnerability of such systems against users who try to copy the database by sending automated queries. We analyze two models for similarity sear...", "weights": [[8, 18, 1.0], [188, 198, 1.0]]}, "2017.wwwconf_conference-2017.109": {"snippet": "...data similarity are the cosine and the resemblance (the latter is for binary data). In this study, we develop theoretical results for both the cosine and the GMM (generalized min-max) kernel , which i...", "weights": [[8, 18, 1.0], [27, 33, 1.0], [146, 152, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.0": {"snippet": "...y of similarity predicate committee\n\n\n A B S T R A C TIn the last decades, many similarity measures are proposed, such as Jaccard coefficient, cosine similarity, BM25, language model, etc. Despite the...", "weights": [[8, 18, 1.0], [83, 93, 1.0], [146, 152, 1.0], [153, 163, 1.0]]}, "2016.cikm_conference-2016.204": {"snippet": "Personalized Semantic Word Vectors", "weights": []}, "2014.cikm_workshop-2014esair.12": {"snippet": "...ased Similarity Measure for Concept Hierarchies and Ontologies\n\n\n ABSTRACTDespite the popularity of concept hierarchies and ontologies, such as Yahoo! Directory, a similarity measure that considers bo...", "weights": [[8, 18, 1.0], [167, 177, 1.0]]}, "2018.cikm_conference-2018.125": {"snippet": "...etic Similarity Measure\n\n\n ABSTRACTDetermining the similarity between two objects is pertinent to many applications. When the basis for similarity is a set of objectto-object relationships, it is natu...", "weights": [[8, 18, 1.0], [54, 64, 1.0], [139, 149, 1.0]]}, "2009.jasis_journal-ir0volumeA60A2.1": {"snippet": "...ween similarity measures for vectors based on vector norms", "weights": [[8, 18, 1.0], [49, 55, 1.0]]}, "W16-4118": {"snippet": "...that cosine similarity relates better to human ranks than the Dice coefficient. We also saw a tendency that including syntactic context to the TF-IDF vector space model is beneficial for this kind of ...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [153, 159, 1.0]]}, "C16-1222": {"snippet": "...this vector space, and the query-document similarity score depends on the length of the vectors and the relationships between them. Introduction Vector-space in text retrieval is old news. Cosine simi...", "weights": [[8, 14, 1.0], [45, 55, 1.0], [148, 154, 1.0], [192, 198, 1.0], [199, 209, 1.0]]}, "2016.cikm_conference-2016.18": {"snippet": "...tent Vector Spaces for Product Search", "weights": [[8, 14, 1.0]]}, "1992.sigirconf_conference-92.14": {"snippet": "...s of vector-based models for information retrieval from the computational geometry point of view. It is shown that both the pseudo-cosine and the standard vector space models can be viewed as special ...", "weights": [[8, 14, 1.0], [134, 140, 1.0], [158, 164, 1.0]]}, "N19-1181": {"snippet": "Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing\n\n\n The prevalent way to estimate the similarity of two documents based on word embeddings is to apply the co...", "weights": [[5, 15, 1.0], [129, 139, 1.0], [198, 204, 1.0]]}, "1999.sigirconf_conference-99.9": {"snippet": "... the cosine similarity measure. Both the document-document similarity matrix and the term-term similarity matrix naturally arise from the maximum likelihood estimation of the model parameters, and the...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [62, 72, 1.0], [98, 108, 1.0]]}, "W11-0329": {"snippet": "...onal vector space. Our approach operates by finding the optimal matrix to minimize the loss of the pre-selected similarity function (e.g., cosine) of the projected vectors, and is able to efficiently ...", "weights": [[8, 14, 1.0], [115, 125, 1.0], [142, 148, 1.0]]}, "L16-1723": {"snippet": "... and Vector Cosine in the {ESL} and {TOEFL} Datasets\n\n\n In this paper, we claim that Vector Cosine -which is generally considered one of the most efficient unsupervised measures for identifying word s...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [88, 94, 1.0], [95, 101, 1.0], [202, 212, 1.0]]}, "2017.clef_conference-2017w.10": {"snippet": "...sine Similarity\n\n\n The 2017 Microblog Cultural Contextualization task consists in three challenges: (1) Content Analysis, (2) Microblog search, and (3) TimeLine illustration. This paper describes the ...", "weights": [[8, 18, 1.0]]}, "D17-1193": {"snippet": "...ring Vector Spaces for Semantic Relations\n\n\n Word embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods a...", "weights": [[8, 14, 1.0]]}, "N19-1100": {"snippet": "...d to similarity measures between these embeddings, with cosine similarity being used unquestionably in the majority of cases. In this work, we illustrate that for all common word vectors, cosine simil...", "weights": [[8, 18, 1.0], [59, 65, 1.0], [66, 76, 1.0], [191, 197, 1.0], [198, 208, 1.0]]}, "Y16-2021": {"snippet": "...inst Vector Cosine on Similarity Estimation\n\n\n In Distributional Semantic Models (DSMs), Vector Cosine is widely used to estimate similarity between word vectors, although this measure was noticed to ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [25, 35, 1.0], [92, 98, 1.0], [99, 105, 1.0], [133, 143, 1.0]]}, "2007.wwwconf_conference-2007.14": {"snippet": "...airs similarity search\n\n\n ABSTRACTGiven a large collection of sparse vector data in a high dimensional space, we investigate the problem of finding all pairs of vectors whose similarity score (as dete...", "weights": [[8, 18, 1.0], [72, 78, 1.0], [178, 188, 1.0]]}, "2013.clef_conference-2013w.98": {"snippet": "...port Vector Machines\n\n\n In this paper we present two systems that address the issues of disorder recognition and normalization submitted by the authors as defined by the CLEF/ShARe Evaluation Lab. The...", "weights": [[8, 14, 1.0]]}, "2013.ipm_journal-ir0volumeA49A2.10": {"snippet": "...ific similarity are still limited to vector space models wherein there is no connection to probabilistic approaches. We suggest a probabilistic framework that defines query-sensitive similarity based ...", "weights": [[8, 18, 1.0], [40, 46, 1.0], [186, 196, 1.0]]}, "2020.lt4hala-1.10": {"snippet": "...onal similarity. To this end, we apply established algorithms to find connections between headwords based on existing lexicons and other digital resources. For semantic similarity, we utilize the cosi...", "weights": [[8, 18, 1.0], [172, 182, 1.0], [199, 205, 1.0]]}, "2000.cikm_conference-2000.28": {"snippet": "Vector Approximation based Indexing for Non-uniform High Dimensional Data Sets\n\n\n AbstractWith the proliferation of multimedia data, there is increasing need to support the indexing and searching of h...", "weights": [[0, 6, 1.0]]}, "P19-2057": {"snippet": "...ngth vector. Document embedding models map each document to a dense, lowdimensional vector in continuous vector space. This paper proposes training document embeddings using cosine similarity instead ...", "weights": [[8, 14, 1.0], [87, 93, 1.0], [108, 114, 1.0], [177, 183, 1.0], [184, 194, 1.0]]}, "2005.cikm_conference-2005.161": {"snippet": "...sing vector space bases\n\n\n ABSTRACTIn this paper, context is modeled by vector space bases and its evolution is modeled by linear transformations from one base to another. Each document or query can b...", "weights": [[8, 14, 1.0], [75, 81, 1.0]]}, "2004.airs_conference-2004.20": {"snippet": "...ment Similarity\n\n\n Abstract. Measuring pairwise document similarity is critical to various text retrieval and mining tasks. The most popular measure for document similarity is the Cosine measure in Ve...", "weights": [[8, 18, 1.0], [60, 70, 1.0], [165, 175, 1.0], [183, 189, 1.0], [201, 207, 1.0]]}, "2008.cikm_conference-2008.8": {"snippet": "...hms' similarity measurement implies high probability of similarity between documents, which is different from high similarity of the documents. But how similar two documents are is what we really need...", "weights": [[8, 18, 1.0], [59, 69, 1.0], [118, 128, 1.0]]}, "D19-1008": {"snippet": "...like cosine similarity is used to compare individual word vectors, we introduce a novel application of the centered kernel alignment (CKA) as a natural generalisation of squared cosine similarity for ...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [181, 187, 1.0], [188, 198, 1.0]]}, "I05-2026": {"snippet": "...tent similarity evaluation employs ngrams of lexical chains and measures similarity using the cosine of vectors of n-grams of lexical chains, vectors of tf*idfweighted keywords, and vectors of unweigh...", "weights": [[8, 18, 1.0], [76, 86, 1.0], [97, 103, 1.0]]}, "2014.cikm_workshop-2014esair.8": {"snippet": "...evel vector, such as a vector of Wikipedia concepts . It is also considered a human-friendly way to annotate text -it generates concept vector that can be easily interpreted by human. We propose an ap...", "weights": [[8, 14, 1.0], [26, 32, 1.0], [139, 145, 1.0]]}, "2013.cikm_conference-2013.169": {"snippet": "...ling similarity measures outperform alternative measures in both experiments. This outcome strongly recommends the adoption of these measures, replacing cosine similarity in future work.", "weights": [[8, 18, 1.0], [156, 162, 1.0], [163, 173, 1.0]]}, "2016.clef_conference-2016w.23": {"snippet": "...ture vector was built for each training set code by merging all the statements found for this code in the training data. Given a new statement, we ranked candidate codes with Cosine similarity. Featur...", "weights": [[8, 14, 1.0], [178, 184, 1.0], [185, 195, 1.0]]}, "P06-2110": {"snippet": "...s of similarity, i.e., taxonomic similarity and associative similarity. The result of the comparison was that the dictionary-based word vectors better reflect taxonomic similarity, while the LSAbased ...", "weights": [[8, 18, 1.0], [36, 46, 1.0], [63, 73, 1.0], [172, 182, 1.0]]}, "2013.dir_workshop-2013.3": {"snippet": "...tive similarity measure. We show that this new similarity measure is superior to both the Jensen-Shannon divergence and the cosine similarity in a task, in which pairs of words, taken from Wordnet, ha...", "weights": [[8, 18, 1.0], [50, 60, 1.0], [127, 133, 1.0], [134, 144, 1.0]]}, "P18-2088": {"snippet": "...with vector cosine being typically used as the only similarity metric. In this paper, we report experiments with a rank-based metric for WE, which performs comparably to vector cosine in similarity es...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [55, 65, 1.0], [173, 179, 1.0], [180, 186, 1.0], [190, 200, 1.0]]}, "2013.wsdm_conference-2013.72": {"snippet": "...heir similarity. 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Intr~cluction In this paper, we describe the linguistic solutions to some of the problems encou...", "weights": []}, "1988.riao_conference-1988.22": {"snippet": "... for Indexing and Retrieving", "weights": [[8, 16, 1.0]]}, "2015.ictir_conference-2015.38": {"snippet": "...rted indexing. In this paper, we explore a radical proposition: What if we abandon document inversion and instead adopt an architecture based on brute force scans of document representations? In such ...", "weights": [[8, 16, 1.0]]}, "W11-2708": {"snippet": "Discriminative features in reversible stochastic attribute-value grammars\n\n\n Reversible stochastic attribute-value grammars (de Kok et al., 2011) use one model for parse disambiguation and fluency ran...", "weights": []}, "2015.tois_journal-ir0volumeA33A4.3": {"snippet": "...nant indexing method in information retrieval systems. It enables fast return of the list of all documents containing a given query term. However, for retrieval schemes involving query expansion, as i...", "weights": [[8, 16, 1.0]]}, "1982.sigirjournals_journal-ir0volumeA16A3.3": {"snippet": "...atic Indexing: A Tutorial", "weights": [[8, 16, 1.0]]}, "2020.ecir_workshop-2020siirh.6": {"snippet": "...ntic Indexing and Information Retrieval for Health from heterogeneous content types and languages", "weights": [[8, 16, 1.0]]}, "2016.ipm_journal-ir0volumeA52A3.7": {"snippet": "A query term re-weighting approach using document similarity\n\n\n a b s t r a c tPseudo-relevance feedback is the basis of a category of automatic query modification techniques. 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One important ...", "weights": []}, "2011.cikm_conference-2011.42": {"snippet": "Factorization-based lossless compression of inverted indices\n\n\n ABSTRACTMany large-scale Web applications that require ranked top-k retrieval are implemented using inverted indices. An inverted index ...", "weights": []}, "2020.acl-main.382": {"snippet": "...ide. REVERSE HYPOTHESIS: The guy is outside. PREDICTED LABEL: contradiction REVERSE EXPLANATION: Snowboarding is not done outside. PREMISE: A man talks to two guards as he holds a drink. 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For every term in the vocabulary, a list...", "weights": [[8, 16, 1.0]]}, "2008.sigirconf_conference-2008.179": {"snippet": "...that reverse natural graphs tend to have low indegree while maintaining fast PageRank convergence. It follows that calculating Reverse PageRank locally is frequently more feasible than computing PageR...", "weights": [[8, 15, 1.0], [130, 137, 1.0]]}, "2011.tweb_journal-ir0volumeA5A4.1": {"snippet": "Efficient Search Engine Measurements\n\n\n We address the problem of externally measuring aggregate functions over documents indexed by search engines, like corpus size, index freshness, and density of d...", "weights": []}, "W05-1623": {"snippet": "Reversibility and Re-usability of Resources in {NLG} and Natural Language Dialog Systems\n\n\n Reversibility is a key to efficient and maintainable NLG systems. In this paper, we present a formal definit...", "weights": []}, "2020.findings-emnlp.388": {"snippet": "...gual Reverse Dictionary\n\n\n Reverse dictionary is the task to find the proper target word given the word description. In this paper, we tried to incorporate BERT into this task. However, since BERT is ...", "weights": [[8, 15, 1.0], [30, 37, 1.0]]}, "W77-0106": {"snippet": "... the reverse of those studied by Europeans. One begins with basic roots, neatly separated, and a \"machine translation* must m show how they are put together. The Chinese written language, being compos...", "weights": [[8, 15, 1.0]]}, "W91-0104": {"snippet": "Inherently Reversible Grammars, Logic Programming and Computability\n\n\n This paper attempts to clarify two distinct notions of \"reversibility\": (i) Uniformity of implementation of parsing and generatio...", "weights": []}, "2020.emnlp-demos.23": {"snippet": "...line Reverse Dictionary System\n\n\n A reverse dictionary takes descriptions of words as input and outputs words semantically matching the input descriptions. Reverse dictionaries have great practical va...", "weights": [[8, 15, 1.0], [39, 46, 1.0], [159, 166, 1.0]]}, "1977.ipm_journal-ir0volumeA13A6.3": {"snippet": "...y of indexing", "weights": [[8, 16, 1.0]]}, "2013.clef_workshop-2013bioasq.1": {"snippet": "...MeSH Indexing\n\n\n As an increasing number of new journal articles being added to the MEDLINE database each year, it becomes imperative to build effective systems that can automatically suggest Medical ...", "weights": [[8, 16, 1.0]]}, "1977.jasis_journal-ir0volumeA28A1.5": {"snippet": "On indexing, retrieval and the meaning of about", "weights": [[3, 11, 1.0]]}}}
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There are many index compression ...", "weights": [[8, 13, 1.0], [14, 25, 1.0], [27, 38, 1.0], [185, 190, 1.0], [191, 202, 1.0]]}, "2010.cikm_conference-2010.128": {"snippet": "...ease index size.We propose new index compression techniques for versioned document collections that achieve reductions in index size over previous methods. 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A large body of research has been built in the last 30 years around gap compression, including theoretical modeling of the gap distribution, specialized instantaneous codes suitable f...", "weights": [[8, 18, 1.0], [92, 103, 1.0]]}, "2010.wwwconf_conference-2010.32": {"snippet": "...rted index, a plethora of research has focused on fast and effective techniques for compressing this structure. Recently, researchers have proposed techniques for improving index compression by optimi...", "weights": [[8, 13, 1.0], [72, 82, 1.0], [151, 161, 1.0], [176, 181, 1.0], [182, 193, 1.0]]}, "2014.sigirconf_conference-2014.207": {"snippet": "...data compression\n\n\n ABSTRACTWe propose a two-stage lossless compression approach on large scale RDF data. Our approach exploits both Representation Compression and Component Compression techniques to ...", "weights": [[8, 19, 1.0], [63, 74, 1.0], [151, 162, 1.0], [177, 188, 1.0], [189, 199, 1.0]]}, "2013.clef_workshop-2013bioasq.7": {"snippet": "... the Index Section at NLM, MTI continues to grow and expand its ability to provide assistance to the indexers. This paper provides an overview of MTI's functionality, performance, and its evolution ov", "weights": [[8, 13, 1.0]]}, "2005.wwwconf_conference-2005.26": {"snippet": "...less compression methods, while keeping the loss in retrieval precision to a minimum. When compared to the indices size with no compression at all, the compression rate is higher than 88%, i.e., less ...", "weights": [[8, 19, 1.0], [131, 142, 1.0], [155, 166, 1.0]]}, "2014.airs_conference-2014.19": {"snippet": "...rted Index Compression", "weights": [[8, 13, 1.0], [14, 25, 1.0]]}, "2018.wsdm_conference-2018.52": {"snippet": "Index Compression Using Byte-Aligned ANS Coding and Two-Dimensional Contexts\n\n\n ABSTRACTWe examine approaches used for block-based inverted index compression, such as the OptPFOR mechanism, in which f...", "weights": [[0, 5, 1.0], [6, 17, 1.0], [140, 145, 1.0], [146, 157, 1.0]]}, "2019.iir_workshop-2019.10": {"snippet": "...raph Index Compression Techniques", "weights": [[8, 13, 1.0], [14, 25, 1.0], [26, 36, 1.0]]}, "2017.spire_conference-2017.10": {"snippet": "...ient Compression and Indexing of Trajectories", "weights": [[8, 19, 1.0]]}, "2011.sigirconf_conference-2011.58": {"snippet": "...rted index stores a list of the documents in which this word occurs. Compression techniques are often applied to further reduce the space requirement of these lists. However, the index has a shortcomi...", "weights": [[8, 13, 1.0], [72, 83, 1.0], [84, 94, 1.0], [182, 187, 1.0]]}, "2004.airs_conference-2004.7": {"snippet": "... 30% compression and 10% compression, our method is compared with Title, Location, Aggregation Similarity, and DOCUSUM methods. As a result, our method showed higher performance than other methods.", "weights": [[8, 19, 1.0], [28, 39, 1.0]]}, "2019.cikm_conference-2019.270": {"snippet": "...ossy Compression Method on Positional Index for Efficient and Effective Retrieval", "weights": [[8, 19, 1.0], [41, 46, 1.0]]}, "2012.sigirconf_conference-2012.27": {"snippet": "...text index structures.In this paper, we study index organization and compression techniques for such versioned full-text index structures. In particular, we focus on the case of positional index struc...", "weights": [[8, 13, 1.0], [49, 54, 1.0], [72, 83, 1.0], [84, 94, 1.0], [124, 129, 1.0], [192, 197, 1.0]]}, "2015.airs_conference-2015.3": {"snippet": "Large Scale Sentiment Analysis with Locality Sensitive BitHash\n\n\n Abstract. As social media data rapidly grows, sentiment analysis plays an increasingly more important role in classifying users' opini...", "weights": []}, "2008.wwwconf_conference-2008.40": {"snippet": "...ding index compression, caching, and early termination.We focus on two techniques, inverted index compression and index caching, which play a crucial rule in web search engines as well as other high-p...", "weights": [[8, 13, 1.0], [14, 25, 1.0], [74, 84, 1.0], [95, 100, 1.0], [101, 112, 1.0], [117, 122, 1.0]]}, "2008.wsdm_conference-2008.12": {"snippet": "...raph compression with communities\n\n\n ABSTRACTA link server is a system designed to support efficient implementations of graph computations on the web graph. In this work, we present a compression sche...", "weights": [[8, 19, 1.0], [187, 198, 1.0]]}, "2000.ir_journal-ir0volumeA3A1.3": {"snippet": "...ding Compression to Block Addressing Inverted Indexes", "weights": [[8, 19, 1.0]]}, "2013.clef_workshop-2013bioasq.1": {"snippet": "...eval techniques for finding similar articles whose existing annotations are used for MeSH suggestion; 3) LLDA-based labeling, which further trains a multi-label classifier based on MeSH ontology for M...", "weights": [[8, 18, 1.0]]}, "2019.wsdm_conference-2019.6": {"snippet": "... and compression efficiency can be achieved. Our observations are supported by experiments using the document-level inverted index data for two large text collections, and a wide range of other index ...", "weights": [[8, 19, 1.0], [128, 133, 1.0], [197, 202, 1.0]]}, "2005.airs_conference-2005.4": {"snippet": "...onal Index Structure", "weights": [[8, 13, 1.0]]}, "2009.airs_conference-2009.34": {"snippet": "Searching Polyphonic Indonesian Folksongs Based on N-gram Indexing Technique", "weights": []}}}
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Yet, despite the deluge of information, query log analysis often su...", "weights": [[8, 12, 1.0], [40, 46, 1.0], [107, 113, 1.0], [114, 120, 1.0], [176, 181, 1.0]]}, "2018.sigirconf_conference-2018.182": {"snippet": "...erce Search\n\n\n ABSTRACTUnderstanding the search tasks and search behavior of users is necessary for optimizing search engine results. While much work has been done on understanding the users in Web se...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [61, 67, 1.0], [114, 120, 1.0], [121, 127, 1.0], [201, 207, 1.0]]}, "2011.wwwconf_conference-2011c.32": {"snippet": "... web search query logs\n\n\n ABSTRACTQuery logs of a Web search engine have been increasingly used as a vital source for data mining. This paper presents a study on largescale domain-independent entity e...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 25, 1.0], [43, 47, 1.0], [57, 63, 1.0], [64, 70, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A3.6": {"snippet": "... the search queries is critical for search engines. Hence, there has been an increasing attention on studying how to effectively mine the intents of search queries by analyzing search engine query log...", "weights": [[8, 14, 1.0], [39, 45, 1.0], [152, 158, 1.0], [180, 186, 1.0], [187, 193, 1.0], [194, 199, 1.0]]}, "2009.cikm_conference-2009.161": {"snippet": "...n of query logs\n\n\n ABSTRACTUser search query logs have proven to be very useful, but have vast potential for misuse. Several incidents have shown that simple removal of identifiers is insufficient to ...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [35, 41, 1.0], [42, 47, 1.0], [48, 52, 1.0]]}, "2012.sigirconf_conference-2012.213": {"snippet": "...ets. Query logs have also revolutionized query assistance tools such as related queries (recommended queries after the search was issued) or query completions (suggested queries as the query is being ...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [44, 49, 1.0], [122, 128, 1.0], [144, 149, 1.0], [188, 193, 1.0]]}, "2017.wsdm_conference-2017.15": {"snippet": "...ware Query Auto-Completion via Hawkes Processes\n\n\n ABSTRACTQuery auto completion (QAC) is a prominent feature in modern search engines. High quality QAC substantially improves search experiences by he...", "weights": [[8, 13, 1.0], [123, 129, 1.0], [179, 185, 1.0]]}, "2007.sigirjournals_journal-ir0volumeA41A2.17": {"snippet": "Query log analysis: social and technological challenges\n\n\n AbstractAnalysis of search engine query logs is an important tool for developers and researchers.However, the potentially personal content of...", "weights": [[0, 5, 1.0], [79, 85, 1.0], [86, 92, 1.0], [93, 98, 1.0], [99, 103, 1.0]]}, "2005.jasis_journal-ir0volumeA56A13.1": {"snippet": "... the query logs of a Web site search engine", "weights": [[8, 13, 1.0], [14, 18, 1.0], [33, 39, 1.0], [40, 46, 1.0]]}, "2017.wsdm_conference-2017.19": {"snippet": "...ocal search differs from traditional web-search because to correctly capture users' click behavior, the estimation of relevance between query and candidate results must be integrated with geographical...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [139, 144, 1.0]]}, "2011.wwwconf_conference-2011c.9": {"snippet": "...n of search results using query logs\n\n\n ABSTRACTWe study the problem of diversifying search results by exploiting the knowledge mined from query logs. Our proposal exploits the presence of different \"...", "weights": [[8, 14, 1.0], [29, 34, 1.0], [35, 39, 1.0], [88, 94, 1.0], [142, 147, 1.0], [148, 152, 1.0]]}, "2016.ipm_journal-ir0volumeA52A6.12": {"snippet": "...sing search logs by (semi-)automatically identifying user search tactics from logged events, allowing large-scale analysis that is comparable across search systems. In addition, as the resulting analy...", "weights": [[8, 14, 1.0], [15, 19, 1.0], [61, 67, 1.0], [152, 158, 1.0]]}, "2006.cikm_conference-2006.127": {"snippet": "...iven query by constructing the query context from search query logs. Evaluation results show that Q-Rank gains a considerable advantage over the current ranking system of a large-scale commercial Web ...", "weights": [[8, 13, 1.0], [34, 39, 1.0], [53, 59, 1.0], [60, 65, 1.0], [66, 70, 1.0]]}, "2019.wsdm_conference-2019.50": {"snippet": "...g of Query Rewriting andSemantic Matching for e-Commerce\n\n\n ABSTRACTRelevance is the core problem of a search engine, and one of the main challenges is the vocabulary gap between user queries and docu...", "weights": [[8, 13, 1.0], [106, 112, 1.0], [113, 119, 1.0]]}, "2007.jasis_journal-ir0volumeA58A14.18": {"snippet": "...g of search engine logs", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 26, 1.0]]}, "2003.sigirconf_conference-2003.86": {"snippet": "...sWeb search, query reformulation, query modeling Spink et al. [4]  found that reformulations of queries constituted 40% to 52% of queries. Other research using web search query logs has sought to char...", "weights": [[8, 14, 1.0], [16, 21, 1.0], [37, 42, 1.0], [167, 173, 1.0], [174, 179, 1.0], [180, 184, 1.0]]}, "2006.wwwconf_conference-2006.146": {"snippet": "...from search engine query logs\n\n\n ABSTRACTIn this work we propose a method that retrieves a list of related queries given an initial input query. The related queries are based on the query log of previ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [28, 32, 1.0], [141, 146, 1.0], [185, 190, 1.0]]}, "2004.cikm_conference-2004.14": {"snippet": "...t on search performance over the naive query log mining algorithm as well as the baseline search engine.", "weights": [[8, 14, 1.0], [42, 47, 1.0], [93, 99, 1.0], [100, 106, 1.0]]}, "2011.wsdm_conference-2011.35": {"snippet": "...s in search engine query logs", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [28, 32, 1.0]]}, "2011.cikm_conference-2011.246": {"snippet": "...that query logs from Web search are a gold mine for search business, because they reflect users' preference over Web pages presented by search engines, so a lot of studies based on query logs have bee...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [28, 34, 1.0], [55, 61, 1.0], [139, 145, 1.0], [184, 189, 1.0], [190, 194, 1.0]]}, "2005.ecir_conference-2005.2": {"snippet": "...site logs where queries and later actions are recorded from search engine referrers or from an internal search box. In this paper we present two applications based in analyzing and clustering queries....", "weights": [[8, 12, 1.0], [63, 69, 1.0], [70, 76, 1.0], [107, 113, 1.0]]}, "2020.sigirconf_conference-2020.291": {"snippet": "...able search engines. Query logs registered by search engines enable the automatic identification of the search tasks that users perform to fulfill their information needs. Search engine logs contain q...", "weights": [[8, 14, 1.0], [24, 29, 1.0], [30, 34, 1.0], [49, 55, 1.0], [107, 113, 1.0], [175, 181, 1.0], [182, 188, 1.0], [189, 193, 1.0]]}, "2009.clef_workshop-2009.64": {"snippet": "A Search Engine Based on Query Logs, and Search Log Analysis by Automatic Language Identification", "weights": [[2, 8, 1.0], [9, 15, 1.0], [25, 30, 1.0], [31, 35, 1.0], [41, 47, 1.0]]}, "2020.sigirconf_conference-2020.290": {"snippet": "...ting Search Query Logs by Learning to Detect Search Task Boundaries\n\n\n To fulfill their information needs, users submit sets of related queries to available search engines. Query logs record users' ac...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 25, 1.0], [48, 54, 1.0], [160, 166, 1.0], [176, 181, 1.0], [182, 186, 1.0]]}, "2006.sigirconf_conference-2006.124": {"snippet": "...user query logs\n\n\n ABSTRACTThe exponential growth of the Web and the increasing ability of web search engines to index data have led to a problem of plenty. The number of results returned per query is...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [98, 104, 1.0], [195, 200, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.7": {"snippet": "...oice search is still in its infancy, it is gradually becoming more widespread. In this article, we report a comprehensive voice search query log analysis of a commercial web search engine's mobile app...", "weights": [[8, 14, 1.0], [131, 137, 1.0], [138, 143, 1.0], [177, 183, 1.0], [184, 190, 1.0]]}, "2014.sigirconf_conference-2014.114": {"snippet": "...ng a query log via crowdsourcing\n\n\n ABSTRACTA query log is a key asset in a commercial search engine. Everyday millions of users rely on search engines to find information on the Web by entering a few...", "weights": [[8, 13, 1.0], [49, 54, 1.0], [90, 96, 1.0], [97, 103, 1.0], [140, 146, 1.0]]}, "2002.wwwconf_conference-2002.32": {"snippet": "... web query logs. In this study, we propose a new method for query expansion based on query logs. The central idea is to extract probabilistic correlations between query terms and document terms by ana...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [63, 68, 1.0], [88, 93, 1.0], [94, 98, 1.0], [166, 171, 1.0]]}, "2009.clef_workshop-2009w.5": {"snippet": "A Search Engine based on Query Logs and Search Log Analysis at the University of Sunderland\n\n\n This work describes a variation on the traditional Information Retrieval paradigm, where instead of text ...", "weights": [[2, 8, 1.0], [9, 15, 1.0], [25, 30, 1.0], [31, 35, 1.0], [40, 46, 1.0]]}, "2008.wwwconf_conference-2008.7": {"snippet": "... the query logs of the search engine. We identify queries related to a campaign by exploiting the associations between queries and URLs as they are captured by the user's clicks. These queries form go...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [26, 32, 1.0], [33, 39, 1.0]]}, "2010.wsdm_conference-2010.19": {"snippet": "...ough logs of a web search engine\n\n\n ABSTRACTWe propose a new model to interpret the clickthrough logs of a web search engine. This model is based on explicit assumptions on the user behavior. In parti...", "weights": [[8, 12, 1.0], [22, 28, 1.0], [29, 35, 1.0], [100, 104, 1.0], [114, 120, 1.0], [121, 127, 1.0]]}, "2011.sigirconf_conference-2011.82": {"snippet": "...y on search engine query logs to determine possible query suggestions. However, for customized search systems in the enterprise domain, intranet search, or personalized search such as email or desktop...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [28, 32, 1.0], [55, 60, 1.0], [98, 104, 1.0], [148, 154, 1.0], [172, 178, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A1.3": {"snippet": "Query Recommendation for Improving Search Engine Results", "weights": [[0, 5, 1.0], [35, 41, 1.0], [42, 48, 1.0]]}, "2011.sigirconf_conference-2011.217": {"snippet": "... web search by mining search and browse logs\n\n\n ABSTRACTHuge amounts of search log data have been accumulated in various search engines. Currently, a commercial search engine receives billions of quer...", "weights": [[8, 14, 1.0], [25, 31, 1.0], [43, 47, 1.0], [75, 81, 1.0], [124, 130, 1.0], [164, 170, 1.0], [171, 177, 1.0]]}, "2006.wwwconf_conference-2006.194": {"snippet": "...ning search engine query logs for query recommendation\n\n\n ABSTRACTThis paper presents a simple and intuitive method for mining search engine query logs to get fast query recommendations on a large sca...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [28, 32, 1.0], [37, 42, 1.0], [130, 136, 1.0], [137, 143, 1.0], [144, 149, 1.0], [150, 154, 1.0], [167, 172, 1.0]]}, "2017.wsdm_conference-2017.96": {"snippet": "...yond Query Logs: Recommendation and Evaluation\n\n\n ABSTRACTQuery recommendation in Web search is typically manifested in algorithms that 1) recommend previously issued queries from a query log or 2) ma...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [89, 95, 1.0], [185, 190, 1.0]]}, "2019.wsdm_conference-2019.110": {"snippet": "...mage Search\n\n\n ABSTRACTWeb-based image search engines di er from Web search engines greatly. The intents or goals behind human interactions with image search engines are di erent. In image search, use...", "weights": [[8, 14, 1.0], [42, 48, 1.0], [72, 78, 1.0], [154, 160, 1.0], [192, 198, 1.0]]}, "2011.wsdm_conference-2011.48": {"snippet": "...om a search engine. We model the conditional probability of a searchable web site being relevant to a given query in term of three main components: the language model of the query, the language model ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [111, 116, 1.0], [177, 182, 1.0]]}, "2009.sigirconf_workshop-2009uiir.5": {"snippet": "... web search and implemented the technology in a browser extension that can dynamically modify search engine results pages (Google, Yahoo!, and Live Search). A combination of explicit (queries, reformu...", "weights": [[8, 14, 1.0], [97, 103, 1.0], [104, 110, 1.0], [151, 157, 1.0]]}, "2021.ecir_conference-20211.26": {"snippet": "...able search engines. The mining of query logs from search engines enables the automatic extraction of search tasks by clustering related queries into groups representing search tasks. The extraction o...", "weights": [[8, 14, 1.0], [38, 43, 1.0], [44, 48, 1.0], [54, 60, 1.0], [105, 111, 1.0], [173, 179, 1.0]]}, "2002.tois_journal-ir0volumeA20A1.3": {"snippet": "Query clustering using user logs\n\n\n Query clustering is a process used to discover frequently asked questions or most popular topics on a search engine. This process is crucial for search engines base...", "weights": [[0, 5, 1.0], [28, 32, 1.0], [36, 41, 1.0], [138, 144, 1.0], [145, 151, 1.0], [181, 187, 1.0]]}, "2010.wsdm_conference-2010.5": {"snippet": "...d on query logs have been studied as a method of capturing user intent and improving retrieval effectiveness. The evaluation of these techniques has primarily, however, focused on proprietary query lo...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [195, 200, 1.0], [201, 205, 1.0]]}, "2012.wwwconf_conference-2012c.106": {"snippet": "... the search engine query stream\n\n\n ABSTRACTSearch engines record a large amount of metadata each time a user issues a query. While efficiently mining this data can be challenging, the results can be u...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [121, 126, 1.0]]}, "2007.jasis_journal-ir0volumeA58A12.13": {"snippet": "... Web search engine query logs using an improved association rule mining model", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [28, 32, 1.0]]}, "2010.wsdm_conference-2010.43": {"snippet": "...cale search bot detection\n\n\n ABSTRACTIn this paper, we study search bot traffic from search engine query logs at a large scale. Although bots that generate search traffic aggressively can be easily de...", "weights": [[8, 14, 1.0], [64, 70, 1.0], [88, 94, 1.0], [95, 101, 1.0], [102, 107, 1.0], [108, 112, 1.0], [159, 165, 1.0]]}, "2008.cikm_conference-2008.50": {"snippet": "...from search engine logs. These two patterns can be used to address the mis-specification and under-specification problems of ineffective queries. Experiment results on real search engine logs show tha...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 26, 1.0], [176, 182, 1.0], [183, 189, 1.0], [190, 194, 1.0]]}, "2009.sigirconf_workshop-2009uiir.7": {"snippet": "...s of search query logs or Web logs is not sufficient to study users and interpret their preferences. In fact the combination of implicitly and explicitly collected data improves understanding of behav...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 25, 1.0], [33, 37, 1.0]]}, "2016.wwwconf_conference-2016.126": {"snippet": "...sing Query Logs and Table Corpora\n\n\n ABSTRACTAttribute synonyms are important ingredients for keywordbased search systems. For instance, web search engines, recognize queries that seek the value of an...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [110, 116, 1.0], [144, 150, 1.0]]}, "2016.ictir_conference-2016.25": {"snippet": "...lled search tasks, from user queries stored in a search engine log. The framework is made of two main modules: Query Similarity Learning (QSL) and Graph-based Query Clustering (GQC). The former is dev...", "weights": [[8, 14, 1.0], [52, 58, 1.0], [59, 65, 1.0], [114, 119, 1.0], [162, 167, 1.0]]}, "2010.tweb_journal-ir0volumeA4A2.2": {"snippet": "...fast search on compressed time-series query logs\n\n\n Consider a database of time-series, where each datapoint in the series records the total number of users who asked for a specific query at an intern...", "weights": [[8, 14, 1.0], [41, 46, 1.0], [47, 51, 1.0], [185, 190, 1.0]]}, "2010.wwwconf_conference-2010.91": {"snippet": "... the query-URL bipartite graphs. Consequently, our model is capable of scaling up to the need of commercial search engines. Experimental results on one-month query logs from a large commercial search ...", "weights": [[8, 13, 1.0], [111, 117, 1.0], [161, 166, 1.0], [167, 171, 1.0], [196, 202, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.8": {"snippet": "...uted search engines\n\n\n AbstractWeb search engines have to deal with a rapidly increasing amount of information, high query loads and tight performance constraints. The success of a search engine depen...", "weights": [[8, 14, 1.0], [38, 44, 1.0], [120, 125, 1.0], [184, 190, 1.0], [191, 197, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.13": {"snippet": "... Web search engine data logs\n\n\n AbstractThe analysis of contextual information in search engine query logs enhances the understanding of Web users' search patterns. Obtaining contextual information on...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [27, 31, 1.0], [85, 91, 1.0], [92, 98, 1.0], [99, 104, 1.0], [105, 109, 1.0], [151, 157, 1.0]]}, "2011.cikm_conference-2011.87": {"snippet": "...king query recommendations which not only consider relevance to the original query but also take into account efficiency of a query at accomplishing a user search task at hand. We formalize the notion...", "weights": [[8, 13, 1.0], [80, 85, 1.0], [129, 134, 1.0], [159, 165, 1.0]]}, "C10-1148": {"snippet": "...with Search Engine Query Logs\n\n\n This paper proposes a method that extracts paraphrases from search engine query logs. The method first extracts paraphrase query-title pairs based on an assumption tha...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [28, 32, 1.0], [96, 102, 1.0], [103, 109, 1.0], [110, 115, 1.0], [116, 120, 1.0], [159, 164, 1.0]]}, "2020.wwwconf_conference-2020.278": {"snippet": "... for Search Engine Optimization", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 34, 1.0]]}, "2009.sigirconf_workshop-2009uiir.4": {"snippet": "...dual query but also the complete search process is necessary. Only in this way it is possible to satisfy an information need.", "weights": [[8, 13, 1.0], [36, 42, 1.0]]}, "2013.wsdm_conference-2013.89": {"snippet": "...ets. Query logs have also revolutionized query assistance tools such as related queries (recommended queries after the search was issued) or query completions (suggested queries as the query is being ...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [44, 49, 1.0], [122, 128, 1.0], [144, 149, 1.0], [188, 193, 1.0]]}, "2009.cikm_conference-2009.281": {"snippet": "... the search engine recorded in query logs.A recent query-log mining approach for query recommendation is based on Query Flow Graphs (QFG). In this paper we propose an evaluation of the effects of time...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [34, 39, 1.0], [40, 44, 1.0], [54, 59, 1.0], [84, 89, 1.0], [117, 122, 1.0]]}, "2015.wwwconf_conference-2015c.217": {"snippet": "... Web search, any change to a component of the search engine may result in a different search result page for the same query, but we normally cannot infer reliably from search log how users would react...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [56, 62, 1.0], [89, 95, 1.0], [121, 126, 1.0], [171, 177, 1.0]]}, "2008.cikm_conference-2008.64": {"snippet": "... the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more.In this paper we introduce the query-flow ...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [61, 66, 1.0], [130, 135, 1.0], [192, 197, 1.0]]}, "2016.tois_journal-ir0volumeA35A2.0": {"snippet": "... web search services for millions of users who compose search queries by different languages. Hence, the search engine query log, which serves as the backbone of many search engine applications, recor...", "weights": [[8, 14, 1.0], [58, 64, 1.0], [108, 114, 1.0], [115, 121, 1.0], [122, 127, 1.0], [170, 176, 1.0], [177, 183, 1.0]]}, "2011.sigirconf_conference-2011.7": {"snippet": "... the search engines themselves. In this paper we analyze the search logs of one particular vertical: people search engines. Based on an extensive analysis of the logs of a search engine geared towards...", "weights": [[8, 14, 1.0], [64, 70, 1.0], [71, 75, 1.0], [111, 117, 1.0], [165, 169, 1.0], [175, 181, 1.0], [182, 188, 1.0]]}, "2009.wsdm_workshop-2009wscd.10": {"snippet": "...sing query logs and click data to create improved document descriptions\n\n\n ABSTRACTLogfiles of search engines are a promising resource for data mining, since they provide raw data associated to users ...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [98, 104, 1.0]]}, "2013.cikm_conference-2013.144": {"snippet": "...amic query intents from search query logs. By regarding the query logs as a data stream, we identify constant intents while quickly capturing new bursty intents. To evaluate the accuracy and efficienc...", "weights": [[8, 13, 1.0], [27, 33, 1.0], [34, 39, 1.0], [40, 44, 1.0], [63, 68, 1.0], [69, 73, 1.0]]}, "2016.wsdm_conference-2016.15": {"snippet": "...zing search result presentation for queries with diverse intents, by selectively presenting query suggestions for leading users to more relevant search results. The optimization is based on a probabil...", "weights": [[8, 14, 1.0], [95, 100, 1.0], [148, 154, 1.0], [168, 180, 1.0]]}, "2013.wsdm_conference-2013.13": {"snippet": "... web search engines use significant hardware and energy resources to process hundreds of millions of queries each day, and a lot of research has focused on how to improve query processing efficiency. ...", "weights": [[8, 14, 1.0], [174, 179, 1.0]]}}}
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Natural language processing (NLP) is one of the key technologies for solving the above Web usability problems. Almost all the Web page provide with the essential information in the form of natura...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [197, 204, 1.0]]}, "W07-2432": {"snippet": "...hich makes natural language processing based on the English language not always applicable for Swedish material. One area where there is a difference is compounding. The word-forming process of compou...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 30, 1.0], [31, 41, 1.0], [63, 71, 1.0]]}, "2000.cikm_conference-2000.57": {"snippet": "...sing Natural Language Processing\n\n\n ABSTRACTA t rst sight, it might appear that natural language processing should improve the accuracy of information retrieval systems, by making available a more det...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [83, 90, 1.0], [91, 99, 1.0], [100, 110, 1.0]]}, "W05-0109": {"snippet": "Natural Language Processing at the {S}chool of {I}nformation {S}tudies for {A}frica\n\n\n The lack of persons trained in computational linguistic methods is a severe obstacle to making the Internet and c...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2021.cl-4.24": {"snippet": "Natural Language Processing and Computational Linguistics", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2007.wwwconf_conference-2007.6": {"snippet": "...of a natural-language-processing system and an intelligent form system that observes an assistant processing requests. The intelligent form system then generates a labeled training corpus by interpret...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [101, 111, 1.0]]}, "W18-1106": {"snippet": "...Make Natural Language Processing about People again\n\n\n Over the years, natural language processing has increasingly focused on tasks that can be solved by statistical models, but ignored the social as...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [74, 81, 1.0], [82, 90, 1.0], [91, 101, 1.0]]}, "2014.ipm_journal-ir0volumeA50A5.12": {"snippet": "...t in Natural Language Processing. In order to overcome this limitation we propose an inference method based on Integer Programming formulation of the problem, where long distance dependencies are incl...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "W13-3815": {"snippet": "...d in natural language. Even if ad-hoc natural language examples are often provided in argumentation theory works, no automated processing of such natural language arguments is carried out, making it i...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [41, 48, 1.0], [49, 57, 1.0], [130, 140, 1.0], [149, 156, 1.0], [157, 165, 1.0]]}, "2016.wsdm_conference-2016.92": {"snippet": "...sing natural language, in a way similar to how they document code or communicate with peers. However, using automatic natural language processing techniques to address programming questions is very di...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [121, 128, 1.0], [129, 137, 1.0], [138, 148, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.5": {"snippet": "Natural language processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2010.clef_workshop-2010w.61": {"snippet": "... the natural language used in the queries. Language identification is in fact a key task within Cross Language Information Retrieval systems and the challenge is particularly difficult in the case of ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [46, 54, 1.0], [105, 113, 1.0]]}, "W11-2205": {"snippet": "... for natural language processing tasks\n\n\n The development of unsupervised learning methods for natural language processing tasks has become an important and popular area of research. The primary advan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [98, 105, 1.0], [106, 114, 1.0], [115, 125, 1.0]]}, "2000.riao_conference-2000.38": {"snippet": "Natural Language Processing for Text Mining and Decision Making", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "1985.tois_journal-ir0volumeA3A2.5": {"snippet": "... The Natural Language Processing Project in the AI Program at MCC\n\n\n We discuss a recently launched, long-term project in natural language processing, the primary concern of which is that natural lang...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [125, 132, 1.0], [133, 141, 1.0], [142, 152, 1.0], [191, 198, 1.0], [199, 207, 1.0]]}, "2021.teachingnlp-1.3": {"snippet": "Natural Language Processing 4 All ({NLP}4{A}ll): A New Online Platform for Teaching and Learning {NLP} Concepts\n\n\n Natural Language Processing offers new insights into language data across almost all ...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [115, 122, 1.0], [123, 131, 1.0], [132, 142, 1.0], [168, 176, 1.0]]}, "2014.sigirconf_conference-2014.40": {"snippet": "...nize natural language questions or assertions into an analytic hierarchy, which can be evaluated as part of an ad hoc decision process or as a documented, repeatable analytic process. We then introduc...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "N18-5019": {"snippet": "...of a Natural Language Processing system aimed at group decision making facilitation. Borrowing from the scientific field of Decision Analysis, its essential role is to identify alternatives and criter...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "W14-4919": {"snippet": "...tudy makes preliminary steps towards detection of decision style, based on an annotated dataset of image-based clinical reasoning in which speech data were collected from physicians as they inspected ...", "weights": [[8, 13, 1.0]]}, "1990.ipm_journal-ir0volumeA26A1.3": {"snippet": "Natural language processing and information retrieval", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "P96-1004": {"snippet": "...Most natural language processing tasks require lexical semantic information. Automated acquisition of this information would thus increase the robustness and portability of NLP systems. This paper des...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "1973.sigirconf_conference-73.4": {"snippet": "...in a natural language vocabulary is first characterized in terms of their frequency distribution over the documents of a collection. The construction of \"good\" natural language vocabularies is then de...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [163, 170, 1.0], [171, 179, 1.0]]}, "2005.wwwconf_conference-2005.49": {"snippet": "... for natural language applications\n\n\n ABSTRACTMany modern natural language-processing applications utilize search engines to locate large numbers of Web documents or to compute statistics over the Web...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [61, 68, 1.0], [69, 77, 1.0], [78, 88, 1.0]]}, "P80-1030": {"snippet": "...ased Natural Language Understander\n\n\n We have developed an approach to natural language processing in which the natural language processor is viewed as a knowledge-based system whose knowledge is abou...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [74, 81, 1.0], [82, 90, 1.0], [91, 101, 1.0], [115, 122, 1.0], [123, 131, 1.0]]}, "2015.fire_workshop-2015w.20": {"snippet": "...s\n\n\n Natural Language processing (NLP) in its pure sense, is a platform that provides the ability for transforming natural language text to useful information. Named Entity Recognition (NER) is a key ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [118, 125, 1.0], [126, 134, 1.0]]}, "2020.rocling-1.36": {"snippet": "Natural Language Processing for Digital Humanities", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "W12-1501": {"snippet": "...loys Natural Language Generation technologies to generate appropriate textual summaries of the information graphics. As such, it makes contributions to several areas within the field of Natural Langua...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [132, 137, 1.0], [189, 196, 1.0], [197, 205, 1.0]]}, "W04-0609": {"snippet": "...ased Natural Language Processing\n\n\n Conceptualising a domain has long been recognised as a prerequisite for understanding that domain and processing information about it. Ontologies are explicit speci...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [141, 151, 1.0]]}, "1986.jasis_journal-ir0volumeA37A4.2": {"snippet": "Natural language processing in information retrieval", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2020.wsdm_conference-2020.120": {"snippet": "...p on Natural Language Processing for Recommendations\n\n\n Natural language processing is becoming more and more important in recommender systems. This half day workshop explores challenges and potential...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [59, 66, 1.0], [67, 75, 1.0], [76, 86, 1.0]]}, "2011.ipm_journal-ir0volumeA47A4.4": {"snippet": "... for natural language applications on mobile devices is increasing. This paper proposes an automatic word spacing system, the first step module of natural language processing (NLP) for many languages ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [150, 157, 1.0], [158, 166, 1.0], [167, 177, 1.0]]}, "1985.tmi-1.17": {"snippet": "... and Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2016.sigirconf_conference-2016.1": {"snippet": "...uman Language: Can NLP and Deep Learning Help?\n\n\n ABSTRACTThere is a lot of overlap between the core problems of information retrieval (IR) and natural language processing (NLP). An IR system gains fr...", "weights": [[8, 16, 1.0], [147, 154, 1.0], [155, 163, 1.0], [164, 174, 1.0]]}, "J81-4005": {"snippet": "Natural Language Processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2022.acl-demo.12": {"snippet": "...a\n\n\n Natural Language Processing (NLP)'s applied nature makes it necessary to select the most effective and robust models. However, just producing slightly higher performance is insufficient; we want ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [59, 64, 1.0]]}, "J80-2001": {"snippet": "...ward Natural Language Computation {I}\n\n\n A computer programming system called the \"Natural Language Computer\" (NLC) is described which allows a user to type English commands while watching them execut...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [86, 93, 1.0], [94, 102, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.17": {"snippet": "...with natural language is a fundamental challenge for computer vision and natural language processing. Recently, progress in this problem has been achieved through two steps: 1) employing 2-D and/or 3-...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [76, 83, 1.0], [84, 92, 1.0], [93, 103, 1.0]]}, "L06-1315": {"snippet": "Natural Language Processing: A Terminological and Statistical Approach", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "A88-1026": {"snippet": "... the natural language interface. Luke also makes natural language available in its own user interface. This makes it possible for a knowledge base builder to exploit natural language both as a way of ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [46, 51, 1.0], [52, 59, 1.0], [60, 68, 1.0], [111, 116, 1.0], [169, 176, 1.0], [177, 185, 1.0]]}, "2021.teachingnlp-1.22": {"snippet": "...k on Natural Language Processing\n\n\n There are thousands of papers about natural language processing and computational linguistics, but very few textbooks. I describe the motivation and process for wri...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [75, 82, 1.0], [83, 91, 1.0], [92, 102, 1.0]]}, "1982.sigirjournals_journal-ir0volumeA17A3.3": {"snippet": "...3\n\n\n Natural language processing techniques developed for Artificial Intelligence programs can aid in constructing powerful information retrieval systems in at least two areas. Automatic construction ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "U05-1027": {"snippet": "...cted Natural Language\n\n\n We argue it is better to program in a natural language such as English, instead of a programming language like Java. A natural language interface for programming should result...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [66, 73, 1.0], [74, 82, 1.0], [125, 133, 1.0], [147, 154, 1.0], [155, 163, 1.0]]}, "W13-0206": {"snippet": "...s in natural language semantics have been characterized in regular languages, with strings representing sequences of temporal observations. We extend this approach to natural language constructions wh...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [170, 177, 1.0], [178, 186, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...t of natural language processing techniques. Different groups focused on different aspects of the overall problem, ranging from the development of language-independent tools such as stemmers to much w...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [150, 158, 1.0]]}, "H94-1119": {"snippet": "...h in Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2001.ipm_journal-ir0volumeA37A6.0": {"snippet": "Natural language processing in support of decision-making: phrases and part-of-speech tagging", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "1995.sigirjournals_journal-ir0volumeA29A2.2": {"snippet": "...ion: Natural Language Processing and Information Retrieval Group Information Access and User Interfaces Division National Institute of Standards and Technology\n\n\n OverviewThe Natural Language Processi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [178, 185, 1.0], [186, 194, 1.0], [195, 205, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.0": {"snippet": "...y in Natural Language Processing - Colocated with WSDM 2020, Houston, USA, Feb 7, 2020", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "A88-1034": {"snippet": "...l in natural language processing systems which makes use of canonical structures as a way of collapsing multiple analyses in individual components. We give an overview here of how the Lucy natural lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [50, 55, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "2017.tois_journal-ir0volumeA35A3.10": {"snippet": "...s in natural language processing. Many neural network models such as Convolutional Neural Network (CNN), Recursive Neural Network (RNN), and Long Short-Term Memory (LSTM) have been proposed to learn r...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "H93-1101": {"snippet": "...h in Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "H86-1007": {"snippet": "...y of natural language processing outside of a laboratory setting. The paper will be most valuable to those engaged in applying state-of-the-art techniques to deliver natural language interfaces and to...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [169, 176, 1.0], [177, 185, 1.0]]}, "P15-1120": {"snippet": "Can Natural Language Processing Become Natural Language Coaching?\n\n\n How we teach and learn is undergoing a revolution, due to changes in technology and connectivity. Education may be one of the best ...", "weights": [[4, 11, 1.0], [12, 20, 1.0], [21, 31, 1.0], [39, 46, 1.0], [47, 55, 1.0]]}, "2019.ipm_journal-ir0volumeA56A4.14": {"snippet": "Exploring the group holiday decision-making process with the support of technology\n\n\n A B S T R A C TNumerous studies have examined the decision-making process and the associated information search be...", "weights": []}, "J75-4040": {"snippet": "A Natural Language Processing Package\n\n\n A set of SAIL programs has been implemented for analyzing large bodies of natural language data in which associations exist between strings and sets of strings...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [19, 29, 1.0], [115, 122, 1.0], [123, 131, 1.0]]}, "W18-3719": {"snippet": "...ncy: Natural Language Processing for Translator Training\n\n\n This study explores the use of natural language processing techniques to enhance bilingual lexical access beyond simple equivalents, to enab...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [94, 101, 1.0], [102, 110, 1.0], [111, 121, 1.0]]}, "W17-8106": {"snippet": "Natural Language Processing in Political Campaigns", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2020.emnlp-demos.6": {"snippet": "...-Art Natural Language Processing\n\n\n Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilita...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [58, 65, 1.0], [66, 74, 1.0], [75, 85, 1.0]]}, "2017.sigirconf_conference-2017.255": {"snippet": "...ding natural language processing, information extraction, knowledge base construction and population, reasoning, and question answering. With the goal to join forces in the collaborative development o...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2010.cikm_conference-2010.240": {"snippet": "...g of natural language processing\n\n\n ABSTRACTWe present a new framework for feature engineering of natural language processing that is based on a relational data model of text. It includes fast and fle...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [101, 108, 1.0], [109, 117, 1.0], [118, 128, 1.0]]}, "M95-1021": {"snippet": "...UNO} Natural Language Processing System as Used for {MUC}-6\n\n\n The UNO natural language processing (NLP) system implements a Boolean algebra computational model o f natural language [Iwadska, 1992 ] [...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [74, 81, 1.0], [82, 90, 1.0], [91, 101, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "2018.ecir_conference-2018.70": {"snippet": "...many natural language processing tasks, which can lead to enhanced performance and execution time. Many existing methods either rely on a predefined list of stopwords or compute word significance base...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2004.wwwconf_workshop-2004sw.12": {"snippet": "...ence makes it appropriate for usage in scenarios involving distributed and collaborative design and manufacturing process. The use of OWL and Process Grammar makes SOM very suitable for coordination o...", "weights": [[8, 13, 1.0], [161, 166, 1.0]]}, "P16-4010": {"snippet": "...n of language and other modalities, most notably vision, is becoming increasingly important in natural language processing. We introduce a toolkit that can be used to obtain feature representations fo...", "weights": [[8, 16, 1.0], [98, 105, 1.0], [106, 114, 1.0], [115, 125, 1.0]]}, "W08-0506": {"snippet": "...n in Natural Language Processing\n\n\n It is a widely accepted belief in natural language processing research that naturally occurring data is the best (and perhaps the only appropriate) data for testing...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [73, 80, 1.0], [81, 89, 1.0], [90, 100, 1.0]]}, "L18-1025": {"snippet": "...s in natural language processing papers, can account for a variety of types of research reports. These dimensions are reproducibility of a conclusion, of a finding, and of a value. Three biomedical na...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [201, 208, 1.0]]}, "H89-2078": {"snippet": "...r on Natural Language Processing\n\n\n I. Scope Major Challenges We take the ultimate goal of natural language processing (NLP) to be the ability to use natural languages as effectively as humans do. Nat...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [94, 101, 1.0], [102, 110, 1.0], [111, 121, 1.0], [153, 160, 1.0], [200, 207, 1.0]]}, "Y09-1039": {"snippet": "...nal) Natural Language Processing: A Cognitive Perspective\n\n\n In this paper I aim at sketching out in bare outline a new model/framework of language processing with its implications for natural languag...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [142, 150, 1.0], [151, 161, 1.0], [188, 195, 1.0], [196, 204, 1.0]]}, "P92-1029": {"snippet": "...ased Natural Language Processing with Neural Networks\n\n\n This paper describes a natural language processing system reinforced by the use of association of words and concepts, implemented as a neural n...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [83, 90, 1.0], [91, 99, 1.0], [100, 110, 1.0]]}, "2016.fire_conference-2016w.53": {"snippet": "...ce \u2192 Natural language processing \u2192 Language resources Computing methodologies \u2192 Artificial intelligence \u2192 Natural language processing \u2192 Lexical semantics", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [38, 46, 1.0], [109, 116, 1.0], [117, 125, 1.0], [126, 136, 1.0]]}, "W05-1613": {"snippet": "...s in natural language. Natural language directed inference represents an interesting challenge to research in automated reasoning and natural language processing.", "weights": [[8, 15, 1.0], [16, 24, 1.0], [26, 33, 1.0], [34, 42, 1.0], [137, 144, 1.0], [145, 153, 1.0], [154, 164, 1.0]]}, "C69-4101": {"snippet": "...ated Language Processing\n\n\n This paper is concerned with natural language, computers, and two groups of people interested in natural language: ]inguists, and persons engaged in computer processing of ...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [60, 67, 1.0], [68, 76, 1.0], [128, 135, 1.0], [136, 144, 1.0], [189, 199, 1.0]]}, "2017.wsdm_conference-2017.32": {"snippet": "...s in Natural Language Processing\n\n\n ABSTRACTIncreasingly central to online search and information discovery are methods from Natural Language Processing (NLP). Research in the field, however, is progr...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [128, 135, 1.0], [136, 144, 1.0], [145, 155, 1.0]]}, "2007.sigirconf_conference-2007.3": {"snippet": "...NLP -natural language processing -or NLIP -processing information conveyed in natural language. At that time, most researchers thought primarily of the efficiency gains that could be Copyright is held...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [46, 56, 1.0], [81, 88, 1.0], [89, 97, 1.0]]}, "2011.cikm_conference-2011.257": {"snippet": "...ars, natural language processing techniques have been used more and more in IR. Among other syntactic and semantic parsing are effective methods for the design of complex applications like for example...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2020.emnlp-main.703": {"snippet": "...e\n\n\n Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness...", "weights": [[8, 16, 1.0], [76, 84, 1.0]]}, "2020.cikm_conference-2020.67": {"snippet": "...d in natural language and convert it into forms that can be more easily and precisely processed by computers. Knowledge Graphs (KGs) offer offer a good solution for such processing. Natural Language P...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [173, 183, 1.0], [185, 192, 1.0], [193, 201, 1.0], [202, 212, 1.0]]}, "1985.tois_journal-ir0volumeA3A2.1": {"snippet": "...LSP) natural language processing system has been developed as a domain-independent natural language processing system. Initially utilized for processing sets of medical messages and other texts in the...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [86, 93, 1.0], [94, 102, 1.0], [103, 113, 1.0], [145, 155, 1.0]]}, "J89-3012": {"snippet": "...ews: Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "N12-4008": {"snippet": "...s in natural language processing problems often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate what assignments ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "P16-2043": {"snippet": "Natural Language Generation enhances human decision-making with uncertain information\n\n\n Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilit...", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "W05-0101": {"snippet": "...lied Natural Language Processing: Triumphs and Tribulations\n\n\n In Fall 2004 I introduced a new course called Applied Natural Language Processing, in which students acquire an understanding of which te...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [120, 127, 1.0], [128, 136, 1.0], [137, 147, 1.0]]}, "2017.clef_conference-2017.9": {"snippet": "...o CM language pairs: Hinglish (Matrix language: Hindi, Embedded language: English) and Tenglish (Matrix language: Telugu, Embedded language: English). Lack of language resources such as annotated corp...", "weights": [[8, 16, 1.0], [41, 49, 1.0], [67, 75, 1.0], [107, 115, 1.0], [134, 142, 1.0], [162, 170, 1.0]]}, "2004.ipm_journal-ir0volumeA40A6.4": {"snippet": "... the natural language processing (NLP) system for improving the system. Therefore, the necessity of co-occurrence word information in the natural language processing system increases further and vario...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [141, 148, 1.0], [149, 157, 1.0], [158, 168, 1.0]]}, "H94-1109": {"snippet": "...h in Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "J86-2001": {"snippet": "...y, a natural language interface should be constructed in accord with principles of integrated processing: processing syntax and semantics at the same time, processing syntax and semantics using the sa...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [97, 107, 1.0], [109, 119, 1.0], [159, 169, 1.0]]}, "C14-1088": {"snippet": "...uage processing\n\n\n This paper proposes an architecture for an open-domain conversational system and evaluates an implemented system. The proposed architecture is fully composed of modules based on nat...", "weights": [[8, 18, 1.0], [200, 207, 1.0]]}, "2016.wwwconf_conference-2016.82": {"snippet": "...s in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text un...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "1983.sigirconf_conference-83.4": {"snippet": "...sing natural language processing techniques to conceptually analyze the documents being stored in a system greatly expands the effectiveness of queries about given pieces of text. However, only robust...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "T87-1023": {"snippet": "Natural Language Processing: What{'}s Really Involved?", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "W15-5955": {"snippet": "Natural Language Processing for Solving Simple Word Problems\n\n\n This paper describes our system which solves simple arithmetic word problems. The system takes a word problem described in natural langu...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [187, 194, 1.0], [195, 203, 1.0]]}, "P14-6001": {"snippet": "... for Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "T87-1044": {"snippet": "...}\n\n\n Natural language understanding and natural language generation could employ the same knowledge of language. 1 They could even represent their knowledge in the same way, provided that it was a non...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [43, 50, 1.0], [51, 59, 1.0], [106, 114, 1.0]]}, "2013.wwwconf_conference-2013c.133": {"snippet": "...into natural language. In addition to generating verbalizations, our approach can also explain the output of queries by providing a natural-language description of the reasons that led to each element...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [135, 142, 1.0], [143, 151, 1.0]]}, "2018.sigirconf_conference-2018.88": {"snippet": "...ces. Natural language interfaces, exemplified by virtual assistants like Apple Siri and Microsoft Cortana, are widely believed to be a promising direction. However, current natural language interfaces...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [176, 183, 1.0], [184, 192, 1.0]]}, "2006.claw-1.5": {"snippet": "...lled Natural Language\n\n\n In this paper, we present PENG Online, a web-based authoring tool which allows authors to write RSS feeds in a machine-processable controlled natural language. The authoring t...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [170, 177, 1.0], [178, 186, 1.0]]}, "E91-1043": {"snippet": "... for Natural Language Processing\n\n\n In this paper* I will argue for a model of grammatical processing that is based on uniform processing and knowledge sources. The main feature of this model is to vi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [94, 104, 1.0], [130, 140, 1.0]]}}}
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It is tackled in a general situation where the relevance predictions of individual documents have uncertain...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1998.tois_journal-ir0volumeA16A4.1": {"snippet": "...ntic Indexing Information Retrieval\n\n\n The vast amount of textual information available today is useless unless it can be effectively and efficiently searched. The goal in information retrieval is to ...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [69, 80, 1.0], [175, 186, 1.0], [187, 196, 1.0]]}, "W99-1009": {"snippet": "...\n An indexing tool was built to provide for one of several information seeking tasks. In ac cordance with the basic principles of work held by the HUMLE laboratory at SICS, a so lution regarding index...", "weights": [[8, 16, 1.0], [62, 73, 1.0], [198, 206, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.3": {"snippet": "...lop' information retrieval beyond the mere keyword approach. In the closing pages of my earlier book on Information Retrieval (Van Rijsbergen, 1979) I said the following: \"It has never been assuned th...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [107, 118, 1.0], [119, 128, 1.0]]}, "2021.wsdm_conference-2021.148": {"snippet": "...king Principle: Modeling the Dependencies among Documents\n\n\n Probability Ranking Principle (PRP) [30], which assumes that each document has a unique and independent probability to satisfy a particular...", "weights": [[8, 17, 1.0], [84, 93, 1.0]]}, "2010.tois_journal-ir0volumeA28A1.0": {"snippet": "...s\n\n\n Information retrieval (IR) systems typically compress their indexes in order to increase their efficiency. Static pruning is a form of lossy data compression: it removes from the index, data that...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2006.jasis_journal-ir0volumeA57A10.13": {"snippet": "Information retrieval design: Principles and options for information description, organization, display, and access in information retrieval databases, digital libraries, catalogs, and indexes", "weights": [[0, 11, 1.0], [12, 21, 1.0], [57, 68, 1.0], [119, 130, 1.0], [131, 140, 1.0]]}, "2009.cikm_conference-2009.47": {"snippet": "... for Information Retrieval systems. The new model is based on partitioning of the inverted index across the terms in it. We exploit the query log to partition the on-disk inverted index into two types...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1986.sigirconf_conference-86.34": {"snippet": "...ropy Principle in Information Retrieval\n\n\n ABSTRACTApplications, assumptions and properties of the maximum entropy principle ate discussed. The maximum", "weights": [[8, 17, 1.0], [21, 32, 1.0], [33, 42, 1.0], [118, 127, 1.0]]}, "N09-1020": {"snippet": "... for Information Retrieval\n\n\n We propose a principled probabilisitc framework which uses trees over the vocabulary to capture similarities among terms in an information retrieval setting. This allows ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [160, 171, 1.0], [172, 181, 1.0]]}, "1998.sigirconf_conference-98.42": {"snippet": "...ment Indexing: A Linguistic Contribution to Knowledge-Based IR\n\n\n AbstractThis poster presents Condorcet, a domain-specific prototype indexing system for tens of thousands of documents covering two sc...", "weights": [[8, 16, 1.0], [137, 145, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.22": {"snippet": "...ased retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic i...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [202, 213, 1.0]]}, "2005.sigirconf_conference-2005.35": {"snippet": "...ntic indexing\n\n\n ABSTRACTLatent semantic indexing (LSI) is a well-known unsupervised approach for dimensionality reduction in information retrieval. However if the output information (i.e. category la...", "weights": [[8, 16, 1.0], [44, 52, 1.0], [129, 140, 1.0], [141, 150, 1.0], [174, 185, 1.0]]}, "1986.sigirconf_conference-86.31": {"snippet": "...s of Retrieval with Probabilistic Indexing\n\n\n AbstractWe describe two retrieval modeb for probabilistic indexing. The binary independence indexing (BII)  model is a generalised version of the Maron & ...", "weights": [[8, 17, 1.0], [37, 45, 1.0], [73, 82, 1.0], [107, 115, 1.0], [141, 149, 1.0]]}, "W89-0207": {"snippet": "...ting principle-based syntactic theories directly in a parsing algorithm in order to determ ine whether or not a principle-based parsing algorithm can be com patible with psycholinguistic evidence. Pri...", "weights": [[8, 17, 1.0], [115, 124, 1.0], [200, 209, 1.0]]}, "2020.sigirconf_conference-2020.279": {"snippet": "...ment indexing is a key component for efficient information retrieval (IR). After preprocessing steps such as stemming and stopword removal, document indexes usually store term-frequencies (tf). Along ...", "weights": [[8, 16, 1.0], [50, 61, 1.0], [62, 71, 1.0]]}, "2013.cikm_conference-2013.240": {"snippet": "...tion indexing: fast approximate retrieval from large corpora\n\n\n ABSTRACTInverted indexing is a ubiquitous technique used in retrieval systems including web search. Despite its popularity, it has a dra...", "weights": [[8, 16, 1.0], [35, 44, 1.0], [84, 92, 1.0], [127, 136, 1.0]]}, "2011.sigirconf_conference-2011.58": {"snippet": "...s in information retrieval. For each unique word occurring in a document collection, the inverted index stores a list of the documents in which this word occurs. Compression techniques are often appli...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1998.sigirconf_conference-98.34": {"snippet": "...h to Information Retrieval\n\n\n AbstractModels of document indexing and document retrieval have been extensively studied. The integration of these two classes of models has been the goal of several rese...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [60, 68, 1.0], [82, 91, 1.0]]}, "1993.cikm_conference-93.22": {"snippet": "...Join indexing is needed for the efficient maintenance of such rule-derived daa but existing join indexing methods are not suitable for this purpose. We develop a family of join indexing techniques --j...", "weights": [[8, 16, 1.0], [100, 108, 1.0], [180, 188, 1.0]]}, "P99-1045": {"snippet": "...ring indexing for information retrieval. By eliminating index terms that occur only in subordinate clauses, index size is reduced by approximately 30% without adversely affecting precision or recall. ...", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0]]}, "W03-2001": {"snippet": "...ment Retrieval System Addressing Both Semantic and Syntactic Properties\n\n\n Combining the principle of Differential Latent Semantic Index (DLSI) (Chen et  al., 2001)  and the Template Matching Techniqu...", "weights": [[8, 17, 1.0], [92, 101, 1.0]]}, "2006.clef_workshop-2006.122": {"snippet": "...ased Indexing Technique for Geographical Information Retrieval", "weights": [[8, 16, 1.0], [44, 55, 1.0], [56, 65, 1.0]]}, "N07-5002": {"snippet": "... for Information Retrieval\n\n\n Statistical language models have recently been successfully applied to many information retrieval problems. A great deal of recent work has shown that statistical languag...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "2004.sigirconf_conference-2004.14": {"snippet": "... and indexing is a key problem for document analysis and processing, such as clustering, classification and retrieval. Conventionally, Latent Semantic Indexing (LSI) is considered effective in derivin...", "weights": [[8, 16, 1.0], [111, 120, 1.0], [154, 162, 1.0]]}, "1996.cikm_conference-96.18": {"snippet": "...ntic Indexing into a Neural Network Model for Information Retrieval", "weights": [[8, 16, 1.0], [49, 60, 1.0], [61, 70, 1.0]]}, "2001.sigirconf_conference-2001.55": {"snippet": "...ntic Indexing (FLSI)\n\n\n ABSTRACTLatent Semantic Indexing (LSI) dramatically reduces the dimension of the document space by mapping it into a space spanned by conceptual indices. Empirically, the numbe...", "weights": [[8, 16, 1.0], [51, 59, 1.0]]}, "2013.sigirconf_conference-2013.18": {"snippet": "An information-theoretic account of static index pruning\n\n\n ABSTRACTIn this paper, we recast static index pruning as a model induction problem under the framework of Kullback's principle of minimum cr...", "weights": [[3, 14, 1.0], [177, 186, 1.0]]}, "2010.sigirconf_conference-2010.123": {"snippet": "... for Information Retrieval. However, under non-trivial conditions PT violates the original Probability Ranking Principle (PRP). In this poster, we shall explore whether PT upholds a different ranking ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [114, 123, 1.0]]}, "2000.ir_journal-ir0volumeA2A4.2": {"snippet": "... for Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1986.tc-1.6": {"snippet": "... the information to manageable proportions. Information overload and underload are facts of life, and any methods which can help to optimise information retrieval are of value. In this presentation, I...", "weights": [[8, 19, 1.0], [47, 58, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "2012.trec_conference-2012.60": {"snippet": "...Page Indexing\n\n\n Indexing is a crucial technique for dealing with the massive amount of data present on the web. In our third participation in the web track at TREC 2012, we explore the idea of buildi...", "weights": [[8, 16, 1.0], [20, 28, 1.0]]}, "2007.sigirconf_conference-2007.68": {"snippet": "A time machine for text search\n\n\n ABSTRACTText search over temporally versioned document collections such as web archives has received little attention as a research problem. As a consequence, there i...", "weights": []}, "2011.sigirjournals_journal-ir0volumeA45A2.15": {"snippet": "... XML retrieval techniques to the EAD finding aids, develop system evaluation of EAD retrieval, and study information seeking behavior of archival search. The main information retrieval (IR) contributi...", "weights": [[8, 17, 1.0], [87, 96, 1.0], [108, 119, 1.0], [166, 177, 1.0], [178, 187, 1.0]]}, "2007.sigirconf_conference-2007.86": {"snippet": "...s in information retrieval and databases. Efficient indexing for this operation is known to be a hard problem for arbitrary data distributions. However, text corpora used in Information Retrieval appl...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [55, 63, 1.0], [177, 188, 1.0], [189, 198, 1.0]]}, "2010.cikm_conference-2010.10": {"snippet": "...g to information retrieval\n\n\n ABSTRACTThe probabilistic formalism of quantum physics is said to provide a sound basis for building a principled information retrieval framework. Such a framework can be...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "2015.ictir_conference-2015.38": {"snippet": "...rted indexing. In this paper, we explore a radical proposition: What if we abandon document inversion and instead adopt an architecture based on brute force scans of document representations? In such ...", "weights": [[8, 16, 1.0]]}, "1991.sigirconf_conference-91.23": {"snippet": "...uted Indexing: A Scalable Mechanism for Distributed Information Retrieval", "weights": [[8, 16, 1.0], [55, 66, 1.0], [67, 76, 1.0]]}, "2005.clef_workshop-2005.11": {"snippet": "...tual Indexing for Multilingual Information Retrieval", "weights": [[8, 16, 1.0], [34, 45, 1.0], [46, 55, 1.0]]}, "2015.tois_journal-ir0volumeA33A4.3": {"snippet": "...back Retrieval\n\n\n The inverted index is the dominant indexing method in information retrieval systems. It enables fast return of the list of all documents containing a given query term. However, for r...", "weights": [[8, 17, 1.0], [56, 64, 1.0], [75, 86, 1.0], [87, 96, 1.0], [202, 211, 1.0]]}, "2006.cikm_conference-2006.111": {"snippet": "...rted indexing, TFIDF and Latent Semantic Indexing(LSI)) to content-based brain image retrieval. Our results show that efficient and accurate retrieval of brain images is possible, and that representat...", "weights": [[8, 16, 1.0], [44, 52, 1.0], [88, 97, 1.0], [144, 153, 1.0]]}, "P13-4024": {"snippet": "...file indexing, PoS information from BNC, and semantic indexing based on Latent Dirichlet Allocation with Google Web 1T. The method involves parsing the query to transforming it into several keyword re...", "weights": [[8, 16, 1.0], [22, 33, 1.0], [57, 65, 1.0], [201, 210, 1.0]]}, "1997.ecir_conference-1997.2": {"snippet": "... for Information Retrieval Indexing", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 38, 1.0]]}, "2012.sigirconf_conference-2012.220": {"snippet": "...sual information retrieval using Java and LIRE\n\n\n ABSTRACTVisual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotat...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [68, 79, 1.0], [80, 89, 1.0], [186, 194, 1.0]]}, "1999.cikm_conference-99.17": {"snippet": "... (or principle) for model selection in learning. Our method is unique in that decision lists are constructed on the basis of the principle of minimizing a quantity called Extended Stochastic Complexit...", "weights": [[8, 17, 1.0], [132, 141, 1.0]]}, "2011.ictir_conference-2011.15": {"snippet": "... and Retrieval Strategies", "weights": [[8, 17, 1.0]]}, "2003.spire_conference-2003.11": {"snippet": "...irst principle, in linear time. The algorithm employs a suitable index structure for a text, and involves technically efficient operations on the structure.", "weights": [[8, 17, 1.0]]}, "2008.ipm_journal-ir0volumeA44A5.1": {"snippet": "...tThe principle of polyrepresentation offers a theoretical framework for handling multiple contexts in information retrieval (IR). This paper presents an empirical laboratory study of polyrepresentatio...", "weights": [[8, 17, 1.0], [105, 116, 1.0], [117, 126, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.8": {"snippet": "...sfer principle, the j-index satisfies the opposite transfer principle while the h-and R-indices do not satisfy any of these principles. We study general inequalities between these measures and also de...", "weights": [[8, 17, 1.0], [63, 72, 1.0]]}, "1991.jasis_journal-ir0volumeA42A10.2": {"snippet": "...king principle in information retrieval", "weights": [[8, 17, 1.0], [21, 32, 1.0], [33, 42, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.7": {"snippet": "...rent indexing languages\n\n\n AbstractAn integrated information retrieval system generally contains multiple databases that are inconsistent in terms of their content and indexing. This paper proposes a ...", "weights": [[8, 16, 1.0], [52, 63, 1.0], [64, 73, 1.0], [171, 179, 1.0]]}, "2004.spire_conference-2004.15": {"snippet": "Indexing Text Documents Based on Topic Identification\n\n\n Abstract. This work provides algorithms and heuristics to index text documents by determining important topics in the documents. To index text ...", "weights": [[0, 8, 1.0]]}, "1993.sigirconf_conference-93.14": {"snippet": "...Text Retrieval System\n\n\n AbstractThere has been a great deal of interest within the Information Retrieval community in evahrating the use of linguistic knowledge to improve the indexing and searching ...", "weights": [[8, 17, 1.0], [87, 98, 1.0], [99, 108, 1.0], [180, 188, 1.0]]}, "2004.sigirconf_conference-2004.13": {"snippet": "...able indexing and ranking for XML information retrieval\n\n\n ABSTRACTIndexing and ranking are two key factors for efficient and effective XML information retrieval. Inappropriate indexing may result in ...", "weights": [[8, 16, 1.0], [37, 48, 1.0], [49, 58, 1.0], [143, 154, 1.0], [155, 164, 1.0], [180, 188, 1.0]]}, "P90-1014": {"snippet": "... The principle known as 'free indexation' plays an important role in the determination of the referential properties of noun phrases in the principleand-parameters language framework. First, by invest...", "weights": [[8, 17, 1.0]]}, "2011.sigirconf_conference-2011.219": {"snippet": "... for information retrieval: principles, methods, and applications", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2007.sigirconf_conference-2007.88": {"snippet": "...text retrieval with a P2P query-driven index\n\n\n ABSTRACTIn this paper, we present a query-driven indexing/retrieval strategy for efficient full text retrieval from large document collections distribut...", "weights": [[8, 17, 1.0], [100, 108, 1.0], [109, 118, 1.0], [152, 161, 1.0]]}, "2011.cikm_conference-2011.406": {"snippet": "...ical information retrieval modelling: from the probability ranking principle to recent advances in diversity, portfolio theory, and beyond\n\n\n ABSTRACTStatistical modelling of Information Retrieval (IR...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [70, 79, 1.0], [178, 189, 1.0], [190, 199, 1.0]]}, "2018.sigirconf_conference-2018.141": {"snippet": "...call Principle for Recommender Systems\n\n\n ABSTRACTWe revisit the Probability Ranking Principle in the context of recommender systems. We find a key difference in the retrieval protocol with respect to...", "weights": [[8, 17, 1.0], [88, 97, 1.0], [169, 178, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ment retrieval. The shot retrieval and the video segment model re-use the probability of relevance and a language modeling ranking function respectively, basing themselves on decades of text retrieval...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [194, 203, 1.0]]}, "1998.jasis_journal-ir0volumeA49A6.5": {"snippet": "...ropy Principle for Information Retrieval", "weights": [[8, 17, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "Y03-1031": {"snippet": "...word Indexing with Maximum Entropy Principle\n\n\n In our information era, keywords are very useful to information retrieval, text clustering and so on. News is always a domain attracting a large amount ...", "weights": [[8, 16, 1.0], [38, 47, 1.0], [58, 69, 1.0], [103, 114, 1.0], [115, 124, 1.0]]}, "2005.trec_conference-2005.27": {"snippet": "...cale Information Retrieval\n\n\n We describe indexing and retrieval techniques that are suited to perform terabyte-scale information retrieval tasks on a standard desktop PC. Starting from an Okapi-BM25-...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [45, 53, 1.0], [58, 67, 1.0], [121, 132, 1.0], [133, 142, 1.0]]}, "2000.cikm_conference-2000.29": {"snippet": "... New Indexing Scheme for High-Dimensional Data\n\n\n ABSTRACTThis paper presents a new indexing scheme, the Subspace Coding Method (SCM), that offers high performance similarity retrieval. It is based on...", "weights": [[8, 16, 1.0], [87, 95, 1.0], [178, 187, 1.0]]}, "2016.jasis_journal-ir0volumeA67A1.0": {"snippet": "...atic indexing or classification in the context of retrieval", "weights": [[8, 16, 1.0], [53, 62, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.25": {"snippet": "...line information retrieval concepts, principles, and techniques", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2009.ictir_conference-2009.21": {"snippet": "...king Principle for Information Retrieval", "weights": [[8, 17, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2015.ictir_conference-2015.44": {"snippet": "... the Principle of Polyrepresentation in MIR\n\n\n ABSTRACTThe principle of polyrepresentation -a representative of the cognitive viewpoint on IR, takes a holistic perspective on interactive IR research.O...", "weights": [[8, 17, 1.0], [62, 71, 1.0]]}, "2008.cikm_conference-2008.45": {"snippet": "...onal information retrieval (IR) approaches assume that the indexing terms are independent, which is not true in reality. Although some previous studies have tried to consider term relationships, stron...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [62, 70, 1.0]]}, "2011.cikm_workshop-2011pikm.2": {"snippet": "... the principle of polyrepresentation\n\n\n ABSTRACTRecently, the cognitively motivated principle of polyrepresentation has been shown to correlate with quantum mechanics-inspired IR models. The principle...", "weights": [[8, 17, 1.0], [87, 96, 1.0], [194, 203, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.11": {"snippet": "... web retrieval effectiveness\n\n\n AbstractThis paper talks about several schemes for improving retrieval effectiveness that can be used in the named page finding tasks of web information retrieval (Over...", "weights": [[8, 17, 1.0], [96, 105, 1.0], [176, 187, 1.0], [188, 197, 1.0]]}, "2006.sigirconf_conference-2006.57": {"snippet": "...lly, information retrieval systems aim to maximize the number of relevant documents returned to a user within some window of the top. For that goal, the probability ranking principle, which ranks docu...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [176, 185, 1.0]]}, "2010.cikm_conference-2010.109": {"snippet": "...rted indexing for feedback and expansion\n\n\n ABSTRACTTraditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a list...", "weights": [[8, 16, 1.0], [79, 90, 1.0], [91, 100, 1.0]]}, "1983.jasis_journal-ir0volumeA34A4.14": {"snippet": "...s in information retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2008.ir_journal-ir0volumeA11A3.3": {"snippet": "...king principle for interactive information retrieval", "weights": [[8, 17, 1.0], [34, 45, 1.0], [46, 55, 1.0]]}, "1977.jasis_journal-ir0volumeA28A1.5": {"snippet": "On indexing, retrieval and the meaning of about", "weights": [[3, 11, 1.0], [13, 22, 1.0]]}, "2011.jasis_journal-ir0volumeA62A1.5": {"snippet": "...dge: Indexing and information retrieval as an example", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0]]}}}
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Theory and background", "weights": [[8, 11, 1.0], [12, 18, 1.0]]}, "2012.wwwconf_workshop-2012dec3d.6": {"snippet": "...D in Web Architecture\n\n\n The recent WebGL integration in major web browser has open the way to many 3D applications as well as high-level libraries targeting 3D content developers. While most of these...", "weights": [[8, 11, 1.0], [12, 24, 1.0], [66, 69, 1.0]]}, "2008.cikm_workshop-2008lsdsir.1": {"snippet": "Search engine architectures from conventional to Brian Ulicny and Ken Baclawski. New metrics for newsblog\n\n\n ABSTRACTA significant amount of research has focused on the problem of implementing large-s...", "weights": [[0, 6, 1.0], [7, 13, 1.0]]}, "W03-1115": {"snippet": "...onal search engines based on a centralized architecture to retrieve fresh information, because they take a long time to collect documents via Web robots. 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It offers a personalized,...", "weights": [[0, 12, 1.0], [31, 34, 1.0], [35, 41, 1.0], [42, 48, 1.0], [67, 73, 1.0], [125, 131, 1.0], [132, 138, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.20": {"snippet": "...edia Web search functionality is generally limited. Our findings show that despite the increasing level of interest in multimedia Web search, those few Web search engines offering multimedia Web searc...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [133, 136, 1.0], [137, 143, 1.0], [155, 158, 1.0], [159, 165, 1.0], [194, 197, 1.0], [198, 204, 1.0]]}, "2012.wwwconf_workshop-2012dec3d.8": {"snippet": "... the Web Architecture\n\n\n In this paper we present a guide for writing use cases for the Declarative 3D for the Web Architecture, use cases where embedding 3D data in HTML using declarative approach pr...", "weights": [[8, 11, 1.0], [12, 24, 1.0], [114, 117, 1.0], [118, 130, 1.0]]}, "2016.sigirconf_conference-2016.224": {"snippet": "...cale web search engines. In particular, the tutorial provides an in-depth architectural overview of a web search engine, mainly focusing on the web crawling, indexing, and query processing components....", "weights": [[8, 11, 1.0], [12, 18, 1.0], [105, 108, 1.0], [109, 115, 1.0], [116, 122, 1.0], [147, 150, 1.0]]}, "1999.cikm_conference-99.28": {"snippet": "Architecture of a Metasearch Engine That Supports User Information Needs\n\n\n AbstractWhen a query is submitted to a metasearch engine, decisions are made with respect to the underlying search engines t...", "weights": [[0, 12, 1.0], [29, 35, 1.0], [126, 132, 1.0], [184, 190, 1.0]]}, "2017.sigirconf_workshop-2017ecom.10": {"snippet": "The Architecture of eBay Search\n\n\n The architecture of a large-scale search engine is dependent on the application. In the case of a web search engine, the document collection can be considered to be ...", "weights": [[4, 16, 1.0], [25, 31, 1.0], [39, 51, 1.0], [69, 75, 1.0], [76, 82, 1.0], [133, 136, 1.0], [137, 143, 1.0], [144, 150, 1.0]]}, "2011.wsdm_conference-2011.48": {"snippet": "...able web sites recommendation. Given a query, our system will recommend a list of searchable web sites ranked by relevance, which can be used to complement the web page results and ads from a search e...", "weights": [[8, 11, 1.0], [96, 99, 1.0], [163, 166, 1.0], [195, 201, 1.0], [202, 208, 1.0]]}, "2014.sigirconf_conference-2014.216": {"snippet": "...cale web search engines\n\n\n ABSTRACTLarge-scale web search engines rely on massive compute infrastructures to be able to cope with the continuous growth of the Web and their user bases. In such search ...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [50, 53, 1.0], [54, 60, 1.0], [162, 165, 1.0], [196, 202, 1.0]]}, "2019.wsdm_conference-2019.110": {"snippet": "... for Web Image Search\n\n\n ABSTRACTWeb-based image search engines di er from Web search engines greatly. The intents or goals behind human interactions with image search engines are di erent. In image s...", "weights": [[8, 11, 1.0], [18, 24, 1.0], [52, 58, 1.0], [78, 81, 1.0], [82, 88, 1.0], [164, 170, 1.0], [202, 208, 1.0]]}, "2010.sigirconf_conference-2010.74": {"snippet": "... the search engine's architecture for rank-time query reduction. Experiments on a large collection of long queries issued to a commercial search engine show that the proposed techniques significantly ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [24, 36, 1.0], [141, 147, 1.0], [148, 154, 1.0]]}, "2008.sigirconf_conference-2008.19": {"snippet": "...ance web search engines\n\n\n ABSTRACTResults caching is an efficient technique for reducing the query processing load, hence it is commonly used in real search engines. This technique, however, bounds t...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [154, 160, 1.0]]}, "2007.wwwconf_conference-HeMLYW07.0": {"snippet": "... the Search Interfaces of the Deep Web\n\n\n Abstract Many databases have become Web-accessible through form-based search interfaces (i.e., HTML forms) that allow users to specify complex and precise que...", "weights": [[8, 14, 1.0], [38, 41, 1.0], [81, 84, 1.0], [115, 121, 1.0]]}, "2009.cikm_conference-2009.1": {"snippet": "...lt a web search engine that is capable of managing 100 million web pages per node in a non-parallel configuration using Odysseus. This engine has been successfully tested in many commercial environmen...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [19, 25, 1.0], [66, 69, 1.0], [138, 144, 1.0]]}, "2012.ipm_journal-ir0volumeA48A5.2": {"snippet": "...ache architecture for web search engines\n\n\n a b s t r a c tCaching is a crucial performance component of large-scale web search engines, as it greatly helps reducing average query response times and q...", "weights": [[8, 20, 1.0], [25, 28, 1.0], [29, 35, 1.0], [120, 123, 1.0], [124, 130, 1.0]]}, "2013.sigirconf_conference-2013.184": {"snippet": "...cale web search engines . In particular, the tutorial provides an in-depth architectural overview of a web search engine, mainly focusing on the web crawling, indexing, query processing, and caching c...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [106, 109, 1.0], [110, 116, 1.0], [117, 123, 1.0], [148, 151, 1.0]]}, "2015.wsdm_conference-2015.26": {"snippet": "...ting Search with Deep Understanding of Contents and Users\n\n\n ABSTRACTRecent years have witnessed dramatic changes in how people interact with search engines. Search engines are expected to be more int...", "weights": [[8, 14, 1.0], [145, 151, 1.0], [161, 167, 1.0]]}, "2005.wwwconf_conference-2005si.46": {"snippet": "... the search engine that works for you\n\n\n ABSTRACTA search engine evaluation model that considers over seventy performance and feature parameters is presented. The design of a web-based system that all...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [54, 60, 1.0], [61, 67, 1.0], [178, 181, 1.0]]}, "2017.wsdm_conference-2017.1": {"snippet": "...e on web search and data mining. We start from its inception during 2006 to the first conference held at Stanford in 2008, driven by some key people in the main web search engines of that time. We wer...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [164, 167, 1.0], [168, 174, 1.0]]}, "2010.wwwconf_conference-2010.44": {"snippet": "...cial search engine like Aardvark lies in finding the right person to satisfy a user's information need. Further, while trust in a traditional search engine is based on authority, in a social search en...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [145, 151, 1.0], [152, 158, 1.0], [194, 200, 1.0], [201, 207, 1.0]]}, "2011.wwwconf_conference-2011c.11": {"snippet": "Web-scale entity-relation search architecture\n\n\n ABSTRACTEnabling entity search and ranking at Web-scale is fraught with many challenges: annotating the corpus with entities and types, query language ...", "weights": [[0, 3, 1.0], [26, 32, 1.0], [33, 45, 1.0], [73, 79, 1.0], [95, 98, 1.0]]}, "2008.ipm_journal-ir0volumeA44A6.3": {"snippet": "... the Web based on thematic contexts can be built on top of a conventional search engine and benefit from the huge amount of content as well as from the functionality available through the search engin...", "weights": [[8, 11, 1.0], [77, 83, 1.0], [84, 90, 1.0], [191, 197, 1.0], [198, 204, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.15": {"snippet": "...peer search. Furthermore, we introduce our own architecture for peer-to-peer search systems, inspired by BitTorrent.Distributing the task of providing search results for queries introduces the problem...", "weights": [[8, 14, 1.0], [50, 62, 1.0], [80, 86, 1.0], [154, 160, 1.0]]}, "2014.tois_journal-ir0volumeA32A4.5": {"snippet": "...ased Search Engine Architectures: An Experimental Study\n\n\n Caching is an important optimization in search engine architectures. Existing caching techniques for search engine optimization are mostly bi...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [102, 108, 1.0], [109, 115, 1.0], [163, 169, 1.0], [170, 176, 1.0]]}, "2014.wwwconf_conference-2014c.40": {"snippet": "...cale web search engines. In particular, the tutorial provides an in-depth architectural overview of a web search engine, mainly focusing on the web crawling, indexing, and query processing components....", "weights": [[8, 11, 1.0], [12, 18, 1.0], [105, 108, 1.0], [109, 115, 1.0], [116, 122, 1.0], [147, 150, 1.0]]}, "2012.wsdm_conference-2012.45": {"snippet": "...zing web search\n\n\n ABSTRACTWe present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorit...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [74, 77, 1.0], [78, 84, 1.0], [135, 138, 1.0], [139, 145, 1.0]]}, "2006.wwwjournals_journal-ir0volumeA9A4.1": {"snippet": "...ords web search . search engine architecture . search engine query processing . inverted index . caching World Wide Web (2006) 9: 369-395", "weights": [[8, 11, 1.0], [12, 18, 1.0], [21, 27, 1.0], [28, 34, 1.0], [35, 47, 1.0], [50, 56, 1.0], [57, 63, 1.0], [119, 122, 1.0]]}, "2001.wwwconf_conference-2001p.86": {"snippet": "Web Query Characteristics and their Implications on Search Engines", "weights": [[0, 3, 1.0], [52, 58, 1.0]]}, "2010.tois_journal-ir0volumeA28A2.0": {"snippet": "...y of search engines: Load-driven routing and incremental caching to reduce and balance the load\n\n\n This article introduces an architecture for a document-partitioned search engine, based on a novel ap...", "weights": [[8, 14, 1.0], [129, 141, 1.0], [169, 175, 1.0], [176, 182, 1.0]]}, "2006.wwwconf_conference-2006.116": {"snippet": "...: an architecture and web service design for an academic document search engine\n\n\n ABSTRACTCiteSeer is a scientific literature digital library and search engine which automatically crawls and indexes ...", "weights": [[8, 20, 1.0], [25, 28, 1.0], [69, 75, 1.0], [76, 82, 1.0], [150, 156, 1.0], [157, 163, 1.0]]}, "2015.ictir_conference-2015.35": {"snippet": "...ined Search Engine in the Browser\n\n\n ABSTRACTJavaScript engines inside modern web browsers are capable of running sophisticated multi-player games, rendering impressive 3D scenes, and supporting compl...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [81, 84, 1.0]]}}}
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Information in t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [33, 44, 1.0], [45, 54, 1.0], [130, 141, 1.0], [187, 198, 1.0]]}, "2020.sigirconf_conference-2020.367": {"snippet": "... for Information Retrieval: Fundamentals and Advances\n\n\n Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users' information needs by suggesting...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [60, 71, 1.0], [72, 81, 1.0], [172, 183, 1.0]]}, "2017.ictir_conference-2017.57": {"snippet": "...s of information retrieval, such as online learning to rank, interactive information retrieval or dynamic information retrieval. ese Permission to make digital or hard copies of part or all of this wo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [76, 87, 1.0], [88, 97, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords Information Retrieval (IR) solutions to retrieve passages matching a set of terms extracted from the question.There is a fundamental disconnect between the capabilities of the bag-of-words retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [197, 206, 1.0]]}, "1981.sigirconf_conference-81.7": {"snippet": "...stic Retrieval\n\n\n ABSTRACTThe objective is to relate the effectiveness of retrieval, the fuzzy set concept and the processing of Boolean query. The use of a probabilistic retrieval scheme is motivated...", "weights": [[8, 17, 1.0], [77, 86, 1.0], [174, 183, 1.0]]}, "2017.airs_conference-2017.16": {"snippet": "... for Information Retrieval Models Over Industry Data Sets\n\n\n Abstract. The development of Information Retrieval (IR) techniques heavily depends on empirical studies over real world data collections. U...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [93, 104, 1.0], [105, 114, 1.0]]}, "2015.ictir_conference-2015.9": {"snippet": "...tive Information Retrieval variant have guided the development of ranking and retrieval algorithms for decades, yet they are not capable of helping us model problems in Dynamic Information Retrieval w...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [81, 90, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "2013.ictir_conference-2013.19": {"snippet": "... for Information Retrieval\n\n\n ABSTRACTOver many years a number of theoretical constructs and models have evolved for the study of information retrieval in its various manifestations. Gradually these h...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2012.tist_journal-ir0volumeA3A4.10": {"snippet": "...edia retrieval. The first three studies deal primarily with comment-targeting retrieval, while the last one deals with commentexploiting retrieval. Throughout the article, connections to information r...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [140, 149, 1.0], [190, 201, 1.0], [202, 211, 1.0]]}, "2014.sigirconf_conference-2014.222": {"snippet": "... for Information Retrieval, where his research interests include: (i) Models for the retrieval of documents, sentences, experts and other information objects ; (ii) Probabilistic models of user intera...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [88, 97, 1.0], [141, 152, 1.0]]}, "2016.ictir_conference-2016.2": {"snippet": "...tric Information Retrieval\n\n\n AbstractGeneral-purpose knowledge bases are increasingly growing in terms of depth (content) and width (coverage). Moreover, algorithms for entity linking and entity retr...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [199, 208, 1.0]]}, "2020.sigirconf_conference-2020.301": {"snippet": "...Fast Retrieval-based Chat-bots\n\n\n Response retrieval is a subset of neural ranking in which a model selects a suitable response from a set of candidates given a conversation history. Retrieval-based c...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [186, 195, 1.0]]}, "2015.ictir_conference-2015.2": {"snippet": "...y of Retrieval: The Retrievability of Information\n\n\n ABSTRACTRetrievability is an important and interesting indicator that can be used in a number of ways to analyse Information Retrieval systems and ...", "weights": [[8, 17, 1.0], [41, 52, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2018.chiir_conference-2018.33": {"snippet": "... the information to write the paper. Information use is another component in information behavior models such as Wilson's [10]  model. In the Information Retrieval (IR) research community, tasks invol...", "weights": [[8, 19, 1.0], [40, 51, 1.0], [80, 91, 1.0], [145, 156, 1.0], [157, 166, 1.0]]}, "2006.jasis_journal-ir0volumeA57A10.13": {"snippet": "Information retrieval design: Principles and options for information description, organization, display, and access in information retrieval databases, digital libraries, catalogs, and indexes", "weights": [[0, 11, 1.0], [12, 21, 1.0], [57, 68, 1.0], [119, 130, 1.0], [131, 140, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2016.sigirconf_conference-2016.36": {"snippet": "...ding Information Need: An fMRI Study\n\n\n ABSTRACTThe raison d'etre of IR is to satisfy human information need. But, do we really understand information need? Despite advances in the past few decades in...", "weights": [[8, 19, 1.0], [95, 106, 1.0], [142, 153, 1.0]]}, "2014.cikm_conference-2014.9": {"snippet": "...ween Retrieval Bias and Retrieval Performance\n\n\n ABSTRACTRetrievability provides an alternative way to assess an Information Retrieval (IR) system by measuring how easily documents can be retrieved. R...", "weights": [[8, 17, 1.0], [27, 36, 1.0], [116, 127, 1.0], [128, 137, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...n of Information Retrieval Models\n\n\n Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is nece...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [61, 70, 1.0], [118, 129, 1.0], [130, 139, 1.0]]}, "2001.ntcir_workshop-2001.29": {"snippet": "On a Retrieval Support System by Suggesting Terms to a User\n\n\n We propose a support method for information retrieval. This method automatically suggests terms relevant to a query, to a user, and when ...", "weights": [[5, 14, 1.0], [95, 106, 1.0], [107, 116, 1.0]]}, "2016.ictir_conference-2016.14": {"snippet": "...y of Information Retrieval Models\n\n\n ABSTRACTDeveloping effective information retrieval models has been a long standing challenge in Information Retrieval (IR), and significant progresses have been ma...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [69, 80, 1.0], [81, 90, 1.0], [136, 147, 1.0], [148, 157, 1.0]]}, "I05-2024": {"snippet": "Information Retrieval Capable of Visualization and High Precision\n\n\n We present a neural-network based selforganizing approach that enables visualization of the information retrieval while at the same...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [161, 172, 1.0], [173, 182, 1.0]]}, "2008.sigirconf_conference-2008.170": {"snippet": "...ment retrieval has shown to be a competitive approach in XML element retrieval, which is counter-intuitive since the element retrieval task requests all and only relevant document parts to be retrieve...", "weights": [[8, 17, 1.0], [72, 81, 1.0], [128, 137, 1.0]]}, "2020.sigirconf_conference-2020.360": {"snippet": "...tive Information Retrieval: Models, Algorithms, and Evaluation\n\n\n Since Information Retrieval (IR) is an interactive process in general, it is important to study Interactive Information Retrieval (IIR...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [75, 86, 1.0], [87, 96, 1.0], [177, 188, 1.0], [189, 198, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.1": {"snippet": "...ased retrieval in language modeling approach\n\n\n AbstractThe term mismatch problem in information retrieval is a critical problem, and several techniques have been developed, such as query expansion, c...", "weights": [[8, 17, 1.0], [88, 99, 1.0], [100, 109, 1.0]]}, "2003.sigirconf_conference-2003.73": {"snippet": "XML retrieval: what to retrieve?\n\n\n ABSTRACTThe fundamental difference between standard information retrieval and XML retrieval is the unit of retrieval. In traditional IR, the unit of retrieval is fi...", "weights": [[4, 13, 1.0], [88, 99, 1.0], [100, 109, 1.0], [118, 127, 1.0], [143, 152, 1.0], [185, 194, 1.0]]}, "2005.clef_workshop-2005w.119": {"snippet": "... All retrieval results rely heavily on two retrieval systems: for visual retrieval we use the GNU Image Finding Tool (GIFT), and for textual retrieval the EasyIR retrieval system. For the ad-hoc retri...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [76, 85, 1.0], [144, 153, 1.0], [165, 174, 1.0], [198, 207, 1.0]]}, "2012.ipm_journal-ir0volumeA48A1.9": {"snippet": "... and information science, the same cannot be said about the subfield of information retrieval. We address that in this work by sketching the information retrieval intellectual landscape through visual...", "weights": [[8, 19, 1.0], [75, 86, 1.0], [87, 96, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "2017.trec_conference-2017.21": {"snippet": "...ular information retrieval application is web search engine such as Google [2] , Baidu [3] , Bing [4] and Sogou [5]. These application will return top-N best retrieval result to users. Information Ret...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [161, 170, 1.0], [188, 199, 1.0], [200, 209, 1.0]]}, "2018.sigirconf_conference-2018.223": {"snippet": "....com.Information retrieval has a natural role to play in e-commerce, especially in connecting people to goods and services. Information discovery in e-commerce concerns different types of search (expl...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [127, 138, 1.0]]}, "1987.jasis_journal-ir0volumeA38A2.2": {"snippet": "...ment retrieval: II. The retrieval process", "weights": [[8, 17, 1.0], [27, 36, 1.0]]}, "1986.sigirjournals_journal-ir0volumeA20A14.4": {"snippet": "... the retrieval processes of document retrieval systems\u00b0 Two basic categories of models that have been employed in information retrieval are the vector processing models and the Boolean retrieval model", "weights": [[8, 17, 1.0], [40, 49, 1.0], [117, 128, 1.0], [129, 138, 1.0], [188, 197, 1.0]]}, "2016.ictir_conference-2016.16": {"snippet": "Retrievability in API-Based \"Evaluation as a Service\"\n\n\n ABSTRACT \"Evaluation as a service\" (EaaS) refers to a family of related evaluation methodologies that enables community-wide evaluations and th...", "weights": []}, "2016.ictir_conference-2016.20": {"snippet": "...t of retrieval systems that performance and efficiency measures do not. Retrievability is often used to calculate the retrievability bias, an indication of how accessible a system makes all the docume...", "weights": [[8, 17, 1.0]]}, "2009.cikm_conference-2009.134": {"snippet": "...opic retrieval\n\n\n ABSTRACTTraditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [63, 72, 1.0], [167, 178, 1.0], [194, 203, 1.0]]}, "2006.airs_conference-2006.36": {"snippet": "...mage Retrieval Using the Insight Toolkit (ITK)\n\n\n Abstract. Visual information retrieval is an emerging domain in the medical field as it has been in computer vision for more than ten years. It has th...", "weights": [[8, 17, 1.0], [70, 81, 1.0], [82, 91, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.2": {"snippet": "... for information retrieval\n\n\n AbstractThis paper presents a probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [77, 88, 1.0], [89, 98, 1.0], [122, 131, 1.0]]}, "1987.sigirconf_conference-87.21": {"snippet": "...near Information Retrieval Models\n\n\n AbstractLinear decision (retrieval)functions have been widely adopted in information retrieval systems such as in Boolean, vector, and probabilistic", "weights": [[8, 19, 1.0], [20, 29, 1.0], [65, 74, 1.0], [113, 124, 1.0], [125, 134, 1.0]]}, "1990.jasis_journal-ir0volumeA41A2.10": {"snippet": "...s of information retrieval and automatic text processing: The transformation analysis, and retrieval of information by computer", "weights": [[8, 19, 1.0], [20, 29, 1.0], [94, 103, 1.0], [107, 118, 1.0]]}, "2005.spire_conference-2005.25": {"snippet": "Retrieval Status Values in Information Retrieval Evaluation", "weights": [[0, 9, 1.0], [27, 38, 1.0], [39, 48, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ical information retrieval\n\n\n AbstractIn this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 153, 1.0], [154, 163, 1.0]]}, "1999.sigirconf_conference-99.73": {"snippet": "Information Retrieval Library (IRLIB) (demonstration abstract)\n\n\n ABSTRACTThe IRLIB is a full-text web searchable digital library, whose contents are publications and documents relative to the field o...", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2013.sigirconf_conference-2013.125": {"snippet": "...e an Information Retrieval (IR) system as it focuses on how easily documents can be found. It is intrinsically related to retrieval performance because a document needs to be retrieved before it can b...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [125, 134, 1.0]]}, "2016.ictir_conference-2016.7": {"snippet": "... for Retrieval and Ranking\n\n\n ABSTRACTData fusion has been shown to be a simple and effective way to improve retrieval results. Most existing data fusion methods combine ranked lists from different re...", "weights": [[8, 17, 1.0], [112, 121, 1.0], [201, 210, 1.0]]}, "2005.sigirconf_conference-2005.9": {"snippet": "... hoc information retrieval\n\n\n ABSTRACTInformation retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information nee...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 62, 1.0], [164, 173, 1.0], [188, 199, 1.0]]}, "2020.sigirconf_conference-2020.172": {"snippet": "...cipe Retrieval with Visual Query of Ingredients\n\n\n Recipe retrieval is a representative and useful application of crossmodal information retrieval. Recent studies have proposed frameworks for retrievi...", "weights": [[8, 17, 1.0], [61, 70, 1.0], [128, 139, 1.0], [140, 149, 1.0]]}, "2016.ictir_conference-2016.32": {"snippet": "...tity Retrieval\n\n\n ABSTRACTThe premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing ...", "weights": [[8, 17, 1.0], [51, 60, 1.0]]}, "W09-4632": {"snippet": "...e\n\n\n Information retrieval is a field of research reaching from computer and information science to lingusitics. As a linguist in the information retrieval field, I leave the quest for effective searc...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [137, 148, 1.0], [149, 158, 1.0]]}, "2015.ictir_conference-2015.28": {"snippet": "...text Retrieval for Web Tables\n\n\n ABSTRACTMany modern knowledge bases are built by extracting information from millions of web pages. Though existing extraction methods primarily focus on web pages' ma...", "weights": [[8, 17, 1.0], [96, 107, 1.0]]}, "2018.ictir_conference-2018.10": {"snippet": "...ment Retrieval\n\n\n ABSTRACTIn this paper, we study jointly query reformulation and document relevance estimation, the two essential aspects of information retrieval (IR). Their interactions are modelle...", "weights": [[8, 17, 1.0], [145, 156, 1.0], [157, 166, 1.0]]}, "1988.riao_conference-1988.41": {"snippet": "...Text Retrieval for Legal Information Retrieval Systems", "weights": [[8, 17, 1.0], [28, 39, 1.0], [40, 49, 1.0]]}, "2004.sigirconf_conference-2004.132": {"snippet": "...Most retrieval and database, even those tailored to geographic information, provide only nascent approaches to the technologically and conceptually difficult challenges of Geographic Information Retri...", "weights": [[8, 17, 1.0], [66, 77, 1.0], [186, 197, 1.0], [198, 207, 1.0]]}, "2004.airs_conference-2004.26": {"snippet": "...bile information services focus on providing location information related to the user's current location. In contrast, we propose an information retrieval method that searches for 'local information' ...", "weights": [[8, 19, 1.0], [57, 68, 1.0], [136, 147, 1.0], [148, 157, 1.0], [190, 201, 1.0]]}, "2019.ictir_conference-2019.39": {"snippet": "...sage Retrieval for Entity Retrieval", "weights": [[8, 17, 1.0], [29, 38, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "1995.jasis_journal-ir0volumeA46A9.12": {"snippet": "Information Retrieval: New Systems and Current Research. Proceedings of the 15th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, edited by Ruben Leon", "weights": [[0, 11, 1.0], [12, 21, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2017.clef_conference-2017w.74": {"snippet": "...ered Information Retrieval Task-1: Improving Medical Document Retrieval with Query Expansion\n\n\n In this report, we describe our retrieval framework for participating in CLEF eHealth 2017 Patient-Cente...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [65, 74, 1.0], [131, 140, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ment retrieval. The shot retrieval and the video segment model re-use the probability of relevance and a language modeling ranking function respectively, basing themselves on decades of text retrieval...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [194, 203, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...n of retrieval models particularly for recall oriented retrieval domains, where the focus is on retrieving all relevant information but not just retrieving a subset of relevant information. Available ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [123, 134, 1.0], [180, 191, 1.0]]}, "2004.sigirconf_conference-2004.8": {"snippet": "...y of information retrieval heuristics\n\n\n ABSTRACTEmpirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [73, 84, 1.0], [85, 94, 1.0], [118, 127, 1.0], [181, 190, 1.0]]}}}
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The framework is based on a coop...", "weights": [[8, 17, 1.0], [29, 38, 1.0], [144, 153, 1.0], [154, 163, 1.0]]}, "1995.ipm_journal-ir0volumeA31A3.6": {"snippet": "Efficient Retrieval of Partial Documents", "weights": [[0, 9, 1.0], [10, 19, 1.0]]}, "2017.sigirconf_conference-2017.58": {"snippet": "Retrieval Algorithms Optimized for Human Learning\n\n\n ABSTRACTWhile search technology is widely used for learning-oriented information needs, the results provided by popular services such as Web search...", "weights": [[0, 9, 1.0], [10, 20, 1.0]]}, "2012.ijirr_journal-ir0volumeA2A2.3": {"snippet": "Efficient Retrieval Technique for Microarray Gene Expression", "weights": [[0, 9, 1.0], [10, 19, 1.0]]}, "1999.tois_journal-ir0volumeA17A4.2": {"snippet": "Efficient passage ranking for document databases\n\n\n Queries to text collections are resolved by ranking the documents in the collection and returning the highest-scoring documents to the user. An alte...", "weights": [[0, 9, 1.0]]}, "2010.airs_conference-2010.21": {"snippet": "An Integrated Deterministic and Nondeterministic Inference Algorithm for Sequential Labeling\n\n\n Abstract. In this paper, we present a new search algorithm for sequential labeling tasks based on the co...", "weights": []}, "1991.riao_conference-1991.33": {"snippet": "... for efficient full-text retrieval", "weights": [[8, 17, 1.0], [28, 37, 1.0]]}, "2011.sigirconf_conference-2011.227": {"snippet": "Efficient and effective solutions for search engines\n\n\n AbstractEffectiveness and efficiency are two of the main issues in Information Retrieval (IR). IR efficiency is normally addressed in terms of a...", "weights": [[0, 9, 1.0], [135, 144, 1.0]]}, "2012.spire_conference-2012.33": {"snippet": "...eral algorithms have been proposed to count runs in a string efficiently, and \u03c1(n) can be obtained for small n by these algorithms. In this paper, we focus on computing \u03c1(n) for given length parameter...", "weights": [[8, 18, 1.0], [123, 133, 1.0]]}, "2011.sigirconf_conference-2011.55": {"snippet": "Efficient manifold ranking for image retrieval\n\n\n ABSTRACTManifold Ranking (MR), a graph-based ranking algorithm, has been widely applied in information retrieval and shown to have excellent performan...", "weights": [[0, 9, 1.0], [37, 46, 1.0], [153, 162, 1.0]]}, "2012.sigirconf_conference-2012.64": {"snippet": "...rmit efficient retrieval by not fully scoring all postings of the documents matching a query -without degrading the retrieval effectiveness of the topranked results. However, the amount of pruning ach...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [119, 128, 1.0]]}, "1981.sigirconf_conference-81.6": {"snippet": "...ment Retrieval System\n\n\n AbstractMany effective search strategies derived from different models are available for document retrieval systems.However, it does not appear that there is a single most eff...", "weights": [[8, 17, 1.0], [126, 135, 1.0]]}, "2011.cikm_conference-2011.348": {"snippet": "...more efficient solutions. We propose two branch and bound algorithms, the Estimation Expanding Pruning (EEP) algorithm and the Bounding Influence Pruning (BIP) algorithm. These algorithms exploit vari...", "weights": [[8, 17, 1.0], [61, 71, 1.0], [180, 190, 1.0]]}, "2011.tois_journal-ir0volumeA29A4.2": {"snippet": "... and efficient stemming algorithm for information retrieval\n\n\n A novel graph-based language-independent stemming algorithm suitable for information retrieval is proposed in this article. The main feat...", "weights": [[8, 17, 1.0], [53, 62, 1.0], [151, 160, 1.0]]}, "C10-1096": {"snippet": "... and Efficient Algorithm for Approximate Dictionary Matching\n\n\n This paper presents a simple and efficient algorithm for approximate dictionary matching designed for similarity measures such as cosine...", "weights": [[8, 17, 1.0], [100, 109, 1.0]]}, "2003.cikm_conference-2003.60": {"snippet": "Efficient query evaluation using a two-level retrieval process\n\n\n ABSTRACTWe present an efficient query evaluation method based on a two level approach: at the first level, our method iterates in para...", "weights": [[0, 9, 1.0], [45, 54, 1.0], [88, 97, 1.0]]}, "P06-2052": {"snippet": "Efficient Sentence Retrieval Based on Syntactic Structure\n\n\n This paper proposes an efficient method of sentence retrieval based on syntactic structure. Collins proposed Tree Kernel to calculate struc...", "weights": [[0, 9, 1.0], [19, 28, 1.0], [84, 93, 1.0], [113, 122, 1.0]]}, "2004.trec_conference-2004.8": {"snippet": "...tter Retrieval Function From Machine?\n\n\n The quality of an information retrieval system heavily depends on its retrieval function, which returns a similarity measurement between the query and each doc...", "weights": [[8, 17, 1.0], [74, 83, 1.0], [114, 123, 1.0]]}, "2018.sigirconf_conference-2018.11": {"snippet": "...tion algorithms such as Rank-S or Taily to rank index shards and determine how many to search for a given query. Most prior research evaluated solutions by their ability to improve efficiency without ...", "weights": [[8, 18, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A2.6": {"snippet": "...onal algorithms based on CPU, we aim at designing efficient GPU based algorithms. We firstly design inverted index in GPU, then choose ScanCount, a straightforward but efficient T-Overlap algorithm, a...", "weights": [[8, 18, 1.0], [53, 62, 1.0], [73, 83, 1.0], [171, 180, 1.0]]}, "2018.sigirconf_workshop-2018birndl.5": {"snippet": "...ning efficient algorithms for expert finding in large databases in various application domains. The evaluation of such recommender systems lies most of the time on humanannotated sets of experts assoc...", "weights": [[8, 17, 1.0], [18, 28, 1.0]]}, "2014.sigirconf_conference-2014.73": {"snippet": "... and retrieval methods. This paper proposes an innovative mathematics retrieval system along with the novel algorithms, which enables efficient formula index and retrieval from both webpages and PDF d...", "weights": [[8, 17, 1.0], [73, 82, 1.0], [111, 121, 1.0], [137, 146, 1.0], [165, 174, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A4.1": {"snippet": "...tion Retrieval Algorithms for Efficient Ranking of Webpages", "weights": [[8, 17, 1.0], [18, 28, 1.0], [33, 42, 1.0]]}, "1991.sigirconf_conference-91.30": {"snippet": "An Efficient Directory System for Document Retrieval", "weights": [[3, 12, 1.0], [43, 52, 1.0]]}, "2018.wsdm_conference-2018.6": {"snippet": "...ing, efficient algorithms are now in higher demand more than ever before. Big Data fundamentally challenges the classical notion of efficient algorithms: Algorithms that used to be considered efficien...", "weights": [[8, 17, 1.0], [18, 28, 1.0], [135, 144, 1.0], [145, 155, 1.0], [157, 167, 1.0], [195, 204, 1.0]]}, "2014.wsdm_conference-2014.79": {"snippet": "...tion retrieval (LSDS-IR 2014)\n\n\n ABSTRACTThe LSDS-IR'14 workshop aims to bring together information retrieval practitioners from industry and academic researchers concerned with efficient and distribu...", "weights": [[8, 17, 1.0], [103, 112, 1.0], [181, 190, 1.0]]}, "2005.ismir_conference-2005.114": {"snippet": "... and Efficient Match Algorithm for Content-Based Music Retrieval on Acoustic Data", "weights": [[8, 17, 1.0], [58, 67, 1.0]]}, "2012.sigirconf_conference-2012.25": {"snippet": "Efficient in-memory top-k document retrieval", "weights": [[0, 9, 1.0], [35, 44, 1.0]]}, "2005.airs_conference-2005.63": {"snippet": "An Efficient Incremental Nearest Neighbor Algorithm for Processing k-Nearest Neighbor Queries with Visal and Semantic Predicates in Multimedia Information Retrieval System", "weights": [[3, 12, 1.0], [155, 164, 1.0]]}, "2006.spire_conference-2006.23": {"snippet": "...t an efficient algorithm which recognizes tight matrices over a certain ring, M, implies an efficient algorithm that solves SBT on an important class of permutations, called simple permutations. Such ...", "weights": [[8, 17, 1.0], [95, 104, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2009.sigirconf_conference-2009.21": {"snippet": "...duce algorithms are introduced: one based on brute force, a second where the problem is treated as large-scale ad hoc retrieval, and a third based on the Cartesian product of postings lists. Each algo...", "weights": [[8, 18, 1.0], [121, 130, 1.0]]}, "2017.spire_conference-2017.18": {"snippet": "...ngth Algorithms\n\n\n Abstract. Some of the most efficient algorithms for computing the length of a longest common subsequence (LLCS) between two strings are based on so-called \"bit-parallelism\". They ac...", "weights": [[8, 18, 1.0], [49, 58, 1.0], [59, 69, 1.0]]}, "2012.sigirconf_conference-2012.38": {"snippet": "... new algorithms from scratch, we study challenges when adopting existing top-k algorithms in the literature that heavily rely on two basic list-access methods: random access and sorted access. We pres...", "weights": [[8, 18, 1.0], [82, 92, 1.0]]}, "2012.mir_conference-2012.110": {"snippet": "Efficient Medical Information Retrieval in Encrypted Electronic Health Records", "weights": [[0, 9, 1.0], [30, 39, 1.0]]}, "2003.sigirconf_conference-2003.65": {"snippet": "...ems, retrieval of relevant documents, and generation of metasearch lists.\nCategories and Subject Descriptors:H.3.3 [Information Search and Retrieval]: Retrieval Models.General Terms: Algorithms, Theor...", "weights": [[8, 17, 1.0], [142, 151, 1.0], [154, 163, 1.0], [186, 196, 1.0]]}, "2011.cikm_conference-2011.313": {"snippet": "...ting efficient information extraction pipelines\n\n\n ABSTRACTInformation Extraction (IE) pipelines analyze text through several stages. The pipeline's algorithms determine both its effectiveness and its...", "weights": [[8, 17, 1.0], [152, 162, 1.0]]}, "2007.cikm_conference-2007.133": {"snippet": "An efficient algorithm for approximate biased quantile computation in data streams\n\n\n ABSTRACTWe propose an efficient algorithm for approximate biased quantile computation in large data streams. Our a...", "weights": [[3, 12, 1.0], [108, 117, 1.0]]}, "1991.riao_conference-1991.36": {"snippet": "Efficient probabilistic Inference for text retrieval", "weights": [[0, 9, 1.0], [43, 52, 1.0]]}, "2014.trec_conference-2014.70": {"snippet": "...pass retrieval framework, with the first pass consisting of a simple and efficient retrieval model that focuses on recall, and the second pass a wave of feature extraction algorithms run on the set of...", "weights": [[8, 17, 1.0], [76, 85, 1.0], [86, 95, 1.0], [175, 185, 1.0]]}, "2018.wsdm_conference-2018.51": {"snippet": "...tage Retrieval\n\n\n ABSTRACTLarge scale retrieval systems often employ cascaded ranking architectures, in which an initial set of candidate documents is iteratively refined and re-ranked by increasingly...", "weights": [[8, 17, 1.0], [41, 50, 1.0]]}, "2011.wsdm_conference-2011.32": {"snippet": "...n of algorithms for this interactive retrieval setting. Furthermore, we propose two dynamic ranking algorithms, both of which are computationally efficient. We prove that these algorithms provide retr...", "weights": [[8, 18, 1.0], [40, 49, 1.0], [103, 113, 1.0], [149, 158, 1.0], [180, 190, 1.0], [199, 208, 1.0]]}, "2011.sigirconf_conference-2011.13": {"snippet": "... for efficient ranked retrieval\n\n\n ABSTRACTThere is a fundamental tradeoff between effectiveness and efficiency when designing retrieval models for large-scale document collections. Effectiveness tend...", "weights": [[8, 17, 1.0], [25, 34, 1.0], [130, 139, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "Y18-1019": {"snippet": "... FCA Retrieval a fast and efficient algorithm for information retrieval from a binary concept lattice. This algorithm can respond to queries containing several types of logical operators (conjunction,...", "weights": [[8, 17, 1.0], [29, 38, 1.0], [65, 74, 1.0]]}, "2016.ijirr_journal-ir0volumeA6A4.1": {"snippet": "Efficient Textual Web Retrieval using Wavelet Tree", "weights": [[0, 9, 1.0], [22, 31, 1.0]]}, "2003.spire_conference-2003.18": {"snippet": "...L-M\" algorithms. Of them, the FINDSTEM and A-F algorithms employ inflectional and derivational stemmers, whereas the L-M one handles only inflectional rules. Comparison of stemming algorithms was done...", "weights": [[8, 18, 1.0], [50, 60, 1.0], [184, 194, 1.0]]}, "2008.mir_conference-2008.62": {"snippet": "An efficient algorithm for keyframe-based motion retrieval in the presence of temporal deformations", "weights": [[3, 12, 1.0], [49, 58, 1.0]]}, "2012.ecir_conference-2012.49": {"snippet": "...s an efficient dynamic processing of data points as they arrive in groups. Our experiments on the TDT5 benchmark collection show the superiority of both versions of this algorithm compared to other st...", "weights": [[8, 17, 1.0]]}, "2012.cikm_conference-2012.269": {"snippet": "...op-k retrieval using conditional preference networks\n\n\n ABSTRACTThis paper considers top-k retrieval using Conditional Preference Network (CP-Net). As a model for expressing user preferences on multip...", "weights": [[8, 17, 1.0], [94, 103, 1.0]]}, "1991.ipm_journal-ir0volumeA27A23.1": {"snippet": "...s of retrieval algorithms", "weights": [[8, 17, 1.0], [18, 28, 1.0]]}, "1969.ipm_journal-ir0volumeA5A3.1": {"snippet": "...tion retrieval system", "weights": [[8, 17, 1.0]]}, "2004.trec_conference-2004.41": {"snippet": "...deed efficient in improving retrieval, although further work is needed in order to improve the performance of the prediction algorithms and their uses.", "weights": [[8, 17, 1.0], [31, 40, 1.0], [128, 138, 1.0]]}, "D11-1047": {"snippet": "Efficient retrieval of tree translation examples for Syntax-Based Machine Translation\n\n\n We propose an algorithm allowing to efficiently retrieve example treelets in a parsed tree database in order to...", "weights": [[0, 9, 1.0], [10, 19, 1.0]]}, "2014.sigirconf_conference-2014.50": {"snippet": "...stic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy.We first point out that greedy algorithms are essentially finding a self-cons...", "weights": [[8, 18, 1.0], [54, 63, 1.0], [157, 167, 1.0]]}, "2012.wsdm_conference-2012.50": {"snippet": "... the retrieval has to be fast (few milliseconds) to load the page quickly. Fast retrieval is well studied in the information retrieval (IR) literature, especially in the context of document retrieval ...", "weights": [[8, 17, 1.0], [83, 92, 1.0], [128, 137, 1.0], [193, 202, 1.0]]}, "2016.wsdm_conference-2016.10": {"snippet": "... how efficient are social media users at selecting their information sources.In this work, we propose a computational framework to quantify users' efficiency at selecting information sources. Our fram...", "weights": [[8, 17, 1.0]]}, "2001.jasis_journal-ir0volumeA52A5.6": {"snippet": "... for efficient retrieval", "weights": [[8, 17, 1.0], [18, 27, 1.0]]}, "2005.cikm_conference-2005.126": {"snippet": "...kNN: efficient reverse k-nearest neighbors retrieval with local kNN-distance estimation\n\n\n ABSTRACTThe Reverse k-Nearest Neighbors (RkNN) queries are important in profile-based marketing, information ...", "weights": [[8, 17, 1.0], [46, 55, 1.0]]}, "2006.sigirconf_conference-2006.24": {"snippet": "...-hoc retrieval\n\n\n ABSTRACTSearch algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and...", "weights": [[8, 17, 1.0], [36, 46, 1.0], [121, 130, 1.0], [159, 168, 1.0]]}, "2012.cikm_conference-2012.57": {"snippet": "...m of efficient retrieval of recommendations in a MF framework. We reduce the retrieval in a MF model to an apparently simple task of finding the maximum dot-product for the user vector over the set of...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [80, 89, 1.0]]}, "2008.spire_conference-2008.16": {"snippet": "An Efficient Linear Space Algorithm for Consecutive Suffix Alignment under Edit Distance (Short Preliminary Paper)", "weights": [[3, 12, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.19": {"snippet": "...base retrieval. To overcome the problem of over-fitting when the number of training samples in dataset is limited, this paper proposes an image database retrieval algorithm based on the framework of t...", "weights": [[8, 17, 1.0], [156, 165, 1.0]]}, "1996.tois_journal-ir0volumeA14A2.1": {"snippet": "... for Efficient Information Retrieval with Redundant Information Filters\n\n\n We consider information retrieval when the data-for instance, multimedia-is computationally expensive to fetch. Our approach ...", "weights": [[8, 17, 1.0], [30, 39, 1.0], [102, 111, 1.0]]}, "2013.wsdm_conference-2013.93": {"snippet": "...tion retrieval (LSDS-IR 2013)\n\n\n ABSTRACTThe LSDS-IR'13 workshop aims to bring together both information retrieval practitioners from industry, as well as academic researchers concerned with efficient...", "weights": [[8, 17, 1.0], [108, 117, 1.0], [194, 203, 1.0]]}, "2004.airs_conference-2004.13": {"snippet": "Efficient PageRank with Same Out-Link Groups\n\n\n Abstract. Traditional PageRank algorithm suffers from heavy computation cost due to the huge number of web pages. In this paper, we propose a more effic...", "weights": [[0, 9, 1.0], [195, 204, 1.0]]}, "2017.ictir_conference-2017.57": {"snippet": "...ndit algorithms as well as new applications for these algorithms. 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Results obtained for two large datasets and four large query logs confirm that selective search is sign...", "weights": [[8, 18, 1.0]]}, "2002.cikm_conference-2002.69": {"snippet": "...ting algorithms.\nCATEGORIES AND SUBJECT DESCRIPTORSInformation systems: information storage and retrieval: information search and retrieval: search process.\nGENERAL TERMSAlgorithms and performance.\nIN...", "weights": [[8, 18, 1.0], [99, 108, 1.0], [133, 142, 1.0]]}, "2016.ipm_journal-ir0volumeA52A6.9": {"snippet": "... LtR algorithms has tuning parameters that can influence both effectiveness and efficiency, where higher ranking quality is generally obtained with more complex and expensive models. Moreover, LtR alg...", "weights": [[8, 18, 1.0], [200, 210, 1.0]]}, "2017.ecir_conference-2017.4": {"snippet": "Entity Linking in Queries: Efficiency vs. Effectiveness\n\n\n Abstract. Identifying and disambiguating entity references in queries is one of the core enabling components for semantic search. While there...", "weights": []}, "C69-5101": {"snippet": "...r an efficient information system. It can be implemented for text processing and document retrieval; numerical data retrieval; and for handling of large files such as dictionaries, catalogs, and perso...", "weights": [[8, 17, 1.0], [93, 102, 1.0], [119, 128, 1.0]]}, "2008.ecir_conference-2008.49": {"snippet": "Efficient Multimedia Time Series Data Retrieval Under Uniform Scaling and Normalisation", "weights": [[0, 9, 1.0], [38, 47, 1.0]]}, "2018.tist_journal-ir0volumeA9A6.1": {"snippet": "...tion retrieval systems such as Web search engines, which have to return highly relevant documents in response to user query within fractions of seconds. The most effective LtR algorithms adopt a gradi...", "weights": [[8, 17, 1.0], [179, 189, 1.0]]}, "2012.spire_conference-2012.30": {"snippet": "Efficient Data Structures for the Factor Periodicity Problem\n\n\n Abstract. We present several efficient data structures for answering queries related to periods in words. For a given word w of length n...", "weights": [[0, 9, 1.0], [93, 102, 1.0]]}, "1995.sigirconf_conference-95.6": {"snippet": "Efficient Recompression Techniques for Dynamic Full-Text Retrieval Systems", "weights": [[0, 9, 1.0], [57, 66, 1.0]]}, "J08-4003": {"snippet": "...zing algorithms for deterministic incremental dependency parsing, formalized as transition systems. We then describe and analyze two families of such algorithms: stack-based and list-based algorithms....", "weights": [[8, 18, 1.0], [153, 163, 1.0], [192, 202, 1.0]]}, "1999.tois_journal-ir0volumeA17A2.2": {"snippet": "...ased Retrieval by Spatial Similarity in Image Databases\n\n\n A framework for retrieving images by spatial similarity (FRISS) in image databases is presented. 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Spamming involves floo...", "weights": [[8, 12, 1.0], [164, 168, 1.0]]}, "2015.wwwconf_conference-2015.88": {"snippet": "...n of Spam Mobile Apps\n\n\n ABSTRACTIncreased popularity of smartphones has attracted a large number of developers to various smartphone platforms. As a result, app markets are also populated with spam a...", "weights": [[8, 12, 1.0], [197, 201, 1.0]]}, "2007.sigirconf_conference-2007.180": {"snippet": "...ting spam from non-spam pages.", "weights": [[8, 12, 1.0], [22, 26, 1.0]]}, "2008.wwwconf_conference-2008.127": {"snippet": "...king results have a strong bias towards seeds. In this paper, we analyze the relationship between the result bias and the number of seeds. Furthermore, we experimentally show that an automatically sel...", "weights": [[8, 15, 1.0]]}, "2011.wsdm_conference-2011.86": {"snippet": "... the spam filtering problem are not handled appropriately in existing work. Principal among these are adversarial spammer efforts -spammers routinely tune their spam emails to bypass spam-filters, and...", "weights": [[8, 12, 1.0], [164, 168, 1.0], [186, 190, 1.0]]}, "W10-2110": {"snippet": "Consonant Co-Occurrence in Stems across Languages: Automatic Analysis and Visualization of a Phonotactic Constraint\n\n\n In this paper, we explore the phenomenon of Similar Place Avoidance (SPA), accord...", "weights": []}, "2017.ipm_journal-ir0volumeA53A4.5": {"snippet": "...uted results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache ca...", "weights": [[8, 15, 1.0], [161, 168, 1.0]]}, "2008.tweb_journal-ir0volumeA2A1.1": {"snippet": "... Web spam detection\n\n\n We propose link-based techniques for automatic detection of Web spam, a term referring to pages which use deceptive techniques to obtain undeservedly high scores in search engin...", "weights": [[8, 12, 1.0], [90, 94, 1.0]]}, "2016.wwwconf_conference-2016c.47": {"snippet": "...eing spam and ham (i.e., non-spam). In our experiments, we observe that it is much easier to recommend \"correct\" hashtags for spam tweets than ham tweets, because of the near duplicates in spam tweets...", "weights": [[8, 12, 1.0], [32, 36, 1.0], [129, 133, 1.0], [192, 196, 1.0]]}, "2007.wwwconf_conference-2007.30": {"snippet": "...heir spam links into top search results. In this paper, we focus on one prevalent type of spam -redirection spam -where one can identify spam pages by the third-party domains that these pages redirect...", "weights": [[8, 12, 1.0], [35, 42, 1.0], [93, 97, 1.0], [111, 115, 1.0], [140, 144, 1.0]]}, "2013.jasis_journal-ir0volumeA64A3.15": {"snippet": "...w to avoid them", "weights": [[8, 13, 1.0]]}, "2005.trec_conference-2005.110": {"snippet": "...2005 Spam Track Overview", "weights": [[8, 12, 1.0]]}, "2013.wwwconf_conference-2013c.208": {"snippet": "...uals avoid regrettable online disclosures, we employed lessons from behavioral decision research and research on soft paternalism to design mechanisms that \"nudge\" users to consider the content and co...", "weights": [[8, 13, 1.0]]}, "2009.airweb_workshop-2009.1": {"snippet": "... web spam", "weights": [[8, 12, 1.0]]}, "2008.cikm_conference-2008.36": {"snippet": "... web spam with HTTP session information\n\n\n ABSTRACTWeb spam is a widely-recognized threat to the quality and security of the Web. Web spam pages pollute search engine indexes, burden Web crawlers and ...", "weights": [[8, 12, 1.0], [58, 62, 1.0], [137, 141, 1.0]]}, "2006.wwwconf_conference-2006.8": {"snippet": "... web spam\n\n\n ABSTRACTWeb spam is behavior that attempts to deceive search engine ranking algorithms. TrustRank is a recent algorithm that can combat web spam. However, TrustRank is vulnerable in the s...", "weights": [[8, 12, 1.0], [28, 32, 1.0], [156, 160, 1.0]]}, "2018.cikm_conference-2018.249": {"snippet": "CAPatternMiner: Mining Ship Collision Avoidance Behavior from AIS Trajectory Data\n\n\n ABSTRACTThe improvement of collision avoidance for ship navigation in encounter situation is an important topic in ...", "weights": []}, "2009.airweb_workshop-2009.7": {"snippet": "...cial spam detection", "weights": [[8, 12, 1.0]]}, "2015.wwwconf_conference-2015.17": {"snippet": "...ting spam opinions in threads. The results of spam detection on first posts show: (1) spam first posts put more focus on certain topics such as the user experiences' on the promoted items, (2) spam fi...", "weights": [[8, 12, 1.0], [38, 45, 1.0], [49, 53, 1.0], [89, 93, 1.0], [196, 200, 1.0]]}, "2005.trec_conference-2005.73": {"snippet": "...Anti-SPAM Solution at the TREC 2005 Spam Track: Improving the Performance of Naive Bayes for Spam Detection\n\n\n Recently, the spam already constituted a serious problem for both e-mail users and Intern...", "weights": [[8, 12, 1.0], [39, 43, 1.0], [96, 100, 1.0], [128, 132, 1.0]]}, "2009.cikm_conference-2009.254": {"snippet": "...ntal results show that with such two metrics, our method can filter out spam sites and identify reputable ones more effectively than previous algorithms such as TrustRank.", "weights": [[8, 15, 1.0], [75, 79, 1.0]]}, "2011.ipm_journal-ir0volumeA47A2.8": {"snippet": "...ting spam blogs from blog search results\n\n\n a b s t r a c tBlogging has been an emerging media for people to express themselves. However, the presence of spam blogs (also known as splogs) may reduce t...", "weights": [[8, 12, 1.0], [36, 43, 1.0], [157, 161, 1.0]]}, "2011.wwwconf_conference-2011c.21": {"snippet": "...n to avoid detection and to increase the reach of their generated spam, such as forming 'spam-farms' and creating large number of links with targeted legitimate users. The observations are verified th...", "weights": [[8, 13, 1.0], [69, 73, 1.0], [92, 96, 1.0]]}, "2007.wwwconf_conference-2007.154": {"snippet": "Spam and popularity ratings for combating link spam\n\n\n ABSTRACTWe present a new approach for propagating spam scores in web graphs, in order to combat link spam. The resulting spam rating is then used...", "weights": [[0, 4, 1.0], [47, 51, 1.0], [105, 109, 1.0], [156, 160, 1.0], [176, 180, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A6.30": {"snippet": "...heir spam behavior in a very low density to try to conceal themselves in normal users. These camouflaged spammers, driven by profits, are hired by some stores to write fake reviews in groups so as to ...", "weights": [[8, 12, 1.0]]}, "2008.cikm_workshop-2008lsdsir.4": {"snippet": "...ntal results show that the proposed techniques successfully decrease the amount of spam by 9% in the top-200 results and by 92% in the top-20 results with reasonable cost.", "weights": [[8, 15, 1.0], [86, 90, 1.0], [112, 119, 1.0], [145, 152, 1.0]]}, "2007.sigirconf_conference-2007.56": {"snippet": "... web spam detection using the web topology\n\n\n ABSTRACTWeb spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to dete...", "weights": [[8, 12, 1.0], [61, 65, 1.0], [125, 132, 1.0]]}, "2020.coling-main.423": {"snippet": "...hod (SpAM) to a typologically diverse language sample, and its potential to produce semantic evaluation resources to support multilingual NLP, with a focus on verb semantics. We demonstrate SpAM's uti...", "weights": [[8, 12, 1.0], [193, 197, 1.0]]}, "2013.ijirr_journal-ir0volumeA3A1.2": {"snippet": "...g of Spam", "weights": [[8, 12, 1.0]]}, "2007.trec_conference-2007.72": {"snippet": "...2007 Spam Track Overview", "weights": [[8, 12, 1.0]]}, "2020.ecir_conference-20202.60": {"snippet": ".... To avoid re-entering formulas, chips can be reused, edited, or used in creating other formulas. As users enter formulas, our novel autocompletion facility returns entity cards searchable by formula ...", "weights": [[8, 13, 1.0]]}, "2021.ecir_conference-20212.68": {"snippet": "...d to avoid these sources due to their complex language, internal vernacular, or lacking prior background knowledge. Text simplification approaches can remove some of these barriers, thereby avoiding t...", "weights": [[8, 13, 1.0]]}, "W17-1908": {"snippet": "...r to avoid erroneous (spam) crowdsourced results, we used a novel taskspecific two-phase filtering process where users were asked to identify synonyms in the target language, and remove erroneous sens", "weights": [[8, 13, 1.0], [25, 29, 1.0], [44, 51, 1.0]]}, "2020.ecir_conference-20202.58": {"snippet": "...king results. We present the content-focused Neural-IR-Explorer, which empowers users to browse through retrieval results and inspect the inner workings and fine-grained results of neural re-ranking m...", "weights": [[8, 15, 1.0], [117, 124, 1.0], [173, 180, 1.0]]}, "2006.trec_conference-2006.91": {"snippet": "...2006 Spam Track Overview", "weights": [[8, 12, 1.0]]}, "2015.cikm_conference-2015.162": {"snippet": "... and spam message detection as two separate tasks. However, social spammers and spam messages have strong connections, since social spammers tend to post more spam messages and spam messages have high...", "weights": [[8, 12, 1.0], [83, 87, 1.0], [162, 166, 1.0], [180, 184, 1.0]]}, "2013.cikm_conference-2013.52": {"snippet": "...work spam detection\n\n\n ABSTRACTSocial network spam increases explosively with the rapid development and wide usage of various social networks on the Internet. To timely detect spam in large social net...", "weights": [[8, 12, 1.0], [49, 53, 1.0], [179, 183, 1.0]]}, "2018.tweb_journal-ir0volumeA12A4.5": {"snippet": "... web spam and infinite autogenerated web content, large-scale web crawlers require low-complexity ranking methods to effectively budget their limited resources and allocate bandwidth to reputable site...", "weights": [[8, 12, 1.0]]}, "2012.tweb_journal-ir0volumeA6A1.1": {"snippet": "... Web spam and are incapable of dealing with newly appearing spam types efficiently. With user-behavior analyses from Web access logs, a spam page-detection algorithm is proposed based on a learning sc...", "weights": [[8, 12, 1.0], [63, 67, 1.0], [139, 143, 1.0]]}, "2006.trec_conference-2006.83": {"snippet": "...c of spam filtering. The hypothesis were that: 1 Spammers were changing tactics to successfully evade contentbased spam filters; 2 A pretrained database of known spam and nonspam improves overall accu...", "weights": [[8, 12, 1.0], [118, 122, 1.0], [165, 169, 1.0]]}, "2010.trec_conference-2010.19": {"snippet": "...2010 Spam Task\n\n\n Web Spamming refers those web pages deceive search engines so as to get a higher rank in their search result. We work on the data set TrecWeb09, based on a content-based spamming cla...", "weights": [[8, 12, 1.0]]}, "2008.cikm_conference-2008.35": {"snippet": "...ting spam in P2P query result sets. Experimental results show that the proposed techniques successfully decrease the amount of spam by 9% in the top-200 results and by 92% in the top-20 results.", "weights": [[8, 12, 1.0], [52, 59, 1.0], [130, 134, 1.0], [156, 163, 1.0], [189, 196, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.5": {"snippet": "...h as spam filtering, blog classification, and web resource categorization. We extend the ideas of online clustering and batch-mode centroid-based classification to online learning with negative feedba...", "weights": [[8, 12, 1.0]]}, "2010.ntcir_workshop-2010.3": {"snippet": "... Web spam task. The track uses the ClueWeb09 dataset as its document collection. This collection consists of roughly 1 billion web pages in multiple languages, comprising approximately 25TB of uncompr...", "weights": [[8, 12, 1.0]]}, "2006.trec_conference-2006.86": {"snippet": "...ntal results on the TREC 2006 spam data sets and on other publicly available spam data, including near-perfect performance on the TREC 2006 Chinese spam data set. These results invite further explorat...", "weights": [[8, 15, 1.0], [33, 37, 1.0], [80, 84, 1.0], [151, 155, 1.0], [172, 179, 1.0]]}, "2008.wsdm_conference-2008.23": {"snippet": "...nion spam and analysis", "weights": [[8, 12, 1.0]]}, "2008.wwwconf_conference-2008.154": {"snippet": "... web spam detection with re-extracted features\n\n\n ABSTRACTWeb spam detection has become one of the top challenges for the Internet search industry. Instead of using some heuristic rules, we propose a ...", "weights": [[8, 12, 1.0], [65, 69, 1.0]]}, "2011.cikm_conference-2011.282": {"snippet": "...n of spam and irrelevant documents. Additionally we find that when users are given two documents of equal utility, the one with the lower spam score will be preferred; a result page without any spam d...", "weights": [[8, 12, 1.0], [141, 145, 1.0], [197, 201, 1.0]]}}}
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However, Syllabification by Analogy performs better than the other algorithms tested with a mean word accuracy of 96.84% (...", "weights": [[8, 18, 1.0], [148, 158, 1.0]]}, "2017.sigirconf_conference-2017.58": {"snippet": "Retrieval Algorithms Optimized for Human Learning\n\n\n ABSTRACTWhile search technology is widely used for learning-oriented information needs, the results provided by popular services such as Web search...", "weights": [[0, 9, 1.0], [10, 20, 1.0], [122, 133, 1.0]]}, "1996.ipm_journal-ir0volumeA32A2.4": {"snippet": "...etic Algorithms for Information Retrieval and Filtering: An Empirical Basis for Grammatical Rules", "weights": [[8, 18, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "2018.airs_conference-2018.4": {"snippet": "...ster information between a searcher and authors of contents. This paper also describes the evaluation experiments to confirm effectiveness of the proposed algorithm by implementing the proposed algori...", "weights": [[8, 19, 1.0]]}, "2009.spire_conference-2009.9": {"snippet": "...time algorithms.", "weights": [[8, 18, 1.0]]}, "2022.naacl-srw.8": {"snippet": "...hing algorithms based on term matching have been widely used. Recently, neural-based algorithms (termed as neural retrievers) have gained more attention which can mitigate the limitations of tradition...", "weights": [[8, 18, 1.0], [88, 98, 1.0]]}, "2011.tois_journal-ir0volumeA29A4.2": {"snippet": "... for information retrieval\n\n\n A novel graph-based language-independent stemming algorithm suitable for information retrieval is proposed in this article. The main features of the algorithm are retriev...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [106, 117, 1.0], [118, 127, 1.0], [196, 205, 1.0]]}, "1994.riao_conference-1994.13": {"snippet": "...o an Information Retrieval System", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1993.sigirjournals_journal-ir0volumeA27A3.6": {"snippet": "Information Retrieval Data Structures & Algorithms, by William B. Frakes and Ricardo Baeza-Yates (Book Review)", "weights": [[0, 11, 1.0], [12, 21, 1.0], [40, 50, 1.0]]}, "2013.wwwconf_conference-2013c.31": {"snippet": "... and algorithms for composite retrieval", "weights": [[8, 18, 1.0], [33, 42, 1.0]]}, "1988.jasis_journal-ir0volumeA39A3.0": {"snippet": "...king algorithms to improve the effectiveness of full-text retrieval. II. On the effectiveness of ranking algorithms on full-text retrieval", "weights": [[8, 18, 1.0], [61, 70, 1.0], [108, 118, 1.0], [132, 141, 1.0]]}, "2002.ir_journal-ir0volumeA5A23.8": {"snippet": "Information Retrieval: Algorithms and Heuristics", "weights": [[0, 11, 1.0], [12, 21, 1.0], [23, 33, 1.0]]}, "2004.sigirconf_conference-2004.41": {"snippet": "...ring algorithms for information retrieval models with named attributes and proximity operators\n\n\n ABSTRACTIn the selective dissemination of information (or publish/ subscribe) paradigm, clients subscr...", "weights": [[8, 18, 1.0], [23, 34, 1.0], [35, 44, 1.0], [143, 154, 1.0]]}, "2002.cikm_conference-2002.43": {"snippet": "... the retrieval accuracy of information retrieval systems of Latin-based languages. We present two stemming algorithms for Arabic information retrieval systems. We empirically investigate the effective...", "weights": [[8, 17, 1.0], [30, 41, 1.0], [42, 51, 1.0], [110, 120, 1.0], [132, 143, 1.0], [144, 153, 1.0]]}, "N06-5003": {"snippet": "...ased Algorithms for Natural Language Processing and Information Retrieval\n\n\n Graph theory is a well studied discipline, and so are the fields of natural language processing and information retrieval. ...", "weights": [[8, 18, 1.0], [55, 66, 1.0], [67, 76, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "2004.sigirconf_conference-2004.132": {"snippet": "...phic information from texts can be exploited to provide effective and accurate ranked retrieval of geospatial information and relevant text documents using retrieval algorithms based on logistic regre...", "weights": [[8, 19, 1.0], [89, 98, 1.0], [113, 124, 1.0], [159, 168, 1.0], [169, 179, 1.0]]}, "2019.ecir_workshop-2019amir.3": {"snippet": "Algorithm Selection with Librec-auto", "weights": []}, "2009.sigirconf_workshop-2009lsdsir.6": {"snippet": "... for information retrieval applications. Sorting is a fundamental and universal problem in computer science. Even if sort has been extensively addressed by many research works, it still remains an int...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2008.sigirconf_conference-2008.42": {"snippet": "...h to information retrieval: algorithmic mediation for intentional, synchronous collaborative exploratory search. Using our system, two or more users with a common information need search together, sim...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [166, 177, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...n of retrieval models particularly for recall oriented retrieval domains, where the focus is on retrieving all relevant information but not just retrieving a subset of relevant information. Available ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [123, 134, 1.0], [180, 191, 1.0]]}, "2019.ecir_workshop-2019amir.6": {"snippet": "... the algorithms that generated the recommendations. The logs enable researchers to conduct various analyses about a real-world recommender system. This includes the evaluation of meta-learning approac...", "weights": [[8, 18, 1.0]]}, "2015.spire_conference-2015.9": {"snippet": "...hese algorithms achieve remarkable performance in terms of I/O complexity and speed, their designs are quite complex and their disk requirements remain rather heavy. Currently, the core algorithmic pa...", "weights": [[8, 18, 1.0]]}, "2016.sigirconf_conference-2016.201": {"snippet": "...tual information into RS algorithms? This question is broken down into four specific questions: RQ1. Which information can be considered as contextual information for a specific recommendation task? I...", "weights": [[8, 19, 1.0], [28, 38, 1.0], [110, 121, 1.0], [154, 165, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A4.1": {"snippet": "...ased Information Retrieval Algorithms for Efficient Ranking of Webpages", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 40, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.12": {"snippet": "...ent. Information retrieval systems offering automated assistance could greatly improve search effectiveness by suggesting or implementing assistance automatically. A critical issue in designing such s...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2019.ecir_workshop-2019amir.1": {"snippet": "... The information retrieval (IR) community, however, has paid relatively little attention to the algorithm selection problem. The 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2007.cikm_conference-2007.76": {"snippet": "...tive retrieval\n\n\n ABSTRACTIt has been shown that using phrases properly in the document retrieval leads to higher retrieval effectiveness. In this paper, we define four types of noun phrases and prese...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [117, 126, 1.0]]}, "2004.wwwconf_conference-2004.67": {"snippet": "...ring algorithms to demonstrate its superiority. Even though our algorithm is slightly more computationally intensive than one of the algorithms, it generates better hierarchies. Our user studies also ...", "weights": [[8, 18, 1.0], [136, 146, 1.0]]}, "2016.tois_journal-ir0volumeA34A4.1": {"snippet": "...s an information retrieval metric of choice (in our case, NDCG) in a greedy manner. Direct optimization of the listwise objective functions is computationally prohibitive for most learning algorithms,...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [192, 202, 1.0]]}, "1996.sigirconf_conference-96.35": {"snippet": "...ning Algorithms for Linear Text Classifiers\n\n\n AbstractSystems for text retrieval, routing, categorization and other IR tasks rely heavily on linear classifiers. We propose that two machine learning a...", "weights": [[8, 18, 1.0], [75, 84, 1.0], [202, 212, 1.0]]}, "2005.cikm_conference-2005.154": {"snippet": "... set retrieval\n\n\n ABSTRACTThis paper presents a novel formulation and approach to the minimal document set retrieval problem. Minimal Document Set Retrieval (MDSR) is a promising information retrieval...", "weights": [[8, 17, 1.0], [110, 119, 1.0], [150, 159, 1.0], [182, 193, 1.0], [194, 203, 1.0]]}, "1998.ipm_journal-ir0volumeA34A4.2": {"snippet": "...m in Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2017.ipm_journal-ir0volumeA53A2.5": {"snippet": "...arch algorithms is a key research avenue. In particular, thematic search algorithms should lead to and benefit from the emergence of semantic communities that are the result of the interaction among p...", "weights": [[8, 18, 1.0], [76, 86, 1.0]]}, "2013.ictir_conference-2013.14": {"snippet": "Information Retrieval with Time Series Query\n\n\n ABSTRACTWe study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find rele...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [73, 84, 1.0], [85, 94, 1.0], [170, 179, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2000.cikm_conference-2000.40": {"snippet": "...s of algorithms for knowledge discovery in databases (KDD) relies heavily on similarity queries, i.e. range queries or nearest neighbor queries, in multidimensional feature spaces. Many KDD algorithms...", "weights": [[8, 18, 1.0], [193, 203, 1.0]]}, "2007.sigirconf_conference-2007.36": {"snippet": "...tive algorithms for constructing refined query language models from user term feedback. Our algorithms are shown to bring significant improvement in retrieval accuracy over a non-feedback baseline, an...", "weights": [[8, 18, 1.0], [95, 105, 1.0], [152, 161, 1.0]]}, "2002.jasis_journal-ir0volumeA53A11.6": {"snippet": "...etic algorithms for multimodal relevance optimization in information retrieval", "weights": [[8, 18, 1.0], [60, 71, 1.0], [72, 81, 1.0]]}, "2020.sigirconf_conference-2020.360": {"snippet": "...tive Information Retrieval: Models, Algorithms, and Evaluation\n\n\n Since Information Retrieval (IR) is an interactive process in general, it is important to study Interactive Information Retrieval (IIR...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [39, 49, 1.0], [75, 86, 1.0], [87, 96, 1.0], [177, 188, 1.0], [189, 198, 1.0]]}, "2012.sigirconf_conference-2012.84": {"snippet": "...ized retrieval algorithms. One simple approach is to learn a set of combination weights for multiple centralized retrieval algorithms (e.g., logistic regression) to compute comparable document scores....", "weights": [[8, 17, 1.0], [18, 28, 1.0], [116, 125, 1.0], [126, 136, 1.0]]}, "2010.sigirconf_conference-2010.138": {"snippet": "...uted information retrieval system . However, most post-retrieval query performance predictors are expensive to compute, and not well-suited for the web.The key idea behind RAPP is to use retrieval sco...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [58, 67, 1.0], [190, 199, 1.0]]}, "2010.ecir_conference-2010.30": {"snippet": "...king Algorithms for Information Retrieval", "weights": [[8, 18, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "2011.wsdm_conference-2011.32": {"snippet": "...n of algorithms for this interactive retrieval setting. Furthermore, we propose two dynamic ranking algorithms, both of which are computationally efficient. We prove that these algorithms provide retr...", "weights": [[8, 18, 1.0], [40, 49, 1.0], [103, 113, 1.0], [180, 190, 1.0], [199, 208, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "1990.sigirconf_conference-90.27": {"snippet": "... for Information Retrieval, Based on the Theory of Relative Concentration\n\n\n ABSTRACTThis paper introduces a new method for information retrieval of documents that are represented by a vector. The nov...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [127, 138, 1.0], [139, 148, 1.0]]}, "Y18-1019": {"snippet": "... FCA Retrieval a fast and efficient algorithm for information retrieval from a binary concept lattice. This algorithm can respond to queries containing several types of logical operators (conjunction,...", "weights": [[8, 17, 1.0], [53, 64, 1.0], [65, 74, 1.0]]}, "1978.sigirconf_conference-78.8": {"snippet": "...tive retrieval, in which searches are performed using more than one key.A nearest neighbors associative retrieval algorithm, suitable for document retrieval using similarity matching, is described. Th...", "weights": [[8, 17, 1.0], [107, 116, 1.0], [150, 159, 1.0]]}, "2006.clef_workshop-2006w.1": {"snippet": "...sion Information Retrieval Method for WiQA\n\n\n This paper presents Wolverhampton University's participation in the WiQA competition. The method chosen for this task combines a high precision, but low r...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "C14-1057": {"snippet": "...ely, information retrieval research has focused on the retrieval of text documents and images, with almost no attention specifically directed toward the retrieval of information graphics. Our work is ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [58, 67, 1.0], [156, 165, 1.0], [169, 180, 1.0]]}, "2000.jasis_journal-ir0volumeA51A11.9": {"snippet": "...iew: Information retrieval: Algorithms and heuristics, by David A. Grossman and Ophir Frieder", "weights": [[8, 19, 1.0], [20, 29, 1.0], [31, 41, 1.0]]}, "1991.ipm_journal-ir0volumeA27A23.1": {"snippet": "...s of retrieval algorithms", "weights": [[8, 17, 1.0], [18, 28, 1.0]]}, "2021.ecir_conference-20212.83": {"snippet": "...tion algorithms has been traditionally the main objective for the industrial and academic research communities. However, recent studies have shown that optimizing models through these algorithms may r...", "weights": [[8, 18, 1.0], [187, 197, 1.0]]}, "I11-1117": {"snippet": "...sage Retrieval for Information Extraction using Distant Supervision\n\n\n In this paper, we propose a keyword-based passage retrieval algorithm for information extraction, trained by distant supervision....", "weights": [[8, 17, 1.0], [22, 33, 1.0], [124, 133, 1.0], [148, 159, 1.0]]}, "2014.sigirconf_conference-2014.219": {"snippet": "...3.3 [Information Search and Retrieval]: Retrieval modelsGeneral Terms: Algorithms Keywords: axiomatic analysis; information retrieval models; retrieval constraints; optimization\nOVERVIEWOne of the mos...", "weights": [[8, 19, 1.0], [31, 40, 1.0], [43, 52, 1.0], [74, 84, 1.0], [115, 126, 1.0], [127, 136, 1.0], [145, 154, 1.0]]}, "S15-2084": {"snippet": "...: An Information Retrieval-based System For Multi-Domain Sentiment Analysis\n\n\n This paper describes the SHELLFBK system that participated in SemEval 2015 Tasks 9, 10, and 11. Our system takes a superv...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2006.ecir_conference-2006.14": {"snippet": "... new algorithms that support topic detection, search, and navigation of news documents. This work presents an algorithm for topic detection that considers the temporal evolution of news and the struct...", "weights": [[8, 18, 1.0]]}, "2019.ecir_conference-20192.53": {"snippet": "...d in information retrieval, and also how techniques from IR can be used for automated algorithm selection and metalearning.\nMotivationThere is a plethora of algorithms for information retrieval applic...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [160, 170, 1.0], [175, 186, 1.0], [187, 196, 1.0]]}, "1992.trec_conference-1992.3": {"snippet": "...t in Information Retrieval Using Genetic Algorithms - A Report on the Experiments of the TREC Project", "weights": [[8, 19, 1.0], [20, 29, 1.0], [44, 54, 1.0]]}, "1981.sigirconf_conference-81.9": {"snippet": "...king Algorithms in Information Retrieval", "weights": [[8, 18, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ical information retrieval\n\n\n AbstractIn this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 153, 1.0], [154, 163, 1.0]]}, "1999.jasis_journal-ir0volumeA50A9.2": {"snippet": "...tive Information Retrieval Agent", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.16": {"snippet": "... for information access\n\n\n AbstractSince the middle of last century, information retrieval has gained an increasing interest. Since its inception, much research has been devoted to finding optimal way...", "weights": [[8, 19, 1.0], [72, 83, 1.0], [84, 93, 1.0]]}, "2010.sigirconf_conference-2010.173": {"snippet": "...ning retrieval scores (or the rankings) assigned by different retrieval models or ranking algorithms. In this work, we focus on the problem of choosing between retrieval models using performance estim...", "weights": [[8, 17, 1.0], [65, 74, 1.0], [93, 103, 1.0], [163, 172, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.19": {"snippet": "... and retrieval to access information quickly on social media. However, even with humongous information available, there are certain categories of images which are important for certain applications bu...", "weights": [[8, 17, 1.0], [28, 39, 1.0], [94, 105, 1.0]]}, "1996.tois_journal-ir0volumeA14A2.1": {"snippet": "...ient Information Retrieval with Redundant Information Filters\n\n\n We consider information retrieval when the data-for instance, multimedia-is computationally expensive to fetch. Our approach uses \"info...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [45, 56, 1.0], [80, 91, 1.0], [92, 101, 1.0], [199, 210, 1.0]]}, "2019.ecir_workshop-2019amir.2": {"snippet": "...rent algorithms on many problem instances, developers learn an intuitive mapping from some characteristics of a given problem instance (e.g., the number of features of a dataset) to a well-performing ...", "weights": [[8, 18, 1.0]]}, "2017.ictir_conference-2017.57": {"snippet": "...ndit algorithms; 2) application of bandits in information retrieval; 3) Optimization of information retrieval systems based on bandit algorithms. In the rst part will provide an overview of bandit alg...", "weights": [[8, 18, 1.0], [49, 60, 1.0], [61, 70, 1.0], [91, 102, 1.0], [103, 112, 1.0], [137, 147, 1.0], [200, 210, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "... for information retrieval\n\n\n ABSTRACTIn recent years, we have witnessed successful application of machine learning techniques to a wide range of information retrieval problems, including Web search e...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [149, 160, 1.0], [161, 170, 1.0]]}, "2012.ipm_journal-ir0volumeA48A5.11": {"snippet": "...k in information retrieval\n\n\n a b s t r a c tIn this paper we present a new algorithm for relevance feedback (RF) in information retrieval. Unlike conventional RF algorithms which use the top ranked d...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [120, 131, 1.0], [132, 141, 1.0], [166, 176, 1.0]]}, "2013.riao_conference-2013.25": {"snippet": "...nder algorithms for enhanced information retrieval", "weights": [[8, 18, 1.0], [32, 43, 1.0], [44, 53, 1.0]]}, "2000.ipm_journal-ir0volumeA36A5.2": {"snippet": "...etic algorithms to query optimization in document retrieval", "weights": [[8, 18, 1.0], [53, 62, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.25": {"snippet": "...ards information retrieval in complex networks\n\n\n Abstract Spanking information retrieval in large-scale Web and network has attracted increasing interest in the research community, many typical appro...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [71, 82, 1.0], [83, 92, 1.0]]}, "2004.airs_conference-2004.4": {"snippet": "...sian Information Criterion. Experimental results on two different corpora are given to show the effectiveness of our algorithm.", "weights": [[8, 19, 1.0]]}, "2004.ntcir_workshop-2004.24": {"snippet": "...text retrieval algorithm that is specialized for QA and a new algorithm for sorting the answer candidates. Using our new algorithm for text retrieval, articles containing the answer for the question c...", "weights": [[8, 17, 1.0], [143, 152, 1.0]]}, "1995.jasis_journal-ir0volumeA46A3.2": {"snippet": "... for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms", "weights": [[8, 19, 1.0], [20, 29, 1.0], [79, 89, 1.0]]}, "2008.cikm_workshop-2008lsdsir.10": {"snippet": "...uted information retrieval environments\n\n\n ABSTRACTIn this paper, a new source selection algorithm for uncooperative distributed information retrieval environments is presented. The algorithm function...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [132, 143, 1.0], [144, 153, 1.0]]}, "2020.ecir_conference-20202.84": {"snippet": "... the information retrieval community. In this workshop, we aim to collect novel contributions in this emerging field and to provide a common ground for interested researchers and practitioners.", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2018.wwwconf_conference-2018c.14": {"snippet": "...rent algorithms. Given a network G, the ASCD problem is \nA Community Detection ToolboxFigure 1: An illustration of the ASCD problem. A community detection toolbox, such as CoDeT , includes CNM, Spectr...", "weights": [[8, 18, 1.0]]}, "2002.sigirconf_conference-2002.21": {"snippet": "...line algorithms for category ranking\n\n\n ABSTRACTWe describe a new family of topic-ranking algorithms for multi-labeled documents. The motivation for the algorithms stems from recent advances in online...", "weights": [[8, 18, 1.0], [93, 103, 1.0], [156, 166, 1.0]]}, "1999.tois_journal-ir0volumeA17A2.2": {"snippet": "...ased Retrieval by Spatial Similarity in Image Databases\n\n\n A framework for retrieving images by spatial similarity (FRISS) in image databases is presented. In this framework, a robust retrieval by spa...", "weights": [[8, 17, 1.0], [187, 196, 1.0]]}, "P09-3008": {"snippet": "...odel Information Retrieval System with Expectation Maximization Algorithm\n\n\n Statistical language modeling (SLM) has been used in many different domains for decades and has also been applied to inform...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [197, 208, 1.0]]}, "2003.sigirconf_conference-2003.8": {"snippet": "...sage retrieval algorithms vary with the choice of document retriever, which suggests significant interactions between document retrieval and passage retrieval. The best algorithms in our evaluation em...", "weights": [[8, 17, 1.0], [18, 28, 1.0], [130, 139, 1.0], [152, 161, 1.0], [172, 182, 1.0]]}}}
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"2014.sigirconf_conference-2014.143"}, {"rank": 8, "score": 14.352198987089041, "doc_id": "2008.cikm_workshop-2008wicow.8"}, {"rank": 9, "score": 13.62176686511829, "doc_id": "P06-1129"}, {"rank": 10, "score": 13.59962281737521, "doc_id": "2020.emnlp-demos.21"}]}], "docs": {"N19-1326": {"snippet": "...t to misspellings. Existing word embeddings have limited applicability to malformed texts, which contain a non-negligible amount of outof-vocabulary words. We propose a method combining FastText with ...", "weights": [[8, 20, 1.0]]}, "2013.cikm_conference-2013.11": {"snippet": "...tual queries. However, these queries do not always include well-formed addresses or place names. They may contain transpositions, misspellings, fragments and so on. Queries may significantly differ fr...", "weights": [[8, 15, 1.0], [32, 39, 1.0], [133, 145, 1.0], [168, 175, 1.0]]}, "2020.coling-industry.12": {"snippet": "...s as misspellings. In this paper, we propose a method to automatically detect misspellings from product images in an attempt to reduce false positive detections. We curate a large scale corpus, define...", "weights": [[8, 20, 1.0], [81, 93, 1.0]]}, "2021.hcinlp-1.2": {"snippet": "Spellchecking for Children in Web Search: a Natural Language Interface Case-study\n\n\n Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are...", "weights": []}, "2005.ecir_conference-2005.8": {"snippet": "... and queries in vector spaces and build indexes for processing text-centric semi-structured queries that support a proximity measure between XML documents. The idea of using vector spaces for XML retr...", "weights": [[8, 15, 1.0], [95, 102, 1.0]]}, "2011.wwwconf_conference-2011.15": {"snippet": "...ines queries. In order to help users effectively express their information needs, mechanisms for automatically correcting misspelled queries are required. Online spelling correction aims to provide sp...", "weights": [[8, 15, 1.0], [136, 143, 1.0]]}, "2019.sigirconf_workshop-2019ecom.11": {"snippet": "...lled queries, modifying correct queries into wrong ones. In this paper, three novel components / algorithms currently used in The Home Depot (THD) spell correction service is presented: (1) word embed...", "weights": [[8, 15, 1.0], [35, 42, 1.0]]}, "2012.wwwconf_conference-2012c.141": {"snippet": "CloudSpeller: query spelling correction by using a unified hidden markov model with web-scale resources\n\n\n ABSTRACTQuery spelling correction is an important component of modern search engines that can...", "weights": []}, "2013.tois_journal-ir0volumeA31A4.1": {"snippet": "...many queries, such approaches also produce retrieval performance that is poorer than no stemming, thereby degrading the robustness. The main goal of this article is to present corpus-based fully autom...", "weights": [[8, 15, 1.0]]}, "2011.ipm_journal-ir0volumeA47A2.9": {"snippet": "...lled queries in IR applications", "weights": [[8, 15, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...rse\" queries were more effective than the \"short\" queries. This unlikely result may be because of how queries were constructed. In the example listed, the terse query is a phrase that is probably used...", "weights": [[8, 15, 1.0], [53, 60, 1.0], [105, 112, 1.0]]}, "2001.tois_journal-ir0volumeA19A1.2": {"snippet": "...ural queries, which retrieve configurations matching a particular spatio-temporal structure, and (iv) special cases of motion queries. Considering the current large availability of multidimensional da...", "weights": [[8, 15, 1.0], [129, 136, 1.0]]}, "2012.wwwconf_conference-2012c.168": {"snippet": "Query spelling correction using multi-task learning\n\n\n ABSTRACTThis paper explores the use of online multi-task learning for search query spelling correction, by effectively transferring information f...", "weights": []}, "2004.tois_journal-ir0volumeA22A4.2": {"snippet": "...uate queries extremely fast, a challenging task given the quantities of data being indexed. A significant proportion of the queries posed to search engines involve phrases. In this article we consider...", "weights": [[8, 15, 1.0], [127, 134, 1.0]]}, "D14-1171": {"snippet": "...ding misspellings fast. However, the classical approach is based on a quadratic time algorithm with 80% coverage. We present a novel algorithm for misspelling detection, which runs in constant time an...", "weights": [[8, 20, 1.0]]}, "2021.eacl-main.250": {"snippet": "...t to misspellings and perturbations of the input text. We empirically demonstrate that the LSH projection based classifiers are more robust to common misspellings compared to BiL-STMs (with both word-...", "weights": [[8, 20, 1.0], [153, 165, 1.0]]}, "2008.tois_journal-ir0volumeA27A1.3": {"snippet": "Extended probabilistic HAL with close temporal association for psychiatric query document retrieval\n\n\n Psychiatric query document retrieval can assist individuals to locate query documents relevant to...", "weights": []}, "2022.findings-acl.98": {"snippet": "{MDCS}pell: A Multi-task Detector-Corrector Framework for {C}hinese Spelling Correction\n\n\n Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts. CSC...", "weights": []}, "2016.sigirconf_conference-2016.43": {"snippet": "...ting queries to explore diverse aspects and deciding when to stop searching. With the goal of helping searchers discover unexplored aspects and find the appropriate timing for search stopping in intri...", "weights": [[8, 15, 1.0]]}, "1987.ipm_journal-ir0volumeA23A5.7": {"snippet": "... the misspellings of poor spellers", "weights": [[8, 20, 1.0]]}, "2004.cikm_conference-2004.7": {"snippet": "Scoring missing terms in information retrieval tasks\n\n\n ABSTRACTAn usual approach to address mismatching vocabulary problem is to augment the original query using dictionaries and other lexical resour...", "weights": []}, "2016.clef_conference-2016.7": {"snippet": "...tive queries combining the users' native language and English language with lay and medico-scientific terminology. To assess how the language and terminology impact the use of suggestions, we conducte...", "weights": [[8, 15, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.9": {"snippet": "...lled queries in Cross-Language Information Retrieval", "weights": [[8, 15, 1.0]]}, "2012.tois_journal-ir0volumeA30A1.1": {"snippet": "Static index pruning in web search engines: Combining term and document popularities with query views\n\n\n Static index pruning techniques permanently remove a presumably redundant part of an inverted f...", "weights": []}, "H05-1120": {"snippet": "Learning a Spelling Error Model from Search Query Logs\n\n\n Applying the noisy channel model to search query spelling correction requires an error model and a language model. Typically, the error model ...", "weights": []}, "W15-1901": {"snippet": "Misspellings in Responses to Listening Comprehension Questions: Prospects for Scoring based on Phonetic Normalization\n\n\n Automated scoring systems which evaluate content require robust ways of dealing...", "weights": [[0, 12, 1.0]]}, "2020.findings-emnlp.37": {"snippet": "...from misspellings. On the contrary, humans can easily infer the corresponding correct words from their misspellings and surrounding context. Inspired by this, we address the standalone spelling correc...", "weights": [[8, 20, 1.0], [106, 118, 1.0]]}, "P06-1129": {"snippet": "Exploring Distributional Similarity Based Models for Query Spelling Correction\n\n\n A query speller is crucial to search engine in improving web search relevance. This paper describes novel methods for ...", "weights": []}, "2013.tois_journal-ir0volumeA31A3.3": {"snippet": "...user queries with lists of ten blue links to webpages, people are increasingly issuing queries to accomplish their daily tasks (e.g., finding a recipe, booking a flight, reading online news, etc.). In...", "weights": [[8, 15, 1.0], [90, 97, 1.0]]}, "2015.jasis_journal-ir0volumeA66A6.15": {"snippet": "On searching misspelled collections", "weights": []}, "2002.tois_journal-ir0volumeA20A3.1": {"snippet": "...uage queries\n\n\n Missing information, imprecision, inconsistency, vagueness, uncertainty, and ignorance abound in information systems. Such imperfection is a fact of life in database systems. Although ...", "weights": [[8, 15, 1.0]]}, "L18-1134": {"snippet": "... all queries for lexical entries not currently in the database are indistinguishable from a larger list of misspellings, meaning that potential new or trending entries can get lost easily. In this pro...", "weights": [[8, 15, 1.0], [110, 122, 1.0]]}, "2020.emnlp-demos.21": {"snippet": "...ring misspellings from multiple sources. We find that many systems do not adequately leverage the context around the misspelt token. To remedy this, (i) we train neural models using spelling errors in...", "weights": [[8, 20, 1.0]]}, "R13-1023": {"snippet": "... for misspellings. The method is unsupervised and combines the application of anomalous pattern initialization and partition around medoids (PAM). To evaluate the method, we used an English misspellin...", "weights": [[8, 20, 1.0]]}, "2007.clef_workshop-2007.98": {"snippet": "... Web Queries\n\n\n Abstract. In this paper we present preliminary results for a patternbased approach to parse web-based queries. The approach is designed to identify and categorize queries that include ...", "weights": [[8, 15, 1.0], [121, 128, 1.0], [182, 189, 1.0]]}, "2014.sigirconf_conference-2014.143": {"snippet": "...arch queries, in particular by avoiding common mistakes such as misspellings and using particularly ambiguous queries.\nCategories and Subject DescriptorsHowever, the details of user's interactions wit...", "weights": [[8, 15, 1.0], [67, 79, 1.0], [113, 120, 1.0]]}, "2012.sigirconf_conference-2012.63": {"snippet": "A generalized hidden Markov model with discriminative training for query spelling correction\n\n\n ABSTRACTQuery spelling correction is a crucial component of modern search engines. Existing methods in t...", "weights": []}, "2006.tois_journal-ir0volumeA24A1.1": {"snippet": "...ssed queries. SDC extracts from historical usage data the results of the most frequently submitted queries and stores them in a static, read-only portion of the cache. The remaining entries of the cac...", "weights": [[8, 15, 1.0], [102, 109, 1.0]]}, "O12-1012": {"snippet": "\u5b57\u5f62\u76f8\u4f3c\u5225\u5b57\u4e4b\u81ea\u52d5\u6821\u6b63\u65b9\u6cd5 (Automatic Correction for Graphemic {C}hinese Misspelled Words) [In {C}hinese]\n\n\n No matter that learning Chinese as a first or second language, a quite important issue, misspelled words...", "weights": []}, "W19-4429": {"snippet": "Anglicized Words and Misspelled Cognates in Native Language Identification\n\n\n In this paper, we present experiments that estimate the impact of specific lexical choices of people writing in a second l...", "weights": []}, "2009.ipm_journal-ir0volumeA45A2.3": {"snippet": "...uous queries in web search\n\n\n a b s t r a c tIt is widely believed that many queries submitted to search engines are inherently ambiguous (e.g., java and apple). However, few studies have tried to cla...", "weights": [[8, 15, 1.0], [80, 87, 1.0]]}, "N10-1064": {"snippet": "...ling misspellings. The enhanced system decodes a word-based confusion network representing spelling variations of the input text. We present extensive experimental results on two translation tasks of ...", "weights": [[8, 20, 1.0]]}, "2007.cikm_conference-2007.4": {"snippet": "...ting queries for xml\n\n\n ABSTRACTIt has been observed that queries over XML data sources are often unsatisfiable. Unsatisfiability may stem from several different sources, e.g., the user may be insuffi...", "weights": [[8, 15, 1.0], [61, 68, 1.0]]}, "2010.tweb_journal-ir0volumeA4A4.2": {"snippet": "...gine Queries\n\n\n Web search engines can perform poorly for long queries (i.e., those containing four or more terms), in part because of their high level of query specificity. The automatic assignment o...", "weights": [[8, 15, 1.0], [66, 73, 1.0]]}, "2001.tois_journal-ir0volumeA19A4.1": {"snippet": "...ical queries over XML data\n\n\n The rapid evolution of XML from a mere data exchange format to a universal syntax for encoding domain-specific information raises the need for new query languages specifi...", "weights": [[8, 15, 1.0]]}, "Y11-1032": {"snippet": "...s\n\n\n Misspellings, abbreviations and acronyms are very popular in clinical notes and can be an obstacle to high quality information extraction and classification. In addition, another important part o...", "weights": [[8, 20, 1.0]]}, "A97-1025": {"snippet": "Contextual Spelling Correction Using Latent Semantic Analysis\n\n\n Contextual spelling errors are defined as the use of an incorrect, though valid, word in a particular sentence or context. Traditional ...", "weights": []}, "2008.cikm_workshop-2008wicow.8": {"snippet": "...gine queries on a pre-determined set of commonly misspelled words.", "weights": [[8, 15, 1.0]]}, "2019.wsdm_conference-2019.9": {"snippet": "...sive misspellings and user-created abbreviations for these mentions. In addition, these mentions are usually context dependent. In this paper, we propose a neural approach with hierarchical tweet repr...", "weights": [[8, 20, 1.0]]}, "2012.cikm_conference-2012.94": {"snippet": "...s of queries over SQL databases\n\n\n ABSTRACTData completeness is an important aspect of data quality. We consider a setting, where databases can be incomplete in two ways: records may be missing and re...", "weights": [[8, 15, 1.0]]}, "W18-1208": {"snippet": "Fast Query Expansion on an Accounting Corpus using Sub-Word Embeddings\n\n\n We present early results from a system under development which uses sub-word embeddings for query expansion in the presence of...", "weights": []}, "2016.tois_journal-ir0volumeA35A2.0": {"snippet": "...arch queries by different languages. Hence, the search engine query log, which serves as the backbone of many search engine applications, records millions of users' search history in a wide spectrum o...", "weights": [[8, 15, 1.0]]}, "2006.wwwjournals_journal-ir0volumeA9A4.1": {"snippet": "...s of queries per second with interactive response times. Due to the sizes of the data sets involved, often in the range of multiple terabytes, a single query may require the processing of hundreds of ...", "weights": [[8, 15, 1.0]]}, "P19-1561": {"snippet": "...rial Misspellings with Robust Word Recognition\n\n\n To combat adversarial spelling mistakes, we propose placing a word recognition model in front of the downstream classifier. Our word recognition model...", "weights": [[8, 20, 1.0]]}, "2015.fire_conference-2015.10": {"snippet": "MESS: A Multilingual Error based String Similarity measure for transliterated name variants\n\n\n ABSTRACTCross-lingual name matching is an important problem in the fields of machine translation and data...", "weights": []}, "W12-2012": {"snippet": "...word misspellings in student essays\n\n\n In this paper we present a new spell-checking system that utilizes contextual information for automatic correction of non-word misspellings. The system is evalua...", "weights": [[8, 20, 1.0], [169, 181, 1.0]]}}}
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Experiments on 20 languages and 294 distant language pairs demonstrate the ...", "weights": [[8, 16, 1.0], [93, 101, 1.0], [172, 180, 1.0]]}, "2004.clef_workshop-2004.13": {"snippet": "...ross-Language Information Retrieval: An Experiment at CLEF 2004", "weights": [[8, 16, 1.0], [17, 28, 1.0]]}, "2012.wwwconf_conference-2012c.153": {"snippet": "...lish language, Yoruba language, and other commonly used \"pidgin\" Yoruba language words. Since Yoruba is still a resource constrained language, existing sentiment or subjectivity analysis algorithms ha...", "weights": [[8, 16, 1.0], [25, 33, 1.0], [75, 83, 1.0], [136, 144, 1.0]]}, "2016.ictir_conference-2016.45": {"snippet": "...ross-language information retrieval approaches. In this paper, we address the task of retrieving relevant tweets given another tweet in a different language. We build parallel corpora for tweets in di...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [141, 150, 1.0], [151, 159, 1.0], [201, 210, 1.0]]}, "E17-2102": {"snippet": "...with language vectors\n\n\n Most existing models for multilingual natural language processing (NLP) treat language as a discrete category, and make predictions for either one language or the other. In co...", "weights": [[8, 16, 1.0], [74, 82, 1.0], [106, 114, 1.0], [175, 183, 1.0]]}, "2021.vardial-1.8": {"snippet": "...atic language identification is a challenging problem. Discriminating between closely related languages is especially difficult. This paper presents a machine learning approach for automatic language ...", "weights": [[8, 16, 1.0], [194, 202, 1.0]]}, "1990.tois_journal-ir0volumeA8A4.0": {"snippet": "...bout Information Systems\n\n\n We describe Telos, a language intended to support the development of information systems. 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The sys...", "weights": [[8, 16, 1.0], [151, 159, 1.0], [186, 194, 1.0]]}, "2022.acl-long.504": {"snippet": "...cial Language: Studying Transferable Knowledge in Language Models\n\n\n We investigate what kind of structural knowledge learned in neural network encoders is transferable to processing natural language....", "weights": [[8, 16, 1.0], [53, 61, 1.0], [194, 202, 1.0]]}, "2018.fire_conference-2018.5": {"snippet": "... and Information Extraction (IE) tasks. This overview paper will be detailing more on the existing Indian language corpora development and the steps taken for building our own corpus along with its st", "weights": [[8, 19, 1.0], [109, 117, 1.0]]}, "I08-3008": {"snippet": "...ross-Language Parser Adaptation between Related Languages\n\n\n The present paper describes an approach to adapting a parser to a new language. Presumably the target language is much poorer in linguistic...", "weights": [[8, 16, 1.0], [134, 142, 1.0], [166, 174, 1.0]]}, "2001.clef_workshop-2000w.5": {"snippet": "...tudy different types of compound languages, and to study the effects query structuring in different languages. We designed and implemented a method for automated query construction in FIN \uf8ecSWE \uf8ecGER-> ...", "weights": [[8, 17, 1.0], [93, 102, 1.0]]}, "2022.naacl-main.114": {"snippet": "...rive language-universal abstractions about grammar. In particular, we investigate whether morphosyntactic information is encoded in the same subset of neurons in different languages. We conduct the fi...", "weights": [[8, 16, 1.0], [109, 120, 1.0], [165, 174, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.5": {"snippet": "...sian language, and then unifies the extracted terms and their equivalences in the native language as one index unit. For Cross-Language Information Retrieval (CLIR), one of the major hindrances to ach...", "weights": [[8, 16, 1.0], [92, 100, 1.0], [130, 138, 1.0], [139, 150, 1.0]]}, "C18-1293": {"snippet": "...tive language identification. Punctuation is specific to each language, and is part of the indicators that overtly represent the manner in which each language organizes and conveys information. Our ex...", "weights": [[8, 16, 1.0], [65, 73, 1.0], [153, 161, 1.0], [184, 195, 1.0]]}, "D19-1089": {"snippet": "... for language clustering: (1) using prior knowledge, where we cluster languages according to language family, and (2) using language embedding, in which we represent each language by an embedding vect...", "weights": [[8, 16, 1.0], [96, 104, 1.0], [127, 135, 1.0], [174, 182, 1.0]]}, "2015.wwwconf_conference-2015c.204": {"snippet": "...eign Language\n\n\n ABSTRACTOnline searching is a central element of internet users' information behaviors. Searching is usually executed in a user's native language, but searching in English as a foreig...", "weights": [[8, 16, 1.0], [85, 96, 1.0], [157, 165, 1.0]]}, "2021.mrl-1.11": {"snippet": "...gual Language Models for Low-resourced Languages\n\n\n Pretrained multilingual language models have been shown to work well on many languages for a variety of downstream NLP tasks. However, these models ...", "weights": [[8, 16, 1.0], [79, 87, 1.0]]}, "2002.sigirconf_conference-2002.7": {"snippet": "...itle language model for information retrieval\n\n\n ABSTRACTIn this paper, we propose a new language model, namely, a title language model, for information retrieval. Different from the traditional langu...", "weights": [[8, 16, 1.0], [27, 38, 1.0], [92, 100, 1.0], [124, 132, 1.0], [144, 155, 1.0], [167, 176, 1.0], [198, 206, 1.0]]}, "2015.wwwconf_conference-2015c.201": {"snippet": "...iple language pairs and for two very different CLIR tasks. Our experiments show that the MT systems perform differently on average for different tasks and language pairs, but more significantly for di...", "weights": [[8, 16, 1.0], [40, 49, 1.0], [138, 147, 1.0], [158, 166, 1.0], [201, 210, 1.0]]}, "I08-6001": {"snippet": "...s of Language Relatedness on Multilingual Information Retrieval: A Case Study With {I}ndo-{E}uropean and {S}emitic Languages\n\n\n We explore the effects of language relatedness within a multilingual inf...", "weights": [[8, 16, 1.0], [45, 56, 1.0], [157, 165, 1.0], [200, 211, 1.0]]}, "R15-1065": {"snippet": "...ural Language Query into Different {SQL} Clauses\n\n\n Attribute information in a natural language query is one of the key features for converting a natural language query into a Structured Query Languag...", "weights": [[8, 16, 1.0], [28, 37, 1.0], [65, 76, 1.0], [90, 98, 1.0], [157, 165, 1.0], [196, 204, 1.0]]}, "2004.clef_workshop-2004.33": {"snippet": "...ross-Language Question Answering: Searching Passages Versus Searching Documents\n\n\n Abstract. iCLEF 2004 is the first comparative evaluation of interactive Cross-Language Question Answering systems. Th...", "weights": [[8, 16, 1.0], [164, 172, 1.0]]}, "L16-1710": {"snippet": "... and Information for Language and Linguistic Analysis ({GORILLA})\n\n\n The infrastructure Global Open Resources and Information for Language and Linguistic Analysis (GORILLA) was created as a resource t...", "weights": [[8, 19, 1.0], [24, 32, 1.0], [117, 128, 1.0], [133, 141, 1.0]]}, "2004.clef_workshop-2004.66": {"snippet": "...ross-Language and Cross-Media Image Retrieval", "weights": [[8, 16, 1.0]]}, "W14-4207": {"snippet": "...ring Language Closeness by Modeling Regularity\n\n\n This paper addresses the problems of measuring similarity between languageswhere the term language covers any of the senses denoted by language, diale...", "weights": [[8, 16, 1.0], [143, 151, 1.0], [188, 196, 1.0]]}, "2016.sigirconf_conference-2016.27": {"snippet": "...rget language, and the classification performance is far from satisfactory because of the language gap between the source language and the translated target language.In this paper, to address the abov...", "weights": [[8, 16, 1.0], [93, 101, 1.0], [125, 133, 1.0], [160, 168, 1.0]]}, "2016.chiir_conference-2016.62": {"snippet": "...cond language or foreign languages when they search for information online. The difference between a second language and a foreign language is that the second language is spoken in the immediate envir...", "weights": [[8, 16, 1.0], [59, 70, 1.0], [111, 119, 1.0], [134, 142, 1.0], [162, 170, 1.0]]}, "1999.sigirconf_conference-99.43": {"snippet": "...ding language models of these documents. One obstacle in applying statistical language modeling to information retrieval is the sparse data problem. It is serious in information retrieval for two reas...", "weights": [[8, 16, 1.0], [81, 89, 1.0], [102, 113, 1.0], [169, 180, 1.0]]}, "2004.ipm_journal-ir0volumeA40A6.3": {"snippet": "... the information in languages other than English has grown exponentially. Cross-lingual information retrieval (CLIR) across different European languages, such as English, Spanish, and French, has been...", "weights": [[8, 19, 1.0], [91, 102, 1.0], [127, 136, 1.0]]}, "2020.ldl-1.4": {"snippet": "...ning different types of knowledge about a given natural language. As a case study, we develop and present a language profile of an example language. At this stage, a language profile is an independent...", "weights": [[8, 17, 1.0], [59, 67, 1.0], [111, 119, 1.0], [142, 150, 1.0], [169, 177, 1.0]]}, "1999.trec_conference-1999.18": {"snippet": "...many different languages. Compared to the usual definition of cross-language information retrieval, where systems work with a single language pair, retrieving documents in a language L1 using queries ...", "weights": [[8, 17, 1.0], [71, 79, 1.0], [80, 91, 1.0], [136, 144, 1.0], [177, 185, 1.0]]}, "N03-1025": {"snippet": "...mple Language Models\n\n\n We present a simple method for language independent and task independent text categorization learning, based on character-level n-gram language models. Our approach uses simple...", "weights": [[8, 16, 1.0], [58, 66, 1.0], [162, 170, 1.0]]}, "2011.sigirconf_conference-2011.76": {"snippet": "...n in different languages about a hot news event. The news articles in different languages are written in different ways to reflect different standpoints. For example, the Chinese news agencies and the...", "weights": [[8, 17, 1.0], [73, 82, 1.0], [108, 117, 1.0], [134, 143, 1.0]]}, "2013.fire_conference-2013.11": {"snippet": "...ther language and even more challenging if translation language uses a different script and syntactical structure. This year, in CLINSS, focus was on journalistic text reuse between texts written in t...", "weights": [[8, 16, 1.0], [58, 66, 1.0], [74, 83, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.11": {"snippet": "...urce language differ from the grammatical structures of the target language because the acquired rules have the information to cope with the different word orders of source language and target languag...", "weights": [[8, 16, 1.0], [70, 78, 1.0], [115, 126, 1.0], [144, 153, 1.0], [176, 184, 1.0], [196, 204, 1.0]]}, "2016.sigirconf_workshop-2016sal.6": {"snippet": "...cond-Language Searching\n\n\n In this paper we describe learning that happens when searchers look for information in a foreign language. A strong disparity exists between the language distribution of Web...", "weights": [[8, 16, 1.0], [102, 113, 1.0], [127, 135, 1.0], [175, 183, 1.0]]}, "H89-2077": {"snippet": "...same language. The ultimate goal of work on spoken language systems is to overcome this language barrier by building systems that provide the necessary interpretive function between various languages,...", "weights": [[8, 16, 1.0], [54, 62, 1.0], [91, 99, 1.0]]}, "W16-3716": {"snippet": "...ross-Language Information Retrieval using Multilingual Word Clusters\n\n\n In Cross-Language Information Retrieval, finding the appropriate translation of the source language query has always been a diff...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [84, 92, 1.0], [93, 104, 1.0], [166, 174, 1.0]]}, "1963.ipm_journal-ir0volumeA1A23.3": {"snippet": "...n an information language for geometry and a translation algorithm from Russian into this information language", "weights": [[8, 19, 1.0], [20, 28, 1.0], [93, 104, 1.0], [105, 113, 1.0]]}, "P19-1395": {"snippet": "... one language. However, current representations in machine learning are language dependent. In this work, we present a method to decouple the language from the problem by learning language agnostic re...", "weights": [[8, 16, 1.0], [75, 83, 1.0], [145, 153, 1.0], [183, 191, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.7": {"snippet": "...acy. Information retrieval tests on KTSET and NTCIR-1 test collection show that our transliteration model can improve the performance of an information retrieval system about 10-34%.", "weights": [[8, 19, 1.0], [143, 154, 1.0]]}, "N10-1027": {"snippet": "Language Identification: The Long and the Short of the Matter\n\n\n Language identification is the task of identifying the language a given document is written in. This paper describes a detailed examina...", "weights": [[0, 8, 1.0], [65, 73, 1.0], [120, 128, 1.0]]}, "2004.clef_workshop-2004.79": {"snippet": "...ross-Language Spoken Document Retrieval Track", "weights": [[8, 16, 1.0]]}, "2020.lrec-1.458": {"snippet": "...ghly language-specific: adding more languages to a training set is often better, but too many harms performance-the best number depends on the source language. Furthermore, training on related languag...", "weights": [[8, 16, 1.0], [153, 161, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.13": {"snippet": "... The language modeling approach to information retrieval has recently attracted much attention. In the language modeling approach, we assume that a query is a sample drawn from a language model: given...", "weights": [[8, 16, 1.0], [38, 49, 1.0], [106, 114, 1.0], [182, 190, 1.0]]}, "L08-1007": {"snippet": "Language-Sites: Accessing and Presenting Language Resources via Geographic Information Systems", "weights": [[0, 8, 1.0], [41, 49, 1.0], [75, 86, 1.0]]}, "2011.clef_workshop-2011w.44": {"snippet": "...face Language, User Language and Success Rates in the European Library\n\n\n In this paper, TEL 2010 action logs are analyzed with a particular focus on the impact of language (user native language and i...", "weights": [[8, 16, 1.0], [23, 31, 1.0], [167, 175, 1.0], [189, 197, 1.0]]}, "W13-0206": {"snippet": "...free language and a regular language results in a context-free language. Previous work on subsumption and entailment is inapplicable to context-free languages, due to the undecidability of the subset ...", "weights": [[8, 16, 1.0], [31, 39, 1.0], [66, 74, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [42, 53, 1.0], [92, 103, 1.0], [122, 130, 1.0], [147, 158, 1.0]]}, "C69-3401": {"snippet": "...ible Language System (The {REL} Language Processor)\n\n\n REL, Rapidly Extenslble Language System, permits a variety of languages to coexist within a single computer system. Here the term \"language\" is u...", "weights": [[8, 16, 1.0], [35, 43, 1.0], [82, 90, 1.0], [189, 197, 1.0]]}, "W03-1120": {"snippet": "...ross-Language Information Retrieval Based on Category Matching Between Language Versions of a Web Directory\n\n\n Since the Web consists of documents in various domains or genres, the method for Cross-La...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [74, 82, 1.0], [201, 209, 1.0]]}, "U05-1012": {"snippet": "...ajor language (the base language) to 'fake' TTS for a minor language (the target language). This paper describes the design for an experiment which involves finding a suitable base language for the Au...", "weights": [[8, 16, 1.0], [27, 35, 1.0], [63, 71, 1.0], [84, 92, 1.0], [184, 192, 1.0]]}, "E17-1087": {"snippet": "...gual Language Identification on Character Window\n\n\n In language identification, a common first step in natural language processing, we want to automatically determine the language of some input text. ...", "weights": [[8, 16, 1.0], [58, 66, 1.0], [114, 122, 1.0], [174, 182, 1.0]]}, "2016.fire_conference-2016.3": {"snippet": "...gual Information Retrieval for Bantu Languages\n\n\n ABSTRACTDespite the availability of massive open information and efforts to promote multilingualism on the Web, content in Bantu languages remains neg...", "weights": [[8, 19, 1.0], [102, 113, 1.0]]}, "2022.acl-long.539": {"snippet": "... can language technology address the diverse situations of the world's languages? In one view, languages exist on a resource continuum and the challenge is to scale existing solutions, bringing under-...", "weights": [[8, 16, 1.0]]}, "I08-1058": {"snippet": "...me a different language is used. This is cumbersome, especially when language changes are frequent. To solve this problem, we propose TypeAny, a novel front-end interface that detects the language of ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [72, 80, 1.0], [191, 199, 1.0]]}, "2003.clef_workshop-2003w.36": {"snippet": "...ross-language Information Retrieval\n\n\n The aim of this project is the automatic conversion of query terms in one language into their equivalents in a second, historically related, language, so that do...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [116, 124, 1.0], [183, 191, 1.0]]}, "N18-2085": {"snippet": "...same information. We then conduct a study on 21 languages, demonstrating that in some languages, the textual expression of the information is harder to predict with both n-gram and LSTM language model...", "weights": [[8, 19, 1.0], [130, 141, 1.0], [189, 197, 1.0]]}, "2011.ipm_journal-ir0volumeA47A5.0": {"snippet": "...e of information processing management\n\n\n Due to the popularity of the World Wide Web and the advance of Internet search engines, information in many different languages is accessible online nowadays....", "weights": [[8, 19, 1.0], [133, 144, 1.0], [153, 162, 1.0]]}, "2007.ntcir_workshop-2007.77": {"snippet": "...gram Language Models for Monolingual and Cross-Language IR\n\n\n Due to the lack of explicit word boundaries in Chinese, and Japanese, and to some extent in Korean, an additional problem in IR in these l...", "weights": [[8, 16, 1.0], [50, 58, 1.0]]}, "D17-1053": {"snippet": "...ific language, and different classification models are used in different languages. In this paper we aim to build a universal sentiment classifier with a single classification model in multiple differ...", "weights": [[8, 16, 1.0], [22, 31, 1.0], [66, 75, 1.0], [197, 206, 1.0]]}, "2013.cikm_conference-2013.185": {"snippet": "...ross language similarity and activity\n\n\n ABSTRACTWikipedia is an online free and open access encyclopedia available in many languages. Wikipedia articles across over 280 languages are written by milli...", "weights": [[8, 16, 1.0]]}, "2021.bea-1.7": {"snippet": "...tive language transfer in learner errors using {POS} information\n\n\n A common mistake made by language learners is the misguided usage of first language rules when communicating in another language. In...", "weights": [[8, 16, 1.0], [56, 67, 1.0], [96, 104, 1.0], [146, 154, 1.0], [191, 199, 1.0]]}, "C10-2128": {"snippet": "...rget language and then be used for automated reasoning. We present and compare the most mature of these novel languages, show how they can balance the disadvantages of natural languages and formal lan...", "weights": [[8, 16, 1.0]]}, "2022.computel-1.4": {"snippet": "...2$}} Language Documentation Meets Natural Language Processing for Revitalising Endangered Languages\n\n\n Language revitalisation should not be understood as a direct outcome of language documentation, w...", "weights": [[8, 16, 1.0], [45, 53, 1.0], [106, 114, 1.0], [178, 186, 1.0]]}, "2013.ecir_conference-2013.66": {"snippet": "...ross-Language Plagiarism Detection Using a Multilingual Semantic Network\n\n\n Abstract. Cross-language plagiarism refers to the type of plagiarism where the source and suspicious documents are in differ...", "weights": [[8, 16, 1.0], [95, 103, 1.0], [197, 206, 1.0]]}, "2004.clef_workshop-2004.22": {"snippet": "... our information retrieval system to document languages other than English, and completing the multilingual task comprising four languages: English, French, Russian and Finnish. Our retrieval system i...", "weights": [[8, 19, 1.0]]}, "N16-2017": {"snippet": "...late language models trained on the target language and on the related language. In our second experiment, we select the sentences most similar to the target language and add them to our training corp...", "weights": [[8, 16, 1.0], [46, 54, 1.0], [74, 82, 1.0], [161, 169, 1.0]]}, "2013.wwwconf_conference-2013c.27": {"snippet": "...tive language. This makes it non-trivial for a machine to distinguish the language of one comment from another. This situation presents an opportunity for offering following language based services fo...", "weights": [[8, 16, 1.0], [77, 85, 1.0], [177, 185, 1.0]]}, "P11-1155": {"snippet": "... the information from the original documents in the source language or the information from the translated documents in the target language. In this study, we propose to use the bilingual information ...", "weights": [[8, 19, 1.0], [62, 70, 1.0], [78, 89, 1.0], [134, 142, 1.0], [191, 202, 1.0]]}, "2004.clef_workshop-2004.59": {"snippet": "...ross-Language Image Retrieval Track\n\n\n Abstract. The purpose of this paper is to outline efforts from the 2004 CLEF cross-language image retrieval campaign (ImageCLEF). The aim of this CLEF track is t...", "weights": [[8, 16, 1.0], [125, 133, 1.0]]}, "Y05-1027": {"snippet": "Language Identification for Person Names Based on Statistical Information\n\n\n Language identification has been an interesting and fascinating issue in natural language processing for decades, and there...", "weights": [[0, 8, 1.0], [62, 73, 1.0], [77, 85, 1.0], [158, 166, 1.0]]}, "2018.fire_conference-2018w.14": {"snippet": "...tive Language Identification\n\n\n Native Language Identification (NLI) is the task which involves identification of native language (L1) of an individual based on his/her language production in a learne...", "weights": [[8, 16, 1.0], [42, 50, 1.0], [124, 132, 1.0], [172, 180, 1.0]]}, "1998.sigirconf_conference-98.8": {"snippet": "...urce language word is translated to a target language by replacing it with all of its target language equivalents. 'The INQUERY information retrieval system is provided by the Information", "weights": [[8, 16, 1.0], [48, 56, 1.0], [96, 104, 1.0], [131, 142, 1.0], [179, 190, 1.0]]}, "2020.coling-main.141": {"snippet": "...atic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines. Lan...", "weights": [[8, 16, 1.0], [169, 177, 1.0], [200, 208, 1.0]]}, "C12-1057": {"snippet": "... use information from documents in the source language. We overcome this limitation by using information from retrieved documents in the source language to improve the retrieval quality of the target ...", "weights": [[8, 19, 1.0], [49, 57, 1.0], [96, 107, 1.0], [147, 155, 1.0]]}, "2006.wwwconf_conference-2006.201": {"snippet": "... the different data formats available on the Web. In contrast, Xcerpt is a versatile semi-structured query language, i.e., a query language able to access all kinds of Web data such as XML and RDF in ...", "weights": [[8, 17, 1.0], [110, 118, 1.0], [134, 142, 1.0]]}, "P16-1176": {"snippet": "...tive language and translations are closer to each other than each of them is to native language; and (3) some of these characteristics depend on the source or native language, while others do not, ref...", "weights": [[8, 16, 1.0], [90, 98, 1.0], [169, 177, 1.0]]}, "O00-3003": {"snippet": "...gual information can be divided into two major categories: (1) alphabetic language information (English-like alphabetic languages) and ( 2 ) ideographic language information (Chinese-like ideographic ...", "weights": [[8, 19, 1.0], [77, 85, 1.0], [86, 97, 1.0], [156, 164, 1.0], [165, 176, 1.0]]}, "2004.clef_workshop-2004.45": {"snippet": "...n of Language Resources\n\n\n Abstract. This paper describes the Bulgarian part of a BulgarianEnglish question answering system. The Bulgarian modules are implemented as a question analysis procedure wit...", "weights": [[8, 16, 1.0]]}, "W15-4623": {"snippet": "...oken information access in English. Our new demo shows a robot speaking different languages, getting content from different language Wikipedias, and switching languages to meet the linguistic capabili...", "weights": [[8, 19, 1.0], [75, 84, 1.0], [117, 126, 1.0], [127, 135, 1.0]]}, "W05-0705": {"snippet": "...ural Language Processing System for {E}uropean Languages to Treat {A}rabic in Information Processing and Information Retrieval Applications\n\n\n The goal of many natural language processing platforms is...", "weights": [[8, 16, 1.0], [81, 92, 1.0], [108, 119, 1.0], [171, 179, 1.0]]}, "D17-1302": {"snippet": "...with language-adversarial training and bidirectional language modeling as auxiliary objectives to better represent language-general information while not losing the information about a specific target...", "weights": [[8, 16, 1.0], [56, 64, 1.0], [118, 126, 1.0], [135, 146, 1.0], [168, 179, 1.0]]}, "1997.mtsummit-workshop.6": {"snippet": "..., on language-specific information in an interlingual approach to machine translation. At that workshop I argued that language-specific information must be present in the IL representation, since any ...", "weights": [[8, 16, 1.0], [26, 37, 1.0], [121, 129, 1.0], [139, 150, 1.0]]}, "2013.fire_conference-2013.10": {"snippet": "...ross Language News Story Search\n\n\n ABSTRACTCross Language News story search (CLNSS) is concerned with finding documents describing the same events in documents in different languages. As well as suppo...", "weights": [[8, 16, 1.0], [52, 60, 1.0], [166, 175, 1.0]]}, "2018.chiir_conference-2018.23": {"snippet": "...ment information, and academic resources, go online shopping, seek personal health information, and need social networking. Findings highlight when the participants need prompt information, they searc...", "weights": [[8, 19, 1.0], [86, 97, 1.0], [180, 191, 1.0]]}, "2020.lt4gov-1.5": {"snippet": "...rian Language Resource Portal stresses the importance of language resources specific to a language variety, thus paving the way for the re-use of variety-specific language data for human language tech...", "weights": [[8, 16, 1.0], [60, 68, 1.0], [93, 101, 1.0], [166, 174, 1.0], [190, 198, 1.0]]}, "W12-3902": {"snippet": "...lish language. This paper details a novel clustering and co-occurrence based approach to map English NEs with their equivalent representations from different languages recognized in a language-indepen...", "weights": [[8, 16, 1.0], [151, 160, 1.0], [187, 195, 1.0]]}, "D08-1063": {"snippet": "...ting information from a language with richer resources such as English into a foreign language alone (no resources or models in the foreign language). Furthermore, while examining the performance usin...", "weights": [[8, 19, 1.0], [27, 35, 1.0], [89, 97, 1.0], [143, 151, 1.0]]}, "W16-1208": {"snippet": "...ding Language Divergence ({WELD}) as a Quantitative Measure of Language Distance\n\n\n We introduce a new measure of distance between languages based on word embedding, called word embedding language div...", "weights": [[8, 16, 1.0], [66, 74, 1.0], [191, 199, 1.0]]}, "2004.airs_conference-2004.26": {"snippet": "...ocal information through the Internet for use in daily activities. Conventional mobile information services focus on providing location information related to the user's current location. In contrast,...", "weights": [[8, 19, 1.0], [90, 101, 1.0], [139, 150, 1.0]]}, "2004.clef_workshop-2004.36": {"snippet": "...ross-language information access. The task was to answer factual questions from a large collection of documents written in a language in which the user has little proficiency. An interactive cross-lan...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [128, 136, 1.0], [200, 208, 1.0]]}, "2019.ipm_journal-ir0volumeA56A4.12": {"snippet": "...line information seeker group had geographically wider movement patterns than the online information seekers. In other words, the offline information seeker group showed movements in various direction...", "weights": [[8, 19, 1.0], [92, 103, 1.0], [141, 152, 1.0]]}}}
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In this paper I will demonstrate a method of automatically genera...", "weights": [[8, 21, 1.0]]}, "2013.clef_conference-2013w.109": {"snippet": "...n of Abbreviations/Acronyms: THCIB at CLEF eHealth 2013 Task 2\n\n\n This paper describes the THCIB systems that used in the ShARe/CLEF eHealth Lab 2013 task 2. We built a baseline system using open sour...", "weights": [[8, 21, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A2.4": {"snippet": "ELM-based name disambiguation in bibliography\n\n\n AbstractIt is common that different people share the same name. When it occurs in bibliography databases, it worsens the performance of information ret...", "weights": []}, "1988.tois_journal-ir0volumeA6A3.0": {"snippet": "...ague queries directly, forcing their users to retry specific queries repeatedly with minor modifications until they match data that are satisfactory. This article describes a system called VAGUE that ...", "weights": [[8, 15, 1.0], [64, 71, 1.0]]}, "2010.tois_journal-ir0volumeA28A2.1": {"snippet": "...vant queries for a given query, which helps users better specify their information needs. Previous work on query suggestion has been limited to the same language. In this article, we extend it to cros...", "weights": [[8, 15, 1.0]]}, "W14-1211": {"snippet": "...n of Abbreviations in {CL}inical text\n\n\n In the medical domain, especially in clinical texts, non-standard abbreviations are prevalent, which impairs readability for patients. To ease the understandin...", "weights": [[8, 21, 1.0], [110, 123, 1.0]]}, "U08-1001": {"snippet": "...arch Queries\n\n\n Traditionally, information retrieval examines the search query in isolation: a query is used to retrieve documents, and the relevance of the documents returned is evaluated in relation...", "weights": [[8, 15, 1.0]]}, "I11-1046": {"snippet": "...arch queries. A label propagation algorithm was applied on a clickthrough graph, and the candidates were reranked using a query language model. Our method first enumerates query candidates with common...", "weights": [[8, 15, 1.0]]}, "1977.jasis_journal-ir0volumeA28A2.16": {"snippet": "...sics abbreviations and acronyms", "weights": [[8, 21, 1.0]]}, "1986.tois_journal-ir0volumeA4A4.1": {"snippet": "...with queries. These queries reflect the presuppositions of their authors about the system and the information it contains. With most query processors, queries that are based on erroneous presuppositio...", "weights": [[8, 15, 1.0], [23, 30, 1.0], [154, 161, 1.0]]}, "2001.tois_journal-ir0volumeA19A2.2": {"snippet": "...tree queries-a variant and extension of Kilpel\u00e4i-nen's Tree Matching formalism-we introduce the notion of a \"complete answer aggregate\" for a given query. This new data structure offers a compact view...", "weights": [[8, 15, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A5.12": {"snippet": "...word queries by suggesting keywords that are unfamiliar to users, offering alternative queries that deviate from the original ones, and even correcting spelling errors. The design goal of QS is to enr...", "weights": [[8, 15, 1.0], [90, 97, 1.0]]}, "2004.tois_journal-ir0volumeA22A4.2": {"snippet": "...uate queries extremely fast, a challenging task given the quantities of data being indexed. A significant proportion of the queries posed to search engines involve phrases. In this article we consider...", "weights": [[8, 15, 1.0], [127, 134, 1.0]]}, "L18-1325": {"snippet": "...heir abbreviations. However, due to the deficiency in the abbreviation corpora, such a task is limited in current studies, especially considering general abbreviation prediction should also include th...", "weights": [[8, 21, 1.0]]}, "2013.wwwconf_conference-2013.109": {"snippet": "... are abbreviations formed from the initial components of words or phrases. Acronym usage is becoming more common in web searches, email, text messages, tweets, blogs and posts. Acronyms are typically ...", "weights": [[8, 21, 1.0]]}, "2008.tois_journal-ir0volumeA27A1.3": {"snippet": "Extended probabilistic HAL with close temporal association for psychiatric query document retrieval\n\n\n Psychiatric query document retrieval can assist individuals to locate query documents relevant to...", "weights": []}, "2015.clef_conference-2015.5": {"snippet": "...tive abbreviations. As mentioned earlier, these context sensitive abbreviations hinder accuracy of the classification and retrieval methods. We try to address this issue by adding more Tweet Expansion...", "weights": [[8, 21, 1.0], [69, 82, 1.0]]}, "C08-1083": {"snippet": "...ting abbreviations and their full forms appearing in actual text. The task of abbreviation recognition is formalized as a sequential alignment problem, which finds the optimal alignment (origins of ab...", "weights": [[8, 21, 1.0]]}, "1990.tois_journal-ir0volumeA8A4.2": {"snippet": "Queries and Query Processing in Object-Oriented Database Systems\n\n\n Object-oriented database management systems (OODBMS) combine the data abstraction and computational models of object-oriented progra...", "weights": [[0, 7, 1.0]]}, "W06-0103": {"snippet": "...cter abbreviations more easily. With only a few training iterations, the acquisition accuracy of the proposed SCR model achieves 62% and 50 % precision for training set and test set, respectively, fro...", "weights": [[8, 21, 1.0]]}, "2004.spire_conference-2004.21": {"snippet": "...s in queries, and if not properly translated or transliterated, query performance may deteriorate significantly. As proper names often need no translation, a trivial solution is to include the untrans...", "weights": [[8, 15, 1.0]]}, "2012.tois_journal-ir0volumeA30A1.1": {"snippet": "Static index pruning in web search engines: Combining term and document popularities with query views\n\n\n Static index pruning techniques permanently remove a presumably redundant part of an inverted f...", "weights": []}, "2012.tweb_journal-ir0volumeA6A4.0": {"snippet": "...ilar queries that are previously cached in order to create synthetic results for new queries. The second strategy forms an inverted index over the textual information (i.e., query terms and result sni...", "weights": [[8, 15, 1.0], [88, 95, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.25": {"snippet": "...fied queries. When a classifier that predicts the most appropriate mapping for each query is used, an additional 5.2% improvement over non-clarified queries is achieved.Furthermore, I introduce a set ...", "weights": [[8, 15, 1.0], [152, 159, 1.0]]}, "O08-3005": {"snippet": "...n of Queries with Infrequent Unknown Abbreviations and Proper Names\n\n\n Unknown term translation is important to CLIR and MT systems, but it is still an unsolved problem. Recently, a few researchers ha...", "weights": [[8, 15, 1.0], [40, 53, 1.0]]}, "2013.clef_conference-2013w.167": {"snippet": "... and abbreviations normalized to concepts from a standardized, medical vocabulary for the ShARe/CLEF eHealth 2013 challenge. In this paper, we review prior text normalization shared tasks, reference s...", "weights": [[8, 21, 1.0]]}, "2002.tois_journal-ir0volumeA20A3.0": {"snippet": "...ngle queries, consistent with the fact that the ordered sets of expansion terms suggested for each query by the different functions are largely uncorrelated. Motivated by these findings, we argue that...", "weights": [[8, 15, 1.0]]}, "2012.wwwconf_conference-2012.42": {"snippet": "...arch queries\n\n\n ABSTRACTWeb search queries are an encoding of the user's search intent and extracting structured information from them can facilitate central search engine operations like improving th...", "weights": [[8, 15, 1.0], [38, 45, 1.0]]}, "D09-1154": {"snippet": "...arch Queries\n\n\n In this paper we address the problem of identifying a broad range of term variations in Japanese web search queries, where these variations pose a particularly thorny problem due to th...", "weights": [[8, 15, 1.0], [127, 134, 1.0]]}, "2006.trec_conference-2006.1": {"snippet": "...ured queries comparing to Okapi? 2. Will the query expansion based on domain knowledge increase retrieval effectiveness? 3. Will our abbreviation database from MEDLINE help improve query expansion and...", "weights": [[8, 15, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.2": {"snippet": "...when queries are not expanded. On the other hand, when queries are expanded, language modeling gives better results, but only when using a probabilistic dictionary derived from a parallel corpus.We pu...", "weights": [[8, 15, 1.0], [58, 65, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...nced queries. We submitted both the original and modified queries to three major Web search engines; a total of 600 queries were submitted and 5,748 documents evaluated. We compared the results from t...", "weights": [[8, 15, 1.0], [61, 68, 1.0], [119, 126, 1.0]]}, "2008.cikm_workshop-2008ssm.7": {"snippet": "...many queries.In this paper, we propose a Tag Suggestion System that takes advantage of tags associated with query results to expand a searcher's query. Since not every web page is associated with exis...", "weights": [[8, 15, 1.0]]}, "2020.wwwconf_conference-2020.32": {"snippet": "... SQL queries. In this paper, we tackle these challenges by developing a deep learning based TRanslate-Edit Model for Question-to-SQL (TREQS) generation, which adapts the widely used sequence-to-sequen...", "weights": [[8, 15, 1.0]]}, "N09-2069": {"snippet": "...ting abbreviations for Chinese organization names. Abbreviations are commonly used in spoken Chinese, especially for organization names. The generation of Chinese abbreviation is much more complex tha...", "weights": [[8, 21, 1.0], [54, 67, 1.0]]}, "2011.sigirconf_conference-2011.176": {"snippet": "... the queries from the adhoc task of the TREC 2004 Genomics track. Our preliminary analysis shows the potential of both acronym and long-form expansions for biomedical IR.", "weights": [[8, 15, 1.0]]}, "C18-2001": {"snippet": "...s\n\n\n Abbreviations and acronyms are a part of textual communication in most domains. However, abbreviations are not necessarily defined in documents that employ them. Understanding all abbreviations u...", "weights": [[8, 21, 1.0], [97, 110, 1.0], [188, 201, 1.0]]}, "2002.tois_journal-ir0volumeA20A1.3": {"snippet": "...s of queries, approaches based on keywords are not suitable for query clustering. This paper describes a new query clustering method that makes use of user logs which allow us to identify the document...", "weights": [[8, 15, 1.0]]}, "2008.trec_conference-2008.5": {"snippet": "CSIR at TREC 2008 Expert Search Task: Modeling Expert Evidence in Expertise Retrieval\n\n\n In this paper, we described our method for the expert search task in TREC 2008. First, we proposed an adaption ...", "weights": []}, "2008.sigirconf_conference-2008.122": {"snippet": "...n in queries. Experiments on real Web search queries show promising results.", "weights": [[8, 15, 1.0], [48, 55, 1.0]]}, "1976.jasis_journal-ir0volumeA27A2.0": {"snippet": "...e of abbreviations and acronyms in the physics literature", "weights": [[8, 21, 1.0]]}, "C12-1187": {"snippet": "...heir abbreviations will be helpful in many applications. For example, it can improve the recall of information retrieval systems. An intuition to solve this is to build an abbreviation dictionary in a...", "weights": [[8, 21, 1.0]]}, "2016.wwwconf_conference-2016.126": {"snippet": "...nize queries that seek the value of an entity on a specific attribute (referred to as e+a queries) and provide direct answers for them using a combination of knowledge bases, web tables and documents....", "weights": [[8, 15, 1.0], [93, 100, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.3": {"snippet": "...uous queries in web search\n\n\n a b s t r a c tIt is widely believed that many queries submitted to search engines are inherently ambiguous (e.g., java and apple). However, few studies have tried to cla...", "weights": [[8, 15, 1.0], [80, 87, 1.0]]}, "2010.tweb_journal-ir0volumeA4A4.2": {"snippet": "...gine Queries\n\n\n Web search engines can perform poorly for long queries (i.e., those containing four or more terms), in part because of their high level of query specificity. The automatic assignment o...", "weights": [[8, 15, 1.0], [66, 73, 1.0]]}, "2003.cikm_conference-2003.2": {"snippet": "...ated queries", "weights": [[8, 15, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.10": {"snippet": "...ique queries per session. The final data set was composed of 313 search sessions. Three facets of query reformulation (content, format, and resource) as well as nine sub-facets were derived from the d...", "weights": [[8, 15, 1.0]]}, "2006.tois_journal-ir0volumeA24A3.3": {"snippet": "...ical abbreviations\n\n\n Abbreviations and acronyms are widely used in the biomedical literature and many of them represent important biomedical concepts. Because many abbreviations are ambiguous (e.g., ...", "weights": [[8, 21, 1.0], [25, 38, 1.0], [168, 181, 1.0]]}, "2008.ipm_journal-ir0volumeA44A6.3": {"snippet": "...n of queries with new terms, study the effect of different mutation rates on the exploration of query-space, and discuss the use of a especially developed fitness function that favors the construction...", "weights": [[8, 15, 1.0]]}, "2008.trec_conference-2008.46": {"snippet": "... the abbreviations of query concepts in documents are recognized. This helps in identifying whether an opinion is in the vicinity of a query concept (which can be an abbreviation) in a document. The t...", "weights": [[8, 21, 1.0]]}, "I08-2127": {"snippet": "...nese abbreviations through the use of previous approaches, examining actual usages of parenthetical expressions in newspaper articles. In order to bridge the gap between Japanese abbreviations and the...", "weights": [[8, 21, 1.0], [182, 195, 1.0]]}, "W09-1309": {"snippet": "...ical Abbreviations\n\n\n Abbreviations are common in biomedical documents and many are ambiguous in the sense that they have several potential expansions. Identifying the correct expansion is necessary f...", "weights": [[8, 21, 1.0], [25, 38, 1.0]]}, "2004.ipm_journal-ir0volumeA40A1.2": {"snippet": "...n of abbreviations by using context and character information", "weights": [[8, 21, 1.0]]}, "2012.cikm_conference-2012.124": {"snippet": "...tify queries that are likely to have low ranking quality to improve the user satisfaction. For instance, the system may suggest to the user alternative queries for such hard queries. In this paper, we...", "weights": [[8, 15, 1.0], [155, 162, 1.0], [177, 184, 1.0]]}, "2014.fire_conference-2014.24": {"snippet": "...ular abbreviations through text messaging. The system receives a text input of acronym (possibly wrongly typed) in Roman script. The application returns a very brief information from the first few lin...", "weights": [[8, 21, 1.0]]}, "U07-1015": {"snippet": "...rack queries and collection. Our results show that for highly ambiguous abbreviations the query collocation effect isn't strong enough to deter the retrieval of erroneous passages. We conclude that fu...", "weights": [[8, 15, 1.0], [75, 88, 1.0]]}, "2002.ntcir_workshop-2002.70": {"snippet": "Unsupervised Acquisition of Knowledge about the Abbreviation Possibility of some of Multiple Phrases modifying the same Verb/Noun\n\n\n This paper proposes a statistical method of acquiring knowledge abo...", "weights": []}}}
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Therefore, the aim of this paper is to evaluate the impact of three different Arabic stemmers (i.e. 'Information ...", "weights": [[8, 18, 1.0]]}, "2007.clef_workshop-2007w.15": {"snippet": "...age, lemmatization significantly improves retrieval results and manually created queries are only slightly better than queries automatically generated from topic specifications.", "weights": [[8, 21, 1.0]]}, "D18-1532": {"snippet": "... and lemmatization in Czech, German, and Arabic.", "weights": [[8, 21, 1.0]]}, "P17-1136": {"snippet": "...tive Lemmatization Using Two Successive Bidirectional Gated Recurrent Networks\n\n\n We introduce a composite deep neural network architecture for supervised and language independent context sensitive le...", "weights": [[8, 21, 1.0], [201, 214, 1.0]]}, "2021.eacl-main.272": {"snippet": "...ural Lemmatization with External Resources\n\n\n We propose a novel hybrid approach to lemmatization that enhances the seq2seq neural model with additional lemmas extracted from an external lexicon or a ...", "weights": [[8, 21, 1.0], [87, 100, 1.0]]}, "2019.ecir_workshop-2019amir.1": {"snippet": "...tial algorithms that can solve a problem, but which algorithm would perform best (e.g. in terms of runtime or accuracy) is often unclear. In many domains, particularly artificial intelligence, the alg...", "weights": [[8, 18, 1.0]]}, "2016.spire_conference-2016.7": {"snippet": "...free algorithms, i.e., alternative algorithms that avoid the computationally expensive alignment, has become one of the recent hot topics in algorithmic bioinformatics.Analysis of protein structures i...", "weights": [[8, 18, 1.0], [38, 48, 1.0]]}, "2003.clef_workshop-2003w.3": {"snippet": "... the lemmatization of the text in order to avoid inflectional variation. Our second approach consists of the employment of syntactic dependencies as complex index terms, in an attempt to solve the pro...", "weights": [[8, 21, 1.0]]}, "2018.wwwconf_conference-2018.57": {"snippet": "...ring algorithms are a fundamental tool in any machine learning library. Most of the eorts in developing dynamic machine learning and data mining algorithms have been focusing on the sliding window mod...", "weights": [[8, 18, 1.0], [148, 158, 1.0]]}, "2020.lt4hala-1.4": {"snippet": "Lemmatization and {POS}-tagging process by using joint learning approach. Experimental results on {C}lassical {A}rmenian, {O}ld {G}eorgian, and {S}yriac\n\n\n Classical Armenian, Old Georgian and Syriac ...", "weights": [[0, 13, 1.0]]}, "L14-1142": {"snippet": "...ting Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage", "weights": [[8, 21, 1.0]]}, "W09-4006": {"snippet": "...ines lemmatization, hand-crafted transformation rules, and weighted Levenshtein distance. The experimental results show an 11-pt interpolated average precision of 90.58%, which represents a sizeable i...", "weights": [[8, 21, 1.0]]}, "2013.ecir_conference-2013.58": {"snippet": "... for lemmatization and phrase (multi word units) retrieval. We show by applying simple rule-based modifications and only targeting specific language instances (noun phrases) the usage of general NLP t...", "weights": [[8, 21, 1.0]]}, "L18-1181": {"snippet": "...test lemmatization accuracy, and an efficient lemmatization algorithm that outperforms state-of-the-art Arabic lemmatization in terms of accuracy and speed. We share the dataset and the code for resea", "weights": [[8, 21, 1.0], [49, 62, 1.0], [114, 127, 1.0]]}, "W07-1711": {"snippet": "Multilingual Word Sense Discrimination: A Comparative Cross-Linguistic Study\n\n\n We describe a study that evaluates an approach to Word Sense Discrimination on three languages with different linguistic...", "weights": []}, "2014.wwwjournals_journal-ir0volumeA17A5.15": {"snippet": "Extreme learning machine with errors in variables\n\n\n Abstract Extreme learning machine (ELM) is widely used in training single-hidden layer feedforward neural networks (SLFNs) because of its good gene...", "weights": []}, "W19-4206": {"snippet": "... and lemmatization in context. Our model is a multi-task sequence to sequence neural network, which jointly learns morphological tagging and lemmatization. On the encoding side, we exploit character-l...", "weights": [[8, 21, 1.0], [144, 157, 1.0]]}, "P09-1055": {"snippet": "...oint lemmatization and part-of-speech prediction\n\n\n We present a global joint model for lemmatization and part-of-speech prediction. Using only morphological lexicons and unlabeled data, we learn a pa...", "weights": [[8, 21, 1.0], [91, 104, 1.0]]}, "2017.spire_conference-2017.18": {"snippet": "...ngth Algorithms\n\n\n Abstract. Some of the most efficient algorithms for computing the length of a longest common subsequence (LLCS) between two strings are based on so-called \"bit-parallelism\". They ac...", "weights": [[8, 18, 1.0], [59, 69, 1.0]]}, "W12-5213": {"snippet": "...cing Lemmatization for {M}ongolian and its Application to Statistical Machine Translation\n\n\n Lemmatization is crucial in natural language processing and information retrieval especially for highly inf...", "weights": [[8, 21, 1.0], [96, 109, 1.0]]}, "2016.spire_conference-2016.20": {"snippet": "...mory algorithms that construct the suffix array and the LCP array simultaneously in the optimal I/O complexity of O(sort(n)), for several reasons it would be desirable to construct the suffix array fi...", "weights": [[8, 18, 1.0]]}, "2017.ipm_journal-ir0volumeA53A1.16": {"snippet": "...tion algorithms, and they also fail to capture the correlation between link formulation and community evolution. In this paper we introduce a balanced Modularity-Maximization Link Prediction (MMLP) mo...", "weights": [[8, 18, 1.0]]}, "2013.clef_conference-2013w.75": {"snippet": "... and lemmatization. Then each tweet content is analyzed and 18 features are obtained, related to presence of polarized term, negation before polarized expression and entity reference. For the first ru...", "weights": [[8, 21, 1.0]]}, "W03-0305": {"snippet": "Reducing Parameter Space for Word Alignment\n\n\n This paper presents the experimental results of our attemps to reduce the size of the parameter space in word alignment algorithm. We use IBM Model 4 as ...", "weights": []}, "2020.lt4hala-1.19": {"snippet": "... and Lemmatization\n\n\n The paper presents the system used in the EvaLatin shared task to POS tag and lemmatize Latin. It consists of two components. A gradient boosting machine (LightGBM) is used for P...", "weights": [[8, 21, 1.0]]}, "W79-0115": {"snippet": "...dsk (Lemmatization of {I}celandic) [In {D}anish]", "weights": [[8, 21, 1.0]]}, "W19-4009": {"snippet": "...iple lemmatization strategies that different projects adopt. In this paper we discuss how we tackle the challenges raised by harmonizing different lemmatization criteria in a project that aims to conn...", "weights": [[8, 21, 1.0], [150, 163, 1.0]]}, "2006.tois_journal-ir0volumeA24A4.3": {"snippet": "... and algorithms are among the first steps in developing document-type-independent indexing methods for the full text in heterogeneous XML collections.", "weights": [[8, 18, 1.0]]}, "2019.wsdm_conference-2019.47": {"snippet": "A General View for Network Embedding as Matrix Factorization\n\n\n ABSTRACTWe propose a general view that demonstrates the relationship between network embedding approaches and matrix factorization. Unli...", "weights": []}, "W19-4205": {"snippet": "...tual Lemmatization and Morphological Tagging\n\n\n In this study, we present Morpheus, a joint contextual lemmatizer and morphological tagger. Morpheus is based on a neural sequential architecture where ...", "weights": [[8, 21, 1.0]]}, "2013.clef_conference-2013w.11": {"snippet": "An Approach to the Main Task of QA4MRE-2013\n\n\n This article describes the participation of a group from the University of\u00c9vora in the CLEF2013 QA4MRE main task. Our system has a superficial text analy...", "weights": []}, "R13-1012": {"snippet": "...h as lemmatization, syllabification, lexical stress prediction and phonetic transcription. These steps usually require knowledge of the word's lexical composition (derivative morphology, inflectional ...", "weights": [[8, 21, 1.0]]}, "2014.fire_conference-2014.18": {"snippet": "... and Lemmatization for Tamil using Machine Learning\n\n\n ABSTRACTThis paper presents the method of Morpheme Extraction and lemmatization for Tamil language in Morpheme Extraction Task (MET) of FIRE-2014...", "weights": [[8, 21, 1.0], [124, 137, 1.0]]}, "2006.clef_workshop-2006w.16": {"snippet": "...ased Lemmatization for Information Retrieval\n\n\n This paper reports on our system used in the CLEF 2006 ad hoc mono-lingual Hungarian retrieval task. Our experiments focus on the benefits that deeper N...", "weights": [[8, 21, 1.0]]}, "U06-1005": {"snippet": "...tion algorithms with various popular feature selection methods. The results highlight similarities between sentence and text classification, such as the superiority of Support Vector Machines, as well...", "weights": [[8, 18, 1.0]]}, "W15-3716": {"snippet": "... and lemmatization in {L}atin: A comparison of six taggers and two lemmatization methods\n\n\n We present a survey of tagging accuracies -concerning part-of-speech and full morphological tagging -for sev...", "weights": [[8, 21, 1.0], [70, 83, 1.0]]}, "R17-1064": {"snippet": "Lemmatization of Multi-word Common Noun Phrases and Named Entities in {P}olish", "weights": [[0, 13, 1.0]]}, "L16-1585": {"snippet": "...-art algorithms for extractive summarization and the effect of stopword removal, lemmatization, and stemming is analyzed. Results showed that these pre-processing settings improve the results.", "weights": [[8, 18, 1.0], [84, 97, 1.0]]}, "N18-1126": {"snippet": "...ural Lemmatization with {L}ematus\n\n\n The main motivation for developing contextsensitive lemmatizers is to improve performance on unseen and ambiguous words. Yet previous systems have not carefully ev...", "weights": [[8, 21, 1.0]]}, "W10-1410": {"snippet": "Lemmatization and Lexicalized Statistical Parsing of Morphologically-Rich Languages: the Case of {F}rench\n\n\n This paper shows that training a lexicalized parser on a lemmatized morphologically-rich tr...", "weights": [[0, 13, 1.0]]}, "2021.naacl-main.322": {"snippet": "...s\n\n\n Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Most prior work on ML based lemmatization has focused on high resource langu...", "weights": [[8, 21, 1.0], [155, 168, 1.0]]}, "2008.sigirconf_workshop-2008and.16": {"snippet": "...val, lemmatization and stemming. Both stemming and lemmatization share a common goal of reducing a word to its base. However, lemmatization is more robust than stemming as it often involves usage of v...", "weights": [[8, 21, 1.0], [54, 67, 1.0], [129, 142, 1.0]]}, "2021.nodalida-main.25": {"snippet": "...e\n\n\n Lemmatization is often used with morphologically rich languages to address issues caused by morphological complexity, performed by grammar-based lemmatizers. We propose an alternative for this, i...", "weights": [[8, 21, 1.0]]}, "P16-1108": {"snippet": "... and Lemmatization.\n\n\n We present several methods for stemming and lemmatization based on discriminative string transduction. We exploit the paradigmatic regularity of semi-structured inflection table...", "weights": [[8, 21, 1.0], [70, 83, 1.0]]}, "2013.wwwconf_conference-2013.131": {"snippet": "Localized matrix factorization for recommendation based on matrix block diagonal forms\n\n\n ABSTRACTMatrix factorization on user-item rating matrices has achieved significant success in collaborative fi...", "weights": []}, "L16-1406": {"snippet": "...ural lemmatization model which is language independent and supervised in nature. To handle the words in a neural framework, word embedding technique is used to represent words as vectors. The proposed...", "weights": [[8, 21, 1.0]]}, "2011.cikm_conference-2011.194": {"snippet": "The list Viterbi training algorithm and its application to keyword search over databases\n\n\n ABSTRACTHidden Markov Models (HMMs) are today employed in a variety of applications, ranging from speech rec...", "weights": []}, "2015.cikm_conference-2015.82": {"snippet": "...ased algorithms can reduce the expensive computation cost of SVD for nuclear norm minimization. However, most matrix factorization based algorithms fail to provide the theoretical guarantee for conver...", "weights": [[8, 18, 1.0], [140, 150, 1.0]]}, "N19-1153": {"snippet": "...ving Lemmatization of Non-Standard Languages with Joint Learning\n\n\n Lemmatization of standard languages is concerned with (i) abstracting over morphological differences and (ii) resolving token-lemma ...", "weights": [[8, 21, 1.0], [71, 84, 1.0]]}, "2004.cikm_conference-2004.91": {"snippet": "... and lemmatization in the clustering of finnish text documents\n\n\n ABSTRACTStemming and lemmatization were compared in the clustering of Finnish text documents. Since Finnish is a highly inflectional a...", "weights": [[8, 21, 1.0], [90, 103, 1.0]]}, "N19-1155": {"snippet": "...ural Lemmatization\n\n\n English verbs have multiple forms. For instance, talk may also appear as talks, talked or talking, depending on the context. The NLP task of lemmatization seeks to map these dive...", "weights": [[8, 21, 1.0], [166, 179, 1.0]]}, "W07-1704": {"snippet": "Lemmatization of {P}olish Person Names\n\n\n The paper presents two techniques for lemmatization of Polish person names. First, we apply a rule-based approach which relies on linguistic information and h...", "weights": [[0, 13, 1.0], [80, 93, 1.0]]}, "2010.clef_conference-2010.3": {"snippet": "A Dictionary- and Corpus-Independent Statistical Lemmatizer for Information Retrieval in Low Resource Languages", "weights": []}, "2014.airs_conference-2014.28": {"snippet": "...ming algorithms (i.e., morphologyand syntax-based lemmatization; and morphology-based Information Science Research Institute (ISRI) stemming on clustering Arabic text dataset. We aim to identify the b...", "weights": [[8, 18, 1.0], [53, 66, 1.0]]}, "L16-1081": {"snippet": "... and Lemmatization\n\n\n In this paper we present a rule-based method for multi-word term extraction that relies on extensive lexical resources in the form of electronic dictionaries and finite-state tra...", "weights": [[8, 21, 1.0]]}, "2019.ecir_workshop-2019amir.3": {"snippet": "Algorithm Selection with Librec-auto", "weights": []}, "W19-4204": {"snippet": "... and lemmatization in context, using a transformer-based sequence to sequence model and a biaffine attention based BiLSTM model. First, a lemma is produced for a given word, and then both the lemma an...", "weights": [[8, 21, 1.0]]}, "P09-1017": {"snippet": "...g of lemmatization rules that handle morphological changes in pre-, in- and suffixes alike\n\n\n We propose a method to automatically train lemmatization rules that handle prefix, infix and suffix change...", "weights": [[8, 21, 1.0], [140, 153, 1.0]]}, "2009.ipm_journal-ir0volumeA45A4.3": {"snippet": "...se a lemmatization method for Mongolian. The advantage of our lemmatization method is that it does not rely on noun dictionaries, enabling us to lemmatize out-of-dictionary words. We also apply our me...", "weights": [[8, 21, 1.0], [65, 78, 1.0]]}, "I08-1001": {"snippet": "A Lemmatization Method for {M}odern {M}ongolian and its Application to Information Retrieval\n\n\n In Modern Mongolian, a content word can be inflected when concatenated with suffixes. Identifying the or...", "weights": [[2, 15, 1.0]]}, "H05-1080": {"snippet": "...rate lemmatization of German nouns mandates the use of a lexicon. Comprehensive lexicons, however, are expensive to build and maintain. We present a selflearning lemmatizer capable of automatically cr...", "weights": [[8, 21, 1.0]]}, "2019.ecir_workshop-2019amir.6": {"snippet": "... the algorithms that generated the recommendations. The logs enable researchers to conduct various analyses about a real-world recommender system. This includes the evaluation of meta-learning approac...", "weights": [[8, 18, 1.0]]}, "2005.spire_conference-2005.9": {"snippet": "...nown algorithms). To our knowledge, our algorithm is the first that spends in the worst case O(log n) per every single input symbol.We also provide a simple algorithm that constructs online an indexin...", "weights": [[8, 18, 1.0]]}}}
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The whole-page results must satisfy ...", "weights": [[8, 10, 1.0]]}, "2001.sigirconf_conference-2001.36": {"snippet": "...ile. Documents with scores above profile-specific dissemination thresholds are delivered.An optimal dissemination threshold is one that maximizes a given utility function based on the distributions of...", "weights": [[8, 17, 1.0]]}, "1999.trec_conference-1999.56": {"snippet": "...EC-8 Ad-Hoc, Query and Filtering Track Experiments using PIRCS\n\n\n In TREC-8, we participated in automatic ad-hoc retrieval as well as the query and filtering tracks. The theme of our participation is ...", "weights": [[8, 10, 1.0], [109, 111, 1.0]]}, "2004.riao_conference-2004.57": {"snippet": "Rich Document Representation for Document Clustering", "weights": [[0, 4, 1.0]]}, "2012.cikm_conference-2012.55": {"snippet": "... the ad income. In this paper, we study how online publishers could optimally select ads to maximize their ad incomes over time. The conventional offline, content-based matching between webpages and a...", "weights": [[8, 10, 1.0], [110, 112, 1.0]]}, "2017.wwwconf_conference-2017c.165": {"snippet": "...Text-Rich Setting. In the context-rich environment, there exists not only different kinds of relationships between users, items and objects of various types, but also a rich amount of text information...", "weights": [[8, 12, 1.0], [37, 41, 1.0], [172, 176, 1.0]]}, "2010.cikm_conference-2010.100": {"snippet": "...ting rich user information\n\n\n ABSTRACTOne-Class Collaborative Filtering (OCCF) is an emerging setup in collaborative filtering in which only positive examples or implicit feedback can be observed. Com...", "weights": [[8, 12, 1.0]]}, "2002.trec_conference-2002.35": {"snippet": "Kernel Methods for Document Filtering", "weights": []}, "2010.wsdm_conference-2010.37": {"snippet": "...ving ad relevance in sponsored search\n\n\n ABSTRACTWe describe a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap an...", "weights": [[8, 10, 1.0], [124, 126, 1.0]]}, "2000.sigirconf_conference-2000.57": {"snippet": "...ding documents to users based on how similar their tastes are to other users. If two users tend to agree on what they like, the system will recommend the same documents to them. The generalized vector...", "weights": [[8, 17, 1.0], [162, 171, 1.0]]}, "W19-5345": {"snippet": "...both rich modeling of limited contextual information and integration of large-scale document-level context with a less rich representation. We train this model by fine-tuning a big Transformer baselin...", "weights": [[8, 12, 1.0], [122, 126, 1.0]]}, "2018.airs_conference-2018.15": {"snippet": "Ad-hoc Video Search Improved by the Word Sense Filtering of Query Terms", "weights": [[0, 2, 1.0]]}, "2014.clef_conference-2014w.73": {"snippet": "...edia Documents in ImageCLEF 2014\n\n\n The Image search by content is an area that is based on a set of low-level features such as histograms, textures, the distribution of colors, shapes an brightness. ...", "weights": [[8, 17, 1.0]]}, "2005.wwwconf_conference-2005si.44": {"snippet": "...n to filter unwanted images (such as advertisements or flashy graphics) based on minimal user feedback. To do so, we apply the weighted majority algorithm using pieces of the Uniform Resource Locators...", "weights": [[8, 14, 1.0]]}, "R11-1104": {"snippet": "...from documents according only to the context of the question. In this work, we complemented this approach by adding a filtering process on top of the document retrieval. This way, the system reevaluat...", "weights": [[8, 17, 1.0]]}, "2010.wwwconf_conference-2010.11": {"snippet": "... the ad indexing problem. Unlike most traditional search problems, the ad corpus is defined hierarchically in terms of advertiser accounts, campaigns, and ad groups, which further consist of creatives...", "weights": [[8, 10, 1.0], [74, 76, 1.0], [158, 160, 1.0]]}, "2013.trec_conference-2013.24": {"snippet": "...tric Documents using Numerics and Temporals features within RF Classifier", "weights": [[8, 17, 1.0]]}, "2009.sigirconf_conference-2009.148": {"snippet": "...ased filter for improving ad quality", "weights": [[8, 14, 1.0], [29, 31, 1.0]]}, "2005.ecir_conference-2005.10": {"snippet": "...t of documents their most significant excerpts in relation to a user profile. This method relies on both structured profiles and a topical analysis of documents. The topical analysis is also used for ...", "weights": [[8, 17, 1.0], [154, 163, 1.0]]}, "H93-1072": {"snippet": "An Overview of {DR-LINK} and Its Approach to Document Filtering", "weights": []}, "2016.cikm_conference-2016.81": {"snippet": "Document Filtering for Long-tail Entities", "weights": []}, "1994.sigirconf_conference-94.35": {"snippet": "Document Filtering for Fast Ranking", "weights": []}, "2013.trec_conference-2013.23": {"snippet": "...ring Documents over Time on Evolving Topics - The University of Amsterdam at TREC 2013 KBA CCR", "weights": [[8, 17, 1.0]]}, "2017.wwwconf_conference-2017.8": {"snippet": "...with Rich Ads\n\n\n ABSTRACTThe generalized second price (GSP) auction has served as the core selling mechanism for sponsored search ads for over a decade. However, recent trends expanding the set of all...", "weights": [[8, 12, 1.0]]}, "2001.cikm_conference-2001.37": {"snippet": "...lion documents. Using a training collection of approximately 30,000 previously edited cases, the filter application provides ranked sets of textual evidence for current case law documents in the edito...", "weights": [[8, 17, 1.0], [100, 106, 1.0], [181, 190, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.1": {"snippet": "...able documents into suitable categories. A popular way to achieve integrated DF and DC is to associate each category with a threshold. A document d may be classified into a category c only if its degr...", "weights": [[8, 17, 1.0]]}, "2007.wwwconf_conference-2007.61": {"snippet": "...ally filter the content to prevent injected script code from running in browsers that view the site. The diversity of browser rendering algorithms and the desire to allow rich content make filtering q...", "weights": [[8, 14, 1.0], [174, 178, 1.0]]}, "2000.sigirconf_conference-2000.23": {"snippet": "...rack documents, the proposed method obtained a significant improvement in LF1, LF2, F1 and F3 measures compared to the best results submitted by other TREC entries.", "weights": [[8, 17, 1.0]]}, "2001.trec_conference-2001.40": {"snippet": "...nked documents per category, and the training process and similarity score calculation algorithm are the same for batch filtering and routing, we will focus our discussion on batch filtering and adapt", "weights": [[8, 17, 1.0]]}, "1998.trec_conference-1998.19": {"snippet": "...atic Ad Hoc, Filtering, VLC and Interactive", "weights": [[8, 10, 1.0]]}, "2008.cikm_conference-2008.153": {"snippet": "...ture-rich sequence classifier that removes irrelevant parts from pages, while keeping the content intact. Most of the features the system uses are purely form-related: HTML tags and their positions, s...", "weights": [[8, 12, 1.0]]}, "1998.trec_conference-1998.45": {"snippet": "... for Ad-Hoc and Filtering", "weights": [[8, 10, 1.0]]}, "2013.trec_conference-2013.28": {"snippet": "...e of documents. These methods are based on linear combination with scores of document fields. The distinction is different weight factors. Score of base line method is the combination with score of ba...", "weights": [[8, 17, 1.0]]}, "2015.ecir_conference-2015.33": {"snippet": "...able Documents", "weights": [[8, 17, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.8": {"snippet": "...eled documents as training examples. Manually labeling documents is costly and time-consuming. More importantly, it becomes unrealistic to know all the categories covered by the documents beforehand. ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [181, 190, 1.0]]}, "2012.cikm_conference-2012.347": {"snippet": "...t of documents is generated using a \"cheap\" but low-quality scoring function, which is then reranked by an \"expensive\" but high-quality method (usually machine-learned). This paper focuses on the prob...", "weights": [[8, 17, 1.0]]}, "2010.ecir_conference-2010.60": {"snippet": "...ring Documents with Subspaces", "weights": [[8, 17, 1.0]]}, "2003.wwwconf_conference-2003.22": {"snippet": "...HTML documents\n\n\n AbstractWeb pages often contain clutter (such as pop-up ads, unnecessary images and extraneous links) around the body of an article that distracts a user from actual content. Extract...", "weights": [[8, 17, 1.0]]}, "2017.ecir_conference-2017.33": {"snippet": "...thms filter large volumes of streaming documents and emit sentences that constitute salient event updates. Systems developed typically combine in an ad-hoc fashion traditional retrieval and document s...", "weights": [[8, 14, 1.0], [42, 51, 1.0], [152, 154, 1.0]]}, "2011.sigirconf_conference-2011.67": {"snippet": "...ured documents based on faceted feedback\n\n\n ABSTRACTExisting adaptive filtering systems learn user profiles based on users' relevance judgments on documents. In some cases, users have some prior knowl...", "weights": [[8, 17, 1.0], [150, 159, 1.0]]}, "2019.wwwconf_conference-2019.134": {"snippet": "...alid Ad Traffic", "weights": [[8, 10, 1.0]]}, "2007.sigirconf_conference-2007.133": {"snippet": "...ming documents in order to estimate their relevance to a user's profile, and in order to help users better understand documents, as distinct from solely attempting to either select relevant material f...", "weights": [[8, 17, 1.0], [121, 130, 1.0]]}, "P16-1065": {"snippet": "...ween documents-citations, hyperlinks, or revisions-and which links are added is often based on topical similarity. To model these intuitions, we introduce a new topic model for documents situated with...", "weights": [[8, 17, 1.0], [180, 189, 1.0]]}, "2004.cikm_conference-2004.22": {"snippet": "Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model\n\n\n ABSTRACTCollaborative filtering and content-based filtering are two types ...", "weights": []}, "2003.trec_conference-2003.1": {"snippet": "...o an ad hoc search but the target of the search is a particular document (or a small set of documents) that the searcher knows to exist in the collection and wants to find again. Once again, the retri...", "weights": [[8, 10, 1.0], [95, 104, 1.0]]}, "I08-2121": {"snippet": "...ated Documents Detection using Multi-level Prefix-filter\n\n\n Duplicate document detection is the problem of finding all document-pairs rapidly whose similarities are equal to or greater than a given th...", "weights": [[8, 17, 1.0], [53, 59, 1.0]]}, "2016.cikm_conference-2016.63": {"snippet": "...with Rich Ad Formats", "weights": [[8, 12, 1.0], [13, 15, 1.0]]}, "2004.sigirconf_conference-2004.76": {"snippet": "...vant documents has a much worse impact than not retrieving relevant ones. We adapt cost-based metrics from the document filtering domain to this result filtering problem in order to explicitly examine...", "weights": [[8, 17, 1.0]]}, "2000.sigirconf_conference-2000.24": {"snippet": "...ieve documents relevant to a user's profile from a flow of documents. Generally, filtering systems calculate the similarity between the profile and each incoming document, and retrieve documents with ...", "weights": [[8, 17, 1.0], [62, 71, 1.0], [188, 197, 1.0]]}, "2010.ipm_journal-ir0volumeA46A5.9": {"snippet": "...ring documents in several thousand daily news sources in 11 different languages. This paper describes the filtering algorithm, statistical procedures for estimating Precision and Recall in an operatio...", "weights": [[8, 17, 1.0]]}, "2010.cikm_conference-2010.171": {"snippet": "...vant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents accor...", "weights": [[8, 17, 1.0], [118, 122, 1.0], [188, 197, 1.0]]}, "2020.sigirconf_workshop-2020birds.12": {"snippet": "Content-based Image Search System Design for Capturing User Preferences during Query Formulation", "weights": []}, "2015.clef_conference-2015.5": {"snippet": "...s to filter new tweets using cosine similarity.\nOverviewThe short length of tweets is the main reason that we believe content expansion can be helpful. The users on Twitter website must enter their me...", "weights": [[8, 14, 1.0]]}, "1997.eamt-1.7": {"snippet": "...g of documents written in any of the following formats: Interleaf, FrameMaker, WordPerfect, Word for Windows, Lotus WordPro, Lotus AmiPro, and other formats including SGML and HTML.", "weights": [[8, 17, 1.0]]}}}
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We build and evaluate models that predict transitions from searches on symptoms to searches on health conditions, and escalations from symptoms to s...", "weights": [[8, 17, 1.0]]}, "2001.wwwconf_conference-2001p.91": {"snippet": "...ding Online Services?\n\n\n We report results for an independent, blind evaluation of the performance of 11 commercial search engines on 106 online service queries and on 54 topic relevance queries. We f...", "weights": [[8, 14, 1.0], [141, 147, 1.0]]}, "W18-5616": {"snippet": "...ning Symptom Detection Using Deep Attention-Based Neural Networks\n\n\n We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is abl...", "weights": [[8, 15, 1.0]]}, "2018.wwwconf_conference-2018.74": {"snippet": "... are searching. 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In order to further enhance the performance of symptom diagnosis over dialogues, we propose a global attention mechanism to capture more symptom related information, and build ...", "weights": [[8, 15, 1.0], [74, 81, 1.0], [164, 171, 1.0]]}, "R19-1019": {"snippet": "Risk Factors Extraction from Clinical Texts based on Linked Open Data\n\n\n This paper presents experiments in risk factors analysis based on clinical texts enhanced with Linked Open Data (LOD). The idea...", "weights": []}, "2011.sigirconf_conference-2011.9": {"snippet": "Intentions and attention in exploratory health search\n\n\n ABSTRACTWe study information goals and patterns of attention in exploratory search for health information on the Web, reporting results of a la...", "weights": []}, "2019.ecir_conference-20192.29": {"snippet": "... the online doctor appointment and medical consultation sites to route the patient or question to the right medical specialty. The system returns the top three relevant specialties when the estimated ...", "weights": [[8, 14, 1.0]]}, "2021.eacl-main.114": {"snippet": "...n of Online Misogyny\n\n\n Online misogyny is a pernicious social problem that risks making online platforms toxic and unwelcoming to women. We present a new hierarchical taxonomy for online misogyny, as...", "weights": [[8, 14, 1.0], [27, 33, 1.0], [92, 98, 1.0], [99, 108, 1.0], [184, 190, 1.0]]}, "2020.clinicalnlp-1.16": {"snippet": "...onal Symptom Detection with Graph Memory Networks\n\n\n In this work, we propose a novel goal-oriented dialog task, automatic symptom detection. We build a system that can interact with patients through ...", "weights": [[8, 15, 1.0], [126, 133, 1.0]]}, "2020.lrec-1.836": {"snippet": "...from online forums and social networking services. 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We find that important measures such as symptom severity can be predicted with comparable accuracy to face-to-face data, using general features such as ...", "weights": [[8, 14, 1.0], [91, 98, 1.0], [141, 149, 1.0]]}, "W17-2346": {"snippet": "...s in online support groups using minimal supervision. In recent years, several online support groups have been established which has led to a huge increase in the amount of patient-authored text avail...", "weights": [[8, 14, 1.0], [82, 88, 1.0]]}, "2020.coling-main.63": {"snippet": "...rent online medical platforms provide millions of public available conversations between real patients and doctors, where the patients propose their medical problems and the registered doctors offer d...", "weights": [[8, 14, 1.0], [23, 32, 1.0]]}, "2007.sigirconf_conference-2007.67": {"snippet": "...t of online distributed collections, a federated information retrieval system typically represents each collection, centrally, by a set of vocabularies or sampled documents. Accurate retrieval is ther...", "weights": [[8, 14, 1.0]]}, "R19-1096": {"snippet": "...ion, symptom-disease relations, as well as drug-symptom relations (e.g., searching for the drug that might be most useful for treating a given set of symptoms).", "weights": [[8, 15, 1.0], [51, 58, 1.0], [76, 85, 1.0]]}, "W17-2332": {"snippet": "Toward Automated Early Sepsis Alerting: Identifying Infection Patients from Nursing Notes\n\n\n Severe sepsis and septic shock are conditions that affect millions of patients and have close to 50% mortal...", "weights": []}, "2008.cikm_conference-2008.16": {"snippet": "MedSearch: a specialized search engine for medical information retrieval\n\n\n ABSTRACTPeople are thirsty for medical information. Existing Web search engines often cannot handle medical search well beca...", "weights": []}, "L16-1306": {"snippet": "...tion online. Developing search techniques that aid these information needs is challenging. In this paper we present the datasets created by CLEF eHealth Lab from 2013-2015 for evaluation of search sol...", "weights": [[8, 14, 1.0]]}, "2016.wwwconf_conference-2016.27": {"snippet": "...ning Online Social Data for Detecting Social Network Mental Disorders\n\n\n ABSTRACTAn increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Over...", "weights": [[8, 14, 1.0]]}, "1991.tois_journal-ir0volumeA9A3.3": {"snippet": "... for online search assistance was developed. This prototype augments the searching capabilities of novice users by providing automatic query reformulation to improve the search results, and automatic ...", "weights": [[8, 14, 1.0], [76, 85, 1.0]]}, "W16-4320": {"snippet": "...edia platforms like Twitter, providing a potentially useful data source for monitoring population-level mental health risk factors. 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"weights": [[8, 16, 1.0], [46, 55, 1.0], [76, 84, 1.0], [90, 97, 1.0], [98, 106, 1.0]]}, "2008.trec_conference-2008.42": {"snippet": "...s of techniques, query expansion and search result re-ranking based on document relevance model, are adopted to improve the retrieval performance. The TMiner search engine, from IR group of Tsinghua U...", "weights": [[8, 18, 1.0], [127, 136, 1.0]]}, "2018.chiir_conference-2018.62": {"snippet": "Diversity-Enhanced Recommendation Interface and Evaluation\n\n\n ABSTRACTe beyond accuracy user experience of using recommender system is drawing more and more a ention. For example, the system interface...", "weights": []}, "1992.trec_conference-1992.31": {"snippet": "Machine Learning and Relevance Feedback", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2013.sigirconf_conference-2013.200": {"snippet": "...ovel information for the given object and user. Moreover, as we argued in [1], objects on the Web 2.0 may be multifaceted, being related to various topics, thus requiring more diversified tags to cove...", "weights": [[8, 19, 1.0]]}, "2009.cikm_conference-2009.247": {"snippet": "...lick information no better than a linear function of the field attributes. We also find query click information to be the single most important field for retrieval. In response, we develop a machine l...", "weights": [[8, 19, 1.0], [103, 114, 1.0], [157, 166, 1.0], [194, 201, 1.0], [202, 210, 1.0]]}, "2017.ictir_workshop-2017learner.8": {"snippet": "...s on Learning to Rank\n\n\n Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of documents an...", "weights": [[8, 16, 1.0], [28, 36, 1.0], [51, 61, 1.0], [71, 78, 1.0], [79, 87, 1.0]]}, "2011.cikm_conference-2011.248": {"snippet": "...opic retrieval that have been used to motivate diverse retrieval. \nCategories and Subject Descriptors\nGeneral TermsAlgorithms Keywords diversity, set-level relevance, maximal marginal relevance\nDIVERS...", "weights": [[8, 17, 1.0], [58, 67, 1.0]]}, "2008.sigirconf_conference-2008.198": {"snippet": "...tive information retrieval (IR). The value of relevance assessment lies in the disambiguation of the user's information need, which is achieved by applying various feedback techniques. Such techniques...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [111, 122, 1.0], [176, 186, 1.0], [193, 203, 1.0]]}, "2012.sigirconf_conference-2012.171": {"snippet": "Learning to select a time-aware retrieval model\n\n\n ABSTRACTTime-aware retrieval models exploit one of two time dimensions, namely, (a) publication time or (b) content time (temporal expressions mentio...", "weights": [[0, 8, 1.0], [32, 41, 1.0], [70, 79, 1.0]]}, "2017.ictir_conference-2017.57": {"snippet": "...s of information retrieval, such as online learning to rank, interactive information retrieval or dynamic information retrieval. ese Permission to make digital or hard copies of part or all of this wo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 54, 1.0], [76, 87, 1.0], [88, 97, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "2020.sigirconf_conference-2020.367": {"snippet": "...ment Learning for Information Retrieval: Fundamentals and Advances\n\n\n Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users' information needs ...", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0], [73, 84, 1.0], [85, 94, 1.0], [100, 110, 1.0], [185, 196, 1.0]]}, "2018.sigirconf_conference-2018.219": {"snippet": "...sly, machine learning methods have been exploited to address the problem, which learns a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning h...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [150, 158, 1.0], [193, 201, 1.0]]}, "1995.sigirconf_conference-95.28": {"snippet": "...pare learning techniques based on statistical classification to traditional methods of relevance feedback for the document routing problem. We consider three classification techniques which have decis...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [176, 186, 1.0]]}, "2008.cikm_conference-2008.48": {"snippet": "...ving retrieval accuracy. The existing relevance feedback algorithms based on language models and vector space models are not effective in learning from negative feedback documents, which are abundant ...", "weights": [[8, 17, 1.0], [141, 149, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "Machine learning for information retrieval\n\n\n ABSTRACTIn recent years, we have witnessed successful application of machine learning techniques to a wide range of information retrieval problems, includ...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0], [115, 122, 1.0], [123, 131, 1.0], [132, 142, 1.0], [162, 173, 1.0], [174, 183, 1.0]]}, "2007.sigirconf_conference-2007.51": {"snippet": "...y in information retrieval (IR). Many machine learning techniques have been proposed for ranking problem, such as RankSVM, RankBoost, and RankNet. Among them, RankNet, which is based on a probabilisti...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [41, 48, 1.0], [49, 57, 1.0], [58, 68, 1.0]]}, "2014.sigirconf_conference-2014.221": {"snippet": "...amic information retrieval modeling\n\n\n ABSTRACTDynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [69, 80, 1.0], [81, 90, 1.0]]}, "2019.wsdm_conference-2019.113": {"snippet": "...sly, machine learning methods have been exploited to address the problem, which learn a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning ha...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [149, 157, 1.0], [192, 200, 1.0]]}, "2006.airs_conference-2006.59": {"snippet": "...ised learning algorithm to integrate pseudo relevant documents with documents to be re-ranked. Given an initial ranked list of retrieved documents, the document re-ranking approach picks a set of docu...", "weights": [[8, 16, 1.0]]}, "2008.ecir_conference-2008.44": {"snippet": "...sage Retrieval\n\n\n Abstract. Passage retrieval and pseudo relevance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, ...", "weights": [[8, 17, 1.0], [39, 48, 1.0], [160, 169, 1.0]]}, "N06-1051": {"snippet": "A Machine Learning based Approach to Evaluating Retrieval Systems\n\n\n Test collections are essential to evaluate Information Retrieval (IR) systems. The relevance assessment set has been recognized as ...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [48, 57, 1.0], [112, 123, 1.0], [124, 133, 1.0]]}, "2008.sigirconf_conference-2008.31": {"snippet": "...back information. The new approach addresses the overfitting problem by projecting the original feature space onto a more compact set which retains the necessary information. The new set of features c...", "weights": [[8, 19, 1.0], [165, 176, 1.0]]}, "W97-1002": {"snippet": "...onal Learning of Pattern-Match Rules for Information Extraction\n\n\n Information extraction systems process natural language documents and locate a specific set of relevant items. Given the recent succe...", "weights": [[8, 16, 1.0], [44, 55, 1.0], [70, 81, 1.0]]}, "2013.ictir_conference-2013.12": {"snippet": "...nced Information Retrieval by Exploiting Recommender Techniques in Cluster-Based Link Analysis\n\n\n ABSTRACTInspired by the use of PageRank algorithms in document ranking, we develop and evaluate a clus...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [56, 66, 1.0]]}, "2011.sigirconf_conference-2011.12": {"snippet": "...se a machine-learning framework for simultaneously optimizing freshness and relevance, in which the trade-off is automatically adaptive to query temporal characteristics. We start by illustrating diff...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "....3.3 Information Search and Retrieval -relevance feedback\nGeneral TermsAlgorithms, Performance, Experimentation\nKeywordsRelevance Feedback, Web Search, Evaluation\n1.INTRODUCTIONRelevance feedback is a...", "weights": [[8, 19, 1.0], [31, 40, 1.0]]}, "2007.ntcir_workshop-2007.53": {"snippet": "...king Techniques: A Comparison of Label Propagation, KNN, and Relevance Feedback\n\n\n This paper describes our work at the sixth NTCIR workshop on the subtask of CC single language information retrieval....", "weights": [[8, 18, 1.0], [181, 192, 1.0], [193, 202, 1.0]]}, "2011.sigirconf_conference-2011.43": {"snippet": "...n: a machine learning approach\n\n\n ABSTRACTAutomatic query expansion technologies have been proven to be effective in many information retrieval tasks. Most existing approaches are based on the assumpt...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [125, 136, 1.0], [137, 146, 1.0]]}, "2007.clef_workshop-2007w.70": {"snippet": "...e in information retrieval systems. In the MorphoChallenge 2007 evaluation we applied a simple zero-knowledge technique that is based on frequency counts rather than machine learning. Our method is ba...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [169, 176, 1.0], [177, 185, 1.0]]}, "2004.cikm_conference-2004.48": {"snippet": "...hm's learning function gradually improves document representations, over time and across users. Results show that the modified document representations yield lower dimensionality while improving retri...", "weights": [[8, 16, 1.0], [198, 207, 1.0]]}, "I08-1068": {"snippet": "...that learning these user profiles using snippets surrounding the results for a query gives better performance than learning from entire document collection.", "weights": [[8, 16, 1.0], [118, 126, 1.0]]}, "2009.trec_conference-2009.53": {"snippet": "...ment retrieval. These features include co-citation measures, URL similarities, as well as features often used in machine learning systems for document ranking such as the difference in scores assigned...", "weights": [[8, 17, 1.0], [116, 123, 1.0], [124, 132, 1.0]]}, "2009.trec_conference-2009.35": {"snippet": "Machine Learning for Information Retrieval: TREC 2009 Web, Relevance Feedback and Legal Tracks", "weights": [[0, 7, 1.0], [8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0]]}, "2007.sigirconf_conference-2007.184": {"snippet": "... and machine meet: a study of combining cognitive and algorithmic relevance feedback\n\n\n ABSTRACTUsing Saracevic's relevance types, we explore approaches to combining algorithm and cognitive relevance ...", "weights": [[8, 15, 1.0]]}, "2007.clef_workshop-2007w.104": {"snippet": "...p on machine learning, image processing and information retrieval (TIA) in the photographic retrieval task at ImageCLEF2007. Many experiments were performed comprising most of the query and target lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [47, 58, 1.0], [59, 68, 1.0], [95, 104, 1.0]]}, "L16-1312": {"snippet": "A Machine Learning based Music Retrieval and Recommendation System\n\n\n In this paper, we present a music retrieval and recommendation system using machine learning techniques. We propose a query by hum...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [31, 40, 1.0], [104, 113, 1.0], [146, 153, 1.0], [154, 162, 1.0], [163, 173, 1.0]]}, "2010.cikm_conference-2010.51": {"snippet": "...ased retrieval comprising multiple ranked lists. Our ground truth is defined in terms of \"information nuggets\", which are obviously not known to the retrieval system when processing a new user query. ...", "weights": [[8, 17, 1.0], [93, 104, 1.0], [152, 161, 1.0]]}, "2014.sigirconf_workshop-2014medir.7": {"snippet": "... for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use. A novel ...", "weights": [[8, 19, 1.0], [84, 95, 1.0], [96, 105, 1.0], [147, 157, 1.0]]}, "2017.tist_journal-ir0volumeA8A3.12": {"snippet": "... for Information Retrieval\n\n\n In many research and application areas, such as information retrieval and machine learning, we often encounter dealing with a probability distribution that is mixed by on...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [81, 92, 1.0], [93, 102, 1.0], [107, 114, 1.0], [115, 123, 1.0]]}, "2004.trec_conference-2004.8": {"snippet": "...good retrieval function can be discovered by a pure machine learning approach, without using probabilistic theories and knowledge-based techniques. Two machine learning algorithms, Support Vector Mach...", "weights": [[8, 17, 1.0], [55, 62, 1.0], [63, 71, 1.0], [139, 149, 1.0], [155, 162, 1.0], [163, 171, 1.0], [199, 206, 1.0]]}, "2007.sigirconf_conference-2007.18": {"snippet": "...mage retrieval\n\n\n ABSTRACTRelevance feedback is a powerful technique to enhance ContentBased Image Retrieval (CBIR) performance. It solicits the user's relevance judgments on the retrieved images retu...", "weights": [[8, 17, 1.0], [102, 111, 1.0]]}, "2011.sigirconf_conference-2011.220": {"snippet": "...lick information.Yisong Yue is a postdoctoral researcher in the Machine Learning Department and the Heinz College at Carnegie Mellon University, where he works on machine learning approaches to inform...", "weights": [[8, 19, 1.0], [67, 74, 1.0], [75, 83, 1.0], [166, 173, 1.0], [174, 182, 1.0], [197, 208, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.8": {"snippet": "...this information overload, because rich information about various kinds of events, user opinions, and daily life activities are generated in an unprecedented speed. This timely information breeds the ...", "weights": [[8, 19, 1.0], [43, 54, 1.0], [180, 191, 1.0]]}, "2011.cikm_conference-2011.267": {"snippet": "...h on learning to rank concerns the problem of appropriate choice of the objective function to maximize by means of machine learning algorithms. We describe a novel technique of smoothing an arbitrary ...", "weights": [[8, 16, 1.0], [118, 125, 1.0], [126, 134, 1.0]]}, "2007.ipm_journal-ir0volumeA43A5.3": {"snippet": "...eral machine learning algorithms for cross-lan7guage patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [63, 72, 1.0], [144, 151, 1.0], [152, 160, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "2014.sigirconf_conference-2014.138": {"snippet": "...into learning-to-rank models\n\n\n ABSTRACTRelevance feedback has been shown to improve retrieval for a broad range of retrieval models. It is the most common way of adapting a retrieval model for a spec...", "weights": [[8, 16, 1.0], [88, 97, 1.0], [119, 128, 1.0], [177, 186, 1.0]]}, "2010.cikm_conference-2010.189": {"snippet": "...line learning for recency search ranking using real-time user feedback\n\n\n ABSTRACTTraditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance...", "weights": [[8, 16, 1.0], [97, 104, 1.0]]}, "2003.sigirjournals_journal-ir0volumeA37A2.1": {"snippet": "...icit techniques can be limited.In this paper we consider the use of implicit feedback techniques for query expansion and user profiling in information retrieval tasks. These techniques unobtrusively o...", "weights": [[8, 18, 1.0], [89, 99, 1.0], [142, 153, 1.0], [154, 163, 1.0], [177, 187, 1.0]]}, "2008.trec_conference-2008.1": {"snippet": "... and machine learning approach for estimating and ranking the opinionated documents. For the Polarized Opinion Retrieval subtask, we employ machine learning for predicting the polarity and linear comb...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [114, 123, 1.0], [143, 150, 1.0], [151, 159, 1.0]]}, "2009.ipm_journal-ir0volumeA45A3.2": {"snippet": "... new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSRANK, aims to use the advantages of both the traditional Information Retrieval...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [76, 85, 1.0], [182, 193, 1.0], [194, 203, 1.0]]}, "2017.sigirconf_conference-2017.241": {"snippet": "...PTS\u2022 Information systems \u2192 Web searching and information discovery; Information retrieval; Information retrieval query processing; Query representation; Query intent; Query languages; \u2022 Software and i...", "weights": [[8, 19, 1.0], [48, 59, 1.0], [71, 82, 1.0], [83, 92, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2004.ir_journal-ir0volumeA7A12.7": {"snippet": "...gual Information Retrieval Using Machine Translation, Relevance Feedback and Decompounding", "weights": [[8, 19, 1.0], [20, 29, 1.0], [36, 43, 1.0]]}, "2016.tist_journal-ir0volumeA7A3.0": {"snippet": "...tary information (i.e., tags), which helps to cover multiple aspects or topics related to the target object. Previous work has addressed the tag recommendation problem by exploiting at most two of the...", "weights": [[8, 19, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.23": {"snippet": "... for information retrieval\n\n\n AbstractMany machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieva...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 53, 1.0], [54, 62, 1.0], [183, 194, 1.0], [195, 204, 1.0]]}, "2018.sigirconf_workshop-2018profs.3": {"snippet": "...call information retrieval is crucial to tasks such as electronic discovery and systematic review. Continuous Active Learning (CAL) is a technique where a human reviewer works in loop with a machine l...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [120, 128, 1.0], [194, 201, 1.0], [202, 210, 1.0]]}, "2005.sigirconf_conference-2005.9": {"snippet": "... hoc information retrieval\n\n\n ABSTRACTInformation retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information nee...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 62, 1.0], [164, 173, 1.0], [188, 199, 1.0]]}, "2016.sigirconf_workshop-2016sal.11": {"snippet": "Retrieval Techniques for Contextual Learning\n\n\n Following constructivist models of contextual learning, knowledge acquisition goes beyond mere absorption of isolated facts, and, instead is enabled, st...", "weights": [[0, 9, 1.0], [10, 20, 1.0], [36, 44, 1.0], [94, 102, 1.0]]}, "H93-1077": {"snippet": "Machine Learning Techniques for Document Filtering\n\n\n BoozoAllen & Hamilton's Advanced Decision Systems Division is conducting a program of research to investigate machine learning techniques that can...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [164, 171, 1.0], [172, 180, 1.0], [181, 191, 1.0]]}, "2012.wsdm_conference-2012.50": {"snippet": "...ical/machine-learned model. This is challenging since the retrieval has to be fast (few milliseconds) to load the page quickly. Fast retrieval is well studied in the information retrieval (IR) literat...", "weights": [[8, 15, 1.0], [61, 70, 1.0], [136, 145, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2011.cikm_conference-2011.406": {"snippet": "...ical information retrieval modelling: from the probability ranking principle to recent advances in diversity, portfolio theory, and beyond\n\n\n ABSTRACTStatistical modelling of Information Retrieval (IR...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [178, 189, 1.0], [190, 199, 1.0]]}, "W99-0625": {"snippet": "... via machine learning. 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This paper studies retrieval of sentences with annotations for a question a...", "weights": [[8, 15, 1.0], [67, 74, 1.0], [119, 126, 1.0]]}, "2010.airs_conference-2010.55": {"snippet": "... and Boolean IR Models for Question Answering", "weights": [[8, 15, 1.0]]}, "2012.ipm_journal-ir0volumeA48A6.9": {"snippet": "...s as Boolean filters. The best working queries were structured into 2-3 main facets out of 3-5 possible search facets, and expressed with terms reflecting the focal view of the search task. The findin...", "weights": [[8, 15, 1.0], [42, 49, 1.0]]}, "2004.ntcir_workshop-2004.80": {"snippet": "...ying queries that combines the probabilistic IR model and the Boolean IR model. Since it is not easy to construct a Boolean query that covers all relevant documents, a mechanism for clarifying the Boo...", "weights": [[8, 15, 1.0], [65, 72, 1.0], [119, 126, 1.0], [200, 207, 1.0]]}, "2012.sigirconf_conference-2012.106": {"snippet": "...late Boolean queries to represent their information need. We want to work on three basic problems:1. Boolean query formulation -Our primary goal is to study Boolean query formulation in the light of t...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [104, 111, 1.0], [160, 167, 1.0]]}, "R15-1095": {"snippet": "...than pro or contra. Second, they cannot be learned as there is no labeled data available. We address the challenge using graph clustering methods to group the members, including the similarity of thei...", "weights": [[8, 11, 1.0], [15, 21, 1.0]]}, "2016.ecir_workshop-2016newsir.3": {"snippet": "Boolean Queries for News Monitoring: Suggesting New Query Terms to Expert Users\n\n\n In this paper, we evaluate query suggestion for Boolean queries in a news monitoring system. Users of this system rec...", "weights": [[0, 7, 1.0], [8, 15, 1.0], [131, 138, 1.0], [139, 146, 1.0]]}, "2010.cikm_conference-2010.75": {"snippet": "... for queries over text databases. Research on selectivity estimation for such queries has focused on Boolean retrieval, i.e., a document may be relevant for the query or not. But with the coalescence ...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [104, 111, 1.0]]}, "2015.ictir_conference-2015.40": {"snippet": "...tive queries in a boolean model. We show that this represents an upper bound on retrievability for all other best match algorithms. 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We also discus...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [93, 100, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.4": {"snippet": "Boolean interpretation of conjunctions for document retrieval", "weights": [[0, 7, 1.0]]}, "2011.sigirconf_conference-2011.85": {"snippet": "... for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [151, 158, 1.0]]}, "2009.jasis_journal-ir0volumeA60A6.9": {"snippet": "...earn Boolean queries: A comparative study", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "... for boolean query formulation\n\n\n AbstractA new model named Boolean Latent Semantic Indexing model based on the Singular Value Decomposition and Boolean query formulation is introduced. While the Sing...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [148, 155, 1.0]]}, "2008.wwwconf_conference-2008.201": {"snippet": "...s of queries per second over billions of documents. To deal with this heavy workload, current engines use massively parallel architectures of thousands of machines that require large hardware investme...", "weights": [[8, 15, 1.0]]}, "2007.trec_conference-2007.51": {"snippet": "...rent Boolean queries were provided for each topic (defendant, plaintiff and final). In 2006, the plaintiff and final queries had (usually) been the same. 1 Although often referred to as \"Boolean,\" the...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [120, 127, 1.0], [190, 197, 1.0]]}, "1985.sigirconf_conference-85.3": {"snippet": "...s of Boolean Structures for Probabilistic Retrieval", "weights": [[8, 15, 1.0]]}, "2005.trec_conference-2005.42": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator. The fuzzy term proximity is controlled with an influenc...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2019.clef_conference-2019w.27": {"snippet": "...e, a boolean query constructed by Cochrane experts, and a set of PubMed Document Identifiers (PID's) returned by running the boolean query in MEDLINE. We handle the problem under the Continuous Active...", "weights": [[8, 15, 1.0], [128, 135, 1.0]]}, "1993.jasis_journal-ir0volumeA44A6.2": {"snippet": "...n of Boolean Queries: A Prototype Implementation and Evaluation", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1982.jasis_journal-ir0volumeA33A6.1": {"snippet": "...with boolean search request formulations", "weights": [[8, 15, 1.0]]}, "2006.trec_conference-2006.32": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator because it can not be generalized to nodes. The fuzzy te...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2010.tois_journal-ir0volumeA29A1.0": {"snippet": "...tive Boolean queries are a key component of modern information retrieval systems, especially when Webscale repositories are being searched. A conjunctive query q is equivalent to a |q|-way intersectio...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2021.paclic-1.11": {"snippet": "...ion, contra Kishimoto's (2001) classification of Japanese ditransitive verbs, in which send-type verbs carry only a caused motion meaning whereas sell-type verbs carry both caused motion and caused po...", "weights": [[8, 14, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "... the Boolean operators and search terms that comprise the submitted queries. Our analysis shows that both Boolean training and the use of an assisted interface improved the participants' ability to co...", "weights": [[8, 15, 1.0], [71, 78, 1.0], [109, 116, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.10": {"snippet": "...n in Boolean IR queries\n\n\n a b s t r a c tThe negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include negated terms in Boole...", "weights": [[8, 15, 1.0], [19, 26, 1.0], [130, 137, 1.0], [198, 205, 1.0]]}, "2020.paclic-1.3": {"snippet": "...wel, contra earlier reports. We further investigated the robustness of this contrast using linear discriminant analysis. Results show that discrimination is above chance, but poor (~62%). The large ov...", "weights": [[8, 14, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...nced queries. We submitted both the original and modified queries to three major Web search engines; a total of 600 queries were submitted and 5,748 documents evaluated. We compared the results from t...", "weights": [[8, 15, 1.0], [61, 68, 1.0], [119, 126, 1.0]]}, "1989.ipm_journal-ir0volumeA25A1.3": {"snippet": "...fied boolean queries: A comparison of inverted and signature file organizations", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2018.sigirconf_conference-2018.50": {"snippet": "...hese Boolean queries are carefully crafted by researchers and information specialists, and often reviewed by a panel of experts. However, little is known about the effectiveness of the Boolean queries...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [188, 195, 1.0], [196, 203, 1.0]]}, "1997.sigirconf_conference-97.15": {"snippet": "...g of Boolean Operators\n\n\n The inference network model of information retrieval allows for a probabilistic interpretation of Boolean query operators. Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [127, 134, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...nded Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulate...", "weights": [[8, 15, 1.0], [73, 80, 1.0], [81, 88, 1.0]]}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0], [8, 15, 1.0]]}, "2003.clef_workshop-2003.32": {"snippet": "...dish Queries for Boolean Combinations of Truncated Terms\n\n\n Abstract. In languages that use compound words such as Swedish, it is often neccessary to split compound words when indexing documents or qu...", "weights": [[8, 15, 1.0], [20, 27, 1.0], [201, 208, 1.0]]}, "2004.trec_conference-2004.90": {"snippet": "...y of boolean approaches to IR and the positive remarks made about their utility for QA by other TREC participants [Harabagiu et al., 2000], for TREC 2003 we implemented a boolean search engine called ...", "weights": [[8, 15, 1.0], [174, 181, 1.0]]}, "1984.ipm_journal-ir0volumeA20A56.6": {"snippet": "... for boolean query relevancy feedback", "weights": [[8, 15, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [124, 131, 1.0], [196, 203, 1.0]]}, "2003.trec_conference-2003.94": {"snippet": "...that Boolean structured queries expanded with automatically collected high frequency words and names of organisms performed considerably better than queries containing gene names only as keys. In the ...", "weights": [[8, 15, 1.0], [27, 34, 1.0], [152, 159, 1.0]]}, "2005.clef_workshop-2005w.11": {"snippet": "Boolean Operators in Interactive Search\n\n\n This paper presents the participation of the MIRACLE team at the ImageCLEF interactive search task. Basically, queries consisting on several terms can be pro...", "weights": [[0, 7, 1.0], [154, 161, 1.0]]}, "2000.sigirconf_conference-2000.5": {"snippet": "... for Boolean IR systems is proposed. It is based on a controlled formulation of inclusive query plans, on an automatic conversion of query plans into elementary queries, and on combining elementary qu...", "weights": [[8, 15, 1.0], [164, 171, 1.0], [201, 208, 1.0]]}, "2007.spire_conference-2007.13": {"snippet": "...tive Boolean queries are a fundamental operation in web search engines. These queries can be reduced to the problem of intersecting ordered sets of integers, where each set represents the documents co...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "Y00-1041": {"snippet": "...ment contra Belletti and Rizzi 1988, Grimshaw 1990  and that the structure proposed by Belletti and Rizzi 1988 is not correct. Bouchard 1995, Iwata 1995, and Chung 1998 have already pointed out that t...", "weights": [[8, 14, 1.0]]}, "2012.sigirconf_workshop-2012osir.0": {"snippet": "... CNF queries. These CNF queries are highly effective, and can outperform bag of word queries by a large margin. However, not many effective tools exist that can facilitate the efficient manual creatio...", "weights": [[8, 15, 1.0], [27, 34, 1.0], [88, 95, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0], [133, 140, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...d on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in answer to search queries but they are not easy to use by uninitiated patrons. An extended Boo...", "weights": [[8, 15, 1.0], [127, 134, 1.0], [200, 207, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...sing Boolean queries. The model is applied to the task of ranking documents, or other structural elements, in the order of their expected relevance. Features such as phrase matching, truncation, and s...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [99, 106, 1.0], [176, 183, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.7": {"snippet": "...onal Boolean retrieval systems", "weights": [[8, 15, 1.0]]}, "1985.riao_conference-1985.20": {"snippet": "...ting boolean structure within a probabilistic framework", "weights": [[8, 15, 1.0]]}, "2014.sigirconf_conference-2014.152": {"snippet": "...lity Boolean queries that can be applied deterministically during filtering. We call these Boolean statements sufficient queries. We argue that using deterministic queries for entity-centric filtering...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [94, 101, 1.0], [124, 131, 1.0], [167, 174, 1.0]]}, "2003.tois_journal-ir0volumeA21A3.0": {"snippet": "...user queries with a variable number of keywords in two manners. Positive expansion adds terms to a user's keywords with a Boolean \"and,\" negative expansion adds terms to the user's keywords with a Boo...", "weights": [[8, 15, 1.0], [125, 132, 1.0], [200, 207, 1.0]]}, "2006.trec_conference-2006.28": {"snippet": "...ated Boolean Queries of the TREC 2006 Legal Discovery Track\n\n\n We analyze the results of several experimental runs submitted to the Legal Discovery and Terabyte Tracks of TREC 2006. In the Legal Track...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2006.clef_workshop-2006w.37": {"snippet": "...dles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes it possible to...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2003.trec_conference-2003.11": {"snippet": "...nded Boolean model has been widely used in business system for its easiness to be complemented and not bad results. In this paper, a statistical model and modified Boolean model and natural language p...", "weights": [[8, 15, 1.0], [167, 174, 1.0]]}, "2006.wwwconf_conference-2006.42": {"snippet": "...ough boolean or TFIDF retrieval, which reduces the specificity of the query. We define a scale for evaluating query substitution, and show that our method performs well at generating new queries relat...", "weights": [[8, 15, 1.0], [190, 197, 1.0]]}}}
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Besides the operators 'and', 'or' and' not' the GI~PS retrieval language contains thesaurus --operators to extend the query and truncation --and context-operators for free...", "weights": [[8, 15, 1.0], [86, 95, 1.0]]}, "1997.jasis_journal-ir0volumeA48A2.3": {"snippet": "...ring Boolean and Probabilistic Information Retrieval Systems across Queries and Disciplines", "weights": [[8, 15, 1.0], [46, 55, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.12": {"snippet": "...fied boolean document retrieval systems", "weights": [[8, 15, 1.0], [25, 34, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.4": {"snippet": "Boolean interpretation of conjunctions for document retrieval", "weights": [[0, 7, 1.0], [52, 61, 1.0]]}, "1998.sigirconf_conference-98.70": {"snippet": "... and Boolean Retrieval", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2011.sigirconf_conference-2011.85": {"snippet": "... for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we...", "weights": [[8, 15, 1.0], [151, 158, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.5": {"snippet": "...nced Boolean retrieval: Experiences with the smart and sire systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2018.sigirconf_conference-2018.82": {"snippet": "...item retrieval systems assume the user can formulate a good Boolean or SQL-style query to retrieve items, as one would do with a database, but this is often challenging, particularly given multiple nu...", "weights": [[8, 17, 1.0], [63, 70, 1.0]]}, "1978.jasis_journal-ir0volumeA29A3.7": {"snippet": "...ging boolean and weighted retrieval systems", "weights": [[8, 15, 1.0], [29, 38, 1.0]]}, "1981.jasis_journal-ir0volumeA32A4.5": {"snippet": "...hted boolean retrieval", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1987.sigirconf_conference-87.38": {"snippet": "...text retrieval systems, which accommodate metrical as well as Boolean operators, the traditional approach to query processing uses a \"concordance\", from which large sets of coordinates are retrieved a...", "weights": [[8, 17, 1.0], [65, 72, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "... for boolean query formulation\n\n\n AbstractA new model named Boolean Latent Semantic Indexing model based on the Singular Value Decomposition and Boolean query formulation is introduced. While the Sing...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [148, 155, 1.0]]}, "1981.ipm_journal-ir0volumeA17A3.0": {"snippet": "... and Boolean retrieval systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1985.sigirconf_conference-85.3": {"snippet": "...s of Boolean Structures for Probabilistic Retrieval", "weights": [[8, 15, 1.0], [45, 54, 1.0]]}, "1982.sigirconf_conference-82.12": {"snippet": "...into Boolean Retrieval System", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...tion retrieval skills by (a) comparing student use of printed and electronic forms of an encyclopedia, (b) examining the effects of menu or command driven interfaces and task difficulty on students' s...", "weights": [[8, 17, 1.0]]}, "2003.trec_conference-2003.11": {"snippet": "...l in retrieval has been shown to perform well empirically. Extended Boolean model has been widely used in business system for its easiness to be complemented and not bad results. In this paper, a stat...", "weights": [[8, 17, 1.0], [71, 78, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "...tion retrieval performance: training in Boolean logic and the type of search interface. As reported previously, we found that both Boolean training and the use of an assisted interface improved the pa...", "weights": [[8, 17, 1.0], [43, 50, 1.0], [134, 141, 1.0]]}, "1994.ipm_journal-ir0volumeA30A2.2": {"snippet": "... for Retrieval Performance and Their Use Measuring Performance and Generating Optimal Boolean Queries: Can It Get Any Better Than This?", "weights": [[8, 17, 1.0], [89, 96, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.1": {"snippet": "...tion retrieval \u2013 The potential for improvements in conventional Boolean retrieval systems", "weights": [[8, 17, 1.0], [67, 74, 1.0], [75, 84, 1.0]]}, "1990.sigirconf_conference-90.28": {"snippet": "...nded Boolean Retrieval: A Heuristic Approach?", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "... and retrieval performance. In extended Boolean models, the relevance feedback implies not only that new query terms must be identified and re-weighted, but also that the terms must be connected with ...", "weights": [[8, 17, 1.0], [43, 50, 1.0]]}, "1988.sigirconf_conference-88.39": {"snippet": "...tion retrieval systems accessing collections of documents. Advantages offered by the theory are: the implicit inclusion of boolean logic; term weighting; and the ability to rank retrieved documents. I...", "weights": [[8, 17, 1.0], [62, 72, 1.0], [126, 133, 1.0]]}, "1997.sigirconf_conference-97.15": {"snippet": "...g of Boolean Operators\n\n\n The inference network model of information retrieval allows for a probabilistic interpretation of Boolean query operators. Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [72, 81, 1.0], [127, 134, 1.0]]}, "2017.chiir_conference-2017.73": {"snippet": "...tion Retrieval. Recently, there has been a move to developing formal models of the interaction between the user and the system, whether it be to: (i) run a simulation, (ii) conduct an economic analysi...", "weights": [[8, 17, 1.0]]}, "1996.tois_journal-ir0volumeA14A4.0": {"snippet": "...Text Retrieval\n\n\n and JUSTIN ZOBEL RMITQuery-processing costs on large text databases are dominated by the need to retrieve and scan the inverted list of each query term. Retrieval time for inverted l...", "weights": [[8, 17, 1.0], [174, 183, 1.0]]}, "2018.clef_conference-2018w.15": {"snippet": "...ng a boolean query and submitting it to a database, and then screening the retrieved documents for relevant ones. Task 2 of CLEF 2018 eHealth lab focuses on automating this process on two fronts: Sub-...", "weights": [[8, 15, 1.0]]}, "1979.ipm_journal-ir0volumeA15A5.2": {"snippet": "...hted boolean retrieval system", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [50, 59, 1.0], [85, 94, 1.0], [124, 131, 1.0], [132, 141, 1.0], [196, 203, 1.0]]}, "2016.ictir_conference-2016.34": {"snippet": "...nked retrieval have been studied for decades. In this paper we propose a new strategy, which we call rank-at-a-time query processing, that evaluates documents in descending order of quantized scores a...", "weights": [[8, 17, 1.0]]}, "1994.ipm_journal-ir0volumeA30A1.5": {"snippet": "...ased Boolean Retrieval Systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0]]}, "2013.ictir_conference-2013.28": {"snippet": "... and Retrieval]: Retrieval models\nGeneral Terms\nTheory\nKeywordsClassical Probabilistic Models, Information Visualization\nBACKGROUNDIn the Binary Independence Model (BIM) , documents and queries are de...", "weights": [[8, 17, 1.0], [20, 29, 1.0]]}, "2012.sigirconf_workshop-2012osir.0": {"snippet": "...tion Retrieval, OSIR@SIGIR 2012, Portland, Oregon, USA, 16th August 2012\n\n\n Boolean Conjunctive Normal Form (CNF) expansion can effectively address the vocabulary mismatch problem, a problem that curr...", "weights": [[8, 17, 1.0], [79, 86, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...Soft Boolean Operators\n\n\n AbstractThe conventional bibliographic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [68, 77, 1.0], [99, 106, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...s of Boolean algebra are obeyed, and the exact-match semantics of Boolean retrieval are preserved. Simple inverted-list file structures provide an efficient implementation. Retrieval effectiveness is ...", "weights": [[8, 15, 1.0], [69, 76, 1.0], [77, 86, 1.0], [176, 185, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [45, 54, 1.0], [99, 106, 1.0], [107, 116, 1.0], [176, 183, 1.0], [199, 208, 1.0]]}, "1986.sigirconf_conference-86.21": {"snippet": "... for Boolean Query Processing\n\n\n Abstract. An infamation retrieval model, named the Generaliied Vectm Spice Model (GVSM). is extended m handle situations where queries are specitied as (extended) Bool...", "weights": [[8, 15, 1.0], [60, 69, 1.0], [199, 206, 1.0]]}, "1980.sigirconf_conference-80.3": {"snippet": "... for Boolean Retrieval", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1998.ipm_journal-ir0volumeA34A23.3": {"snippet": "... vs. Boolean: A Comparison of Partial and Exact Match Retrieval Systems", "weights": [[8, 15, 1.0], [57, 66, 1.0]]}, "2022.naacl-main.226": {"snippet": "...eral advantages compared to standard dense retrieval models. It produces representations which are 4x more compact, while delivering comparable retrieval results. It also performs surprisingly well un...", "weights": [[8, 18, 1.0], [46, 55, 1.0], [147, 156, 1.0]]}, "1988.tois_journal-ir0volumeA6A1.1": {"snippet": "... but retrieval is usually limited to finding documents that contain a specified Boolean pattern of words. Effective retrieval requires that documents with similar meanings be found through a process o...", "weights": [[8, 17, 1.0], [83, 90, 1.0], [119, 128, 1.0]]}}}
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We partly defuse the Boolean challenge by showing that the data actually argue against a ...", "weights": [[8, 15, 1.0], [135, 142, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": []}, "2021.findings-acl.146": {"snippet": "Documents Representation via Generalized Coupled Tensor Chain with the Rotation Group constraint\n\n\n Continuous representations of linguistic structures are an important part of modern natural language...", "weights": []}, "1999.tois_journal-ir0volumeA17A4.1": {"snippet": "...uery operators. In particular, Boolean query operators are conveniently modeled as link matrices of the Bayesian Network. Prior work has shown, however, that these operators do not perform as well as ...", "weights": [[8, 17, 1.0], [34, 41, 1.0], [48, 57, 1.0], [167, 176, 1.0]]}, "2009.tois_journal-ir0volumeA27A2.3": {"snippet": "...udes Boolean and word proximity operators.", "weights": [[8, 15, 1.0], [35, 44, 1.0]]}, "1985.sigirconf_conference-85.25": {"snippet": "...nded Boolean language / 10/. Besides the operators 'and', 'or' and' not' the GI~PS retrieval language contains thesaurus --operators to extend the query and truncation --and context-operators for free...", "weights": [[8, 15, 1.0], [44, 53, 1.0], [126, 135, 1.0], [185, 194, 1.0]]}, "J98-1001": {"snippet": "Introduction to the Special Issue on Word Sense Disambiguation: The State of the Art\n\n\n and is masculine in the former sense, feminine in the latter) to properly tag it as a masculine noun. Sense disa...", "weights": []}, "1999.sigirconf_conference-99.10": {"snippet": "...nded Boolean Models", "weights": [[8, 15, 1.0]]}, "2011.sigirconf_conference-2011.85": {"snippet": "... for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we...", "weights": [[8, 15, 1.0], [151, 158, 1.0]]}, "2007.wwwconf_conference-2007.106": {"snippet": "...d OR boolean operators as well. We first analyze properties of the LCA computation and propose improved algorithms to solve the traditional keyword search problem (with only AND semantics). We then ex...", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2008.wwwconf_conference-2008.201": {"snippet": "...ther Boolean filter between the lists is applied to determine those docIDs that contain all or most of the query terms. For these docIDs, the additional information associated with the docID in the in...", "weights": [[8, 15, 1.0]]}, "2007.trec_conference-2007.51": {"snippet": "... as \"Boolean,\" these queries contain additional operators (e.g., proximity and truncation operators) that are commonly found in the query languages of commercial search systems that employ Boolean log", "weights": [[8, 15, 1.0], [51, 60, 1.0], [93, 102, 1.0], [192, 199, 1.0]]}, "2005.trec_conference-2005.42": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator. The fuzzy term proximity is controlled with an influenc...", "weights": [[8, 15, 1.0], [81, 88, 1.0]]}, "P03-1018": {"snippet": "...dard Boolean methods. By altering the query vector itself, vector negation removes not only unwanted strings but unwanted meanings.", "weights": [[8, 15, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "... the Boolean operators and search terms that comprise the submitted queries. Our analysis shows that both Boolean training and the use of an assisted interface improved the participants' ability to co...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [109, 116, 1.0]]}, "1952.earlymt-1.13": {"snippet": "...uman operators, and above all, in what fashion the vocabulary of the FL is to be transferred to the TL. There is no a priori \"operational syntax.\" There are only operational syntaxes, whose elaboratio...", "weights": [[8, 17, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.10": {"snippet": "...n in Boolean IR queries\n\n\n a b s t r a c tThe negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include negated terms in Boole...", "weights": [[8, 15, 1.0], [198, 205, 1.0]]}, "2014.tois_journal-ir0volumeA32A2.0": {"snippet": "Efficient Index-Based Snippet Generation\n\n\n Ranked result lists with query-dependent snippets have become state of the art in text search. They are typically implemented by searching, at query time, f...", "weights": []}, "2007.trec_conference-2007.65": {"snippet": "...lds, boolean constraints and structured operators. Evaluation results show that structured queries outperform bag-of-words ones. Boolean constraints improve both precision and recall. For the routing ...", "weights": [[8, 15, 1.0], [43, 52, 1.0], [132, 139, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...uery operators, with the other 90% using extremely simple queries. It is often assumed that the use of query operators, such as Boolean operators and phrase searching, improves the effectiveness of We...", "weights": [[8, 17, 1.0], [112, 121, 1.0], [131, 138, 1.0], [139, 148, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.12": {"snippet": "... and Boolean operators used as well as their relationships with search failure. Among other findings, results indicate that a majority of the queries were simple, with short query lengths and a low us...", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2003.tc-1.15": {"snippet": "Convergence in {CAT}: blending {MT}, {TM}, {OCR} {\\&} {SR} to boost productivity\n\n\n 2. Brain strain. After having translated a sentence, you will have to look again at your paper sheet and locate the ...", "weights": []}, "1997.sigirconf_conference-97.15": {"snippet": "...g of Boolean Operators\n\n\n The inference network model of information retrieval allows for a probabilistic interpretation of Boolean query operators. Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [127, 134, 1.0], [141, 150, 1.0], [194, 203, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...nded Boolean models, the relevance feedback implies not only that new query terms must be identified and re-weighted, but also that the terms must be connected with Boolean And/Or operators properly. ...", "weights": [[8, 15, 1.0], [168, 175, 1.0], [183, 192, 1.0]]}, "J90-1008": {"snippet": "Book Reviews: Prosody and Speech Recognition\n\n\n Book Reviews Prosody and Speech Recognition chart parsing is reduced into a set of matrix operations dealing with sparse matrices. Appendices list sampl...", "weights": []}, "1993.sigirconf_conference-93.7": {"snippet": "The Identification of Important Concepts in Highly Structured Technical Papers\n\n\n AbstractAutomatic abstracting, typically based on extraction of important sentences from a text, has been treated as a...", "weights": []}, "2003.clef_workshop-2003.32": {"snippet": "...se a Boolean quorum-level search method to rank documents both according to a tf-idf factor but also to the number of matching Boolean combinations.The Boolean combinations performed relatively well, ...", "weights": [[8, 15, 1.0], [130, 137, 1.0], [155, 162, 1.0]]}, "C18-1211": {"snippet": "...onal Operators for Analogy Detection\n\n\n Representing the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as anal...", "weights": [[8, 17, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [42, 49, 1.0], [124, 131, 1.0], [196, 203, 1.0]]}, "2005.clef_workshop-2005w.11": {"snippet": "Boolean Operators in Interactive Search\n\n\n This paper presents the participation of the MIRACLE team at the ImageCLEF interactive search task. Basically, queries consisting on several terms can be pro...", "weights": [[0, 7, 1.0], [8, 17, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0]]}, "2007.spire_conference-2007.13": {"snippet": "...tive Boolean queries are a fundamental operation in web search engines. These queries can be reduced to the problem of intersecting ordered sets of integers, where each set represents the documents co...", "weights": [[8, 15, 1.0]]}, "2000.sigirconf_conference-2000.5": {"snippet": "...n of Boolean query effectiveness across a wide operational range\n\n\n ABSTRACTTraditional methods for the system-oriented evaluation of Boolean IR systems suffer from validity and reliability problems. ...", "weights": [[8, 15, 1.0], [137, 144, 1.0]]}, "1982.sigirconf_conference-82.5": {"snippet": "...d on Boolean combinations. These novel methods have not been adopted by the large operational systems. In this paper, a software implementation of an ' i n t e l l i g e n t terminal' is described. I ...", "weights": [[8, 15, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...Soft Boolean Operators\n\n\n AbstractThe conventional bibliographic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [99, 106, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [99, 106, 1.0], [176, 183, 1.0]]}, "2018.wwwconf_conference-2018c.48": {"snippet": "Crowd-Machine Collaboration for Item Screening\n\n\n ABSTRACTIn this paper we describe how crowd and machine classifier can be efficiently combined to screen items that satisfy a set of predicates. We sh...", "weights": []}, "2008.ipm_journal-ir0volumeA44A6.6": {"snippet": "Adapting information retrieval to query contexts\n\n\n a b s t r a c tIn current IR approaches documents are retrieved only according to the terms specified in the query. The same answers are returned fo...", "weights": []}, "E85-1017": {"snippet": "...lean Operators", "weights": [[8, 17, 1.0]]}, "2016.chiir_conference-2016.21": {"snippet": "History by Diversity: Helping Historians search News Archives\n\n\n ABSTRACTLongitudinal corpora like newspaper archives are of immense value to historical research, and time as an important factor for h...", "weights": []}, "2001.cikm_conference-2001.28": {"snippet": "...lude operators such as projection and selection using comparison predicates.Based on its knowledge about the abstract table representations, the wrapper prepares a local access plan. In the first plac...", "weights": [[8, 17, 1.0]]}, "P84-1038": {"snippet": "A General Computational Model for Word-Form Recognition and Production\n\n\n A language independent model for recognition and production of word forms is presented. This \"two-level model\" is based on a n...", "weights": []}}}
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Pepper seamlessly combines hierarchical networks wi...", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1997.tois_journal-ir0volumeA15A2.0": {"snippet": "...ng a Digital Library: The Contents of the CORE Project", "weights": [[8, 15, 1.0]]}, "2009.cikm_conference-2009.314": {"snippet": "...s in digital libraries\n\n\n ABSTRACTQueries on digital libraries generally involve the retrieval of specific documents, but most techniques lack the ability to efficiently explore these collections. The...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [48, 55, 1.0], [56, 65, 1.0]]}, "2016.eamt-2.23": {"snippet": "Digital curation technologies ({DKT})\n\n\n Digital Curation Technologies (\"Digitale Kuratierungstechnologien\", DKT) is a project that involves four Berlin-based SMEs (ART+COM AG, Condat AG, 3pc GmbH and...", "weights": [[0, 7, 1.0], [17, 29, 1.0], [41, 48, 1.0], [58, 70, 1.0]]}, "L14-1164": {"snippet": "Digital Library 2.0: Source of Knowledge and Research Collaboration Platform", "weights": [[0, 7, 1.0]]}, "2017.wwwconf_conference-2017c.191": {"snippet": "Digital City, Smart City and Beyond\n\n\n ABSTRACTThis article revisited past digital cities, and discussed smart cities and the future. If we understand digital cities as exploration of cyber space and ...", "weights": [[0, 7, 1.0], [75, 82, 1.0], [151, 158, 1.0]]}, "2004.cikm_conference-2004.85": {"snippet": "...OSU) Libraries. SERF encourages users to enter longer and more informative queries, and collects ratings from users as to whether search results meet their information need or not. 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This causes a lack of representativeness in our NLP tasks, leading to models that can capture the head phenomena in language, but fail when dealing with the long tail. We therefore pr...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "1986.jasis_journal-ir0volumeA37A5.4": {"snippet": "...ka's Law, Bradford's Law, and Zipf's Law", "weights": [[8, 11, 1.0], [24, 27, 1.0], [33, 37, 1.0], [40, 43, 1.0]]}, "1998.sigirconf_conference-98.3": {"snippet": "...o an information need. In particular, the relationship between the document frequency and the weight of evidence is investigated.A correlation between document frequency normalized by collection size ...", "weights": [[8, 19, 1.0]]}, "P14-1075": {"snippet": "...lows Zipf's law, we derive tradeoff formulae of the perplexity of k-gram models and topic models with respect to the size of the reduced vocabulary. In addition, we show an approximate behavior of eac...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2017.sigirconf_conference-2017.41": {"snippet": "...ago, Zipf postulated that the word frequency distribution of languages is a power law, i.e., it is a straight line on a log-log plot. 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Information Retrieval via Natural Language Query", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0]]}, "1994.sigirconf_conference-94.19": {"snippet": "...nded Boolean Models in Information Retrieval", "weights": [[8, 15, 1.0], [26, 37, 1.0], [38, 47, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.10": {"snippet": "...ment Retrieval\n\n\n AbstractThe use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolea", "weights": [[8, 17, 1.0], [79, 88, 1.0], [121, 130, 1.0], [197, 204, 1.0]]}, "2004.ntcir_workshop-2004.80": {"snippet": "... and Boolean IR Models for WWW Documents Retrieval\n\n\n In this paper, we describe our information retrieval (IR) system that is used for the NTCIR-4 Web Task A. First, we introduce our IR system, which...", "weights": [[8, 15, 1.0], [44, 53, 1.0], [88, 99, 1.0], [100, 109, 1.0]]}, "2012.sigirconf_conference-2012.106": {"snippet": "...late Boolean queries to represent their information need. We want to work on three basic problems:1. Boolean query formulation -Our primary goal is to study Boolean query formulation in the light of t...", "weights": [[8, 15, 1.0], [43, 54, 1.0], [104, 111, 1.0], [160, 167, 1.0]]}, "2010.cikm_conference-2010.75": {"snippet": "...d on Boolean retrieval, i.e., a document may be relevant for the query or not. But with the coalescence of database and information retrieval (IR) technology, selectivity estimation for other, more so...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.22": {"snippet": "... the information available to us has been notably increasing. In this overload of information, Information Retrieval (IR) has been playing a prominent role by developing systems capable of separating ...", "weights": [[8, 19, 1.0], [85, 96, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "1994.ipm_journal-ir0volumeA30A3.4": {"snippet": "...s in Boolean Retrieval Systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2006.clef_workshop-2006w.121": {"snippet": "... Our retrieval system consists of four modules: the geographic knowledge base; the indexing module; the document retrieval module and the ranking module. The geographic knowledge base provides informa...", "weights": [[8, 17, 1.0], [116, 125, 1.0], [196, 207, 1.0]]}, "1997.jasis_journal-ir0volumeA48A2.3": {"snippet": "...ring Boolean and Probabilistic Information Retrieval Systems across Queries and Disciplines", "weights": [[8, 15, 1.0], [34, 45, 1.0], [46, 55, 1.0]]}, "1987.sigirconf_conference-87.25": {"snippet": "...Text Retrieval and Analysis System\n\n\n AbstractMICROARRAS is an advanced full-text retrieval and analysis system. It supports fast, efficient browsing of a document's vocabulary as well as its text, re...", "weights": [[8, 17, 1.0], [85, 94, 1.0]]}, "1985.sigirconf_conference-85.25": {"snippet": "...heir retrieval experlrnent. FIZ4 IS using the Information retrieval system GRIPS \u2022 (General Relation based Information l~oceeeing System) which was developed by DIMDI (Deutsches Institut flLer Medlzin...", "weights": [[8, 17, 1.0], [49, 60, 1.0], [61, 70, 1.0], [110, 121, 1.0]]}, "1993.jasis_journal-ir0volumeA44A2.1": {"snippet": "...zing Boolean Information Retrieval: A Model and Its Evaluation", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0]]}, "2012.sigirconf_conference-2012.111": {"snippet": "...p in information systems is to afford seamless movement between boolean and ranked retrieval of such different retrieval types enabling users to easily negotiate this complex information space. We int...", "weights": [[8, 19, 1.0], [67, 74, 1.0], [86, 95, 1.0], [114, 123, 1.0], [178, 189, 1.0]]}, "1993.sigirconf_conference-93.36": {"snippet": "...s on Information Retrieval System Performance\n\n\n AbstractFive independently generated Boolean query formulations for ten different TREC topics were produced by ten different expert online searchers. T...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [89, 96, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.4": {"snippet": "Boolean interpretation of conjunctions for document retrieval", "weights": [[0, 7, 1.0], [52, 61, 1.0]]}, "1989.sigirconf_conference-89.5": {"snippet": "... for Information Retrieval\n\n\n AbstractIn conventional Boolean retrieval systems,", "weights": [[8, 19, 1.0], [20, 29, 1.0], [57, 64, 1.0], [65, 74, 1.0]]}, "1990.sigirconf_conference-90.1": {"snippet": "...ment Retrieval\n\n\n AbstractThe use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolea", "weights": [[8, 17, 1.0], [79, 88, 1.0], [121, 130, 1.0], [197, 204, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "...onal information retrieval model, Boolean query formulation can help users to make precise representation of their information search needs. Retrieval experiments on a number of test collections seem ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [37, 44, 1.0], [118, 129, 1.0], [144, 153, 1.0]]}, "1985.sigirconf_conference-85.3": {"snippet": "...s of Boolean Structures for Probabilistic Retrieval", "weights": [[8, 15, 1.0], [45, 54, 1.0]]}, "2017.cikm_conference-2017.268": {"snippet": "Boolean Matrix Decomposition by Formal Concept Sampling\n\n\n ABSTRACTFinding interesting patterns is a classical problem in data mining. Boolean matrix decomposition is nowadays a standard tool that can...", "weights": [[0, 7, 1.0], [135, 142, 1.0]]}, "1982.sigirconf_conference-82.12": {"snippet": "...into Boolean Retrieval System", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1982.jasis_journal-ir0volumeA33A6.1": {"snippet": "...h to information retrieval in systems with boolean search request formulations", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 53, 1.0]]}, "2015.jasis_journal-ir0volumeA66A8.2": {"snippet": "... for boolean retrieval and human decision-making during searches", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1997.sigirjournals_journal-ir0volumeA31A1.8": {"snippet": "...atic Boolean query processing. Information Processing and  Management, 24, 3 (1988), 269-280.    Historical note: the past thirty years in information retrieval. Journal of the American Society for In...", "weights": [[8, 15, 1.0], [34, 45, 1.0], [142, 153, 1.0], [154, 163, 1.0], [201, 212, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "...user information retrieval performance: training in Boolean logic and the type of search interface. As reported previously, we found that both Boolean training and the use of an assisted interface imp...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [55, 62, 1.0], [146, 153, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.1": {"snippet": "...h on information retrieval \u2013 The potential for improvements in conventional Boolean retrieval systems", "weights": [[8, 19, 1.0], [20, 29, 1.0], [79, 86, 1.0], [87, 96, 1.0]]}, "1986.sigirjournals_journal-ir0volumeA20A14.4": {"snippet": "... the retrieval processes of document retrieval systems\u00b0 Two basic categories of models that have been employed in information retrieval are the vector processing models and the Boolean retrieval model", "weights": [[8, 17, 1.0], [40, 49, 1.0], [117, 128, 1.0], [129, 138, 1.0], [180, 187, 1.0], [188, 197, 1.0]]}, "1980.sigirconf_conference-80.22": {"snippet": "...ased Information Retrieval System with a Boolean Search Request Formulation", "weights": [[8, 19, 1.0], [20, 29, 1.0], [44, 51, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...uses information obtained from a user about a set of initially retrieved documents to improve subsequent search formulations and retrieval performance. In extended Boolean models, the relevance feedba...", "weights": [[8, 19, 1.0], [132, 141, 1.0], [167, 174, 1.0]]}, "1997.sigirconf_conference-97.15": {"snippet": "...g of Boolean Operators\n\n\n The inference network model of information retrieval allows for a probabilistic interpretation of Boolean query operators. Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [60, 71, 1.0], [72, 81, 1.0], [127, 134, 1.0]]}, "2001.ipm_journal-ir0volumeA37A3.2": {"snippet": "...ated information retrieval with real users: Boolean searching and batch retrieval evaluations", "weights": [[8, 19, 1.0], [20, 29, 1.0], [47, 54, 1.0], [75, 84, 1.0]]}, "1988.sigirconf_conference-88.39": {"snippet": "...s of information retrieval Finally we present a small scale evaluation of rough sets which indicates its potential in information retrieval.", "weights": [[8, 19, 1.0], [20, 29, 1.0], [121, 132, 1.0], [133, 142, 1.0]]}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0], [55, 64, 1.0]]}, "2004.trec_conference-2004.90": {"snippet": "...ed a boolean search engine called MadCow which does word indexing at the sentence level. This year we decided to explore systematically strategies for boolean retrieval for the purpose of QA measuring...", "weights": [[8, 15, 1.0], [154, 161, 1.0], [162, 171, 1.0]]}, "1979.ipm_journal-ir0volumeA15A5.2": {"snippet": "...hted boolean retrieval system", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2020.sigirconf_conference-2020.379": {"snippet": "...sing Boolean techniques. Given this situation, an e cient system for legal case retrieval can be of great bene t and thus has drawn increasing attention in academic and industrial IR research. General...", "weights": [[8, 15, 1.0], [83, 92, 1.0]]}, "1994.ipm_journal-ir0volumeA30A1.5": {"snippet": "...ased Boolean Retrieval Systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [50, 59, 1.0], [85, 94, 1.0], [124, 131, 1.0], [132, 141, 1.0], [196, 203, 1.0]]}, "1982.sigirconf_conference-82.18": {"snippet": "...uage Information System\n\n\n AbstractLarge operational information retrieval systems typically employ inverted file structures and Boolean logic operators for efficient text retrieval. These systems req...", "weights": [[8, 19, 1.0], [56, 67, 1.0], [68, 77, 1.0], [132, 139, 1.0], [175, 184, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0]]}, "2007.spire_conference-2007.13": {"snippet": "... for Information Retrieval\n\n\n Abstract. Conjunctive Boolean queries are a fundamental operation in web search engines. These queries can be reduced to the problem of intersecting ordered sets of integ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [55, 62, 1.0]]}, "2013.ictir_conference-2013.28": {"snippet": "...3.3 [Information Search and Retrieval]: Retrieval models\nGeneral Terms\nTheory\nKeywordsClassical Probabilistic Models, Information Visualization\nBACKGROUNDIn the Binary Independence Model (BIM) , docum...", "weights": [[8, 19, 1.0], [31, 40, 1.0], [43, 52, 1.0], [121, 132, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...Soft Boolean Operators\n\n\n AbstractThe conventional bibliographic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [68, 77, 1.0], [99, 106, 1.0]]}, "1991.sigirconf_conference-91.35": {"snippet": "...tual Information Retrieval System\n\n\n ABSTRACTFERRET is a full text, conceptual information retrieval system that uses a partial understanding of its texts to provide greater precision and recall perfo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [82, 93, 1.0], [94, 103, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...s of Boolean algebra are obeyed, and the exact-match semantics of Boolean retrieval are preserved. Simple inverted-list file structures provide an efficient implementation. Retrieval effectiveness is ...", "weights": [[8, 15, 1.0], [69, 76, 1.0], [77, 86, 1.0], [176, 185, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [45, 54, 1.0], [99, 106, 1.0], [107, 116, 1.0], [176, 183, 1.0], [199, 208, 1.0]]}, "2008.trec_conference-2008.30": {"snippet": "...veR) Information Retrieval platform, DFRee model, Bo1 (Bose-Einstein statistics) term weighting models, Boolean re-rank, query lexicon, query performance prediction, Z-Score. \u2022 Open Text Corporation: ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [107, 114, 1.0]]}, "1986.sigirconf_conference-86.21": {"snippet": "... for Boolean Query Processing\n\n\n Abstract. An infamation retrieval model, named the Generaliied Vectm Spice Model (GVSM). is extended m handle situations where queries are specitied as (extended) Bool...", "weights": [[8, 15, 1.0], [60, 69, 1.0], [199, 206, 1.0]]}, "2006.clef_workshop-2006w.37": {"snippet": "...dles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes it possible to...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [89, 100, 1.0], [101, 110, 1.0]]}, "1988.tois_journal-ir0volumeA6A1.1": {"snippet": "... but retrieval is usually limited to finding documents that contain a specified Boolean pattern of words. Effective retrieval requires that documents with similar meanings be found through a process o...", "weights": [[8, 17, 1.0], [83, 90, 1.0], [119, 128, 1.0]]}}}
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The mode...", "weights": [[8, 15, 1.0], [16, 19, 1.0], [182, 189, 1.0], [190, 193, 1.0]]}, "1982.ipm_journal-ir0volumeA18A5.4": {"snippet": "... the Boolean logic of a program", "weights": [[8, 15, 1.0]]}, "2015.jasis_journal-ir0volumeA66A8.2": {"snippet": "... for boolean retrieval and human decision-making during searches", "weights": [[8, 15, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "... the Boolean operators and search terms that comprise the submitted queries. Our analysis shows that both Boolean training and the use of an assisted interface improved the participants' ability to co...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [109, 116, 1.0], [134, 137, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.10": {"snippet": "... the use of negation in Boolean IR queries\n\n\n a b s t r a c tThe negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include neg...", "weights": [[8, 11, 1.0], [27, 34, 1.0]]}, "2007.sigirconf_conference-2007.184": {"snippet": "...mple Boolean operators. Results show minimal differences in performance with respect to the different techniques.", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...uery operators, with the other 90% using extremely simple queries. It is often assumed that the use of query operators, such as Boolean operators and phrase searching, improves the effectiveness of We...", "weights": [[8, 17, 1.0], [99, 102, 1.0], [112, 121, 1.0], [131, 138, 1.0], [139, 148, 1.0]]}, "2007.trec_conference-2007.65": {"snippet": "...lds, boolean constraints and structured operators. Evaluation results show that structured queries outperform bag-of-words ones. Boolean constraints improve both precision and recall. For the routing ...", "weights": [[8, 15, 1.0], [43, 52, 1.0], [132, 139, 1.0]]}, "1997.sigirconf_conference-97.15": {"snippet": "...g of Boolean Operators\n\n\n The inference network model of information retrieval allows for a probabilistic interpretation of Boolean query operators. Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [127, 134, 1.0], [141, 150, 1.0], [194, 203, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...nded Boolean models, the relevance feedback implies not only that new query terms must be identified and re-weighted, but also that the terms must be connected with Boolean And/Or operators properly. ...", "weights": [[8, 15, 1.0], [168, 175, 1.0], [183, 192, 1.0]]}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0]]}, "1997.sigirjournals_journal-ir0volumeA31A1.7": {"snippet": "...pert systems research is the new frontier and the next area of development in library information science. Expert systems will enable users to make more effective use of the automated systems and on-l...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [117, 124, 1.0], [166, 169, 1.0], [187, 194, 1.0]]}, "1979.ipm_journal-ir0volumeA15A5.2": {"snippet": "...hted boolean retrieval system", "weights": [[8, 15, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [42, 49, 1.0], [124, 131, 1.0], [196, 203, 1.0]]}, "1982.sigirconf_conference-82.18": {"snippet": "...eval systems typically employ inverted file structures and Boolean logic operators for efficient text retrieval. These systems require considerable user training for effective use. As a consequence, s...", "weights": [[8, 15, 1.0], [62, 69, 1.0], [76, 85, 1.0], [122, 129, 1.0], [179, 182, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0]]}, "2007.spire_conference-2007.13": {"snippet": "...tive Boolean queries are a fundamental operation in web search engines. These queries can be reduced to the problem of intersecting ordered sets of integers, where each set represents the documents co...", "weights": [[8, 15, 1.0]]}, "2005.clef_workshop-2005w.11": {"snippet": "Boolean Operators in Interactive Search\n\n\n This paper presents the participation of the MIRACLE team at the ImageCLEF interactive search task. Basically, queries consisting on several terms can be pro...", "weights": [[0, 7, 1.0], [8, 17, 1.0]]}, "2000.sigirconf_conference-2000.5": {"snippet": "...n of Boolean query effectiveness across a wide operational range\n\n\n ABSTRACTTraditional methods for the system-oriented evaluation of Boolean IR systems suffer from validity and reliability problems. ...", "weights": [[8, 15, 1.0], [137, 144, 1.0], [148, 155, 1.0]]}, "1982.sigirconf_conference-82.5": {"snippet": "...eval Systems\n\n\n ABSTRACTResearch has shown that document r e t r i e v a l based on weighting functions and incorporating relevance feedback may be more effective than r e t r i e v a l based on Boole...", "weights": [[8, 15, 1.0], [198, 205, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...Soft Boolean Operators\n\n\n AbstractThe conventional bibliographic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [78, 85, 1.0], [99, 106, 1.0], [166, 173, 1.0]]}, "2008.trec_conference-2008.43": {"snippet": "...ment Library Boolean search system. Our experiment achieved reasonable precision but suffered significantly from low recall. These results, together with the appealing and adjudication results, sugges...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...s of Boolean algebra are obeyed, and the exact-match semantics of Boolean retrieval are preserved. Simple inverted-list file structures provide an efficient implementation. Retrieval effectiveness is ...", "weights": [[8, 15, 1.0], [69, 76, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [99, 106, 1.0], [176, 183, 1.0]]}, "1985.riao_conference-1985.20": {"snippet": "...ting boolean structure within a probabilistic framework", "weights": [[8, 15, 1.0]]}, "2006.clef_workshop-2006w.37": {"snippet": "...dles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes it possible to...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [127, 130, 1.0]]}, "2019.wwwconf_conference-2019.151": {"snippet": "...atic Boolean Query Refinement for Systematic Review Literature Search", "weights": [[8, 15, 1.0]]}}}
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Hence, diverse Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee...", "weights": [[8, 15, 1.0]]}, "2020.sigirconf_workshop-2020birds.2": {"snippet": "...y in Ranking\n\n\n Ranking in Information Retrieval (IR) has been traditionally evaluated from the perspective of the relevance of search engine results to people searching for information, i.e., the ext...", "weights": [[8, 15, 1.0], [19, 26, 1.0], [131, 137, 1.0], [145, 152, 1.0]]}, "2013.tweb_journal-ir0volumeA7A2.6": {"snippet": "... For ranking results, we analyze the second-order agreement between the results. Further extending SourceRank to multidomain search, we propose a source ranking sensitive to the query domains. Multipl...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [75, 82, 1.0], [128, 134, 1.0], [156, 163, 1.0]]}, "2015.cikm_conference-2015.141": {"snippet": "...ting search engines often rely on crowdsourced assessments of document relevance with respect to a user query. To use such assessments for either evaluation or learning, we propose a new framework for...", "weights": [[8, 14, 1.0], [149, 159, 1.0]]}, "2012.cikm_conference-2012.220": {"snippet": "...zing search results for non-native speakers\n\n\n ABSTRACTBlog posts, news articles and other webpages are present on the web in multiple languages. Standard search engines evaluate the relevance of the ...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [158, 164, 1.0]]}, "2008.sigirconf_conference-2008.173": {"snippet": "Re-ranking search results using document-passage graphs\n\n\n ABSTRACTWe present a novel passage-based approach to re-ranking documents in an initially retrieved list so as to improve precision at top ra...", "weights": [[3, 10, 1.0], [11, 17, 1.0], [18, 25, 1.0], [115, 122, 1.0]]}, "2017.cikm_conference-2017.79": {"snippet": "...Many search e ectiveness evaluation measures penalize the importance of results at lower ranks.is is usually explained as an a empt to model users' persistence when sequentially examining results-lowe...", "weights": [[8, 14, 1.0], [28, 38, 1.0], [75, 82, 1.0], [191, 198, 1.0]]}, "2016.trec_conference-2016.9": {"snippet": "...1) a ranking method using information crawled from an authoritative search engine; (2) search ranking based on clickthrough data taken from user feedback; and (3) a unified modeling method that combin...", "weights": [[8, 15, 1.0], [71, 77, 1.0], [90, 96, 1.0], [97, 104, 1.0]]}, "2006.cikm_conference-2006.127": {"snippet": "... the ranking of search results for any given query by constructing the query context from search query logs. Evaluation results show that Q-Rank gains a considerable advantage over the current ranking...", "weights": [[8, 15, 1.0], [19, 25, 1.0], [26, 33, 1.0], [93, 99, 1.0], [112, 122, 1.0], [123, 130, 1.0], [196, 203, 1.0]]}, "2011.sigirconf_conference-2011.190": {"snippet": "...s of search quality, such as freshness and diversity. Prior work on multi-objective ranking trained rankers focused on using hybrid labels that combine overall quality of documents, and implicitly inc...", "weights": [[8, 14, 1.0], [87, 94, 1.0]]}, "2004.wwwconf_conference-2004at.87": {"snippet": "... web search\n\n\n ABSTRACTMaximizing only the relevance between queries and documents will not satisfy users if they want the top search results to present a wide coverage of topics by a few representati...", "weights": [[8, 14, 1.0], [130, 136, 1.0], [137, 144, 1.0]]}, "2009.cikm_conference-2009.182": {"snippet": "... web search ranking using data from a search engine. Our results demonstrate that multi-task learning methods bring significant relevance improvements over existing baseline methods.", "weights": [[8, 14, 1.0], [15, 22, 1.0], [41, 47, 1.0], [60, 67, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.13": {"snippet": "...ment ranking.To validate our proposal and to gain more insights about approaches for document ranking, we (1) analyse PRP, qPRP and other ranking approaches, exposing the assumptions underlying their ...", "weights": [[8, 15, 1.0], [97, 104, 1.0], [141, 148, 1.0]]}, "2012.sigirconf_conference-2012.77": {"snippet": "...ing, ranking and evaluation\n\n\n ABSTRACTIn this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes ...", "weights": [[8, 15, 1.0], [20, 30, 1.0], [144, 151, 1.0], [162, 172, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.5": {"snippet": "...y in Ranking\n\n\n AbstractRanking in Information Retrieval (IR) has been traditionally evaluated from the perspective of the relevance of search engine results to people searching for information, i.e.,...", "weights": [[8, 15, 1.0], [139, 145, 1.0], [153, 160, 1.0]]}, "2005.sigirconf_conference-2005.67": {"snippet": "... web search results using affinity graph\n\n\n ABSTRACTIn this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics:(1) diversity --whi...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [89, 96, 1.0], [119, 126, 1.0], [143, 149, 1.0], [150, 157, 1.0]]}, "2018.cikm_conference-2018.226": {"snippet": "... Web search results that are useful in discovering the veracity of claims from the ones that are not. We also propose a re-ranking method to improve ranking of search results for fact-checking. To eva...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [126, 133, 1.0], [152, 159, 1.0], [163, 169, 1.0], [170, 177, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "...em's evaluation of document relevance back to the system, which then uses this information to provide a revised list of search results. Even with state-of-the-art search engines users are often dissat...", "weights": [[8, 18, 1.0], [123, 129, 1.0], [130, 137, 1.0], [166, 172, 1.0]]}, "2020.ipm_journal-ir0volumeA57A1.15": {"snippet": "...irer ranking in search engine results", "weights": [[8, 15, 1.0], [19, 25, 1.0], [33, 40, 1.0]]}, "2007.jasis_journal-ir0volumeA58A9.3": {"snippet": "...s of search engine results", "weights": [[8, 14, 1.0], [22, 29, 1.0]]}, "2015.wwwconf_conference-2015c.218": {"snippet": "...ased Ranking Evaluation on a Budget\n\n\n ABSTRACTWe address the problem of assessing the quality of a ranking system (e.g., search engine, recommender system, review ranker) given a fixed budget for col...", "weights": [[8, 15, 1.0], [16, 26, 1.0], [103, 110, 1.0], [125, 131, 1.0]]}, "2001.ntcir_workshop-2001.50": {"snippet": "... the search results in each of the sub-tasks and evaluated the search results using the relevance judgments in the pools. Almost the same rankings were produced by all the relevance judgments. Therefo...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [66, 72, 1.0], [73, 80, 1.0]]}, "2001.wwwconf_conference-2001p.91": {"snippet": "...hich Search Engine is Best at Finding Online Services?\n\n\n We report results for an independent, blind evaluation of the performance of 11 commercial search engines on 106 online service queries and on...", "weights": [[8, 14, 1.0], [71, 78, 1.0], [105, 115, 1.0], [152, 158, 1.0]]}, "2010.tois_journal-ir0volumeA28A4.3": {"snippet": "...rent search engines. A measure of the similarity between incomplete rankings should handle nonconjointness, weight high ranks more heavily than low, and be monotonic with increasing depth of evaluatio...", "weights": [[8, 14, 1.0], [194, 204, 1.0]]}, "2019.trec_conference-2019.25": {"snippet": "... the ranking results while ensuring the relevance of the ranking results. We will show how to use our model to achieve the relevance of query results, then adjust the overall ranking results according...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [60, 67, 1.0], [68, 75, 1.0], [145, 152, 1.0], [178, 185, 1.0], [186, 193, 1.0]]}, "2013.sigirconf_conference-2013.116": {"snippet": "... for search results\n\n\n ABSTRACTInternet search engines typically compute a relevance score for webpages given the query terms, and then rank the pages by decreasing relevance scores. The popular searc...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [43, 49, 1.0], [198, 204, 1.0]]}, "2007.wwwconf_conference-2007.124": {"snippet": "... the evaluation of search results\n\n\n ABSTRACTWe investigate the effect of search engine brand (i.e., the identifying name or logo that distinguishes a product from its competitors) on evaluation of sy...", "weights": [[8, 18, 1.0], [22, 28, 1.0], [29, 36, 1.0], [77, 83, 1.0], [187, 197, 1.0]]}, "2010.wsdm_conference-2010.19": {"snippet": "...good results. This is particularly notable given that the baseline we use is the heavily optimized ranking function of a leading commercial search engine. A deeper analysis shows that the new feature ...", "weights": [[8, 15, 1.0], [102, 109, 1.0], [143, 149, 1.0]]}, "L16-1583": {"snippet": "...ring Evaluation Measure\n\n\n Ranking is used for a wide array of problems, most notably information retrieval (search). Kendall's \u03c4 , Average Precision, and nDCG are a few popular approaches to the eval...", "weights": [[8, 18, 1.0], [30, 37, 1.0], [112, 118, 1.0], [199, 209, 1.0]]}, "2003.jasis_journal-ir0volumeA54A2.3": {"snippet": "...ased Ranking of Search Engine Results", "weights": [[8, 15, 1.0], [19, 25, 1.0], [33, 40, 1.0]]}, "2011.sigirconf_conference-2011.81": {"snippet": "...ocal search ranking through external logs\n\n\n ABSTRACTThe signals used for ranking in local search are very different from web search: in addition to (textual) relevance, measures of (geographic) dista...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [77, 84, 1.0], [94, 100, 1.0], [129, 135, 1.0]]}, "2013.cikm_conference-2013.72": {"snippet": "...line evaluation method to compare two alternative ranking functions based on the users' implicit feedback. In an interleaving experiment, the results from two ranking functions are merged in a single ...", "weights": [[8, 18, 1.0], [53, 60, 1.0], [145, 152, 1.0], [162, 169, 1.0]]}, "2013.sigirconf_conference-2013.119": {"snippet": "... two search result lists. Unlike the traditional method of collecting relevance labels per single result, this method allows to consider the interaction between search results as part of the judging c...", "weights": [[8, 14, 1.0], [164, 170, 1.0], [171, 178, 1.0]]}, "2008.cikm_conference-2008.190": {"snippet": "Ranking in folksonomy systems: can context help?\n\n\n ABSTRACTFolksonomy systems have shown to contribute to the quality of Web search ranking strategies. In this paper, we analyze and compare different...", "weights": [[0, 7, 1.0], [126, 132, 1.0], [133, 140, 1.0]]}, "2013.clef_conference-2013.7": {"snippet": "...rent evaluation criteria prior to conducting an evaluation. We analyse results to show which criteria users most value, together with the inter-relationships between them. Our results highlight the ne...", "weights": [[8, 18, 1.0], [51, 61, 1.0], [74, 81, 1.0], [179, 186, 1.0]]}, "2001.wwwconf_conference-2001p.73": {"snippet": "...ance Evaluation of Search Engines' Query Results", "weights": [[8, 18, 1.0], [22, 28, 1.0], [44, 51, 1.0]]}, "2009.cikm_conference-2009.296": {"snippet": "... the ranking score distribution across search results. We then propose two alternatives to the NDCG metric that both incorporate ranking robustness into ranking function evaluation and selection. A ma...", "weights": [[8, 15, 1.0], [42, 48, 1.0], [49, 56, 1.0], [132, 139, 1.0], [156, 163, 1.0], [173, 183, 1.0]]}, "1995.trec_conference-1995.36": {"snippet": "... and Ranking in Interactive Searching", "weights": [[8, 15, 1.0]]}, "2013.sigirconf_conference-2013.68": {"snippet": "...n of results from multiple search engines. We introduce copula-based versions of standard relevance estimators and fusion methods and show that these lead to significant performance improvements on se...", "weights": [[8, 15, 1.0], [30, 36, 1.0]]}, "2007.sigirconf_conference-2007.39": {"snippet": "...ning ranking functions using relative relevance judgments\n\n\n ABSTRACTEffective ranking functions are an essential part of commercial search engines. We focus on developing a regression framework for l...", "weights": [[8, 15, 1.0], [82, 89, 1.0], [136, 142, 1.0]]}, "2007.wwwconf_conference-2007.121": {"snippet": "...atic search engine performance evaluation with click-through data analysis\n\n\n ABSTRACTPerformance evaluation is an important issue in Web search engine researches. Traditional evaluation methods rely ...", "weights": [[8, 14, 1.0], [34, 44, 1.0], [101, 111, 1.0], [141, 147, 1.0], [179, 189, 1.0]]}, "2010.wwwconf_conference-2010.185": {"snippet": "...k on search result lists. We tried to incorporate this measure of reliability into relevance feedback for improving ranking performances. Experimental results indicate that the proposed approach is bo...", "weights": [[8, 14, 1.0], [119, 126, 1.0], [154, 161, 1.0]]}, "2005.wwwconf_workshop-2004webdyn.1": {"snippet": "...-ten results, since users usually inspect only the first page returned by the search engine, which normally contains ten results. In particular, we compare rankings of the top ten results of the searc...", "weights": [[8, 15, 1.0], [81, 87, 1.0], [124, 131, 1.0], [183, 190, 1.0], [198, 204, 1.0]]}, "2005.wwwconf_conference-2005si.46": {"snippet": "...ng a search engine's efficiency and effectiveness, each user has his/her own preference and is highly subjective in judging the relevance of the search results.We have proposed a formal evaluation mod...", "weights": [[8, 14, 1.0], [148, 154, 1.0], [155, 162, 1.0], [189, 199, 1.0]]}, "2014.ipm_journal-ir0volumeA50A2.9": {"snippet": "...w of ranking approaches for semantic search on Web\n\n\n a b s t r a c tWith ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining...", "weights": [[8, 15, 1.0], [40, 46, 1.0], [139, 145, 1.0]]}, "2011.cikm_conference-2011.202": {"snippet": "...plex results in databases. While there is a large body of work that provides different mechanisms for computing keyword search results efficiently, a recent study has shown that the problem of ranking...", "weights": [[8, 15, 1.0], [123, 129, 1.0], [130, 137, 1.0], [196, 203, 1.0]]}, "2002.sigirconf_conference-2002.9": {"snippet": "... top ranking sentences: an evaluation of two alternative schemes\n\n\n ABSTRACTIn this paper we present an evaluation of techniques that are designed to encourage web searchers to interact more with the ...", "weights": [[8, 15, 1.0], [30, 40, 1.0], [107, 117, 1.0]]}, "2011.ipm_journal-ir0volumeA47A3.5": {"snippet": "...pert search\n\n\n a b s t r a c tThe retrieval effectiveness of the underlying document search component of an expert search engine can have an important impact on the effectiveness of the generated expe...", "weights": [[8, 14, 1.0], [88, 94, 1.0], [118, 124, 1.0]]}, "2012.ipm_journal-ir0volumeA48A1.10": {"snippet": "...tion search process\n\n\n a b s t r a c tRelevance judgments occur within an information search process, where time, context and situation can impact the judgments. The determination of relevance is depe...", "weights": [[8, 14, 1.0], [89, 95, 1.0]]}, "2011.wsdm_conference-2011.53": {"snippet": "... for ranking, which enables principled and natural integration with features derived from both quality-based and relevance-based algorithms. Through experimental verification, the combined methods can...", "weights": [[8, 15, 1.0]]}, "2013.wwwconf_conference-2013c.278": {"snippet": "...Thus search engines face the challenge of ranking results that are equally relevant from the perspective of the traditional relevance grading approach. To solve this problem we propose to consider add...", "weights": [[8, 14, 1.0], [45, 52, 1.0], [53, 60, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "... the evaluation of retrieval results. Very high correlations were found among the rankings of systems produced using different relevance judgment sets. The high correlations indicate that the comparat...", "weights": [[8, 18, 1.0], [32, 39, 1.0]]}, "2002.sigirconf_conference-2002.83": {"snippet": "...item search results. Because the engines perform their search on overlapping (but different) subsets of the web collected at different points in time, evaluation of search engines poses significant ch...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [58, 64, 1.0], [154, 164, 1.0], [168, 174, 1.0]]}, "2017.tist_journal-ir0volumeA8A3.7": {"snippet": "...sity Evaluation Measures: A New Approach to Search Result Diversification\n\n\n The queries issued to search engines are often ambiguous or multifaceted, which requires search engines to return diverse r...", "weights": [[8, 18, 1.0], [47, 53, 1.0], [102, 108, 1.0], [169, 175, 1.0], [202, 209, 1.0]]}, "2010.tois_journal-ir0volumeA29A1.2": {"snippet": "...king search results\n\n\n We present a novel language-model-based approach to reranking search results; that is, reordering the documents in an initially retrieved list so as to improve precision at top ...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [88, 94, 1.0], [95, 102, 1.0]]}, "2021.emnlp-main.575": {"snippet": "...NLG} Evaluation Metrics\n\n\n Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequ...", "weights": [[8, 18, 1.0], [64, 74, 1.0]]}, "2009.eamt-1.11": {"snippet": "Relevance of Different Segmentation Options on {S}panish-{B}asque {SMT}\n\n\n Segmentation is widely used in adapting Statistical Machine Translation to highly inflected languages as Basque. The way this...", "weights": []}, "2013.ecir_conference-2013.52": {"snippet": "...d Re-ranking of Text-Based Image Search Results\n\n\n Abstract. This article presents a method for re-ranking images retrieved by classical search engine using key words for entering queries. This method...", "weights": [[8, 15, 1.0], [36, 42, 1.0], [43, 50, 1.0], [102, 109, 1.0], [140, 146, 1.0]]}, "2018.cikm_conference-2018.66": {"snippet": "... 000 search results. Experimental results show that the proposed JRE model achieves better performance than state-of-the-art ranking solutions as well as the original ranking of commercial search engi", "weights": [[8, 14, 1.0], [15, 22, 1.0], [37, 44, 1.0], [128, 135, 1.0], [170, 177, 1.0], [192, 198, 1.0]]}, "2012.sigirconf_conference-2012.24": {"snippet": "...ated search engine results pages (SERPs) for each query, which were manipulated to exhibit different levels of performance. Following this, subjects reevaluated the queries, were interviewed about the...", "weights": [[8, 14, 1.0], [22, 29, 1.0]]}, "2013.dir_workshop-2013.19": {"snippet": "...n of Search Facets\n\n\n We present a novel method for summarization and expansion of search facets. To dynamically extract key facets, the ranked list of search results generated from a keyword search i...", "weights": [[8, 14, 1.0], [86, 92, 1.0], [155, 161, 1.0], [162, 169, 1.0], [195, 201, 1.0]]}, "2013.wwwconf_conference-2013.57": {"snippet": "...tory search for multi page search results\n\n\n ABSTRACTModern information retrieval interfaces typically involve multiple pages of search results, and users who are recall minded or engaging in explorat...", "weights": [[8, 14, 1.0], [30, 36, 1.0], [37, 44, 1.0], [132, 138, 1.0], [139, 146, 1.0]]}, "2004.ntcir_workshop-2004.3": {"snippet": "...e by ranking each document using the density of keywords and by a threshold. For evaluation of our system, we participated in NTCIR4-WEB Topical classification task. Only a part of \"evaluation based o...", "weights": [[8, 15, 1.0], [84, 94, 1.0], [185, 195, 1.0]]}, "2011.cikm_conference-2011.282": {"snippet": "...with search engines\n\n\n ABSTRACTMeaningful evaluation of web search must take account of spam. Here we conduct a user experiment to investigate whether satisfaction with search engine result pages as a...", "weights": [[8, 14, 1.0], [45, 55, 1.0], [63, 69, 1.0], [172, 178, 1.0]]}}}
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This was the first paper to present a probabilistic approach to information retrieval, and perhaps the first paper on ranked retrieval. Although it is one of the m...", "weights": [[8, 19, 1.0], [21, 30, 1.0], [104, 115, 1.0], [116, 125, 1.0], [165, 174, 1.0]]}, "P19-3008": {"snippet": "...emic jobs. We set out to make the market for advanced research skills more visible to job seekers. In this paper, we present PostAc \u00ae , an online platform of authentic job postings that helps PhD grad...", "weights": [[8, 12, 1.0]]}, "2013.wwwconf_conference-2013c.246": {"snippet": "... and jobs. The data collected from the website is modeled using a directed, weighted, and multirelational graph, and the 3A ranking algorithm is exploited to rank items according to their relevance to...", "weights": [[8, 12, 1.0]]}, "2008.ntcir_workshop-2008.28": {"snippet": "Information Retrieval Using PU Learning Based Re-ranking\n\n\n In this paper, we describe our approach for information retrieval for question answering (IR4QA) on simple Chinese language of NTCIR-7 tasks...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [104, 115, 1.0], [116, 125, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.11": {"snippet": "Information retrieval in the workplace: A comparison of professional search practices\n\n\n Legal researchers, recruitment professionals, healthcare information professionals, and patent analysts all und...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [146, 157, 1.0]]}, "2017.fire_conference-2017w.9": {"snippet": "...017: Information Retrieval from Microblog during Disasters\n\n\n Microblogging sites like Twitter are increasingly being used for aiding relief operations during disaster events. In such situations, iden...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2007.trec_conference-2007.88": {"snippet": "...nion retrieval task. An information retrieval step retrieves the query-relevant documents. A following opinion identification step identifies the opinionative texts in these documents. A ranking step ...", "weights": [[8, 17, 1.0], [27, 38, 1.0], [39, 48, 1.0]]}, "2004.ntcir_workshop-2004.57": {"snippet": "...onal Retrieval Task 2 that was conducted from 2003 to 2004 as a subtask of the WEB Task at the Fourth NTCIR Workshop ('NTCIR-4 WEB'). In the Informational Retrieval Task, we attempted to assess the re...", "weights": [[8, 17, 1.0], [158, 167, 1.0], [201, 210, 1.0]]}, "2017.fire_conference-2017w.18": {"snippet": "...ack: Information Retrieval from Legal Documents\n\n\n The FIRE 2017 IRLeD Track focused on creating a framework for evaluating different methods of Information Retrieval from legal documents. There were ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [148, 159, 1.0], [160, 169, 1.0]]}, "2007.tc-1.13": {"snippet": "...ated information such as manuals, clerical, technical and legal texts are translated into a current total of 10 languages. The majority of the workload of approx. 150 translation jobs per month, i.e. ...", "weights": [[8, 19, 1.0], [182, 186, 1.0]]}, "2010.sigirconf_conference-2010.10": {"snippet": "...y of information retrieval approaches applied to match-making systems. Specifically, we focus on retrieval for a dating service. This domain offers several unique problems not found in traditional inf...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [100, 109, 1.0], [200, 211, 1.0]]}, "1991.sigirconf_conference-91.24": {"snippet": "...ssor Information Retrieval Systems\n\n\n Abstract.Information retrieval is the selection of documents that are potentially relevant to a user's information need. Given the vast volume of data stored in m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0], [144, 155, 1.0]]}, "2011.eamt-1.9": {"snippet": "...ting information would have to be manually added by localization professionals. A few attempts to bridge the localization industry and Moses were already done in the past, however, the method describe...", "weights": [[8, 19, 1.0]]}, "2016.wsdm_conference-2016.31": {"snippet": "... for Information Retrieval\n\n\n ABSTRACT Document relevance has been the primary focus in the design, optimization and evaluation of retrieval systems. Traditional test collections are constructed by as...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [134, 143, 1.0]]}, "2014.ipm_journal-ir0volumeA50A5.9": {"snippet": "...tive information retrieval\n\n\n a b s t r a c tCollaborative information retrieval involves retrieval settings in which a group of users collaborates to satisfy the same underlying need. One core issue ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [62, 73, 1.0], [74, 83, 1.0], [93, 102, 1.0]]}, "2008.airs_conference-2008.16": {"snippet": "Job Information Retrieval Based on Document Similarity", "weights": [[4, 15, 1.0], [16, 25, 1.0]]}, "1986.tc-1.6": {"snippet": "... the information to manageable proportions. Information overload and underload are facts of life, and any methods which can help to optimise information retrieval are of value. In this presentation, I...", "weights": [[8, 19, 1.0], [47, 58, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "2021.ecir_conference-20212.18": {"snippet": "...s in Information Retrieval Relevance Judgement Datasets\n\n\n Recent studies in information retrieval have shown that gender biases have found their way into representational and algorithmic aspects of c...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [92, 101, 1.0]]}, "2012.tist_journal-ir0volumeA3A4.10": {"snippet": "...edia retrieval. The first three studies deal primarily with comment-targeting retrieval, while the last one deals with commentexploiting retrieval. Throughout the article, connections to information r...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [140, 149, 1.0], [190, 201, 1.0], [202, 211, 1.0]]}, "2007.sigirconf_conference-2007.150": {"snippet": "... and jobs based on relevance models\n\n\n ABSTRACTWe investigate the difficult problem of matching semi-structured resumes and jobs in a large scale real-world collection. We compare standard approaches ...", "weights": [[8, 12, 1.0], [127, 131, 1.0]]}, "2007.tc-1.14": {"snippet": "...ness information management in the language industry\n\n\n LTC has almost a decade of experience in helping corporate clients and language companies implement business information and workflow control to...", "weights": [[8, 19, 1.0], [168, 179, 1.0]]}, "2020.sigirconf_conference-2020.317": {"snippet": "...hing jobs with processionals. Improving job targeting accuracy and hire efficiency align with LinkedIn's Member First Motto. To achieve those goals, we need to understand unstructured job postings wit...", "weights": [[8, 12, 1.0]]}, "2006.sigirconf_conference-2006.149": {"snippet": "...es a retrieval methodology based on the Divergence from Randomness framework to rank candidate profiles in response to a user query. Each document is represented by a set of fields (e.g. Content, Titl...", "weights": [[8, 17, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.14": {"snippet": "... for retrieval with automatically generated content metadata from video, Specifically we focus on their application for improving audiovisual fragment search in the audiovisual broadcast archive. We u...", "weights": [[8, 17, 1.0]]}, "2015.ictir_conference-2015.2": {"snippet": "...y of Retrieval: The Retrievability of Information\n\n\n ABSTRACTRetrievability is an important and interesting indicator that can be used in a number of ways to analyse Information Retrieval systems and ...", "weights": [[8, 17, 1.0], [41, 52, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2022.naacl-srw.8": {"snippet": "...l\n\n\n Information Retriever (IR) aims to find the relevant documents (e.g. snippets, passages, and articles) to a given query at large scale. IR plays an important role in many tasks such as open domai...", "weights": [[8, 19, 1.0]]}, "2013.sigirconf_conference-2013.88": {"snippet": "...vant information graphics\n\n\n ABSTRACTInformation retrieval research has made significant progress in the retrieval of text documents and images. However, relatively little attention has been given to ...", "weights": [[8, 19, 1.0], [52, 61, 1.0], [108, 117, 1.0]]}, "2013.ictir_conference-2013.14": {"snippet": "Information Retrieval with Time Series Query\n\n\n ABSTRACTWe study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find rele...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [73, 84, 1.0], [85, 94, 1.0], [170, 179, 1.0]]}, "2013.sigirconf_conference-2013.47": {"snippet": "... the Information Retrieval (IR) task that performs document retrieval for a search session. During a session, a user constantly modifies queries in order to find relevant documents that fulfill the in...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [63, 72, 1.0], [201, 212, 1.0]]}, "2018.sigirconf_conference-2018.191": {"snippet": "An Information Retrieval Experiment Framework for Domain Specific Applications\n\n\n ABSTRACTWe present a framework for constructing and executing information retrieval experiment pipelines. The framewor...", "weights": [[3, 14, 1.0], [15, 24, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "2008.sigirjournals_journal-ir0volumeA42A2.18": {"snippet": "...t of information retrieval in the workplace\n\n\n Increasingly, workplace information seeking takes place in digital information environments and is reliant upon search systems. Existing systems are desi...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [74, 85, 1.0], [117, 128, 1.0]]}, "2011.cikm_conference-2011.325": {"snippet": "...ital information on daily basis. Previously efforts have been made to support their work by employing different indexing and search tools that make information more accessible. However, recently it ha...", "weights": [[8, 19, 1.0], [151, 162, 1.0]]}, "2001.ntcir_workshop-2001.29": {"snippet": "On a Retrieval Support System by Suggesting Terms to a User\n\n\n We propose a support method for information retrieval. This method automatically suggests terms relevant to a query, to a user, and when ...", "weights": [[5, 14, 1.0], [95, 106, 1.0], [107, 116, 1.0]]}, "2007.ecir_conference-2007.59": {"snippet": "... XML Retrieval\n\n\n Abstract. The objective of XML retrieval is to return relevant XML document fragments that answer a given user information need, by exploiting the document structure. The focus in th...", "weights": [[8, 17, 1.0], [52, 61, 1.0], [132, 143, 1.0]]}, "2020.alta-1.8": {"snippet": "A machine-learning based model to identify {P}h{D}-level skills in job ads\n\n\n Around 60% of doctoral graduates worldwide ended up working in industry rather than academia. There have been calls to mor...", "weights": []}, "2010.sigirconf_conference-2010.136": {"snippet": "...TXML retrieval provides a focused access to the relevant content of documents. However, in evaluation, full document retrieval has appeared competitive to focused XML retrieval. We analyze the density...", "weights": [[8, 17, 1.0], [120, 129, 1.0], [170, 179, 1.0]]}, "1999.ipm_journal-ir0volumeA35A6.6": {"snippet": "... and information actions - Integrating studies on information seeking and retrieval", "weights": [[8, 19, 1.0], [53, 64, 1.0], [77, 86, 1.0]]}, "2017.trec_conference-2017.21": {"snippet": "...ular information retrieval application is web search engine such as Google [2] , Baidu [3] , Bing [4] and Sogou [5]. These application will return top-N best retrieval result to users. Information Ret...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [161, 170, 1.0], [188, 199, 1.0], [200, 209, 1.0]]}, "2020.sigirconf_conference-2020.179": {"snippet": "...able Retrieval Models for Precision Medicine Literature Search\n\n\n In professional search tasks such as precision medicine literature search, queries often involve multiple aspects. To assess the relev...", "weights": [[8, 17, 1.0]]}, "2019.ecir_conference-20192.55": {"snippet": "...nced Information Retrieval: 8th International BIR Workshop\n\n\n Abstract. The Bibliometric-enhanced Information Retrieval workshop series (BIR) at ECIR tackles issues related to academic search, at the ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [101, 112, 1.0], [113, 122, 1.0]]}, "2012.sigirconf_conference-2012.132": {"snippet": "...n of information retrieval\n\n\n ABSTRACTAmazon CloudSearch is a new hosted search service, built on top of many cloud-based AWS services, and based on the same technology that powers search on Amazon's ...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2007.sigirconf_conference-2007.76": {"snippet": "...n of information retrieval systems is one of the core tasks in information retrieval. Problems include the inability to exhaustively label all documents for a topic, nongeneralizability from a small n...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [66, 77, 1.0], [78, 87, 1.0]]}, "2017.sigirconf_conference-2017.248": {"snippet": "... for Information Retrieval: And Related Tasks\n\n\n MOTIVATIONe main task of an Information Retrieval (IR) system is to return relevant documents to users in response to a query. However, this task is in...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [92, 101, 1.0]]}, "1999.tc-1.4": {"snippet": "Resource and Workflow Management Support in Teletranslation\n\n\n Value-added service providers in the technical and business translation market are increasingly organizing themselves into teams whose me...", "weights": []}, "2017.cikm_conference-2017.143": {"snippet": "... the jobs in the corpus for all members is not possible given the latency constraints. On the other hand, reducing the candidate set could potentially involve loss of relevant jobs. We present a way t...", "weights": [[8, 12, 1.0], [179, 183, 1.0]]}, "2010.sigirconf_conference-2010.201": {"snippet": "Web retrieval: the role of users\n\n\n Abstract:Web retrieval methods have evolved through three major steps in the last decade or so. They started from standard document-centric IR in the early days of ...", "weights": [[4, 13, 1.0], [49, 58, 1.0]]}, "2012.ecir_conference-2012.9": {"snippet": "...ment Retrieval", "weights": [[8, 17, 1.0]]}, "O01-2005": {"snippet": "...s of Information Retrieval with Clustering and Fusion\n\n\n Fusion and clustering are two approaches to improving the effectiveness of information retrieval. In fusion, ranked lists are combined together...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [135, 146, 1.0], [147, 156, 1.0]]}, "2009.cikm_conference-2009.134": {"snippet": "...opic retrieval\n\n\n ABSTRACTTraditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [63, 72, 1.0], [167, 178, 1.0], [194, 203, 1.0]]}, "2021.rocling-1.22": {"snippet": "...l of information retrieval is to search the most relevant results for the user's query from a large set of documents. 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Specifically, we propose a conceptual framework of retrieval templates that can adapt cluster-b...", "weights": [[8, 17, 1.0], [34, 45, 1.0], [159, 168, 1.0]]}, "2006.trec_conference-2006.110": {"snippet": "... the retrieval step. In the retrieval step, we try to improve the retrieval effectiveness by retrieving based on concepts, and doing query expansion using pseudo feedback, Wikipedia feedback and web f...", "weights": [[8, 17, 1.0], [31, 40, 1.0], [69, 78, 1.0]]}, "2008.ntcir_workshop-2008.81": {"snippet": "The Information Retrieval Facility and Professional Search", "weights": [[4, 15, 1.0], [16, 25, 1.0]]}, "2011.wwwconf_workshop-2011ldow.1": {"snippet": "...oral Information Retrieval: Challenges and Opportunities\n\n\n Time is an important dimension of any information space. 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That task is to identify as many relevant documents, and as few non-relevant documents as possible, such that ...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "P80-1039": {"snippet": "... for Information Retrieval\n\n\n The representation of whole texts is a major concern of the field known as information retrieval (IR), an importaunt aspect of which might more precisely be called 'docum...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [108, 119, 1.0], [120, 129, 1.0]]}, "2018.ictir_conference-2018.10": {"snippet": "...ment Retrieval\n\n\n ABSTRACTIn this paper, we study jointly query reformulation and document relevance estimation, the two essential aspects of information retrieval (IR). Their interactions are modelle...", "weights": [[8, 17, 1.0], [145, 156, 1.0], [157, 166, 1.0]]}, "W14-1118": {"snippet": "... for information retrieval, focusing on care episode retrieval, based on textual similarity, where similarity is measured through domain-specific modelling of the distributional semantics of words. Mo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [56, 65, 1.0]]}, "2021.triton-1.22": {"snippet": "The Post-Editing Workflow: Training Challenges for {LSP}s, Post-Editors and Academia\n\n\n Language technology is already largely adopted by most Language Service Providers (LSPs) and integrated into the...", "weights": []}, "2014.tc-1.14": {"snippet": "Top-down or bottom-up: what do industry approaches to translation quality mean for effective integration of standards and tools?\n\n\n The diverse approaches to translation quality in the industry can be...", "weights": []}, "2019.ecir_conference-20192.52": {"snippet": "...rds: Information need \u00b7 Backstory \u00b7 Intent ambiguity\nMotivationAmbiguity in information retrieval processes is a common problem which retrieval systems need to handle to provide suitable results for s...", "weights": [[8, 19, 1.0], [79, 90, 1.0], [91, 100, 1.0], [137, 146, 1.0]]}, "2007.sigirconf_conference-2007.213": {"snippet": "...ment retrieval. Some of this new attention has fallen on entity retrieval. This emerging area of entity retrieval differs from traditional document retrieval in a number of ways. Entities are not repr...", "weights": [[8, 17, 1.0], [67, 76, 1.0], [107, 116, 1.0], [151, 160, 1.0]]}, "2020.sigirconf_conference-2020.10": {"snippet": "...ired retrieval models, and some well-known neural retrieval models in the ad-hoc retrieval task. CCS CONCEPTS \u2022 Information systems \u2192 Retrieval models and ranking.", "weights": [[8, 17, 1.0], [53, 62, 1.0], [84, 93, 1.0], [115, 126, 1.0], [137, 146, 1.0]]}, "2018.sigirconf_workshop-2018profs.5": {"snippet": "...f an information retrieval (IR) system is to identify and return to the user content relevant or useful in addressing the information need which required them to use the system. The development and ev...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [125, 136, 1.0]]}, "2012.wwwconf_conference-2012c.163": {"snippet": "...onal Information Retrieval (IR) models, which assume that scores (relevance) for documents given an information need are static. In this paper, we formulate a general Dynamical Information Retrieval p...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [103, 114, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "P06-2068": {"snippet": "...e of Information Retrieval in Answering Complex Questions\n\n\n This paper explores the role of information retrieval in answering \"relationship\" questions, a new class complex information needs formally...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [96, 107, 1.0], [108, 117, 1.0], [177, 188, 1.0]]}}}
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The applications include negated terms in Boole...", "weights": [[8, 15, 1.0], [198, 205, 1.0]]}, "W89-0203": {"snippet": "...to a boolean combination of propositional variables. We call P 4> a contexted constraint, where P is the context and 4> is called the base constraint.", "weights": [[8, 15, 1.0]]}, "1997.sigirconf_conference-97.15": {"snippet": "...g of Boolean Operators\n\n\n The inference network model of information retrieval allows for a probabilistic interpretation of Boolean query operators. Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [127, 134, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...nded Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulate...", "weights": [[8, 15, 1.0], [73, 80, 1.0]]}, "W11-0118": {"snippet": "Formalising and specifying underquantification\n\n\n This paper argues that all subject noun phrases can be given a quantified formalisation in terms of the intersection between their denotation set and ...", "weights": []}, "P95-1003": {"snippet": "The Replace Operator\n\n\n This paper introduces to the calculus of regular expressions a replace operator and defines a set of replacement expressions that concisely encode alternate variations of the o...", "weights": []}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0]]}, "2016.cikm_conference-2016.92": {"snippet": "Empowering Truth Discovery with Multi-Truth Prediction\n\n\n ABSTRACTTruth discovery is the problem of detecting true values from the conflicting data provided by multiple sources on the same data items....", "weights": []}, "1996.cikm_conference-96.17": {"snippet": "...ts.O-Algebra is an algebra whose operands are collections of internal objects. 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General...", "weights": [[8, 15, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [124, 131, 1.0], [196, 203, 1.0]]}, "1997.jasis_journal-ir0volumeA48A8.1": {"snippet": "...h to Boolean Query Reformulation", "weights": [[8, 15, 1.0]]}, "2005.clef_workshop-2005w.11": {"snippet": "Boolean Operators in Interactive Search\n\n\n This paper presents the participation of the MIRACLE team at the ImageCLEF interactive search task. Basically, queries consisting on several terms can be pro...", "weights": [[0, 7, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. 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Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [99, 106, 1.0]]}, "P83-1021": {"snippet": "Parsing as Deduction\n\n\n By exploring the relationship between parsing and deduction, a new and more general view of chart parsing is obtained, which encompasses parsing for grammar formalisms based on...", "weights": []}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...s of Boolean algebra are obeyed, and the exact-match semantics of Boolean retrieval are preserved. Simple inverted-list file structures provide an efficient implementation. Retrieval effectiveness is ...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [69, 76, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [99, 106, 1.0], [176, 183, 1.0]]}, "1995.iwpt-1.20": {"snippet": "...ling algebra. The theorem proving strategy is particularly well suited to the treatment of categorial gram mar, because it allows us to distribute the computational cost between the algorithm which de...", "weights": [[8, 15, 1.0]]}, "1985.ipm_journal-ir0volumeA21A6.2": {"snippet": "... for boolean search request formulations, including some experimental results", "weights": [[8, 15, 1.0]]}, "1985.riao_conference-1985.20": {"snippet": "...ting boolean structure within a probabilistic framework", "weights": [[8, 15, 1.0]]}, "1995.iwpt-1.9": {"snippet": "An Abstract Machine for Attribute-Value Logics\n\n\n A direct abstract machine implementation of the core attribute-value logic operations is shown to decrease the number of operations and conserve the a...", "weights": []}, "Y15-2021": {"snippet": "Distinguishing between True and False Stories using various Linguistic Features\n\n\n This paper analyzes what linguistic features differentiate true and false stories written in Hebrew. To do so, we hav...", "weights": []}, "1997.tois_journal-ir0volumeA15A1.1": {"snippet": "...onal Algebra for the Integration of Information Retrieval and Database Systems\n\n\n We present a probabilistic relational algebra (PRA) which is a generalization of standard relational algebra. In PRA, ...", "weights": [[8, 15, 1.0], [123, 130, 1.0], [186, 193, 1.0]]}, "2001.cikm_conference-2001.28": {"snippet": "... any boolean combination of comparison predicates of columns with constants or list of constants. Such comparison predicates contain an operator 0 E {=, f, <, 5, >, 2).Core and base functionality coul...", "weights": [[8, 15, 1.0]]}}}
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By following external links and searching for the retrie...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [70, 78, 1.0], [126, 134, 1.0]]}, "2014.cikm_conference-2014.164": {"snippet": "...uery criteria. The latter is measured using the relevance towards each of the criteria stated in the query, represented in the form of sub-queries. We thoroughly evaluate our proposed approach using t...", "weights": [[8, 16, 1.0], [51, 60, 1.0], [81, 89, 1.0]]}, "1998.ipm_journal-ir0volumeA34A23.5": {"snippet": "...ers' Criteria for Relevance Evaluation: A Cross-situational Comparison", "weights": [[8, 16, 1.0], [21, 30, 1.0]]}, "2006.jasis_journal-ir0volumeA57A10.8": {"snippet": "Relevance criteria identified by health information users during Web searches", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2004.trec_conference-2004.53": {"snippet": "...t of criteria aiming at capturing complementary aspects of relevance. Moreover, we provide aggregation procedures that are based on decision rules, to get the ranking of relevant documents.", "weights": [[8, 16, 1.0], [62, 71, 1.0], [173, 181, 1.0]]}, "2018.sigirconf_conference-2018.154": {"snippet": "...find relevant social images using keyword queries. State-of-the-art TagIR techniques typically rank query results based on relevance, temporal or popularity criteria. However, these criteria may not a...", "weights": [[8, 16, 1.0], [109, 116, 1.0], [126, 135, 1.0], [160, 168, 1.0], [185, 193, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "...ment relevance back to the system, which then uses this information to provide a revised list of search results. Even with state-of-the-art search engines users are often dissatisfied with the returne...", "weights": [[8, 17, 1.0], [100, 106, 1.0], [107, 114, 1.0], [143, 149, 1.0]]}, "2013.sigirconf_conference-2013.209": {"snippet": "...t of search results. When more relevant search results are present, it is likely to give the impression that more relevant results can be found beyond the first SERP; searchers are therefore more like...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [34, 42, 1.0], [43, 49, 1.0], [50, 57, 1.0], [117, 125, 1.0], [126, 133, 1.0]]}, "2002.sigirconf_conference-2002.52": {"snippet": "... and relevance criteria", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2002.sigirconf_conference-2002.44": {"snippet": "...aded relevance corpus.Relevance criteria were defined so that a distinction was made between documents rich in topical information (relevant and highly relevant documents) and poor in topical informat...", "weights": [[8, 17, 1.0], [25, 34, 1.0], [35, 43, 1.0], [135, 143, 1.0], [155, 163, 1.0]]}, "2001.jasis_journal-ir0volumeA52A8.7": {"snippet": "...e of relevance criteria across stages of document evaluation: On the complementarity of experimental and naturalistic studies", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2009.sigirconf_conference-2009.147": {"snippet": "Relevance criteria for e-commerce: a crowdsourcing-based experimental analysis", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "D19-1351": {"snippet": "...ping Criteria for Search Results using {P}oisson Processes\n\n\n Text retrieval systems often return large sets of documents, particularly when applied to large collections. Stopping criteria can reduce ...", "weights": [[8, 16, 1.0], [21, 27, 1.0], [28, 35, 1.0], [183, 191, 1.0]]}, "2003.sigirconf_conference-2003.61": {"snippet": "...ers' criteria For assessing web pages\n\n\n ABSTRACTWe investigate the criteria used by online searchers when assessing the relevance of web pages to information-seeking tasks. Twenty four searchers were...", "weights": [[8, 16, 1.0], [71, 79, 1.0], [124, 133, 1.0]]}, "2007.ipm_journal-ir0volumeA43A4.15": {"snippet": "...tify relevance criteria beyond topical relevance. Our results also find a statistically significant relationship between the users' stage in the search process and relevance categories chosen using th...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [42, 51, 1.0], [57, 64, 1.0], [148, 154, 1.0], [167, 176, 1.0]]}, "2014.sigirconf_conference-2014.103": {"snippet": "...ince search engines can only observe server-side actions (i.e., activity on the search engine result page), server-side dwell times are estimated by measuring the time between a search result click an...", "weights": [[8, 14, 1.0], [83, 89, 1.0], [181, 187, 1.0]]}, "2013.sigirconf_conference-2013.119": {"snippet": "... two search result lists. Unlike the traditional method of collecting relevance labels per single result, this method allows to consider the interaction between search results as part of the judging c...", "weights": [[8, 14, 1.0], [73, 82, 1.0], [164, 170, 1.0], [171, 178, 1.0], [202, 210, 1.0]]}, "2013.clef_conference-2013.7": {"snippet": "...cess criteria that can be measured in some way. Success might refer to whether a search system retrieves relevant (compared with non-relevant) documents; how quickly results are returned; how well the...", "weights": [[8, 16, 1.0], [84, 90, 1.0], [108, 116, 1.0], [136, 144, 1.0], [169, 176, 1.0]]}, "2012.ipm_journal-ir0volumeA48A2.10": {"snippet": "...ulti-criteria relevance. The following criteria are considered to estimate document relevance: aboutness, coverage, appropriateness, and reliability.The originality of this approach lies in the aggreg...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [42, 50, 1.0], [87, 96, 1.0]]}, "1994.jasis_journal-ir0volumeA45A3.3": {"snippet": "...ined Relevance Criteria: An Exploratory Study", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2002.jasis_journal-ir0volumeA53A5.1": {"snippet": "...s on relevance criteria: A comparison among relevant, partially relevant, and not-relevant judgments", "weights": [[8, 17, 1.0], [18, 26, 1.0], [47, 55, 1.0], [67, 75, 1.0], [85, 93, 1.0]]}, "2012.sigirconf_conference-2012.176": {"snippet": "... the search result is satisfactory but the user may be looking for alternative solutions. Based on the two scenarios, we propose two assessment criteria. Our labeling results indicate that the new ass...", "weights": [[8, 14, 1.0], [147, 155, 1.0], [170, 177, 1.0]]}, "2010.ismir_conference-2010.100": {"snippet": "...ers' Relevance Criteria in Music Retrieval in Everyday Life: An Exploratory Study", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2013.sigirconf_conference-2013.68": {"snippet": "...onal relevance estimates and fusion of results from multiple search engines. We introduce copula-based versions of standard relevance estimators and fusion methods and show that these lead to signific...", "weights": [[8, 17, 1.0], [42, 49, 1.0], [64, 70, 1.0], [127, 136, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...ses.\nRelevance assessmentsThe relevance assessments for each query were made by the psrson that generated that query. Rather than manually assess all images in the collection for relevance, the struct...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [182, 191, 1.0]]}, "2007.ecir_conference-2007.43": {"snippet": "... for Relevance Criteria: A Framework and Experiments in Web Retrieval", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2007.tweb_journal-ir0volumeA1A1.2": {"snippet": "...ored search campaigns are more relevant than the average sponsored link. We discuss the implications for Web search engines and sponsored search as a long-term business model and as a mechanism for fi...", "weights": [[8, 14, 1.0], [34, 42, 1.0], [112, 118, 1.0], [141, 147, 1.0]]}, "2021.ecir_conference-20212.80": {"snippet": "...onal Relevance in Task-Specific Retrieval\n\n\n Several criteria of relevance have been proposed in the literature. However, relevance criteria are strongly related to the search task. Thus, it is import...", "weights": [[8, 17, 1.0], [56, 64, 1.0], [68, 77, 1.0], [125, 134, 1.0], [135, 143, 1.0], [172, 178, 1.0]]}, "2014.ipm_journal-ir0volumeA50A2.9": {"snippet": "...wned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance ...", "weights": [[8, 14, 1.0], [101, 107, 1.0], [148, 154, 1.0], [193, 202, 1.0]]}, "2012.ipm_journal-ir0volumeA48A1.10": {"snippet": "...n of relevance is dependent on a number of factors and variables which include the criteria used to determine relevance. The relevance judgment process and the criteria used to make those judgments ar...", "weights": [[8, 17, 1.0], [86, 94, 1.0], [113, 122, 1.0], [128, 137, 1.0], [163, 171, 1.0]]}, "2008.ntcir_workshop-2008evia.8": {"snippet": "...User Relevance Criteria\n\n\n Recently, Scholer and Turpin [Proc. SIGIR 2008] proposed the use of techniques from the field of psychophysics to determine a relevance threshold for a user. Using this thre...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [156, 165, 1.0]]}, "1997.sigirconf_conference-97.36": {"snippet": "...e of relevant documents may be very small, terms maybe extracted from non-relevant sections of otherwise relevant documents, and some relevant terms may also attract non-relevant topics that are alrea...", "weights": [[8, 16, 1.0], [77, 85, 1.0], [108, 116, 1.0], [137, 145, 1.0], [173, 181, 1.0]]}, "2013.sigirconf_conference-2013.82": {"snippet": "... the relevance of document, investigating the effect of relevance level and document size. Results suggest that \"relevant\" documents require more effort to judge when compared to highly relevant and n...", "weights": [[8, 17, 1.0], [59, 68, 1.0], [94, 101, 1.0], [116, 124, 1.0], [189, 197, 1.0]]}, "2006.ecir_conference-2006.40": {"snippet": "...line Search Behaviour and Use of Relevance Criteria", "weights": [[8, 14, 1.0], [36, 45, 1.0], [46, 54, 1.0]]}, "2021.birws_workshop-2021.7": {"snippet": "... and Relevance Criteria - An Online Experiment", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2002.ipm_journal-ir0volumeA38A5.5": {"snippet": "...ers' relevance criteria in image retrieval in American history", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2014.cikm_conference-2014.192": {"snippet": "...plex Relevance Spaces with Copulas\n\n\n ABSTRACTModern relevance models consider a wide range of criteria in order to identify those documents that are expected to satisfy the user's information need. W...", "weights": [[8, 17, 1.0], [56, 65, 1.0], [98, 106, 1.0]]}}}
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Others have proposed modifications to Zipf's Law, and closer examination un...", "weights": [[8, 11, 1.0], [45, 49, 1.0], [80, 83, 1.0], [166, 170, 1.0], [173, 176, 1.0]]}, "2010.jasis_journal-ir0volumeA61A12.15": {"snippet": "...ka's law and Zipf's law", "weights": [[8, 11, 1.0], [16, 20, 1.0], [23, 26, 1.0]]}, "I08-3003": {"snippet": "...ower law or the Zipf's law. This makes NLP hard. Interestingly, the distribution of speakers over the world, content over the web and linguistic resources available across languages also follow power ...", "weights": [[8, 11, 1.0], [19, 23, 1.0], [26, 29, 1.0]]}, "2005.ipm_journal-ir0volumeA41A6.4": {"snippet": "...g of Zipf-type. A generalization of Zipf distribution (a size-frequency form of the Zipf\u00d5s law), named the generalized Zipf distribution, is introduced. Two main characterizations of the generalized Z...", "weights": [[8, 12, 1.0], [39, 43, 1.0], [94, 97, 1.0], [122, 126, 1.0], [202, 206, 1.0]]}, "1987.ipm_journal-ir0volumeA23A3.1": {"snippet": "...s of Zipf's law: An index approach", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2009.ipm_journal-ir0volumeA45A4.6": {"snippet": "...ower-law regularities have been discovered behind many complex natural and social phenomenons. We discover that the power-law regularities, especially the Zipf's and Heaps' laws, also exist in large-s...", "weights": [[8, 11, 1.0], [125, 128, 1.0], [158, 162, 1.0]]}, "1999.jasis_journal-ir0volumeA50A3.4": {"snippet": "... the Law of Zipf-Mandelbrot for Multi-Wort Phrases", "weights": [[8, 11, 1.0], [15, 19, 1.0]]}, "E17-4009": {"snippet": "...t to Zipf's law, an empirical law referring to the phenomenon that many types of data in social sciences can be approximated with a Zipfian distribution. Additionally, we also analyse Benford's law, i...", "weights": [[8, 12, 1.0], [15, 18, 1.0], [33, 36, 1.0], [197, 200, 1.0]]}, "O03-4004": {"snippet": "...pus, Zipf 's law for both words in English and characters in Chinese does not hold for all ranks. The frequency falls below the frequency predicted by Zipf's law for English words for rank greater tha...", "weights": [[8, 12, 1.0], [16, 19, 1.0], [154, 158, 1.0], [161, 164, 1.0]]}, "N18-1102": {"snippet": "...e of Zipf's law, which states that most content words occur very rarely. In this paper, we propose novel methods with a neural model of P (path|w 1 , w 2 ) to solve this problem. Our proposed model of...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "C14-1035": {"snippet": "... The Zipf's law of meaning is used as a way of pre-setting the sense number for the parametric model. We further extend the parametric model to non-parametric model which not only simplifies the probl...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2021.acl-srw.6": {"snippet": "...{'}s Law of Abbreviation and Interfering Noise in Emergent Languages\n\n\n This paper studies whether emergent languages in a signaling game follow Zipf's law of abbreviation (ZLA), especially when the c...", "weights": [[8, 11, 1.0], [148, 152, 1.0], [155, 158, 1.0]]}, "2007.sigirconf_conference-2007.86": {"snippet": "...ower-law constraints (also known as Zipf's Law and long tails) that allow us to materialize carefully chosen combinations of multi-keyword indexes, which significantly improve worst-case performance w...", "weights": [[8, 11, 1.0], [39, 43, 1.0], [46, 49, 1.0]]}, "2019.gwc-1.44": {"snippet": "...ency law with wordnets as sense inventories\n\n\n According to George K. Zipf, more frequent words have more senses. We have tested this law using corpora and wordnets of English, Spanish, Portuguese, Fr...", "weights": [[8, 11, 1.0], [73, 77, 1.0], [137, 140, 1.0]]}, "1982.jasis_journal-ir0volumeA33A5.5": {"snippet": "...n of Zipf's Law and Its Application to the Bibliographic Database Environment", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2021.ccl-1.93": {"snippet": "...{'}s law: evidence from danmu comments\n\n\n Zipf's law is a succinct yet powerful mathematical law in linguistics. However, the meaningfulness and units of the law have remained controversial. The curre...", "weights": [[8, 11, 1.0], [45, 49, 1.0], [52, 55, 1.0], [96, 99, 1.0], [161, 164, 1.0]]}, "2001.jasis_journal-ir0volumeA52A12.3": {"snippet": "... and Zipf models", "weights": [[8, 12, 1.0]]}, "1987.jasis_journal-ir0volumeA38A6.3": {"snippet": "...n of Zipf parameters", "weights": [[8, 12, 1.0]]}, "1987.ipm_journal-ir0volumeA23A3.0": {"snippet": "...of a zipf size variable: Sampling properties and relationship to other parameters", "weights": [[8, 12, 1.0]]}, "1986.jasis_journal-ir0volumeA37A5.4": {"snippet": "...ka's Law, Bradford's Law, and Zipf's Law", "weights": [[8, 11, 1.0], [24, 27, 1.0], [33, 37, 1.0], [40, 43, 1.0]]}, "1972.jasis_journal-ir0volumeA23A3.8": {"snippet": "...eral Zipf-type distributions in their goodness of fit to language data", "weights": [[8, 12, 1.0]]}, "2017.sigirconf_conference-2017.41": {"snippet": "...ago, Zipf postulated that the word frequency distribution of languages is a power law, i.e., it is a straight line on a log-log plot. Over the years, this phenomenon has been documented and studied ex...", "weights": [[8, 12, 1.0], [85, 88, 1.0]]}, "2021.naacl-main.350": {"snippet": "...ngs (Zipf's law of abbreviation), the need for a productive and open-ended vocabulary, local constraints on sequences of symbols, and various other factors all shape the lexicons of the world's langua...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "W96-0106": {"snippet": "...{'}s Law\n\n\n An asymptote is derived from Turing's local reestimation formula for population frequencies, and a local reestimation formula is derived from Zipf's law for the asymptotic behavior of popu...", "weights": [[8, 11, 1.0], [157, 161, 1.0], [164, 167, 1.0]]}, "C02-1117": {"snippet": "...{'}s Law to Words and Phrases\n\n\n Zipf's law states that the frequency of word tokens in a large corpus of natural language is inversely proportional to the rank. The law is investigated for two langua...", "weights": [[8, 11, 1.0], [36, 40, 1.0], [43, 46, 1.0], [169, 172, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.10": {"snippet": "... of \"Zipf's law\" was cited most (64.8%), followed by \"the principle of least effort\" (24.5%). Although the concept of \"the principle of least effort,\" the focus of HBPLE, was not most frequently obser...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}}}
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To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we...", "weights": [[8, 15, 1.0], [151, 158, 1.0]]}, "2018.sigirconf_conference-2018.82": {"snippet": "...item retrieval systems assume the user can formulate a good Boolean or SQL-style query to retrieve items, as one would do with a database, but this is often challenging, particularly given multiple nu...", "weights": [[8, 17, 1.0], [63, 70, 1.0]]}, "1990.sigirconf_conference-90.1": {"snippet": "...ment Retrieval\n\n\n AbstractThe use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolea", "weights": [[8, 17, 1.0], [79, 88, 1.0], [121, 130, 1.0], [131, 136, 1.0], [197, 204, 1.0]]}, "1981.jasis_journal-ir0volumeA32A4.5": {"snippet": "...hted boolean retrieval", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "...xing model for boolean query formulation\n\n\n AbstractA new model named Boolean Latent Semantic Indexing model based on the Singular Value Decomposition and Boolean query formulation is introduced. Whil...", "weights": [[8, 13, 1.0], [18, 25, 1.0], [61, 66, 1.0], [73, 80, 1.0], [106, 111, 1.0], [158, 165, 1.0]]}, "1985.sigirconf_conference-85.3": {"snippet": "...s of Boolean Structures for Probabilistic Retrieval", "weights": [[8, 15, 1.0], [45, 54, 1.0]]}, "2007.trec_conference-2007.51": {"snippet": "...rent Boolean queries were provided for each topic (defendant, plaintiff and final). In 2006, the plaintiff and final queries had (usually) been the same. 1 Although often referred to as \"Boolean,\" the...", "weights": [[8, 15, 1.0], [190, 197, 1.0]]}, "2008.wwwconf_conference-2008.201": {"snippet": "...ming model offered by the GPU, which is a data-parallel model reminiscent of the massively parallel SIMD models studied in the 1980s.Recently, GPU vendors have started to offer better support for gene...", "weights": [[8, 13, 1.0], [59, 64, 1.0]]}, "1982.jasis_journal-ir0volumeA33A6.1": {"snippet": "...tion retrieval in systems with boolean search request formulations", "weights": [[8, 17, 1.0], [34, 41, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "...tion retrieval performance: training in Boolean logic and the type of search interface. As reported previously, we found that both Boolean training and the use of an assisted interface improved the pa...", "weights": [[8, 17, 1.0], [43, 50, 1.0], [134, 141, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.1": {"snippet": "...tion retrieval \u2013 The potential for improvements in conventional Boolean retrieval systems", "weights": [[8, 17, 1.0], [67, 74, 1.0], [75, 84, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...h as Boolean operators and phrase searching, improves the effectiveness of Web searching. We test this assumption by examining the effects of query operators on the performance of three major Web sear...", "weights": [[8, 15, 1.0]]}, "2007.trec_conference-2007.65": {"snippet": "...lds, boolean constraints and structured operators. Evaluation results show that structured queries outperform bag-of-words ones. Boolean constraints improve both precision and recall. For the routing ...", "weights": [[8, 15, 1.0], [132, 139, 1.0]]}, "1990.sigirconf_conference-90.28": {"snippet": "...nded Boolean Retrieval: A Heuristic Approach?", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1980.sigirconf_conference-80.22": {"snippet": "A Model of a Document-Clustering-Based Information Retrieval System with a Boolean Search Request Formulation", "weights": [[2, 7, 1.0], [51, 60, 1.0], [75, 82, 1.0]]}, "2010.sigirconf_conference-2010.48": {"snippet": "... two retrieval models that incorporate user feedback on document facets. Evaluated based on user feedback collected through Amazon Mechanical Turk, our experimental results show that the Boolean filte...", "weights": [[8, 17, 1.0], [190, 197, 1.0]]}, "1997.sigirconf_conference-97.15": {"snippet": "...g of Boolean Operators\n\n\n The inference network model of information retrieval allows for a probabilistic interpretation of Boolean query operators. Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [51, 56, 1.0], [72, 81, 1.0], [127, 134, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...nded Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulate...", "weights": [[8, 15, 1.0], [16, 21, 1.0], [73, 80, 1.0]]}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0], [55, 64, 1.0]]}, "1979.ipm_journal-ir0volumeA15A5.2": {"snippet": "...ical model of a weighted boolean retrieval system", "weights": [[8, 13, 1.0], [28, 35, 1.0], [36, 45, 1.0]]}, "2002.ipm_journal-ir0volumeA38A6.0": {"snippet": "...nded Boolean retrieval model", "weights": [[8, 15, 1.0], [16, 25, 1.0], [26, 31, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [50, 59, 1.0], [85, 94, 1.0], [124, 131, 1.0], [132, 141, 1.0], [173, 178, 1.0], [196, 203, 1.0]]}, "1982.sigirconf_conference-82.18": {"snippet": "...tion retrieval systems typically employ inverted file structures and Boolean logic operators for efficient text retrieval. These systems require considerable user training for effective use. As a cons...", "weights": [[8, 17, 1.0], [72, 79, 1.0], [115, 124, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0]]}, "2007.spire_conference-2007.13": {"snippet": "...tion Retrieval\n\n\n Abstract. Conjunctive Boolean queries are a fundamental operation in web search engines. These queries can be reduced to the problem of intersecting ordered sets of integers, where e...", "weights": [[8, 17, 1.0], [43, 50, 1.0]]}, "1982.sigirconf_conference-82.5": {"snippet": "...onal Retrieval Systems\n\n\n ABSTRACTResearch has shown that document r e t r i e v a l based on weighting functions and incorporating relevance feedback may be more effective than r e t r i e v a l base...", "weights": [[8, 17, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...Soft Boolean Operators\n\n\n AbstractThe conventional bibliographic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [68, 77, 1.0], [99, 106, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.7": {"snippet": "...onal Boolean retrieval systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...ring retrieval and ranking\n\n\n We present a model for arbitrary passage retrieval using Boolean queries. The model is applied to the task of ranking documents, or other structural elements, in the orde...", "weights": [[8, 17, 1.0], [46, 51, 1.0], [74, 83, 1.0], [90, 97, 1.0], [111, 116, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [45, 54, 1.0], [99, 106, 1.0], [107, 116, 1.0], [176, 183, 1.0], [199, 208, 1.0]]}, "1986.sigirconf_conference-86.21": {"snippet": "...pace Model for Boolean Query Processing\n\n\n Abstract. An infamation retrieval model, named the Generaliied Vectm Spice Model (GVSM). is extended m handle situations where queries are specitied as (exte...", "weights": [[8, 13, 1.0], [18, 25, 1.0], [70, 79, 1.0], [80, 85, 1.0], [121, 126, 1.0]]}, "2006.clef_workshop-2006w.37": {"snippet": "... Our model handles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes ...", "weights": [[8, 13, 1.0], [22, 29, 1.0], [95, 102, 1.0], [115, 124, 1.0], [125, 130, 1.0]]}, "2003.trec_conference-2003.11": {"snippet": "...nded Boolean model has been widely used in business system for its easiness to be complemented and not bad results. In this paper, a statistical model and modified Boolean model and natural language p...", "weights": [[8, 15, 1.0], [16, 21, 1.0], [148, 153, 1.0], [167, 174, 1.0], [175, 180, 1.0]]}}}
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The goal is to in...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0], [155, 166, 1.0], [167, 176, 1.0], [177, 184, 1.0]]}, "2000.ir_journal-3.6": {"snippet": "...gent Indexing and Semantic Retrieval of Multimodal Documents", "weights": [[8, 16, 1.0], [30, 39, 1.0]]}, "2005.cikm_conference-2005.81": {"snippet": "Indexing time vs. query time: trade-offs in dynamic information retrieval systems\n\n\n ABSTRACTWe examine issues in the design of fully dynamic information retrieval systems supporting both document ins...", "weights": [[0, 8, 1.0], [52, 63, 1.0], [64, 73, 1.0], [74, 81, 1.0], [142, 153, 1.0], [154, 163, 1.0], [164, 171, 1.0]]}, "1983.sigirconf_conference-83.1": {"snippet": "...ware Systems for Text Information Retrieval\n\n\n As databases become very large, conventional digital computers cannot provide satisfactory response time. This is particularly true for text databases, w...", "weights": [[8, 15, 1.0], [25, 36, 1.0], [37, 46, 1.0]]}, "2013.wsdm_conference-2013.93": {"snippet": "...uted information retrieval (IR) systems. Scalability and efficiency of these systems also have an impact on their effectiveness. Consequently, scalability and efficiency in the context of retrieval sy...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [35, 42, 1.0], [80, 87, 1.0], [191, 200, 1.0], [201, 208, 1.0]]}, "1988.tois_journal-ir0volumeA6A1.1": {"snippet": "... but retrieval is usually limited to finding documents that contain a specified Boolean pattern of words. Effective retrieval requires that documents with similar meanings be found through a process o...", "weights": [[8, 17, 1.0], [119, 128, 1.0]]}, "1967.ipm_journal-ir0volumeA3A4.9": {"snippet": "Indexing and classification for interactive retrieval systems", "weights": [[0, 8, 1.0], [44, 53, 1.0], [54, 61, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.3": {"snippet": "...text retrieval systems use high-performance inverted indexes to provide efficient text query evaluation. Algorithms for fast query evaluation and index construction are well-known, but relatively litt...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "1999.cikm_conference-99.51": {"snippet": "...ntic indexing and retrieval in Information Retrieval. The prototype is motivated by a desire to provide a more efficient and effective information retrieval system compared to the current state of the...", "weights": [[8, 16, 1.0], [21, 30, 1.0], [34, 45, 1.0], [46, 55, 1.0], [138, 149, 1.0], [150, 159, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A3.0": {"snippet": "...ng \u00b7 Information retrieval \u00b7 DB-IR integration \u00b7 Odysseus\nDB-IR integrationDatabases (DB) and information retrieval (IR) have been parallel universes. However, many recent applications such as custome...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "1982.ipm_journal-ir0volumeA18A4.3": {"snippet": "...tive indexing in a document retrieval system model", "weights": [[8, 16, 1.0], [31, 40, 1.0]]}, "2005.sigirconf_conference-2005.124": {"snippet": "Indexing emails and email threads for retrieval\n\n\n ABSTRACTElectronic mail poses a number of unusual challenges for the design of information retrieval systems and test collections, including informal...", "weights": [[0, 8, 1.0], [38, 47, 1.0], [130, 141, 1.0], [142, 151, 1.0], [152, 159, 1.0]]}, "1988.ipm_journal-ir0volumeA24A4.2": {"snippet": "... for indexing and retrieval", "weights": [[8, 16, 1.0], [21, 30, 1.0]]}, "2005.ipm_journal-ir0volumeA41A2.6": {"snippet": "...Many information retrieval systems use the inverted file as indexing structure. The inverted file, however, requires inefficient reorganization when new documents are to be added to an existing collec...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0], [63, 71, 1.0]]}, "2005.ipm_journal-ir0volumeA41A2.0": {"snippet": "...stic information retrieval model for a dependency structured indexing system\n\n\n AbstractMost previous information retrieval (IR) models assume that terms of queries and documents are statistically ind...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [64, 72, 1.0], [105, 116, 1.0], [117, 126, 1.0]]}, "2014.wsdm_conference-2014.79": {"snippet": "...uted information retrieval (IR) systems. Scalability and efficiency of these systems also have an impact on their effectiveness. Consequently, scalability and efficiency in the context of retrieval sy...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [35, 42, 1.0], [80, 87, 1.0], [191, 200, 1.0], [201, 208, 1.0]]}, "2014.tois_journal-ir0volumeA32A1.2": {"snippet": "Indexing Word Sequences for Ranked Retrieval\n\n\n Formulating and processing phrases and other term dependencies to improve query effectiveness is an important problem in information retrieval. However,...", "weights": [[0, 8, 1.0], [35, 44, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2004.sigirconf_conference-2004.40": {"snippet": "...ment retrieval service by processing huge Inverted File indexes. Inverted File indexes allow fast query resolution and good memory utilization since their d-gaps representation can be effectively and ...", "weights": [[8, 17, 1.0]]}, "2010.cikm_conference-2010.127": {"snippet": "... and information retrieval. Such techniques return the top-k documents without scanning and evaluating the full inverted lists of the query terms. Thus, they can greatly improve query processing effic...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2009.trec_conference-2009.65": {"snippet": "...onal Indexing Model for Chemical Patent Search\n\n\n For the TREC-2009 Chemical IR Track, we explore development of a distributed information retrieval system based on a dimensional data model. The index...", "weights": [[8, 16, 1.0], [130, 141, 1.0], [142, 151, 1.0], [198, 206, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.7": {"snippet": "...rent indexing languages\n\n\n AbstractAn integrated information retrieval system generally contains multiple databases that are inconsistent in terms of their content and indexing. This paper proposes a ...", "weights": [[8, 16, 1.0], [52, 63, 1.0], [64, 73, 1.0], [171, 179, 1.0]]}, "2010.wsdm_conference-2010.38": {"snippet": "...ment retrieval\n\n\n ABSTRACTThere has been a large amount of research on efficient document retrieval in both IR and web search areas. One important technique to improve retrieval efficiency is early te...", "weights": [[8, 17, 1.0], [93, 102, 1.0], [171, 180, 1.0]]}, "2013.tweb_journal-ir0volumeA7A3.3": {"snippet": "...g on retrieval systems that adopt a term-based index partitioning strategy, the high communication overhead due to the transfer of large amounts of data from the index servers forms a major performanc...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2009.cikm_conference-2009.47": {"snippet": "... for Information Retrieval systems. The new model is based on partitioning of the inverted index across the terms in it. We exploit the query log to partition the on-disk inverted index into two types...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0]]}, "2010.cikm_conference-2010.128": {"snippet": "...rent Information Retrieval systems use inverted index structures for efficient query processing. Due to the extremely large size of many data sets, these index structures are usually kept in compresse...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0]]}, "2000.tois_journal-ir0volumeA18A2.2": {"snippet": "...ased indexing of large image databases\n\n\n Large image databases have emerged in various applications in recent years. A prime requisite of these databases is the means by which their contents can be i...", "weights": [[8, 16, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...ased information systems have indicated that the content of the knowledge base and the reasoning behavior of systems analysts are two important factors in the development of computer based information...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [112, 119, 1.0], [192, 203, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.3": {"snippet": "...uted Indexing: To MapReduce or Not?\n\n\n Information Retrieval (IR) systems require input corpora to be indexed. The advent of terabyte-scale Web corpora has reinvigorated the need for efficient indexin...", "weights": [[8, 16, 1.0], [42, 53, 1.0], [54, 63, 1.0], [69, 76, 1.0], [196, 204, 1.0]]}, "2004.sigirconf_conference-2004.13": {"snippet": "...able indexing and ranking for XML information retrieval\n\n\n ABSTRACTIndexing and ranking are two key factors for efficient and effective XML information retrieval. Inappropriate indexing may result in ...", "weights": [[8, 16, 1.0], [37, 48, 1.0], [49, 58, 1.0], [143, 154, 1.0], [155, 164, 1.0], [180, 188, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.22": {"snippet": "...ased retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic i...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [202, 213, 1.0]]}, "2006.ecir_conference-2006.21": {"snippet": "...Text Retrieval Systems\n\n\n Abstract. In-place and merge-based index maintenance are the two main competing strategies for on-line index construction in dynamic information retrieval systems based on in...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [162, 173, 1.0], [174, 183, 1.0], [184, 191, 1.0]]}, "2004.sigirconf_conference-2004.15": {"snippet": "...ntic indexing for information retrieval\n\n\n ABSTRACTLatent Semantic Indexing (LSI) is a well established and effective framework for conceptual information retrieval. In traditional implementations of ...", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0], [70, 78, 1.0], [146, 157, 1.0], [158, 167, 1.0]]}, "2001.sigirconf_conference-2001.6": {"snippet": "... for Information Retrieval Systems\n\n\n ABSTRACTWe introduce static index pruning methods that significantly reduce the index size in information retrieval systems. We investigate uniform and term-based...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0], [135, 146, 1.0], [147, 156, 1.0], [157, 164, 1.0]]}, "2004.ntcir_workshop-2004.49": {"snippet": "...tion Retrieval with Site Anchor Text\n\n\n In this paper we present an information retrieval system that indexes only site anchor text to verify the efficiency of reference information in a navigation re...", "weights": [[8, 17, 1.0], [71, 82, 1.0], [83, 92, 1.0], [173, 184, 1.0], [201, 210, 1.0]]}, "1991.sigirjournals_journal-ir0volumeA25A2.6": {"snippet": "... and Retrieval of Documents Using Index Expressions", "weights": [[8, 17, 1.0]]}, "2010.wwwconf_conference-2010.32": {"snippet": "Scalable techniques for document identifier assignment ininverted indexes\n\n\n ABSTRACTWeb search engines use the full-text inverted index data structure. Because query processing performance is impacte...", "weights": []}, "2016.sigirconf_conference-2016.224": {"snippet": "...Web, indexing discovered pages, and processing user queries. The scalability and efficiency of these infrastructures require careful performance optimizations in every major component of the search en...", "weights": [[8, 16, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.1": {"snippet": "...text retrieval systems using inverted indices\n\n\n AbstractSimilarity calculations and document ranking form the computationally expensive parts of query processing in ranking-based text retrieval. In t...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [188, 197, 1.0]]}, "2019.wsdm_conference-2019.119": {"snippet": "... two information objects is the core of many different information retrieval (IR) applications including Web search, question answering, and recommendation. Recently, deep learning methods have yielde...", "weights": [[8, 19, 1.0], [58, 69, 1.0], [70, 79, 1.0]]}, "1989.sigirconf_conference-89.11": {"snippet": "... for Information Retrieval\n\n\n ABSTRACTIn this paper we present a parallel document ranking algorithm suitable for use on databases of l-1000 GB, resident on primary or secondary storage. The algorithm...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2014.ntcir_conference-2014.31": {"snippet": "...Math Retrieval System for NTCIR-11 Math-2 Task\n\n\n In NTCIR-11, the NTCIR-Math-2 Task is organized for mathematical information retrieval. This paper proposes an innovative system for efficient formula...", "weights": [[8, 17, 1.0], [118, 129, 1.0], [130, 139, 1.0]]}, "2013.sigirconf_conference-2013.204": {"snippet": "Indexing and querying overlapping structures\n\n\n ABSTRACTStructural information retrieval is mostly based on hierarchy. However, in real life information is not purely hierarchical and structural eleme...", "weights": [[0, 8, 1.0], [67, 78, 1.0], [79, 88, 1.0], [141, 152, 1.0]]}, "2011.cikm_conference-2011.43": {"snippet": "...ased indexing and retrieval. Performance is comparable to the state of the art inverted file based systems, including language models and BM25. These findings suggest that file signatures offer a viab...", "weights": [[8, 16, 1.0], [21, 30, 1.0], [102, 109, 1.0]]}, "2002.ntcir_workshop-2002.68": {"snippet": "...form Indexing and Retrieval Scheme for Chinese, Japanese, and Korean\n\n\n This paper reports on our work at the third NTCIR workshop on the subtasks of Chinese, Japanese, and Korean monolingual informat...", "weights": [[8, 16, 1.0], [21, 30, 1.0], [195, 206, 1.0]]}, "2020.sigirconf_conference-2020.279": {"snippet": "...ment indexing is a key component for efficient information retrieval (IR). After preprocessing steps such as stemming and stopword removal, document indexes usually store term-frequencies (tf). Along ...", "weights": [[8, 16, 1.0], [50, 61, 1.0], [62, 71, 1.0]]}, "2013.cikm_conference-2013.240": {"snippet": "...tion indexing: fast approximate retrieval from large corpora\n\n\n ABSTRACTInverted indexing is a ubiquitous technique used in retrieval systems including web search. Despite its popularity, it has a dra...", "weights": [[8, 16, 1.0], [35, 44, 1.0], [84, 92, 1.0], [127, 136, 1.0], [137, 144, 1.0]]}, "2001.tois_journal-ir0volumeA19A3.0": {"snippet": "... Web indexing system that we have implemented.", "weights": [[8, 16, 1.0]]}, "2007.riao_conference-2007.31": {"snippet": "...s of information retrieval systems: a machine learning based solution", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0]]}, "2006.cikm_conference-2006.23": {"snippet": "...text retrieval systems\n\n\n ABSTRACTWe present a static index pruning method, to be used in ad-hoc document retrieval tasks, that follows a documentcentric approach to decide whether a posting for a giv...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [109, 118, 1.0]]}, "1998.sigirconf_conference-98.33": {"snippet": "... our indexing language. This choice allows for relational indexing support and captures all the useful properties of the logical information retrieval model, in a workable system. First order logic an...", "weights": [[8, 16, 1.0], [61, 69, 1.0], [132, 143, 1.0], [144, 153, 1.0]]}, "1986.sigirconf_conference-86.20": {"snippet": "...ment retrieval [GRIFM. GRIF851. Recent research by the author corroborates these findings and also shows that the partial ranking produced by a topdown search of the complete link hierarchy can be mor...", "weights": [[8, 17, 1.0]]}, "2008.tois_journal-ir0volumeA26A3.3": {"snippet": "...ased retrieval using compressed cluster-skipping inverted files\n\n\n We propose a unique cluster-based retrieval (CBR) strategy using a new cluster-skipping inverted file for improving query processing ...", "weights": [[8, 17, 1.0], [104, 113, 1.0]]}, "2011.sigirconf_conference-2011.58": {"snippet": "...s in information retrieval. For each unique word occurring in a document collection, the inverted index stores a list of the documents in which this word occurs. Compression techniques are often appli...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.sigirconf_conference-2011.153": {"snippet": "...s an indexing method to speed-up music retrieval\n\n\n ABSTRACTComputations in most music retrieval systems strongly depend on the size of data compared. We propose to enhance performances of a music ret...", "weights": [[8, 16, 1.0], [42, 51, 1.0], [90, 99, 1.0], [100, 107, 1.0], [200, 209, 1.0]]}, "2010.cikm_conference-2010.109": {"snippet": "...rted indexing for feedback and expansion\n\n\n ABSTRACTTraditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a list...", "weights": [[8, 16, 1.0], [79, 90, 1.0], [91, 100, 1.0], [101, 108, 1.0]]}, "2012.clef_workshop-2012w.187": {"snippet": "Word Indexing Versus Conceptual Indexing in Medical Image Retrieval", "weights": [[5, 13, 1.0], [32, 40, 1.0], [58, 67, 1.0]]}, "2005.cikm_conference-2005.127": {"snippet": "...amic indexing, and identifies features worth demonstrating. The concept of dynamic index implies that the aim is to reflect the creation of, deletion of, and updates to relevant files in the search in...", "weights": [[8, 16, 1.0]]}, "W00-1104": {"snippet": "...lean Information l~.etrieval system that adds word semantics to the classic word based indexing. Two of the main tasks of our system, namely the indexing and retrieval components, are using a combined...", "weights": [[8, 19, 1.0], [90, 98, 1.0], [148, 156, 1.0], [161, 170, 1.0]]}, "1997.jasis_journal-ir0volumeA48A10.1": {"snippet": "...atic Indexing for Information Retrieval with Arabic Documents", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0]]}, "2017.sigirconf_conference-2017.63": {"snippet": "...text indexing. In recent years the Bing search engine has developed and deployed an index based on bit-sliced signatures. This index, known as BitFunnel, replaced an existing production system based o...", "weights": [[8, 16, 1.0]]}, "1976.ipm_journal-ir0volumeA12A2.6": {"snippet": "...ased indexing of natural language texts for information retrieval systems", "weights": [[8, 16, 1.0], [47, 58, 1.0], [59, 68, 1.0], [69, 76, 1.0]]}, "2003.spire_conference-2003.21": {"snippet": "...rase Indexing and Mining for an Information Retrieval System\n\n\n Abstract. In this paper, we present a noun phrase indexing and mining methodology for French Information Retrieval. Our assumption is th...", "weights": [[8, 16, 1.0], [35, 46, 1.0], [47, 56, 1.0], [117, 125, 1.0], [160, 171, 1.0], [172, 181, 1.0]]}, "2015.tois_journal-ir0volumeA33A4.3": {"snippet": "...back Retrieval\n\n\n The inverted index is the dominant indexing method in information retrieval systems. It enables fast return of the list of all documents containing a given query term. However, for r...", "weights": [[8, 17, 1.0], [56, 64, 1.0], [75, 86, 1.0], [87, 96, 1.0], [97, 104, 1.0], [202, 211, 1.0]]}, "C02-1045": {"snippet": "...ased Indexing of Textual Data\n\n\n This paper presents a framework for clustering in text-based information retrieval systems. The prominent feature of the proposed method is that documents, terms, and ...", "weights": [[8, 16, 1.0], [97, 108, 1.0], [109, 118, 1.0], [119, 126, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.4": {"snippet": "...ient indexing in information retrieval systems\n\n\n AbstractThis paper presents a size reduction method for the inverted file, the most suitable indexing structure for an information retrieval system (I...", "weights": [[8, 16, 1.0], [20, 31, 1.0], [32, 41, 1.0], [42, 49, 1.0], [146, 154, 1.0], [172, 183, 1.0], [184, 193, 1.0]]}, "2006.clef_workshop-2006.122": {"snippet": "...ased Indexing Technique for Geographical Information Retrieval", "weights": [[8, 16, 1.0], [44, 55, 1.0], [56, 65, 1.0]]}, "1971.ipm_journal-ir0volumeA7A5.3": {"snippet": "Retrieval efficiency from titles and the cost of indexing", "weights": [[0, 9, 1.0], [49, 57, 1.0]]}}}
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[69, 78, 1.0]]}, "2001.wwwconf_conference-2001p.48": {"snippet": "...tion retrieval systems or search engines. We present a model for visualizing large collections of documents in World Wide Web retrieval, independently of the retrieval system.", "weights": [[8, 17, 1.0], [18, 25, 1.0], [129, 138, 1.0], [161, 170, 1.0]]}, "2008.cikm_conference-2008.183": {"snippet": "...nion retrieval system with the proposed opinion retrieval techniques and the polarity classification model outperformed the best reported systems respectively.", "weights": [[8, 17, 1.0], [51, 60, 1.0], [141, 148, 1.0]]}, "2007.sigirconf_conference-2007.25": {"snippet": "...ment retrieval in main memory\n\n\n ABSTRACTDisk access performance is a major bottleneck in traditional information retrieval systems. Compared to system memory, disk bandwidth is poor, and seek times a...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [127, 134, 1.0]]}, "2013.wsdm_conference-2013.93": {"snippet": "...tion retrieval (IR) systems. Scalability and efficiency of these systems also have an impact on their effectiveness. Consequently, scalability and efficiency in the context of retrieval systems contin...", "weights": [[8, 17, 1.0], [23, 30, 1.0], [68, 75, 1.0], [179, 188, 1.0], [189, 196, 1.0]]}, "2007.ipm_journal-ir0volumeA43A4.14": {"snippet": "...rank retrieval systems without relevance judgments\n\n\n AbstractThis paper addresses the problem of how to rank retrieval systems without the need for human relevance judgments, which are very resource ...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [113, 122, 1.0], [123, 130, 1.0]]}, "2003.sigirconf_conference-2003.58": {"snippet": "...g of retrieval systems in imperfect environments\n\n\n ABSTRACTThe empirical investigation of the effectiveness of information retrieval (IR) systems requires a test collection, a set of query topics, an...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [127, 136, 1.0], [142, 149, 1.0]]}, "2011.cikm_conference-2011.248": {"snippet": "...opic retrieval that have been used to motivate diverse retrieval. \nCategories and Subject Descriptors\nGeneral TermsAlgorithms Keywords diversity, set-level relevance, maximal marginal relevance\nDIVERS...", "weights": [[8, 17, 1.0], [58, 67, 1.0]]}, "1985.riao_conference-1985.38": {"snippet": "...tion retrieval system based on user problem statements", "weights": [[8, 17, 1.0]]}, "2015.sigirconf_workshop-2015gsb.6": {"snippet": "...tion Retrieval Boosted by Category for Troubleshooting Search System\n\n\n Troubleshooting search system aims at extracting relevant information to solve the problem at hand. It is often the case that do...", "weights": [[8, 17, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.13": {"snippet": "...ence retrieval and novelty detection. We analyze the strengths and weaknesses of current state of the art methods and, subsequently, new mechanisms to address sentence retrieval and novelty detection ...", "weights": [[8, 17, 1.0], [171, 180, 1.0]]}, "1991.sigirconf_conference-91.24": {"snippet": "...tion Retrieval Systems\n\n\n Abstract.Information retrieval is the selection of documents that are potentially relevant to a user's information need. Given the vast volume of data stored in modern inform...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [50, 59, 1.0]]}, "2013.tois_journal-ir0volumeA31A2.2": {"snippet": "... and systems in these environments. Searching for information in these information spaces has brought about problems beyond traditional boundaries of information retrieval (IR) research. This article ...", "weights": [[8, 15, 1.0], [111, 119, 1.0], [165, 174, 1.0]]}, "1968.ipm_journal-ir0volumeA4A4.2": {"snippet": "... and retrieval system evaluation", "weights": [[8, 17, 1.0]]}, "2000.cikm_conference-2000.17": {"snippet": "...tion retrieval system. We describe an interactive relevance feedback agent that analyzes the inter-document similarities and can help the user to locate the interesting information quickly. We show ho...", "weights": [[8, 17, 1.0]]}, "1983.sigirconf_conference-83.28": {"snippet": "Open Problems in Information Retrieval\n\n\n What are the crucial issues and open problems in our field? I propose to answer this question indirectly by offering you a view of the terrain that we call in...", "weights": [[5, 13, 1.0], [29, 38, 1.0], [79, 87, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.2": {"snippet": "...tive systems.CCS Concepts: \u2022 Information systems \u2192 Retrieval effectiveness; Learning to rank; Information retrieval query processing;Additional Key Words and Phrases: Information systems, information ...", "weights": [[8, 15, 1.0], [44, 51, 1.0], [54, 63, 1.0], [109, 118, 1.0], [182, 189, 1.0]]}, "1986.tc-1.6": {"snippet": "...tion retrieval are of value. In this presentation, I shall attempt to outline the main techniques for personal information retrieval systems, both manual and computer-based. Whilst I shall be talking ...", "weights": [[8, 17, 1.0], [126, 135, 1.0], [136, 143, 1.0]]}, "1995.sigirconf_conference-95.28": {"snippet": "...oved retrieval performance for the routing problem. We use the Tipster collection and the TREC-2 and TREC-3 routing tasks to test classifiers and representations .There are some risks associated with ...", "weights": [[8, 17, 1.0]]}, "1982.sigirconf_conference-82.9": {"snippet": "...ment Retrieval Problem", "weights": [[8, 17, 1.0]]}, "1989.sigirconf_conference-89.8": {"snippet": "Retrieval System Evaluation Using Recall and Precision: Problems and Answers", "weights": [[0, 9, 1.0], [56, 64, 1.0]]}, "W99-1017": {"snippet": "...ment Retrieval\n\n\n It is only during the last few years that attention has started to shift from pure textbased retrieval towards other media. Information retrieval from spoken documents is analogous t...", "weights": [[8, 17, 1.0], [114, 123, 1.0], [157, 166, 1.0]]}, "2015.ictir_conference-2015.2": {"snippet": "...y of Retrieval: The Retrievability of Information\n\n\n ABSTRACTRetrievability is an important and interesting indicator that can be used in a number of ways to analyse Information Retrieval systems and ...", "weights": [[8, 17, 1.0], [181, 190, 1.0], [191, 198, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.14": {"snippet": "... for retrieval evaluation. These are used as the basis for experiments aimed at solving specific problems that are faced when searching with automatically generated descriptions of video content. Fina...", "weights": [[8, 17, 1.0], [100, 108, 1.0]]}, "H94-1073": {"snippet": "...eech retrieval system affects the retrieval effectiveness. A speech retrieval system facilitates content-based retrieval of speech documents, i.e. audio recordings containing spoken text. The speech r...", "weights": [[8, 17, 1.0], [37, 46, 1.0], [71, 80, 1.0], [114, 123, 1.0], [202, 211, 1.0]]}, "2012.cikm_conference-2012.135": {"snippet": "...mage retrieval systems have to cope with two different regimes: understanding broadly the categories of interest to the user, and refining the search in this or these categories to converge to specifi...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.4": {"snippet": "...s of Problems in Online Judge Systems\n\n\n Online Judge (OJ) systems have been widely used in many areas, including programming, mathematical problems solving, and job interviews. Unlike other online le...", "weights": [[8, 16, 1.0], [33, 40, 1.0], [62, 69, 1.0], [143, 151, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "... and Retrieval -relevance feedback\nGeneral TermsAlgorithms, Performance, Experimentation\nKeywordsRelevance Feedback, Web Search, Evaluation\n1.INTRODUCTIONRelevance feedback is a classical information ...", "weights": [[8, 17, 1.0]]}, "2005.sigirconf_conference-2005.7": {"snippet": "...tion retrieval using implicit feedback\n\n\n ABSTRACTA major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collect...", "weights": [[8, 17, 1.0], [89, 98, 1.0], [110, 117, 1.0], [130, 139, 1.0]]}, "2013.ictir_conference-2013.14": {"snippet": "...tion Retrieval with Time Series Query\n\n\n ABSTRACTWe study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find relevant do...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [166, 175, 1.0]]}, "2000.sigirconf_conference-2000.12": {"snippet": "...ment retrieval\n\n\n ABSTRACTCombined word-based indexes and phonetic indexes have been used to improve the performance of spoken document retrieval systems primarily by addressing the out-of-vocabulary ...", "weights": [[8, 17, 1.0], [139, 148, 1.0], [149, 156, 1.0]]}, "2012.airs_conference-2012.0": {"snippet": "...tion Retrieval Technology, 8th Asia Information Retrieval Societies Conference, AIRS 2012, Tianjin, China, December 17-19, 2012. Proceedings\n\n\n The annual AIRS conference has established its reputatio...", "weights": [[8, 17, 1.0], [51, 60, 1.0]]}, "2012.cikm_conference-2012.18": {"snippet": "...tion retrieval system is to satisfy a user's information need-e.g., by providing an answer or information \"nugget\"-while the actual search space of a typical information retrieval system consists of d...", "weights": [[8, 17, 1.0], [173, 182, 1.0]]}, "2008.sigirjournals_journal-ir0volumeA42A2.18": {"snippet": "...tion retrieval in the workplace\n\n\n Increasingly, workplace information seeking takes place in digital information environments and is reliant upon search systems. Existing systems are designed to retr...", "weights": [[8, 17, 1.0], [157, 164, 1.0], [175, 182, 1.0]]}, "2011.cikm_conference-2011.245": {"snippet": "...rove retrieval quality over the initial query. Typical relevance feedback methods usually select top-ranked documents for relevance judgments, then query expansion or model updating are carried out ba...", "weights": [[8, 17, 1.0]]}, "2017.cikm_conference-2017.301": {"snippet": "... the systems that perform be er, tend to have a higher chance of retrieving a relevant document rather than a non-relevant document for a topic prior to retrieval, indicating that retrieval systems wh...", "weights": [[8, 15, 1.0], [156, 165, 1.0], [183, 192, 1.0], [193, 200, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A4.15": {"snippet": "...ated retrieval, that is, the retrieval of blog entries which contain opinions about a topic. There has been a remarkable amount of work towards the improvement of the effectiveness of the opinion retr...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [199, 208, 1.0]]}, "2001.ntcir_workshop-2001.29": {"snippet": "On a Retrieval Support System by Suggesting Terms to a User\n\n\n We propose a support method for information retrieval. This method automatically suggests terms relevant to a query, to a user, and when ...", "weights": [[5, 14, 1.0], [107, 116, 1.0]]}, "2003.sigirconf_conference-2003.65": {"snippet": "... and Retrieval]: Retrieval Models.General Terms: Algorithms, Theory. Keywords: Metasearch, Pooling, Retrieval Systems.\nINTRODUCTIONIn the annual TREC competition, participating systems return ranked l...", "weights": [[8, 17, 1.0], [20, 29, 1.0], [103, 112, 1.0], [113, 120, 1.0], [180, 187, 1.0]]}, "2012.wsdm_conference-2012.41": {"snippet": "...tion retrieval systems such as search engines relies on good test collections, including assessments of retrieved content. The widely employed \"Cranfield paradigm\" dictates that the information releva...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2010.sigirconf_conference-2010.136": {"snippet": "...TXML retrieval provides a focused access to the relevant content of documents. However, in evaluation, full document retrieval has appeared competitive to focused XML retrieval. We analyze the density...", "weights": [[8, 17, 1.0], [120, 129, 1.0], [170, 179, 1.0]]}, "2005.sigirconf_conference-2005.85": {"snippet": "Characterization of a simple case of the reassignment of document identifiers as a pattern sequencing problem\n\n\n ABSTRACTIn this poster, we analyze recent work in the document identifiers reassignment...", "weights": []}, "2007.sigirconf_conference-2007.76": {"snippet": "...tion retrieval systems is one of the core tasks in information retrieval. Problems include the inability to exhaustively label all documents for a topic, nongeneralizability from a small number of top...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [66, 75, 1.0], [77, 85, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.16": {"snippet": "...text retrieval\n\n\n Even though modern retrieval systems typically use a multitude of features to rank documents, the backbone for search ranking is usually the standard tf.idf retrieval models.This the...", "weights": [[8, 17, 1.0], [40, 49, 1.0], [50, 57, 1.0], [178, 187, 1.0]]}, "2011.cikm_conference-2011.78": {"snippet": "...ting systems, and (ii) generalize to new, previously unseen systems. We propose a two stage approach. For a given set of queries, we adopt the traditional pooling method and use a portion of the budge...", "weights": [[8, 15, 1.0], [63, 70, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.22": {"snippet": "...t of retrieval systems empirically designed to be biased towards relevant information. However, other biases have been observed, which counteract retrieval performance. In this thesis, the reduction o...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [149, 158, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...s of retrieval models on the basis of their retrieval bias. This also offers a basis for optimizing retrieval systems for specific collections without the need to provide manually annotated ground tru...", "weights": [[8, 17, 1.0], [47, 56, 1.0], [103, 112, 1.0], [113, 120, 1.0]]}, "2006.sigirconf_conference-2006.57": {"snippet": "...tion retrieval systems aim to maximize the number of relevant documents returned to a user within some window of the top. For that goal, the probability ranking principle, which ranks documents in dec...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2004.ntcir_workshop-2004.18": {"snippet": "...tent Retrieval\n\n\n This paper describes our retrieval system participated in the Patent Retrieval Task at the Fourth NTCIR Workshop. The main task was an associative patent retrieval task, in which a p...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [90, 99, 1.0], [175, 184, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...mage Retrieval by Hypertext Links\n\n\n This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, th...", "weights": [[8, 17, 1.0], [72, 81, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.2": {"snippet": "...ting retrieval models and accommodate systematic development of new retrieval models. As an example of using the framework to model non-traditional retrieval problems, we derive retrieval models for s...", "weights": [[8, 17, 1.0], [71, 80, 1.0], [151, 160, 1.0], [161, 169, 1.0], [181, 190, 1.0]]}, "1983.sigirconf_conference-83.26": {"snippet": "...tion retrieval components are currently incorporated in several types of information systems, including bibliographic retrieval systems, data base management systems and question-answering systems.Som...", "weights": [[8, 17, 1.0], [88, 95, 1.0], [121, 130, 1.0], [131, 138, 1.0], [161, 168, 1.0], [192, 199, 1.0]]}, "2013.sigirconf_conference-2013.118": {"snippet": "...tion retrieval systems. Traditional methods of collecting relevance judgments are based on collecting binary or graded nominal judgments, but such judgments are limited by factors such as inter-assess...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2003.sigirconf_conference-2003.4": {"snippet": "...opic retrieval\n\n\n ABSTRACTWe present a non-traditional retrieval problem we call subtopic retrieval. The subtopic retrieval problem is concerned with finding documents that cover many different subtop...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [93, 102, 1.0], [117, 126, 1.0]]}, "2014.sigirconf_workshop-2014medir.6": {"snippet": "...ical retrieval task is cognitively demanding. Previous research has highlighted the challenges and issues information retrieval systems are faced with when determining the relevance of documents in th...", "weights": [[8, 17, 1.0], [121, 130, 1.0], [131, 138, 1.0]]}, "2005.ntcir_workshop-2005.25": {"snippet": "...tent Retrieval Task\n\n\n This paper describes our system participated in the Document and Passage Retrieval Subtasks at the NTCIR-5 Patent Retrieval Task. The purpose of these subtasks was the invalidit...", "weights": [[8, 17, 1.0], [99, 108, 1.0], [140, 149, 1.0]]}, "2005.sigirconf_conference-2005.9": {"snippet": "...tion retrieval\n\n\n ABSTRACTInformation retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information need. The retri...", "weights": [[8, 17, 1.0], [41, 50, 1.0], [152, 161, 1.0], [198, 207, 1.0]]}, "1989.tois_journal-ir0volumeA7A3.1": {"snippet": "...s of Retrieval System Performance\n\n\n Recall and precision are often used to evaluate the effectiveness of information retrieval systems. They are easy to define if there is a single query and if the r...", "weights": [[8, 17, 1.0], [121, 130, 1.0], [131, 138, 1.0], [202, 211, 1.0]]}, "2006.cikm_conference-2006.124": {"snippet": "...ased retrieval model with limited relevance judgments for document re-ranking. Probabilities of the document-context based retrieval model are estimated from the top N (=20) documents in the initial r...", "weights": [[8, 17, 1.0], [126, 135, 1.0], [202, 211, 1.0]]}, "1992.tois_journal-ir0volumeA10A2.0": {"snippet": "...tion retrieval systems, We report on an analysis of lexlcal ambiguity in information retrieval test collections and on experiments to determine the utility of word meanings for separating relevant fro...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [88, 97, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2011.cikm_workshop-2011pikm.12": {"snippet": "...tion retrieval testcollections\n\n\n ABSTRACTWe consider the problem of optimally allocating limited resources to construct relevance judgements for a test collection that facilities reliable evaluation ...", "weights": [[8, 17, 1.0]]}, "2010.cikm_conference-2010.167": {"snippet": "...text retrieval systems are not very successful in mathematics retrieval. The lack of a definition for similarity between mathematical expressions, and the inadequacy of searching for exact matches onl...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [65, 74, 1.0]]}, "1991.tois_journal-ir0volumeA9A3.3": {"snippet": "...line retrieval systems experience many difficulties, particularly with search tactics, User studies have indicated that searchers use vocabulary incorrectly and do not take full advantage of iteration...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2018.cikm_conference-2018.22": {"snippet": "...call Retrieval: Less is More\n\n\n ABSTRACTHigh-recall retrieval -finding all or nearly all relevant documents -is critical to applications such as electronic discovery, systematic review, and the constr...", "weights": [[8, 17, 1.0], [55, 64, 1.0]]}, "1981.sigirconf_conference-81.21": {"snippet": "...or a Retrieval Data-Base with Semantic Network of Concepts\n\n\n AbstractThis paper describes a development and implementation of an expert/consultation system for a retrieval data-base, that interfaces ...", "weights": [[8, 17, 1.0], [166, 175, 1.0]]}, "2004.sigirconf_conference-2004.132": {"snippet": "...tion retrieval (GIR): searching where and what\n\n\n EXTENDED ABSTRACTInformation retrieval systems in operation today for applications ranging from Digital Libraries to Web Search make very little use o...", "weights": [[8, 17, 1.0], [82, 91, 1.0], [92, 99, 1.0]]}, "2006.sigirconf_conference-2006.80": {"snippet": "...ting retrieval systems using a limited number of relevance judgments. Recent work has demonstrated that one can accurately estimate average precision via a judged pool corresponding to a relatively sm...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2021.naacl-main.428": {"snippet": "...tion systems 050 (Jiang et al., 2019). 051 In many scenarios, we need to distinguish dif-052 ferent keyword-document (query-document) rele-053 vance levels. For instance, in our recommendation 054 sys...", "weights": [[8, 15, 1.0]]}, "1980.sigirconf_conference-80.15": {"snippet": "Problems in the Simulation of Bibliographic Retrieval Systems", "weights": [[0, 8, 1.0], [44, 53, 1.0], [54, 61, 1.0]]}, "2010.ntcir_workshop-2010evia.5": {"snippet": "...king Retrieval Systems without Relevance Assessments: Revisited\n\n\n We reexamine the problem of ranking retrieval systems without relevance assessments in the context of collaborative evaluation forums...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [106, 115, 1.0], [116, 123, 1.0]]}, "1983.ipm_journal-ir0volumeA19A4.4": {"snippet": "...s in retrieval systems", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "T87-1043": {"snippet": "Limits on the human sentence generator\n\n\n The problem of language generation is the problem of translating communicative intent into linguistic form. As such, it implicates the entire richness and com...", "weights": []}, "1971.sigirconf_conference-71.6": {"snippet": "...ment Retrieval: Hebrew Legal Texts\n\n\n ABSTRACTA full text retrieval system was designed for the responsa literature, which is a large corpus of Hebrew legal cases. The unique problems of the data base...", "weights": [[8, 17, 1.0], [61, 70, 1.0], [178, 186, 1.0]]}}}
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Batch RLSI and online RLSI are as effective as existing topic modeling techniques and can scale...", "weights": [[8, 14, 1.0], [54, 60, 1.0], [123, 129, 1.0]]}, "1980.tc-1.4": {"snippet": "... and online\n\n\n Information can be retrieved by direct interrogation of a remote computer by means of a keyboard terminal and a telephone. The advantages of such an online system are fast access to lar...", "weights": [[8, 14, 1.0], [167, 173, 1.0]]}, "2015.ipm_journal-ir0volumeA51A4.5": {"snippet": "...ase, online news sources) and experimental conditions (i.e., the type of media used to present online content).", "weights": [[8, 14, 1.0], [98, 104, 1.0]]}, "P19-3034": {"snippet": "... and quizzes for every target word, and automatically linking named entities to corresponding Wikipedia information. We present a prototype vocabulary learning system, Linggle Booster, that applies th...", "weights": [[8, 15, 1.0]]}, "2016.sigirconf_conference-2016.218": {"snippet": "...mize online metrics without subjecting users to an online learning algorithm? This tutorial summarizes and unifies the emerging body of methods on counterfactual evaluation and learning. These counter...", "weights": [[8, 14, 1.0], [54, 60, 1.0]]}, "2009.cikm_workshop-2009swsm.9": {"snippet": "...s in online social networks\n\n\n ABSTRACTToday, online social networks in the World Wide Web become increasingly interactive and networked. Web 2.0 technologies provide a multitude of platforms, such as...", "weights": [[8, 14, 1.0], [49, 55, 1.0]]}, "2013.wwwconf_conference-2013c.152": {"snippet": "...from quizzes using intelligent learning companions\n\n\n ABSTRACTIt is widely recognised that engaging games can have a profound impact on learning. Integrating a conversational Artificial Intelligence (...", "weights": [[8, 15, 1.0]]}, "2014.wwwconf_conference-2014.46": {"snippet": "...open online programming courses\n\n\n ABSTRACTMassive open online courses (MOOCs), one of the latest internet revolutions have engendered hope that constant iterative improvement and economies of scale m...", "weights": [[8, 14, 1.0], [59, 65, 1.0]]}, "2020.lrec-1.852": {"snippet": "...ling Quizzes Designed for Evaluation of Machine Reading Comprehension\n\n\n This paper describes our developing dataset of Japanese slot filling quizzes designed for evaluation of machine reading compreh...", "weights": [[8, 15, 1.0], [145, 152, 1.0]]}, "Y00-1007": {"snippet": "...s in quizzes, though not often in daily conversations. 2 I assume that, despite their restriction in the range of registers, WLWQs are also constrained by the knowledge of human language. To my knowle...", "weights": [[8, 15, 1.0]]}, "2010.cikm_conference-2010.189": {"snippet": "Online learning for recency search ranking using real-time user feedback\n\n\n ABSTRACTTraditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevan...", "weights": [[0, 6, 1.0]]}, "2006.jasis_journal-ir0volumeA57A14.1": {"snippet": "...ring online information seeking context, Part 2: Findings and discussion", "weights": [[8, 14, 1.0]]}, "1990.jasis_journal-ir0volumeA41A8.1": {"snippet": "...e of online searching: An analysis of Online and Online Review", "weights": [[8, 14, 1.0], [41, 47, 1.0], [52, 58, 1.0]]}, "P07-2030": {"snippet": "...rate Quizzes for Interactive Information Presentation\n\n\n This paper proposes the idea of ranking definitions of a person (a set of biographical facts) to automatically generate \"Who is this?\" quizzes....", "weights": [[8, 15, 1.0], [195, 202, 1.0]]}, "2014.amta-workshop.4": {"snippet": "...{MT} online learning in computer assisted translation\n\n\n In this paper we propose a cascading framework for optimizing online learning in machine translation for a computer assisted translation scenar...", "weights": [[8, 14, 1.0], [122, 128, 1.0]]}, "2013.ecir_conference-2013.107": {"snippet": "...rast online evaluation with standard offline evaluation, and provide an overview of online approaches.Keywords: Interleaving, Clicks, Search Engine, Online Evaluation.\nMotivationGiven the growing brea...", "weights": [[8, 14, 1.0], [87, 93, 1.0], [152, 158, 1.0]]}, "1998.sigirconf_conference-98.19": {"snippet": "...Many online documents are similarly structured, though not identically structured. We study the problem of discovering \"typical\" structures of a collection of such documents, where the user specifies ...", "weights": [[8, 14, 1.0]]}, "2010.cikm_conference-2010.7": {"snippet": "Online annotation of text streams with structured entities", "weights": [[0, 6, 1.0]]}, "P15-1008": {"snippet": "... for Online Course Discussion Forums\n\n\n Massive open online courses (MOOCs) are redefining the education system and transcending boundaries posed by traditional courses. With the increase in popularit...", "weights": [[8, 14, 1.0], [56, 62, 1.0]]}, "W14-1305": {"snippet": "...s in Online Debates\n\n\n This work suggests a fine-grained mining of contentious documents, specifically online debates, towards a summarization of contention issues. We propose a Joint Topic Viewpoint ...", "weights": [[8, 14, 1.0], [106, 112, 1.0]]}, "2021.ranlp-srw.15": {"snippet": "...ment quizzes and games. Although the research on the automatic or semi-automatic generation of multiple-choice test items has been conducted since the beginning of this millennium, most approaches foc...", "weights": [[8, 15, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A2.9": {"snippet": "...p of online short texts, are becoming increasingly prevalent with the development of online communications. These short texts are characterized by their co-occurrences with usually lengthier normal do...", "weights": [[8, 14, 1.0], [88, 94, 1.0]]}, "2021.ranlp-srw.28": {"snippet": "... for Quizzes and Answer-Aware Question Generators\n\n\n In education, quiz questions have become an important tool for assessing the knowledge of students. Yet, manually preparing such questions is a ted...", "weights": [[8, 15, 1.0]]}, "2013.cikm_workshop-2013ueo.5": {"snippet": "...hms, online controlled experiments are now utilized to make data-driven decisions at many other companies. While the theory of a controlled experiment is simple, running online controlled experiments ...", "weights": [[8, 14, 1.0], [173, 179, 1.0]]}, "C10-2100": {"snippet": "...s in online debates, i.e., support or oppose the main topic of an online debate, by exploiting local information in their remarks within the debate. An online debate is a forum where each user post an...", "weights": [[8, 14, 1.0], [69, 75, 1.0], [155, 161, 1.0]]}, "2001.wwwconf_conference-2001p.63": {"snippet": "Online Learning in Hong Kong: A Preliminary Comparison of the Lecture and Online Versions of a Computing Fundamentals Course\n\n\n This case study compares students' learning experience and outcomes in t...", "weights": [[0, 6, 1.0], [74, 80, 1.0]]}, "2011.ir_journal-ir0volumeA14A6.0": {"snippet": "Online community search using conversational structures", "weights": [[0, 6, 1.0]]}, "C10-1083": {"snippet": "...ered Online Opinions\n\n\n We study the problem of integrating scattered online opinions. For this purpose, we propose to exploit structured ontology to obtain well-formed relevant aspects to a topic and...", "weights": [[8, 14, 1.0], [73, 79, 1.0]]}, "2017.ictir_workshop-2017learner.4": {"snippet": "... for Online Evaluation of Rankers\n\n\n In online learning to rank we are faced with a tradeoff between exploring new, potentially superior rankers, and exploiting our preexisting knowledge of what ranke...", "weights": [[8, 14, 1.0], [43, 49, 1.0]]}, "W18-3722": {"snippet": "...lank Quizzes\n\n\n In this paper we formalize the problem automatic fill-in-the-blank question generation using two standard NLP machine learning schemes, proposing concrete deep learning models for each...", "weights": [[8, 15, 1.0]]}, "2012.wwwconf_workshop-2012lile.10": {"snippet": "...n of Quizzes and Learning Artifacts from Linked Data\n\n\n In this position paper, we illustrate how Linked Data can be effectively used in a Technology-enhanced Learning scenario. Specifically, we aim a...", "weights": [[8, 15, 1.0]]}, "2014.wwwconf_conference-2014c.287": {"snippet": "...ent (quizzes, problems, interactive examples). The first part of the talk will review our exploration of a more traditional knowledge-based personalization approach [2; 3] and the second part will foc...", "weights": [[8, 15, 1.0]]}, "D08-1043": {"snippet": "...arge Online {Q}{\\&}{A} Collections with Compact Translation Models\n\n\n Lexical gaps between queries and questions (documents) have been a major issue in question retrieval on large online question and ...", "weights": [[8, 14, 1.0], [183, 189, 1.0]]}, "2011.cikm_conference-2011.60": {"snippet": "...ning online search results\n\n\n ABSTRACTWe present a new image search and ranking algorithm for retrieving unannotated images by collaboratively mining online search results which consist of online imag...", "weights": [[8, 14, 1.0], [153, 159, 1.0], [192, 198, 1.0]]}}}
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Seasoned researchers ...", "weights": [[0, 10, 1.0], [104, 114, 1.0]]}, "2001.sigirconf_conference-2001.10": {"snippet": "Evaluation by Highly Relevant Documents\n\n\n ABSTRACTGiven the size of the w eb,the search engine industry has argued that engines should be evaluated by their ability t o retrieve highly relevant pages...", "weights": [[0, 10, 1.0]]}, "2014.sigirconf_conference-2014.5": {"snippet": "Temporal feedback for tweet search with non-parametric density estimation\n\n\n ABSTRACTThis paper investigates the temporal cluster hypothesis: in search tasks where time plays an important role, do rel...", "weights": []}, "Y04-1032": {"snippet": "Three {E}nglish Learner Assistance Systems Using Automatic Paraphrasing Techniques\n\n\n We developed three systems based on automatic paraphrasing techniques to help English learners and English-languag...", "weights": []}, "2022.acl-short.92": {"snippet": "{C}o{DA}21: Evaluating Language Understanding Capabilities of {NLP} Models With Context-Definition Alignment\n\n\n Pretrained language models (PLMs) have achieved superhuman performance on many benchmark...", "weights": []}, "J06-1006": {"snippet": "Introducing Speech and Language Processing, by John Coleman\n\n\n a group of multidisciplinary researchers convened at the Symposium on Next Generation Automatic Speech Recognition (ASR) to consider new ...", "weights": []}, "W18-3721": {"snippet": "...ing. Evaluation results show that heterogeneous features perform better than each individual type of features.", "weights": [[8, 18, 1.0]]}, "L16-1302": {"snippet": "Evaluation Set for {S}lovak News Information Retrieval\n\n\n This work proposes an information retrieval evaluation set for the Slovak language. A set of 80 queries written in the natural language is giv...", "weights": [[0, 10, 1.0], [102, 112, 1.0]]}, "2007.ecir_conference-2007.26": {"snippet": "...eval Evaluation Methodology for Incomplete Relevance Assessments", "weights": [[8, 18, 1.0]]}, "2005.sigirconf_conference-2005.52": {"snippet": "Evaluation of resources for question answering evaluation\n\n\n ABSTRACTControlled and reproducible laboratory experiments, enabled by reusable test collections, represent a well-established methodology ...", "weights": [[0, 10, 1.0], [47, 57, 1.0]]}, "1998.sigirconf_conference-98.40": {"snippet": "...with evaluation of interactive IR systems. The RR measure describes the degree of agreement between the types of relevance applied in evaluation of information retrieval (IR) systems in a non-binary a...", "weights": [[8, 18, 1.0], [137, 147, 1.0]]}, "1994.jasis_journal-ir0volumeA45A3.7": {"snippet": "...eval Evaluation: Perspectives from Medicine", "weights": [[8, 18, 1.0]]}, "2021.cl-1.8": {"snippet": "Python for Linguists\n\n\n Teaching programming skills is a hard task. It is even harder if one targets an audience with no or little mathematical background. Although there are books on programming that...", "weights": []}, "2000.clef_workshop-2000.6": {"snippet": "Evaluating Interactive Cross-Language Information Retrieval: Document Selection", "weights": []}, "2005.mir_workshop-2005.30": {"snippet": "Evaluation strategies for image understanding and retrieval", "weights": [[0, 10, 1.0]]}, "2012.sigirconf_conference-2012.24": {"snippet": "User evaluation of query quality\n\n\n ABSTRACTAlthough a great deal of research has been conducted about automatic techniques for determining query quality, there have been relatively few studies about ...", "weights": [[5, 15, 1.0]]}, "2018.cikm_conference-2018.205": {"snippet": "...only evaluation measures ignore these aspects. We used synthetic and real data to compare our proposed framework, named MM, to the understandability-biased information evaluation (UBIRE), an existing ...", "weights": [[8, 18, 1.0], [171, 181, 1.0]]}, "2015.sigirconf_conference-2015.110": {"snippet": "... two evaluation metrics: Distributional Overlap (DO), which compares the clustering of scores of relevant and non-relevant documents, and Histogram Slope Analysis (HSA), which examines the log of the ...", "weights": [[8, 18, 1.0]]}, "2020.lrec-1.815": {"snippet": "{FAB}: The {F}rench Absolute Beginner Corpus for Pronunciation Training\n\n\n We introduce the French Absolute Beginner (FAB) speech corpus. The corpus is intended for the development and study of Comput...", "weights": []}, "2017.cikm_conference-2017.301": {"snippet": "...e. e evaluation of bias has recently been made possible through the use of retrievability, a quanti able measure of bias. While evaluating bias is relatively novel, the evaluation of performance has b...", "weights": [[8, 18, 1.0], [172, 182, 1.0]]}, "F12-1103": {"snippet": "Perception de la Langue fran{\\c{c}}aise Parl{\\'e}e Compl{\\'e}t{\\'e}e ({LPC}) et effet d{'}expertise chez les normo-entendants ({F}rench Cued Speech perception and expertise effect in hearing people) [...", "weights": []}, "2013.sigirconf_conference-2013.43": {"snippet": "...ased evaluation can then take this user profile information into account to better model utility to the space of users.In this work, we propose an evaluation framework that not only can consider impli...", "weights": [[8, 18, 1.0], [150, 160, 1.0]]}, "1992.tc-1.11": {"snippet": "Evaluation of software\n\n\n Two aspects of the selection and evaluation of software packages are reviewed: the strategy for evaluation and selection, and criteria that might be applied in selection. 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We analyze the density of relevance in documents, and show that in sparsely relevant documents focused retri...", "weights": [[8, 18, 1.0]]}, "2005.sigirconf_conference-2005.59": {"snippet": "Modeling task-genre relationships for IR in the workplace\n\n\n ABSTRACTContext influences the search process, but to date research has not definitively identified which aspects of context are the most i...", "weights": []}, "2010.tois_journal-ir0volumeA28A2.4": {"snippet": "...cts' evaluation ratings and resulted in subjects using a greater range of evaluation scores.", "weights": [[8, 18, 1.0], [77, 87, 1.0]]}, "2012.wsdm_conference-2012.41": {"snippet": "...stem evaluation using nugget-based test collections\n\n\n ABSTRACTThe development of information retrieval systems such as search engines relies on good test collections, including assessments of retriev...", "weights": [[8, 18, 1.0]]}, "2016.ictir_conference-2016.16": {"snippet": "...sed \"Evaluation as a Service\"\n\n\n ABSTRACT \"Evaluation as a service\" (EaaS) refers to a family of related evaluation methodologies that enables community-wide evaluations and the construction of test c...", "weights": [[8, 18, 1.0], [46, 56, 1.0], [108, 118, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...l IR evaluation paradigm a list of query topics along with their relevance judgments are given. The main limitation of this kind of evaluation paradigm is that it focuses almost exclusively on a small...", "weights": [[8, 18, 1.0], [135, 145, 1.0]]}, "2000.sigirconf_conference-2000.7": {"snippet": "IR evaluation methods for retrieving highly relevant documents\n\n\n AbstractThis paper proposes evaluation methods based on the use of non-dichotomous relevance judgements in IR experiments. It is argue...", "weights": [[3, 13, 1.0], [94, 104, 1.0]]}, "C18-1294": {"snippet": "Investigating Productive and Receptive Knowledge: A Profile for Second Language Learning\n\n\n The literature frequently addresses the differences in receptive and productive vocabulary, but grammar is o...", "weights": []}, "2011.wwwconf_conference-2011c.99": {"snippet": "Visual query system for analyzing social semantic web\n\n\n ABSTRACTThe social web is becoming increasingly popular and important, because it creates the collective intelligence, which can produce more v...", "weights": []}, "2007.ntcir_workshop-2007evia.9": {"snippet": "...eval Evaluation with Graded Relevance\n\n\n Large-scale information retrieval evaluation efforts such as TREC and NTCIR have tended to adhere to binary-relevance evaluation metrics, even when graded rele...", "weights": [[8, 18, 1.0], [78, 88, 1.0], [162, 172, 1.0]]}, "2017.ictir_conference-2017.13": {"snippet": "... the evaluation of both relevance and credibility (and applying different criteria introduces bias to the evaluation); (II) many more and richer measures exist for assessing relevance effectiveness th...", "weights": [[8, 18, 1.0], [109, 119, 1.0]]}, "L16-1306": {"snippet": "...ding Evaluation Datasets for Consumer-Oriented Information Retrieval\n\n\n Common people often experience difficulties in accessing relevant, correct, accurate and understandable health information onlin...", "weights": [[8, 18, 1.0]]}, "2013.ecir_conference-2013.107": {"snippet": "...line evaluation with standard offline evaluation, and provide an overview of online approaches.Keywords: Interleaving, Clicks, Search Engine, Online Evaluation.\nMotivationGiven the growing breadth of ...", "weights": [[8, 18, 1.0], [41, 51, 1.0], [152, 162, 1.0]]}, "2010.ntcir_workshop-2010.24": {"snippet": "...e an evaluation experiment on GeoTemporal Document Retrieval created for the GeoTime evaluation task of NTCIR 2010. GeoTemporal Retrieval aims at to improve retrieval results using Geographic and Temp...", "weights": [[8, 18, 1.0], [88, 98, 1.0]]}, "2007.cikm_conference-2007.96": {"snippet": "...atic evaluation of retrieval systems using document similarities\n\n\n ABSTRACTTaking advantage of the well-known cluster hypothesis that \"closely associated documents tend to be relevant to the same req...", "weights": [[8, 18, 1.0]]}, "L18-1594": {"snippet": "...ding Evaluation Datasets for Cultural Microblog Retrieval\n\n\n ECLEF Microblog Cultural Contextualization is an evaluation challenge aiming at providing the research community with datasets to gather, o...", "weights": [[8, 18, 1.0], [113, 123, 1.0]]}, "2003.cikm_conference-2003.67": {"snippet": "...stem evaluation\n\n\n ABSTRACTWe present a unified model which, given the ranked lists of documents returned by multiple retrieval systems in response to a given query, simultaneously solves the problems...", "weights": [[8, 18, 1.0]]}, "2015.sigirconf_conference-2015.59": {"snippet": "...eval Evaluation", "weights": [[8, 18, 1.0]]}, "2018.sigirconf_conference-2018.186": {"snippet": "...ased evaluation represents the standard of evaluation for information retrieval systems. Legal IR, more specifically case law retrieval, has no such standard test collection for evaluation. In this pa...", "weights": [[8, 18, 1.0], [46, 56, 1.0], [181, 191, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.11": {"snippet": "Exploring topic structure: coherence, diversity and relatedness\n\n\n AbstractThe use of topical information has long been studied in the context of information retrieval. For example, grouping search re...", "weights": []}, "2013.sigirconf_conference-2013.82": {"snippet": "...ical evaluation of the effort users must exert to judge the relevance of document, investigating the effect of relevance level and document size. Results suggest that \"relevant\" documents require more...", "weights": [[8, 18, 1.0]]}, "L18-1644": {"snippet": "{R}evita: a Language-learning Platform at the Intersection of {ITS} and {CALL}\n\n\n This paper presents Revita, a Web-based platform for language learning-beyond the beginner level. We anchor the presen...", "weights": []}, "O17-1008": {"snippet": "\u624b\u6a5f\u5e73\u53f0 {APP} \u4e4b\u56db\u7e23\u5ba2\u8a9e\u8f38\u5165\u6cd5\u7684\u7814\u767c (Research and Implementation of {S}ixian {H}akka {P}inyin Input Method for Mobile Cell {APP}) [In {C}hinese]\n\n\n The proposal scheme called Hakka pinyin input method is based on ...", "weights": []}, "2001.ntcir_workshop-2001.52": {"snippet": "...sive evaluation, and that evaluation has often used test collections. In 1992 a new test collection was built at the National Institute of Standards and Technology (NIST) as part of the TIPSTER projec...", "weights": [[8, 18, 1.0], [29, 39, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.23": {"snippet": "... and Evaluation\n\n\n AbstractProgress and innovation are driven by experiments, but experimentation is useless without an objective evaluation measure that allow researchers to detect the improvements a...", "weights": [[8, 18, 1.0], [133, 143, 1.0]]}, "W09-4408": {"snippet": "...g an evaluation scheme for definition extraction (DE) in terminology. The premise is that not just any information is relevant to defining a given concept in a given expert domain. Therefore, evaluati...", "weights": [[8, 18, 1.0], [195, 205, 1.0]]}, "2006.sigirconf_conference-2006.36": {"snippet": "...eval evaluation\n\n\n ABSTRACTAccurate estimation of information retrieval evaluation metrics such as average precision require large sets of relevance judgments. Building sets large enough for evaluatio...", "weights": [[8, 18, 1.0], [75, 85, 1.0], [194, 204, 1.0]]}, "2006.sigirconf_conference-2006.35": {"snippet": "Evaluation in (XML) information retrieval: expected precision-recall with user modelling (EPRUM)\n\n\n ABSTRACTStandard Information Retrieval (IR) metrics assume a simple model where documents are unders...", "weights": [[0, 10, 1.0]]}, "2008.ir_journal-ir0volumeA11A5.3": {"snippet": "... for evaluation with incomplete relevance assessments", "weights": [[8, 18, 1.0]]}, "2004.ntcir_workshop-2004.19": {"snippet": "...tems evaluation remains based on the use of precision and recall (and related measures), thus hindering IR development and evaluation. We recently questioned these assumptions, and proposed a new meas...", "weights": [[8, 18, 1.0], [126, 136, 1.0]]}, "2011.wwwconf_conference-2011c.152": {"snippet": "...odel evaluation performed between our contextual model, BM25, and language model shows that the model can be effective for contextual opinion retrieval such as faceted opinion retrieval.", "weights": [[8, 18, 1.0]]}, "2020.lrec-1.461": {"snippet": "...tion Evaluation\n\n\n Despite increasing efforts to improve evaluation of machine translation (MT) by going beyond the sentence level to the document level, the definition of what exactly constitutes a \"...", "weights": [[8, 18, 1.0], [60, 70, 1.0]]}, "1999.sigirjournals_journal-ir0volumeA33A1.5": {"snippet": "Evaluation of Web Document Retrieval: A SIGIR'99 Workshop", "weights": [[0, 10, 1.0]]}}}
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23.712143683789627, "doc_id": "2020.sigirconf_conference-2020.367"}, {"rank": 5, "score": 23.555332641061124, "doc_id": "2018.sigirconf_conference-2018.238"}, {"rank": 6, "score": 22.611708028951423, "doc_id": "H05-1020"}, {"rank": 7, "score": 22.428790629432452, "doc_id": "2018.cikm_conference-2018.68"}, {"rank": 8, "score": 22.33530418163773, "doc_id": "2008.sigirconf_conference-2008.164"}, {"rank": 9, "score": 22.120285428301845, "doc_id": "2009.ipm_journal-ir0volumeA45A3.2"}, {"rank": 10, "score": 21.689946368944188, "doc_id": "2016.clef_conference-2016w.106"}]}], "docs": {"2008.sigirconf_conference-2008.37": {"snippet": "Learning to rank with ties\n\n\n ABSTRACTDesigning effective ranking functions is a core problem for information retrieval and Web search since the ranking functions directly impact the relevance of the ...", "weights": [[0, 8, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "2011.tist_journal-ir0volumeA2A2.1": {"snippet": "...tive learning in multimedia annotation and retrieval: A survey\n\n\n Active learning is a machine learning technique that selects the most informative samples for labeling and uses them as training data....", "weights": [[8, 16, 1.0], [46, 55, 1.0], [76, 84, 1.0], [90, 97, 1.0], [98, 106, 1.0]]}, "2013.ipm_journal-ir0volumeA49A5.5": {"snippet": "...uter information retrieval\n\n\n EditorialIntroduction to special issue on human-computer information retrieval Contemporary search engines are optimized for look-up scenarios where the information targe...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [90, 101, 1.0], [102, 111, 1.0], [186, 197, 1.0]]}, "2018.sigirconf_conference-2018.150": {"snippet": "...t of machine learning technologies. Recently, within the information retrieval field, a number of neural ranking frameworks have been proposed to address the ad-hoc search. These models usually need a...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [60, 71, 1.0], [72, 81, 1.0]]}, "2011.sigirconf_conference-2011.229": {"snippet": "...gual information retrieval\n\n\n AbstractA general characteristic of Information Retrieval (IR) and Multilingual IR (MIR)  systems is that if the same query was submitted by different users, the system w...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [69, 80, 1.0], [81, 90, 1.0]]}, "2009.cikm_conference-2009.247": {"snippet": "...lick information no better than a linear function of the field attributes. We also find query click information to be the single most important field for retrieval. In response, we develop a machine l...", "weights": [[8, 19, 1.0], [103, 114, 1.0], [157, 166, 1.0], [194, 201, 1.0], [202, 210, 1.0]]}, "2018.desires_conference-2018.13": {"snippet": "Retrieval and Richness when Querying by Document\n\n\n A single relevant document can be viewed as a long query for ad hoc retrieval, or a tiny training set for supervised learning. We tested techniques ...", "weights": [[0, 9, 1.0], [120, 129, 1.0], [169, 177, 1.0]]}, "2016.clef_conference-2016w.106": {"snippet": "...ited machine learning techniques to rank query terms and assign an appropriate weight to each one before applying a probabilistic information retrieval model (BM15). Thereafter, only the top-k terms a...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "1998.sigirconf_conference-98.56": {"snippet": "... the information corpus available for access symbolizes the wealth and benefits of this medium. At the same time this immense pool of information has created an information overflow which requires use...", "weights": [[8, 19, 1.0], [137, 148, 1.0], [164, 175, 1.0]]}, "2017.sigirconf_conference-2017.231": {"snippet": "... and retrieval frameworks that are able to e ciently answer most queries. However, the current literature generally focuses on improving the mean or median query processing time in a given system. In ...", "weights": [[8, 17, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A3.0": {"snippet": "...ng \u00b7 Information retrieval \u00b7 DB-IR integration \u00b7 Odysseus\nDB-IR integrationDatabases (DB) and information retrieval (IR) have been parallel universes. However, many recent applications such as custome...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "2020.sigirconf_conference-2020.5": {"snippet": "...Deep Learning Works for Information Retrieval\n\n\n Information retrieval (IR) is the science of search, the search of user query relevant pieces of information from a collection of unstructured resource...", "weights": [[8, 16, 1.0], [27, 38, 1.0], [39, 48, 1.0], [52, 63, 1.0], [64, 73, 1.0], [149, 160, 1.0]]}, "2015.wsdm_conference-2015.58": {"snippet": "... the information needs of end users continue being refined. One of the emerging trends is using vertical search intent. For example, a user may want to find a restaurant near her current location; ano...", "weights": [[8, 19, 1.0]]}, "2020.sigirconf_conference-2020.367": {"snippet": "...ment Learning for Information Retrieval: Fundamentals and Advances\n\n\n Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users' information needs ...", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0], [73, 84, 1.0], [85, 94, 1.0], [185, 196, 1.0]]}, "2017.ictir_conference-2017.57": {"snippet": "...s of information retrieval, such as online learning to rank, interactive information retrieval or dynamic information retrieval. ese Permission to make digital or hard copies of part or all of this wo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 54, 1.0], [76, 87, 1.0], [88, 97, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "2018.sigirconf_conference-2018.219": {"snippet": "...sly, machine learning methods have been exploited to address the problem, which learns a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning h...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [150, 158, 1.0], [193, 201, 1.0]]}, "2012.sigirconf_conference-2012.171": {"snippet": "Learning to select a time-aware retrieval model\n\n\n ABSTRACTTime-aware retrieval models exploit one of two time dimensions, namely, (a) publication time or (b) content time (temporal expressions mentio...", "weights": [[0, 8, 1.0], [32, 41, 1.0], [70, 79, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "...s in machine learning, and will present recent developments in machine learning and its application to information retrieval. Each season is self-contained.\nSession 1: Core Learning Technologies for I...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [66, 73, 1.0], [74, 82, 1.0], [106, 117, 1.0], [118, 127, 1.0], [176, 184, 1.0], [202, 213, 1.0]]}, "2017.trec_conference-2017.62": {"snippet": "...call retrieval system for assessors to e ciently nd relevant documents within a limited period of time. e system consists of an AutoTAR Continuous Active Learning (CAL) system based on paragraph/docum...", "weights": [[8, 17, 1.0], [157, 165, 1.0]]}, "D09-1068": {"snippet": "...ised machine-learned ranking approach, applying a set of emerging machine learning techniques. Our approach is especially useful for queries that contain multiple types of concepts. Comparing to a maj...", "weights": [[8, 15, 1.0], [69, 76, 1.0], [77, 85, 1.0]]}, "2019.wsdm_conference-2019.113": {"snippet": "...sly, machine learning methods have been exploited to address the problem, which learn a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning ha...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [149, 157, 1.0], [192, 200, 1.0]]}, "N06-1051": {"snippet": "A Machine Learning based Approach to Evaluating Retrieval Systems\n\n\n Test collections are essential to evaluate Information Retrieval (IR) systems. The relevance assessment set has been recognized as ...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [48, 57, 1.0], [112, 123, 1.0], [124, 133, 1.0]]}, "2011.sigirconf_conference-2011.12": {"snippet": "...se a machine-learning framework for simultaneously optimizing freshness and relevance, in which the trade-off is automatically adaptive to query temporal characteristics. We start by illustrating diff...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "....3.3 Information Search and Retrieval -relevance feedback\nGeneral TermsAlgorithms, Performance, Experimentation\nKeywordsRelevance Feedback, Web Search, Evaluation\n1.INTRODUCTIONRelevance feedback is a...", "weights": [[8, 19, 1.0], [31, 40, 1.0]]}, "1999.mtsummit-1.45": {"snippet": "...gual information retrieval, summarization, and translation system. MuST integrates machine translation and other text processing services to enable users to perform cross-language information retrieva...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [86, 93, 1.0], [183, 194, 1.0], [195, 204, 1.0]]}, "2020.sigirconf_conference-2020.375": {"snippet": "... and Retrieval with Neural Networks\n\n\n Relevance in ad-hoc retrieval is a fundamental problem of text understanding. Developing neural network methods for this foundational task of Information Retriev...", "weights": [[8, 17, 1.0], [62, 71, 1.0], [184, 195, 1.0], [196, 205, 1.0]]}, "2008.sigirconf_conference-2008.164": {"snippet": "Learning with support vector machines for query-by-multiple-examples\n\n\n ABSTRACTWe explore an alternative Information Retrieval paradigm called Query-By-Multiple-Examples (QBME) where the information ...", "weights": [[0, 8, 1.0], [106, 117, 1.0], [118, 127, 1.0], [188, 199, 1.0]]}, "2012.sigirconf_conference-2012.5": {"snippet": "...se a machine learning approach to learn the probability that a user may find a follow-up query both useful and relevant, given his initial query. Our approach is based on a machine learning model whic...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [176, 183, 1.0], [184, 192, 1.0]]}, "2011.sigirconf_conference-2011.43": {"snippet": "...n: a machine learning approach\n\n\n ABSTRACTAutomatic query expansion technologies have been proven to be effective in many information retrieval tasks. Most existing approaches are based on the assumpt...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [125, 136, 1.0], [137, 146, 1.0]]}, "2014.cikm_conference-2014.6": {"snippet": "Machine-Assisted Search Preference Evaluation\n\n\n ABSTRACTInformation Retrieval systems are traditionally evaluated using the relevance of web pages to individual queries. Other work on IR evaluation h...", "weights": [[0, 7, 1.0], [69, 78, 1.0]]}, "2020.wsdm_conference-2020.84": {"snippet": "...ting learning to rank models for information retrieval are trained based on explicit or implicit query-document relevance information. In this paper, we study the task of learning a retrieval model ba...", "weights": [[8, 16, 1.0], [36, 47, 1.0], [48, 57, 1.0], [125, 136, 1.0], [174, 182, 1.0], [185, 194, 1.0]]}, "I08-1068": {"snippet": "...that learning these user profiles using snippets surrounding the results for a query gives better performance than learning from entire document collection.", "weights": [[8, 16, 1.0], [118, 126, 1.0]]}, "2009.trec_conference-2009.35": {"snippet": "Machine Learning for Information Retrieval: TREC 2009 Web, Relevance Feedback and Legal Tracks", "weights": [[0, 7, 1.0], [8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0]]}, "2010.cikm_conference-2010.181": {"snippet": "...ized information retrieval\n\n\n ABSTRACTIndividuals often use search engines to return to web pages they have previously visited. This behaviour, called refinding, accounts for about 38% of all queries....", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2017.ictir_conference-2017.59": {"snippet": "...e of Information Retrieval (IR) since it allows to sift out non relevant information and to select a list of items ordered by their estimated relevance to a given query. Documents, information needs, ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [76, 87, 1.0], [184, 195, 1.0]]}, "2007.sigirconf_conference-2007.212": {"snippet": "...tive information retrieval system?\n\n\n ABSTRACTThis research explores the relationship between Information Retrieval (IR) systems' effectiveness and users' performance (accuracy and speed) and their sa...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "2004.trec_conference-2004.8": {"snippet": "...good retrieval function can be discovered by a pure machine learning approach, without using probabilistic theories and knowledge-based techniques. Two machine learning algorithms, Support Vector Mach...", "weights": [[8, 17, 1.0], [55, 62, 1.0], [63, 71, 1.0], [155, 162, 1.0], [163, 171, 1.0], [199, 206, 1.0]]}, "2011.sigirconf_conference-2011.220": {"snippet": "...lick information.Yisong Yue is a postdoctoral researcher in the Machine Learning Department and the Heinz College at Carnegie Mellon University, where he works on machine learning approaches to inform...", "weights": [[8, 19, 1.0], [67, 74, 1.0], [75, 83, 1.0], [166, 173, 1.0], [174, 182, 1.0], [197, 208, 1.0]]}, "2011.wsdm_conference-2011.81": {"snippet": "...eful information, are either totally ignored or just explored very limitedly. In this paper, we propose to explore multiple pairwise relationships between documents in a learning setting to rerank sea...", "weights": [[8, 19, 1.0], [173, 181, 1.0]]}, "2007.sigirconf_conference-2007.39": {"snippet": "... for learning ranking functions for improving relevance of search engines serving diverse streams of user queries. We explore supervised learning methodology from machine learning, and we distinguish ...", "weights": [[8, 16, 1.0], [140, 148, 1.0], [166, 173, 1.0], [174, 182, 1.0]]}, "2010.cikm_conference-2010.189": {"snippet": "...line learning for recency search ranking using real-time user feedback\n\n\n ABSTRACTTraditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance...", "weights": [[8, 16, 1.0], [97, 104, 1.0]]}, "2007.riao_conference-2007.31": {"snippet": "...s of information retrieval systems: a machine learning based solution", "weights": [[8, 19, 1.0], [20, 29, 1.0], [41, 48, 1.0], [49, 57, 1.0]]}, "2015.tist_journal-ir0volumeA7A1.0": {"snippet": "Learning to Rank from Noisy Data\n\n\n Learning to rank, which learns the ranking function from training data, has become an emerging research area in information retrieval and machine learning. Most exi...", "weights": [[0, 8, 1.0], [36, 44, 1.0], [148, 159, 1.0], [160, 169, 1.0], [174, 181, 1.0], [182, 190, 1.0]]}, "2015.clef_conference-2015.34": {"snippet": "...w an information retrieval system works.\nState-of-the-ArtFern\u00e1ndez-Luna et al.  gave an overview of the state-of-the-art for teaching and learning in information retrieval. They discuss various, exist...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [141, 149, 1.0], [153, 164, 1.0], [165, 174, 1.0]]}, "2017.wsdm_conference-2017.103": {"snippet": "...ting Information Retrieval to User Signals via Stochastic Models\n\n\n ABSTRACTTo address the challenge of adapting Information Retrieval (IR) to the constantly evolving user tasks and needs and to adjus...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [116, 127, 1.0], [128, 137, 1.0]]}, "2017.sigirconf_conference-2017.241": {"snippet": "...PTS\u2022 Information systems \u2192 Web searching and information discovery; Information retrieval; Information retrieval query processing; Query representation; Query intent; Query languages; \u2022 Software and i...", "weights": [[8, 19, 1.0], [48, 59, 1.0], [71, 82, 1.0], [83, 92, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2009.ipm_journal-ir0volumeA45A3.2": {"snippet": "... new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSRANK, aims to use the advantages of both the traditional Information Retrieval...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [76, 85, 1.0], [182, 193, 1.0], [194, 203, 1.0]]}, "2012.wsdm_conference-2012.84": {"snippet": "Machine learning for query-document matching in search", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2012.wsdm_conference-2012.47": {"snippet": "Learning to rank with multi-aspect relevance for vertical search\n\n\n ABSTRACTMany vertical search tasks such as local search focus on specific domains. The meaning of relevance in these verticals is do...", "weights": [[0, 8, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.23": {"snippet": "... for information retrieval\n\n\n AbstractMany machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieva...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 53, 1.0], [54, 62, 1.0], [183, 194, 1.0], [195, 204, 1.0]]}, "2018.sigirconf_workshop-2018profs.3": {"snippet": "...call information retrieval is crucial to tasks such as electronic discovery and systematic review. Continuous Active Learning (CAL) is a technique where a human reviewer works in loop with a machine l...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [120, 128, 1.0], [194, 201, 1.0], [202, 210, 1.0]]}, "2005.sigirconf_conference-2005.9": {"snippet": "... hoc information retrieval\n\n\n ABSTRACTInformation retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information nee...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 62, 1.0], [164, 173, 1.0], [188, 199, 1.0]]}, "2016.sigirconf_workshop-2016sal.11": {"snippet": "Retrieval Techniques for Contextual Learning\n\n\n Following constructivist models of contextual learning, knowledge acquisition goes beyond mere absorption of isolated facts, and, instead is enabled, st...", "weights": [[0, 9, 1.0], [36, 44, 1.0], [94, 102, 1.0]]}, "H93-1077": {"snippet": "Machine Learning Techniques for Document Filtering\n\n\n BoozoAllen & Hamilton's Advanced Decision Systems Division is conducting a program of research to investigate machine learning techniques that can...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [164, 171, 1.0], [172, 180, 1.0]]}, "2012.wsdm_conference-2012.50": {"snippet": "...ical/machine-learned model. This is challenging since the retrieval has to be fast (few milliseconds) to load the page quickly. Fast retrieval is well studied in the information retrieval (IR) literat...", "weights": [[8, 15, 1.0], [61, 70, 1.0], [136, 145, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2018.sigirconf_conference-2018.238": {"snippet": "...p on Learning from Limited or Noisy Data for Information Retrieval\n\n\n ABSTRACTIn recent years, machine learning approaches, and in particular deep neural networks, have yielded significant improvement...", "weights": [[8, 16, 1.0], [48, 59, 1.0], [60, 69, 1.0], [98, 105, 1.0], [106, 114, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2018.cikm_conference-2018.22": {"snippet": "... for information retrieval tasks. The effectiveness of current methods for high-recall information retrieval is limited by their reliance on human input, either to generate queries, or to assess the r...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [90, 101, 1.0], [102, 111, 1.0]]}, "2012.wsdm_conference-2012.45": {"snippet": "...d by machine learning algorithms using either direct human relevance judgments or indirect judgments obtained from click-through data from millions of users. The rankings are thus optimized to this ge...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2008.sigirconf_conference-2008.35": {"snippet": "Learning to rank with SoftRank and Gaussian processes\n\n\n ABSTRACTIn this paper we address the issue of learning to rank for document retrieval using Thurstonian models based on sparse Gaussian process...", "weights": [[0, 8, 1.0], [103, 111, 1.0], [133, 142, 1.0]]}, "2012.tois_journal-ir0volumeA30A4.0": {"snippet": "...line Learning Framework for Refining Recency Search Results with User Click Feedback\n\n\n Traditional machine-learned ranking systems for Web search are often trained to capture stationary relevance of ...", "weights": [[8, 16, 1.0], [103, 110, 1.0]]}, "2018.wsdm_conference-2018.111": {"snippet": "...ding information retrieval and exploration. With improved speech recognition and information retrieval systems, more Permission to make digital or hard copies of part or all of this work for personal ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [84, 95, 1.0], [96, 105, 1.0]]}, "2018.cikm_conference-2018.68": {"snippet": "...task Learning of Information Retrieval and Reading Comprehension\n\n\n ABSTRACTThis study considers the task of machine reading at scale (MRS) wherein, given a question, a system first performs the infor...", "weights": [[8, 16, 1.0], [20, 31, 1.0], [32, 41, 1.0], [112, 119, 1.0], [198, 209, 1.0]]}, "H05-1020": {"snippet": "...ased Learning for Information Retrieval\n\n\n We approached the problem as learning how to order documents by estimated relevance with respect to a user query. Our support vector machines based classifie...", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0], [75, 83, 1.0]]}, "2006.ecir_conference-2006.30": {"snippet": "Machine Learning Ranking for Structured Information Retrieval\n\n\n Abstract. We consider the Structured Information Retrieval task which consists in ranking nested textual units according to their relev...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [40, 51, 1.0], [52, 61, 1.0], [102, 113, 1.0], [114, 123, 1.0]]}}}
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In this paper we explore various methods to estimate the collection parameter of the information based models for ad hoc information retrieval. In previous ...", "weights": [[8, 19, 1.0], [132, 143, 1.0], [168, 179, 1.0], [180, 189, 1.0]]}, "2020.sigirconf_conference-2020.379": {"snippet": "...Case Retrieval\n\n\n Precedents (prior cases decided in courts of law) are primary legal materials in both common and civil law systems. 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The law is investigated for two langua...", "weights": [[8, 11, 1.0], [36, 40, 1.0], [43, 46, 1.0], [169, 172, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.10": {"snippet": "...s of information behavior and served to support the citing authors' claims. By contrast, the concept of \"Zipf's law\" received the most attention from bibliometrics research and was used mainly for com...", "weights": [[8, 19, 1.0], [108, 112, 1.0], [115, 118, 1.0]]}}}
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Various algorithms have been put up in terms of maximizing the query efficiency. In re...", "weights": [[8, 15, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.9": {"snippet": "...ting Boolean queries in conjunctive normal form with probabilistic retrieval models", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2001.ir_journal-ir0volumeA4A34.4": {"snippet": "...s of Boolean Queries in Large Full-Text Databases", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2018.clef_conference-2018w.48": {"snippet": "... the Boolean search as the starting point(sub-task 2). We adopt BM25 with query expansion to acquire basic relationship and utilize a customized Paragraph2Vector to represent queries / documents train...", "weights": [[8, 15, 1.0], [178, 185, 1.0]]}, "1979.jasis_journal-ir0volumeA30A6.10": {"snippet": "...sing boolean queries with a clustered file organization", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2003.clef_workshop-2003w.44": {"snippet": "...ed a Boolean quorum-level type of query combination so that documents were ranked according to both the tf-idf factor but also to the number of matching Boolean combinations. The Boolean combinations ...", "weights": [[8, 15, 1.0], [156, 163, 1.0], [182, 189, 1.0]]}, "2004.ntcir_workshop-2004.80": {"snippet": "...ying queries that combines the probabilistic IR model and the Boolean IR model. Since it is not easy to construct a Boolean query that covers all relevant documents, a mechanism for clarifying the Boo...", "weights": [[8, 15, 1.0], [65, 72, 1.0], [119, 126, 1.0], [200, 207, 1.0]]}, "2012.sigirconf_conference-2012.106": {"snippet": "...late Boolean queries to represent their information need. We want to work on three basic problems:1. Boolean query formulation -Our primary goal is to study Boolean query formulation in the light of t...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [104, 111, 1.0], [160, 167, 1.0]]}, "2016.ecir_workshop-2016newsir.3": {"snippet": "Boolean Queries for News Monitoring: Suggesting New Query Terms to Expert Users\n\n\n In this paper, we evaluate query suggestion for Boolean queries in a news monitoring system. Users of this system rec...", "weights": [[0, 7, 1.0], [8, 15, 1.0], [131, 138, 1.0], [139, 146, 1.0]]}, "W14-1411": {"snippet": "...with Boolean operations. The second relates to adjectival modification of question and propositional entities. We partly defuse the Boolean challenge by showing that the data actually argue against a ...", "weights": [[8, 15, 1.0], [135, 142, 1.0]]}, "1999.tois_journal-ir0volumeA17A4.1": {"snippet": "...lar, Boolean query operators are conveniently modeled as link matrices of the Bayesian Network. Prior work has shown, however, that these operators do not perform as well as the the pnorm operators us...", "weights": [[8, 15, 1.0]]}, "1982.jasis_journal-ir0volumeA33A1.1": {"snippet": "... for boolean search request formulations", "weights": [[8, 15, 1.0]]}, "1997.jasis_journal-ir0volumeA48A2.3": {"snippet": "...ring Boolean and Probabilistic Information Retrieval Systems across Queries and Disciplines", "weights": [[8, 15, 1.0], [71, 78, 1.0]]}, "2020.ecir_conference-20201.27": {"snippet": "...iven Boolean queries that is now several decades old. Rank fusion is a relatively recent method for combining runs from multiple systems into a single ranking, and has been shown to significantly impr...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1999.tois_journal-ir0volumeA17A1.0": {"snippet": "...s in Boolean queries into native subsuming forms, which is a basis of translating complex queries. In addition, we present experimental results for evaluating the cost of postfiltering. We also discus...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [93, 100, 1.0]]}, "1999.jasis_journal-ir0volumeA50A1.7": {"snippet": "Boolean Search: Current State and Perspectives", "weights": [[0, 7, 1.0]]}, "2011.sigirconf_conference-2011.85": {"snippet": "... for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [151, 158, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "... for boolean query formulation\n\n\n AbstractA new model named Boolean Latent Semantic Indexing model based on the Singular Value Decomposition and Boolean query formulation is introduced. While the Sing...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [148, 155, 1.0]]}, "2009.jasis_journal-ir0volumeA60A6.9": {"snippet": "...earn Boolean queries: A comparative study", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2008.trec_conference-2008.15": {"snippet": "...ated Boolean Queries of the TREC 2008 Legal Track\n\n\n We analyze the results of several experimental runs submitted for the TREC 2008 Legal Track. In the Ad Hoc task, we found that rank-based merging o...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1993.jasis_journal-ir0volumeA44A6.2": {"snippet": "...n of Boolean Queries: A Prototype Implementation and Evaluation", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2007.trec_conference-2007.51": {"snippet": "...rent Boolean queries were provided for each topic (defendant, plaintiff and final). In 2006, the plaintiff and final queries had (usually) been the same. 1 Although often referred to as \"Boolean,\" the...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [120, 127, 1.0], [190, 197, 1.0]]}, "2008.wwwconf_conference-2008.201": {"snippet": "...s of queries per second over billions of documents. To deal with this heavy workload, current engines use massively parallel architectures of thousands of machines that require large hardware investme...", "weights": [[8, 15, 1.0]]}, "1982.sigirjournals_journal-ir0volumeA17A2.2": {"snippet": "...atic Boolean Query Processing", "weights": [[8, 15, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.1": {"snippet": "...g of Boolean queries by means of a multiobjective IQBE evolutionary algorithm", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2006.trec_conference-2006.32": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator because it can not be generalized to nodes. The fuzzy te...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2005.trec_conference-2005.42": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator. The fuzzy term proximity is controlled with an influenc...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2010.tois_journal-ir0volumeA29A1.0": {"snippet": "...tive Boolean queries are a key component of modern information retrieval systems, especially when Webscale repositories are being searched. A conjunctive query q is equivalent to a |q|-way intersectio...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "... the Boolean operators and search terms that comprise the submitted queries. Our analysis shows that both Boolean training and the use of an assisted interface improved the participants' ability to co...", "weights": [[8, 15, 1.0], [71, 78, 1.0], [109, 116, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.10": {"snippet": "...n in Boolean IR queries\n\n\n a b s t r a c tThe negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include negated terms in Boole...", "weights": [[8, 15, 1.0], [19, 26, 1.0], [130, 137, 1.0], [198, 205, 1.0]]}, "2007.trec_conference-2007.65": {"snippet": "...lds, boolean constraints and structured operators. Evaluation results show that structured queries outperform bag-of-words ones. Boolean constraints improve both precision and recall. For the routing ...", "weights": [[8, 15, 1.0], [94, 101, 1.0], [132, 139, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...nced queries. We submitted both the original and modified queries to three major Web search engines; a total of 600 queries were submitted and 5,748 documents evaluated. We compared the results from t...", "weights": [[8, 15, 1.0], [61, 68, 1.0], [119, 126, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.12": {"snippet": "... and Boolean operators used as well as their relationships with search failure. Among other findings, results indicate that a majority of the queries were simple, with short query lengths and a low us...", "weights": [[8, 15, 1.0], [145, 152, 1.0]]}, "2006.tois_journal-ir0volumeA24A4.4": {"snippet": "...Path queries, as well as other related XPath decision problems such as satisfiability, equivalence, overlap, and coverage. The considered XPath fragment covers most of the language features used in pr...", "weights": [[8, 15, 1.0]]}, "1989.ipm_journal-ir0volumeA25A1.3": {"snippet": "...fied boolean queries: A comparison of inverted and signature file organizations", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2018.sigirconf_conference-2018.50": {"snippet": "...hese Boolean queries are carefully crafted by researchers and information specialists, and often reviewed by a panel of experts. However, little is known about the effectiveness of the Boolean queries...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [188, 195, 1.0], [196, 203, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...nded Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulate...", "weights": [[8, 15, 1.0], [73, 80, 1.0], [81, 88, 1.0]]}, "2007.trec_conference-2007.22": {"snippet": "...ated Boolean Queries of the TREC 2007 Legal Discovery Track\n\n\n We analyze the results of several experimental runs submitted for the TREC 2007 Legal Track (also sometimes known as the Legal Discovery ...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0], [8, 15, 1.0]]}, "2003.clef_workshop-2003.32": {"snippet": "...dish Queries for Boolean Combinations of Truncated Terms\n\n\n Abstract. In languages that use compound words such as Swedish, it is often neccessary to split compound words when indexing documents or qu...", "weights": [[8, 15, 1.0], [20, 27, 1.0], [201, 208, 1.0]]}, "1997.jasis_journal-ir0volumeA48A8.1": {"snippet": "...h to Boolean Query Reformulation", "weights": [[8, 15, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [124, 131, 1.0], [196, 203, 1.0]]}, "1994.ipm_journal-ir0volumeA30A1.5": {"snippet": "...ased Boolean Retrieval Systems", "weights": [[8, 15, 1.0]]}, "2005.clef_workshop-2005w.11": {"snippet": "Boolean Operators in Interactive Search\n\n\n This paper presents the participation of the MIRACLE team at the ImageCLEF interactive search task. Basically, queries consisting on several terms can be pro...", "weights": [[0, 7, 1.0], [154, 161, 1.0]]}, "2000.sigirconf_conference-2000.5": {"snippet": "... for Boolean IR systems is proposed. It is based on a controlled formulation of inclusive query plans, on an automatic conversion of query plans into elementary queries, and on combining elementary qu...", "weights": [[8, 15, 1.0], [164, 171, 1.0], [201, 208, 1.0]]}, "2007.spire_conference-2007.13": {"snippet": "...tive Boolean queries are a fundamental operation in web search engines. These queries can be reduced to the problem of intersecting ordered sets of integers, where each set represents the documents co...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2012.sigirconf_workshop-2012osir.0": {"snippet": "... CNF queries. These CNF queries are highly effective, and can outperform bag of word queries by a large margin. However, not many effective tools exist that can facilitate the efficient manual creatio...", "weights": [[8, 15, 1.0], [27, 34, 1.0], [88, 95, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...d on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in answer to search queries but they are not easy to use by uninitiated patrons. An extended Boo...", "weights": [[8, 15, 1.0], [127, 134, 1.0], [200, 207, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...sing Boolean queries. The model is applied to the task of ranking documents, or other structural elements, in the order of their expected relevance. Features such as phrase matching, truncation, and s...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [99, 106, 1.0], [176, 183, 1.0]]}, "R09-1020": {"snippet": ".... As Boolean model allows a fine-grained control over query expansion, these semantically related terms are added to the original query to form an expanded Boolean query. We conducted experiments on T...", "weights": [[8, 15, 1.0], [159, 166, 1.0]]}, "1986.sigirconf_conference-86.21": {"snippet": "... for Boolean Query Processing\n\n\n Abstract. An infamation retrieval model, named the Generaliied Vectm Spice Model (GVSM). is extended m handle situations where queries are specitied as (extended) Bool...", "weights": [[8, 15, 1.0], [163, 170, 1.0], [199, 206, 1.0]]}, "2014.sigirconf_conference-2014.152": {"snippet": "...lity Boolean queries that can be applied deterministically during filtering. We call these Boolean statements sufficient queries. We argue that using deterministic queries for entity-centric filtering...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [94, 101, 1.0], [124, 131, 1.0], [167, 174, 1.0]]}, "2006.trec_conference-2006.28": {"snippet": "...ated Boolean Queries of the TREC 2006 Legal Discovery Track\n\n\n We analyze the results of several experimental runs submitted to the Legal Discovery and Terabyte Tracks of TREC 2006. In the Legal Track...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2006.clef_workshop-2006w.37": {"snippet": "...dles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes it possible to...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2001.cikm_conference-2001.28": {"snippet": "...only queries regarding abstract tables, there is no need to implement any functions beyond a subselect (such as union, except, intersect). Separate subselects are usually represented by independent qu...", "weights": [[8, 15, 1.0], [201, 208, 1.0]]}}}
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Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2007.sigirconf_conference-2007.187": {"snippet": "... the development of interactive information retrieval (IR) systems. This paper investigated a simple method to facilitate such a task by looking at aggregated relevance judgements of retrieved documen...", "weights": [[8, 19, 1.0], [35, 46, 1.0], [47, 56, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "2007.ecir_conference-2007.2": {"snippet": "...n in Information Retrieval\n\n\n Abstract. The experimental evaluation of information retrieval systems has a venerable history. 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The widely employed \"Cranfield paradigm\" dictates ...", "weights": [[8, 19, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "1999.ntcir_workshop-1999.9": {"snippet": "Development of a Related Document Retrieval System and Evaluation of the System Using NTCIR-1", "weights": [[0, 11, 1.0], [34, 43, 1.0]]}, "1996.amta-1.9": {"snippet": "... and development methodology\n\n\n The history of MT shows how the shortcomings of one approach become the catalyst for a new approach. New systems are designed in reaction to old systems' weaknesses. Ye...", "weights": [[8, 19, 1.0], [39, 46, 1.0]]}, "2003.clef_workshop-2003w.18": {"snippet": "...h in information access technologies, such as information retrieval, text summarization, question answering, text mining, etc., by providing infrastructure of large-scale evaluation. A brief history, ...", "weights": [[8, 19, 1.0], [49, 60, 1.0], [61, 70, 1.0], [194, 201, 1.0]]}, "2014.tc-1.2": {"snippet": "... its development and will reflect as to what the future has in store. To start with, the early developments after the Second World War II will be outlined. Next, the presentation will elaborate that t...", "weights": [[8, 19, 1.0]]}, "2017.sigirconf_conference-2017.248": {"snippet": "... for Information Retrieval: And Related Tasks\n\n\n MOTIVATIONe main task of an Information Retrieval (IR) system is to return relevant documents to users in response to a query. However, this task is in...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [92, 101, 1.0]]}, "W02-1302": {"snippet": "Towards a Road Map on Human Language Technology: Natural Language Processing\n\n\n This document summarizes contributions and discussions from two workshops that took place in November 2000 and July 2001...", "weights": []}, "2021.latechclfl-1.1": {"snippet": "...n of information in the European early modern period is a wellresearched topic, the question how people were engaging with the information explosion that occurred in early modern Europe, is still unde...", "weights": [[8, 19, 1.0], [130, 141, 1.0]]}, "2021.eacl-main.273": {"snippet": "Summarising Historical Text in Modern Languages\n\n\n We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern ...", "weights": []}, "2017.sigirconf_conference-2017.1": {"snippet": "... Pre-history of Web Search\n\n\n ABSTRACTTo me, an awareness of history is a fundamental requirement for progress; and I believe that we in the eld of information retrieval are currently ill-served in th...", "weights": [[8, 15, 1.0], [64, 71, 1.0], [151, 162, 1.0], [163, 172, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [42, 53, 1.0], [54, 63, 1.0], [92, 103, 1.0], [104, 113, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "1995.sigirconf_conference-95.17": {"snippet": "...n in Information Retrieval\n\n\n ABSTRACTEvaluation is a major force in research, development and applications related to information retrieval (IR). This paper is a critical and historical analysis of e...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [82, 93, 1.0], [122, 133, 1.0], [134, 143, 1.0]]}, "W12-3202": {"snippet": "...onal History of the {ACL}: 1980-2008\n\n\n We develop a people-centered computational history of science that tracks authors over topics and apply it to the history of computational linguistics. We prese...", "weights": [[8, 15, 1.0], [86, 93, 1.0], [157, 164, 1.0]]}, "1976.sigirjournals_journal-ir0volumeA11A2.4": {"snippet": "... of \"Information Retrieval and Processing by Lauren B. Doyle\"; Los Angeles, Melville Publishing Company, 1975\n\n\n While the titles of these two recent books on information retrieval suggest that they m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [162, 173, 1.0], [174, 183, 1.0]]}, "2017.ipm_journal-ir0volumeA53A1.9": {"snippet": "... the development of multilingual information access systems. However, a systematic and quantitative assessment of the impact of evaluation initiatives on multilingual information access and retrieval ...", "weights": [[8, 19, 1.0], [36, 47, 1.0], [170, 181, 1.0], [193, 202, 1.0]]}, "1997.sigirjournals_journal-ir0volumeA31A1.7": {"snippet": "... and information retrieval consists in introducing the expert system approach:  \"I believe that expert systems research is the new frontier and the next area of development in library information scie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [164, 175, 1.0], [187, 198, 1.0]]}, "2002.tmi-tmiw.8": {"snippet": "Towards a road map on human language technology: Natural language processing\n\n\n This document summarizes contributions and discussions from two workshops that took place in November 2000 and July 2001...", "weights": []}, "2012.tois_journal-ir0volumeA30A2.2": {"snippet": "...Peer Information Retrieval: An Overview\n\n\n Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been develo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [157, 168, 1.0], [169, 178, 1.0]]}, "2018.sigirconf_conference-2018.234": {"snippet": "...many information retrieval (IR) tasks. Entity retrieval from KGs has been a long-studied IR research topic for many years . The utilization of entities and KG information for text-centric retrieval ta...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [49, 58, 1.0], [162, 173, 1.0], [191, 200, 1.0]]}, "2011.cikm_conference-2011.406": {"snippet": "...ical information retrieval modelling: from the probability ranking principle to recent advances in diversity, portfolio theory, and beyond\n\n\n ABSTRACTStatistical modelling of Information Retrieval (IR...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [178, 189, 1.0], [190, 199, 1.0]]}, "2010.trec_conference-2010.65": {"snippet": "...ment Retrieval: A Way of Thinking that Leads to Method Choices\n\n\n This paper discusses a theory and proposed design for a retrieval artifact using a bag of words (BOW) approach for terms and a standar...", "weights": [[8, 17, 1.0], [125, 134, 1.0]]}, "O98-4002": {"snippet": "Information Extraction: Beyond Document Retrieval\n\n\n In this paper we give a synoptic view of the growth text processing technology of information extraction (IE) whose function is to extract informat...", "weights": [[0, 11, 1.0], [40, 49, 1.0], [135, 146, 1.0], [192, 203, 1.0]]}, "2003.ecir_conference-2003.1": {"snippet": "...ment Retrieval: Shallow Data, Deep Theories; Historical Reflections, Potential Directions\n\n\n Abstract. This paper reviews the development of statistically-based retrieval. Since the 1950s statistical ...", "weights": [[8, 17, 1.0], [129, 140, 1.0], [164, 173, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2010.airs_conference-2010.12": {"snippet": "... for Retrieval in Historic Documents", "weights": [[8, 17, 1.0]]}, "2006.trec_conference-2006.103": {"snippet": "...ment retrieval, passage retrieval, etc.) and statistical classifiers were developed that we use for various classification tasks.", "weights": [[8, 17, 1.0], [27, 36, 1.0]]}, "2015.clef_conference-2015w.86": {"snippet": "...urce Retrieval: Notebook for PAN at CLEF 2015\n\n\n Source retrieval is a part of a plagiarism discovery process, where only a selected set of candidate documents is retrieved from a large corpus of pote...", "weights": [[8, 17, 1.0], [59, 68, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.23": {"snippet": "...rove Information Retrieval Systems and Evaluation\n\n\n AbstractProgress and innovation are driven by experiments, but experimentation is useless without an objective evaluation measure that allow resear...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2016.trec_conference-2016.54": {"snippet": "...ised Information Retrieval System for Clinical Decision Support\n\n\n This article summarizes the approach developed for TREC 2016 Clinical Decision Support Track. In order to address the daunting challe...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1991.sigirconf_conference-91.25": {"snippet": "... the History of Optimal Retrievals\n\n\n AbstractLearning techniques are used in IR to exploit userfeedback in order that the system can improve its peflormance with respect to particular queries. This p...", "weights": [[8, 15, 1.0]]}, "2002.ipm_journal-ir0volumeA38A5.5": {"snippet": "...mage retrieval in American history", "weights": [[8, 17, 1.0], [30, 37, 1.0]]}, "2018.sigirconf_workshop-2018birndl.2": {"snippet": "... and information retrieval communities can lead the way in addressing this problem through the development of automation technologies that facilitate search and synthesis of evidence; but developing t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [98, 109, 1.0]]}}}
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A...", "weights": []}, "1986.jasis_journal-ir0volumeA37A5.4": {"snippet": "...ka's Law, Bradford's Law, and Zipf's Law", "weights": [[8, 11, 1.0], [24, 27, 1.0], [33, 37, 1.0], [40, 43, 1.0]]}, "2017.sigirconf_conference-2017.41": {"snippet": "...ago, Zipf postulated that the word frequency distribution of languages is a power law, i.e., it is a straight line on a log-log plot. 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The law is investigated for two langua...", "weights": [[8, 11, 1.0], [36, 40, 1.0], [43, 46, 1.0], [169, 172, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.10": {"snippet": "... of \"Zipf's law\" was cited most (64.8%), followed by \"the principle of least effort\" (24.5%). Although the concept of \"the principle of least effort,\" the focus of HBPLE, was not most frequently obser...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2011.sigirconf_conference-2011.212": {"snippet": "The Meta-Dex Suite: generating and analyzing indexes and meta-indexes\n\n\n ABSTRACTOur Meta-dex software suite extracts content and index text from a corpus of PDF files, and generates a meta-index that...", "weights": []}}}
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We discover that the power-law regularities, especially the Zipf's and Heaps' laws, also exist in large-s...", "weights": [[8, 11, 1.0], [125, 128, 1.0], [158, 162, 1.0]]}, "1999.jasis_journal-ir0volumeA50A3.4": {"snippet": "... the Law of Zipf-Mandelbrot for Multi-Wort Phrases", "weights": [[8, 11, 1.0], [15, 19, 1.0]]}, "2010.wsdm_conference-2010.38": {"snippet": "Revisiting globally sorted indexes for efficient document retrieval\n\n\n ABSTRACTThere has been a large amount of research on efficient document retrieval in both IR and web search areas. One important ...", "weights": []}, "E17-4009": {"snippet": "...t to Zipf's law, an empirical law referring to the phenomenon that many types of data in social sciences can be approximated with a Zipfian distribution. Additionally, we also analyse Benford's law, i...", "weights": [[8, 12, 1.0], [15, 18, 1.0], [33, 36, 1.0], [197, 200, 1.0]]}, "2012.airs_conference-2012.0": {"snippet": "Information Retrieval Technology, 8th Asia Information Retrieval Societies Conference, AIRS 2012, Tianjin, China, December 17-19, 2012. Proceedings\n\n\n The annual AIRS conference has established its re...", "weights": []}, "2012.spire_conference-2012.15": {"snippet": "A Zipf-Like Distant Supervision Approach for Multi-document Summarization Using Wikinews Articles", "weights": [[2, 6, 1.0]]}, "O03-4004": {"snippet": "...pus, Zipf 's law for both words in English and characters in Chinese does not hold for all ranks. The frequency falls below the frequency predicted by Zipf's law for English words for rank greater tha...", "weights": [[8, 12, 1.0], [16, 19, 1.0], [154, 158, 1.0], [161, 164, 1.0]]}, "1981.sigirconf_conference-81.5": {"snippet": "A Comparison of Search Term Weighting: Term Relevance vs. Inverse Document Frequency\n\n\n AbstractThe term relevance weighting method has been shown to produce optimal information retrieval queries unde...", "weights": []}, "P06-1076": {"snippet": "...ower law distribution with a slope coefficient of 1.6 for documents and 1.7 for sentences and terms. We conclude with an analysis of IDF stability with respect to random, journal, and section partitio...", "weights": [[8, 11, 1.0]]}, "1982.jasis_journal-ir0volumeA33A5.5": {"snippet": "...n of Zipf's Law and Its Application to the Bibliographic Database Environment", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2019.gwc-1.44": {"snippet": "...ency law with wordnets as sense inventories\n\n\n According to George K. Zipf, more frequent words have more senses. We have tested this law using corpora and wordnets of English, Spanish, Portuguese, Fr...", "weights": [[8, 11, 1.0], [73, 77, 1.0], [137, 140, 1.0]]}, "C14-1032": {"snippet": "...{'}s Law and Statistical Data on {M}odern {T}ibetan\n\n\n In this paper, a large scale modern Tibetan text corpus is built, which includes about 190 thousands documents, 67.21 million words, 93.66 millio...", "weights": [[8, 11, 1.0]]}, "2002.ecir_conference-2002.13": {"snippet": "...ve a law relating the probability of a given term frequency in a document and its the length. A similar law was derived by Mandelbrot. We exploit the paretian distribution to obtain a term frequency n...", "weights": [[8, 11, 1.0], [107, 110, 1.0]]}, "2021.ccl-1.93": {"snippet": "...{'}s law: evidence from danmu comments\n\n\n Zipf's law is a succinct yet powerful mathematical law in linguistics. However, the meaningfulness and units of the law have remained controversial. The curre...", "weights": [[8, 11, 1.0], [45, 49, 1.0], [52, 55, 1.0], [96, 99, 1.0], [161, 164, 1.0]]}, "2001.jasis_journal-ir0volumeA52A12.3": {"snippet": "... and Zipf models", "weights": [[8, 12, 1.0]]}, "2010.cikm_conference-2010.109": {"snippet": "Reverted indexing for feedback and expansion\n\n\n ABSTRACTTraditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a ...", "weights": []}, "2010.spire_conference-2010.32": {"snippet": "... and Zipf-Like Scoring Function for Web Document Similarity Retrieval", "weights": [[8, 12, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "Learning Task Experiments in the TREC 2011 Legal Track\n\n\n The Learning Task of the TREC 2011 Legal Track investigated the effectiveness of e-Discovery search techniques at selecting training examples ...", "weights": []}, "1986.jasis_journal-ir0volumeA37A5.4": {"snippet": "...ka's Law, Bradford's Law, and Zipf's Law", "weights": [[8, 11, 1.0], [24, 27, 1.0], [33, 37, 1.0], [40, 43, 1.0]]}, "1998.sigirconf_conference-98.3": {"snippet": "A Theory of Term Weighting Based on Exploratory Data Analysis\n\n\n AbstractTechniques of exploratory data analysis are used to study the weight of evidence that the occurrence of a query term provides i...", "weights": []}, "2017.sigirconf_conference-2017.41": {"snippet": "...ago, Zipf postulated that the word frequency distribution of languages is a power law, i.e., it is a straight line on a log-log plot. Over the years, this phenomenon has been documented and studied ex...", "weights": [[8, 12, 1.0], [85, 88, 1.0]]}, "2020.wosp-1.5": {"snippet": "Term-Recency for {TF}-{IDF}, {BM}25 and {USE} Term Weighting\n\n\n Effectiveness of a recommendation in an Information Retrieval (IR) system is determined by relevancy scores of retrieved results. Term w...", "weights": []}, "W96-0106": {"snippet": "...{'}s Law\n\n\n An asymptote is derived from Turing's local reestimation formula for population frequencies, and a local reestimation formula is derived from Zipf's law for the asymptotic behavior of popu...", "weights": [[8, 11, 1.0], [157, 161, 1.0], [164, 167, 1.0]]}, "C02-1117": {"snippet": "...{'}s Law to Words and Phrases\n\n\n Zipf's law states that the frequency of word tokens in a large corpus of natural language is inversely proportional to the rank. The law is investigated for two langua...", "weights": [[8, 11, 1.0], [36, 40, 1.0], [43, 46, 1.0], [169, 172, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.10": {"snippet": "... of \"Zipf's law\" was cited most (64.8%), followed by \"the principle of least effort\" (24.5%). Although the concept of \"the principle of least effort,\" the focus of HBPLE, was not most frequently obser...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2015.sigirconf_conference-2015.61": {"snippet": "A Probabilistic Model for Information Retrieval Based on Maximum Value Distribution\n\n\n ABSTRACTThe main goal of a retrieval model is to measure the degree of relevance of a document with respect to th...", "weights": []}, "2011.wwwconf_conference-2011c.88": {"snippet": "Leveraging auxiliary text terms for automatic image annotation\n\n\n ABSTRACTThis paper proposes a novel algorithm to annotate web images by automatically aligning the images with their most relevant aux...", "weights": []}}}
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Previous stud...", "weights": [[8, 12, 1.0], [16, 20, 1.0], [26, 34, 1.0], [83, 91, 1.0], [106, 114, 1.0], [122, 126, 1.0], [161, 170, 1.0]]}, "2008.sigirconf_conference-2008.52": {"snippet": "...User feedback is considered to be a critical element in the information seeking process, especially in relation to relevance assessment. Current feedback techniques determine content relevance with re...", "weights": [[8, 16, 1.0], [118, 127, 1.0], [148, 156, 1.0], [186, 195, 1.0]]}, "2005.tois_journal-ir0volumeA23A2.0": {"snippet": "... the search experience is the collection of implicit user behavior measures (implicit measures) as indications of user interest and user satisfaction. Rather than having to submit explicit user feedba...", "weights": [[8, 14, 1.0], [56, 60, 1.0], [117, 121, 1.0], [135, 139, 1.0], [192, 196, 1.0], [197, 205, 1.0]]}, "2017.sigirconf_conference-2017.69": {"snippet": "...eral user clicking strategies. We highlight in particular what we de ne as a smart strategy, leading to a Nash equilibrium, where every user reaches the maximum possible privacy while preserving the a...", "weights": [[8, 12, 1.0], [139, 143, 1.0]]}, "C10-2068": {"snippet": "...ated Relevance Feedback: A Prognostic Search approach\n\n\n Implicit relevance feedback has proved to be a important resource in improving search accuracy and personalization. However, researchers who re...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [41, 47, 1.0], [69, 78, 1.0], [79, 87, 1.0], [139, 145, 1.0]]}, "2019.ecir_conference-20192.51": {"snippet": "...s of search intents and relevance over the entire search session. In multi-page search , user clicks of documents and dwell times are implicit signals of relevance. In professional search such as pate...", "weights": [[8, 14, 1.0], [27, 36, 1.0], [53, 59, 1.0], [83, 89, 1.0], [92, 96, 1.0], [157, 166, 1.0], [184, 190, 1.0]]}, "2008.sigirconf_conference-2008.198": {"snippet": "... the role of emotions in the information seeking process\n\n\n ABSTRACTUser feedback is considered to be a critical element in the information seeking process. An important aspect of the feedback cycle i...", "weights": [[8, 12, 1.0], [76, 84, 1.0], [187, 195, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.1": {"snippet": "...s of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We ...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [51, 57, 1.0], [148, 152, 1.0], [185, 191, 1.0], [192, 198, 1.0]]}, "2005.sigirconf_conference-2005.6": {"snippet": "...icit relevance feedback\n\n\n ABSTRACTImplicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [47, 56, 1.0], [57, 65, 1.0], [98, 104, 1.0]]}, "2016.sigirconf_conference-2016.209": {"snippet": "...ling User Feedback in Dynamic Search and Browsing", "weights": [[8, 12, 1.0], [13, 21, 1.0], [33, 39, 1.0]]}, "I11-1116": {"snippet": "...n in user feedback and in each document in the search results using latent Dirichlet allocation, and then we modify the search results so that documents with a similar topic distribution to that of th...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [50, 56, 1.0], [123, 129, 1.0]]}, "2012.sigirconf_conference-2012.197": {"snippet": "...icit relevance feedback\n\n\n ABSTRACTUsers' search activity has been used as implicit feedback to model search interests and improve the performance of search systems. In search engines, this behavior u...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [45, 51, 1.0], [87, 95, 1.0], [105, 111, 1.0], [153, 159, 1.0], [172, 178, 1.0]]}, "2005.sigirconf_conference-2005.7": {"snippet": "...tual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval acc...", "weights": [[8, 12, 1.0], [17, 23, 1.0], [100, 108, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "...s.\n2.RELEVANCE FEEDBACKSearch results are typically presented to the user as a ranked list of documents, in the decreasing order of relevance to the user's query. In a system that involves user releva...", "weights": [[8, 17, 1.0], [72, 76, 1.0], [135, 144, 1.0], [152, 156, 1.0], [192, 196, 1.0], [197, 206, 1.0]]}, "2006.sigirconf_conference-2006.124": {"snippet": "...hoo! Search. We consider these refinements as sub-concepts which help define user intent and use them to improve search relevance. The results obtained via live testing on Yahoo! Search are encouragin", "weights": [[8, 14, 1.0], [80, 84, 1.0], [116, 122, 1.0], [123, 132, 1.0], [182, 188, 1.0]]}, "2006.sigirconf_conference-2006.4": {"snippet": "...s of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We ...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [51, 57, 1.0], [148, 152, 1.0], [185, 191, 1.0], [192, 198, 1.0]]}, "2003.jasis_journal-ir0volumeA54A6.5": {"snippet": "...ting user search behavior into relevance feedback", "weights": [[8, 12, 1.0], [13, 19, 1.0], [34, 43, 1.0], [44, 52, 1.0]]}, "2014.sigirconf_conference-2014.103": {"snippet": "... the search engine) and 40s of \"client-side\" dwell time (observable from the browser). Since search engines can only observe server-side actions (i.e., activity on the search engine result page), serv...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [96, 102, 1.0], [171, 177, 1.0], [178, 184, 1.0]]}, "I08-1068": {"snippet": "... the relevance feedback obtained from the user. The user profile thus learnt is applied in a re-ranking phase to rescore the search results retrieved using an underlying search engine. We evaluate our...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [45, 49, 1.0], [55, 59, 1.0], [128, 134, 1.0], [173, 179, 1.0], [180, 186, 1.0]]}, "2018.ictir_conference-2018.1": {"snippet": "...icit user feedback (such as clicks and dwell time) is an important source of data for modern search engines. While heavily biased , it is cheap to collect and particularly useful for user-centric retr...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [96, 102, 1.0], [186, 190, 1.0]]}, "2018.sigirconf_conference-2018.46": {"snippet": "...lick user experience. Very commonly, the user rst clicked an item and then found it is not what he wanted after reading its content, which shows there is a gap between user click and user actual prefe...", "weights": [[8, 12, 1.0], [44, 48, 1.0], [171, 175, 1.0], [186, 190, 1.0]]}, "2006.sigirconf_conference-2006.20": {"snippet": "... for relevance feedback in web search\n\n\n ABSTRACTWe study the effect of user supplied relevance feedback in improving web search results. Rather than using query refinement or document similarity meas...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [34, 40, 1.0], [75, 79, 1.0], [89, 98, 1.0], [99, 107, 1.0], [125, 131, 1.0]]}, "2009.sigirconf_workshop-2009uiir.5": {"snippet": "...eous user intent. This user intent model is combined with the initial search result rankings in order to present recommended search results to the user as well as to reorder subsequent search engine r...", "weights": [[8, 12, 1.0], [26, 30, 1.0], [73, 79, 1.0], [128, 134, 1.0], [150, 154, 1.0], [188, 194, 1.0], [195, 201, 1.0]]}, "2007.sigirconf_conference-2007.212": {"snippet": "... web search engines, in the context of simple search task, are more concerned with precision than completeness of the search. This stemmed from the stronger correlation between users' satisfaction wit...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [121, 127, 1.0]]}, "2016.sigirconf_conference-2016.64": {"snippet": "...icit relevance feedback for improving search ranking, beyond using clicks on images displayed in the search results page. In this paper we propose a number of implicit relevance feedback features base...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [41, 47, 1.0], [104, 110, 1.0], [171, 180, 1.0], [181, 189, 1.0]]}, "2009.trec_conference-2009.73": {"snippet": "...009: Relevance Feedback Track\n\n\n In the Relevance Feedback (RF) task the user is directly involved in the search process: given an initial set of results, he specifies if they are relevant or not to t...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [43, 52, 1.0], [53, 61, 1.0], [76, 80, 1.0], [109, 115, 1.0]]}, "2007.cikm_conference-2007.31": {"snippet": "...icit feedback models personalized for each user and tailored for each search task. We use rich interaction logs (and associated metadata such as relevance judgments), gathered during a longitudinal us...", "weights": [[8, 16, 1.0], [46, 50, 1.0], [73, 79, 1.0], [148, 157, 1.0], [201, 205, 1.0]]}, "2017.tois_journal-ir0volumeA35A3.0": {"snippet": "...icit relevance feedback for commercial search engines. As existing studies have shown that the search result position in a search engine result page (SERP) has a very strong influence on users' examin...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [42, 48, 1.0], [98, 104, 1.0], [126, 132, 1.0], [133, 139, 1.0]]}, "2003.sigirjournals_journal-ir0volumeA37A2.1": {"snippet": "...icit feedback for inferring user preference: a bibliography\n\n\n Relevance feedback has a history in information retrieval that dates back well over thirty years (c.f [SL96]). Relevance feedback is typi...", "weights": [[8, 16, 1.0], [31, 35, 1.0], [66, 75, 1.0], [76, 84, 1.0], [177, 186, 1.0], [187, 195, 1.0]]}, "2014.wsdm_conference-2014.21": {"snippet": "...s on search results are the most widely used behavioral signals for predicting search satisfaction. Even though clicks are correlated with satisfaction, they can also be noisy. Previous work has shown...", "weights": [[8, 14, 1.0], [82, 88, 1.0]]}, "2021.ecir_conference-20211.41": {"snippet": "...onal search and modern Web search engines. Prompting the user for clarification in a search session can be very beneficial to the system as the user's explicit feedback helps the system improve retrie...", "weights": [[8, 14, 1.0], [30, 36, 1.0], [60, 64, 1.0], [88, 94, 1.0], [147, 151, 1.0], [163, 171, 1.0]]}, "2007.ipm_journal-ir0volumeA43A3.15": {"snippet": "...ance feedback information to the search engine. We are interested in how that relevance feedback can be used to improve the search results quality for all users, not just the current user. For example...", "weights": [[8, 16, 1.0], [36, 42, 1.0], [43, 49, 1.0], [81, 90, 1.0], [91, 99, 1.0], [127, 133, 1.0], [186, 190, 1.0]]}, "2009.cikm_conference-2009.256": {"snippet": "...ring relevance for clicks\n\n\n ABSTRACTMining feedback information from user click-through data is an important issue for modern Web retrieval systems in terms of architecture analysis, performance eval...", "weights": [[8, 17, 1.0], [47, 55, 1.0], [73, 77, 1.0]]}, "2016.sigirconf_conference-2016.118": {"snippet": "...text feedback. We collect in situ feedback from a large commercial search engine, and compare this feedback with the judgments provided by trained assessors. We find that in situ feedback differs sign...", "weights": [[8, 16, 1.0], [37, 45, 1.0], [70, 76, 1.0], [77, 83, 1.0], [102, 110, 1.0], [182, 190, 1.0]]}, "2013.sigirconf_conference-2013.157": {"snippet": "...icit feedback in local search tasks\n\n\n ABSTRACTModern search engines make extensive use of people's contextual information to finesse result rankings. Using a searcher's location provides an especiall...", "weights": [[8, 16, 1.0], [26, 32, 1.0], [57, 63, 1.0]]}, "2006.ecir_conference-2006.18": {"snippet": "...s of Relevance Feedback Quality and Quantity in Interactive Relevance Feedback: A Simulation Based on User Modeling", "weights": [[8, 17, 1.0], [18, 26, 1.0], [63, 72, 1.0], [73, 81, 1.0], [105, 109, 1.0]]}, "2018.ecir_conference-2018.17": {"snippet": "...s of search results by either the explicit relevance annotations from external assessors or implicit behavior signals from users. Because the mobile search is different from the desktop search in term...", "weights": [[8, 14, 1.0], [46, 55, 1.0], [152, 158, 1.0], [189, 195, 1.0]]}, "2012.tois_journal-ir0volumeA30A4.0": {"snippet": "...ency Search Results with User Click Feedback\n\n\n Traditional machine-learned ranking systems for Web search are often trained to capture stationary relevance of documents to queries, which have limited...", "weights": [[8, 14, 1.0], [28, 32, 1.0], [39, 47, 1.0], [103, 109, 1.0], [150, 159, 1.0]]}, "2003.tois_journal-ir0volumeA21A3.0": {"snippet": "...on a relevance feedback or genetic algorithm using dynamic query contexts can help casual users search the Internet. These algorithms can draw on implicit user feedback based on the surrounding links ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [99, 105, 1.0], [158, 162, 1.0], [163, 171, 1.0]]}, "2017.sigirconf_conference-2017.238": {"snippet": "...ct a user's rating on venues by exploiting explicit feedback (e.g. users' ratings on venues). However, such explicit feedback may not be available, particularly for inactive users, while implicit feed...", "weights": [[8, 12, 1.0], [55, 63, 1.0], [120, 128, 1.0], [199, 207, 1.0]]}, "2013.wwwconf_conference-2013.57": {"snippet": "... the user's feedback from the first page. We show that document correlations used in result diversification have a significant impact on relevance feedback and its effectiveness over a search session....", "weights": [[8, 12, 1.0], [15, 23, 1.0], [140, 149, 1.0], [150, 158, 1.0], [188, 194, 1.0]]}, "2008.ir_journal-ir0volumeA11A3.1": {"snippet": "...s of relevance feedback based on a user simulation model: effects of a user scenario on cumulated gain value", "weights": [[8, 17, 1.0], [18, 26, 1.0], [38, 42, 1.0], [74, 78, 1.0]]}, "2002.ipm_journal-ir0volumeA38A5.4": {"snippet": "... the role of context of user relevance feedback", "weights": [[8, 12, 1.0], [27, 31, 1.0], [32, 41, 1.0], [42, 50, 1.0]]}, "2005.sigirconf_conference-2005.116": {"snippet": "...icit user feedback\n\n\n ABSTRACTMeasuring the information retrieval effectiveness of Web search engines can be expensive if human relevance judgments are required to evaluate search results. Using impli...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [90, 96, 1.0], [131, 140, 1.0], [176, 182, 1.0]]}}}
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Based on this feature of social media, it is difficult for people's posts/comments on social media to be...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [66, 72, 1.0], [73, 78, 1.0], [124, 130, 1.0], [131, 136, 1.0], [185, 191, 1.0], [192, 197, 1.0]]}, "2021.jasis_journal-ir0volumeA72A1.6": {"snippet": "...ving social media data: Understanding data management practices among social media researchers and their implications for archives", "weights": [[8, 14, 1.0], [15, 20, 1.0], [73, 79, 1.0], [80, 85, 1.0]]}, "2013.ictir_conference-2013.10": {"snippet": "...ead; social media and information interaction in many forms are increasingly used in daily life, etc. Simultaneously, the information experiences of users and use of information has become increasingl...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [25, 36, 1.0], [125, 136, 1.0], [169, 180, 1.0]]}, "2011.mir_conference-2011.33": {"snippet": "Social media driven image retrieval", "weights": [[0, 6, 1.0], [7, 12, 1.0], [26, 35, 1.0]]}, "2019.ecir_conference-20192.47": {"snippet": "... for Social Media Retrieval\n\n\n Abstract. The huge amount of information that is available in social media today has resulted in a pressing demand for information retrieval models to support users in d...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 30, 1.0], [63, 74, 1.0], [96, 102, 1.0], [103, 108, 1.0], [153, 164, 1.0], [165, 174, 1.0]]}, "2013.sigirconf_conference-2013.210": {"snippet": "...e in social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2013.cikm_workshop-2013unlp.8": {"snippet": "...d on social media sites and blogs. In this work we evaluate different textual representations and retrieval models that aim to make sense of social media data for retail applications. Our task is to l...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [101, 110, 1.0], [144, 150, 1.0], [151, 156, 1.0]]}, "2016.fire_conference-2016w.30": {"snippet": "...ita: Information Retrieval on CodeMixed Hindi-English Tweets Using Vector Space Models\n\n\n One of the major challenges nowadays is Information retrieval from social media platforms. Most of the informa...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [133, 144, 1.0], [145, 154, 1.0], [160, 166, 1.0], [167, 172, 1.0], [196, 207, 1.0]]}, "2020.sigirjournals_journal-ir0volumeA54A1.16": {"snippet": "Information extraction from digital social trace data with applications to social media and scholarly communication data", "weights": [[0, 11, 1.0], [36, 42, 1.0], [75, 81, 1.0], [82, 87, 1.0]]}, "2020.wsdm_conference-2020.119": {"snippet": "...y in Social Networks and Media\n\n\n The first Workshop on Integrity in Social Networks and Media is held in conjunction with the 13th ACM Conference on Web Search and Data Mining (WSDM) in Houston, Texa...", "weights": [[8, 14, 1.0], [28, 33, 1.0], [72, 78, 1.0], [92, 97, 1.0]]}, "W14-6202": {"snippet": "Information Extraction for Social Media\n\n\n The rapid growth in IT in the last two decades has led to a growth in the amount of information available online. A new style for sharing information is soci...", "weights": [[0, 11, 1.0], [27, 33, 1.0], [34, 39, 1.0], [127, 138, 1.0], [181, 192, 1.0], [196, 202, 1.0]]}, "2013.wwwconf_conference-2013c.216": {"snippet": "...sion retrieval from twitter\n\n\n ABSTRACTWhile social media receive a lot of attention from the scientific community in general, there is little work on high recall retrieval of messages relevant to a d...", "weights": [[8, 17, 1.0], [48, 54, 1.0], [55, 60, 1.0], [166, 175, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.14": {"snippet": "...sing information in social media: (i) the people creating the social media and (ii) their individual utterances (e.g., blog posts, tweets). These entry points act as a doorway to the information in so...", "weights": [[8, 19, 1.0], [23, 29, 1.0], [30, 35, 1.0], [65, 71, 1.0], [72, 77, 1.0], [186, 197, 1.0], [201, 207, 1.0]]}, "2021.wsdm_conference-2021.155": {"snippet": "...y in Social Networks and Media\n\n\n The second Workshop on Integrity in Social Networks and Media is held in conjunction with the 14th ACM Conference on Web Search and Data Mining (WSDM) in Jerusalem, I...", "weights": [[8, 14, 1.0], [28, 33, 1.0], [73, 79, 1.0], [93, 98, 1.0]]}, "2018.ecir_conference-2018.76": {"snippet": "... the Retrieval of Related Tweets to News Articles\n\n\n Abstract. Nowadays, social media users react in real-time to local and global events. Therefore, social media can be used to measure the impact of ...", "weights": [[8, 17, 1.0], [76, 82, 1.0], [83, 88, 1.0], [153, 159, 1.0], [160, 165, 1.0]]}, "2018.wwwconf_conference-2018c.138": {"snippet": "...sing Social Media Messages in Mass Emergency: Survey Summary\n\n\n ABSTRACTMillions of people use social media to share information during disasters and mass emergencies. Information available on social ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [98, 104, 1.0], [105, 110, 1.0], [120, 131, 1.0], [171, 182, 1.0], [196, 202, 1.0]]}, "2015.fire_workshop-2015w.21": {"snippet": "... for Social Media Texts in Indian Languages\n\n\n This contemporary work is done as a slice of the shared task on Entity Extraction from Social Media Text Indian Languages in Forum for Information Retrie...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [137, 143, 1.0], [144, 149, 1.0], [185, 196, 1.0], [197, 206, 1.0]]}, "2017.wwwconf_conference-2017.139": {"snippet": "...d to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for soci...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [96, 102, 1.0], [103, 108, 1.0], [158, 164, 1.0], [165, 170, 1.0], [199, 205, 1.0]]}, "2015.fire_workshop-2015w.25": {"snippet": "... for social media content in English as part of our participation in the shared task on Entity Extraction from Social Media Text in Indian Languages (ESM-IL) organized by Forum for Information Retriev...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [114, 120, 1.0], [121, 126, 1.0], [184, 195, 1.0], [196, 205, 1.0]]}, "2018.wsdm_conference-2018.65": {"snippet": "...on a social media site is an important task, which not only brings conveniences to users but also benefits platform providers. However, recommendation for newly launched social media sites is challeng...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [173, 179, 1.0], [180, 185, 1.0]]}, "2013.cikm_conference-2013.270": {"snippet": "...e of social networks on social media content providers. Additionally, we propose several document expansion methods, which leverage the content generated by the social networks of the authors of socia...", "weights": [[8, 14, 1.0], [27, 33, 1.0], [34, 39, 1.0], [164, 170, 1.0], [198, 204, 1.0]]}, "2018.wsdm_conference-2018.108": {"snippet": "...t of information is posted by the public in social media. Collecting, aggregating, and presenting this information to stakeholders can be extremely challenging, particularly if an understanding of the...", "weights": [[8, 19, 1.0], [47, 53, 1.0], [54, 59, 1.0], [106, 117, 1.0]]}, "2017.cikm_conference-2017.349": {"snippet": "...ning social media data for urban informatics. The workshop hopes to foster collaboration between researchers working in information retrieval, social media analytics, linguistics; social scientists, a...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [123, 134, 1.0], [135, 144, 1.0], [146, 152, 1.0], [153, 158, 1.0], [183, 189, 1.0]]}, "2011.wsdm_conference-2011.64": {"snippet": "... and social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A4.11": {"snippet": "...ered social media preservation as necessary. It is critical to preserve social media as they are an essential part of our heritage and they can prove valuable for current and future generations.Social...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [75, 81, 1.0], [82, 87, 1.0], [197, 203, 1.0]]}, "2016.cikm_conference-2016.99": {"snippet": "... App Retrieval for Social Media Users via Inference of Implicit Intent in Social Media Text", "weights": [[8, 17, 1.0], [22, 28, 1.0], [29, 34, 1.0], [77, 83, 1.0], [84, 89, 1.0]]}, "2017.fire_conference-2017w.39": {"snippet": "...from Social Media text using Conditional Random Fields\n\n\n Social Media tools popularized the digital devices among masses making information dissemination easier and faster. Exchange of text is most p...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [61, 67, 1.0], [68, 73, 1.0], [132, 143, 1.0]]}, "N18-2094": {"snippet": "...mber Retrieval on Social Media Using Textual Information\n\n\n This paper addresses the problem of community membership detection using only text features in a scenario where a small number of positive l...", "weights": [[8, 17, 1.0], [21, 27, 1.0], [28, 33, 1.0], [48, 59, 1.0]]}, "2019.chiir_conference-2019.20": {"snippet": "...mage Retrieval Approach to Searching for Images on Social Media\n\n\n ABSTRACTSearching for images posted within social media services such as Twitter relies on matching textual queries to the contents o...", "weights": [[8, 17, 1.0], [54, 60, 1.0], [61, 66, 1.0], [113, 119, 1.0], [120, 125, 1.0]]}, "R13-1064": {"snippet": "...s of social media marketing campaigns. However, the informal features of Web 2.0 texts can affect the performance of automated opinion mining tools. These are usually short and noisy texts with presen...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2012.wsdm_conference-2012.55": {"snippet": "...rent social media sites. Further, we explore ways in which event content identified on one social media site can be used to retrieve additional relevant event content on other social media sites. We a...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [94, 100, 1.0], [101, 106, 1.0], [179, 185, 1.0], [186, 191, 1.0]]}, "2017.ecir_workshop-2017smerp.0": {"snippet": "...n of Social Media for Emergency Relief and Preparedness co-located with European Conference on Information Retrieval, SMERP@ECIR 2017, Aberdeen, UK, April 9, 2017", "weights": [[8, 14, 1.0], [15, 20, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "2014.cikm_conference-2014.151": {"snippet": "...n of Social Media Contents using Focused Topic Hierarchy\n\n\n ABSTRACTWith the popularity of social media platforms such as Facebook and Twitter, the amount of useful data in these sources is rapidly in...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [94, 100, 1.0], [101, 106, 1.0]]}, "L16-1259": {"snippet": "...eful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose m...", "weights": [[8, 19, 1.0], [172, 178, 1.0], [179, 184, 1.0], [185, 196, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A2.7": {"snippet": "...s on social media data\n\n\n Abstract Social media sites contain a considerable amount of data for natural calamities events, such as earthquakes, snowstorms, mud-rock flows. With the increasing amount o...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [38, 44, 1.0], [45, 50, 1.0]]}, "2014.wsdm_conference-2014.62": {"snippet": "...eous social media\n\n\n ABSTRACTThe problem of image search has been studied extensively in recent years because of the large and increasing repositories of images on the web, social media, and other lin...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [176, 182, 1.0], [183, 188, 1.0]]}, "W10-0504": {"snippet": "An Information-Retrieval Approach to Language Modeling: Applications to Social Data\n\n\n In this paper we propose the IR-LM (Information Retrieval Language Model) which is an approach to carrying out la...", "weights": [[3, 14, 1.0], [15, 24, 1.0], [72, 78, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2016.sigirconf_conference-2016.232": {"snippet": "...ving Information Retrieval\nMOTIVATIONInformation retrieval and information privacy/security are two fast-growing computer science disciplines. 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However, the potential lack of quality of user generated c...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [46, 51, 1.0]]}, "L12-1124": {"snippet": "...sing Social Media {E}nglish Text", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2014.wwwconf_conference-2014snowdc.8": {"snippet": "...n in Social Media Streaming Data\n\n\n The continuous growth of social networks and the active use of social media services result in massive amounts of user-generated data. Worldwide, more and more peop...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [64, 70, 1.0], [102, 108, 1.0], [109, 114, 1.0]]}, "2013.mir_conference-2013.21": {"snippet": "...sual information for event-related social media retrieval", "weights": [[8, 19, 1.0], [38, 44, 1.0], [45, 50, 1.0], [51, 60, 1.0]]}, "2007.sigirjournals_journal-ir0volumeA41A2.7": {"snippet": "... for information retrieval (LR4IR 2007)\n\n\n AbstractWe propose a set of novel quantitative measures for controversy derived from statistical properties of textual social media. We empirically establish...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [165, 171, 1.0], [172, 177, 1.0]]}, "2014.sigirconf_conference-2014.228": {"snippet": "...014: social media retrieval and analysis workshop\n\n\n ABSTRACTThe SoMeRA workshop targets cutting edge research from all fields of retrieval, recommendation, and browsing in social media, as well as th...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 30, 1.0], [133, 142, 1.0], [176, 182, 1.0], [183, 188, 1.0]]}, "2014.sigirconf_workshop-2014somera.0": {"snippet": "...p on Social Media Retrieval and Analysis, July 11, 2014, Gold Coast, Queensland, Australia", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 30, 1.0]]}, "2017.sigirconf_conference-2017.239": {"snippet": "...line Social Media (OSM) in providing actionable situational information. However, in such media, the crucial information is typically obscured by a lot of insignificant information (e.g., personal opi...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [63, 74, 1.0], [93, 98, 1.0], [112, 123, 1.0], [172, 183, 1.0]]}, "2012.ecir_conference-2012.53": {"snippet": "...ople Retrieval in Social Media with Multiple Roles", "weights": [[8, 17, 1.0], [21, 27, 1.0], [28, 33, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A2.10": {"snippet": "...ment retrieval\n\n\n AbstractThe first international workshop on Social Media Engagement 1 was held on March 29, 2011 in conjunction with the International World Wide Web Conference (WWW 2011 2 ) in Hyde...", "weights": [[8, 17, 1.0], [65, 71, 1.0], [72, 77, 1.0]]}, "W19-5803": {"snippet": "...nese Social Media Texts with Foreign Words\n\n\n Multilingual speakers often switch between languages and generate enormous quantities of cross-language data. This phenomenon is more frequent observed in...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2017.ecir_workshop-2017somepeas.0": {"snippet": "...p on Social Media for Personalization and Search co-located with 39th European Conference on Information Retrieval, SoMePeAS@ECIR 2017, Aberdeen, Scotland, UK, April 9, 2017", "weights": [[8, 14, 1.0], [15, 20, 1.0], [96, 107, 1.0], [108, 117, 1.0]]}, "2020.coling-main.242": {"snippet": "...) on social media such as Twitter is a critical and challenging task due to its noisy, informal, but informative nature. Most existing RC models are developed on formal datasets such as news articles ...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2012.cikm_conference-2012.446": {"snippet": "... and social sensor data sources.\nCategories and Subject Descriptors\nH.4: Information Systems applications\nGeneral TermsAlgorithms, Experimentation, Performance, Human Factors.\nKeywordsSocial networks,...", "weights": [[8, 14, 1.0], [76, 87, 1.0]]}, "2018.wwwconf_conference-2018c.305": {"snippet": "... for social media applications. In essence it is completely at the discretion of the social network provider how the user's data is used and what content is delivered. However, even though social medi...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [88, 94, 1.0], [192, 198, 1.0], [199, 204, 1.0]]}}}
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A performance comparison between a term and sense based system was carried out indicating increased retrieval effectiveness using a s...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [45, 54, 1.0], [55, 68, 1.0], [126, 132, 1.0], [170, 179, 1.0], [180, 193, 1.0]]}, "2011.sigirconf_conference-2011.13": {"snippet": "...nked retrieval\n\n\n ABSTRACTThere is a fundamental tradeoff between effectiveness and efficiency when designing retrieval models for large-scale document collections. Effectiveness tends to derive from ...", "weights": [[8, 17, 1.0], [69, 82, 1.0], [113, 122, 1.0], [168, 181, 1.0]]}, "2010.riao_conference-2010.33": {"snippet": "...tion retrieval effectiveness", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A4.15": {"snippet": "...ated retrieval, that is, the retrieval of blog entries which contain opinions about a topic. There has been a remarkable amount of work towards the improvement of the effectiveness of the opinion retr...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [170, 183, 1.0], [199, 208, 1.0]]}, "2004.clef_workshop-2004w.28": {"snippet": "...e to Retrieval Effectiveness?\n\n\n Named entities in topics are a major factor contributing to the quality of retrieval results. In this paper, we report on an analysis on the correlation between the nu...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [111, 120, 1.0]]}, "2003.sigirconf_conference-2003.49": {"snippet": "... and Retrieval]: Retrieval Models.General Terms: Theory, Experimentation. Keywords: Ranking Retrieval Systems.\nINTRODUCTIONSoboroff, Nicholas and Cahan [2] recently proposed a method for evaluating re...", "weights": [[8, 17, 1.0], [20, 29, 1.0], [95, 104, 1.0], [201, 210, 1.0]]}, "2001.sigirconf_conference-2001.5": {"snippet": "...d in improving the effectiveness of information retrieval systems. Techniques such as relevance feedback, thesaural expansion, and pivoting all provide better quality responses to queries when tested ...", "weights": [[8, 17, 1.0], [22, 35, 1.0], [51, 60, 1.0]]}, "2008.tois_journal-ir0volumeA27A1.1": {"snippet": "...t of retrieval effectiveness\n\n\n A range of methods for measuring the effectiveness of information retrieval systems has been proposed. These are typically intended to provide a quantitative single-val...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [72, 85, 1.0], [101, 110, 1.0]]}, "1972.ipm_journal-ir0volumeA8A5.1": {"snippet": "... the effectiveness of an on-line, natural language retrieval system", "weights": [[8, 21, 1.0], [54, 63, 1.0], [64, 70, 1.0]]}, "2001.ipm_journal-ir0volumeA37A4.0": {"snippet": "Retrieval effectiveness on the web", "weights": [[0, 9, 1.0], [10, 23, 1.0]]}, "2007.sigirconf_conference-2007.25": {"snippet": "...ance retrieval systems without resorting to methods that may harm effectiveness.We evaluate our techniques using Galago, a new retrieval system designed for efficient query processing. Our system achi...", "weights": [[8, 17, 1.0], [69, 82, 1.0], [130, 139, 1.0], [140, 146, 1.0], [192, 198, 1.0]]}, "C10-2130": {"snippet": "...free retrieval evaluation. By using one system to represent all the systems that are similar to it, we can largely reduce the negative effect of similar retrieval results in Retrieval evaluation. Expe...", "weights": [[8, 17, 1.0], [43, 49, 1.0], [156, 165, 1.0], [177, 186, 1.0]]}, "2014.cikm_conference-2014.9": {"snippet": "...ween Retrieval Bias and Retrieval Performance\n\n\n ABSTRACTRetrievability provides an alternative way to assess an Information Retrieval (IR) system by measuring how easily documents can be retrieved. R...", "weights": [[8, 17, 1.0], [27, 36, 1.0], [128, 137, 1.0], [143, 149, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords retrieval model and the retrieval needs of the QA system. Bag-of-words IR retrieves documents matching a query, but the QA system really needs documents that contain answers. Through question ana...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [58, 64, 1.0], [131, 137, 1.0]]}, "2007.ipm_journal-ir0volumeA43A1.13": {"snippet": "...tion retrieval systems to effectively search for and locate the required information. In this study, we present several improvements over the two main bottlenecks in a distributed information retrieva...", "weights": [[8, 17, 1.0], [195, 204, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.2": {"snippet": "...ment retrieval behaviour. In particular relevance feedback query-expansion methods, which are often effective for improving electronic text retrieval, are observed to be less reliable for retrieval of...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [143, 152, 1.0], [191, 200, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "... the effectiveness of retrieval models from the documents retrievability point of view. We focus particularly on the retrieval bias of different retrieval models, and try to examine to what extent thi...", "weights": [[8, 21, 1.0], [25, 34, 1.0], [120, 129, 1.0], [148, 157, 1.0]]}, "P92-1014": {"snippet": "...tion retrieval system which uses advanced natural language processing techniques to enhance the effectiveness of traditional key-word based document retrieval. The backbone of our system is a statisti...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [99, 112, 1.0], [152, 161, 1.0], [183, 189, 1.0]]}, "1988.jasis_journal-ir0volumeA39A3.0": {"snippet": "... the effectiveness of full-text retrieval. II. On the effectiveness of ranking algorithms on full-text retrieval", "weights": [[8, 21, 1.0], [35, 44, 1.0], [57, 70, 1.0], [106, 115, 1.0]]}, "2008.cikm_conference-2008.183": {"snippet": "... the effectiveness of the opinion retrieval and opinion polarity classification\n\n\n ABSTRACTOpinion retrieval is a document retrieving and ranking process. A relevant document must be relevant to the q...", "weights": [[8, 21, 1.0], [37, 46, 1.0], [102, 111, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.2": {"snippet": "...ms \u2192 Retrieval effectiveness; Learning to rank; Information retrieval query processing;Additional Key Words and Phrases: Information systems, information retrieval, learning to rank, retrieval system ...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [63, 72, 1.0], [157, 166, 1.0], [186, 195, 1.0], [196, 202, 1.0]]}, "2004.cikm_conference-2004.47": {"snippet": "...tion retrieval systems. Each system demonstrated improved effectiveness with the use of blind feedback, in which the results of a preliminary retrieval step were used to augment the efficacy of a seco...", "weights": [[8, 17, 1.0], [32, 38, 1.0], [61, 74, 1.0], [145, 154, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.12": {"snippet": "...tion retrieval systems offering automated assistance could greatly improve search effectiveness by suggesting or implementing assistance automatically. A critical issue in designing such systems is de...", "weights": [[8, 17, 1.0], [85, 98, 1.0]]}, "1998.sigirconf_conference-98.15": {"snippet": "... the retrieval effectiveness of distributed information retrieval systems in realistic environments.We find that when a large number of collections are available, the retrieval effectiveness is signif...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [59, 68, 1.0], [170, 179, 1.0], [180, 193, 1.0]]}, "H94-1073": {"snippet": "...eech retrieval system affects the retrieval effectiveness. A speech retrieval system facilitates content-based retrieval of speech documents, i.e. audio recordings containing spoken text. The speech r...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [37, 46, 1.0], [47, 60, 1.0], [71, 80, 1.0], [81, 87, 1.0], [114, 123, 1.0], [202, 211, 1.0]]}, "A94-1028": {"snippet": "...tion Retrieval\n\n\n We report on the results of a series of experiments with a prototype text retrieval system which uses relatively advanced natural language processing techniques in order to enhance t...", "weights": [[8, 17, 1.0], [95, 104, 1.0], [105, 111, 1.0]]}, "2017.ipm_journal-ir0volumeA53A4.6": {"snippet": "...arch effectiveness for different kinds of users? We observed four different kinds of information seekers using an experimental information retrieval system: (1) search novices; (2) domain experts; (3)...", "weights": [[8, 21, 1.0], [142, 151, 1.0], [152, 158, 1.0]]}, "O01-2005": {"snippet": "Improving the Effectiveness of Information Retrieval with Clustering and Fusion\n\n\n Fusion and clustering are two approaches to improving the effectiveness of information retrieval. In fusion, ranked l...", "weights": [[0, 9, 1.0], [14, 27, 1.0], [43, 52, 1.0], [127, 136, 1.0], [141, 154, 1.0], [170, 179, 1.0]]}, "1989.ipm_journal-ir0volumeA25A4.2": {"snippet": "Improving the effectiveness of retrieval systems by information structures", "weights": [[0, 9, 1.0], [14, 27, 1.0], [31, 40, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.11": {"snippet": "... for improving web retrieval effectiveness\n\n\n AbstractThis paper talks about several schemes for improving retrieval effectiveness that can be used in the named page finding tasks of web information r...", "weights": [[8, 17, 1.0], [22, 31, 1.0], [32, 45, 1.0], [100, 109, 1.0], [110, 119, 1.0], [120, 133, 1.0], [202, 211, 1.0]]}, "2005.trec_conference-2005.11": {"snippet": "...rove Effectiveness and Robustness of Ad Hoc Information Retrieval\n\n\n Our participation in this year's robust track aims to: (1) test how to improve the effectiveness of IR (according to MAP) using dif...", "weights": [[8, 21, 1.0], [59, 68, 1.0], [155, 168, 1.0]]}, "1988.jasis_journal-ir0volumeA39A2.0": {"snippet": "... the effectiveness of full-text retrieval. I. On the effectiveness of full-text retrieval", "weights": [[8, 21, 1.0], [35, 44, 1.0], [56, 69, 1.0], [83, 92, 1.0]]}, "2009.cikm_conference-2009.277": {"snippet": "... for improving retrieval effectiveness, but the noise in the top retrieved documents still can cause topic drift problem that affects the performance of certain topics. By viewing a document as an int...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [28, 41, 1.0]]}, "2007.sigirconf_conference-2007.212": {"snippet": "...ween system effectiveness and users' performance. People learn to adapt to a system regardless to its effectiveness. This study recommends that a combination of measures (e.g. system effectiveness, us...", "weights": [[8, 14, 1.0], [15, 28, 1.0], [80, 86, 1.0], [105, 118, 1.0], [179, 185, 1.0], [186, 199, 1.0]]}, "O07-2010": {"snippet": "...\u4e4b\u7814\u7a76 (Improving Retrieval Effectiveness by Document Reranking and Local Expansion) [In {C}hinese]", "weights": [[8, 17, 1.0], [18, 27, 1.0], [28, 41, 1.0]]}, "2004.ecir_conference-2004.21": {"snippet": "Improving Retrieval Effectiveness by Reranking Documents Based on Controlled Vocabulary", "weights": [[0, 9, 1.0], [10, 19, 1.0], [20, 33, 1.0]]}, "1973.jasis_journal-ir0volumeA24A2.1": {"snippet": "...e of retrieval effectiveness", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2008.tois_journal-ir0volumeA26A4.2": {"snippet": "...file retrieval events but searched for only 4-15% of events. Second, the effect of improving the quality of the search engine on search usage was limited and inconsistent. Third, search was used mainl...", "weights": [[8, 17, 1.0], [86, 95, 1.0]]}, "2005.ipm_journal-ir0volumeA41A1.7": {"snippet": "... the retrieval effectiveness of information retrieval systems, however the designing and the implementation of stemmers requires a laborious amount of effort due to the fact that documents and queries...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [47, 56, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...pert System based control system for the control of an Automated Storage and Retrieval System (AS/RS) is created. The control system includes several unique elements, including the hierarchical partit...", "weights": [[8, 14, 1.0], [29, 35, 1.0], [80, 89, 1.0], [90, 96, 1.0], [129, 135, 1.0]]}, "2001.sigirconf_conference-2001.47": {"snippet": "...tion Retrieval System\n\n\n ABSTRACTThis paper presents a novel information retrieval system that includes 1) the addition of concepts to facilitate the identification of the correct word sense, 2) a nat...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [76, 85, 1.0], [86, 92, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [196, 202, 1.0]]}, "2007.tois_journal-ir0volumeA26A1.4": {"snippet": "...ment retrieval\n\n\n Due to the existence of large numbers of legacy documents (such as old books and newspapers), improving retrieval effectiveness for OCR'ed documents continues to be an important prob...", "weights": [[8, 17, 1.0], [115, 124, 1.0], [125, 134, 1.0], [135, 148, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.0": {"snippet": "...tion retrieval systems using data fusion\n\n\n AbstractMeasuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic...", "weights": [[8, 17, 1.0], [65, 78, 1.0], [94, 103, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.7": {"snippet": "...tion retrieval\n\n\n a b s t r a c tThe Condorcet fusion is a distinctive fusion method and was found useful in information retrieval. Two basic requirements for the Condorcet fusion to improve retrieval...", "weights": [[8, 17, 1.0], [124, 133, 1.0], [194, 203, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...heir retrieval strategies. To be viable as a laboratory tool, a collection must reliably rank different retrieval variants according to their true effectiveness. In particular, the relative effectiven...", "weights": [[8, 17, 1.0], [107, 116, 1.0], [150, 163, 1.0], [193, 206, 1.0]]}, "2018.ipm_journal-ir0volumeA54A2.1": {"snippet": "...meta-system. Even simple ensemble techniques, when used in an informed manner, prove to be very effective in improving the overall performance and consistency of summarization systems. A statistically...", "weights": [[8, 14, 1.0], [112, 121, 1.0]]}, "2008.sigirconf_conference-2008.127": {"snippet": "... web retrieval\n\n\n ABSTRACTDocument prior features, such as Pagerank and URL depth, can improve the retrieval effectiveness of Web Information Retrieval (IR) systems. However, not all queries equally b...", "weights": [[8, 17, 1.0], [102, 111, 1.0], [112, 125, 1.0], [145, 154, 1.0]]}, "2017.sigirconf_conference-2017.144": {"snippet": "... the effectiveness of information retrieval systems by augmenting documents' term probability estimates with those of similar documents, producing higher quality document representations. We propose a...", "weights": [[8, 21, 1.0], [37, 46, 1.0]]}, "2003.sigirconf_conference-2003.23": {"snippet": "...s in retrieval effectiveness. In this study we explore the development and subsequent evaluation of a statistical word sense disambiguation system which demonstrates increased precision from a sense b...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [143, 149, 1.0]]}, "2002.cikm_conference-2002.43": {"snippet": "...rch: improving the retrieval effectiveness via a light stemming approach\n\n\n ABSTRACTThe inflectional structure of a word impacts the retrieval accuracy of information retrieval systems of Latin-based ...", "weights": [[8, 17, 1.0], [22, 31, 1.0], [32, 45, 1.0], [136, 145, 1.0], [170, 179, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.9": {"snippet": "...to a retrieval system and obtains no relevant documents, he or she is bound to feel frustrated. We call these weak queries and retrievals. Improving their effectiveness is an important issue for ad ho...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [142, 151, 1.0], [158, 171, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of the system. Instead, most effort has been concentrated on the building and testing of the image analysis techniques: \nExperimental designIn order to evaluate the effect...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [39, 45, 1.0], [197, 210, 1.0]]}, "2016.ntcir_conference-2016.90": {"snippet": "...tent Retrieval (SCR) subtask. We investigated the use of multiple ASR hypotheses (words) and subword units (syllables) for improving retrieval effectiveness. We also compared the retrieval effectivene...", "weights": [[8, 17, 1.0], [126, 135, 1.0], [136, 145, 1.0], [146, 159, 1.0], [182, 191, 1.0], [192, 205, 1.0]]}, "1990.jasis_journal-ir0volumeA41A5.0": {"snippet": "... the effectiveness of information retrieval systems using simulated queries", "weights": [[8, 21, 1.0], [37, 46, 1.0]]}, "W05-0704": {"snippet": "...n IR effectiveness and that context sensitive morphology further improves retrieval effectiveness, but the improvement is not statistically significant. Furthermore, the improvement obtained by the us...", "weights": [[8, 21, 1.0], [77, 86, 1.0], [87, 100, 1.0]]}, "2015.ipm_journal-ir0volumeA51A5.7": {"snippet": "... the effectiveness of a non-deterministic system with a deterministic or another non-deterministic system. In addition, we present a statistical best-practice that can be used to safely show how a non...", "weights": [[8, 21, 1.0], [45, 51, 1.0], [102, 108, 1.0]]}, "2004.cikm_conference-2004.48": {"snippet": "...hile improving retrieval effectiveness. The algorithm is efficient and scalable, suited for retrieval systems managing large document collections.", "weights": [[8, 17, 1.0], [18, 27, 1.0], [28, 41, 1.0], [95, 104, 1.0]]}, "2004.clef_workshop-2004.81": {"snippet": "...e to Retrieval Effectiveness?", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "W97-0315": {"snippet": "...tion Retrieval\n\n\n The main application of name searching has be~ name matching in a database of names. This paper discusses a different application: improving information retrieval through name recogn...", "weights": [[8, 17, 1.0], [152, 161, 1.0], [174, 183, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...good retrieval performance is closely related to the use of various retrieval heuristics. We connect the weaknesses and strengths of a retrieval function with its implementations of these retrieval he...", "weights": [[8, 17, 1.0], [71, 80, 1.0], [138, 147, 1.0], [191, 200, 1.0]]}, "2003.trec_conference-2003.21": {"snippet": "...rove Retrieval Effectiveness", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "1999.cikm_conference-99.43": {"snippet": "...ious retrieval approaches and query representations can improve search effectiveness. Today, many me&search engines exist which combine the results of various search engines in the hopes of improving ...", "weights": [[8, 17, 1.0], [74, 87, 1.0], [193, 202, 1.0]]}, "2012.sigirconf_conference-2012.134": {"snippet": "...tion retrieval\n\n\n ABSTRACTMany information retrieval (IR) techniques have been proposed to improve the performance, and some combinations of these techniques has been demonstrated to be effective. How...", "weights": [[8, 17, 1.0], [46, 55, 1.0]]}, "2018.ictir_conference-2018.20": {"snippet": "...d by retrieval systems in terms of Information Theory. The proposed framework allows to formalize: (i) system effectiveness as an information theoretic similarity between system outputs and human asse...", "weights": [[8, 17, 1.0], [106, 112, 1.0], [113, 126, 1.0], [174, 180, 1.0]]}, "1998.sigirconf_conference-98.22": {"snippet": "... our system that included a 3-D visualization.The questions we investigated in the context of this work were:Can we build a system for the aspect retrieval task that is more effective than a basic ret...", "weights": [[8, 14, 1.0], [127, 133, 1.0], [149, 158, 1.0], [200, 209, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A2.12": {"snippet": "...s of improving the effectiveness of current information retrieval systems. This aim can be achieved by accomplishing numerous supporting objectives.", "weights": [[8, 17, 1.0], [22, 35, 1.0], [59, 68, 1.0]]}, "2002.ipm_journal-ir0volumeA38A1.0": {"snippet": "Improving the retrieval effectiveness of very short queries", "weights": [[0, 9, 1.0], [14, 23, 1.0], [24, 37, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...ment retrieval systems is not adversely affected by speech recognition errors. Even with error rates of around 40%, the effectiveness of an IR system falls less than 10%. The paper hypothesizes that t...", "weights": [[8, 17, 1.0], [123, 136, 1.0], [146, 152, 1.0]]}, "2005.trec_conference-2005.26": {"snippet": "... our retrieval system scales up to terabyte-scale collections. We found that our retrieval system can handle 25 million documents, although in terms of indexing time we are approaching the limits of a...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [84, 93, 1.0], [94, 100, 1.0]]}, "2011.ecir_conference-2011.27": {"snippet": "...s of Retrieval Effectiveness", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...tion retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC -required s...", "weights": [[8, 17, 1.0], [42, 51, 1.0], [92, 101, 1.0], [147, 156, 1.0]]}, "2018.wsdm_conference-2018.47": {"snippet": "...zing Retrieval Systems\n\n\n ABSTRACTThe effectiveness of information retrieval systems heavily depends on a large number of hyperparameters that need to be tuned. Hyperparameters range from the choice o...", "weights": [[8, 17, 1.0], [41, 54, 1.0], [70, 79, 1.0]]}, "1991.ipm_journal-ir0volumeA27A23.1": {"snippet": "... the effectiveness of retrieval algorithms", "weights": [[8, 21, 1.0], [25, 34, 1.0]]}, "N07-1028": {"snippet": "...tion retrieval systems are frequently required to handle long queries. Simply using all terms in the query or relying on the underlying retrieval model to appropriately weight terms often leads to ine...", "weights": [[8, 17, 1.0], [139, 148, 1.0]]}, "2008.cikm_conference-2008.76": {"snippet": "...tion retrieval\n\n\n ABSTRACTAs an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and cross-language information retrieval ...", "weights": [[8, 17, 1.0], [59, 68, 1.0], [69, 78, 1.0], [79, 92, 1.0], [193, 202, 1.0]]}, "2012.wwwconf_conference-2012c.84": {"snippet": "...ning system for unrecognized drug side effects discovery based on the text information gathered from the Internet. The system can automatically build a knowledge base for drug side effects by integrat...", "weights": [[8, 14, 1.0], [122, 128, 1.0]]}, "2016.iir_workshop-2016.13": {"snippet": "Improving the Efficiency of Retrieval Effectiveness Evaluation: Finding a Few Good Topics with Clustering?", "weights": [[0, 9, 1.0], [28, 37, 1.0], [38, 51, 1.0]]}, "2008.ipm_journal-ir0volumeA44A3.9": {"snippet": "...tion retrieval systems\n\n\n AbstractNowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval techniques (particularly images retrieval) have beco...", "weights": [[8, 17, 1.0], [141, 150, 1.0], [183, 192, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ased retrieval.Directions for future work are using these concepts for communication between user and retrieval system, extending upon the translation models and extending CLIR-enhanced concept-based ...", "weights": [[8, 17, 1.0], [105, 114, 1.0], [115, 121, 1.0]]}, "1998.sigirconf_conference-98.33": {"snippet": "...tion retrieval model, in a workable system. First order logic and standard information retrieval techniques are combined together, to the same effect: obtaining an expressive system, able to accuratel...", "weights": [[8, 17, 1.0], [39, 45, 1.0], [90, 99, 1.0], [178, 184, 1.0]]}, "1995.sigirconf_conference-95.30": {"snippet": "... the system, computes a score indicating how good those decisions were.Tune the system so that the expected effectiveness of its decisions will be the best possible.Have the system estimate the effect...", "weights": [[8, 14, 1.0], [83, 89, 1.0], [111, 124, 1.0], [177, 183, 1.0], [197, 210, 1.0]]}, "2017.airs_conference-2017.1": {"snippet": "Improving Retrieval Effectiveness for Temporal-Constrained Top-K Query Processing", "weights": [[0, 9, 1.0], [10, 19, 1.0], [20, 33, 1.0]]}, "2008.cikm_conference-2008.59": {"snippet": "...tion Retrieval (IR) has long focused on effectiveness and efficiency. However, new and emerging access tasks now demand alternative evaluation measures which go beyond this traditional view. A retriev...", "weights": [[8, 17, 1.0], [43, 56, 1.0], [196, 205, 1.0]]}, "2021.mrqa-1.16": {"snippet": "...ense retrieval has been shown to be effective for Open Domain Question Answering, surpassing sparse retrieval methods like BM25. One such model, REALM, (Guu et al., 2020) is an end-to-end dense retrie...", "weights": [[8, 17, 1.0], [103, 112, 1.0], [197, 206, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.23": {"snippet": "...tion Retrieval (IR), accurately interpreting the result of a system in terms of user satisfaction is fundamental to push the research in the correct direction. Therefore, measuring systems effectivene...", "weights": [[8, 17, 1.0], [64, 70, 1.0], [192, 205, 1.0]]}, "1978.ipm_journal-ir0volumeA14A1.0": {"snippet": "...y of retrieval effectiveness measures", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "1998.trec_conference-1998.34": {"snippet": "...tion Retrieval Effectiveness", "weights": [[8, 17, 1.0], [18, 31, 1.0]]}, "2014.cikm_conference-2014.212": {"snippet": "...mean retrieval effectiveness (e.g., MAP) may sacrifice the retrieval stability across queries, implying a tradeoff between effectiveness and stability. The evaluation of both effectiveness and stabili...", "weights": [[8, 17, 1.0], [18, 31, 1.0], [62, 71, 1.0], [126, 139, 1.0], [178, 191, 1.0]]}, "1999.ntcir_workshop-1999.31": {"snippet": "...ndex System at NTCIR1: Information Retrieval using Dependency Relationship between Words\n\n\n It is difficult to improve retrieval effectiveness using only keyword-based retrieval, the major method in d...", "weights": [[8, 14, 1.0], [38, 47, 1.0], [122, 131, 1.0], [132, 145, 1.0], [171, 180, 1.0]]}, "2010.irfc_conference-2010.7": {"snippet": "...y in Effectiveness of a Retrieval System", "weights": [[8, 21, 1.0], [27, 36, 1.0], [37, 43, 1.0]]}, "1997.sigirconf_conference-97.36": {"snippet": "...cale retrieval context, or whether inexperienced users can achieve this effectiveness in practice. These experiments address these two questions. A small but significant improvement in potential retri...", "weights": [[8, 17, 1.0], [75, 88, 1.0], [198, 207, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2000.sigirconf_conference-2000.7": {"snippet": "... the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance. The test was run with a best match retrieval system...", "weights": [[8, 21, 1.0], [187, 196, 1.0], [197, 203, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.18": {"snippet": "...mage retrieval from multimedia databases, which improves both the effectiveness and efficiency of traditional CBIR by exploring secondary media. We perform retrieval in a two-stage fashion: first rank...", "weights": [[8, 17, 1.0], [69, 82, 1.0], [159, 168, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A1.9": {"snippet": "... the effectiveness of queries and retrieval systems", "weights": [[8, 21, 1.0], [37, 46, 1.0]]}}}
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We combine string and...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [140, 154, 1.0], [166, 174, 1.0]]}, "2020.coling-main.159": {"snippet": "...tive Language Identification\n\n\n Native language identification (NLI) -identifying the native language (L1) of a person based on his/her writing in the second language (L2) -is useful for a variety of ...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [42, 50, 1.0], [51, 65, 1.0], [96, 104, 1.0], [161, 169, 1.0]]}, "W13-1713": {"snippet": "...y of machine learning approaches that we explored, including Winnow, language modeling, logistic regression and maximum-entropy models. Our primary features were word and character n-grams. We also de...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [72, 80, 1.0]]}, "W18-1203": {"snippet": "...ural language model (LM), and data bootstrapping to overcome the problem of unbalanced limited resources. As a test, we explore the task of language identification in mixed-language short non-edited t...", "weights": [[8, 16, 1.0], [143, 151, 1.0], [152, 166, 1.0], [176, 184, 1.0]]}, "2014.airs_conference-2014.24": {"snippet": "...d on machine learning approaches that extract nationalities, weapons, and crime locations in online crime documents. This study also collected a new corpus of crime and manually labeled them. A machin...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [197, 204, 1.0]]}, "W04-3210": {"snippet": "...raph Identification: A Study across Languages and Domains\n\n\n In this paper we investigate whether paragraphs can be identified automatically in different languages and domains. We propose a machine le...", "weights": [[8, 22, 1.0], [193, 200, 1.0], [201, 209, 1.0]]}, "2020.eamt-1.69": {"snippet": "...ect: Machine Translation impact on language learning", "weights": [[8, 15, 1.0], [38, 46, 1.0], [47, 55, 1.0]]}, "2000.riao_conference-2000.44": {"snippet": "...rase Identification in Cross-Language Information Retrieval", "weights": [[8, 22, 1.0], [32, 40, 1.0]]}, "P18-1113": {"snippet": "...phor Identification and Interpretation\n\n\n Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Transl...", "weights": [[8, 22, 1.0], [94, 102, 1.0], [155, 163, 1.0], [189, 196, 1.0]]}, "2009.ipm_journal-ir0volumeA45A1.5": {"snippet": "...Many machine learning algorithms have been applied to text classification tasks. In the machine learning paradigm, a general inductive process automatically builds a text classifier by learning, gener...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [91, 98, 1.0], [99, 107, 1.0], [188, 196, 1.0]]}, "L02-1094": {"snippet": "...n of Machine Learning Methods for Natural Language Processing Tasks", "weights": [[8, 15, 1.0], [16, 24, 1.0], [45, 53, 1.0]]}, "2015.tist_journal-ir0volumeA6A2.9": {"snippet": "...sign language and sentence-level sign language. An American Sign Language dataset including approximately 2,000 word-level sign language phrases and 2,000 sentence-level sign language phrases was coll...", "weights": [[8, 16, 1.0], [41, 49, 1.0], [68, 76, 1.0], [131, 139, 1.0], [178, 186, 1.0]]}, "D07-1057": {"snippet": "Identification and Resolution of {C}hinese Zero Pronouns: A Machine Learning Approach\n\n\n In this paper, we present a machine learning approach to the identification and resolution of Chinese anaphoric...", "weights": [[0, 14, 1.0], [60, 67, 1.0], [68, 76, 1.0], [117, 124, 1.0], [125, 133, 1.0], [150, 164, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.16": {"snippet": "...ural language models Deep learning A B S T R A C T Automatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms. Generally...", "weights": [[8, 16, 1.0], [29, 37, 1.0], [165, 172, 1.0], [173, 181, 1.0]]}, "2019.fire_conference-2019w.41": {"snippet": "...onal Machine Learning for Hate Speech and Offensive Content Identification\n\n\n In this paper, the results obtained from the Support Vector Machine, XGBoost method by IRLab@IIT(BHU) on HASOC shared task...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [63, 77, 1.0], [141, 148, 1.0]]}, "2021.vardial-1.8": {"snippet": "...atic language identification is a challenging problem. Discriminating between closely related languages is especially difficult. This paper presents a machine learning approach for automatic language ...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [154, 161, 1.0], [162, 170, 1.0], [194, 202, 1.0]]}, "U09-1008": {"snippet": "...tive Language Identification\n\n\n Attempts to profile authors based on their characteristics, including native language, have drawn attention in recent years, via several approaches using machine learni...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [112, 120, 1.0], [189, 196, 1.0], [197, 205, 1.0]]}, "1998.ipm_journal-ir0volumeA34A4.1": {"snippet": "... for Language Identification: A Comparative Study", "weights": [[8, 16, 1.0], [17, 31, 1.0]]}, "2007.ipm_journal-ir0volumeA43A5.3": {"snippet": "...eral machine learning algorithms for cross-lan7guage patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [144, 151, 1.0], [152, 160, 1.0], [171, 179, 1.0]]}, "2015.fire_workshop-2015w.9": {"snippet": "...evel language identification. Our approach consists of a two stage algorithm for language identification. First level classification is done using sentence level character n-grams and second level con...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [84, 92, 1.0], [93, 107, 1.0]]}, "N19-5005": {"snippet": "Language Learning and Processing in People and Machines", "weights": [[0, 8, 1.0], [9, 17, 1.0]]}, "2019.fire_conference-2019w.42": {"snippet": "...019: Identification of Hate Speech using Machine Learning and Deep Learning approaches for social media post\n\n\n This paper describes the research that our team, DA Master, did on the shared task HASOC...", "weights": [[8, 22, 1.0], [44, 51, 1.0], [52, 60, 1.0], [70, 78, 1.0]]}, "2019.fire_conference-2019w.45": {"snippet": "...9: A Machine Learning Framework for Hate Speech and Offensive Language Detection\n\n\n In this paper, we describe the system submitted by our team for Hate Speech and Offensive Content Identification in ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [65, 73, 1.0], [185, 199, 1.0]]}, "S19-2130": {"snippet": "...sive Language Identification in Social Media using Traditional and Deep Machine Learning Approaches\n\n\n Offensive language identification (OLI) in user generated text is automatic detection of any prof...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [75, 82, 1.0], [83, 91, 1.0], [116, 124, 1.0], [125, 139, 1.0]]}, "W16-5818": {"snippet": "...hing language identification using Subword Information Enriched Word Vectors\n\n\n Codeswitching is a widely observed phenomenon among bilingual speakers. By combining subword information enriched word v...", "weights": [[8, 16, 1.0], [17, 31, 1.0]]}, "2020.lrec-1.432": {"snippet": "...uery Language Identification Using Weak Labeling\n\n\n Language identification is a well-known task for natural language documents. In this paper we explore search query language identification which is ...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [55, 63, 1.0], [64, 78, 1.0], [112, 120, 1.0], [170, 178, 1.0], [179, 193, 1.0]]}, "2020.semeval-1.289": {"snippet": "...d of machine learning and deep learning models for all five languages. Three machine learning and four deep learning models were used in the ensemble. In the OffensEval-2020 competition our model achi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [34, 42, 1.0], [80, 87, 1.0], [88, 96, 1.0], [111, 119, 1.0]]}, "W12-5608": {"snippet": "...dary Identification for {M}alayalam Using {CRF}\n\n\n This paper presents a clause boundary identification system for Malayalam sentences using the machine learning approach CRF (Conditional Random Field...", "weights": [[8, 22, 1.0], [92, 106, 1.0], [148, 155, 1.0], [156, 164, 1.0]]}, "2021.wanlp-1.30": {"snippet": "...ical machine learning approaches and deep learning models using small datasets. We examined an integration technique between two machine learning approaches. Additionally, we created dictionaries auto...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [45, 53, 1.0], [132, 139, 1.0], [140, 148, 1.0]]}, "2015.fire_workshop-2015w.7": {"snippet": "...ased machine learning framework was used for labeling the individual words with their corresponding language labels. We used a dictionary based approach for language identification. We also took into ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [103, 111, 1.0], [160, 168, 1.0], [169, 183, 1.0]]}, "2017.fire_conference-2017w.36": {"snippet": "...tive Language Identification\n\n\n Native Language Identification has played an important role in forensics primarily for author profiling and identification. In this work, we discuss our approach to the...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [42, 50, 1.0], [51, 65, 1.0], [143, 157, 1.0]]}, "W19-6301": {"snippet": "...sing machine learning\n\n\n Predicting the knowledge of language learners is crucial for personalized interactions in any intelligent tutoring system for language learning. This study adopts a machine le...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [56, 64, 1.0], [154, 162, 1.0], [163, 171, 1.0], [193, 200, 1.0], [201, 209, 1.0]]}, "2017.fire_conference-2017w.34": {"snippet": "...tive Language Identification using SVM with SGD Training\n\n\n Native Language Identification (NLI), as a variant of Language Identification task, focuses on determining an author's native language, base...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [70, 78, 1.0], [79, 93, 1.0], [117, 125, 1.0], [126, 140, 1.0], [189, 197, 1.0]]}, "W19-1420": {"snippet": "...ting Machine Learning Methods for Language and Dialect Identification of Cuneiform Texts\n\n\n Identification of the languages written using cuneiform symbols is a difficult task due to the lack of resou...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [37, 45, 1.0], [58, 72, 1.0], [95, 109, 1.0]]}, "2018.fire_conference-2018w.11": {"snippet": "A Machine Learning Approach to Indian Native Language Identification\n\n\n NLI (Native Language Identification) determines the native language of the non-native users using their writings in a foreign la...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [45, 53, 1.0], [54, 68, 1.0], [84, 92, 1.0], [93, 107, 1.0], [131, 139, 1.0], [198, 206, 1.0]]}, "2015.fire_workshop-2015w.6": {"snippet": "... own language script and mixed script. The primary challenges in process the short message is identifying languages. Therefore, the language identification is not restricted to a language but also to ...", "weights": [[8, 16, 1.0], [135, 143, 1.0], [144, 158, 1.0], [182, 190, 1.0]]}, "2019.fire_conference-2019w.36": {"snippet": "...sive language on Twitter in English, German and Hindi languages. As a part of this task, we (team A3-108) submitted different machine learning and neural network based models for all the languages. Ou...", "weights": [[8, 16, 1.0], [129, 136, 1.0], [137, 145, 1.0]]}, "2017.fire_conference-2017.3": {"snippet": "...tive language into english. The core part of the problem is identifying the language, looking at small fragments of text among a set of languages. We propose a two stage approach for word-level langua...", "weights": [[8, 16, 1.0], [79, 87, 1.0], [197, 205, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.0": {"snippet": "Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments\n\n\n Eliminating the negative effect of non-stationary environmental noise is a long-standing research top...", "weights": [[5, 13, 1.0]]}, "W19-4628": {"snippet": "...lect Identification. Dialect Identification is one of the prominent tasks in the field of Natural language processing where the subsequent language modules can be improved based on it. We explored the...", "weights": [[8, 22, 1.0], [32, 46, 1.0], [101, 109, 1.0], [142, 150, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...ural language processing techniques. Different groups focused on different aspects of the overall problem, ranging from the development of language-independent tools such as stemmers to much work on l...", "weights": [[8, 16, 1.0], [142, 150, 1.0], [202, 210, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.0": {"snippet": "...m of language identification of noisy texts, which could represent the primary step of many natural language processing or information retrieval tasks. Language identification is the task of automatic...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [103, 111, 1.0], [155, 163, 1.0], [164, 178, 1.0]]}, "W15-0606": {"snippet": "...tive Language Identification\n\n\n The task of Native Language Identification (NLI) is typically solved with machine learning methods, and systems make use of a wide variety of features. Some preliminary...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [54, 62, 1.0], [63, 77, 1.0], [109, 116, 1.0], [117, 125, 1.0]]}, "J96-3002": {"snippet": "Machine Learning Comprehension Grammars for Ten Languages", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "W18-3921": {"snippet": "...ryan Language Identification ({ILI}) at {V}ar{D}ial 2018\n\n\n Text Language Identification is a Natural Language Processing (NLP) task of identifying and recognizing a given language out of many differe...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [68, 76, 1.0], [77, 91, 1.0], [105, 113, 1.0], [175, 183, 1.0]]}, "2017.jasis_journal-ir0volumeA68A10.7": {"snippet": "...ntal machine learning", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "L12-1354": {"snippet": "... for Machine Learning Approach to Authorship Identification", "weights": [[8, 15, 1.0], [16, 24, 1.0], [48, 62, 1.0]]}, "W14-5151": {"snippet": "...evel Language Identification\n\n\n Language identification is a necessary prerequisite for processing any user generated text, where the language is unknown. It becomes even more challenging when the tex...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [35, 43, 1.0], [44, 58, 1.0], [137, 145, 1.0]]}, "W05-0401": {"snippet": "...ovel Machine Learning Approach for the Identification of Named Entity Relations\n\n\n In this paper, a novel machine learning approach for the identification of named entity relations (NERs) called posit...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [42, 56, 1.0], [109, 116, 1.0], [117, 125, 1.0], [143, 157, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "...asic machine learning concepts and tools; in the second session, we will introduce more advanced topics in machine learning, and will present recent developments in machine learning and its applicatio...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [110, 117, 1.0], [118, 126, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "2014.fire_conference-2014.9": {"snippet": "Machine Learning Approach for Language Identification & Transliteration\n\n\n ABSTRACTIn this paper, we describe the system that we developed as part of our participation to the FIRE-2014 Shared Task on ...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [30, 38, 1.0], [39, 53, 1.0]]}, "E17-1087": {"snippet": "...gual Language Identification on Character Window\n\n\n In language identification, a common first step in natural language processing, we want to automatically determine the language of some input text. ...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [58, 66, 1.0], [67, 81, 1.0], [114, 122, 1.0], [174, 182, 1.0]]}, "2020.semeval-1.264": {"snippet": "... 12: Machine Learning and Deep Learning Methods for Offensive Language Identification\n\n\n The paper describes systems that our team IRLab DAIICT employed for shared task OffensEval 2020: Multilingual O...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [34, 42, 1.0], [65, 73, 1.0], [74, 88, 1.0]]}, "S19-2107": {"snippet": "...sive Language Identification using Traditional Machine Learning and Deep Learning approaches\n\n\n In this paper, the used methods and the results obtained by our team, entitled Emad, on the OffensEval 2...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [50, 57, 1.0], [58, 66, 1.0], [76, 84, 1.0]]}, "2018.fire_conference-2018w.10": {"snippet": "...tive Language Identification INLI@FIRE2018\n\n\n Native Language Identification is the process of determining the language native to the author from the written text. We have proposed a system that uses ...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [56, 64, 1.0], [65, 79, 1.0], [114, 122, 1.0]]}, "2014.fire_conference-2014.8": {"snippet": "...ased machine learning model and post-processing heuristics are employed for the WLL identification task. In addition to language identification, two transliteration systems were built to transliterate...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [87, 101, 1.0], [123, 131, 1.0], [132, 146, 1.0]]}, "2009.ipm_journal-ir0volumeA45A1.0": {"snippet": "A language modeling framework for expert finding\n\n\n a b s t r a c tStatistical language models have been successfully applied to many information retrieval tasks, including expert finding: the process...", "weights": [[2, 10, 1.0], [79, 87, 1.0]]}, "1991.riao_conference-1991.48": {"snippet": "Machine learning principles in application to a transportable natural language database interface", "weights": [[0, 7, 1.0], [8, 16, 1.0], [70, 78, 1.0]]}, "2020.semeval-1.287": {"snippet": "...sive Language Identification in {E}nglish, {D}anish, {G}reek Using {BERT} and Machine Learning Approach\n\n\n Offensive language identification is to detect the hurtful tweets, derogatory comments, swear...", "weights": [[8, 16, 1.0], [17, 31, 1.0], [81, 88, 1.0], [89, 97, 1.0], [120, 128, 1.0], [129, 143, 1.0]]}}}
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Although memes are typically humorous in nature, recent da...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2015.wwwconf_conference-2015c.131": {"snippet": "...from Social Media Streams\n\n\n ABSTRACTMy proposed research addresses fundamental deficiencies in social media-based event detection by discovering highimpact moments and evaluating their credibility ra...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [99, 105, 1.0], [106, 111, 1.0]]}, "R17-1062": {"snippet": "...h in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2017.sigirconf_conference-2017.232": {"snippet": "... and Social Media\n\n\n ABSTRACTPeople regularly use web search and social media to investigate health related issues.is type of Internet data might contain misinformation i.e. incorrect information whic...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [68, 74, 1.0], [75, 80, 1.0]]}, "2019.clef_conference-2019w.31": {"snippet": "...into Social Media Texts for the Early Detection of Mental and Food Disorders\n\n\n This paper describes the participation of the Bioinformatics group of the Institute of Electronics and Engineering Infor...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "W17-3108": {"snippet": "...s on social media may reveal themselves to be in various states of crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis from social media text automatically and accurately ca...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [154, 160, 1.0], [161, 166, 1.0]]}, "2019.clef_conference-2019w.19": {"snippet": "...m on Social Media\n\n\n Three tasks are proposed at CLEF eRisk-2019 for predicting mental disorder using users posts on Reddit. Two tasks (T1 and T2) focus on early risk detection of signs of anorexia an...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.jasis_journal-ir0volumeA71A1.3": {"snippet": "...sing social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.wwwconf_conference-2020.216": {"snippet": "...s in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "Y18-1070": {"snippet": "...from Social Media\n\n\n Although depression is one of the most common mental disorders, the depressed individuals may not be aware of their symptoms at all so that they sometimes miss the appropriate tim...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.tweb_journal-ir0volumeA14A4.3": {"snippet": "...n of Social Media Hoaxes at Scale", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.clef_conference-2020w.90": {"snippet": "...s in social media.", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2017.wwwconf_conference-2017.13": {"snippet": "...t in Social Media\n\n\n ABSTRACTStudies suggest that self-harm users found it easier to discuss self-harm-related thoughts and behaviors using social media than in the physical world. Given the enormous ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [143, 149, 1.0], [150, 155, 1.0]]}, "2018.coria_conference-2018.7": {"snippet": "...s in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2019.tweb_journal-ir0volumeA13A3.3": {"snippet": "...n in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2019.clef_conference-2019w.171": {"snippet": "...n in Social Media\n\n\n In this paper we describe the participation of our research group at the CLEF eRisk 2019. The eRisk goal is the early detection of atrisk people by means of machine learning techn...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "W18-4421": {"snippet": "...cale social media monitoring and early detection of undesired situations. This paper presents the Isistanitos's approach for detecting aggressive content in multiple social media sites. The approach i...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [169, 175, 1.0], [176, 181, 1.0]]}, "2014.sigirconf_conference-2014.185": {"snippet": "...y of social media to automatically detect road hazards. We conduct experiments on a testbed of tweets discussing road conditions and the initial results demonstrate the effectiveness of our approach.", "weights": [[8, 14, 1.0], [15, 20, 1.0], [38, 44, 1.0]]}, "2018.wwwconf_conference-2018c.268": {"snippet": "...l on social media. Unfortunately, certain social media users go to extreme lengths to click selfies, which puts their lives at risk. Two hundred and sixteen individuals have died since March 2014 unti...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [45, 51, 1.0], [52, 57, 1.0]]}, "2018.wwwconf_conference-2018.13": {"snippet": "...s in Social Media\n\n\n ABSTRACTMillions of users share their experiences on social media sites, such as Twitter, which in turn generate valuable data for public health monitoring, digital epidemiology, ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [77, 83, 1.0], [84, 89, 1.0]]}, "D17-1322": {"snippet": "Depression and Self-Harm Risk Assessment in Online Forums\n\n\n Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities ...", "weights": []}, "2016.cikm_workshop-2016cybersafety.1": {"snippet": "...s to detect bad actors in social media. More specifically, we will describe both general methods that are platform independent or valid for any malicious user, and methods specific to a particular soc...", "weights": [[8, 14, 1.0], [29, 35, 1.0], [36, 41, 1.0], [200, 206, 1.0]]}, "2015.cikm_conference-2015.185": {"snippet": "Building Effective Query Classifiers: A Case Study in Self-harm Intent Detection", "weights": []}, "2019.clef_conference-2019w.165": {"snippet": "...from Social Media Postings\n\n\n Online social media platforms allow open sharing of thoughts and dialogue. These platforms generate large amounts of data about their users' online behaviour, which can b...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [40, 46, 1.0], [47, 52, 1.0]]}, "2015.wwwconf_conference-2015c.125": {"snippet": "...mely social spams. While it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of users s...", "weights": [[8, 14, 1.0]]}, "2017.wwwconf_conference-2017.14": {"snippet": "Mobile Sensing at the Service of Mental Well-being: a Large-scale Longitudinal Study\n\n\n ABSTRACTMeasuring mental well-being with mobile sensing has been an increasingly active research topic. Pervasiv...", "weights": []}, "2017.wwwjournals_journal-ir0volumeA20A2.11": {"snippet": "... for social user profiling\n\n\n Abstract Traditionally, research about social user profiling assumes that users share some similar interests with their followees. However, it lacks the studies on what t...", "weights": [[8, 14, 1.0], [72, 78, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.17": {"snippet": "...s of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar s...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [60, 66, 1.0], [67, 72, 1.0], [110, 116, 1.0], [117, 122, 1.0], [202, 208, 1.0]]}, "2020.wwwconf_conference-2020c.117": {"snippet": "...g on Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.ipm_journal-ir0volumeA57A3.2": {"snippet": "...n on social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2016.wsdm_conference-2016.88": {"snippet": "...h as social media rumors. Therefore, we adopt this type enquiry activities as signal to detect rumors. Experiment results show that our rumor detection approach can detect social media rumors at early...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [91, 97, 1.0], [168, 174, 1.0], [175, 181, 1.0], [182, 187, 1.0]]}, "2020.clef_conference-2020w.55": {"snippet": "...s on social media. Identifying mental illnesses especially in the early stages can help people and avoid risky behaviors. Personal notes on social media are often indicative of one's psychological sta...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [143, 149, 1.0], [150, 155, 1.0]]}, "2020.clef_conference-2020w.116": {"snippet": "...e to detect these individuals through their content on social media. Early detection is crucial for mental health problems, and for this purpose a shared task named eRisk was proposed. This paper desc...", "weights": [[8, 14, 1.0], [58, 64, 1.0], [65, 70, 1.0]]}, "2019.ecir_conference-20192.48": {"snippet": "...d in Social Media platforms and other online communities an easier way to reveal their minds. The textual content produced by such users proved to be handful in evaluating mental health and detecting ...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.clef_conference-2020w.60": {"snippet": "...hich social media users are at risk of self-harming, with precision up to 91.3% for Task 1. Given these promising results, it will be interesting to further refine the training regime, classifier and ...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.1": {"snippet": "...h of social media, massive misinformation is also spreading widely on social media, e.g., Weibo and Twitter, and brings negative effects to human life. Today, automatic misinformation identification h...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [73, 79, 1.0], [80, 85, 1.0]]}, "2020.clef_conference-2020w.49": {"snippet": "...from social media. Computational methods based on machine learning and natural language processing have a great potential to assist with early detection of mental disorders of social media users, base...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [179, 185, 1.0], [186, 191, 1.0]]}, "2020.clef_conference-2020w.135": {"snippet": "...s in social media.", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2019.wwwconf_conference-2019c.14": {"snippet": "...g in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2019.clef_conference-2019w.1": {"snippet": "A Baseline Approach for Early Detection of Signs of Anorexia and Self-harm in Reddit Posts\n\n\n This paper describes the systems developed by the BiTeM team for the CLEF eRisk Task 1 and 2, 2019. The go...", "weights": []}, "2020.chiir_conference-2020.49": {"snippet": "Detecting Critical Responses from Deliberate Self-harm Videos on YouTube", "weights": []}, "2021.wwwconf_conference-2021c.39": {"snippet": "...with Social Media Data", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.ecir_conference-20202.72": {"snippet": "eRisk 2020: Self-harm and Depression Challenges\n\n\n This paper describes eRisk, the CLEF lab on early risk prediction on the Internet. eRisk started in 2017 as an attempt to set the experimental founda...", "weights": []}, "2018.wwwconf_conference-2018c.181": {"snippet": "...e of social media can be leveraged to identify stress and relaxation. In this PhD research, we introduce an improved method to detect expressions of stress and relaxation in social media content. It u...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [130, 136, 1.0], [177, 183, 1.0], [184, 189, 1.0]]}, "2017.tist_journal-ir0volumeA8A6.11": {"snippet": "...mely social spams. Although it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of user...", "weights": [[8, 14, 1.0]]}, "2019.clef_conference-2019w.170": {"snippet": "...ting Social Media users, namely Reddit, who may be under the risk of Anorexia, self-harm, and depression. We participated in both tasks of early risk detection of Anorexia and self-harm. Our predictio...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2018.wwwconf_conference-2018c.138": {"snippet": "...sing Social Media Messages in Mass Emergency: Survey Summary\n\n\n ABSTRACTMillions of people use social media to share information during disasters and mass emergencies. Information available on social ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [98, 104, 1.0], [105, 110, 1.0], [196, 202, 1.0]]}, "2020.clef_conference-2020w.208": {"snippet": "...m in Social Media Postings\n\n\n This paper describes the University of Hildesheim submission to the CLEF eRisk 2020 shared task on detecting early signs of self-harm in social media posts. We introduce ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [170, 176, 1.0], [177, 182, 1.0]]}, "2017.wwwconf_conference-2017c.3": {"snippet": "... the social media era. People are willing to share their selfies in various social media platforms such as Facebook, Instagram and Flicker. The popularity of selfie have caught researchers' attention,...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [79, 85, 1.0], [86, 91, 1.0]]}, "2015.jasis_journal-ir0volumeA66A6.14": {"snippet": "... for social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}}}
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are spoken varieties of Standard Arabic. These dialects have mixed form with many variation...", "weights": [[8, 14, 1.0], [90, 96, 1.0], [145, 151, 1.0]]}, "2004.sigirconf_conference-2004.112": {"snippet": "... for arabic information retrieval\n\n\n ABSTRACTDue to the high number of inflectional variations of Arabic words, empirical results suggest that stemming is essential for Arabic information retrieval. H...", "weights": [[8, 14, 1.0], [101, 107, 1.0], [146, 154, 1.0], [172, 178, 1.0]]}, "2003.mtsummit-semit.6": {"snippet": "...e of Arabic, Farsi and Urdu lexical entries, is more modular and less costly. ICT for Arabic, Farsi, and Urdu requires the implementation of stem-based lexical entries, the authentic scripts for each ...", "weights": [[8, 14, 1.0], [89, 95, 1.0]]}, "1990.ipm_journal-ir0volumeA26A2.8": {"snippet": "...n of Arabic text", "weights": [[8, 14, 1.0]]}, "2008.cikm_workshop-2008inews.2": {"snippet": "...tive Arabic stemming algorithm that can reduce stemming errors in addition to decreasing computational time and data storage.The novelty of the work arises from the use of neglected Arabic stop-words....", "weights": [[8, 14, 1.0], [15, 23, 1.0], [50, 58, 1.0], [185, 191, 1.0]]}, "2012.cikm_conference-2012.349": {"snippet": "... the Arabic microblogs retrieval. We illustrate some of the challenges associated with Arabic microblog retrieval, which mainly stem from the use of different Arabic dialects that vary in lexical sele...", "weights": [[8, 14, 1.0], [90, 96, 1.0], [162, 168, 1.0]]}, "W19-3512": {"snippet": "...n on Arabic social media. Automatic hate speech and abusive detection systems can facilitate the prohibition of toxic textual contents. The complexity, informality and ambiguity of the Arabic dialects...", "weights": [[8, 14, 1.0], [188, 194, 1.0]]}, "C08-2027": {"snippet": "...s\n\n\n Arabic morphological analysers and stemming algorithms have become a popular area of research. Many computational linguists have designed and developed algorithms to solve the problem of morpholo...", "weights": [[8, 14, 1.0], [43, 51, 1.0]]}, "2011.airs_conference-2011.53": {"snippet": "...ISRI Stemming on Similarity Measure for Arabic Document Clustering", "weights": [[8, 16, 1.0], [43, 49, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.3": {"snippet": "...hile stemming is useful normally, with a very large parallel corpus for Arabic-English, stemming hurt performance in our empirical studies with Arabic, a highly inflected language.", "weights": [[8, 16, 1.0], [75, 81, 1.0], [91, 99, 1.0], [147, 153, 1.0]]}, "2014.airs_conference-2014.28": {"snippet": "... two stemming algorithms (i.e., morphologyand syntax-based lemmatization; and morphology-based Information Science Research Institute (ISRI) stemming on clustering Arabic text dataset. We aim to ident...", "weights": [[8, 16, 1.0], [144, 152, 1.0], [167, 173, 1.0]]}, "2002.cikm_conference-2002.43": {"snippet": "...ight stemming approach\n\n\n ABSTRACTThe inflectional structure of a word impacts the retrieval accuracy of information retrieval systems of Latin-based languages. We present two stemming algorithms for ...", "weights": [[8, 16, 1.0], [153, 162, 1.0], [179, 187, 1.0]]}, "2006.airs_conference-2006.25": {"snippet": "... for stemming for an open class of languages. Since it does not rely on existing large data collections or other linguistic resources than raw text it is especially attractive for low-density language...", "weights": [[8, 16, 1.0], [38, 47, 1.0], [195, 204, 1.0]]}, "W05-0709": {"snippet": "...ical Stemming on {A}rabic Mention Detection and Coreference Resolution\n\n\n Arabic presents an interesting challenge to natural language processing, being a highly inflected and agglutinative language. ...", "weights": [[8, 16, 1.0], [77, 83, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.18": {"snippet": "...eral stemming strategies for this language, and also demonstrates that a light stemming approach could be quite effective. Based on searches done on the CLEF test collection, we find that a more aggre...", "weights": [[8, 16, 1.0], [82, 90, 1.0]]}, "2008.clef_workshop-2008.10": {"snippet": "...sing Stemming and Part of Speech Tagging\n\n\n Abstract. With the emergence of vast resources of information, it is necessary to develop methods that retrieve the most relevant information according to n...", "weights": [[8, 16, 1.0]]}, "2000.spire_conference-2000.16": {"snippet": "...Word Stemming Algorithm for the Spanish Language", "weights": [[8, 16, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A3.3": {"snippet": "...t of Stemming on Arabic Text Classification: An Empirical Study", "weights": [[8, 16, 1.0], [20, 26, 1.0]]}, "2010.airs_conference-2010.27": {"snippet": "...ased Arabic Stemming Using N-Grams\n\n\n Abstract. In languages with high word inflation such as Arabic, stemming improves text retrieval performance by reducing words variants. We propose a change in th...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [54, 63, 1.0], [97, 103, 1.0], [105, 113, 1.0]]}, "1998.tois_journal-ir0volumeA16A1.2": {"snippet": "...ased Stemming Using Cooccurrence of Word Variants\n\n\n Stemming is used in many information retrieval (IR) systems to reduce variant word forms to common roots. It is one of the simplest applications of...", "weights": [[8, 16, 1.0], [56, 64, 1.0]]}, "C98-1018": {"snippet": "...cale Arabic morphological analyzer which is available for testing on the Internet. introduction Most formal analyses of Semitic languages, including Arabic. deDnd the reality of abstract, unpronouncea...", "weights": [[8, 14, 1.0], [131, 140, 1.0], [152, 158, 1.0]]}, "2002.trec_conference-2002.13": {"snippet": "...s of Arabic \"light\" stemming are also presented. A simple variant of an existing stemming algorithm was found to result in significantly better retrieval effectiveness.", "weights": [[8, 14, 1.0], [23, 31, 1.0], [84, 92, 1.0]]}, "2009.ipm_journal-ir0volumeA45A6.6": {"snippet": "... and stemming approaches for the Czech language\n\n\n a b s t r a c tThis paper describes and evaluates various stemming and indexing strategies for the Czech language. Based on Czech test-collection, we...", "weights": [[8, 16, 1.0], [112, 120, 1.0]]}, "2016.tois_journal-ir0volumeA35A1.5": {"snippet": "...ther languages, little effort was directed towards QA systems for Arabic. Due to the difficulty of handling why-questions, most Arabic QA systems tend to ignore it. In this article, we specifically ad...", "weights": [[8, 17, 1.0], [69, 75, 1.0], [131, 137, 1.0]]}, "2008.sigirconf_workshop-2008and.16": {"snippet": "... the Arabic Language. The new lemmatizer proposed here is a part of a comprehensive Arabic tokenization system, with a stop words list exceeding 2200 Arabic words. Currently, there are two Arabic lead...", "weights": [[8, 14, 1.0], [87, 93, 1.0], [153, 159, 1.0], [192, 198, 1.0]]}, "2014.airs_conference-2014.27": {"snippet": "...r on Arabic Text Retrieval\n\n\n Abstract.Stemming is a process of reducing inflected words to their stem, stem or root from a generally written word form. One of the high inflected words in the language...", "weights": [[8, 14, 1.0], [42, 50, 1.0], [195, 204, 1.0]]}, "1994.jasis_journal-ir0volumeA45A8.1": {"snippet": "...n an Arabic Information Retrieval System", "weights": [[8, 14, 1.0]]}, "W06-1631": {"snippet": "...h as stemming should not be applied indiscriminately to all words in a collection, and so before any stemming, foreign words need to be identified. In this paper, we investigate three approaches for t...", "weights": [[8, 16, 1.0], [104, 112, 1.0]]}, "P12-2043": {"snippet": "...e to Arabic's morphological complexity, Arabic retrieval benefits greatly from morphological analysis -particularly stemming. However, the best known stemming does not handle linguistic phenomena such...", "weights": [[8, 14, 1.0], [43, 49, 1.0], [119, 127, 1.0], [153, 161, 1.0]]}, "2002.sigirconf_conference-2002.36": {"snippet": "... for Arabic retrieval\n\n\n ABSTRACTThis work evaluates a few search strategies for Arabic monolingual and cross-lingual retrieval, using the TREC Arabic corpus as the test-bed. The release by NIST in 20...", "weights": [[8, 14, 1.0], [84, 90, 1.0], [147, 153, 1.0]]}, "W16-3912": {"snippet": "...Many stemming algorithms have been developed for Modern Standard Arabic (MSA). 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Therefore, I propose a new stemming approach based o...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [143, 149, 1.0], [178, 186, 1.0]]}, "2003.sigirconf_conference-2003.96": {"snippet": "Stemming in the language modeling framework", "weights": [[0, 8, 1.0]]}, "W04-1605": {"snippet": "...g in Arabic requires an efficient method for matching stems with roots and vice versa. Such mapping is complicated by the hundreds of manifestations of the same root. An algorithm based on the generat...", "weights": [[8, 14, 1.0]]}, "Y15-2019": {"snippet": "...ion (stemming). Stemmers have improved many NLP tasks without explicit knowledge about its stemming accuracy. In this paper, a study is conducted to evaluate various Arabic stemmers. The study is done...", "weights": [[8, 16, 1.0], [94, 102, 1.0], [169, 175, 1.0]]}, "2005.spire_conference-2005.23": {"snippet": "Stemming Arabic Conjunctions and Prepositions", "weights": [[0, 8, 1.0], [9, 15, 1.0]]}, "P03-1050": {"snippet": "...abic Stemming Using a Parallel Corpus\n\n\n This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translatio...", "weights": [[8, 16, 1.0], [125, 131, 1.0], [146, 154, 1.0]]}, "2003.mtsummit-semit.5": {"snippet": "...with Arabic as a source or target language, and focuses on lexical databases. The non-concatenative nature of Arabic morphology, the complex structure of Arabic word-forms, and the general use of vowe...", "weights": [[8, 14, 1.0], [113, 119, 1.0], [157, 163, 1.0]]}, "Y10-1068": {"snippet": "...s in Arabic language can be attached to a stem or to each other without orthographic marks such as an apostrophe. In this paper we present a statistical study of clitics and its effect in Arabic langu...", "weights": [[8, 14, 1.0], [191, 197, 1.0]]}, "P03-1051": {"snippet": "...cted languages provided that one can create a small manually segmented corpus of the language of interest. 1 Arabic is presented in both native and Buckwalter transliterated Arabic whenever possible. ...", "weights": [[8, 17, 1.0], [112, 118, 1.0], [177, 183, 1.0]]}}}
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Different data augmentation techniques are applied to prevent overfitting a...", "weights": [[8, 13, 1.0], [14, 19, 1.0]]}, "2000.riao_conference-2000.9": {"snippet": "...eech Recognition based Audio Indexing System for the Web", "weights": [[8, 19, 1.0], [20, 25, 1.0], [26, 31, 1.0]]}, "W16-2014": {"snippet": "Read my points: Effect of animation type when speech-reading from {EMA} data\n\n\n Three popular vocal-tract animation paradigms were tested for intelligibility when displaying videos of pre-recorded Ele...", "weights": []}, "2019.clef_conference-2019w.125": {"snippet": "...Bird Recognition in Soundscapes\n\n\n The BirdCLEF challenge-as part of the 2019 LifeCLEF Lab [7]-offers a large-scale proving ground for system-oriented evaluation of bird species identification based o...", "weights": [[8, 19, 1.0], [196, 201, 1.0]]}, "2018.clef_conference-2018w.99": {"snippet": "...l or audio observations of living organism. A picture actually contains only a partial information that is usually not sufficient to determine the right species with certainty. Quantifying this uncert...", "weights": [[8, 13, 1.0]]}, "2011.wwwjournals_journal-ir0volumeA14A3.3": {"snippet": "...hing-based skeleton generation method suitable for use in the mobile environment. This method takes user sketching as an input, and based on the mesh segmentation result of a 3D object, it estimates a...", "weights": [[8, 13, 1.0], [135, 140, 1.0]]}, "2020.clef_conference-2020w.143": {"snippet": "...cies recognition systems. We have collected 287,632 images of 783 snake species from 145 countries. Here we report 1) a description of the provided data, 2) evaluation methodology and principles, 3) a...", "weights": [[8, 19, 1.0]]}, "1999.sigirconf_conference-99.6": {"snippet": "...eech recognition (ASR) technology, it is now possible to automatically transcribe speech with reasonable accuracy 17]. 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Such software usually rel...", "weights": []}, "2018.ismir_conference-2018.73": {"snippet": "...n of Audio Features for Recognition of Western and Ethnic Instruments in Polyphonic Mixtures", "weights": [[8, 13, 1.0], [27, 38, 1.0]]}, "W12-1629": {"snippet": "...rate audio and visual features with temporal group interactions and limited, task-independent language. The best model performs identification about 20% better than the model that uses the audio-visua...", "weights": [[8, 13, 1.0], [192, 197, 1.0]]}, "2013.tois_journal-ir0volumeA31A2.1": {"snippet": "...tity recognition\n\n\n Named Entity Recognition (NER) is a fundamental task in information extraction from unstructured text. Most previous machine-learning-based NER systems are domain-specific, which i...", "weights": [[8, 19, 1.0], [36, 47, 1.0], [157, 162, 1.0]]}, "2017.ismir_conference-2017.45": {"snippet": "...king-Based Emotion Recognition for Experimental Music", "weights": [[8, 13, 1.0], [22, 33, 1.0]]}, "2006.ismir_conference-2006.25": {"snippet": "...hord Recognition from Audio Using a HMM with Supervised Learning", "weights": [[8, 19, 1.0], [25, 30, 1.0]]}, "2020.cikm_conference-2020.223": {"snippet": "...aker recognition models show that adding certain background sounds to clean vocal audio leads to a substantial deterioration of recognition performance. In several noisy settings, our findings reveal ...", "weights": [[8, 19, 1.0], [85, 90, 1.0], [131, 142, 1.0]]}, "2015.clef_conference-2015.39": {"snippet": "...s in Audio Recordings", "weights": [[8, 13, 1.0]]}, "W17-7522": {"snippet": "Tutorial for Deaf {--} Teaching {P}unjabi Alphabet using Synthetic Animations\n\n\n Developing an automatic tool for educating students has become essential in today's world of computerization. For diffe...", "weights": []}, "2018.tois_journal-ir0volumeA36A3.7": {"snippet": "...eech recognition technologies, studies of mobile search using voice started to emerge. Many of the studies focused on voice recognition challenges.  defined voice search as \"the technology underlying ...", "weights": [[8, 19, 1.0], [127, 138, 1.0]]}, "2018.mir_conference-2018.12": {"snippet": "... for Audio Event Recognition", "weights": [[8, 13, 1.0], [20, 31, 1.0]]}, "2019.clef_conference-2019w.127": {"snippet": "... and Animal Occurrences\n\n\n The GeoLifeCLEF challenge aim to evaluate location-based species recommendation algorithms through open and perennial datasets in a reproducible way. It offers a ground for ...", "weights": [[8, 14, 1.0], [81, 86, 1.0]]}, "2009.ipm_journal-ir0volumeA45A3.0": {"snippet": "...tion recognition from speech\n\n\n a b s t r a c tEmotional expression and understanding are normal instincts of human beings, but automatical emotion recognition from speech without referring any langua...", "weights": [[8, 19, 1.0], [151, 162, 1.0]]}, "R19-2007": {"snippet": "...n of audio transcriptions based on phone-level alignment between the transcriptions and their corresponding audio recordings. Comparing this approach to a more general rule-based verbalization method ...", "weights": [[8, 13, 1.0], [29, 34, 1.0], [111, 116, 1.0], [176, 181, 1.0]]}, "2019.ismir_conference-2019.4": {"snippet": "...hord Recognition from Audio", "weights": [[8, 19, 1.0], [25, 30, 1.0]]}, "2019.ecir_conference-20192.37": {"snippet": "...mage-based plant identification challenge, (ii) a bird sounds identification challenge and (iii) a location-based species prediction challenge based on spatial occurrence data and environmental tensor", "weights": [[8, 13, 1.0], [111, 116, 1.0], [146, 151, 1.0]]}, "2001.sigirconf_conference-2001.57": {"snippet": "...tent-Based Segmentation of Speech Transcripts\n\n\n ABSTRACTAn algorithm for the segmentation of an audio/video source into topically cohesive segments based on automatic speech recognition (ASR) transcr...", "weights": [[8, 13, 1.0], [100, 105, 1.0], [152, 157, 1.0], [178, 189, 1.0]]}, "2013.wwwconf_conference-2013c.282": {"snippet": "...mage based Captchas and more fun as well. Some solutions are available such as yo!Captcha, NLPcaptcha. Solve Media TYPE-IN Captchas. These Captchas are single image based Captchas which ask users to t...", "weights": [[8, 13, 1.0], [168, 173, 1.0]]}, "2020.lrec-1.441": {"snippet": "...eech Recognition\n\n\n We present a corpus of sentence-aligned triples of German audio, German text, and English translation, based on German audio books. The speech translation data consist of 110 hours...", "weights": [[8, 19, 1.0], [81, 86, 1.0], [126, 131, 1.0], [142, 147, 1.0]]}, "2017.tist_journal-ir0volumeA8A2.12": {"snippet": "...ideo-Based Facial Expression Recognition\n\n\n Video-based facial expression recognition (FER) has recently received increased attention as a result of its widespread application. Using only one type of ...", "weights": [[8, 13, 1.0], [32, 43, 1.0], [53, 58, 1.0], [77, 88, 1.0]]}, "2014.airs_conference-2014.24": {"snippet": "...tity recognition from the newspaper articles. Thus, this study designs and develops a crime named entity recognition based on machine learning approaches that extract nationalities, weapons, and crime...", "weights": [[8, 19, 1.0], [108, 119, 1.0], [120, 125, 1.0]]}, "2016.clef_conference-2016w.53": {"snippet": "...s in Audio Recordings using Deep Convolutional Neural Networks\n\n\n This paper summarizes a method for purely audio-based bird species recognition through the application of convolutional neural network...", "weights": [[8, 13, 1.0], [111, 116, 1.0], [117, 122, 1.0], [136, 147, 1.0]]}, "W13-3916": {"snippet": "...eech Recognition Technologies for Voice-based Home Automation Control in a Smart Home\n\n\n This paper presents an audio-based interaction technology that lets the user have full control over her home en...", "weights": [[8, 19, 1.0], [43, 48, 1.0], [115, 120, 1.0], [121, 126, 1.0]]}, "2021.ecir_conference-20212.70": {"snippet": "...tion based on herbarium sheets, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: location-based prediction of species based on environmental and occurrence data and (iv...", "weights": [[8, 13, 1.0], [63, 74, 1.0], [78, 83, 1.0], [125, 130, 1.0], [153, 158, 1.0]]}, "2017.cikm_conference-2017.221": {"snippet": "...tity Recognition with Character-Word Mixed Embedding\n\n\n ABSTRACTNamed Entity Recognition (NER) is an important basis for the tasks in natural language processing such as relation extraction, entity li...", "weights": [[8, 19, 1.0], [80, 91, 1.0]]}, "2016.clef_conference-2016w.42": {"snippet": "...-set recognition problem, i.e. a problem in which the recognition system has to be robust to unknown and never seen categories. Beyond the brute-force classification across the known classes of the tr...", "weights": [[8, 19, 1.0], [57, 68, 1.0]]}, "2004.jasis_journal-ir0volumeA55A12.7": {"snippet": "Recognition and analysis of audio for copyright protection: The RAA project", "weights": [[0, 11, 1.0], [28, 33, 1.0]]}, "2011.ismir_conference-2011.104": {"snippet": "... for Audio Chord Recognition and Music Analysis", "weights": [[8, 13, 1.0], [20, 31, 1.0]]}, "2007.ismir_conference-2007.59": {"snippet": "...hord Recognition in Audio", "weights": [[8, 19, 1.0], [23, 28, 1.0]]}, "2020.lrec-1.555": {"snippet": "...ties recognition which could be integrated into ISTEX resources. This led to testing two detection tools for animal species names in a corpus of 100 documents in zoology. This makes it possible to pro...", "weights": [[8, 19, 1.0], [112, 118, 1.0]]}, "2020.sigirconf_conference-2020.170": {"snippet": "...eech recognition (ASR) models to transcribe voice queries for further downstream language understanding tasks. Currently, labeling audio data for training is the main bottleneck in deploying accurate ...", "weights": [[8, 19, 1.0], [134, 139, 1.0]]}, "2005.sigirconf_conference-2005.48": {"snippet": "...tion based on audio-visual-text analysis and alignment\n\n\n ABSTRACTIn this paper, we propose a novel approach for automatic music video summarization based on audio-visual-text analysis and alignment. ...", "weights": [[8, 13, 1.0], [17, 22, 1.0], [152, 157, 1.0], [161, 166, 1.0]]}, "2015.tist_journal-ir0volumeA6A2.9": {"snippet": "...uage Recognition with Kinect\n\n\n Vision-based sign language recognition has attracted more and more interest from researchers in the computer vision field. In this article, we propose a novel algorithm...", "weights": [[8, 19, 1.0], [42, 47, 1.0], [62, 73, 1.0]]}, "2020.coling-main.26": {"snippet": "...s of animal species using custom generated scientific name embeddings. We use two different vector embeddings generated using representation learning on Wikipedia text and animal taxonomy data. We gen...", "weights": [[8, 14, 1.0], [175, 181, 1.0]]}, "2019.clef_conference-2019w.102": {"snippet": "...n-v3 Based Method of LifeCLEF 2019 Bird Recognition\n\n\n In this paper, we present a method of bird recognition based on Inception-v3. The goal of the LifeCLEF2019 Bird Recognition is to detect and clas...", "weights": [[8, 13, 1.0], [43, 54, 1.0], [101, 112, 1.0], [113, 118, 1.0], [170, 181, 1.0]]}, "2019.clef_conference-2019w.131": {"snippet": "...t is based on a dataset of 10K species mainly focused on the Guiana shield and the Northern Amazon rainforest, an area known to have one of the greatest diversity of plants and animals in the world. A...", "weights": [[8, 13, 1.0]]}, "1999.sigirconf_conference-99.68": {"snippet": "...ideo/audio in order to facilitate rapid retrieval, assimilation and browsing. CueVideo employs a combination of automatic indexing and analysis techniques in the domain of video, audio, speech recogni...", "weights": [[8, 13, 1.0], [182, 187, 1.0], [196, 207, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A6.19": {"snippet": "...eter-based activity recognition\n\n\n Abstract Accelerometer-based activity recognition (AAR) attracted a lot of attentions due to the wide spread of smartphones with energy-efficiency. However, since ac...", "weights": [[8, 13, 1.0], [23, 34, 1.0], [61, 66, 1.0], [76, 87, 1.0]]}, "2004.wwwconf_conference-2004at.73": {"snippet": "... web-based graphics is that a diagram should be able to directly support interaction.Unfortunately, the current vector-based graphics standard SVG 1.1 does not explicitly support these. Instead, the d...", "weights": [[8, 13, 1.0], [122, 127, 1.0]]}, "O11-2002": {"snippet": "...cies Recognition Based on Speech Processing Techniques) [In {C}hinese]", "weights": [[8, 19, 1.0], [20, 25, 1.0]]}, "H93-1083": {"snippet": "...ment-Based Acoustic Models for Continuous Speech Recognition", "weights": [[8, 13, 1.0], [52, 63, 1.0]]}, "2021.acl-long.177": {"snippet": "... the audio segments. Unlike previous datasets, QASR contains linguistically motivated segmentation, punctuation, speaker information among others. QASR is suitable for training and evaluating speech r...", "weights": [[8, 13, 1.0], [202, 213, 1.0]]}, "2014.mir_workshop-2014emr.3": {"snippet": "...oint-based medialness for animal and plant identification", "weights": [[8, 13, 1.0], [29, 35, 1.0]]}, "2020.acl-main.216": {"snippet": "...eech Recognition with Transformers\n\n\n This paper presents an audio visual automatic speech recognition (AV-ASR) system using a Transformer-based architecture. We particularly focus on the scene contex...", "weights": [[8, 19, 1.0], [64, 69, 1.0], [94, 105, 1.0], [142, 147, 1.0]]}, "2016.clef_conference-2016w.55": {"snippet": "Audio Based Bird Species Identification using Deep Learning Techniques\n\n\n In this paper we present a new audio classification method for bird species identification. Whereas most approaches apply near...", "weights": [[0, 5, 1.0], [6, 11, 1.0], [105, 110, 1.0]]}, "2014.ismir_conference-2014.48": {"snippet": "...ical Audio Recordings", "weights": [[8, 13, 1.0]]}, "2021.findings-acl.455": {"snippet": "...e an animal looks like another animal, the more likely it is to survive. Contextual: It's a way of thinking about how organisms adapt to change. The newly found cat is probably part of a separate bran...", "weights": [[8, 14, 1.0], [34, 40, 1.0]]}, "2016.wwwconf_conference-2016.107": {"snippet": "...eech recognition. Speech recognition systems are trained using audio transcripts, which can be generated by a paid annotator listening to some audio and manually transcribing it. This paper considers ...", "weights": [[8, 19, 1.0], [28, 39, 1.0], [66, 71, 1.0], [146, 151, 1.0]]}, "2001.cikm_conference-2001.63": {"snippet": "...ning based on associated speech transcriptions. We present a novel segmentation algorithm based on automatic speech recognition (ASR) applied to the audio track of the video. Our feature set consists ...", "weights": [[8, 13, 1.0], [93, 98, 1.0], [119, 130, 1.0], [152, 157, 1.0]]}, "2020.clef_conference-2020w.32": {"snippet": "... the audio data. This neural network architecture is based on a neural architecture search and contains multiple auxiliary heads and recurrent layers. During the training process, scheduled drop path ...", "weights": [[8, 13, 1.0], [56, 61, 1.0]]}, "L18-1680": {"snippet": "...ound Audio Data\n\n\n We present a novel and general approach for fast and efficient non-sequential browsing of sound in large archives that we know little or nothing about, e.g. so called found data -da...", "weights": [[8, 13, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.0": {"snippet": "...eech Recognition: An Overview of Recent Developments\n\n\n Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition but sti...", "weights": [[8, 19, 1.0], [184, 195, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...eech recognition will never be perfect, so analyses applied to the resulting transcripts must be robust in the face of recognition errors. The ability to capture speech and apply speech recognition on...", "weights": [[8, 19, 1.0], [122, 133, 1.0], [189, 200, 1.0]]}, "2002.wwwconf_conference-2002.5": {"snippet": "... and audio, but generally have an inflexible model for time itself. In contrast, modern animation models in the computer graphics community generally lack tools for synchronization and structural time...", "weights": [[8, 13, 1.0]]}, "K19-1081": {"snippet": "...tity Recognition (NER) and Taxa Recognition (TR) in biological documents. We use this data to train a number of leading machine learning tools and create a gold standard for TR in biodiversity literat...", "weights": [[8, 19, 1.0], [35, 46, 1.0]]}, "W15-5105": {"snippet": "Synthesizing and Evaluating Animations of {A}merican {S}ign {L}anguage Verbs Modeled from Motion-Capture Data\n\n\n Animations of American Sign Language (ASL) can make information accessible for many sig...", "weights": []}, "2017.clef_conference-2017w.88": {"snippet": "...rine Animal Recognition task, we examined different neural networks to make classification based on weakly-labelled images. Our methods achieve good results in both task1 and task3.", "weights": [[8, 14, 1.0], [15, 26, 1.0], [94, 99, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A2.6": {"snippet": "...tion recognition\n\n\n AbstractAs an important task in computer vision, the interaction recognition has attracted extensive attention due to its widely potential applications. The existing methods mainly...", "weights": [[8, 19, 1.0], [88, 99, 1.0]]}, "2014.clef_conference-2014w.63": {"snippet": "...ance-based classification scheme based on the dense indexing of MFCC features and the pruning of the non-discriminant ones. To make such strategy scalable to the 30M of MFCC features extracted from th...", "weights": [[8, 13, 1.0], [36, 41, 1.0]]}, "2020.ecir_conference-20202.70": {"snippet": "...tion based on herbarium sheets, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: location-based prediction of species based on environmental and occurrence data, and (i...", "weights": [[8, 13, 1.0], [63, 74, 1.0], [78, 83, 1.0], [125, 130, 1.0], [153, 158, 1.0]]}, "2004.ismir_conference-2004.22": {"snippet": "...core-based Gestures In Audio Recordings", "weights": [[8, 13, 1.0], [26, 31, 1.0]]}, "2020.wsdm_conference-2020.42": {"snippet": "...aker Recognition with Limited Data\n\n\n Automatic speaker recognition (ASR) is a stepping-stone technology towards semantic multimedia understanding and benefits versatile downstream applications. In re...", "weights": [[8, 19, 1.0], [59, 70, 1.0]]}, "2016.tist_journal-ir0volumeA7A3.8": {"snippet": "...vity Recognition, and Tracking\n\n\n The machine recognition of user trajectories and activities is fundamental to devise context-aware applications for support and monitoring in daily life. So far, trac...", "weights": [[8, 19, 1.0], [49, 60, 1.0]]}, "2010.wwwconf_conference-2010.47": {"snippet": "...tity recognition\n\n\n ABSTRACTNamed entity recognition studies the problem of locating and classifying parts of free text into a set of predefined categories. Although extensive research has focused on ...", "weights": [[8, 19, 1.0], [44, 55, 1.0]]}, "2013.ismir_conference-2013.55": {"snippet": "Audio Chord Recognition with Recurrent Neural Networks", "weights": [[0, 5, 1.0], [12, 23, 1.0]]}, "2011.tist_journal-ir0volumeA2A4.6": {"snippet": "...y on animal movement data analysis and examine advanced data mining methods for discovery of various animal movement patterns. In particular, we introduce a moving object data mining system, MoveMine,...", "weights": [[8, 14, 1.0], [104, 110, 1.0]]}, "2014.clef_conference-2014w.69": {"snippet": "... raw audio without segmentation and without using any auxiliary metadata. It successfully classifies among 501 bird categories, and was by far the highest scoring audio-only bird recognition algorithm...", "weights": [[8, 13, 1.0], [166, 171, 1.0], [182, 193, 1.0]]}, "2005.ismir_conference-2005.108": {"snippet": "...enre Recognition Systems in Audio Signals", "weights": [[8, 19, 1.0], [31, 36, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.21": {"snippet": "...tion recognition with web-scale video data\n\n\n AbstractAction recognition in surveillance video makes our life safer by detecting the criminal events or predicting violent emergencies. However, efficie...", "weights": [[8, 19, 1.0], [64, 75, 1.0]]}, "W14-5131": {"snippet": "... new audio-visual emotion (AVE) database is developed. The database consists of audio, video and audio-visual clips sourced from TV broadcast like movies and soapoperas in English language. The data c...", "weights": [[8, 13, 1.0], [83, 88, 1.0], [100, 105, 1.0]]}, "S19-1032": {"snippet": "Generating Animations from Screenplays\n\n\n Automatically generating animation from natural language text finds application in a number of areas e.g. movie script writing, instructional videos, and publ...", "weights": []}, "W17-2003": {"snippet": "...gate animal recognition models learned from wildlife video documentaries by using the weak supervision of the textual subtitles. This is a challenging setting, since i) the animals occur in their natu...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2018.clef_conference-2018w.16": {"snippet": "Audio-based Bird Species Identification with Deep Convolutional Neural Networks\n\n\n This paper presents deep learning techniques for audio-based bird identification at very large scale. Deep Convolutio...", "weights": [[0, 5, 1.0], [6, 11, 1.0], [132, 137, 1.0], [138, 143, 1.0]]}, "L08-1469": {"snippet": "...ideo-Based Automatic Sign Language Recognition", "weights": [[8, 13, 1.0], [38, 49, 1.0]]}, "2011.sigirconf_conference-2011.210": {"snippet": "...ning audio-visual and textual cues to improve written word recognition and vocabulary skills. JuSe provides an interactive learning environment that allows parents to introduce new words and concepts ...", "weights": [[8, 13, 1.0], [62, 73, 1.0]]}, "2011.wwwconf_conference-2011c.164": {"snippet": "...g of audio to index the audio content for Spoken Web search. The first stream indexes the meta-data associated with a particular audio document. The meta-data is usually very sparse, but accurate. Thi...", "weights": [[8, 13, 1.0], [27, 32, 1.0], [132, 137, 1.0]]}, "O14-2004": {"snippet": "...sing audio data collected from commercial CDs and websites show that the timbre-based, pitch-based, and the combination thereof systems achieve 71.1%, 72.1%, and 75.0% accuracy of bird sound identific", "weights": [[8, 13, 1.0], [83, 88, 1.0], [96, 101, 1.0]]}, "2015.ismir_conference-2015.17": {"snippet": "Audio Chord Recognition with a Hybrid Recurrent Neural Network", "weights": [[0, 5, 1.0], [12, 23, 1.0]]}, "2018.chiir_conference-2018.58": {"snippet": "...eech recognition (ASR) is reported to be on a par with human performance [25; 26], while high quality synthetic voices generated with deep neural networks (WaveNet Model) can sound almost indistinguis...", "weights": [[8, 19, 1.0]]}}}
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Online Learning to Rank (OLTR) methods optimize ranking models by directly interacting with users, which allows them to be very efficient and responsive. ...", "weights": [[8, 14, 1.0], [105, 111, 1.0]]}, "P06-1076": {"snippet": "A Comparison of Document, Sentence, and Term Event Spaces\n\n\n The trend in information retrieval systems is from document to sub-document retrieval, such as sentences in a summarization system and word...", "weights": [[2, 12, 1.0], [86, 95, 1.0], [137, 146, 1.0]]}, "2001.tois_journal-ir0volumeA19A1.0": {"snippet": "...tter retrieval effectiveness on several performance measures. We also discuss the effect on retrieval effectiveness of the main parameters involved in automatic query expansion, such as data sparsenes...", "weights": [[8, 17, 1.0], [95, 104, 1.0]]}, "2017.sigirconf_conference-2017.203": {"snippet": "...ined models. 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We evaluate three...", "weights": [[2, 12, 1.0], [71, 80, 1.0]]}, "1981.sigirconf_conference-81.10": {"snippet": "...phic Retrieval Systems\n\n\n Abs t ractThe general model and simulation algorithms for bibliographic retrieval systems presented in an earlier paper I are expanded.The new model integrates the physical a...", "weights": [[8, 17, 1.0], [101, 110, 1.0]]}, "2008.ir_journal-ir0volumeA11A2.0": {"snippet": "...mage retrieval: an experimental comparison", "weights": [[8, 17, 1.0], [35, 45, 1.0]]}, "2021.ecir_conference-20212.43": {"snippet": "...nder different retrieval pipelines. We bridge this gap by thoroughly evaluating those question rewriting methods on the TREC CAsT 2019 and 2020 datasets under the same retrieval pipeline. We analyze t...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [171, 180, 1.0]]}, "2004.trec_conference-2004.8": {"snippet": "...tter Retrieval Function From Machine?\n\n\n The quality of an information retrieval system heavily depends on its retrieval function, which returns a similarity measurement between the query and each doc...", "weights": [[8, 17, 1.0], [74, 83, 1.0], [114, 123, 1.0]]}, "N09-2005": {"snippet": "Comparison of Extended Lexicon Models in Search and Rescoring for {SMT}\n\n\n We show how the integration of an extended lexicon model into the decoder can improve translation performance. The model is b...", "weights": [[0, 10, 1.0], [31, 37, 1.0]]}, "W13-2703": {"snippet": "...ng a different subset of attributes. An application of the matching procedure for comparison of family trees is discussed. A visualization tool is described to present an interactive overview of compa", "weights": [[8, 17, 1.0], [85, 95, 1.0], [198, 208, 1.0]]}, "1992.sigirjournals_journal-ir0volumeA26A2.3": {"snippet": "... for Retrieval of Data on Multimedia Documents\n\n\n AbstractTraditionally, two paradigms are discerned with respect to data retrieval :1. A query mechanism enables the user to express conditions that ch...", "weights": [[8, 17, 1.0], [125, 134, 1.0]]}, "2017.ictir_workshop-2017learner.7": {"snippet": "...mark comparison of learning-to-rank methods using two evaluation measures: the Normalized Winning Number and the Ideal Winning Number. Evaluation results of 87 learning-to-rank methods on 20 datasets ...", "weights": [[8, 18, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...s of retrieval models from the documents retrievability point of view. We focus particularly on the retrieval bias of different retrieval models, and try to examine to what extent this bias restricts ...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [103, 112, 1.0], [121, 130, 1.0], [131, 140, 1.0], [141, 147, 1.0]]}, "2017.cikm_conference-2017.12": {"snippet": "...aved comparison methods generalize interleaved comparison methods to provide a scalable approach for comparing ranking systems based on regular user interactions. Such methods enable the increasingly ...", "weights": [[8, 18, 1.0], [50, 60, 1.0]]}, "2018.ictir_conference-2018.1": {"snippet": "...ical models (e.g., click models) with user behavior hypothesis to extract and train ranking systems with unbiased relevance signals. Recently, a novel counterfactual learning framework that estimates ...", "weights": [[8, 14, 1.0], [28, 34, 1.0]]}, "1994.trec_conference-1994.7": {"snippet": "Comparison of Fragmentation Schemes for Document Retrieval", "weights": [[0, 10, 1.0], [49, 58, 1.0]]}, "2005.airs_conference-2005.5": {"snippet": "...mage Retrieval Method Using Color Distribution and Common Bitmap\n\n\n Abstract. Image retrieval has emerged as an important problem in multimedia database management. This paper uses the color distribut...", "weights": [[8, 17, 1.0], [87, 96, 1.0]]}, "2020.sigirconf_conference-2020.110": {"snippet": "...ious retrieval criteria, like different ranking models and values of k. In this paper, we conduct the first extensive comparison between ten effective strategies, many of which were never compared bef...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [51, 57, 1.0], [121, 131, 1.0]]}, "2020.sigirconf_conference-2020.42": {"snippet": "...tion retrieval (IR) ranking models in production systems continually evolve in response to user feedback, insights from research, and new developments. Rather than investing all engineering resources ...", "weights": [[8, 17, 1.0], [31, 37, 1.0]]}, "1998.sigirconf_conference-98.25": {"snippet": "...Text Retrieval and Routing Using the NEAT System\n\n\n AbstractThis paper describes a structured investigation into the retrieval of Japanese text. The study includes a comparison of different indexing s...", "weights": [[8, 17, 1.0], [120, 129, 1.0], [169, 179, 1.0], [183, 192, 1.0]]}, "2014.sigirconf_conference-2014.163": {"snippet": "...ying different retrieval models are more similar than a well-tuned and a poorly tuned implementation of the same retrieval model.", "weights": [[8, 17, 1.0], [18, 27, 1.0], [28, 34, 1.0], [116, 125, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.13": {"snippet": "...tion retrieval: bridging the theory-effectiveness gap\n\n\n AbstractImproving the effectiveness of general retrieval models has been a long-standing difficult challenge in information retrieval research,...", "weights": [[8, 17, 1.0], [107, 116, 1.0], [117, 123, 1.0], [184, 193, 1.0]]}, "2016.wsdm_conference-2016.26": {"snippet": "... and Comparison Data\n\n\n ABSTRACTWe present a method for learning potentially intransitive preference relations from pairwise comparison and matchup data. Unlike standard preference-learning models tha...", "weights": [[8, 18, 1.0], [128, 138, 1.0], [193, 199, 1.0]]}, "2009.clef_workshop-20092.13": {"snippet": "Comparison of Several Combinations of Multimodal and Diversity Seeking Methods for Multimedia Retrieval", "weights": [[0, 10, 1.0], [94, 103, 1.0]]}, "2006.sigirconf_conference-2006.7": {"snippet": "...stic models. The first of these directly models an expert's knowledge based on the documents that they are associated with, whilst the second locates documents on topic, and then finds the associated ...", "weights": [[8, 14, 1.0], [44, 50, 1.0]]}, "O01-2005": {"snippet": "...tion retrieval. In fusion, ranked lists are combined together by various means. The motivation is that different IR systems will complement each other, because they usually emphasize different query f...", "weights": [[8, 17, 1.0], [106, 115, 1.0], [186, 195, 1.0]]}, "2015.ecir_conference-2015.22": {"snippet": "... and Retrieval Bias: A Comparison of Inequality Measures", "weights": [[8, 17, 1.0], [26, 36, 1.0]]}, "2006.airs_conference-2006.60": {"snippet": "...ical Comparison of Translation Disambiguation Techniques for Chinese-English Cross-Language Information Retrieval", "weights": [[8, 18, 1.0], [107, 116, 1.0]]}, "2017.fire_conference-2017w.24": {"snippet": "...tion Retrieval From Legal Documents\n\n\n This paper details the approach of implementing the Catchphrase Extraction and Precedence Retrieval tasks to be presented at Information Retrieval from Legal Doc...", "weights": [[8, 17, 1.0], [132, 141, 1.0], [179, 188, 1.0]]}, "1994.sigirconf_conference-94.22": {"snippet": "...n: A Comparison of Retrieval Performance", "weights": [[8, 18, 1.0], [22, 31, 1.0]]}, "1992.jasis_journal-ir0volumeA43A5.2": {"snippet": "Retrieval Testing by the Comparison of Statistically Independent Retrieval Methods", "weights": [[0, 9, 1.0], [25, 35, 1.0], [65, 74, 1.0]]}, "P99-1027": {"snippet": "...tion models for both translation directions using the same training data. We investigate information retrieval between English and French, incorporating both translations directions into both document...", "weights": [[8, 14, 1.0], [104, 113, 1.0]]}, "2006.clef_workshop-2006.45": {"snippet": "...sage Retrieval for Question Answering\n\n\n Abstract. In this paper we describe the participation of the Universidad Polit\u00e9cnica of Valencia to the 2006 edition, which was focused on the comparison betwe...", "weights": [[8, 17, 1.0], [187, 197, 1.0]]}, "2008.airs_conference-2008.5": {"snippet": "...re's retrieval performance, on different feature spaces? In this paper, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison i...", "weights": [[8, 17, 1.0], [34, 43, 1.0], [191, 201, 1.0]]}, "2008.ipm_journal-ir0volumeA44A5.7": {"snippet": "... and retrieval of the semantic content of images. While the main focus of image research is on indexing and retrieval of individual images, the general topic of image browsing and indexing, and retrie...", "weights": [[8, 17, 1.0], [111, 120, 1.0], [197, 206, 1.0]]}, "2016.sigirconf_conference-2016.94": {"snippet": "...tion Retrieval via Topic Modelling\n\n\n ABSTRACTTraditional information retrieval (IR) models, in which a document is normally represented as a bag of words and their frequencies, capture the term-level...", "weights": [[8, 17, 1.0], [73, 82, 1.0], [88, 94, 1.0]]}, "2005.ntcir_workshop-2005.76": {"snippet": "...tion Retrieval task at NTCIR-5. We focused on Chinese monolingual retrieval, and aimed to study the effectiveness of language models and different tokenization methods for Chinese. Our main findings a...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [129, 135, 1.0], [140, 149, 1.0]]}, "2012.cikm_conference-2012.15": {"snippet": "...tion retrieval evaluation most often involves manually assessing the relevance of particular query-document pairs. In cases where this is difficult (such as personalized search), interleaved compariso...", "weights": [[8, 17, 1.0], [194, 204, 1.0]]}, "2000.ismir_conference-2000.1": {"snippet": "A Comparison of Language Modeling and Probabilistic Text Information Retrieval Approaches to Monophonic Music Retrieval", "weights": [[2, 12, 1.0], [69, 78, 1.0], [110, 119, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...tion retrieval skills by (a) comparing student use of printed and electronic forms of an encyclopedia, (b) examining the effects of menu or command driven interfaces and task difficulty on students' s...", "weights": [[8, 17, 1.0]]}, "2009.sigirconf_conference-2009.92": {"snippet": "A comparison of retrieval-based hierarchical clustering approaches to person name disambiguation", "weights": [[2, 12, 1.0], [16, 25, 1.0]]}, "2004.ipm_journal-ir0volumeA40A5.2": {"snippet": "...rove retrieval effectiveness. This paper examines the impact the various types of link-based evidence and their combination with content-based evidence have on the retrieval effectiveness for the topi...", "weights": [[8, 17, 1.0], [167, 176, 1.0]]}, "2006.sigirconf_conference-2006.16": {"snippet": "...tion retrieval\n\n\n ABSTRACTA common limitation of many retrieval models, including the recently proposed axiomatic approaches, is that retrieval scores are solely based on exact (i.e., syntactic) match...", "weights": [[8, 17, 1.0], [57, 66, 1.0], [67, 73, 1.0], [137, 146, 1.0]]}, "2018.wsdm_conference-2018.38": {"snippet": "...-hoc Retrieval\n\n\n ABSTRACTNeural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals. We argue that the context of these matching signa...", "weights": [[8, 17, 1.0], [39, 45, 1.0]]}, "2009.clef_workshop-2009w.123": {"snippet": "...ment retrieval track (TEL@CLEF) at at the Cross-Language Retrieval Forum (CLEF) Trinity College Dublin and Dublin City University participated in collaboration. Our retrieval experiments focus on i) i...", "weights": [[8, 17, 1.0], [60, 69, 1.0], [168, 177, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.7": {"snippet": "...tion Retrieval Systems: A Posting-Based Approach\n\n\n Static index pruning methods have been proposed to reduce size of the inverted index of information retrieval systems. The goal is to increase effic...", "weights": [[8, 17, 1.0], [155, 164, 1.0]]}, "1999.sigirconf_conference-99.30": {"snippet": "...ased retrieval system performs well in comparison to conventional retrieval techniques. This prototype system only begins to tap the full potential of translation-based retrieval.", "weights": [[8, 17, 1.0], [42, 52, 1.0], [69, 78, 1.0], [172, 181, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.10": {"snippet": "... XML retrieval filtering models.We finally use the theoretical properties of XML retrieval models and their filters to explain experimental results obtained with some of the XML retrieval models withi...", "weights": [[8, 17, 1.0], [28, 34, 1.0], [84, 93, 1.0], [94, 100, 1.0], [181, 190, 1.0], [191, 197, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.2": {"snippet": "...tion retrieval\n\n\n AbstractTwo probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on...", "weights": [[8, 17, 1.0], [75, 84, 1.0], [137, 143, 1.0]]}, "2007.ecir_conference-2007.68": {"snippet": "...rall Comparison at the Standard Levels of Recall of Multiple Retrieval Methods with the Friedman Test", "weights": [[8, 18, 1.0], [64, 73, 1.0]]}, "2008.sigirconf_conference-2008.55": {"snippet": "...stic models for passage based retrieval\n\n\n ABSTRACTThe approach of using passage-level evidence for document retrieval has shown mixed results when it is applied to a variety of test beds with differe...", "weights": [[8, 14, 1.0], [33, 42, 1.0], [112, 121, 1.0], [196, 205, 1.0]]}, "2011.sigirconf_conference-2011.45": {"snippet": "...wise comparison between two users as a two-player competition with one winner and one loser. Two competition models are proposed to estimate user expertise from pairwise comparisons. Using the NTCIR-8...", "weights": [[8, 18, 1.0], [112, 118, 1.0]]}, "1992.ipm_journal-ir0volumeA28A4.5": {"snippet": "...ntal Retrieval Comparisons", "weights": [[8, 17, 1.0]]}, "2003.sigirconf_conference-2003.35": {"snippet": "...ting retrieval models in the context of patent retrieval. The relative superiority among existing retrieval models did not significantly differ depending on the document genre, that is, patents and ne...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [50, 59, 1.0], [101, 110, 1.0], [111, 117, 1.0]]}, "2012.cikm_conference-2012.210": {"snippet": "...aved comparison outcomes from historical click data\n\n\n ABSTRACTInterleaved comparison methods, which compare rankers using click data, are a promising alternative to traditional information retrieval ...", "weights": [[8, 18, 1.0], [78, 88, 1.0], [193, 202, 1.0]]}, "2011.sigirconf_conference-2011.198": {"snippet": "A comparison of time-aware ranking methods\n\n\n ABSTRACTWhen searching a temporal document collection, e.g., news archives or blogs, the time dimension must be explicitly incorporated into a retrieval m...", "weights": [[2, 12, 1.0], [189, 198, 1.0]]}, "1981.jasis_journal-ir0volumeA32A4.5": {"snippet": "A comparison of two systems of weighted boolean retrieval", "weights": [[2, 12, 1.0], [48, 57, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...s of retrieval effectiveness) of the different text sections used to represent the images,\nExperiments with other types of queryTbe queries used in the retrieval effectiveness experiments were all con...", "weights": [[8, 17, 1.0], [40, 49, 1.0], [155, 164, 1.0]]}, "2005.ntcir_workshop-2005.47": {"snippet": "...tent Retrieval Experiments at Hitachi\n\n\n In NTCIR-5, we used five retrieval methods proposed in NTCIR-4: (1) query term weighting using only document frequency, (2) stopword deletion, (3) two-stage pa...", "weights": [[8, 17, 1.0], [69, 78, 1.0]]}, "2017.ictir_conference-2017.17": {"snippet": "...uage models in the language modeling framework to ad hoc retrieval. Since KL is rank equivalent to a speci c weighted geometric mean, we examine alternative weighted means for language-model compariso...", "weights": [[8, 14, 1.0], [60, 69, 1.0], [194, 204, 1.0]]}, "1986.sigirconf_conference-86.32": {"snippet": "...stic Models for Document Retrieval: A Comparison of Performance on Experimental and Synthetic Databases", "weights": [[8, 14, 1.0], [28, 37, 1.0], [41, 51, 1.0]]}, "2002.tois_journal-ir0volumeA20A3.0": {"snippet": "....e., retrieval feedback) that make use of various functions for scoring expansion terms within Rocchio's classical reweighting scheme. An analytical comparison shows that the retrieval performance of ...", "weights": [[8, 17, 1.0], [152, 162, 1.0], [178, 187, 1.0]]}, "2009.cikm_conference-2009.134": {"snippet": "...opic retrieval\n\n\n ABSTRACTTraditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval...", "weights": [[8, 17, 1.0], [41, 47, 1.0], [63, 72, 1.0], [194, 203, 1.0]]}, "1998.tois_journal-ir0volumeA16A3.1": {"snippet": "...tion Retrieval\n\n\n We analyze the kinematics of probabilistic term weights at retrieval time for different Information Retrieval models. We present four models based on different notions of probabilist...", "weights": [[8, 17, 1.0], [80, 89, 1.0], [99, 108, 1.0], [121, 130, 1.0], [131, 137, 1.0], [155, 161, 1.0], [171, 180, 1.0]]}, "2003.clef_workshop-2003.37": {"snippet": "...or a comparison of different domain-specific retrieval systems and techniques. We felt that it was necessary to acquire knowledge on the pros and cons of different retrieval systems through practical ...", "weights": [[8, 18, 1.0], [22, 31, 1.0], [48, 57, 1.0], [157, 166, 1.0], [167, 176, 1.0]]}, "2011.wwwconf_conference-2011.28": {"snippet": "...over different topics of interests that are coherent in geographical regions? (2) how to compare several topics across different geographical locations? To answer these questions, this paper proposes ...", "weights": [[8, 17, 1.0], [122, 131, 1.0]]}, "C10-2174": {"snippet": "...ment Retrieval\n\n\n The field of information retrieval still strives to develop models which allow semantic information to be integrated in the ranking process to improve performance in comparison to st...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [81, 87, 1.0], [187, 197, 1.0]]}, "2017.ictir_conference-2017.45": {"snippet": "...rm a comparison of widely-used click models in terms of the quality of click simulation and analyze this quality for queries with di erent frequencies. We nd that click models fail to accurately simul...", "weights": [[8, 18, 1.0], [40, 46, 1.0], [172, 178, 1.0]]}, "2010.sigirconf_conference-2010.208": {"snippet": "...ring models, making differences hard to appreciate. Seminal efforts on the detection of text reuse  have fostered the composition of standard resources for the accurate evaluation and comparison of me...", "weights": [[8, 14, 1.0], [187, 197, 1.0]]}, "2017.ictir_conference-2017.36": {"snippet": "...post-retrieval QPP method predicts the query's performance based on the quality of its retrieved result list .A common approach utilized by many state-of-the art post-retrieval QPP methods is the anal...", "weights": [[8, 17, 1.0], [170, 179, 1.0]]}, "2021.ranlp-1.157": {"snippet": "...opic Models are recent neural models that aim at extracting the main themes from a collection of documents. The comparison of these models is usually limited because the hyperparameters are held fixed...", "weights": [[8, 14, 1.0], [33, 39, 1.0], [115, 125, 1.0], [135, 141, 1.0]]}, "2005.sigirconf_conference-2005.22": {"snippet": "...tion retrieval system evaluation: effort, sensitivity, and reliability\n\n\n ABSTRACTThe effectiveness of information retrieval systems is measured by comparing performance on a common set of queries and...", "weights": [[8, 17, 1.0], [118, 127, 1.0]]}, "2021.eacl-main.293": {"snippet": "...ased models in real-world ad-hoc retrieval applications by comparison to a \"greener and more sustainable\" alternative, comprising only 620 trainable parameters. We present an analysis of their efficac...", "weights": [[8, 14, 1.0], [36, 45, 1.0], [62, 72, 1.0]]}, "1999.sigirconf_conference-99.43": {"snippet": "...uage models of these documents. One obstacle in applying statistical language modeling to information retrieval is the sparse data problem. It is serious in information retrieval for two reasons. Firs...", "weights": [[8, 14, 1.0], [105, 114, 1.0], [172, 181, 1.0]]}, "1997.ipm_journal-ir0volumeA33A3.3": {"snippet": "...mage Retrieval: A Comparison", "weights": [[8, 17, 1.0], [21, 31, 1.0]]}, "2014.wwwconf_conference-2014c.224": {"snippet": "...work comparison using random walks\n\n\n ABSTRACTIn this paper, we proposed a network comparison method based on the mathematical theory of diffusion over manifolds using random walks over graphs. We sho...", "weights": [[8, 18, 1.0], [86, 96, 1.0]]}, "2014.cikm_conference-2014.12": {"snippet": "A Comparison of Retrieval Models using Term Dependencies\n\n\n ABSTRACTA number of retrieval models incorporating term dependencies have recently been introduced. Most of these modify existing \"bag-ofwor...", "weights": [[2, 12, 1.0], [16, 25, 1.0], [26, 32, 1.0], [80, 89, 1.0], [90, 96, 1.0]]}, "2011.ntcir_workshop-2011.18": {"snippet": "...uage models (LM) representing different semantic roles and scopes of models for documents and a rank aggregation method. The rationale is based on observation that sentence-based language models using...", "weights": [[8, 14, 1.0], [33, 42, 1.0], [72, 78, 1.0], [191, 197, 1.0]]}, "2015.sigirconf_conference-2015.134": {"snippet": "...line Retrieval Evaluation\n\n\n ABSTRACTOnline evaluation methods for information retrieval use implicit signals such as clicks from users to infer preferences between rankers. A highly sensitive way of ...", "weights": [[8, 17, 1.0], [82, 91, 1.0]]}, "2007.sigirconf_conference-2007.152": {"snippet": "A comparison of sentence retrieval techniques", "weights": [[2, 12, 1.0], [25, 34, 1.0]]}, "2019.ecir_conference-20192.47": {"snippet": "...ased Models for Social Media Retrieval\n\n\n Abstract. The huge amount of information that is available in social media today has resulted in a pressing demand for information retrieval models to support...", "weights": [[8, 14, 1.0], [32, 41, 1.0], [176, 185, 1.0], [186, 192, 1.0]]}, "2012.ecir_conference-2012.24": {"snippet": "...tion Retrieval Model\n\n\n Abstract. We present in this paper well-founded cross-language extensions of the recently introduced models in the information-based family for information retrieval, namely th...", "weights": [[8, 17, 1.0], [128, 134, 1.0], [183, 192, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...tion Retrieval Models\n\n\n Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is necessary to und...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [49, 58, 1.0], [59, 65, 1.0], [118, 127, 1.0], [173, 179, 1.0]]}, "2010.sigirconf_conference-2010.49": {"snippet": "A comparison of general vs personalised affective models for the prediction of topical relevance\n\n\n ABSTRACTInformation retrieval systems face a number of challenges, originating mainly from the seman...", "weights": [[2, 12, 1.0], [50, 56, 1.0], [120, 129, 1.0]]}, "W09-1320": {"snippet": "...ical Comparison Questions\n\n\n This paper introduces the task of automatically answering clinical comparison questions using MEDLINE \u00ae abstracts. In the beginning, clinical comparison questions and the ...", "weights": [[8, 18, 1.0], [99, 109, 1.0], [174, 184, 1.0]]}, "2005.sigirconf_conference-2005.64": {"snippet": "...tion retrieval\n\n\n ABSTRACTExisting retrieval models generally do not offer any guarantee for optimal retrieval performance. Indeed, it is even difficult, if not impossible, to predict a model's empiri...", "weights": [[8, 17, 1.0], [38, 47, 1.0], [48, 54, 1.0], [104, 113, 1.0]]}, "2014.cikm_conference-2014.87": {"snippet": "...port comparison shopping. One key user activity is to compare two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors...", "weights": [[8, 18, 1.0], [134, 144, 1.0], [156, 165, 1.0], [186, 195, 1.0]]}, "1993.sigirconf_conference-93.26": {"snippet": "A Comparison of Indexing Techniques for Japanese Text Retrieval\n\n\n Abstract:A series of Japanese full-text retrieval experiments were conducted using an inference network document retrieval model.", "weights": [[2, 12, 1.0], [54, 63, 1.0], [107, 116, 1.0], [180, 189, 1.0]]}, "2020.ictir_conference-2020.24": {"snippet": "Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking\n\n\n Counterfactual evaluation can estimate Click-Through-Rate (CTR) differences between ranking systems based on h...", "weights": []}, "1995.sigirconf_conference-95.34": {"snippet": "...ific retrieval models for arbitrary applications, and classical probabilistic IR models can be implemented by speci@ing the appropriate rules. In comparison to other approaches, the possibility of rec...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [84, 90, 1.0], [149, 159, 1.0]]}, "P01-1004": {"snippet": "...uity models (in the form of N-grams). Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word Ngram mod...", "weights": [[8, 14, 1.0], [140, 149, 1.0], [200, 206, 1.0]]}, "W19-8504": {"snippet": "...n of comparison on a cognitive and functional basis, 1 investigating how two or more entities could be compared in a language, what patterns are used in these various ways of comparison in Latin, and ...", "weights": [[8, 18, 1.0], [178, 188, 1.0]]}, "2008.sigirconf_conference-2008.62": {"snippet": "Retrieval models for question and answer archives\n\n\n ABSTRACTRetrieval in a question and answer archive involves finding good answers for a user's question. In contrast to typical document retrieval, ...", "weights": [[0, 9, 1.0], [10, 16, 1.0], [189, 198, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...uage models. ASR systems generally contain a language model that provides word sequence probabilities. Some information retrieval systems also rely on language models for retrieval. It is likely that ...", "weights": [[8, 14, 1.0], [123, 132, 1.0], [163, 169, 1.0], [174, 183, 1.0]]}, "1980.sigirconf_conference-80.3": {"snippet": "A Comparison of Two Weighting Schemes for Boolean Retrieval", "weights": [[2, 12, 1.0], [50, 59, 1.0]]}, "2016.ictir_conference-2016.35": {"snippet": "...s of retrieval accuracy. This paper attempts to fill in this void by reporting the results of an empirical comparison of document expansion methods using translation models estimated based on word co-...", "weights": [[8, 17, 1.0], [110, 120, 1.0], [169, 175, 1.0]]}, "2008.airs_conference-2008.76": {"snippet": "A Comparison of Textual Data Mining Methods for Sex Identification in Chat Conversations", "weights": [[2, 12, 1.0]]}, "2013.ictir_conference-2013.12": {"snippet": "...Rank models to investigate the effectiveness of our approach. Comparison of our methods with strong baselines demonstrates the solid performance of our approach. Experimental results are reported on a...", "weights": [[8, 14, 1.0], [65, 75, 1.0]]}, "2022.sigtyp-1.4": {"snippet": "...stic Comparison of Linguistic Feature Encoding in {BERT} Models for Typologically Different Languages\n\n\n Though recently there have been an increased interest in how pre-trained language models encode...", "weights": [[8, 18, 1.0], [60, 66, 1.0], [85, 94, 1.0], [190, 196, 1.0]]}, "2014.sigirconf_conference-2014.193": {"snippet": "...tion retrieval models\n\n\n ABSTRACTIn this paper, we describe VIRLab, a novel web-based virtual laboratory for Information Retrieval (IR). Unlike existing command line based IR toolkits, the VIRLab syst...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [124, 133, 1.0]]}, "2016.ictir_conference-2016.14": {"snippet": "...tion Retrieval Models\n\n\n ABSTRACTDeveloping effective information retrieval models has been a long standing challenge in Information Retrieval (IR), and significant progresses have been made over the ...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [69, 78, 1.0], [79, 85, 1.0], [136, 145, 1.0]]}, "1982.jasis_journal-ir0volumeA33A1.3": {"snippet": "...nate retrieval: A comparison of two indexing languages", "weights": [[8, 17, 1.0], [21, 31, 1.0]]}, "2013.tois_journal-ir0volumeA31A4.0": {"snippet": "...aved comparison methods, which compare rankers using click data obtained when interleaving their rankings.In this article, we propose a framework for analyzing interleaved comparison methods. An inter...", "weights": [[8, 18, 1.0], [175, 185, 1.0]]}, "2009.cikm_conference-2009.301": {"snippet": "...tive comparison of retrieval systems based on clickthroughs\n\n\n ABSTRACTThe Cranfield evaluation method has some disadvantages, including its high cost in labor and inadequacy for evaluating interactiv...", "weights": [[8, 18, 1.0], [22, 31, 1.0]]}, "2013.cikm_conference-2013.189": {"snippet": "...ning comparison queries. We present a novel snowballing algorithm that \"crawls\" comparison queries from search engines via their query autocompletion services. We propose a novel modeling approach tha...", "weights": [[8, 18, 1.0], [83, 93, 1.0]]}, "2020.wsdm_conference-2020.105": {"snippet": "... Hoc Retrieval\n\n\n We present Capreolus, a toolkit designed to facilitate end-to-end ad hoc retrieval experiments with neural networks by providing implementations of prominent neural ranking models wi...", "weights": [[8, 17, 1.0], [94, 103, 1.0], [194, 200, 1.0]]}, "1987.sigirconf_conference-87.3": {"snippet": "...lean retrieval with terms from a prescribed indexing vocabulary. This is a quite different test setting in comparison to other experiments where linear retrieval with free text terms was used. The exp...", "weights": [[8, 17, 1.0], [84, 93, 1.0], [110, 120, 1.0], [155, 164, 1.0]]}, "2007.cikm_conference-2007.67": {"snippet": "A comparison of statistical significance tests for information retrieval evaluation\n\n\n ABSTRACTInformation retrieval (IR) researchers commonly use three tests of statistical significance: the Student'...", "weights": [[2, 12, 1.0], [63, 72, 1.0], [107, 116, 1.0]]}, "D10-1011": {"snippet": "...uage Models for Clinical Information Retrieval\n\n\n The PECO framework is a knowledge representation for formulating clinical questions. Queries are decomposed into four aspects, which are Patient-Probl...", "weights": [[8, 14, 1.0], [40, 49, 1.0]]}, "2010.sigirconf_conference-2010.138": {"snippet": "...tion retrieval system . However, most post-retrieval query performance predictors are expensive to compute, and not well-suited for the web.The key idea behind RAPP is to use retrieval scores and feat...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [178, 187, 1.0]]}, "2014.sigirconf_conference-2014.219": {"snippet": "...tion retrieval models; retrieval constraints; optimization\nOVERVIEWOne of the most important research problems in Information Retrieval (IR) is to develop optimal general IR models. Since IR models de...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [26, 35, 1.0], [129, 138, 1.0], [177, 183, 1.0], [194, 200, 1.0]]}, "2002.cikm_conference-2002.54": {"snippet": "...fair comparison, we propose a measurement of the retrieval effectiveness by recallprecision curves with a scaling factor adjustment. Finally, the performance study of the retrieval effectiveness based...", "weights": [[8, 18, 1.0], [52, 61, 1.0], [174, 183, 1.0]]}, "2011.clef_workshop-2011w.33": {"snippet": "... two different methods: one based on simple linear function and one based on scores comparison. In addition, a comparison between monolingual and multilingual image retrieval using queries in a single...", "weights": [[8, 17, 1.0], [87, 97, 1.0], [114, 124, 1.0], [168, 177, 1.0]]}, "2011.sigirconf_conference-2011.145": {"snippet": "...duct comparison using comparative relations\n\n\n ABSTRACTThis paper proposes a novel Product Comparison approach. The comparative relations between products are first mined from both user reviews on mul...", "weights": [[8, 18, 1.0], [94, 104, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "...imal retrieval model, enables analytical comparison of different retrieval models without necessarily requiring empirical evaluation, and has led to the development of multiple more effective retrieva...", "weights": [[8, 17, 1.0], [44, 54, 1.0], [58, 67, 1.0], [68, 77, 1.0], [78, 84, 1.0], [195, 204, 1.0]]}, "2014.wsdm_conference-2014.18": {"snippet": "...aive comparison can be biased because exposed and unexposed populations typically have different features. One solid methodology for a fair comparison is to apply inverse propensity weighting with dou...", "weights": [[8, 18, 1.0], [90, 99, 1.0], [143, 153, 1.0]]}}}
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New Cache", "weights": [[8, 13, 1.0]]}, "2013.cikm_conference-2013.136": {"snippet": "...s as cache.", "weights": [[8, 13, 1.0]]}, "2003.wwwconf_conference-2003.32": {"snippet": "... -an architecture that uses a peer-to-peer approach to cooperatively stream video using patching techniques, while only relying on unicast connections among peers. We address the following two key tec...", "weights": [[8, 20, 1.0]]}, "2017.ir_journal-ir0volumeA20A3.1": {"snippet": "...ated cache of lists + intersections", "weights": [[8, 13, 1.0]]}, "2012.wwwconf_workshop-2012dec3d.6": {"snippet": "... Web Architecture\n\n\n The recent WebGL integration in major web browser has open the way to many 3D applications as well as high-level libraries targeting 3D content developers. While most of these lib...", "weights": [[8, 20, 1.0]]}, "2007.tweb_journal-ir0volumeA1A3.2": {"snippet": "...exed cache buffer management mechanism at the edge cache and employs selective retransmissions of lost packets between the remote and edge cache for a best-effort recovery of the losses. The new Web c...", "weights": [[8, 13, 1.0], [54, 59, 1.0], [142, 147, 1.0], [202, 207, 1.0]]}, "2012.wwwconf_workshop-2012dec3d.9": {"snippet": "... Web Architecture\". In this demo description, we present a selection of features of XML3D, that demonstrate the advantages of a web-based declarative approach to 3D graphics.", "weights": [[8, 20, 1.0]]}, "2001.wwwconf_conference-2001.4": {"snippet": "...ased architecture for accelerating Web browsing in cellular customer premises networks (CPN) is presented. 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for evaluating ranking functions. It is therefore natural...", "weights": []}, "J90-1001": {"snippet": "... and Scoping\n\n\n Certain restrictions on possible scopings of quantified noun phrases in natural language are usually expressed in terms of formal constraints on binding at a level of logical form. Suc...", "weights": [[8, 15, 1.0]]}, "1997.cikm_workshop-97.53": {"snippet": "...nted Document Search System\n\n\n AbstractWith the increasing numbers of documents available from the Internet and other sources, finding anything is becoming increasingly difficult. This paper presents ...", "weights": [[8, 16, 1.0]]}, "2007.wwwconf_conference-2007.26": {"snippet": "...ical formula using a search engine today, she gets articles where the exact keyword string expressing the chemical formula is found. Searching for the exact occurrence of keywords during searching res...", "weights": [[8, 15, 1.0], [118, 125, 1.0]]}, "P92-1005": {"snippet": "...fier scoping and reference resolution, are often realised computationally by non-monotonic operations involving loss of information and destructive manipulation of semantic representations. The paper ...", "weights": [[8, 15, 1.0]]}, "2009.tois_journal-ir0volumeA27A3.4": {"snippet": "Bounded coordinate system indexing for real-time video clip search\n\n\n Recently, video clips have become very popular online. The massive influx of video clips has created an urgent need for video sear...", "weights": []}, "2010.gir_workshop-2010.24": {"snippet": "Using the geographic scopes of web documents for contextual advertising", "weights": []}, "C90-3034": {"snippet": "...fier Scoping Algorithm without A Free Variable Constraint\n\n\n Three recent demands on quantifier scoping algorithms have been that they should be explicitly stated, they should be sound and complete wi...", "weights": [[8, 15, 1.0], [99, 106, 1.0]]}, "J87-1005": {"snippet": "...ored scoping or generated scopings with mechanisms that have often been inexplicit as to the range of scopings they choose among or profligate in the scopings they allow. This paper presents, along wi...", "weights": [[8, 15, 1.0]]}, "2012.wsdm_conference-2012.9": {"snippet": "...oral scoping. We show that joint inference is effective compared to doing temporal scoping of individual facts independently. We conduct our experiments on large scale open-domain publicly available t...", "weights": [[8, 15, 1.0], [86, 93, 1.0]]}, "2009.cikm_workshop-2009swsm.4": {"snippet": "Automobile, car and BMW: horizontal and hierarchical approach in social tagging systems\n\n\n ABSTRACTSocial tagging systems have recently emerged as an effective way for users to annotate and organize l...", "weights": []}, "2007.clef_workshop-2007.101": {"snippet": "... and Document Scopes in Geographic IR", "weights": [[8, 16, 1.0]]}, "2018.wwwconf_conference-2018.163": {"snippet": "...ical Scoping and Improving a Gazetteer\n\n\n ABSTRACTMany applications that use geographical databases (a.k.a. gazetteers) rely on the accuracy of the information in the database. However, poor data qual...", "weights": [[8, 15, 1.0]]}, "2020.sdp-1.16": {"snippet": "... and formula analyses, so-called mathematical language processing, is necessary to enable computers to understand and retrieve information from the documents. However, as we will show in this project,...", "weights": [[8, 15, 1.0]]}, "P11-2025": {"snippet": "...s on scoping sentences with only two quantified noun phrases (NPs), where the quantifiers are restricted to a predefined list. It also ignores negation, modal/logical operators, and other sentential a...", "weights": [[8, 15, 1.0]]}, "2006.cikm_conference-2006.60": {"snippet": "...of a document, our model also takes into account domain specific knowledge, i.e., how the domain-specific concepts contained in the document affect the document's readability. Three major readability ...", "weights": [[8, 16, 1.0], [135, 143, 1.0], [155, 163, 1.0]]}, "2002.spire_conference-2002.13": {"snippet": "...Rank formula and the classic formula proposed in . As an example of the versatility of our general formula we derive from it a version of PageRank based on a user personalization. Finally, we discuss ...", "weights": [[8, 15, 1.0], [32, 39, 1.0], [102, 109, 1.0]]}, "2020.coling-industry.20": {"snippet": "...I}t: Scoping Task Relevant Sentences in Documents\n\n\n A prominent problem faced by conversational agents working with large documents (Eg: emailbased assistants) is the frequent presence of information...", "weights": [[8, 15, 1.0]]}, "P89-1019": {"snippet": "... and Scoping\n\n\n Certain restrictions on possible scopings of quantified noun phrases in natural language are usually expressed in terms of formal constraints on binding at a level of logical form. Suc...", "weights": [[8, 15, 1.0]]}, "P88-1005": {"snippet": "...fier Scoping in the {SRI} Core Language Engine\n\n\n An algorithm for generating the possible quantifier scopings for a sentence, in order of preference, is outlined. The scoping assigned to a quantifier...", "weights": [[8, 15, 1.0], [171, 178, 1.0]]}, "2013.clef_conference-2013w.64": {"snippet": "German-Language Content in Biomedical Vocabularies\n\n\n There are hundreds of English-language biomedical vocabularies, but only a minor part has been translated into other languages. We performed a sur...", "weights": []}, "2016.cikm_workshop-2016fm4m.18": {"snippet": "Augmenting Mathematical Formulae for More Effective Querying & Presentation", "weights": []}, "2019.wwwconf_conference-2019c.169": {"snippet": "...oral Scoping of Facts", "weights": [[8, 15, 1.0]]}, "L12-1534": {"snippet": "A methodology for the extraction of information about the usage of formulaic expressions in scientific texts", "weights": []}, "2019.sigirconf_workshop-2019birndl.12": {"snippet": "...ne a Formula Concept Retrieval challenge with two subtasks: Formula Concept Discovery (FCD) and Formula Concept Recognition (FCR). While the former aims at the definition and exploration of a Formula ...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [99, 106, 1.0], [195, 202, 1.0]]}, "2020.lrec-1.132": {"snippet": "...ting scoping configurations, and is presented specifically as a resource for quantifier scope disambiguation systems, but also more generally as an object of linguistic study.", "weights": [[8, 15, 1.0]]}, "2013.ntcir_conference-2013.75": {"snippet": "...t of formula patterns and a test data corpus. We showcase a simple data analysis program for answering the given queries. We interpret the patterns as regular expressions and assume that matches to th...", "weights": [[8, 15, 1.0]]}, "2012.tois_journal-ir0volumeA30A2.5": {"snippet": "Authorship Attribution Based on Specific Vocabulary\n\n\n In this article we propose a technique for computing a standardized Z score capable of defining the specific vocabulary found in a text (or part ...", "weights": []}, "2009.wsdm_workshop-2009wscd.10": {"snippet": "...rove document descriptions. A pilot experiment demonstrated that document descriptors extracted from the queries that are associated with documents by clicks provide useful semantic information about ...", "weights": [[8, 16, 1.0], [68, 76, 1.0]]}, "2020.ecir_conference-20202.60": {"snippet": "...tive Formula Editing, Reuse, and Lookup\n\n\n There has been growing interest in math-aware search engines that support retrieval using both formulas and keywords. An important unresolved issue is the de...", "weights": [[8, 15, 1.0]]}, "J90-3001": {"snippet": "Resolving Quasi Logical Forms\n\n\n The paper describes intermediate and resolved logical form representations of sentences involving referring expressions and a reference resolution process for mapping ...", "weights": []}, "W19-3306": {"snippet": "Generating Discourse Inferences from Unscoped Episodic Logical Formulas\n\n\n Unscoped episodic logical form (ULF) is a semantic representation capturing the predicateargument structure of English within...", "weights": []}, "2017.ecir_workshop-2017smerp.8": {"snippet": "Extracting Humanitarian Information from Tweets\n\n\n In this paper we describe the application of our methods to humanitarian information extraction from tweets and their performance in the scope of the...", "weights": []}, "2007.riao_conference-2007.16": {"snippet": "An IE and IR Approach to deal with Geographic Information Scope in Textual Documents", "weights": []}, "1999.sigirconf_conference-99.12": {"snippet": "...ical Formula for Chinese Text Segmentation Incorporating Contextual Information\n\n\n ABSTRACTA new statistical formula for identifying 2-character words in Chinese text, called the contextual informatio...", "weights": [[8, 15, 1.0], [112, 119, 1.0]]}, "2006.clef_workshop-2006w.114": {"snippet": "...d in document retrieval through a ranking function that combines BM25 text weighting with a similarity function for geographic scopes. We also tested a topic augmentation method, based on the geograph...", "weights": [[8, 16, 1.0]]}, "2013.ntcir_conference-2013.84": {"snippet": "...each document. We employ not only a \"static\" document collection consisting of targeted documents but also a \"dynamic\" document collection including web documents related with queries. We expand the q...", "weights": [[8, 16, 1.0], [48, 56, 1.0], [122, 130, 1.0]]}, "2007.cikm_workshop-2007cims.8": {"snippet": "...on a document collection. Each method consists of a combination of term scoring scheme, term scoring formula, and term assignment scheme. Our preliminary experiments evaluate these different slot-base...", "weights": [[8, 16, 1.0], [104, 111, 1.0]]}, "J82-1003": {"snippet": "... the formula for the fragment. However, we have sought to reformulate Gazdar's semantic rules so as to obtain more or less 'conventional' logical translations of English sentences, avoiding the interp...", "weights": [[8, 15, 1.0]]}, "2008.gir_workshop-2008.3": {"snippet": "Geographic scope modeling for web documents", "weights": []}, "2018.sigirconf_conference-2018.210": {"snippet": "...-idf formula for expanded words in search ranking.", "weights": [[8, 15, 1.0]]}, "P89-1004": {"snippet": "...s of scoping and reference resolution. The approach has been implemented in the SRI Core Language Engine which handles the English constructions discussed in the paper.", "weights": [[8, 15, 1.0]]}, "2011.sigirconf_conference-2011.20": {"snippet": "...with document length based on a trade-off between the Verbosity hypothesis, which assumes the independence of a document's relevance of its length, and the Scope hypothesis, which assumes the opposite...", "weights": [[8, 16, 1.0], [115, 123, 1.0]]}, "1997.ipm_journal-ir0volumeA33A1.7": {"snippet": "It's 10 A.M. - Do You Know Where Your Documents Are? The Nature and Scope of Information Retrieval Problems in Business", "weights": []}, "2021.newsum-1.14": {"snippet": "...each document and intent (e.g., \"weather\"), a single summary suffices for all users. No datasets exist, however, for subjective intents (e.g., \"interesting places\") where different users will provide ...", "weights": [[8, 16, 1.0]]}, "2020.clef_conference-2020w.45": {"snippet": "... (2) Formula Retrieval. Four runs were submitted for Task 1 using systems that take advantage of text and formula embeddings. For Task 2, three runs were submitted: one uses only formula embedding, an...", "weights": [[8, 15, 1.0], [109, 116, 1.0], [182, 189, 1.0]]}, "2004.spire_conference-2004.15": {"snippet": "Indexing Text Documents Based on Topic Identification\n\n\n Abstract. This work provides algorithms and heuristics to index text documents by determining important topics in the documents. To index text ...", "weights": []}, "W10-3910": {"snippet": "...tion Formula)', for mining coreference relations between formulas and the concepts that refer to them. Using Wikipedia articles as a target corpus, our approach is based on surface level text matching...", "weights": [[8, 15, 1.0]]}, "N15-1126": {"snippet": "...gger Scoping Strategy for Biographical Fact Extraction\n\n\n The rapid growth of information sources brings a unique challenge to biographical information extraction: how to find specific facts without h...", "weights": [[8, 15, 1.0]]}, "J03-1004": {"snippet": "A Machine Learning Approach to Modeling Scope Preferences\n\n\n This article describes a corpus-based investigation of quantifier scope preferences. Following recent work on multimodular grammar framewor...", "weights": []}, "2003.spire_conference-2003.15": {"snippet": "...king formula which we evaluate using a medical reference collection, the OHSUMED collection. Our results indicate that this combination of evidences might yield considerable gains in retrieval perform...", "weights": [[8, 15, 1.0]]}, "Y05-1017": {"snippet": "A study on multiple interpretations of frequency adverbs in {J}apanese\n\n\n This paper is concerned with frequency adverbs in Japanese. Many linguists have pointed out that frequency statements and gene...", "weights": []}, "2018.ictir_conference-2018.36": {"snippet": "...tant document features such as term frequency, document frequency, and document length. We investigate in this paper aggregative and statistical document features for document ranking. Shortly speakin...", "weights": [[8, 16, 1.0], [50, 58, 1.0], [74, 82, 1.0], [148, 156, 1.0], [170, 178, 1.0]]}, "2020.clef_conference-2020w.8": {"snippet": "...ntic Formula Search in zbMATH Open?", "weights": [[8, 15, 1.0]]}, "2014.sigirconf_conference-2014.73": {"snippet": "...ient formula index and retrieval from both webpages and PDF documents. Unlike prior studies, which require users to manually input formula markup language as query, the new system enables users to \"co...", "weights": [[8, 15, 1.0], [134, 141, 1.0]]}, "2014.ntcir_conference-2014.19": {"snippet": "Evaluation of Similarity-Measure Factors for Formulae Based on the NTCIR-11 Math Task\n\n\n In this paper we evaluate the similarity-measure factors proposed by Zhang and Youssef based on the NTCIR-11 go...", "weights": []}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "... the document-at least, not when the query is shorter than about 30 words. For document retrieval, long enough spans of speech can be readily handled.However, information retrieval is not just about d...", "weights": [[8, 16, 1.0], [82, 90, 1.0], [202, 210, 1.0]]}, "2015.wsdm_conference-2015.62": {"snippet": "... for Document-Level Sentiment Analysis\n\n\n ABSTRACTIn this paper, we propose a representation learning research framework for document-level sentiment analysis. Given a document as the input, document-...", "weights": [[8, 16, 1.0], [128, 136, 1.0], [171, 179, 1.0], [194, 202, 1.0]]}, "2004.wwwconf_conference-2004at.37": {"snippet": "Enhancing the SCORM metadata model\n\n\n ABSTRACTNowadays, the leading e-learning platforms are converging towards standardization. This paper presents an extension to the SCORM, today's most well acclai...", "weights": []}, "E91-1044": {"snippet": "...ault scoping first. Removing logical redundancy is not only interesting per se, but also drastically reduces the processing time. The input and output formats are described through a few access and co...", "weights": [[8, 15, 1.0]]}, "W14-1612": {"snippet": "...oral Scoping of Relational Facts based on {W}ikipedia Data\n\n\n Most previous work in information extraction from text has focused on named-entity recognition, entity linking, and relation extraction. L...", "weights": [[8, 15, 1.0]]}, "Y10-1055": {"snippet": "A Modular Architecture for the Wide-Coverage Translation of Natural Language Texts into Predicate Logic Formulas\n\n\n We present a new method for translating unrestricted natural language texts into pre...", "weights": []}, "W06-1510": {"snippet": "Quantifier Scope in {G}erman: An {MCTAG} Analysis\n\n\n Relative quantifier scope in German depends, in contrast to English, very much on word order. The scope possibilities of a quantifier are determine...", "weights": []}, "2019.tois_journal-ir0volumeA37A3.1": {"snippet": "On Annotation Methodologies for Image Search Evaluation\n\n\n Image search engines differ significantly from general web search engines in the way of presenting search results. The difference leads to di...", "weights": []}, "2020.clef_conference-2020w.14": {"snippet": "...t of formula embedding and term-document matrix, in this paper we have introduced the variable size formula embedding approach where the formula is transformed into the variable size vector. In a vect...", "weights": [[8, 15, 1.0], [35, 43, 1.0], [103, 110, 1.0], [140, 147, 1.0]]}, "2020.sigirconf_conference-2020.307": {"snippet": "KLOOS: KL Divergence-based Out-of-Scope Intent Detection in Human-to-Machine Conversations\n\n\n User intent is not restricted in human-to-machine conversations, and sometimes overshoots the scope of a d...", "weights": []}, "2015.tois_journal-ir0volumeA33A2.3": {"snippet": "...each document is prenormalized by dividing the term frequency by the verbosity of the document. In scope normalization, an existing retrieval model is applied in a straightforward manner to the prenor...", "weights": [[8, 16, 1.0], [89, 97, 1.0]]}, "2016.sigirconf_conference-2016.203": {"snippet": "...ased formula retrieval in two different modalities: symbolic for text documents and image-based for videos.We believe that we can achieve high quality formula retrieval results using the visual appear...", "weights": [[8, 15, 1.0], [154, 161, 1.0]]}, "2020.clef_conference-2020w.120": {"snippet": "Named Entity Recognition in Chemical Patents using Ensemble of Contextual Language Models\n\n\n Chemical patent documents describe a broad range of applications holding key reaction and compound informat...", "weights": []}, "2016.ntcir_conference-2016.87": {"snippet": "...t of formula importance within a document is introduced into the model. In order to make the ranking results more reasonable, our system (WikiMir) applies the learning to rank algorithm (RankBoost) to...", "weights": [[8, 15, 1.0], [36, 44, 1.0]]}, "2020.lrec-1.269": {"snippet": "...s of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. We also present a novel evaluation dataset for this task...", "weights": [[8, 15, 1.0], [126, 133, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.5": {"snippet": "...ical formula, all possible (or a significant number of) subformulae of formulae that appear in any document and all possible subterms (e.g., \"methyl\") of chemical names (e.g., \"methylethyl ketone\") mu...", "weights": [[8, 15, 1.0], [102, 110, 1.0]]}, "2012.cikm_workshop-2012esair.9": {"snippet": "...ical formula search\n\n\n ABSTRACTIn recent years, growing numbers of scientific papers have been published in XML format generating a large published base of MathML-style formulas. Although these formul...", "weights": [[8, 15, 1.0]]}, "2013.ntcir_conference-2013.69": {"snippet": "...eing formula as an expression tree. The other index is a table which stores the parent node and the text string for each node in the expression trees. A hundred thousand documents in the NTCIR-10 Math...", "weights": [[8, 15, 1.0]]}, "2011.sigirconf_conference-2011.121": {"snippet": "...tion formula:where |D| represents document length, avdl stands for average document length, c(q, D) is the raw TF of q in D, and b and k1 are two parameters. c (q, D) is the normalized TF by document ...", "weights": [[8, 15, 1.0], [37, 45, 1.0], [78, 86, 1.0], [194, 202, 1.0]]}, "A88-1008": {"snippet": "... the scoping protocol which wcre encountered during the design of the scoping program.", "weights": [[8, 15, 1.0], [73, 80, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.6": {"snippet": "Computing, explaining and visualizing shape similarity in content-based image retrieval\n\n\n AbstractAlthough there is a growing need for Content-Based Image Retrieval systems, their use is often hamper...", "weights": []}, "P92-1031": {"snippet": "Connection Relations and Quantifier Scope\n\n\n A formalism will be presented in this paper which makes it possible to realise the idea of assigning only one scope-ambiguous representation to a sentence ...", "weights": []}, "2004.spire_conference-2004.6": {"snippet": "...hird document. The current technique for concurrently detecting all co-derivatives in a collection is document fingerprinting, which matches documents based on the hash values of selected document sub...", "weights": [[8, 16, 1.0], [105, 113, 1.0], [191, 199, 1.0]]}, "C88-2135": {"snippet": "...lity Formula of {J}apanese Texts for Machine Scoring\n\n\n A readability formula is obtained that can be used by computer programs for style checking of Japanese texts and need not syntactic or semantic ...", "weights": [[8, 15, 1.0], [73, 80, 1.0]]}}}
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Relevance models, while improving retrieval in most cases, hurts performance on ...", "weights": [[8, 16, 1.0]]}, "2006.sigirconf_conference-2006.110": {"snippet": "...ance feedback results\n\n\n ABSTRACTWe explore interactive methods to further improve the performance of pseudo-relevance feedback. Studies  suggest that new methods for tackling difficult queries are re...", "weights": [[8, 16, 1.0], [122, 130, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.3": {"snippet": "...ance feedback\n\n\n a b s t r a c tTypical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval...", "weights": [[8, 16, 1.0], [60, 68, 1.0]]}, "2015.clef_conference-2015.8": {"snippet": "...ance feedback helps in such a scenario. Therefore we implemented a number of pseudo relevance feedback techniques based on knowledge bases, where the knowledge base is either Wikipedia or a combinatio...", "weights": [[8, 16, 1.0], [97, 105, 1.0]]}, "2001.sigirconf_conference-2001.53": {"snippet": "...ance Feedback Using Optimization Tables", "weights": [[8, 16, 1.0]]}, "2010.trec_conference-2010.29": {"snippet": "...ance Feedback: QUT E-Discovery Lab at the TREC 2010 Relevance Feedback Track\n\n\n User relevance feedback is usually utilized by Web systems to interpret user information needs and retrieve effective re...", "weights": [[8, 16, 1.0], [65, 73, 1.0], [98, 106, 1.0]]}, "2018.sigirconf_workshop-2018profs.3": {"snippet": "...ance feedback. Our focus in this paper is on one particular aspect of CAL: refreshing, which is a crucial and recurring event in the CAL process. During a refresh, the machine learning model is traine...", "weights": [[8, 16, 1.0]]}, "2007.clef_workshop-2007.15": {"snippet": "...ance Feedback", "weights": [[8, 16, 1.0]]}, "2017.ecir_conference-2017.65": {"snippet": "...ance Feedback", "weights": [[8, 16, 1.0]]}, "2014.sigirconf_conference-2014.34": {"snippet": "...ance feedback\n\n\n ABSTRACTPseudo Relevance Feedback is an effective technique to improve the performance of ad-hoc information retrieval. Traditionally, the expansion terms are extracted either accordi...", "weights": [[8, 16, 1.0], [45, 53, 1.0]]}, "2010.sigirconf_conference-2010.75": {"snippet": "...ance feedback\n\n\n ABSTRACTPseudo-relevance feedback is an effective technique for improving retrieval results. Traditional feedback algorithms use a whole feedback document as a unit to extract words f...", "weights": [[8, 16, 1.0], [45, 53, 1.0], [125, 133, 1.0], [157, 165, 1.0]]}, "2018.ictir_conference-2018.35": {"snippet": "...ance Feedback in Fusion-based Retrieval\n\n\n ABSTRACTThe usage of positive relevance feedback in fusion-based retrieval was previously shown to be very useful. Yet, in many retrieval usecases, no actual...", "weights": [[8, 16, 1.0], [86, 94, 1.0]]}, "2008.sigirconf_conference-2008.57": {"snippet": "...ance feedback. For disjoint terms, a relationship between probability theory and TF-IDF is established through the integral R 1 x dx = log x. This study uncovers components such as divergence from ran...", "weights": [[8, 16, 1.0]]}, "2007.sigirconf_conference-2007.41": {"snippet": "...line feedback method as a black box, and the output feedback model as a random variable, we estimate a posterior distribution for the feedback model by resampling a given query's top-retrieved documen...", "weights": [[8, 16, 1.0], [55, 63, 1.0], [137, 145, 1.0]]}, "2001.clef_workshop-2001.12": {"snippet": "...ance Feedback Using a Comparable Corpus", "weights": [[8, 16, 1.0]]}, "2010.ecir_conference-2010.50": {"snippet": "...ance Feedback Using Pseudo-Irrelevant Documents", "weights": [[8, 16, 1.0]]}, "2002.ntcir_workshop-2002.18": {"snippet": "...ance Feedback Method Using Taylor Formula at NTCIR-3 Patent Retrieval Task\n\n\n Pseudo relevance feedback is empirically known as a useful method for enhancing retrieval performance. For example, we can...", "weights": [[8, 16, 1.0], [98, 106, 1.0]]}, "2010.cikm_conference-2010.111": {"snippet": "...ance feedback algorithms\n\n\n ABSTRACTWe present a flexible new optimization framework for finding effective, reliable pseudo-relevance feedback models that unifies existing complementary approaches in ...", "weights": [[8, 16, 1.0], [137, 145, 1.0]]}, "2003.sigirjournals_journal-ir0volumeA37A2.1": {"snippet": "...icit feedback for inferring user preference: a bibliography\n\n\n Relevance feedback has a history in information retrieval that dates back well over thirty years (c.f [SL96]). Relevance feedback is typi...", "weights": [[8, 16, 1.0], [76, 84, 1.0], [187, 195, 1.0]]}, "2011.sigirconf_conference-2011.19": {"snippet": "...ance feedback\n\n\n ABSTRACTPseudo-relevance feedback has proven effective for improving the average retrieval performance. Unfortunately, many experiments have shown that although pseudo-relevance feedb...", "weights": [[8, 16, 1.0], [45, 53, 1.0], [198, 206, 1.0]]}, "2010.airs_conference-2010.50": {"snippet": "...ance Feedback", "weights": [[8, 16, 1.0]]}, "2007.cikm_conference-2007.91": {"snippet": "...ance feedback. To derive this measure, it is necessary to make the assumption that a user is seeking an ideal (usually non-existent) document and the actual relevant documents in the collection will c...", "weights": [[8, 16, 1.0]]}, "Y15-1061": {"snippet": "...ance Feedback Based on Weak Relevant Topic Alignment\n\n\n In this paper, a cross-lingual pseudo relevance feedback (PRF) model based on weak relevant topic alignment (WRTA) is proposed for cross languag...", "weights": [[8, 16, 1.0], [107, 115, 1.0]]}, "2009.cikm_conference-2009.277": {"snippet": "...ance feedback using semantic clustering in relevance language model\n\n\n ABSTRACTPseudo relevance feedback has demonstrated to be in general an effective technique for improving retrieval effectiveness,...", "weights": [[8, 16, 1.0], [99, 107, 1.0]]}, "2011.cikm_conference-2011.269": {"snippet": "...ance feedback", "weights": [[8, 16, 1.0]]}, "2016.tois_journal-ir0volumeA34A4.3": {"snippet": "...ance Feedback\n\n\n Traditional pseudo relevance feedback (PRF) models choose top k feedback documents for query expansion and treat those documents equally. When k is determined, feedback terms are sele...", "weights": [[8, 16, 1.0], [49, 57, 1.0], [84, 92, 1.0], [180, 188, 1.0]]}, "2008.ecir_conference-2008.54": {"snippet": "...eudo Feedback Document Selection for Query Expansion", "weights": [[8, 16, 1.0]]}, "2010.airs_conference-2010.20": {"snippet": "...ance Feedback Based on mRMR Criteria", "weights": [[8, 16, 1.0]]}, "2007.cikm_conference-2007.31": {"snippet": "...icit feedback\n\n\n ABSTRACTImplicit feedback algorithms utilize interaction between searchers and search systems to learn more about users' needs and interests than expressed in query statements alone. ...", "weights": [[8, 16, 1.0], [37, 45, 1.0]]}, "2013.cikm_conference-2013.48": {"snippet": "...ance feedback via tweet selection\n\n\n ABSTRACTQuery expansion methods using pseudo-relevance feedback have been shown effective for microblog search because they can solve vocabulary mismatch problems ...", "weights": [[8, 16, 1.0], [95, 103, 1.0]]}, "2007.clef_workshop-2007w.129": {"snippet": "...ance Feedback to Improve Image Retrieval Results\n\n\n In this paper, we propose a pseudo-relevance feedback method to deal with the photographic retrieval and medical retrieval tasks of ImageCLEF 2007. ...", "weights": [[8, 16, 1.0], [100, 108, 1.0]]}, "2011.jasis_journal-ir0volumeA62A4.10": {"snippet": "...ance feedback", "weights": [[8, 16, 1.0]]}, "2004.sigirconf_conference-2004.72": {"snippet": "...eudo feedback documents than an effective existing feedback model. The new model can tolerate a more flexible setting of the number of pseudo feedback documents without the danger of losing much retri", "weights": [[8, 16, 1.0], [54, 62, 1.0], [145, 153, 1.0]]}, "C10-2007": {"snippet": "...ance feedback to obtain expansion terms from definition clusters. We show that this expansion method outperforms both local feedback, based on the document collection, and expansion with WordNet synon...", "weights": [[8, 16, 1.0], [127, 135, 1.0]]}, "2009.trec_conference-2009.5": {"snippet": "...eudo feedback strategy which stops pseudo feedback for precision-oriented queries and only uses it for recall-oriented ones. The proposed PRM has shown clear improvements over the relevance model for ...", "weights": [[8, 16, 1.0], [45, 53, 1.0]]}, "2006.sigirconf_conference-2006.22": {"snippet": "...ance feedback\n\n\n ABSTRACTPseudo-relevance feedback has proven to be an effective strategy for improving retrieval accuracy in all retrieval models. However the performance of existing pseudo feedback ...", "weights": [[8, 16, 1.0], [45, 53, 1.0], [194, 202, 1.0]]}, "W03-2004": {"snippet": "...ance Feedback Method based on {T}aylor Expansion of Retrieval Function in {NTCIR}-3 Patent Retrieval Task\n\n\n Pseudo relevance feedback is empirically known as a useful method for enhancing retrieval p...", "weights": [[8, 16, 1.0], [129, 137, 1.0]]}, "2012.ecir_conference-2012.55": {"snippet": "...ance feedback, based upon the temporal profiles of n-grams extracted from the top N relevance feedback tweets. A weighted graph is used to model temporal correlation between n-grams, with a PageRank v...", "weights": [[8, 16, 1.0], [97, 105, 1.0]]}, "2008.sigirconf_conference-2008.32": {"snippet": "...ance feedback\n\n\n ABSTRACTTypical pseudo-relevance feedback methods assume the topretrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can...", "weights": [[8, 16, 1.0], [53, 61, 1.0]]}, "2016.cikm_conference-2016.153": {"snippet": "...ance Feedback Based on Matrix Factorization", "weights": [[8, 16, 1.0]]}, "U06-1013": {"snippet": "...ance Feedback Using Named Entities for Question Answering\n\n\n Relevance feedback has already proven its usefulness in probabilistic information retrieval (IR). In this research we explore whether a pse...", "weights": [[8, 16, 1.0], [74, 82, 1.0]]}, "2011.ictir_conference-2011.30": {"snippet": "...ance Feedback for Ad-hoc Retrieval", "weights": [[8, 16, 1.0]]}, "2016.sigirconf_conference-2016.129": {"snippet": "...ance Feedback in Content-Based Image Retrieval\n\n\n ABSTRACTThe increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. ...", "weights": [[8, 16, 1.0]]}, "2021.ecir_conference-20212.46": {"snippet": "...ance Feedback Using a Graph-Based Transformer\n\n\n Most research on pseudo relevance feedback (PRF) has been done in vector space and probabilistic retrieval models. This paper shows that Transformer-ba...", "weights": [[8, 16, 1.0], [86, 94, 1.0]]}, "2013.sigirconf_conference-2013.57": {"snippet": "...ance feedback\n\n\n ABSTRACTPseudo-relevance feedback is an important strategy to improve search accuracy. It is often implemented as a tworound retrieval process: the first round is to retrieve an initi...", "weights": [[8, 16, 1.0], [45, 53, 1.0]]}, "2003.wwwconf_conference-2003.2": {"snippet": "...ance feedback in web information retrieval using web page segmentation\n\n\n ABSTRACTIn contrast to traditional document retrieval, a web page as a whole is not a good information unit to search because ...", "weights": [[8, 16, 1.0]]}, "2007.clef_workshop-2007.85": {"snippet": "...ance Feedback to Improve Image Retrieval Results", "weights": [[8, 16, 1.0]]}, "2008.sigirconf_conference-2008.56": {"snippet": "...ance feedback algorithm based on the latent mixture modeling of the Dirichlet compound multinomial distribution to further improve retrieval accuracy. Finally, our experiments show that both the basel...", "weights": [[8, 16, 1.0]]}, "2017.ecir_conference-2017.63": {"snippet": "...tive feedback from the user in a language modeling framework to compute the relevancy score of new documents. In other words, the negative feedback from the user is considered as the representative of...", "weights": [[8, 16, 1.0], [142, 150, 1.0]]}, "2018.ipm_journal-ir0volumeA54A2.6": {"snippet": "...on a feedback query model that easily and typically holds in modern pseudo-relevance feedback methods: 1) the verbosity-preserving effect: the query verbosity of a feedback query model is determined b...", "weights": [[8, 16, 1.0], [88, 96, 1.0], [167, 175, 1.0]]}, "2012.irfc_conference-2012.1": {"snippet": "...ance Feedback for Patent Retrieval", "weights": [[8, 16, 1.0]]}, "2009.cikm_conference-2009.297": {"snippet": "...good feedback documents by classifying all feedback documents using a variety of features such as the distribution of query terms in the feedback document, the similarity between a single feedback doc...", "weights": [[8, 16, 1.0], [46, 54, 1.0], [140, 148, 1.0], [191, 199, 1.0]]}, "2016.ipm_journal-ir0volumeA52A3.7": {"snippet": "...ance feedback is the basis of a category of automatic query modification techniques. Pseudo-relevance feedback methods assume the initial retrieved set of documents to be relevant. Then they use these...", "weights": [[8, 16, 1.0], [105, 113, 1.0]]}, "2009.sigirconf_conference-2009.9": {"snippet": "...ance feedback based on wikipedia", "weights": [[8, 16, 1.0]]}, "2020.ipm_journal-ir0volumeA57A6.55": {"snippet": "...ance feedback framework combining relevance matching and semantic matching for information retrieval", "weights": [[8, 16, 1.0]]}, "2012.wsdm_conference-2012.59": {"snippet": "Exploring social influence via posterior effect of word-of-mouth recommendations\n\n\n ABSTRACTWord-of-mouth has proven an effective strategy for promoting products through social relations. Particularly...", "weights": []}, "2008.sigirconf_conference-2008.31": {"snippet": "...ance feedback\n\n\n ABSTRACTRelevance feedback, which traditionally uses the terms in the relevant documents to enrich the user's initial query, is an effective method for improving retrieval performance...", "weights": [[8, 16, 1.0], [38, 46, 1.0]]}, "2011.cikm_conference-2011.242": {"snippet": "...ance feedback", "weights": [[8, 16, 1.0]]}, "2005.trec_conference-2005.89": {"snippet": "...ance feedback; H2: Query expansion based on the Web will increase effectiveness over baseline queries and baseline queries plus pseudo-relevance feedback; H3: Query expansion using terms selected by t...", "weights": [[8, 16, 1.0], [148, 156, 1.0]]}, "2008.trec_conference-2008.16": {"snippet": "...ers' feedback for an initial result list by the system. With different amount of feedback information available, the optimal feedback strategy might be very different. In TREC Relevance Feedback task,...", "weights": [[8, 16, 1.0], [84, 92, 1.0], [128, 136, 1.0], [189, 197, 1.0]]}, "2012.cikm_workshop-2012dubmmsm.7": {"snippet": "...icit feedback, high proportion of cold-start tweets and short tweet active time. In this paper, we propose a novel retweet recommendation (RTR) framework which leverages the implicit feedback to help ...", "weights": [[8, 16, 1.0], [186, 194, 1.0]]}, "2011.coria_conference-2011.10": {"snippet": "...ance Feedback Models", "weights": [[8, 16, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "...ance feedback\nGeneral TermsAlgorithms, Performance, Experimentation\nKeywordsRelevance Feedback, Web Search, Evaluation\n1.INTRODUCTIONRelevance feedback is a classical information retrieval (IR) techni...", "weights": [[8, 16, 1.0], [89, 97, 1.0], [146, 154, 1.0]]}, "2008.sigirconf_conference-2008.150": {"snippet": "...ance feedback in language modeling with score regularization\n\n\n ABSTRACTWe demonstrate that regularization can improve feedback in a language modeling framework.", "weights": [[8, 16, 1.0], [122, 130, 1.0]]}, "2014.ecir_conference-2014.22": {"snippet": "...ance Feedback Techniques for Informal Text", "weights": [[8, 16, 1.0]]}}}
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Permissio...", "weights": [[8, 15, 1.0]]}, "W98-0718": {"snippet": "...t in Natural Language Generation\n\n\n WordNet has rarely been applied to natural language generation, despite of its wide application in other fields. In this paper, we address three issues in the usage...", "weights": [[8, 15, 1.0], [74, 81, 1.0]]}, "2011.ipm_journal-ir0volumeA47A6.3": {"snippet": "Improving graph-based random walks for complex question answering using syntactic, shallow semantic and extended string subsequence kernels\n\n\n a b s t r a c tThe task of answering complex questions re...", "weights": []}, "W18-1405": {"snippet": "... use natural language to express spatial relations between and across entities in the world with great facility, natural language systems have a facility that depends on that human facility. This posi...", "weights": [[8, 15, 1.0], [116, 123, 1.0]]}, "N04-4034": {"snippet": "...ased word clustering algorithm, we achieve a 8.9% perplexity reduction on Switchboard and a 12.2% reduction on the ICSI Meeting Recorder data.", "weights": [[8, 12, 1.0]]}, "2019.gwc-1.13": {"snippet": "...T to represent semantic information. We decided to use PLWORD-NET lexical units and synsets to describe both the predicate meaning and the semantic fields of its arguments. The original design decisio...", "weights": [[8, 17, 1.0]]}, "2020.conll-1.3": {"snippet": "...roof Nets\n\n\n Linear logic and the linear \u03bb-calculus have a long standing tradition in the study of natural language form and meaning. Among the proof calculi of linear logic, proof nets are of particu...", "weights": [[8, 12, 1.0], [102, 109, 1.0], [184, 188, 1.0]]}, "2021.gwc-1.2": {"snippet": "...f a \"word-net\" for the Uzbek language compatible to Princeton Word-Net. We called it UZWORDNET. 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We show that such 'person vectors' are informative to others, and they influence basic patterns of communication like the choice of one's interlocutor in conversati...", "weights": [[8, 17, 1.0]]}, "2020.jasis_journal-ir0volumeA71A5.4": {"snippet": "Joint Modeling of Characters, Words, and Conversation Contexts for Microblog Keyphrase Extraction", "weights": []}, "2012.cikm_conference-2012.197": {"snippet": "...hich represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also t...", "weights": [[8, 17, 1.0]]}, "2017.sigirconf_workshop-2017birndl2.2": {"snippet": "... 250 word based on the identified sentences in the reference paper from its cited text spans using maximal marginal relevance.", "weights": [[8, 12, 1.0]]}, "W06-3408": {"snippet": "...-art natural language processing components that have been modified to accommodate the unique nature of conversational data. In addition, we leverage the added richness of conversational data by analy...", "weights": [[8, 15, 1.0]]}, "C92-1061": {"snippet": "...roof-Nets which is much related to Dependency Structures similar to those we meet in, for instance (Hudson 1984) . These new Proof-Nets are called Connection Nets. We show that Connection Nets provide...", "weights": [[8, 12, 1.0], [134, 138, 1.0], [161, 165, 1.0], [191, 195, 1.0]]}, "P98-1066": {"snippet": "...th a natural language layer to convert English queries for use by the various search engines. The NLP layer incorporates morphological analysis, noun phrase syntax, and semantic expansion based on Wor", "weights": [[8, 15, 1.0], [200, 204, 1.0]]}, "E87-1030": {"snippet": "Natural and Simulated Pointing\n\n\n Referent identification in human conversation is performed both by describing the objects in question and by pointing at them. Up till now, only the linguistic compon...", "weights": [[0, 7, 1.0]]}, "2021.emnlp-main.403": {"snippet": "...t of natural conversation is to tailor one's response such that it accounts for concepts that the speaker and listener may or may not know and the contextual relevance of all prior concepts used in co...", "weights": [[8, 15, 1.0]]}, "P14-1115": {"snippet": "...tten Conversations Based on Phrasal Queries\n\n\n We propose a novel abstractive querybased summarization system for conversations, where queries are defined as phrases reflecting a user information need...", "weights": [[8, 21, 1.0], [117, 130, 1.0]]}, "2021.naacl-main.246": {"snippet": "...gh a natural conversation. We explore one promising direction for conversational recommendation: mapping a conversational user, for whom there is limited or no data available, to most similar external...", "weights": [[8, 15, 1.0]]}, "2018.sigirconf_conference-2018.221": {"snippet": "...most natural way.\nTHE TUTORIALIn this tutorial, we start with a brief introduction to the recent progress on DL and RL that is related to natural language processing (NLP), information retrieval (IR) ...", "weights": [[8, 15, 1.0], [141, 148, 1.0]]}, "L18-1462": {"snippet": "...el a natural conversation with machines. However, largescale conventional dialogue corpora are mainly built for specified tasks (e.g., task-oriented systems for restaurant or bus information navigatio...", "weights": [[8, 15, 1.0]]}, "W19-7510": {"snippet": "... the word. This allows a thorough understanding of the words, and also the knowledge they embody. Vaijayant\u012bko\u015ba is a popular Sanskrit lexicon that contains words from spoken language as well those us...", "weights": [[8, 12, 1.0]]}, "2011.wsdm_conference-2011.66": {"snippet": "... the natural conversational nature of social messaging systems. Two of the salient features of the proposed approach are (i) an efficient localitybased clustering approach for identifying crowds of us...", "weights": [[8, 15, 1.0]]}, "2020.wwwconf_conference-2020c.2": {"snippet": "...duce word category information via the auxiliary word category label predicting task. To train our model, we construct a new Chinese query rewriting dataset and conduct experiments on it. The experime...", "weights": [[8, 12, 1.0], [52, 56, 1.0]]}, "2020.figlang-1.8": {"snippet": "...n in conversations is a difficult and tricky task. Classifying an utterance as sarcastic or not in isolation can be futile since most of the time the sarcastic nature of a sentence heavily relies on i...", "weights": [[8, 21, 1.0]]}, "2010.sigirconf_conference-2010.107": {"snippet": "Context aware query classification using dynamic query window and relationship net\n\n\n ABSTRACTThe context of the user queries, preceding a given query, is utilized to improve the effectiveness of quer...", "weights": []}, "2018.sigirconf_workshop-2018birndl.4": {"snippet": "...late word importance scores for each target document using a word-level random walk. Next, we optimize sentence embedding vectors using a Siamese neural network. Here, we utilize localized sentence re...", "weights": [[8, 12, 1.0], [64, 68, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.23": {"snippet": "... for natural language conversation\n\n\n AbstractAs an important task of artificial intelligence, natural language conversation has attracted wide attention of researchers in natural language processing....", "weights": [[8, 15, 1.0], [98, 105, 1.0], [175, 182, 1.0]]}, "W16-2802": {"snippet": "...tive Conversations in Reader Comment on News\n\n\n Existing approaches to summarizing multi-party argumentative conversations in reader comment are extractive and fail to capture the argumentative nature...", "weights": [[8, 21, 1.0], [112, 125, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.6": {"snippet": "...pted Word Embeddings using Recurrent Neural Networks", "weights": [[8, 12, 1.0]]}, "2019.ipm_journal-ir0volumeA56A1.9": {"snippet": "...line conversations\n\n\n A B S T R A C TWith the explosion of multilingual content on Web, particularly in social media platforms, identification of languages present in the text is becoming an important...", "weights": [[8, 21, 1.0]]}, "E99-1009": {"snippet": "...roof-nets are to categorial grammar what phrase markers are to phrase structure grammar: unique graphical structures underlying equivalence classes of sequential syntactic derivations; but the role of...", "weights": [[8, 12, 1.0]]}, "W93-0103": {"snippet": "...each word from corpus. The concept of a word is defined within the probabilistic framework. A variation of Belief Net named as Collocation Map is used to compute the probabilities. The Belief Net capt...", "weights": [[8, 12, 1.0], [43, 47, 1.0]]}, "1996.tois_journal-ir0volumeA14A4.1": {"snippet": "...etri nets-namely, the so-called NR/T-nets (Nested-Relation/ Transition Nets). In NR/T-nets, the structural part is modeled by nested relations, and the behavioral part is modeled by a novel Petri net ...", "weights": [[8, 12, 1.0], [40, 44, 1.0], [74, 78, 1.0], [89, 93, 1.0]]}, "C80-1002": {"snippet": "Automatic Processing of Written {F}rench Language\n\n\n An automatic processor of written French language is described. This processor uses syntactic and semantic informations about words in order to con...", "weights": []}, "W00-1423": {"snippet": "...e of natural human conversation. We accomplish this by reasoning from a grammar which describes gesture declaratively in terms of its discourse function, semantics and synchrony with speech.", "weights": [[8, 15, 1.0]]}, "C16-2059": {"snippet": "...ween Natural Language and Knowledge Base in Knowledge Base Construction and Retrieval\n\n\n Words to express relations in natural language (NL) statements may be different from those to represent propert...", "weights": [[8, 15, 1.0], [122, 129, 1.0], [186, 195, 1.0]]}, "2020.cikm_conference-2020.11": {"snippet": "...ural Nets Pay Attention to the Same Words: Studying Eye-tracking Data for Non-factoid QA Evaluation", "weights": [[8, 12, 1.0]]}, "2020.acl-main.166": {"snippet": "...s to represent \"what to say\" and \"how to say\", and edges to represent natural transition between a message (the last utterance in a dialog context) and its response. We then present a novel CG grounde...", "weights": [[8, 17, 1.0], [63, 72, 1.0], [73, 80, 1.0]]}, "2017.wwwconf_conference-2017.142": {"snippet": "...cale natural setting, in the form of an online team-based geography game, that allows us to disentangle confidence from competence and thus separate their effects.We find that in task-oriented discuss...", "weights": [[8, 15, 1.0]]}, "P18-1014": {"snippet": "Extractive Summarization with {SWAP}-{NET}: Sentences and Words from Alternating Pointer Networks\n\n\n We present a new neural sequence-tosequence model for extractive summarization called SWAP-NET (Sen...", "weights": []}, "2018.sigirconf_conference-2018.26": {"snippet": "...e to represent a user conversation history as a semi-structured user query with facet-value pairs. This query is generated and updated by belief tracker that analyzes natural language utterances of us...", "weights": [[8, 17, 1.0], [170, 177, 1.0]]}, "2020.acl-main.127": {"snippet": "...uman conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR-Embedding, which uti...", "weights": [[8, 21, 1.0], [152, 156, 1.0]]}, "W10-4318": {"snippet": "...ized conversations. We investigated whether listeners perceive conversations with these improvements as natural (i.e., human-like) as human-human conversations. We also assessed their ability to corre...", "weights": [[8, 21, 1.0], [66, 79, 1.0], [107, 114, 1.0], [149, 162, 1.0]]}, "2018.gwc-1.25": {"snippet": "...also word embeddings are based on sharing of local contexts represented as vectors. Co-referring words, however, tend to occur in the same topical context but in different local contexts. In addition,...", "weights": [[8, 12, 1.0]]}, "2013.cikm_workshop-2013unlp.10": {"snippet": "Mirroring the real world in social media: twitter, geolocation, and sentiment analysis\n\n\n ABSTRACTIn recent years social media has been used to characterize and predict real world events, and in this ...", "weights": []}, "2018.sigirconf_workshop-2018birndl.7": {"snippet": "...date word selection Keyphrase formation Candidate word scoring Suffering overgeneration error (a) Classic Scheme Candidate phrase selection Keyphrase ranking", "weights": [[8, 12, 1.0], [53, 57, 1.0]]}, "E17-2074": {"snippet": "...from Conversations\n\n\n We introduce a novel, fully unsupervised method to extract keywords from meeting speech in real-time. Our approach represents text as a word co-occurrence network and leverages t...", "weights": [[8, 21, 1.0], [161, 165, 1.0]]}, "2007.sigirconf_conference-2007.3": {"snippet": "...uent word in a document is a good indicator of an important topic in the document, so if the query uses the word, the document is likely to be relevant to your information need.One very simple word we...", "weights": [[8, 12, 1.0], [111, 115, 1.0], [196, 200, 1.0]]}, "P93-1034": {"snippet": "...of a word are formed from entries of its near lexical neighbors. A dimensionality reduction creates a space representing the syntactic categories of unambiguous words. A neural net trained on these sp...", "weights": [[8, 12, 1.0]]}, "2021.paclic-1.17": {"snippet": "...yday Conversations: Factor Analysis of Conversations using Semantic Categories of Functional Expressions\n\n\n To achieve effective dialogue processing, it is important to clarify the kinds of conversati...", "weights": [[8, 21, 1.0], [42, 55, 1.0], [193, 206, 1.0]]}, "S17-1009": {"snippet": "...h in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage. The Paraphrase Database (PPDB) covers 650 times more words, but lacks the semantic str...", "weights": [[8, 15, 1.0]]}, "H91-1064": {"snippet": "...In a natural dialog, a considerable proportion of the utterances actually relate to the maintenance of the dialog itself rather than to furthering the task or goals motivating the conversation. For ex...", "weights": [[8, 15, 1.0]]}, "W10-1302": {"snippet": "...uses Natural Language Generation (NLG) technology to automatically generate conversational utterances from a domain knowledge base modelled from content suggested by a small AAC user group. Findings f...", "weights": [[8, 15, 1.0]]}, "S19-2056": {"snippet": "...n in conversations through Tweets, {CNN} and {LSTM} deep neural networks\n\n\n Emotion detection from user-generated contents is growing in importance in the area of natural language processing. The appr...", "weights": [[8, 21, 1.0], [166, 173, 1.0]]}, "2021.gwc-1.3": {"snippet": "...ican Word-Nets in OMW using NLTK.", "weights": [[8, 12, 1.0], [13, 17, 1.0]]}, "2020.lrec-1.377": {"snippet": "... the Natural Selection of Words\n\n\n There is a growing body of work on how word meaning changes over time: mutation. In contrast, there is very little work on how different words compete to represent t...", "weights": [[8, 15, 1.0], [77, 81, 1.0], [192, 201, 1.0]]}, "2021.emnlp-main.174": {"snippet": "...nted conversations, so as to effectively identify user's intention and generate more accurate responses. Experimental results verify the effectiveness of IR-Net, showing that it achieves the stateof-t...", "weights": [[8, 21, 1.0]]}, "C98-1063": {"snippet": "...th a natural language layer to convert English queries for use by the various search engines. The NI,P layer incorporates morphological analysis, noun phrase syntax, and semantic expansion based on Wo", "weights": [[8, 15, 1.0], [201, 205, 1.0]]}, "D17-1101": {"snippet": "...es a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode ...", "weights": [[8, 15, 1.0], [180, 184, 1.0]]}, "2004.clef_workshop-2004.53": {"snippet": "... the Natural Language Processing techniques we had already developed for single word conflation. In addition, the question analysis is based on complex pattern matching either over forms, part-of-spee...", "weights": [[8, 15, 1.0], [83, 87, 1.0]]}, "2016.cikm_conference-2016.74": {"snippet": "...s in word embedding have shown that semantic representations for words can be efficiently learned by distributional models. A natural generalization is then to represent both queries and documents as ...", "weights": [[8, 12, 1.0], [129, 136, 1.0], [163, 172, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.5": {"snippet": "... the natural language has an extremely rich form and structure with different levels of ambiguities. Therefore, we inquire after a potential framework that could bring this challenge within reach by p...", "weights": [[8, 15, 1.0]]}, "2021.blackboxnlp-1.12": {"snippet": "Word Equations: Inherently Interpretable Sparse Word Embeddings through Sparse Coding\n\n\n Word embeddings are a powerful natural language processing technique, but they are extremely difficult to inter...", "weights": [[0, 4, 1.0], [48, 52, 1.0], [89, 93, 1.0], [120, 127, 1.0]]}, "2020.lrec-1.94": {"snippet": "...e in Natural Dialogue\n\n\n The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only tak...", "weights": [[8, 15, 1.0]]}, "2020.emnlp-main.104": {"snippet": "...s\n\n\n Natural Language Processing (NLP) tasks are usually performed word by word on textual inputs. We can use arbitrary symbols to represent the linguistic meaning of a word and use these symbols as i...", "weights": [[8, 15, 1.0], [70, 74, 1.0], [78, 82, 1.0], [134, 143, 1.0], [172, 176, 1.0]]}, "J77-4003": {"snippet": "...ntic Nets\n\n\n This paper presents an analysis in a semantic net formalism of the semantic structure of English sentences containing references to spatial-location. Spatial reference, hereafter -SR, pro...", "weights": [[8, 12, 1.0]]}, "2015.tois_journal-ir0volumeA34A1.3": {"snippet": "....e., word embeddings) to address text mining, information retrieval, and natural language processing tasks. Most recent efforts have proposed several efficient methods to learn word embeddings from co...", "weights": [[8, 12, 1.0], [76, 83, 1.0], [180, 184, 1.0]]}, "2003.clef_workshop-2003w.3": {"snippet": "...ying Natural Language Processing techniques for single word and multi-word term conflation. Two different conflation approaches have been tested. The first approach is based on the lemmatization of th...", "weights": [[8, 15, 1.0], [58, 62, 1.0], [73, 77, 1.0]]}, "Y98-1029": {"snippet": "...nese word processor cr the computer used in Japan orrinanly employ the Japanese input method through kLyboard strobe combined with Kana (phonetic) to Kanji (ideographic, Chinese) character conversion ...", "weights": [[8, 12, 1.0]]}, "2019.gwc-1.34": {"snippet": "...with Word-Net specifications. Besides presenting the representation effort, we show the utilization of OntoLex-Lemon to bridge from WordNet-like resources to full lexical descriptions and extend the c...", "weights": [[8, 12, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.10": {"snippet": "...d by word embedding models. We present a privacy proof that satises d-privacy where the privacy parameter provides guarantees with respect to a distance metric dened by the word embedding space. We de...", "weights": [[8, 12, 1.0], [176, 180, 1.0]]}, "2011.wwwconf_conference-2011.78": {"snippet": "...s in conversations tend to converge to one another's communicative behavior: they coordinate in a variety of dimensions including choice of words, syntax, utterance length, pitch and gestures. In its ...", "weights": [[8, 21, 1.0]]}, "D19-1186": {"snippet": "...t at word-level or discourse-level with a flat model to alleviate this problem, we propose a hierarchical generation model to capture the different levels of diversity using the conditional variationa...", "weights": [[8, 12, 1.0]]}, "W17-5535": {"snippet": "...s in Natural Language Processing that have been shown to be mutually beneficial. In this work, we design and compare two Neural models for jointly learning both tasks. In the proposed approach, we fir...", "weights": [[8, 15, 1.0]]}, "2020.lrec-1.191": {"snippet": "...{'}s conversations: data preparation and short text classification with contextual word embeddings\n\n\n Abusive texts are reaching the interests of the scientific and social community. How to automatica...", "weights": [[8, 21, 1.0], [86, 90, 1.0]]}, "C16-1130": {"snippet": "...ised Word Sense Disambiguation with Neural Models\n\n\n Determining the intended sense of words in text -word sense disambiguation (WSD) -is a longstanding problem in natural language processing. Recentl...", "weights": [[8, 12, 1.0], [105, 109, 1.0], [167, 174, 1.0]]}, "W16-2522": {"snippet": "...t of word definitions in a cost-efficient manner, we designed a simple interactive word game, Word Sheriff. As a byproduct of game play, it generates short word sequences that can be used to uniquely ...", "weights": [[8, 12, 1.0], [86, 90, 1.0], [97, 101, 1.0], [159, 163, 1.0]]}, "N12-1012": {"snippet": "...e of Conversations?\n\n\n This study aims to infer the social nature of conversations from their content automatically. To place this work in context, our motivation stems from the need to understand how...", "weights": [[8, 21, 1.0], [72, 85, 1.0]]}, "2019.gwc-1.7": {"snippet": "...ng a Word Sense Disambiguation System\n\n\n Arabic WordNet (AWN) represents one of the best-known lexical resources for the Arabic language. However, it contains various issues that affect its use in dif...", "weights": [[8, 12, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.0": {"snippet": "Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments\n\n\n Eliminating the negative effect of non-stationary environmental noise is a long-standing research top...", "weights": []}, "2020.acl-main.184": {"snippet": "...uman conversations naturally evolve around related concepts and scatter to multi-hop concepts. This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowled...", "weights": [[8, 21, 1.0]]}, "W02-1117": {"snippet": "A Character-net Based {C}hinese Text Segmentation Method\n\n\n The segmentation of Chinese texts is a key process in Chinese information processing. The difficulties in segmentation are the process of am...", "weights": []}, "2019.gwc-1.32": {"snippet": "...uate word embeddings in general; and (iii) comparing the performance in that task of the two word embeddings, extracted from the two WordNets. A better performance in that evaluation task results from...", "weights": [[8, 12, 1.0], [96, 100, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...that word, and their appearance is likely to compensate for the missing word (for example, the word earthquake might have words such as quake, tremor, and aftershock providing added context). This the...", "weights": [[8, 12, 1.0], [75, 79, 1.0], [98, 102, 1.0]]}, "N03-2027": {"snippet": "...sian Nets for Syntactic Categorization of Novel Words\n\n\n This paper presents an application of a Dynamic Bayesian Network (DBN) to the task of assigning Part-of-Speech (PoS) tags to novel text. This t...", "weights": [[8, 12, 1.0]]}, "2022.acl-tutorials.1": {"snippet": "...Deep Nets and Opportunities for the Future\n\n\n The first half of this tutorial will make deep nets more accessible to a broader audience, following \"Deep Nets for Poets\" and \"A Gentle Introduction to F...", "weights": [[8, 12, 1.0], [96, 100, 1.0], [156, 160, 1.0]]}, "2020.sigirconf_conference-2020.313": {"snippet": "...ging Word Proximity Networks\n\n\n Question answering (QA) over text passages is a problem of longstanding interest in information retrieval. Recently, the conversational setting has attracted attention,...", "weights": [[8, 12, 1.0]]}, "J75-4028": {"snippet": "... for Word Concepts\n\n\n V a r i c u s representations have been used to partray the ribmiqs of mrd (mtably action) mncepts. The mst pruninent of these include decanp3sition trees, 1 -repreentations such...", "weights": [[8, 12, 1.0]]}, "J18-4008": {"snippet": "...arns word distributions to represent: (1) different roles of conversational discourse, and (2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities o...", "weights": [[8, 12, 1.0], [30, 39, 1.0]]}, "P19-1324": {"snippet": "...ons (word embeddings) which are then put in context in the hidden layers. Since words are often ambiguous, representing the contextually relevant information is not trivial. We investigate how an LSTM...", "weights": [[8, 12, 1.0]]}, "2008.clef_workshop-2008.10": {"snippet": "...from natural language constructs to boost their results by achieving higher precision and recall rates. In this study, we have used part of speech properties of terms as extra source of information ab...", "weights": [[8, 15, 1.0]]}, "W18-3016": {"snippet": "...s to represent lexical semantics. While a unified account of the lexical meaning relies on one being able to convert between these representations, in both directions, the conversion direction from se...", "weights": [[8, 17, 1.0]]}, "Q16-1033": {"snippet": "...ling Conversations: An Application of Natural Language Processing to Mental Health\n\n\n Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy ca...", "weights": [[8, 21, 1.0], [41, 48, 1.0]]}, "2005.wwwconf_conference-2005.25": {"snippet": "Web-assisted annotation, semantic indexing and search of television and radio news\n\n\n ABSTRACTThe Rich News system, that can automatically annotate radio and television news with the aid of resources ...", "weights": []}, "P17-2099": {"snippet": "...s of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of se...", "weights": [[8, 15, 1.0]]}, "2018.ecir_conference-2018.12": {"snippet": "...cess natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by ...", "weights": [[8, 15, 1.0]]}, "2021.wsdm_conference-2021.147": {"snippet": "...sing natural language conversations. Due to users' constant need to look for information to support both work and daily life, conversational recommendation system will be one of the key techniques tow...", "weights": [[8, 15, 1.0], [25, 38, 1.0]]}, "L04-1221": {"snippet": "{F}rame{N}et as a {``}Net{''}", "weights": []}, "2020.coling-main.108": {"snippet": "...tal. Word and Sentence embeddings are one of the most common starting points of any NLP task. A word embedding represents a given word in a predefined vector-space while maintaining vector relations w...", "weights": [[8, 12, 1.0], [99, 103, 1.0], [133, 137, 1.0]]}, "2020.nli-1.4": {"snippet": "...er's natural language questions for questionanswering (QA). Because data can be usually stored in a structured manner, an essential step involves turning a natural language question into its correspon...", "weights": [[8, 15, 1.0], [159, 166, 1.0]]}, "W12-4804": {"snippet": "...gram word-based language model, important word-to-word collocation information are lost during this compression and lead to a drop of the conversion accuracies. In order to overcome this problem, we p...", "weights": [[8, 12, 1.0], [45, 49, 1.0], [53, 57, 1.0]]}, "1998.tois_journal-ir0volumeA16A4.3": {"snippet": "...ntic nets. The relational inconsistency hinders the creation of domain information models. The structural inconsistency leads to unstable documents, especially when a document is composed by computati...", "weights": [[8, 12, 1.0]]}, "P18-2089": {"snippet": "...ctal Word Embeddings\n\n\n Word embeddings are crucial to many natural language processing tasks. The quality of embeddings relies on large nonnoisy corpora. Arabic dialects lack large corpora and are no...", "weights": [[8, 12, 1.0], [27, 31, 1.0], [63, 70, 1.0]]}, "W10-4211": {"snippet": "...ting Conversations: a User Study\n\n\n In this paper we present a complete system for automatically generating natural language abstracts of meeting conversations. This system is comprised of components ...", "weights": [[8, 21, 1.0], [111, 118, 1.0], [149, 162, 1.0]]}, "2018.sigirconf_workshop-2018birndl.6": {"snippet": "...w to represent mathematical formulae in Content MathML referring to the open knowledge-base Wikidata for the grounding of the semantics. By doing so, we link identifiers and symbols in MathML to Wikid...", "weights": [[8, 17, 1.0]]}, "W03-1403": {"snippet": "...y to Represent Metaphors in {W}ord{N}ets? Insights from the {H}amburg Metaphor Database\n\n\n This paper addresses the question whether metaphors can be represented in Word-Nets. For this purpose, domain...", "weights": [[8, 17, 1.0], [168, 172, 1.0], [173, 177, 1.0]]}, "2017.tois_journal-ir0volumeA35A3.10": {"snippet": "...s in natural language processing. Many neural network models such as Convolutional Neural Network (CNN), Recursive Neural Network (RNN), and Long Short-Term Memory (LSTM) have been proposed to learn r...", "weights": [[8, 15, 1.0]]}, "2003.wwwconf_conference-2003.43": {"snippet": "...s of conversations exhibit unexpected behaviors. For example, there exists a composite e-service based on Mealy peers whose set of conversations is not context free (and not regular). (The set of conv...", "weights": [[8, 21, 1.0], [134, 147, 1.0], [199, 212, 1.0]]}, "W06-2105": {"snippet": "...atic natural language understanding. We present an approach towards PP interpretation as part of a natural language understanding system which has been successfully employed in various NLP tasks for i...", "weights": [[8, 15, 1.0], [102, 109, 1.0]]}, "E83-1021": {"snippet": "... (Sl-Nets) paradigm. The operations are reduced to functions of a fo~al language, thus changing the level of abstraction of the operations to be performed on SI-Nets. In this sense, operations on SI-N...", "weights": [[8, 12, 1.0], [164, 168, 1.0], [202, 206, 1.0]]}, "2020.peoples-1.7": {"snippet": "...n in Conversations\n\n\n Since language model pretraining to learn contextualized word representations has been proposed, pretrained language models have made success in many natural language processing ...", "weights": [[8, 21, 1.0], [82, 86, 1.0], [175, 182, 1.0]]}}}
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Rather than communicate their current probability values to their neighbors at each step, nodes instead communicate only cha...", "weights": [[8, 17, 1.0]]}, "2018.tweb_journal-ir0volumeA12A3.5": {"snippet": "Faster Base64 Encoding and Decoding Using AVX2 Instructions\n\n\n Web developers use base64 formats to include images, fonts, sounds, and other resources directly inside HTML, JavaScript, JSON, and XML f...", "weights": []}, "2021.wmt-1.76": {"snippet": "...n an NVIDIA A100, being 3\u00d7 faster than our last year's system. Our system is the fastest and has the lowest memory consumption on the GPU-throughput track. 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Both rely on a notion of preferred attributes that can be learned from human descriptions. In our experiments, preferences are learned from training...", "weights": [[8, 17, 1.0], [44, 53, 1.0]]}, "2015.wwwconf_conference-2015c.35": {"snippet": "...tion algorithm to generate sub-queries online which preserve the intent of the original query the best. Experimental results over a very large real-world data set demonstrate the superb performance of...", "weights": [[8, 17, 1.0]]}, "2016.tois_journal-ir0volumeA34A4.1": {"snippet": "... LtR algorithm. The algorithm directly optimizes an information retrieval metric of choice (in our case, NDCG) in a greedy manner. Direct optimization of the listwise objective functions is computatio...", "weights": [[8, 17, 1.0], [23, 32, 1.0]]}, "2013.wsdm_conference-2013.42": {"snippet": "App recommendation: a contest between satisfaction and temptation\n\n\n ABSTRACTDue to the huge and still rapidly growing number of mobile applications (apps), it becomes necessary to provide users an ap...", "weights": []}, "Y03-1026": {"snippet": "...ased Algorithm for {C}hinese Text Classification\n\n\n In this paper, vector-distance-weighted algorithm and representative-vector-distance algorithm are described and used to implement the process of au...", "weights": [[8, 17, 1.0], [95, 104, 1.0], [140, 149, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.8": {"snippet": "...oved Algorithm to Find the Length of the Longest Common Subsequence of Two Strings\n\n\n (n21ogn).We present an improvement to this algorithm which changes the time complexity to O(r + n(LLCS ). Some exp...", "weights": [[8, 17, 1.0], [132, 141, 1.0]]}, "L18-1060": {"snippet": "...s of mention features, increased number of negative examples, Siamese network architecture and a global mention clustering algorithm. The highest results are achieved by the system combining our best ...", "weights": [[8, 15, 1.0], [107, 114, 1.0], [126, 135, 1.0]]}, "W12-4106": {"snippet": "...ised algorithm for the task for relation mention identification when the underlying concept mentions have already been identified and linked to an ontology. To overcome the lack of annotated data, we ...", "weights": [[8, 17, 1.0], [44, 51, 1.0]]}, "2017.cikm_conference-2017.137": {"snippet": "...hase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.). In the rst phase of our algorithm we propose PhraseType, a probabilistic genera...", "weights": [[8, 17, 1.0], [148, 157, 1.0]]}, "I13-1012": {"snippet": "...gual Mention Detection for Coreference Resolution\n\n\n This paper proposes a novel algorithm for multilingual mention detection: we extract mentions from parse trees via kernelbased SVM learning. Our ap...", "weights": [[8, 15, 1.0], [84, 93, 1.0], [111, 118, 1.0]]}, "2015.tois_journal-ir0volumeA33A3.2": {"snippet": "...mory algorithm, called disk SA-IS (DSA-IS), to exactly emulate the induced sorting algorithm SA-IS previously proposed for sorting suffixes in RAM. DSA-IS is a new diskfriendly method for sequentially...", "weights": [[8, 17, 1.0], [86, 95, 1.0]]}, "2014.sigirconf_workshop-2014erd.11": {"snippet": "...eedy algorithm for finding sets of entity linking interpretations in queries\n\n\n ABSTRACTWe describe our participation in the short text track of the Entity Recognition and Disambiguation (ERD) challen...", "weights": [[8, 17, 1.0]]}, "2017.tist_journal-ir0volumeA8A5.3": {"snippet": "... ToR algorithm is the first ranking-oriented solution, which can effectively generate recommendations in a more direct manner than those regression-oriented methods. Extensive empirical studies show t...", "weights": [[8, 17, 1.0]]}, "D16-1245": {"snippet": "...ural mention-ranking model for coreference evaluation metrics. We experiment with two approaches: the REINFORCE policy gradient algorithm and a rewardrescaled max-margin objective. We find the latter ...", "weights": [[8, 15, 1.0], [131, 140, 1.0]]}, "J94-4002": {"snippet": "An Algorithm for Pronominal Anaphora Resolution\n\n\n This paper presents an algorithm for identifying the noun phrase antecedents of third person pronouns and lexical anaphors (reflexives and reciprocal...", "weights": [[3, 12, 1.0], [74, 83, 1.0]]}, "2010.wwwconf_conference-2010.112": {"snippet": "...This algorithm specific information is then utilized for algorithm ranking in response to user queries. Experimental results show the superiority of our system on other popular search engines.", "weights": [[8, 17, 1.0], [60, 69, 1.0]]}, "P04-1018": {"snippet": "A Mention-Synchronous Coreference Resolution Algorithm Based On the Bell Tree\n\n\n This paper proposes a new approach for coreference resolution which uses the Bell tree to represent the search space an...", "weights": [[2, 9, 1.0], [45, 54, 1.0]]}, "2020.wsdm_conference-2020.49": {"snippet": "...zing mention clustering and imbalanced mention detection, which enables it to detect more gold mentions in a document to make more accurate coreference decisions. Experimental results on the CoNLL-201...", "weights": [[8, 15, 1.0], [42, 49, 1.0]]}, "J04-2005": {"snippet": "...ther algorithm in the older article-the \"algorithm for sorted data\"-can be generalized in a similar way. The new algorithm is faster than the algorithm for addition of strings presented in Carrasco an...", "weights": [[8, 17, 1.0], [44, 53, 1.0], [116, 125, 1.0], [145, 154, 1.0]]}, "P16-1179": {"snippet": "Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation\n\n\n Named Entity Disambiguation (NED) algorithms disambiguate mentions of named entities with respect to a knowledge-b...", "weights": []}, "P16-1061": {"snippet": "...d of mention pairs. We present a neural network based coreference system that produces high-dimensional vector representations for pairs of coreference clusters. Using these representations, our syste...", "weights": [[8, 15, 1.0]]}, "1998.tois_journal-ir0volumeA16A2.0": {"snippet": "...ning algorithm applied to a set of relevance judgments obtained from a single human would be expected to perform on new material at a level somewhat below that human. However, we examine two learning ...", "weights": [[8, 17, 1.0]]}, "S15-1028": {"snippet": "... our algorithm outperforms the most closely related unsupervised solution. The similarity value calculated by our algorithm proved to be an effective feature in a supervised learning setting, helping ...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2015.tois_journal-ir0volumeA33A3.5": {"snippet": "A General SIMD-Based Approach to Accelerating Compression Algorithms\n\n\n Compression algorithms are important for data-oriented tasks, especially in the era of \"Big Data.\" Modern processors equipped wi...", "weights": []}, "S15-1034": {"snippet": "...ular mention-pair models. This study advocates a novel approach that keeps the simplicity of a mention-pair framework, while showing state-of-the-art results. Apart from being very efficient and strai...", "weights": [[8, 15, 1.0], [98, 105, 1.0]]}, "W15-0807": {"snippet": "...tion algorithm and results during the Event Mention Evaluation pilot study. We analyze the problems of evaluating multiple event mention attributes and discontinuous event mention spans. In addition, ...", "weights": [[8, 17, 1.0], [47, 54, 1.0], [132, 139, 1.0], [175, 182, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.12": {"snippet": "Seeking and implementing automated assistance during the search process\n\n\n AbstractSearchers seldom make use of the advanced searching features that could improve the quality of the search process bec...", "weights": []}, "C08-2029": {"snippet": "... new algorithm for attribute selection based on observations from a corpus, which outperformed a simple base algorithm by a significant margin. We then carried out a detailed comparison between our al...", "weights": [[8, 17, 1.0], [112, 121, 1.0], [201, 210, 1.0]]}, "2010.cikm_conference-2010.243": {"snippet": "...cept mention identification is performed by training a sequential tagging model. Each identified concept mention is then associated with a set of candidate ontology concepts along with a feature vecto...", "weights": [[8, 15, 1.0], [108, 115, 1.0]]}, "P94-1017": {"snippet": "...sing Algorithm\n\n\n In this paper we relate a number of parsing algorithms which have been developed in very different areas of parsing theory, and which include deterministic algorithms, tabular algori...", "weights": [[8, 17, 1.0]]}, "W14-2906": {"snippet": "...e an algorithm is implemented to cluster event mentions in a large-scale corpus. Results on test data from AQUAINT TimeML, which we annotated manually with coreference links, reveal how semantic conve...", "weights": [[8, 17, 1.0]]}, "2012.cikm_conference-2012.103": {"snippet": "...osed algorithm, GraphOrder, is a novel and scalable graph-based label propagation algorithm that takes transitivity of temporal order into account, as well as these statistics on narrative order of ve...", "weights": [[8, 17, 1.0], [85, 94, 1.0]]}, "C14-1061": {"snippet": "...of a mention based on the most informative mention pair relation between the given mention entity pair. We introduce an informativeness score for determining the most precise relation of a mention ent...", "weights": [[8, 15, 1.0], [46, 53, 1.0], [86, 93, 1.0], [192, 199, 1.0]]}, "P17-1149": {"snippet": "...same mention phrase usually refers to various entities. In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple ...", "weights": [[8, 15, 1.0], [156, 163, 1.0]]}, "2021.acl-long.9": {"snippet": "Mention Flags ({MF}): Constraining Transformer-based Text Generators\n\n\n This paper focuses on Seq2Seq (S2S) constrained text generation where the text generator is constrained to mention specific word...", "weights": [[0, 7, 1.0], [179, 186, 1.0]]}, "2013.wwwconf_conference-2013.115": {"snippet": "...s to mention, we propose in this paper a novel recommendation scheme named as whom-to-mention. Specifically, we present an in-depth study of mention mechanism and propose a recommendation scheme to so...", "weights": [[8, 15, 1.0], [89, 96, 1.0], [144, 151, 1.0]]}, "J88-2013": {"snippet": "... the algorithm described \"is more efficient than any existing general parsing algorithms in terms of recognition time as far as practical natural language grammars are concerned\" (p. 73). The crucial ...", "weights": [[8, 17, 1.0]]}, "2022.acl-long.147": {"snippet": "... the mention representations. Specifically, we present two different metrics for sibling selection and employ an attentive graph neural network to aggregate information from sibling mentions. The prop...", "weights": [[8, 15, 1.0]]}, "2017.ipm_journal-ir0volumeA53A2.5": {"snippet": "... new algorithm presents some improvements over the previous ones. Several simulations were completed to analyze the connectivity and query propagation patterns of the emergent logical networks. The re...", "weights": [[8, 17, 1.0]]}, "D09-1101": {"snippet": "...tity-mention classifier to determine whether a preceding cluster is coreferent with a given mention. We propose a cluster-ranking approach to coreference resolution that combines the strengths of ment...", "weights": [[8, 15, 1.0], [95, 102, 1.0], [199, 206, 1.0]]}, "2013.clef_conference-2013w.79": {"snippet": "...cept mention detection is based on Conditional Random Fields (CRF) and the concept mention normalization is based on a greedy dictionary lookup algorithm. A distinctive feature in MedTagger compared t...", "weights": [[8, 15, 1.0], [86, 93, 1.0], [147, 156, 1.0]]}, "2009.ipm_journal-ir0volumeA45A5.5": {"snippet": "... SMO algorithm in support vector machine\n\n\n a b s t r a c tSequential minimal optimization (SMO) is quite an efficient algorithm for training the support vector machine. The most important step of thi...", "weights": [[8, 17, 1.0], [122, 131, 1.0]]}, "1996.tois_journal-ir0volumeA14A1.3": {"snippet": "... ASM Algorithm\n\n\n We describe an improvement on Ukkonen's Enhanced Dynamic Programming (EHD) approximate string-matching algorithm for unit-penalty four-edit comparisons. The new algorithm has an asym...", "weights": [[8, 17, 1.0], [124, 133, 1.0], [182, 191, 1.0]]}, "W06-1633": {"snippet": "...raph Algorithm for Coreference Resolution\n\n\n In this paper we describe a coreference resolution method that employs a classification and a clusterization phase. In a novel way, the clusterization is p...", "weights": [[8, 17, 1.0]]}, "2014.wwwconf_workshop-2014ldow.1": {"snippet": "...ping algorithm. It first identifies a set of \"easy\" mentions with low ambiguity and links them to entities in a very efficient manner. This stage also determines the thematic domain of the input text ...", "weights": [[8, 17, 1.0]]}, "P18-2017": {"snippet": "...ible mention, and (2) jointly optimizing the mention detection accuracy and the mention clustering log-likelihood given the mention cluster labels. Our model achieves the stateof-the-art performance o...", "weights": [[8, 15, 1.0], [48, 55, 1.0], [83, 90, 1.0], [127, 134, 1.0]]}, "2004.ipm_journal-ir0volumeA40A2.4": {"snippet": "...ding algorithm", "weights": [[8, 17, 1.0]]}, "W16-2904": {"snippet": "...Gene Mention Tagging\n\n\n The rapidly growing biomedical literature has been a challenging target for natural language processing algorithms. One of the tasks these algorithms focus on is called named e...", "weights": [[8, 15, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A4.12": {"snippet": "...tion algorithm based on integrating social network information such as trust relationships, rating information of users and users' own knowledge. Specifically, since we cannot directly measure a user'...", "weights": [[8, 17, 1.0]]}, "C18-1003": {"snippet": "...tion algorithm that selectively creates clusters to handle both singular and plural mentions, and also a deep learning-based entity linking model that jointly handles both types of mentions through mu...", "weights": [[8, 17, 1.0]]}, "P19-1511": {"snippet": "...tity Mention Detection via Anchor-Region Networks\n\n\n Sequential labeling-based NER approaches restrict each word belonging to at most one entity mention, which will face a serious problem when recogni...", "weights": [[8, 15, 1.0], [148, 155, 1.0]]}, "P17-2085": {"snippet": "List-only Entity Linking\n\n\n Traditional Entity Linking (EL) technologies rely on rich structures and properties in the target knowledge base (KB). However, in many applications, the KB may be as simpl...", "weights": []}, "2018.wwwjournals_journal-ir0volumeA21A1.4": {"snippet": "...tion algorithm. As traditional recommendation algorithms are based on popularity and download, they inadvertently fail to recommend the desirable applications. At the same time, many users tend to pay...", "weights": [[8, 17, 1.0]]}, "D16-1144": {"snippet": "...tity mention's local context. This paper proposes a novel embedding method to separately model \"clean\" and \"noisy\" mentions, and incorporates the given type hierarchy to induce loss functions. We form...", "weights": [[8, 15, 1.0]]}, "2021.acl-short.37": {"snippet": "...AN}: Mention-Only Linking of Entities with a Mention Annotation Network\n\n\n We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems ...", "weights": [[8, 15, 1.0], [48, 55, 1.0]]}, "2017.wwwconf_conference-2017c.103": {"snippet": "Media Attention to Science\n\n\n ABSTRACTHow has media attention to science changed over the past decade? Does media attention bring scientific attention too? To answer these questions, we collected medi...", "weights": []}, "2011.sigirconf_conference-2011.79": {"snippet": "...ence algorithm, which can jointly infer the referent entities of all name mentions by exploiting the interdependence captured in Referent Graph. The key benefit of our method comes from: 1) The global...", "weights": [[8, 17, 1.0]]}, "C04-1091": {"snippet": "...irst algorithm is a provably linear time search algorithm. We use this algorithm as a subroutine in the other algorithms. We believe that decoding algorithms derived from our framework can be of pract", "weights": [[8, 17, 1.0], [51, 60, 1.0], [74, 83, 1.0]]}, "2014.tist_journal-ir0volumeA5A4.14": {"snippet": "... our algorithm.", "weights": [[8, 17, 1.0]]}, "2019.wsdm_conference-2019.9": {"snippet": "MSA: Jointly Detecting Drug Name and Adverse Drug Reaction Mentioning Tweets with Multi-Head Self-Attention\n\n\n ABSTRACTTwitter is a popular social media platform for information sharing and disseminat...", "weights": []}, "H90-1003": {"snippet": "...Best algorithm is an improved algorithm for finding the top N sentence hypotheses. The new algorithm is shown to perform as well as the Exact Sentence-Dependent algorithm presented previously but with...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [94, 103, 1.0], [164, 173, 1.0]]}, "2016.wsdm_conference-2016.15": {"snippet": "To Suggest, or Not to Suggest for Queries with Diverse Intents: Optimizing Search Result Presentation\n\n\n ABSTRACTWe propose a method of optimizing search result presentation for queries with diverse i...", "weights": []}, "2016.wwwconf_conference-2016.112": {"snippet": "...ased algorithm. Based on the concept of multi-view learning, we further enable the algorithm to learn from unlabeled data to alleviate the dearth of labeled data. Extensive experiments are conducted t...", "weights": [[8, 17, 1.0], [86, 95, 1.0]]}, "2007.sigirconf_conference-2007.84": {"snippet": "...ask. Mention detection takes advantages of machine learning approaches and character-based models. It manipulates different types of entities being mentioned and different constitution units (i.e. ext...", "weights": [[8, 15, 1.0]]}, "2019.ecir_workshop-2019amir.2": {"snippet": "...ated Algorithm Selection - Predict which algorithm to use!\n\n\n To achieve state-of-the-art performance, it is often crucial to select a suitable algorithm for a given problem instance. For example, wha...", "weights": [[8, 17, 1.0], [44, 53, 1.0], [147, 156, 1.0]]}, "P14-2006": {"snippet": "...that mention manipulation for scoring predicted mentions is unnecessary, and potentially harmful as it could produce unintuitive results; (ii) illustrate the application of all these measures to scori...", "weights": [[8, 15, 1.0]]}, "S14-2052": {"snippet": "...rder Mention Detection and Disambiguation\n\n\n In this paper we briefly describe our supervised machine learning approach for disorder mention detection system that we submitted as part of our participa...", "weights": [[8, 15, 1.0], [136, 143, 1.0]]}, "W14-4509": {"snippet": "... The algorithm achieves an average F1-measure of 77%, evaluated on reviews about electronic and automobile products.", "weights": [[8, 17, 1.0]]}, "2014.wwwconf_conference-2014c.78": {"snippet": "...tion algorithm based on a node similarity measurement. Using the proposed algorithm, we evaluated the privacy risk of several \"light\" anonymization algorithms on real datasets.", "weights": [[8, 17, 1.0], [77, 86, 1.0]]}, "2004.wwwconf_conference-2004.67": {"snippet": "...ring algorithm to build a topic hierarchy for a collection of search results retrieved in response to a query. At every level of the hierarchy, the new algorithm progressively identifies topics in a w...", "weights": [[8, 17, 1.0], [155, 164, 1.0]]}, "C12-2086": {"snippet": "...ring algorithm to rerank the output of coreference hypotheses. The filter is based on the relational structures between mentions and their corresponding relationships. The second approach is to use a ...", "weights": [[8, 17, 1.0]]}, "2013.cikm_conference-2013.235": {"snippet": "...ping algorithm for identifying and categorizing mentions of concepts. We then propose a new clustering algorithm that uses citations' context as a way to cluster the extracted mentions into coherent c...", "weights": [[8, 17, 1.0], [106, 115, 1.0]]}, "2016.wwwconf_conference-2016.52": {"snippet": "...tity Mention Detection and Linking in Web Queries\n\n\n ABSTRACTIn this paper we study the problem of linking open-domain web-search queries towards entities drawn from the full entity inventory of Wikip...", "weights": [[8, 15, 1.0]]}, "K15-1002": {"snippet": "... and Mention Head Detection\n\n\n In coreference resolution, a fair amount of research treats mention detection as a preprocessed step and focuses on developing algorithms for clustering coreferred menti...", "weights": [[8, 15, 1.0], [94, 101, 1.0]]}, "2012.tist_journal-ir0volumeA3A4.16": {"snippet": "Evaluation of Folksonomy Induction Algorithms\n\n\n Algorithms for constructing hierarchical structures from user-generated metadata have caught the interest of the academic community in recent years. In...", "weights": []}, "S14-2045": {"snippet": "...cing algorithm was used to find different medical entities and a binary SVM classifier was used to find relationship between entities. For Task-B, a dictionary look-up algorithm on a customized UMLS-2...", "weights": [[8, 17, 1.0], [171, 180, 1.0]]}, "N12-1007": {"snippet": "...y on mention (string) matching, syntactic features, and linguistic resources like English WordNet. When co-referent text mentions appear in different languages, these techniques cannot be easily appli...", "weights": [[8, 15, 1.0]]}, "2003.wwwconf_conference-2003.54": {"snippet": "... Our algorithm looks to exploit this. We have defined matrix density, and our algorithm basically uses matrix density considerations in its working.The algorithm is a partitional-agglomerative algorit...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [155, 164, 1.0], [196, 205, 1.0]]}, "2018.sigirconf_conference-2018.173": {"snippet": "...rent Mention Retrieval\n\n\n ABSTRACTThis paper introduces the coreferent mention retrieval task, in which the goal is to retrieve sentences that mention a specific entity based on a query by example in ...", "weights": [[8, 15, 1.0], [74, 81, 1.0], [146, 153, 1.0]]}, "2017.cikm_conference-2017.170": {"snippet": "...e of mention's context to disambiguate (i.e., local objective), and promoting coherence of all the linked entities (i.e., global objective). In this paper, we propose a deep neural network model to ef...", "weights": [[8, 15, 1.0]]}, "2013.clef_conference-2013w.158": {"snippet": "ShARe/CLEF eHealth 2013 Named Entity Recognition and Normalization of Disorders Challenge\n\n\n Objective: There are abundant mentions of clinical conditions, anatomical sites, medications and procedures...", "weights": []}, "W10-4305": {"snippet": "Evaluation Metrics For End-to-End Coreference Resolution Systems\n\n\n Commonly used coreference resolution evaluation metrics can only be applied to key mentions, i.e. already annotated mentions. We her...", "weights": []}, "2018.sigirconf_conference-2018.22": {"snippet": "...n to mention. It is a significant role in promoting the user experience and information propagation. To help users easily find the usernames they want to mention, the mention recommendation task has r...", "weights": [[8, 15, 1.0], [157, 164, 1.0], [170, 177, 1.0]]}, "P08-1096": {"snippet": "...tity-Mention Model for Coreference Resolution with Inductive Logic Programming\n\n\n The traditional mention-pair model for coreference resolution cannot capture information beyond mention pairs for both...", "weights": [[8, 15, 1.0], [101, 108, 1.0], [181, 188, 1.0]]}, "P19-1643": {"snippet": "...odal algorithm that leverages information derived from visual and linguistic clues to automatically infer which actions are visible in a video. We demonstrate that our multimodal algorithm outperforms...", "weights": [[8, 17, 1.0], [182, 191, 1.0]]}}}
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As a result, vertical search engines start attracting more and more attention. For example, many search engines provide specialized vertical sear...", "weights": [[8, 13, 1.0]]}, "W96-0515": {"snippet": "Author Index", "weights": []}, "2001.jasis_journal-ir0volumeA52A2.1": {"snippet": "Authors as citers over time", "weights": [[0, 7, 1.0]]}, "W91-0117": {"snippet": "Author Index", "weights": []}, "2019.fire_conference-2019w.21": {"snippet": "...s of authors based on the Arabic tweets. In the second task, the aim is to Detect deception in Arabic for two genres such as Twitter and News. Deception detection means that the automatic way of ident...", "weights": [[8, 15, 1.0]]}, "2000.cikm_conference-2000.31": {"snippet": "... the least amount of time, we need a system that uses each user's view of the world for classification. This paper explores a way to use a user's personal arrangement of concepts to navigate the Web. ...", "weights": [[8, 13, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...n by authors from 30 different countries. Each submission was reviewed by at least three reviewers, with a maximum of five reviews for particularly challenging papers. The Program Committee accepted 3...", "weights": [[8, 15, 1.0], [80, 85, 1.0], [86, 91, 1.0]]}, "2021.eacl-main.73": {"snippet": "...d by three distinct authors show significant improvements upon existing works to rewrite input texts in target author's style. Our quantitative and qualitative analyses further show that our model has...", "weights": [[8, 13, 1.0], [23, 30, 1.0]]}, "C14-1037": {"snippet": "...s of three well-known Russian authors, Ivan Turgenev, Fyodor Dosteyevsky and Anton Chekhov. This analysis seeks to identify common linguistic patterns which hold for all of the translations from the s...", "weights": [[8, 13, 1.0], [33, 40, 1.0]]}, "2007.sigirconf_workshop-2007pan.7": {"snippet": "...s at least for some of the candidate authors or there is a significant variation in the text-length among the available training texts of the candidate authors. 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Results indicate that the picture produced...", "weights": [[8, 15, 1.0], [136, 143, 1.0]]}, "2019.fire_conference-2019w.22": {"snippet": "... the three traits. SMO classifier with the feature vector that consisted of UniGram and Stem was the best model for each three traits: gender, age and variety 1 .", "weights": [[8, 13, 1.0], [124, 129, 1.0]]}, "1979.jasis_journal-ir0volumeA30A3.10": {"snippet": "...r of authors", "weights": [[8, 15, 1.0]]}, "2006.jasis_journal-ir0volumeA57A13.5": {"snippet": "What does it mean to be an author? The intersection of credit, contribution, and collaboration in science", "weights": []}, "2010.clef_workshop-2010w.30": {"snippet": "...edia authors and content. WikiTrust computes three main quantities: edit quality, author reputation, and content reputation. The edit quality measures how well each edit, that is, each change introduc...", "weights": [[8, 15, 1.0], [48, 53, 1.0]]}, "2018.clef_conference-2018w.8": {"snippet": "...s at least one style change. In other words, the task is to investigate whether it is written by one or multiple authors. The model is composed of two parallel attention networks. Unlike the conventio...", "weights": [[8, 13, 1.0], [116, 123, 1.0]]}, "2015.jasis_journal-ir0volumeA66A6.2": {"snippet": "Are scholarly articles disproportionately read in their own country? An analysis of mendeley readers", "weights": []}, "2003.jasis_journal-ir0volumeA54A9.5": {"snippet": "A cast of thousands: Coauthorship and subauthorship collaboration in the 20th century as manifested in the scholarly journal literature of psychology and philosophy", "weights": []}, "2020.clef_conference-2020w.204": {"snippet": "...rent-authors\"). The paper describes a simple model that performs this task based on a Labb\u00e9 similarity. As features, we employed the most frequent tokens (words, and punctuation symbols) from each aut...", "weights": [[8, 15, 1.0]]}, "2013.clef_conference-2013w.116": {"snippet": "...ring three languages and the evaluation results of the 18 participant teams that submitted their software. Moreover, we survey the characteristics of the submitted approaches and show that a very effe...", "weights": [[8, 13, 1.0]]}, "J85-1010": {"snippet": "AUTHOR INDEX Volumes 6 - 10", "weights": []}, "2014.clef_conference-2014w.112": {"snippet": "...ting three approaches for the 'PAN 2014 Author Identification' [3,1] task using the same representation for the author's style. Two of our approaches extend previous successful approaches: naive Bayes...", "weights": [[8, 13, 1.0]]}, "2019.sigirconf_conference-2019.170": {"snippet": "...ting Least Squares", "weights": [[8, 13, 1.0]]}, "2015.clef_conference-2015w.112": {"snippet": "Overview of the Author Identification Task at PAN 2015\n\n\n This paper presents an overview of the author identification task at PAN-2015 evaluation lab. Similar to previous editions of PAN, this shared...", "weights": []}, "2007.ipm_journal-ir0volumeA43A5.17": {"snippet": "...ting authors ad hoc to examine topics such as structured identification in audiovisual documents, object-based video indexing, and speech and image processing methods.This compilation of authors is on...", "weights": [[8, 15, 1.0], [190, 197, 1.0]]}, "2018.ecir_workshop-2018text2story.3": {"snippet": "... not least, on the basis of pilot analysis, the excellent opportunities of text analysis are demonstrated. Furthermore, the research aims the future application of sentiment and topic analysis in orde...", "weights": [[8, 13, 1.0]]}, "2012.ipm_journal-ir0volumeA48A4.4": {"snippet": "...d co-authors (i.e., co-authors that have joint publications) perform better than those researchers with many relationships to the same group of linked co-authors.", "weights": [[8, 15, 1.0], [26, 33, 1.0], [157, 164, 1.0]]}, "1998.tc-1.8": {"snippet": "... the three most important factors in a company's translation strategy. Or, more pertinently, how to cut costs, how to improve quality and how to save time. Improvements in the quality and efficiency o...", "weights": [[8, 13, 1.0]]}, "2006.jasis_journal-ir0volumeA57A11.2": {"snippet": "Ranking indirect connections in literature-based discovery: The role of medical subject headings", "weights": []}, "2016.jasis_journal-ir0volumeA67A10.16": {"snippet": "...ther authors, institutions, and journals", "weights": [[8, 15, 1.0]]}, "1982.ipm_journal-ir0volumeA18A6.0": {"snippet": "...t vs least-effort", "weights": [[8, 13, 1.0]]}, "W98-1313": {"snippet": "Author Index", "weights": []}, "W96-0419": {"snippet": "Author Index", "weights": []}, "2013.cikm_conference-2013.269": {"snippet": "Topical authority propagation on microblogs\n\n\n ABSTRACTWith a huge number of active users on microblogs, it becomes increasingly important to identify authoritative users on specific topics. This pape...", "weights": []}, "P14-2057": {"snippet": "...ers (authors) only write a few reviews, which are not enough to serve as the training data for accurate classification. In this paper, we present a novel three-view tritraining method to iteratively i...", "weights": [[8, 15, 1.0], [157, 162, 1.0]]}, "2021.ranlp-1.79": {"snippet": "...n\n\n\n Authors of text tend to predominantly use a single sense for a lemma that can differ among different authors. This might not be captured with an author-agnostic word sense disambiguation (WSD) mo...", "weights": [[8, 15, 1.0], [109, 116, 1.0]]}, "2018.sigirconf_conference-2018.212": {"snippet": "...s at least 4? \u2022 Find corporate bonds rated A or better and with coupon higher than 7%.\u2022 Show me news about oil from the Financial Times over the last two months.Our QA (question answering ) systems ma...", "weights": [[8, 13, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A2.4": {"snippet": "...y at least three members of an international two-tier Program Committee. Of the papers submitted to the main research track, 30 were selected for oral presentation and 25 for poster/short presentation...", "weights": [[8, 13, 1.0], [14, 19, 1.0]]}, "2014.sigirconf_conference-2014.105": {"snippet": "...ers (authors) only write a few reviews, which are not enough to serve as the training data for accurate classification.In this paper, we present a novel two-view co-training framework to iteratively i...", "weights": [[8, 15, 1.0]]}, "2017.clef_conference-2017.25": {"snippet": "... the three shared tasks organized this year, namely author identification, author profiling, and author obfuscation. For each task, we give a brief summary of the evaluation data, performance measures...", "weights": [[8, 13, 1.0]]}, "1972.jasis_journal-ir0volumeA23A2.2": {"snippet": "Who cited what? a citation analysis of the four basic cataloging texts", "weights": []}, "2009.wwwconf_conference-2009.105": {"snippet": "... the three regions. The number of authors per paper experienced an increase in a time span of 12 years. We observe that the number of collaborations between authors grows faster than the number of aut...", "weights": [[8, 13, 1.0], [37, 44, 1.0], [160, 167, 1.0], [200, 207, 1.0]]}, "L18-1523": {"snippet": "..., at least in our setting, both methods require relatively long texts and are furthermore highly sensitive to the choice of authors and texts in the comparison corpus.", "weights": [[8, 13, 1.0], [127, 134, 1.0]]}, "2018.cikm_conference-2018.214": {"snippet": "A Network-embedding Based Method for Author Disambiguation\n\n\n ABSTRACTMost existing author disambiguation work relies heavily on feature engineering or cannot use multiple paper relationships. In this...", "weights": []}, "L16-1716": {"snippet": "... not least, provisions have been made towards linked open data (LOD) aspects, by exposing an RDF access point allowing to query on the authors, papers and resources. Finally, a complete technological ...", "weights": [[8, 13, 1.0], [138, 145, 1.0]]}, "2016.wwwconf_conference-2016c.44": {"snippet": "...s of Authors\n\n\n ABSTRACTUnderstanding the qualitative patterns of research endeavor of scientific authors in terms of publication count and their impact (citation) is important in order to quantify su...", "weights": [[8, 15, 1.0], [101, 108, 1.0]]}, "2016.airs_conference-2016.25": {"snippet": "... the Authors of Online Books in Digital Libraries Using Users Affinity", "weights": [[8, 15, 1.0]]}, "2012.jasis_journal-ir0volumeA63A10.6": {"snippet": "Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches", "weights": []}, "L18-1552": {"snippet": "...rary authors over time. Temporal style analysis has received comparatively little attention over the past years in spite of the possibility of an author's style frequently changing over time, a proper...", "weights": [[8, 15, 1.0]]}, "2018.clef_conference-2018w.17": {"snippet": "...s of authors. In the other hand, most of the previous algorithms in author masking try to apply changes to the original texts blindly without considering those fingerprints. In this paper, we propose ...", "weights": [[8, 15, 1.0]]}, "2017.ictir_conference-2017.18": {"snippet": "...d on Least Information Measurement\n\n\n ABSTRACTWe developed a new information retrieval framework based on the Least Information (LI) metric. We derived multiple term weighting schemes and combined the...", "weights": [[8, 13, 1.0], [113, 118, 1.0]]}, "2018.tweb_journal-ir0volumeA12A3.2": {"snippet": "...ets. Authors' addresses: K. Rudra, N. Ganguly, P. Goyal, and S. Ghosh, Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur 721302, India; emails: koustav.rudra@cse...", "weights": [[8, 15, 1.0]]}, "P16-1205": {"snippet": "...f at least 11.3% and 5.3%, in micro and macro F-score, respectively.", "weights": [[8, 13, 1.0]]}, "W15-5315": {"snippet": "...date authors; and the dataset sizes of 100, 300, 500, 1,000, 2,000, and 5,000 instances in each class. The highest 89.2% accuracy in the authorship attribution task using a maximum number of candidate...", "weights": [[8, 15, 1.0]]}, "2020.jasis_journal-ir0volumeA71A3.4": {"snippet": "Authors' status and the perceived quality of their work: Measuring citation sentiment change in nobel articles", "weights": [[0, 7, 1.0]]}, "2017.ecir_workshop-2017bir.4": {"snippet": "...t at least 70% of WoS publications are also indexed by Scopus. Last but not the least, when the parameters leading to the best matching results were applied, it took just about an hour to match 1.6 mi...", "weights": [[8, 13, 1.0], [83, 88, 1.0]]}, "Y02-1045": {"snippet": "Author Index", "weights": []}, "O06-5006": {"snippet": "Author and Subject Index\n\n\n The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the coauthors' names, the title of paper or othe...", "weights": []}, "2013.ecir_conference-2013.79": {"snippet": "Least Square Consensus Clustering: Criteria, Methods, Experiments\n\n\n Abstract. We develop a consensus clustering framework developed three decades ago in Russia and experimentally demonstrate that our...", "weights": [[0, 5, 1.0], [133, 138, 1.0]]}, "2010.jasis_journal-ir0volumeA61A12.0": {"snippet": "... not least: Additional positional effects on citation and readership in arXiv", "weights": [[8, 13, 1.0]]}, "C08-1065": {"snippet": "...rent authors as negative examples. In this paper, we show, on the basis of a new corpus with 145 authors, what the effect is of many authors on feature selection and learning, and show robustness of a...", "weights": [[8, 15, 1.0], [100, 107, 1.0], [136, 143, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A6.1": {"snippet": "... the Least Recently Used algorithm (LRU for short) to evict the least recently watched video when Memcached runs out of space. However, LRU may cause a high miss count, which is the number of times th...", "weights": [[8, 13, 1.0], [67, 72, 1.0]]}, "W15-1813": {"snippet": "...date authors is gradually increasing: i.e. starting from 3, going up to 5, 10, 20, 50, and 100. Using supervised machine learning techniques we also investigated the influence of different features (l...", "weights": [[8, 15, 1.0]]}, "2016.ecir_workshop-2016newsir.3": {"snippet": "...s at least one relevant term among 5 suggestions for 25% of the searches. We found that expert users of news retrieval software are critical in their selection of query terms. Nevertheless, they judge...", "weights": [[8, 13, 1.0]]}, "2015.clef_conference-2015w.126": {"snippet": "...n of authors in a dataset, 2) the known author, 3) the questioned document and combines the three of them in a single representation.", "weights": [[8, 15, 1.0], [95, 100, 1.0]]}, "C82-2075": {"snippet": "Authors Index", "weights": [[0, 7, 1.0]]}, "2019.clef_conference-2019w.156": {"snippet": "...r of authors in a given document. To this end, we present a system consisting of two modules, one for distinguishing the single-author documents from the multiauthor documents and the other for determ...", "weights": [[8, 15, 1.0]]}, "J88-1012": {"snippet": "Author Index: Volumes 6-13", "weights": []}, "2020.clef_conference-2020w.139": {"snippet": "...ific authors for whom all kinds of personal information is public knowledge, and whose large followership enables new kinds of author profiling tasks: At this year's PAN, we study for the first time a...", "weights": [[8, 15, 1.0]]}, "2000.jasis_journal-ir0volumeA51A9.11": {"snippet": "Authors of information science", "weights": [[0, 7, 1.0]]}}}
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Buyers today are not only using t...", "weights": []}, "2021.nllp-1.19": {"snippet": "...ets. German court rulings contain much structural information, so we create a pre-processing pipeline tailored explicitly to the German legal domain. Additionally, we implement multiple extractive as ...", "weights": [[8, 14, 1.0], [132, 138, 1.0], [145, 151, 1.0]]}, "2020.wmt-1.93": {"snippet": "...g on domain-specific bitexts, we explore effects of in-domain dictionaries on enhancing cross-domain neural machine translation performance. We utilize a transfer learning strategy through pre-trained...", "weights": [[8, 14, 1.0], [58, 64, 1.0], [97, 103, 1.0]]}, "2020.lrec-1.537": {"snippet": "A Domain-Specific Dataset of Difficulty Ratings for {G}erman Noun Compounds in the Domains {DIY}, Cooking and Automotive\n\n\n We present a dataset with difficulty ratings for 1,030 German closed noun co...", "weights": [[2, 8, 1.0], [179, 185, 1.0]]}, "W17-7002": {"snippet": "...n of German terminology in an inhomogeneous domain. The text basis is thematically broad and contains various registers, from expert text to user-generated data taken from an online discussion forum. ...", "weights": [[8, 14, 1.0], [47, 53, 1.0]]}, "2020.cmlc-1.2": {"snippet": "...rary German. The dependency parser is trained on the German dataset from the SPMRL 2014 Shared Task which contains text from the news domain, whereas DeReKo also covers other domains including fiction...", "weights": [[8, 14, 1.0], [56, 62, 1.0], [137, 143, 1.0]]}, "C10-2146": {"snippet": "... new domain. We integrate our algorithm into the MXPOST POS tagger (Ratnaparkhi, 1996) and experiment with three languages (English, German and Chinese) in seven in-domain and domain adaptation scenar...", "weights": [[8, 14, 1.0], [136, 142, 1.0], [168, 174, 1.0], [179, 185, 1.0]]}, "E03-2012": {"snippet": "... and German medical scientific abstracts relevant to a German query document (electronic patient record). The modularity of the system allows for deployment in other domains, given appropriate linguis...", "weights": [[8, 14, 1.0], [58, 64, 1.0]]}, "2008.clef_workshop-2008w.122": {"snippet": "The Domain-Specific Track at CLEF 2008\n\n\n The domain-specific track evaluates retrieval models for structured scientific bibliographic collections in English, German and Russian. Documents contain tex...", "weights": [[4, 10, 1.0], [46, 52, 1.0], [159, 165, 1.0]]}, "2021.konvens-1.8": {"snippet": "... for German texts. We have set up a comprehensive compilation of 19 sentiment lexicon resources and 20 sentiment-annotated corpora available for German across multiple domains. In addition to the eval...", "weights": [[8, 14, 1.0], [148, 154, 1.0]]}, "2007.clef_workshop-2007.26": {"snippet": "Domain-Specific IR for German, English and Russian Languages", "weights": [[0, 6, 1.0], [23, 29, 1.0]]}, "W98-0606": {"snippet": "... for German, which interprets natural-language queries to databases in the social insurance domain. These natural language queries are complex NPs, consisting of clusters of NPs and PPs. The parser us...", "weights": [[8, 14, 1.0], [95, 101, 1.0]]}, "2013.clef_conference-2013w.64": {"snippet": "...ough German terms occasionally appear as synonyms in an unsystematic way. The most important German terminology systems are made available by DIMDI, the German Institute for Medical Documentation and ...", "weights": [[8, 14, 1.0], [96, 102, 1.0], [156, 162, 1.0]]}, "W04-1218": {"snippet": "...ical Domain\n\n\n In this paper, we report the adaptation of a named entity recognition (NER) system to the biomedical domain in order to participate in the \"Shared Task Bio-Entity Recognition\". The syst...", "weights": [[8, 14, 1.0], [119, 125, 1.0]]}, "2005.clef_workshop-2005w.111": {"snippet": "...s in Domain-Specific Information Retrieval\n\n\n The third participation of the University of Hagen at the German Indexing and Retrieval Test (GIRT) task of the Cross Language Evaluation Campaign (CLEF 2...", "weights": [[8, 14, 1.0], [107, 113, 1.0]]}, "2016.wwwconf_conference-2016c.120": {"snippet": "...nary domain. It has been recently defined as innovation, not necessarily but mainly through information and communications technologies (ICT), which enhance urban life in terms of people, living, econ...", "weights": [[8, 14, 1.0]]}, "2017.wwwconf_conference-2017c.34": {"snippet": "...Open Domain Entity Search\n\n\n ABSTRACTThe paper introduces an open domain entity search system called ESearch, which aims at finding a list of relevant entities to an open domain entity search query (a...", "weights": [[8, 14, 1.0], [69, 75, 1.0], [174, 180, 1.0]]}, "W11-2147": {"snippet": "... For German-English translation we tried to make the German text more similar to the English text by normalizing German morphology and performing rule-based clause reordering of the German text. This ...", "weights": [[8, 14, 1.0], [56, 62, 1.0], [116, 122, 1.0], [185, 191, 1.0]]}, "S18-2003": {"snippet": "...s in German complex verbs, which frequently undergo meaning shifts. We use a novel strategy to obtain source and target domain characterisations via sentence generation rather than sentence annotation...", "weights": [[8, 14, 1.0], [123, 129, 1.0]]}, "2009.wsdm_conference-2009.9": {"snippet": "Clustering the tagged web\n\n\n ABSTRACTAutomatically clustering web pages into semantic groups promises improved search and browsing on the web. In this paper, we demonstrate how user-generated tags fro...", "weights": []}, "2006.clef_workshop-2006w.137": {"snippet": "...ies (German-English, English-German, German-Russian, English-Russian) were available. Monolingual and cross-language tasks were offered. Topics were prepared in English, German and Russian. Participan...", "weights": [[8, 14, 1.0], [32, 38, 1.0], [40, 46, 1.0], [173, 179, 1.0]]}, "2008.clef_workshop-2008w.41": {"snippet": "...a to Domain-Specific Track in CLEF 2008\n\n\n We participate in 2008 to our first Domain-Specific Track, with the aim to establish a baseline for our Information Retrieval engine in an unknown domain for...", "weights": [[8, 14, 1.0], [82, 88, 1.0], [193, 199, 1.0]]}, "2018.wwwconf_conference-2018.25": {"snippet": "...s of Domain Dropcatching\n\n\n ABSTRACTAn event that is rarely considered by technical users and laymen alike is that of a domain name expiration. The massive growth in the registration of domain names i...", "weights": [[8, 14, 1.0], [123, 129, 1.0], [189, 195, 1.0]]}, "2020.wmt-1.97": {"snippet": "...e in-domain bilingual corpus, we use back-translation of monolingual in-domain data in the English language as additional in-domain training data. Our systems in German\u21d2English and English\u21d2German are ...", "weights": [[8, 14, 1.0], [75, 81, 1.0], [128, 134, 1.0], [165, 171, 1.0], [192, 198, 1.0]]}, "2006.clef_workshop-2006.40": {"snippet": "...gual German-English Framework for Open-Domain Question Answering", "weights": [[8, 14, 1.0], [42, 48, 1.0]]}, "E12-1039": {"snippet": "...ng a domain-independent senseannotated corpus harvested from the web. As a proof of concept, this method has been applied to German, a language for which sense-annotated corpora are still in short sup...", "weights": [[8, 14, 1.0], [128, 134, 1.0]]}, "2021.mtsummit-research.15": {"snippet": "...o in-domain parallel corpora, and only very limited in-domain monolingual corpora. Here, we use the domain classifier to reweight the sentences of our out-of-domain training corpus. This leads to impr...", "weights": [[8, 14, 1.0], [58, 64, 1.0], [103, 109, 1.0], [161, 167, 1.0]]}, "2007.clef_workshop-2007w.30": {"snippet": "Domain-Specific Cross Language Retrieval: Comparing and Merging Structured and Unstructured Indices\n\n\n This year, we participated in all Monolingual, Bilingual and Multilingual tasks of the Domain-Spe...", "weights": [[0, 6, 1.0], [190, 196, 1.0]]}, "2007.clef_workshop-2007w.28": {"snippet": "... the German-German run we achieved an overall accuracy (ACC) of 30%; for the English-German run 18.5% (ACC); for the German-English run 7% (ACC), for the Spanish-English run 10% (ACC) and for the Port...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [88, 94, 1.0], [120, 126, 1.0]]}, "D19-1078": {"snippet": "...n of domain-shared translation knowledge. To this end, we propose an iterative dual domain adaptation framework for NMT. Specifically, we first pretrain in-domain and out-of-domain NMT models using th...", "weights": [[8, 14, 1.0], [87, 93, 1.0], [159, 165, 1.0], [177, 183, 1.0]]}, "W19-5420": {"snippet": "...t-of-domain and in-domain parallel corpora for six translation directions covering English-Chinese, English-French and English-German language pairs. Our submitted systems achieve the best BLEU scores...", "weights": [[8, 14, 1.0], [22, 28, 1.0], [130, 136, 1.0]]}, "2013.wwwconf_conference-2013c.180": {"snippet": "... for german text sentiment classification", "weights": [[8, 14, 1.0]]}, "2020.lt4gov-1.5": {"snippet": "...tion domain, a language guide, named entities based on open public data, translation memories, etc.). German is a pluricentric language that considerably varies in the domain of public administration ...", "weights": [[8, 14, 1.0], [105, 111, 1.0], [171, 177, 1.0]]}, "2003.clef_workshop-2003w.56": {"snippet": "... and Domain-Specific Cross-Language Retrieval\n\n\n As in the previous years, Berkeley's group 1 experimented with the domain-specific CLEF collection GIRT as well as with Russian as query and document l...", "weights": [[8, 14, 1.0], [119, 125, 1.0]]}, "1997.eamt-1.12": {"snippet": "...ment German and Russian). The demo domain is oil/water pollution texts in German and Bulgarian.", "weights": [[8, 14, 1.0], [38, 44, 1.0], [77, 83, 1.0]]}, "K17-1038": {"snippet": "... for German compared with several baseline domain adaptation techniques. As a by-product of this approach, our models that are trained on a combination of English and German utterances perform reasona...", "weights": [[8, 14, 1.0], [46, 52, 1.0], [170, 176, 1.0]]}, "2020.lrec-1.331": {"snippet": "...wiss German dialects normalized into High German. As Swiss German is, for now, a predominantly spoken language, there is a significant variation in the written forms, even between speakers of the same...", "weights": [[8, 14, 1.0], [45, 51, 1.0], [62, 68, 1.0]]}, "2005.clef_workshop-2005.27": {"snippet": "Domain-Specific CLIR of English, German and Russian Using Fusion and Subject Metadata for Query Expansion", "weights": [[0, 6, 1.0], [33, 39, 1.0]]}, "2021.konvens-1.9": {"snippet": "...oth, German and English, from the sub-domain of graph theory. Mathematical texts differ from other domains because sentences which appear as definitions from a linguistic perspective are not necessari...", "weights": [[8, 14, 1.0], [41, 47, 1.0]]}, "2003.clef_workshop-2003.54": {"snippet": "... for German and English", "weights": [[8, 14, 1.0]]}, "2014.wwwconf_conference-2014.86": {"snippet": "A hierarchical Dirichlet model for taxonomy expansion for search engines\n\n\n ABSTRACTEmerging trends and products pose a challenge to modern search engines since they must adapt to the constantly chang...", "weights": []}, "D17-1155": {"snippet": "... and domain weighting with a dynamic weight learning strategy, are proposed for NMT domain adaptation. Empirical results on the IWSLT English-German/French tasks show that the proposed methods can sub...", "weights": [[8, 14, 1.0], [87, 93, 1.0], [145, 151, 1.0]]}, "2004.clef_workshop-2004w.74": {"snippet": "... the German Indexing and Retrieval Test (GIRT) task of the CLEF 2004 evaluation campaign. We conducted both monolingual and bilingual information retrieval experiments. For monolingual experiments wit...", "weights": [[8, 14, 1.0]]}, "2010.tweb_journal-ir0volumeA4A2.0": {"snippet": "...rtal domain refers to a Web domain that shows only advertisements, served by a thirdparty advertisement syndication service, in the form of ads listing. We develop a machine-learningbased classifier t...", "weights": [[8, 14, 1.0], [31, 37, 1.0]]}, "2004.clef_workshop-2004.27": {"snippet": "...in a domain-specific context 1 using the GIRT4 German/English social science database. GIRT4 data is offered as pseudo-parallel German and English corpora. In addition multilingual controlled vocabula...", "weights": [[8, 14, 1.0], [50, 56, 1.0], [131, 137, 1.0]]}, "W14-5310": {"snippet": "...dard German, the official language of the German part of Switzerland. However, dealing with Swiss German in natural language processing, usually the detour through Standard German is taken. As writing...", "weights": [[8, 14, 1.0], [45, 51, 1.0], [101, 107, 1.0], [176, 182, 1.0]]}, "2004.sigirconf_conference-2004.36": {"snippet": "Web-a-where: geotagging web content\n\n\n ABSTRACTWe describe Web-a-Where, a system for associating geography with Web pages. Web-a-Where locates mentions of places and determines the place each name ref...", "weights": []}, "W18-6446": {"snippet": "...lish-German language pair. We have built neural machine translation systems for both translation directions (English\u2192German and German\u2192English) and for two different domains (the biomedical domain and...", "weights": [[8, 14, 1.0], [120, 126, 1.0], [131, 137, 1.0], [193, 199, 1.0]]}, "2004.ir_journal-ir0volumeA7A34.3": {"snippet": "... for German Text Retrieval?", "weights": [[8, 14, 1.0]]}, "W19-8651": {"snippet": "... non-domain specific realisers is sparse, partly due to the complexity of the German language. In this paper, we present SimpleNLG-DE, an adaption of SimpleNLG to German. We discuss which parts of the...", "weights": [[8, 14, 1.0], [81, 87, 1.0], [166, 172, 1.0]]}, "W16-2333": {"snippet": "...}: A Domain Adaptive {MT} System\n\n\n This paper presents the JU-USAAR English-German domain adaptive machine translation (MT) system submitted to the IT domain translation task organized in WMT-2016 . ...", "weights": [[8, 14, 1.0], [80, 86, 1.0], [87, 93, 1.0], [155, 161, 1.0]]}, "W06-3114": {"snippet": "...t-of-domain test set. This allows us to compare machine translation performance in-domain and out-of-domain.", "weights": [[8, 14, 1.0], [86, 92, 1.0], [104, 110, 1.0]]}, "2007.clef_workshop-2007w.31": {"snippet": "Domain-Specific IR for German, English and Russian Languages\n\n\n In participating in this CLEF evaluation campaign, our first objective is to propose and evaluate various indexing and search strategies...", "weights": [[0, 6, 1.0], [23, 29, 1.0]]}, "2001.clef_workshop-2001.44": {"snippet": "...gual German Runs", "weights": [[8, 14, 1.0]]}, "2003.clef_workshop-2003.37": {"snippet": "... the German scientific information domain. One reason for this was that the main ad hoc retrieval tracks in TREC used English news collections for IR system testing. General-purpose news documents req...", "weights": [[8, 14, 1.0], [38, 44, 1.0]]}, "2018.cikm_workshop-2018w.14": {"snippet": "...from German literature in the legal domain which are then integrated into a knowledge base with multiple clusters, allowing for different perspectives and efficient lookups. Having references to regul...", "weights": [[8, 14, 1.0], [39, 45, 1.0]]}, "2012.sigirconf_conference-2012.49": {"snippet": "...k in German is represented by its sequence of words which appear only once. An English-German dictionary is used to transform the word sequence of the English book into German by translating individua...", "weights": [[8, 14, 1.0], [90, 96, 1.0], [172, 178, 1.0]]}, "2020.multilingualbio-1.3": {"snippet": "...e of German medical jargon, in order to optimise terminology grounding of clinical texts by NLP systems. The core resource is a manually maintained table of English-to-German word and chunk translatio...", "weights": [[8, 14, 1.0], [171, 177, 1.0]]}, "2008.wwwconf_conference-2008.102": {"snippet": "Towards a global schema for web entities\n\n\n ABSTRACTPopular entities often have thousands of instances on the Web. In this paper, we focus on the case where they are presented in table-like format, na...", "weights": []}, "W03-2118": {"snippet": "...on \u2212 domain independent speech acts and domain dependent domain actions. Our database contains over 14,000 tagged sentences in English, Italian, and German. We argue that domain actions, and not speec...", "weights": [[8, 14, 1.0], [43, 49, 1.0], [60, 66, 1.0], [152, 158, 1.0], [174, 180, 1.0]]}, "2019.wwwconf_conference-2019.14": {"snippet": "...Open-Domain Entity Descriptions from Facts", "weights": [[8, 14, 1.0]]}, "2006.clef_workshop-2006w.34": {"snippet": "Domain-Specific Track CLEF 2006: Overview of Results and Approaches, Remarks on the Assessment Analysis\n\n\n The CLEF domain-specific track uses databases in different languages from the social science ...", "weights": [[0, 6, 1.0], [116, 122, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "... the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copy...", "weights": [[8, 14, 1.0], [192, 198, 1.0]]}, "1999.jasis_journal-ir0volumeA50A3.3": {"snippet": "...cted German Literature", "weights": [[8, 14, 1.0]]}, "C02-1036": {"snippet": "...n in German that must be addressed in German sentence realization. We present two different approaches to the modeling of extraposition, both based on machine learned decision tree classifiers. The tw...", "weights": [[8, 14, 1.0], [41, 47, 1.0]]}, "L18-1498": {"snippet": "...wiss German speakers, annotated with a standard German transcription. The desired output of the speech recognizer is again standard German, since there is no other official or standardized way to writ...", "weights": [[8, 14, 1.0], [51, 57, 1.0], [135, 141, 1.0]]}, "2006.clef_workshop-2006w.67": {"snippet": "...th a Domain-Specific Document Corpus at the Chemnitz Technical University\n\n\n This article describes the first participation of the Media Informatics Section of the Chemnitz Technical University at the...", "weights": [[8, 14, 1.0]]}, "2021.wmt-1.86": {"snippet": "...i.e. German, Spanish and French) with English as target language. Our NMT systems based on Transformer architecture, were trained on combination of indomain and out-domain parallel corpora developed u...", "weights": [[8, 14, 1.0], [168, 174, 1.0]]}, "2010.eamt-1.29": {"snippet": "Domain Adaptation in Statistical Machine Translation using Factored Translation Models\n\n\n In recent years the performance of SMT increased in domains with enough training data. But under real-world co...", "weights": [[0, 6, 1.0]]}, "2021.konvens-1.15": {"snippet": "...n to German, surpassing the previous state-of-theart performance by a wide margin of 10-30 points F1 across three established datasets for German. This is achieved by a neural end-to-end approach, tra...", "weights": [[8, 14, 1.0], [142, 148, 1.0]]}, "2014.sigirconf_conference-2014.192": {"snippet": "...\"the German chancellor met the Ukrainian opposition leader and former heavy-weight champion\", even if these texts never mention the strings \"Angela Merkel\" and \"Vitali Klitschko\".\nRELATED WORKPrior wo...", "weights": [[8, 14, 1.0]]}, "2018.sigirconf_conference-2018.222": {"snippet": "Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances\n\n\n ABSTRACTGenerative adversarial nets (GANs) have been widely studied during the recent development of deep learning a...", "weights": []}, "2019.gwc-1.4": {"snippet": "...dard German, into GermaNet. This shall give a more inclusive perspective on the German language. We will argue that Swiss Standard German words, Helvetisms, are best included into the already existing...", "weights": [[8, 14, 1.0], [83, 89, 1.0], [134, 140, 1.0]]}, "2018.cikm_conference-2018.52": {"snippet": "GYANI: An Indexing Infrastructure for Knowledge-Centric Tasks\n\n\n ABSTRACTIn this work, we describe gyani (gyan stands for knowledge in Hindi), an indexing infrastructure for search and analysis of lar...", "weights": []}, "P95-1024": {"snippet": "...plex Domain Formation\n\n\n We propose a novel approach to extraposition in German within an alternative conception of syntax in which syntactic structure and linear order are mediated not via encodings ...", "weights": [[8, 14, 1.0], [76, 82, 1.0]]}, "2020.vardial-1.10": {"snippet": "...from German should be further investigated systematically. For translating user reviews, creating synthetic in-domain parallel data through back-and forward-translation and adding them to a small out-...", "weights": [[8, 14, 1.0], [114, 120, 1.0]]}, "2020.wmt-1.57": {"snippet": "...onal German \u2194 English model that is multi-domain, robust to noise, and which can translate entire documents (or bilingual dialogues) at once. We use the same ensemble of such models as our primary sub...", "weights": [[8, 14, 1.0], [45, 51, 1.0]]}, "1998.ecir_conference-1998.9": {"snippet": "German Indexing and Retrieval Test Data Base (GIRT) - Some Results of the Pre-test", "weights": [[0, 6, 1.0]]}, "1973.ipm_journal-ir0volumeA9A12.1": {"snippet": "...as a german language indexing tool", "weights": [[8, 14, 1.0]]}, "2008.clef_workshop-2008w.32": {"snippet": "... the German-German run we achieved a best overall accuracy (ACC) of 37%; for the English-German run 14.5% (ACC); and for the German-English run 14% (ACC).", "weights": [[8, 14, 1.0], [15, 21, 1.0], [92, 98, 1.0], [128, 134, 1.0]]}, "2017.wsdm_conference-2017.35": {"snippet": "German Typographers vs. German Grammar: Decomposition of Wikipedia Category Labels into Attribute-Value Pairs", "weights": [[0, 6, 1.0], [24, 30, 1.0]]}, "2005.clef_workshop-2005.29": {"snippet": "Domain Specific Mono- and Bilingual English to German Retrieval Experiments with a Social Science Document Corpus", "weights": [[0, 6, 1.0], [47, 53, 1.0]]}}}
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The entity linking task aims at identifying all the small text fragments in a...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [84, 88, 1.0], [89, 95, 1.0]]}, "2014.cikm_conference-2014.259": {"snippet": "...y an open source framework, which provides a user friendly and reactive Web interface to support both EL and SE labelling tasks, through a guided two-step process.", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2004.jasis_journal-ir0volumeA55A11.9": {"snippet": "Open knowledge management: Lessons from the open source revolution", "weights": [[0, 4, 1.0], [44, 48, 1.0], [49, 55, 1.0]]}, "C18-1003": {"snippet": "They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking\n\n\n This paper analyzes arguably the most challenging yet under-explored aspect of resolution tasks such as corefere...", "weights": []}, "2007.jasis_journal-ir0volumeA58A13.10": {"snippet": "... of \"open access\" on citation impact: An analysis of ArXiv's condensed matter section", "weights": [[8, 12, 1.0]]}, "P17-4012": {"snippet": "...MT}: Open-Source Toolkit for Neural Machine Translation\n\n\n We describe an open-source toolkit for neural machine translation (NMT). The toolkit prioritizes efficiency, modularity, and extensibility wi...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [77, 81, 1.0], [82, 88, 1.0]]}, "P19-1511": {"snippet": "...tity Mention Detection via Anchor-Region Networks\n\n\n Sequential labeling-based NER approaches restrict each word belonging to at most one entity mention, which will face a serious problem when recogni...", "weights": [[8, 15, 1.0], [148, 155, 1.0]]}, "2019.sigirconf_conference-2019osirrc.1": {"snippet": "...2019 Open-Source IR Replicability Challenge (OSIRRC 2019)", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "L10-1469": {"snippet": "... and Source Information", "weights": [[8, 14, 1.0]]}, "W17-7552": {"snippet": "...tter Mention Network\n\n\n Users in social network either unicast or broadcast their messages. At mention is the popular way of unicasting for Twitter whereas, general tweeting could be considered as bro...", "weights": [[8, 15, 1.0], [98, 105, 1.0]]}, "2006.sigirjournals_journal-ir0volumeA40A2.8": {"snippet": "...ort: Open Source Information Retrieval systems (OSIR06)", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2021.acl-short.37": {"snippet": "...AN}: Mention-Only Linking of Entities with a Mention Annotation Network\n\n\n We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems ...", "weights": [[8, 15, 1.0], [48, 55, 1.0]]}, "P14-2105": {"snippet": "... for open domain question answering (QA). We focus on single-relation questions and decompose each question into an entity mention and a relation pattern. Using convolutional neural network models, we...", "weights": [[8, 12, 1.0], [126, 133, 1.0]]}, "2019.sigirconf_conference-2019osirrc.5": {"snippet": "...ity. Open-Source IR Replicability Challenge (OSIRRC) has addressed this problem by introducing a unified framework for dockerizing a variety of retrieval tasks. In this paper, a Docker image is built ...", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2008.wwwconf_workshop-2008ldow.12": {"snippet": "...king Open Data\" community initiative contributed a great deal to the concretization of the web of data, describing best practice, publishing large sets of RDF data on the web, and consequently giving ...", "weights": [[8, 12, 1.0]]}, "E09-2005": {"snippet": "An Open-Source Natural Language Generator for {OWL} Ontologies and its Use in Protege and Second Life\n\n\n We demonstrate an open-source natural language generation engine that produces descriptions of ...", "weights": [[3, 7, 1.0], [8, 14, 1.0], [123, 127, 1.0], [128, 134, 1.0]]}, "W12-4304": {"snippet": "Open-domain Anatomical Entity Mention Detection\n\n\n Anatomical entities such as kidney, muscle and blood are central to much of biomedical scientific discourse, and the detection of mentions of anatomi...", "weights": [[0, 4, 1.0], [30, 37, 1.0]]}, "2018.cikm_conference-2018.304": {"snippet": "...ased open sources for the startup success prediction task and model the task using a very rich set of signals from such sources. In particular, we enrich structured data about the startup ecosystem wi...", "weights": [[8, 12, 1.0]]}, "2008.wwwconf_workshop-2008ldow.14": {"snippet": "Open Data Commons, a License for Open Data\n\n\n Attendees at the WWW2007 panel session on Open Data [1, 2] will remember a wide-ranging discussion of the role that easily accessible data could play in e...", "weights": [[0, 4, 1.0], [33, 37, 1.0], [88, 92, 1.0]]}, "2009.jasis_journal-ir0volumeA60A11.17": {"snippet": "Open Source: Technology and Policy", "weights": [[0, 4, 1.0], [5, 11, 1.0]]}, "2016.ecir_conference-2016.30": {"snippet": "... The Open-Source IR Reproducibility Challenge", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "P13-4003": {"snippet": "An Open Source Toolkit for Quantitative Historical Linguistics\n\n\n Given the increasing interest and development of computational and quantitative methods in historical linguistics, it is important tha...", "weights": [[3, 7, 1.0], [8, 14, 1.0]]}, "L08-1326": {"snippet": "...y to Open Source", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "P12-3011": {"snippet": "...G}rm open-source finite-state grammar software libraries\n\n\n In this paper, we present a new collection of open-source software libraries that provides command line binary utilities and library classes...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [109, 113, 1.0], [114, 120, 1.0]]}, "2014.wwwconf_conference-2014c.274": {"snippet": "...s in open source communities: an ecological approach applied to github\n\n\n ABSTRACTOpen Source Software (OSS) has gained high amount of popularity during the last few years. It is becoming used by publ...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [90, 96, 1.0]]}, "2022.findings-acl.169": {"snippet": "... for Open-Domain Entity Retrieval\n\n\n Entity retrieval-retrieving information about entity mentions in a query-is a key step in open-domain tasks, such as question answering or fact checking. However, ...", "weights": [[8, 12, 1.0], [130, 134, 1.0]]}, "2007.sigirconf_conference-2007.84": {"snippet": "...data source and evaluation benchmark from the ACE program. The Chinese Entity Detection and Recognition (EDR) task is of particular interest to us. This task presents us several language-independent a...", "weights": [[8, 14, 1.0]]}, "2012.amta-tutorials.5": {"snippet": "Open Source Statistical Machine Translation", "weights": [[0, 4, 1.0], [5, 11, 1.0]]}, "2012.sigirconf_workshop-2012osir.6": {"snippet": "Towards an Efficient and Effective Search Engine", "weights": []}, "2017.clef_conference-2017w.113": {"snippet": "...best source of expansion terms compared to the aliases or combination of both features. Finally, we also found that Relevance Feedback and Pseudo Relevance Feedback are effective to further improve th...", "weights": [[8, 14, 1.0]]}, "2021.conll-1.39": {"snippet": "...t an open-access dataset for cross-document event coreference, CDEC-WN, collected from English Wikinews and open-source our annotation toolkit to encourage further research on cross-document tasks. 1", "weights": [[8, 12, 1.0], [111, 115, 1.0], [116, 122, 1.0]]}, "1993.sigirconf_conference-93.7": {"snippet": "...both source texts and abstracts. This has led Paice to suggest the use of a so-called ubstractframe to guide the extraction of sentences from a source document, thus improving the balance and coverage...", "weights": [[8, 14, 1.0], [147, 153, 1.0]]}, "L04-1136": {"snippet": "Open-source Tools for Creation, Maintenance, and Storage of Lexical Resources for Language Generation from Ontologies", "weights": [[0, 4, 1.0], [5, 11, 1.0]]}, "P07-2053": {"snippet": "...open source, since it is those details of the implementation which are hidden from the user that hold the key for improved POS tagging across a wider variety of languages. We present HunPos 1 , a free...", "weights": [[8, 14, 1.0]]}, "2016.cikm_conference-2016.136": {"snippet": "Geotagging Named Entities in News and Online Documents\n\n\n ABSTRACTNews sources generate constant streams of text with many references to real world entities; understanding the content from such source...", "weights": []}, "2012.sigirconf_workshop-2012osir.2": {"snippet": "ezDL: An Interactive Search and Evaluation System", "weights": []}, "2015.cikm_workshop-2015esair.2": {"snippet": "...sing open information extraction techniques. In this talk we'll discuss some of the differences between open/closed schema knowledge bases including the ideas of objective vs subjective content as wel...", "weights": [[8, 12, 1.0], [107, 111, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A1.0": {"snippet": "Open source search: a data mining platform", "weights": [[0, 4, 1.0], [5, 11, 1.0]]}, "2005.mtsummit-osmtw.3": {"snippet": "Open Source Machine Translation with {DELPH}-{IN}", "weights": [[0, 4, 1.0], [5, 11, 1.0]]}, "W06-0504": {"snippet": "...eing open to a wider spectrum of linguistic phenomena. We describe a manually created benchmark for OPTM and discuss several factors which determine the difficulty of the task.", "weights": [[8, 12, 1.0]]}, "2014.wwwconf_conference-2014c.289": {"snippet": "...m in open source education: a case study\n\n\n ABSTRACTIn this paper, we describe the details of an interactive online web-based degree program in the area of Computer Science with specialization in Free...", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2019.sigirconf_conference-2019osirrc.8": {"snippet": "University of Waterloo Docker Images for OSIRRC at SIGIR 2019", "weights": []}, "2007.jasis_journal-ir0volumeA58A1.11": {"snippet": "... and open source software in developing countries", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2020.emnlp-main.592": {"snippet": "...high mention coverage and sufficient context diversity. Unfortunately, when scaling NER to open situations, these advantages may no longer exist. And therefore it raises a critical question of whether...", "weights": [[8, 15, 1.0], [94, 98, 1.0]]}, "W13-5631": {"snippet": "...g an Open-Source Development Infrastructure for Language Technology Projects\n\n\n The article presents the Giellatekno & Divvun language technology resources, more specifically the effort to utilise ope...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [200, 204, 1.0]]}, "2016.wwwconf_conference-2016.52": {"snippet": "...tity Mention Detection and Linking in Web Queries\n\n\n ABSTRACTIn this paper we study the problem of linking open-domain web-search queries towards entities drawn from the full entity inventory of Wikip...", "weights": [[8, 15, 1.0], [110, 114, 1.0]]}, "K15-1002": {"snippet": "... and Mention Head Detection\n\n\n In coreference resolution, a fair amount of research treats mention detection as a preprocessed step and focuses on developing algorithms for clustering coreferred menti...", "weights": [[8, 15, 1.0], [94, 101, 1.0]]}, "R19-1083": {"snippet": "...e of source codes. Now the system is available on the GitHub repository (https://github.com/ CLARIN-PL/Inforex) as an open source project. The system can be easily setup and run in a Docker container ...", "weights": [[8, 14, 1.0], [121, 125, 1.0], [126, 132, 1.0]]}, "2021.dash-1.6": {"snippet": ".... An open-source and readyto-use implementation based on the text annotation platform INCEpTION 1 is made available 2 .", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2012.sigirconf_workshop-2012osir.3": {"snippet": "Apache Lucene 4", "weights": []}, "2002.wwwconf_conference-2002.69": {"snippet": "...able open source based toolkit that interprets the VoiceXML dialog markup language. It is designed to serve as a framework for system integrators and platform vendors who want to incorporate VoiceXML ...", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2013.tweb_journal-ir0volumeA7A2.6": {"snippet": "...se a source ranking sensitive to the query domains. Multiple domain-specific rankings of a source are computed, and these ranks are combined for the final ranking. We perform extensive evaluations on ...", "weights": [[8, 14, 1.0], [94, 100, 1.0]]}, "2021.eacl-main.21": {"snippet": "...d as open access open source resources.", "weights": [[8, 12, 1.0], [20, 24, 1.0], [25, 31, 1.0]]}, "R11-1010": {"snippet": "An Open Source {P}unjabi Resource Grammar\n\n\n We describe an open source computational grammar for Punjabi; a resource-poor language. The grammar is developed in GF (Grammatical framework), which is a ...", "weights": [[3, 7, 1.0], [8, 14, 1.0], [60, 64, 1.0], [65, 71, 1.0]]}, "2014.sigirconf_workshop-2014erd.1": {"snippet": "...es a mention of \"Milan\" much more likely to refer to the soccer team than the city), and many more features that are strong indicators of the kinds of strategies that should be relied on.Aside from th...", "weights": [[8, 15, 1.0]]}, "2009.trec_conference-2009.21": {"snippet": "...s. 1 Open Text eDOCS SearchServer and Open Text eDOCS Suite are trademarks or registered trademarks of Open Text Corporation in the United States of America, Canada, the European Union and/or other co...", "weights": [[8, 12, 1.0], [41, 45, 1.0], [106, 110, 1.0]]}, "2009.jasis_journal-ir0volumeA60A5.8": {"snippet": "...e on Open Source Software development activity", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "W19-6805": {"snippet": "... and open source software. The system was built using Apertium 1 , a free and open source machine translation system, and is currently unidirectional from Crimean Tatar to Turkish. We describe our tra...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [81, 85, 1.0], [86, 92, 1.0]]}, "L12-1614": {"snippet": "An Open Source {P}ersian Computational Grammar", "weights": [[3, 7, 1.0], [8, 14, 1.0]]}, "C12-3050": {"snippet": "... not open source. However, under the auspices of the CESAR project it has recently been turned open source and a JAVA and a MONO version have been produced. In our view this significant development ju...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [98, 102, 1.0], [103, 109, 1.0]]}, "2020.acl-main.447": {"snippet": "...olar Open Research Corpus\n\n\n We introduce S2ORC, 1 a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, r...", "weights": [[8, 12, 1.0]]}, "2014.wwwconf_conference-2014c.288": {"snippet": "...sive open online courses (MOOC) are to foster knowledge through free high quality learning materials procurement; to create new knowledge through diverse users' interactions with the providing platfor...", "weights": [[8, 12, 1.0]]}, "W11-1902": {"snippet": "...ring mention attributes, such as gender and number, across mentions in the same cluster. We participated in both the open and closed tracks and submitted results using both predicted and gold mentions...", "weights": [[8, 15, 1.0], [120, 124, 1.0]]}, "2010.amta-commercial.12": {"snippet": "...{''} Open Source Project\n\n\n The open source statistical machine translation toolkit Moses has recently drawn a lot of attention in the localization industry. Companies see the chance to use Moses to l...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [35, 39, 1.0], [40, 46, 1.0]]}}}
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This corpus is well represented by a relatively small number of 126 inclusion clusters and 175 exclusion clusters, each of which corresponds to...", "weights": [[8, 17, 1.0], [128, 137, 1.0]]}, "D07-1016": {"snippet": "... our inclusion entity model achieves a performance gain of 4.3 points in F-score over a baseline of no inclusion detection, and even outperforms a parser with access to gold standard part-of-speech ta", "weights": [[8, 17, 1.0], [106, 115, 1.0]]}, "C10-2074": {"snippet": "Text Mining for Automatic Image Tagging\n\n\n This paper introduces several extractive approaches for automatic image tagging, relying exclusively on information mined from texts. Through evaluations on ...", "weights": []}, "2011.ir_journal-ir0volumeA14A2.5": {"snippet": "Michael W. Berry and Jacob Kogan (eds.): Text mining: applications and theory - John Wiley and Sons, Ltd, 2010, xiv + 207 pp, \u00a355.00/66.00, hardcover, ISBN: 978-0-470-74982-1", "weights": []}, "2020.mmw-1.6": {"snippet": "Inclusion of Lithological terms (rocks and minerals) in The Open {W}ordnet for {E}nglish\n\n\n We extend the Open WordNet for English (OWN-EN) with rock-related and other lithological terms using the aut...", "weights": [[0, 9, 1.0]]}, "2015.cikm_workshop-2015dtmbio.9": {"snippet": "Building Text-mining Framework for Gene-Phenotype Relation Extraction using Deep Leaning\n\n\n ABSTRACTThe scientific literature is a rich resource for information retrieval on the biological knowledge. ...", "weights": []}, "2020.wwwconf_conference-2020.190": {"snippet": "Discriminative Topic Mining via Category-Name Guided Text Embedding", "weights": []}, "2001.cikm_conference-2001.84": {"snippet": "... the inclusion relation, i.e., the inclusion of the meaning of a block of text in the meaning of another block. In this paper, we consider the general problem of clustering of asymmetrically related d...", "weights": [[8, 17, 1.0], [38, 47, 1.0]]}, "2012.tist_journal-ir0volumeA3A4.1": {"snippet": "Bootstrapping a Game with a Purpose for Commonsense Collection\n\n\n Text mining has been very successful in extracting huge amounts of commonsense knowledge from data, but the extracted knowledge tends ...", "weights": []}, "2006.ipm_journal-ir0volumeA42A4.15": {"snippet": "Online Course: Text Mining by Statistics.com (http://www.statistics.com/)", "weights": []}, "2017.sigirconf_conference-2017.243": {"snippet": "Probabilistic Topic Models for Text Data Retrieval and Analysis\n\n\n ABSTRACTText data include all kinds of natural language text such as web pages, news articles, scienti c literature, emails, enterpri...", "weights": []}, "2013.ijirr_journal-ir0volumeA3A2.1": {"snippet": "Text Mining in Bioinformatics: Research and Application", "weights": []}, "2022.autosimtrans-1.4": {"snippet": "System Description on Third Automatic Simultaneous Translation Workshop\n\n\n This paper shows my submission to the Third Automatic Simultaneous Translation Workshop at NAACL2022. The submission includes...", "weights": []}, "2011.trec_conference-2011.34": {"snippet": "...hing inclusion/exclusion criteria using plain text from unstructured EMR.", "weights": [[8, 17, 1.0]]}, "2021.ecir_conference-20212.27": {"snippet": "...e as inclusion or exclusion respectively. Further, our inclusion/exclusion classifier provides an F1 of \u223c98 and \u223c97 for 11-class inclusion and exclusion classification respectively. We believe that ou...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [132, 141, 1.0]]}, "2012.tist_journal-ir0volumeA3A2.0": {"snippet": "Introduction to the Special Section on Intelligent Visual Interfaces for Text Analysis\n\n\n Text analysis technologies aim at automatically extracting knowledge and uncovering information buried under s...", "weights": []}, "1999.sigirconf_conference-99.17": {"snippet": "... for inclusion in a summary. This paper presents our analysis of news-article summaries generated by sentence selection. Sentences are ranked for potential inclusion in the summary using a weighted co...", "weights": [[8, 17, 1.0], [159, 168, 1.0]]}, "2010.ecir_conference-2010.14": {"snippet": "Mining Anchor Text Trends for Retrieval", "weights": []}, "2020.peoples-1.5": {"snippet": "... and Inclusion\n\n\n Over the past few years, systems have been developed to control online content and eliminate abusive, offensive or hate speech content. However, people in power sometimes misuse this...", "weights": [[8, 17, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.19": {"snippet": "Tech Mining: Exploiting New Technologies for Competitive Advantage\n\n\n Son, 2005, 384 pages, $84.95, ISBN 0-4714-7567-X    Tech Mining is a book about how to inform technology management by mining a wi...", "weights": []}, "2008.sigirconf_workshop-2008and.12": {"snippet": "Opinion mining from noisy text data\n\n\n ABSTRACTThe proliferation of Internet has not only generated huge volumes of unstructured information in the form of web documents, but a large amount of text is...", "weights": []}, "2016.ipm_journal-ir0volumeA52A5.10": {"snippet": "Helmholtz principle based supervised and unsupervised feature selection methods for text mining\n\n\n a b s t r a c tOne of the important problems in text classification is the high dimensionality of the...", "weights": []}, "2009.ecir_conference-2009.94": {"snippet": "Mining Query Logs", "weights": []}, "2004.sigirconf_conference-2004.118": {"snippet": "Context-based methods for text categorisation\n\n\n ABSTRACTWe propose several context-based methods for text categorization. One method, a small modification to the PPM compression-based model which is ...", "weights": []}, "2015.sigirjournals_journal-ir0volumeA49A2.20": {"snippet": "Utilising Wikipedia for Text Mining Applications", "weights": []}, "2013.jasis_journal-ir0volumeA64A2.4": {"snippet": "A text mining approach to assist the general public in the retrieval of legal documents", "weights": []}, "2015.cikm_workshop-2015dtmbio.0": {"snippet": "Proceedings of the ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2015, Melbourne, Australia, October 23, 2015", "weights": []}, "C86-1118": {"snippet": "{TOPIC} Essentials\n\n\n An overview of TOPIC is provided, a knowledge-based text information system for the analysis of Germanlanguage texts. TOPIC supplies text condensates (summaries) on variable degr...", "weights": []}, "2015.wsdm_conference-2015.60": {"snippet": "Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation\n\n\n ABSTRACTPrevious research on Recommender Systems (RS), especially the continuously popular approach ...", "weights": []}, "W13-4820": {"snippet": "Um reposit{\\'o}rio de verbos para a anota{\\c{c}}{\\~a}o de pap{\\'e}is sem{\\^a}nticos dispon{\\'\\i}vel na web (A Verb Repository for Semantic Role Labeling Available in the Web) [in {P}ortuguese]\n\n\n This...", "weights": []}, "2001.cikm_conference-2001.15": {"snippet": "... the inclusion of the background text, and compare our work to other efforts that can incorporate unlabeled data and other background text in the classification process.", "weights": [[8, 17, 1.0]]}, "2015.jeptalnrecital-court.11": {"snippet": "Adaptation par enrichissement terminologique en traduction automatique statistique fond{\\'e}e sur la g{\\'e}n{\\'e}ration et le filtrage de bi-segments virtuels\n\n\n Nous pr\u00e9sentons des travaux pr\u00e9liminai...", "weights": []}, "S17-2170": {"snippet": "{WING}-{NUS} at {S}em{E}val-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling\n\n\n We describe an end-to-end pipeline processing approach for SemEval 2017's Task 10 to ext...", "weights": []}, "C16-1268": {"snippet": "...onal Inclusion Hypothesis for Tensor-based Composition\n\n\n According to the distributional inclusion hypothesis, entailment between words can be measured via the feature inclusions of their distributio...", "weights": [[8, 17, 1.0], [93, 102, 1.0]]}, "2009.cikm_workshop-2009dtmbio.0": {"snippet": "Proceeding of the 3rd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO 2009, Hong Kong, China, November 6, 2009", "weights": []}, "P05-2023": {"snippet": "An Unsupervised System for Identifying {E}nglish Inclusions in {G}erman Text\n\n\n We present an unsupervised system that exploits linguistic knowledge resources, namely English and German lexical databa...", "weights": []}, "2008.cikm_workshop-2008dtmbio.2": {"snippet": "Text mining in genomics and systems biology\n\n\n AbstractThere is an increasing need of complementing the information available for the analysis of biological systems in Systems Biology and Genomics pro...", "weights": []}, "2017.wsdm_conference-2017.94": {"snippet": "WSDM 2017 Workshop on Mining Online Health Reports: MOHRS 2017\n\n\n ABSTRACTThe workshop on Mining Online Health Reports (MOHRS) draws upon the rapidly developing field of Computational Health, focusing...", "weights": []}, "W10-3404": {"snippet": "...e an inclusion relation, the relationship between them is called entailment. The task of mechanically distinguishing such a relation is called recognising textual entailment (RTE), which is basically ...", "weights": [[8, 17, 1.0]]}, "2013.sigirconf_conference-2013.3": {"snippet": "... for inclusion in the result summary.Our extensive experiments and analysis demonstrate that our method improves the quality of result summaries compared to existing state-of-the-art methods. We belie...", "weights": [[8, 17, 1.0]]}, "2010.cikm_conference-2010.279": {"snippet": "Collaboration analytics: mining work patterns from collaboration activities\n\n\n ABSTRACTPeople are increasingly using more and more social softwares, generating flooding communications. User analytics ...", "weights": []}, "I08-6001": {"snippet": "... the inclusion of genetically related (i.e., other Semitic) languages in the training data; second, the inclusion of non-related languages sharing the same script, and third, the inclusion of morpholo...", "weights": [[8, 17, 1.0], [107, 116, 1.0], [182, 191, 1.0]]}, "L14-1383": {"snippet": "Legal aspects of text mining", "weights": []}, "2013.ir_journal-ir0volumeA16A4.3": {"snippet": "Mining subtopics from text fragments for a web query", "weights": []}, "C14-2022": {"snippet": "A Marketplace for Web Scale Analytics and Text Annotation Services\n\n\n We present MIA, a data marketplace which enables massive parallel processing of data from the Web. End users can combine both text...", "weights": []}, "2007.cikm_conference-2007.25": {"snippet": "Discovering interesting usage patterns in text collections: integrating text mining with visualization\n\n\n ABSTRACT", "weights": []}, "2005.sigirconf_conference-2005.126": {"snippet": "...d an inclusion rate of 95% and overall translation accuracy of 90%, outperforming state-of-the-art OOV translation techniques.", "weights": [[8, 17, 1.0]]}, "2018.jasis_journal-ir0volumeA69A1.2": {"snippet": "Assessing perceived organizational leadership styles through twitter text mining", "weights": []}, "2002.ntcir_workshop-2002.5": {"snippet": "Answer Extraction System by Question Type from Query-Biased Summary for Newspaper Articles\n\n\n Recently, many researchers are focusing on the application of Natural Language Processing (NLP) techniques...", "weights": []}, "W14-3107": {"snippet": "Interactive Exploration of Asynchronous Conversations: Applying a User-centered Approach to Design a Visual Text Analytic System\n\n\n Exploring an online conversation can be very difficult for a user, e...", "weights": []}, "W08-1406": {"snippet": "... the inclusion of related, solid background information to cope with the difficulties of summarizing spoken language and the use of multi-document summarization techniques in single document speechto-...", "weights": [[8, 17, 1.0]]}, "2003.wwwconf_conference-2003p.67": {"snippet": "Disambiguation for Text Mining on the Web", "weights": []}, "2011.sigirconf_conference-2011.27": {"snippet": "DOM based content extraction via text density\n\n\n ABSTRACTIn addition to the main content, most web pages also contain navigation panels, advertisements and copyright and disclaimer notices. This addit...", "weights": []}, "2018.iir_workshop-2018.17": {"snippet": "The Malware Text Collection and Mining Project", "weights": []}, "2014.cikm_conference-2014.164": {"snippet": "... the inclusion criteria by adapting techniques from recent research into coverage-based diversification. We propose a novel approach for modelling the coverage of the query inclusion criteria within t...", "weights": [[8, 17, 1.0], [176, 185, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.15": {"snippet": "Integrating textual and visual information for cross-language image retrieval: A trans-media dictionary approach\n\n\n AbstractThis paper explores the integration of textual and visual information for cr...", "weights": []}, "J07-1007": {"snippet": "Book Reviews: Text Mining for Biology and Biomedicine, edited by Sophia Ananiadou and John {M}c{N}aught\n\n\n Text mining is defined by Hearst (1999) as the automatic discovery of new, previously unknown...", "weights": []}, "2015.cikm_workshop-2015dtmbio.12": {"snippet": "Evaluation of Disease-Associated Text-Mining Databases\n\n\n ABSTRACTThere are about 20 million scientific articles in PubMed and this is a great source of knowledge. Extraction of information from the a...", "weights": []}, "P04-3015": {"snippet": "...d on Inclusion of Appearance\n\n\n In this paper, we propose a method of automatically extracting word hierarchies based on the inclusion relation of appearance patterns from corpora. We apply a compleme...", "weights": [[8, 17, 1.0], [128, 137, 1.0]]}, "1999.cikm_conference-99.31": {"snippet": "Efficient Mining of Association Rules in Text Databases\n\n\n AbstractIn this paper, we propose two new algorithms for mining association rules between words in text databases. The characteristics of tex...", "weights": []}, "2022.acl-long.491": {"snippet": "Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification\n\n\n Hierarchical text classification is a challenging subtask of multi-label classificati...", "weights": []}, "W04-3107": {"snippet": "Text Mining - Next Steps for Drug Discovery\n\n\n The text mining community has shown significant impact for use of text mining in drug discovery and basic research. So far, there have been a lot of poin...", "weights": []}, "2012.cikm_workshop-2012dtmbio.7": {"snippet": "... for inclusion and exclusion criteria) than randomly choosing from a set of candidates obtained from relevant documents. In user simulation experiments, we were able to automatically construct eligibi...", "weights": [[8, 17, 1.0]]}, "2010.airs_conference-2010.52": {"snippet": "Mining Parallel Documents across Web Sites", "weights": []}, "1981.ipm_journal-ir0volumeA17A5.1": {"snippet": "on the inclusiveness of systems for retrieval documents indexed by unweighted descriptors", "weights": []}, "C00-1025": {"snippet": "Mining Tables from Large Scale {HTML} Texts\n\n\n Table is a very common presentation scheme, but few papers touch on table extraction in text data mining. This paper focuses on mining tables from large-...", "weights": []}, "2008.coria_conference-2008.24": {"snippet": "On the use of tolerant graded inclusions in information retrieval", "weights": []}, "D09-1162": {"snippet": "{C}hinese Novelty Mining\n\n\n Automated mining of novel documents or sentences from chronologically ordered documents or sentences is an open challenge in text mining. In this paper, we describe the pre...", "weights": []}, "2011.clef_workshop-2011w.84": {"snippet": "Text Mining in Biograph\n\n\n The Biograph project is a biomedical knowledge discovery project combining graph data mining with structured biomedical information and with text mining on medline abstracts...", "weights": []}, "2013.cikm_workshop-2013unlp.1": {"snippet": "Big data opportunities and challenges for IR, text mining and NLP\n\n\n ABSTRACTBig Data poses challenges for text analysis and natural language processing due to its characteristics of volume, veracity,...", "weights": []}, "J91-1002": {"snippet": "Lexical Cohesion Computed by Thesaural relations as an indicator of the structure of text\n\n\n In text, lexical cohesion is the result of chains of related words that contribute to the continuity of lex...", "weights": []}, "2005.ipm_journal-ir0volumeA41A4.0": {"snippet": "A hybrid approach to protein name identification in biomedical texts\n\n\n AbstractThis paper presents a hybrid approach to identifying protein names in biomedical texts, which is regarded as a crucial s...", "weights": []}, "P11-1162": {"snippet": "Insights from Network Structure for Text Mining\n\n\n Text mining and data harvesting algorithms have become popular in the computational linguistics community. They employ patterns that specify the kind...", "weights": []}, "1997.cikm_workshop-97.42": {"snippet": "Intensional Query Processing Using Data Mining Approaches", "weights": []}, "2001.cikm_conference-2001.89": {"snippet": "A Domain Independent Environment for Creating Information Extraction Modules\n\n\n ABSTRACTText-Mining is a growing area of interest within the field of Data Mining and Knowledge Discovery. Given a colle...", "weights": []}, "N16-1165": {"snippet": "Cross-Domain Mining of Argumentative Text through Distant Supervision\n\n\n Argumentation mining is considered as a key technology for future search engines and automated decision making. In such applica...", "weights": []}, "2001.sigirconf_conference-2001.49": {"snippet": "Anchor Text Mining for Translation Extraction of Query Terms\n\n\n AbstractThis paper presents an approach to automatically extracting the bilingual translations of many Web query terms through mining th...", "weights": []}, "U08-1010": {"snippet": "Text Mining Based Query Expansion for {C}hinese {IR}\n\n\n Query expansion has long been suggested as a technique for dealing with word mismatch problem in information retrieval. In this paper, we descri...", "weights": []}, "2012.cikm_workshop-2012dtmbio.0": {"snippet": "Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics, DTMBIO@CIKM 2012, Maui, Hawaii, USA, October 29, 2012", "weights": []}, "2008.cikm_conference-2008.230": {"snippet": "Effective pattern taxonomy mining in text documents\n\n\n ABSTRACTMany data mining techniques have been proposed for mining useful patterns in databases. However, how to effectively utilize discovered pa...", "weights": []}, "2012.ipm_journal-ir0volumeA48A2.8": {"snippet": "Indices of novelty for emerging topic detection\n\n\n a b s t r a c tEmerging topic detection is a vital research area for researchers and scholars interested in searching for and tracking new research t...", "weights": []}, "2001.jasis_journal-ir0volumeA52A13.6": {"snippet": "Citation mining: Integrating text mining and bibliometrics for research user profiling", "weights": []}}}
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We introduce several experimental techniques allowing us to achieve a balance be...", "weights": [[8, 14, 1.0]]}, "1976.ipm_journal-ir0volumeA12A3.4": {"snippet": "Artificial intelligence in information retrieval systems", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "1991.jasis_journal-ir0volumeA42A8.3": {"snippet": "...e of artificial intelligence in information retrieval", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2007.jasis_journal-ir0volumeA58A2.14": {"snippet": "...tion ethics: Privacy, property, and power", "weights": [[8, 14, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.7": {"snippet": "...?\n\n\n Artificial Intelligence (AI) hopes to provide a positive paradigm shift in technology by providing new features and personalized experience to our digital and physical world. In the future, almos...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "1998.jasis_journal-ir0volumeA49A7.0": {"snippet": "...sue: Artificial Intelligence Techniques for Emerging Information Systems Applications", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2021.jasis_journal-ir0volumeA72A1.8": {"snippet": "Artificial intelligence and the world of work, a co-constitutive relationship", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2021.wwwconf_conference-2021c.33": {"snippet": "...ncy, Ethics and Society on the Web", "weights": [[8, 14, 1.0]]}, "1986.sigirconf_conference-86.26": {"snippet": "...d on Artificial Intelligence Techniques", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2020.wwwconf_conference-2020.240": {"snippet": "IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems\n\n\n Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Corta...", "weights": []}, "2019.tist_journal-ir0volumeA10A2.0": {"snippet": "...ay's artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2001.jasis_journal-ir0volumeA52A1.3": {"snippet": "... and artificial intelligence in the 21st Century", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "1981.jasis_journal-ir0volumeA32A5.13": {"snippet": "... and artificial intelligence", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "J82-1004": {"snippet": "...k of Artificial Intelligence", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "L16-1252": {"snippet": "... for ethics in our community, including a Journ\u00e9e d'\u00e9tudes, a workshop and the Ethics and Big Data Charter. The concern for ethics shows to be quite similar in both surveys, despite a few differences ...", "weights": [[8, 14, 1.0], [82, 88, 1.0], [127, 133, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.2": {"snippet": "Artificial intelligence: Concepts, techniques, applications, promise", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2021.cl-3.22": {"snippet": "... and artificial intelligence. Although some basic knowledge of machine learning may be necessary to follow a few topics, the book includes clear illustrations and explanations, which make it accessibl...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2020.emnlp-main.684": {"snippet": "...y of artificial intelligence is higher than that of linear regression since artificial intelligence possesses a broader meaning scope. Understanding semantic capacity of terms will help many downstrea...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [79, 89, 1.0], [90, 102, 1.0]]}, "2018.wwwconf_conference-2018c.123": {"snippet": "SoBigData: Social Mining & Big Data Ecosystem\n\n\n ABSTRACTOne of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected socie...", "weights": []}, "2020.semeval-1.83": {"snippet": "... for Artificial Intelligence). The experimental results presented in this paper show potential for SOTA-level performance and demonstrate hybrid Artificial Intelligence in action. On SemEval-2020 Task...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [148, 158, 1.0], [159, 171, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.8": {"snippet": "Artificial intelligence: A selected bibliography", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2020.nl4xai-1.6": {"snippet": "...able Artificial Intelligence aims at providing justifications for automatic decisionmaking systems in order to ensure reliability and trustworthiness in users. To achieve this vision, we emphasize the...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "W19-8401": {"snippet": "...able Artificial Intelligence and its potential within Industry\n\n\n The age of Big Data has enabled the creation of artificial intelligence solutions that has allowed systems to better respond to their ...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [117, 127, 1.0], [128, 140, 1.0]]}, "2022.trustnlp-1.3": {"snippet": "Does Moral Code have a Moral Code? Probing Delphi{'}s Moral Philosophy\n\n\n In an effort to guarantee that machine learning model outputs conform with human moral values, recent work has begun exploring...", "weights": []}, "2021.teachingnlp-1.6": {"snippet": "...e on Ethics for Natural Language Processing\n\n\n It is generally agreed upon in the natural language processing (NLP) community that ethics should be integrated into any curriculum. Being aware of and u...", "weights": [[8, 14, 1.0], [134, 140, 1.0]]}, "2010.jec-1.2": {"snippet": "... the ethics of shared resources. The issues are challenging: confidentiality, ownership, copyright, authorial rights, attribution, the law, protectionism, costs, fairness, motivation, trust, quality, ...", "weights": [[8, 14, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.23": {"snippet": "...k of artificial intelligence, natural language conversation has attracted wide attention of researchers in natural language processing. Existing works in this field mainly focus on consistency of neur...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "C90-2042": {"snippet": "...s of Artificial Intelligence is the transfer of the knowledge conveyed by Natural Language into formalisms that a computer can interpret. In the Natural Language Processing department of the IBM Franc...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2010.ipm_journal-ir0volumeA46A3.6": {"snippet": "Automated opinion detection: Implications of the level of agreement between human raters\n\n\n a b s t r a c tThe ability to agree with the TREC Blog06 opinion assessments was measured for seven human as...", "weights": []}, "W19-8403": {"snippet": "...able Artificial Intelligence attempts to solve the problem of algorithmic opacity. Many terms and notions have been introduced recently to define Explainable AI, however, these terms seem to be used i...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2000.jasis_journal-ir0volumeA51A5.6": {"snippet": "... and ethics in professional discourse and information science", "weights": [[8, 14, 1.0]]}, "2020.fire_conference-2020w.11": {"snippet": "Artificial Intelligence as Legal Research Assistant", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2012.jasis_journal-ir0volumeA63A6.13": {"snippet": "...h to Artificial Intelligence Journals", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "C82-2059": {"snippet": "...With Artificial Intelligence", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "W19-8404": {"snippet": "...able Artificial Intelligence\n\n\n Among challenges for eXplainable Artificial Intelligence (XAI) is explanation generation. In this paper we put the stress on this issue by focusing on a semantic repres...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [68, 78, 1.0], [79, 91, 1.0]]}, "2013.tist_journal-ir0volumeA4A2.13": {"snippet": "...ines artificial (societies), computational (experiments), and parallel (execution) methods in intelligent systems and technology for integrative and predictive medicine, or more generally, precision m...", "weights": [[8, 18, 1.0]]}, "T87-1025": {"snippet": "...}\n\n\n Artificial intelligence is, or should be, at the heart of natural language processing research. After all, it is AI more than any other cognitive science that has made processing a central issue ...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2018.sigirconf_conference-2018.240": {"snippet": "... and Ethics in IR\n\n\n ABSTRACTThe interplay between human biases and the underlying data collection and algorithmic methods to present users with relevant information in information retrieval (IR) syst...", "weights": [[8, 14, 1.0]]}, "2020.lrec-1.813": {"snippet": "...e of artificial intelligence (AI) and the growing use of deep-learning architectures, the question of ethics, transparency and fairness of AI systems has become a central concern within the research c...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [105, 111, 1.0]]}, "2014.wwwconf_conference-2014c.34": {"snippet": "...IDR: artificial intelligence for disaster response", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2019.cikm_conference-2019.134": {"snippet": "Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas", "weights": []}, "1997.jasis_journal-ir0volumeA48A7.14": {"snippet": "Ethics and Computing: Living Responsibly in a Computerized World, by Kevin W. Bowyer", "weights": [[0, 6, 1.0]]}, "2021.naacl-main.297": {"snippet": "...ical Ethics in {NLP}\n\n\n Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable c...", "weights": [[8, 14, 1.0]]}, "2019.wsdm_conference-2019.114": {"snippet": "Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned\n\n\n ABSTRACTResearchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by...", "weights": []}, "1987.ipm_journal-ir0volumeA23A4.6": {"snippet": "... for artificial intelligence methods in information retrieval", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2019.fire_conference-2019w.4": {"snippet": "...xts. Artificial Intelligence is now applied in this area to understand and retrieve legal documents. This paper explains a working model developed for the track Artificial Intelligence for Legal Assis...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [164, 174, 1.0], [175, 187, 1.0]]}, "2020.acl-main.466": {"snippet": "...egal Artificial Intelligence\n\n\n Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the l...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [41, 51, 1.0], [52, 64, 1.0], [113, 123, 1.0], [124, 136, 1.0]]}, "2011.jasis_journal-ir0volumeA62A12.13": {"snippet": "...t of Artificial Intelligence Fellowship Award? Evaluating researchers by mining bibliographic data", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2015.tist_journal-ir0volumeA6A4.9": {"snippet": "Analyzing Activity Recognition Uncertainties in Smart Home Environments\n\n\n In spite of the importance of activity recognition (AR) for intelligent human-computer interaction in emerging smart space ap...", "weights": []}, "2015.wwwjournals_journal-ir0volumeA18A2.2": {"snippet": "...ting Artificial Intelligence has been slow, and realistic humanlike intelligence in games is hardly to be found. There have been some attempts to use Machine Learning in games, but such attempts often...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [71, 83, 1.0]]}, "2019.jasis_journal-ir0volumeA70A4.4": {"snippet": "Artificial intelligence in healthcare robots: A social informatics study of knowledge embodiment", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2021.ccl-1.49": {"snippet": "...t of Artificial Intelligence, AI ethics has captured a great deal of public attention. Automatic moral recognition, as an important task in textual morality study, has attracted a lot of interest of N...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [36, 42, 1.0]]}, "2020.ccl-1.50": {"snippet": "... for Artificial Intelligence Ethical Computing)\n\n\n The construction of the moral dictionary is based on artificial intelligence ethical computing. Moral behavior is complex and varied. The existing mo...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [107, 117, 1.0], [118, 130, 1.0]]}, "2015.wsdm_conference-2015.49": {"snippet": "...from artificial intelligence and reinforcement learning. Many of the open problems in current IR research can be described as dynamic systems, for instance, session search or computational advertising...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2015.wwwconf_conference-2015.15": {"snippet": "...arch ethics review. We describe the creation of a new ethics framework and a research ethics submission system (RESS) within Microsoft Research (MSR). This RESS is customized to the needs of web resea...", "weights": [[8, 14, 1.0], [57, 63, 1.0], [89, 95, 1.0]]}, "1996.jasis_journal-ir0volumeA47A10.7": {"snippet": "...g in Artificial Intelligence, by J. F. Baldwin, T. P. Martin, and B. W. Pilsworth", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "1996.ipm_journal-ir0volumeA32A4.9": {"snippet": "Artificial Minds", "weights": [[0, 10, 1.0]]}, "2014.tist_journal-ir0volumeA5A2.8": {"snippet": "A Framework for Effectively Choosing between Alternative Candidate Partners\n\n\n Many multi-agent settings require that agents identify appropriate partners or teammates with whom to work on tasks. When...", "weights": []}, "2006.wwwconf_conference-2006.93": {"snippet": "Detecting online commercial intention (OCI)\n\n\n ABSTRACTUnderstanding goals and preferences behind a user's online activities can greatly help information providers, such as search engine and E-Commerc...", "weights": []}, "1984.jasis_journal-ir0volumeA35A5.4": {"snippet": "Artificial intelligence: Underlying assumptions and basic objectives", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2022.nlp4convai-1.13": {"snippet": "...t of artificial intelligence and natural language processing research. Recent years have seen a dramatic improvement in quality for both task-oriented and open-domain dialogue systems, and an increasi...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "J09-1008": {"snippet": "...uron artificial brain\" talk. The idea behind this project was simple: in order to create artificial intelligence, what was needed first of all was a very large artificial brain; if a big enough set of...", "weights": [[8, 18, 1.0], [92, 102, 1.0], [103, 115, 1.0], [163, 173, 1.0]]}, "2018.cikm_workshop-2018w.29": {"snippet": "...iven artificial intelligence-based technologies is of highest importance to maximize its progressive influence to human life and human society. Life style diseases, social network affinity, impulsive ...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2020.nl4xai-1.10": {"snippet": "...able Artificial Intelligence\n\n\n This paper discusses four major argumentation theoretical frameworks with respect to their use in support of explainable artificial intelligence (XAI). We consider thes...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [156, 166, 1.0], [167, 179, 1.0]]}, "2016.fire_conference-2016w.53": {"snippet": "...es \u2192 Artificial intelligence \u2192 Natural language processing \u2192 Language resources Computing methodologies \u2192 Artificial intelligence \u2192 Natural language processing \u2192 Lexical semantics", "weights": [[8, 18, 1.0], [19, 31, 1.0], [109, 119, 1.0], [120, 132, 1.0]]}, "J90-1015": {"snippet": "... The Artificial Intelligence literature is liberally laced with claims about cognitive reality. Sometimes these are strong claims that cite empirical psychological evidence; but more often these claim...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2016.tist_journal-ir0volumeA7A4.0": {"snippet": "...ween artificial intelligence and human intelligence with the wisdom of a crowd.This special issue contributes to addressing key research questions centered on the role of the crowd in intelligent syst...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [42, 54, 1.0]]}, "J11-2006": {"snippet": "...with Artificial Companions: Key Social, Psychological, Ethical, and Design Issues edited by {Y}orick {W}ilks\n\n\n This book is an edited collection of chapters on artificial companions (ACs) resulting f...", "weights": [[8, 18, 1.0], [164, 174, 1.0]]}, "2020.jasis_journal-ir0volumeA71A6.0": {"snippet": "...tion Ethics. Edited by John T. F. Burgess and Emily J. M. Knox. Chicago: ALA Neal-Schuman, 2019.", "weights": [[8, 14, 1.0]]}, "W17-1604": {"snippet": "...cing ethics issues. While a number of previous works exist that discuss ethical issues, in particular around big data and machine learning, to the authors' knowledge this is the first account of NLP a...", "weights": [[8, 14, 1.0]]}, "2019.clef_conference-2019w.160": {"snippet": "...rong artificial intelligence problem into multiple weak artificial intelligence problems. First, we use a simple classifier to distinguish the four categories by training questions only. Transfer lear...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [59, 69, 1.0], [70, 82, 1.0]]}, "2019.fire_conference-2019w.3": {"snippet": "... for Artificial Intelligence for Legal Assistance Shared Task\n\n\n In this article, we present a description of our systems as a part of our participation in the shared task namely Artificial Intelligen...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [182, 192, 1.0], [193, 205, 1.0]]}, "2020.ipm_journal-ir0volumeA57A5.20": {"snippet": "...with artificial intelligence", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2022.trustnlp-1.4": {"snippet": "...}\n\n\n Artificial Intelligence (AI) and Machine Learning (ML) are rapidly becoming musthave capabilities. According to a 2019 Forbes Insights Report, \"seventy-nine percent [of executives] agree that AI ...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2022.acl-long.573": {"snippet": "... for ethics sheets with 50 ethical considerations, using the task of emotion recognition as a running example. Ethics sheets are a mechanism to engage with and document ethical considerations before b...", "weights": [[8, 14, 1.0], [114, 120, 1.0]]}, "1996.jasis_journal-ir0volumeA47A6.12": {"snippet": "The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents, edited by Luc Steels and Rodney Brooks", "weights": [[4, 14, 1.0], [29, 39, 1.0], [40, 52, 1.0]]}, "2017.wwwconf_conference-2017c.159": {"snippet": "...ars, artificial intelligence has achieved great success in many important applications. Both novel machine learning algorithms (e.g., deep neural networks), and their distributed implementations play ...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2015.wwwconf_conference-2015.48": {"snippet": "Automatic Online Evaluation of Intelligent Assistants\n\n\n ABSTRACTVoice-activated intelligent assistants, such as Siri, Google Now, and Cortana, are prevalent on mobile devices. However, it is challeng...", "weights": []}, "1999.jasis_journal-ir0volumeA50A8.10": {"snippet": "Ethics, Information and Technology: Readings edited by Richard N. Stichler and Robert Hauptman", "weights": [[0, 6, 1.0]]}, "2020.inlg-1.3": {"snippet": "Generating Intelligible Plumitifs Descriptions: Use Case Application with Ethical Considerations\n\n\n Plumitifs (dockets) were initially a tool for law clerks. Nowadays, they are used as summaries prese...", "weights": []}, "2021.wwwconf_conference-2021c.6": {"snippet": "Artificial Neural Network Based Sensor Ontology Matching Technique", "weights": [[0, 10, 1.0]]}, "2019.wsdm_conference-2019.121": {"snippet": "Task Intelligence Workshop @ WSDM 2019", "weights": [[5, 17, 1.0]]}, "2022.naacl-main.56": {"snippet": "...e\n\n\n Ethics is one of the longest standing intellectual endeavors of humanity. In recent years, the fields of AI and NLP have attempted to address ethical issues of harmful outcomes in machine learnin...", "weights": [[8, 14, 1.0]]}}}
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Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2018.wwwconf_conference-2018c.294": {"snippet": "...tual information is available to enrich datasets so as to make the more easily retrieval?\") to optimizing rankers for datasets in the absence of large volumes of interaction data (\"How can we train le...", "weights": [[8, 19, 1.0], [82, 91, 1.0]]}, "2007.ecir_conference-2007.0": {"snippet": "...s in Information Retrieval, 29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2-5, 2007, Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.17": {"snippet": "... and retrieval in large information networks\n\n\n AbstractAmid the rapid growth of information today is the increasing challenge for people to survive and navigate its magnitude. Dynamics and heterogene...", "weights": [[8, 17, 1.0], [27, 38, 1.0], [84, 95, 1.0]]}, "2008.ecir_conference-2008.0": {"snippet": "...s in Information Retrieval , 30th European Conference on IR Research, ECIR 2008, Glasgow, UK, March 30-April 3, 2008. Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2014.clef_conference-2014.5": {"snippet": "An Information Retrieval Ontology for Information Retrieval Nanopublications", "weights": [[3, 14, 1.0], [15, 24, 1.0], [38, 49, 1.0], [50, 59, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.2": {"snippet": "...tive information retrieval\n\n\n AbstractThis paper discusses the role of user-centred evaluations as an essential method for researching interactive information retrieval. It draws mainly on the work ca...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [150, 161, 1.0], [162, 171, 1.0]]}, "2013.ecir_conference-2013.0": {"snippet": "...s in Information Retrieval - 35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013. Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2013.ntcir_workshop-2013evia.9": {"snippet": "...uman information needs and digital information resources. The reliable evaluation of these technologies has been recognized for decades as central to the advancement of the field. As information retri...", "weights": [[8, 19, 1.0], [38, 49, 1.0], [186, 197, 1.0], [198, 207, 1.0]]}, "2021.ecir_conference-20211.0": {"snippet": "...s in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28 - April 1, 2021, Proceedings, Part I", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "N07-5002": {"snippet": "... for Information Retrieval\n\n\n Statistical language models have recently been successfully applied to many information retrieval problems. A great deal of recent work has shown that statistical languag...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "1988.riao_conference-1988.4": {"snippet": "...edia Information Management and Optical Disk Technologies as a Basis for Advanced Information Retrieval", "weights": [[8, 19, 1.0], [85, 96, 1.0], [97, 106, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...n of retrieval models particularly for recall oriented retrieval domains, where the focus is on retrieving all relevant information but not just retrieving a subset of relevant information. Available ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [123, 134, 1.0], [180, 191, 1.0]]}, "2019.chiir_conference-2019.55": {"snippet": "...ised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmic incorporati...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [148, 159, 1.0], [160, 169, 1.0]]}, "2011.wsdm_conference-2011.59": {"snippet": "... two information sources: the bidding behavior of advertisers, and the conversion data. Our key finding suggests that sophisticated advertisers' bids are driven by many factors beyond clicks and immed...", "weights": [[8, 19, 1.0]]}, "2004.ecir_conference-2004.0": {"snippet": "...s in Information Retrieval, 26th European Conference on IR Research, ECIR 2004, Sunderland, UK, April 5-7, 2004, Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1986.sigirconf_conference-86.1": {"snippet": "...atic Information Retrieval\n\n\n AbstractSubstantial successes were achieved", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A4.11": {"snippet": "...ion, information retrieval, content migration, replication, emulation and web preservation strategies. Furthermore, we need to focus not only on theoretical advances but also on innovative application...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "A94-1028": {"snippet": "...ated Information Retrieval\n\n\n We report on the results of a series of experiments with a prototype text retrieval system which uses relatively advanced natural language processing techniques in order ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [107, 116, 1.0]]}, "2009.ictir_conference-2009.0": {"snippet": "...s in Information Retrieval Theory, Second International Conference on the Theory of Information Retrieval, ICTIR 2009, Cambridge, UK, September 10-12, 2009, Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0], [87, 98, 1.0], [99, 108, 1.0]]}, "2004.clef_workshop-2004.6": {"snippet": "...act. Information Retrieval systems can benefit from advanced linguistic resources when carrying out tasks such as word-stemming or query translation. The main goal of our experiments has been the deve...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "X96-1035": {"snippet": "...Text Retrieval", "weights": [[8, 17, 1.0]]}, "2020.sigirconf_conference-2020.354": {"snippet": "...onal Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2009.ecir_conference-2009.92": {"snippet": "...s in Information Retrieval Evaluation", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2020.sigirconf_conference-2020.367": {"snippet": "... for Information Retrieval: Fundamentals and Advances\n\n\n Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users' information needs by suggesting...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [60, 71, 1.0], [72, 81, 1.0], [172, 183, 1.0]]}, "1996.sigirconf_conference-96.43": {"snippet": "...nced Information Visualization for Information Retrieval Szstems (Workshop Abstract)", "weights": [[8, 19, 1.0], [38, 49, 1.0], [50, 59, 1.0]]}, "2020.ecir_conference-20201.0": {"snippet": "...s in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings, Part I", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.ecir_conference-2011.0": {"snippet": "...s in Information Retrieval - 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.sigirconf_conference-2011.207": {"snippet": "...gful information in many cases.This demonstration introduces QuickView, an advanced tweet search service to address this issue. It adopts a series of natural language processing technologies to extrac...", "weights": [[8, 19, 1.0]]}, "1987.sigirconf_conference-87.25": {"snippet": "...Text Retrieval and Analysis System\n\n\n AbstractMICROARRAS is an advanced full-text retrieval and analysis system. It supports fast, efficient browsing of a document's vocabulary as well as its text, re...", "weights": [[8, 17, 1.0], [85, 94, 1.0]]}, "2018.chiir_conference-2018.19": {"snippet": "...e on Information Retrieval\n\n\n ABSTRACTThe success of information retrieval systems depends critically on both the ability of systems to efficiently and effectively retrieve information, and to support...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [56, 67, 1.0], [68, 77, 1.0], [176, 187, 1.0]]}, "2016.sigirconf_conference-2016.230": {"snippet": "...ical information search refers to methodologies and technologies that seek to improve access to medical information archives via a process of information retrieval (IR). Such information is now potent...", "weights": [[8, 19, 1.0], [107, 118, 1.0], [145, 156, 1.0], [157, 166, 1.0], [178, 189, 1.0]]}, "2018.cikm_conference-2018.246": {"snippet": "ASTRO: A Datalog System for Advanced Stream Reasoning\n\n\n ABSTRACTThe rise of the Internet of Things (IoT) and the recent focus on a gamut of 'Smart City' initiatives world-wide have pushed for new adv...", "weights": []}, "1999.cikm_conference-99.52": {"snippet": "...d to Information Retrieval: The Adaptive Information Retrieval System\n\n\n ABSTRACTUsers often quit an information retrieval system (IR system) very frustrated, because they cannot find the information ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [44, 55, 1.0], [56, 65, 1.0], [104, 115, 1.0], [116, 125, 1.0], [191, 202, 1.0]]}, "2021.ecir_conference-20212.26": {"snippet": "...tage Retrieval Pipeline\n\n\n Pre-trained deep language models (LM) have advanced the state-of-the-art of text retrieval. Rerankers fine-tuned from deep LM estimates candidate relevance based on rich con...", "weights": [[8, 17, 1.0], [111, 120, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2010.irfc_conference-2010.0": {"snippet": "...nary Retrieval, First Information Retrieval Facility Conference, IRFC 2010, Vienna, Austria, May 31, 2010. Proceedings", "weights": [[8, 17, 1.0], [25, 36, 1.0], [37, 46, 1.0]]}, "2018.ecir_conference-2018.0": {"snippet": "...s in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018, Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1991.sigirconf_conference-91.18": {"snippet": "...nced Retrieval From Heterogeneous Fact Databases: Integration of Data Retrieval, Conversion, Aggregation and Deductive Techniques\n\n\n Abstract: Modern distributed fact databases are heterogeneous and a", "weights": [[8, 17, 1.0], [73, 82, 1.0]]}, "2010.ecir_conference-2010.1": {"snippet": "...s in Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2006.ecir_conference-2006.0": {"snippet": "...s in Information Retrieval, 28th European Conference on IR Research, ECIR 2006, London, UK, April 10-12, 2006, Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.jasis_journal-ir0volumeA62A6.0": {"snippet": "...s in Information Science", "weights": [[8, 19, 1.0]]}, "1992.sigirconf_conference-92.3": {"snippet": "...of a Retrieval Process", "weights": [[8, 17, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2010.wwwconf_conference-2010.11": {"snippet": "...dard information retrieval (IR). Recently, there has been a great deal of research into developing advanced match ranking algorithms. However, no previous research has addressed the ad indexing proble...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.1": {"snippet": "...h on information retrieval \u2013 The potential for improvements in conventional Boolean retrieval systems", "weights": [[8, 19, 1.0], [20, 29, 1.0], [87, 96, 1.0]]}, "2003.ecir_conference-2003.0": {"snippet": "...s in Information Retrieval, 25th European Conference on IR Research, ECIR 2003, Pisa, Italy, April 14-16, 2003, Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2009.ecir_conference-2009.0": {"snippet": "...s in Information Retrieval, 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "2007.ipm_journal-ir0volumeA43A5.3": {"snippet": "...tent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, which basically used learning techniques for monolingua...", "weights": [[8, 17, 1.0], [125, 136, 1.0], [137, 146, 1.0]]}, "2014.trec_conference-2014.52": {"snippet": "... the advancements that have been made in the field of Information Retrieval, there are still so many challenges. These challenges are magnified when the information that is being retrieved is in a spe...", "weights": [[8, 20, 1.0], [57, 68, 1.0], [69, 78, 1.0], [156, 167, 1.0]]}, "2010.jasis_journal-ir0volumeA61A9.0": {"snippet": "...s in Information Science", "weights": [[8, 19, 1.0]]}, "2010.jasis_journal-ir0volumeA61A4.0": {"snippet": "...s in information science", "weights": [[8, 19, 1.0]]}, "1987.sigirjournals_journal-ir0volumeA21A34.1": {"snippet": "... for Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2010.ecir_conference-2010.0": {"snippet": "...s in Information Retrieval, 32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28-31, 2010. Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2021.ecir_conference-20212.0": {"snippet": "...s in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28 - April 1, 2021, Proceedings, Part II", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2005.ecir_conference-2005.0": {"snippet": "...s in Information Retrieval, 27th European Conference on IR Research, ECIR 2005, Santiago de Compostela, Spain, March 21-23, 2005, Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2014.ecir_conference-2014.0": {"snippet": "...s in Information Retrieval - 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014. Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1985.jasis_journal-ir0volumeA36A3.9": {"snippet": "...s in information retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2013.ijirr_journal-ir0volumeA3A3.0": {"snippet": "... the Information Retrieval in Mobile Learning", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2010.sigirconf_conference-2010.198": {"snippet": "... for information retrieval\n\n\n Abstract: This tutorial is concerned with a comprehensive introduction to the research area of learning to rank for information retrieval. In the first part of the tutori...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [149, 160, 1.0], [161, 170, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...n of Information Retrieval Models\n\n\n Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is nece...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [61, 70, 1.0], [118, 129, 1.0], [130, 139, 1.0]]}, "2012.sigirconf_conference-2012.221": {"snippet": "... for information retrieval\n\n\n ABSTRACTFor many information retrieval applications, we need to deal with the ranking problem on very large scale graphs. However, it is non-trivial to perform efficient ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0]]}, "H92-1040": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a fully automated Information Retrieval System which uses advanced natural language processing techniques to enhance the ...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "2017.sigirconf_conference-2017.68": {"snippet": "...ific Information in Music Retrieval\n\n\n ABSTRACTWith the advancement of mobile computing technology and cloud-based streaming music service, user-centered music retrieval has become increasingly import...", "weights": [[8, 19, 1.0], [29, 38, 1.0], [163, 172, 1.0]]}, "2000.jasis_journal-ir0volumeA51A2.0": {"snippet": "...ical information retrieval for advanced applications", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2007.clef_workshop-2007.0": {"snippet": "...odal Information Retrieval, 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007, Budapest, Hungary, September 19-21, 2007, Revised Selected Papers", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "W15-2618": {"snippet": "{NLP}{--}Based Readability Assessment of Health{--}Related Texts: a Case Study on {I}talian Informed Consent Forms\n\n\n The paper illustrates the results of a case study aimed at investigating and enhan...", "weights": []}, "2018.sigirconf_conference-2018.243": {"snippet": "...PTS\u2022 Information systems \u2192 Document representation; Information extraction; Specialized information retrieval; \nKEYWORDS\nRESEARCH DIRECTIONClinical Decision Support (CDS) systems are designed to assis...", "weights": [[8, 19, 1.0], [55, 66, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "1992.jasis_journal-ir0volumeA43A1.3": {"snippet": "...nced Retrieval Techniques in an Experimental Online Catalog", "weights": [[8, 17, 1.0]]}, "1999.ntcir_workshop-1999.2": {"snippet": "... for Information Retrieval via Key Concepts", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.ntcir_workshop-2011evia.3": {"snippet": "...uman information needs and digital information resources. The reliable evaluation of these technologies has been recognized for decades as central to the advancement of the field. As information retri...", "weights": [[8, 19, 1.0], [38, 49, 1.0], [186, 197, 1.0], [198, 207, 1.0]]}, "2010.airs_conference-2010.12": {"snippet": "... for Retrieval in Historic Documents", "weights": [[8, 17, 1.0]]}, "2014.tist_journal-ir0volumeA5A4.8": {"snippet": "Sponsored Search Auctions: Recent Advances and Future Directions\n\n\n Sponsored search has been proven to be a successful business model, and sponsored search auctions have become a hot research directi...", "weights": []}, "H90-1103": {"snippet": "Opportunities for Advanced Speech Processing in Military Computer-Based Systems*\n\n\n This paper presents a study of military applications of advanced speech processing technology which includes three m...", "weights": []}, "2020.ecir_conference-20202.0": {"snippet": "...s in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings, Part II", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2014.wsdm_conference-2014.75": {"snippet": "...heir information needs, rather than links to documents. Semantic search and its recent applications enabled search engines to organize their wealth of information around entities. Entity linking and r...", "weights": [[8, 19, 1.0], [154, 165, 1.0], [202, 211, 1.0]]}, "2020.ictir_conference-2020.29": {"snippet": "...g in information retrieval (IR), and propose a framework called PyTerrier that allows advanced retrieval pipelines to be expressed, and evaluated, in a declarative manner close to their conceptual des...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [98, 107, 1.0]]}, "2010.ntcir_workshop-2010evia.11": {"snippet": "...uman information needs and digital information resources. The reliable evaluation of these technologies has been recognized for decades as central to the advancement of the field. As information retri...", "weights": [[8, 19, 1.0], [38, 49, 1.0], [186, 197, 1.0], [198, 207, 1.0]]}, "2004.ecir_conference-2004.14": {"snippet": "...ease retrieval accuracy. For Text Categorization (TC) are available fewer and less definitive studies on the use of advanced document representations as it is a relatively new research area (compared ...", "weights": [[8, 17, 1.0]]}, "2018.sigirconf_conference-2018.222": {"snippet": "...s in information retrieval. Finally, we conclude this tutorial with a comprehensive summarization and a prospect of further research directions for GANs in information retrieval. \nKEYWORDS\nBACKGROUND ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [159, 170, 1.0], [171, 180, 1.0]]}, "2007.sigirconf_conference-2007.35": {"snippet": "...vant information and learn improved searching strategies.", "weights": [[8, 19, 1.0]]}, "2014.ntcir_workshop-2014evia.5": {"snippet": "...uman information needs and digital information resources. The reliable evaluation of these technologies has been recognized for decades as central to the advancement of the field. As information retri...", "weights": [[8, 19, 1.0], [38, 49, 1.0], [186, 197, 1.0], [198, 207, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "... for information retrieval\n\n\n ABSTRACTIn recent years, we have witnessed successful application of machine learning techniques to a wide range of information retrieval problems, including Web search e...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [149, 160, 1.0], [161, 170, 1.0]]}, "2017.ecir_conference-2017.0": {"snippet": "...s in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Aberdeen, UK, April 8-13, 2017, Proceedings", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.24": {"snippet": "...of a retrieval density function: perverse retrieval, perfect retrieval, random retrieval, normal retrieval, hereby extending results of Buckland and Gey and of Egghe in the following sense: mathematic...", "weights": [[8, 17, 1.0], [45, 54, 1.0], [64, 73, 1.0], [82, 91, 1.0], [100, 109, 1.0]]}, "2006.ecir_conference-2006.1": {"snippet": "...s in Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2008.sigirconf_conference-2008.140": {"snippet": "...ched information retrieval system for biology\n\n\n ABSTRACTKleio is an advanced information retrieval (IR) system developed at the UK National Centre for Text Mining (NaCTeM)1 . The system offers textua...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [81, 92, 1.0], [93, 102, 1.0]]}, "1992.sigirjournals_journal-ir0volumeA26A1.2": {"snippet": "...Text Retrieval System", "weights": [[8, 17, 1.0]]}, "1999.jasis_journal-ir0volumeA50A9.10": {"snippet": "...nced Information Visualization for Visual Information (Retrieval) Systems", "weights": [[8, 19, 1.0], [45, 56, 1.0], [58, 67, 1.0]]}, "2017.cikm_conference-2017.134": {"snippet": "...ited information of hardware specifications and external conditions, e.g., outdoor weather. In recent years, new generation of sensors is becoming essential part of new chiller plants, for the first t...", "weights": [[8, 19, 1.0]]}, "2016.ecir_conference-2016.0": {"snippet": "...s in Information Retrieval - 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20-23, 2016. 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These expectations can be based on the current information need, but are also influenced by how the searcher believes the search engine works, where relevant result...", "weights": [[8, 15, 1.0], [188, 196, 1.0], [197, 204, 1.0]]}, "W07-2402": {"snippet": "... and Machine Learning for Stylometrics and Stylogenetics\n\n\n Automatic Text Categorization, learning to assign documents to specific categories (e.g. in topic assignment or spam filtering), has been an...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [94, 102, 1.0]]}, "2013.wsdm_conference-2013.53": {"snippet": "...long machine learning and transfer learning", "weights": [[8, 15, 1.0], [16, 24, 1.0], [38, 46, 1.0]]}, "2019.wsdm_conference-2019.5": {"snippet": "...dard machine learning has helped us a lot in the singleturn paradigm, where we use it to predict: intent, relevance, user satisfaction, etc. When we think of search or recommendation as a series of ex...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2001.trec_conference-2001.32": {"snippet": "... Our results show that about 84% of the testing queries had the correct homepage returned within the top 10 pages. This shows that our machine learning approaches are effective since without any machi...", "weights": [[8, 15, 1.0], [138, 145, 1.0], [146, 154, 1.0], [198, 205, 1.0]]}, "2010.wsdm_conference-2010.37": {"snippet": "...be a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model to learn from past user clicks ...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2005.cikm_conference-2005.3": {"snippet": "...e of machine learning and statistical modeling techniques. 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This report describes the track, presents some evaluation results, and provides a general comme...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [166, 173, 1.0]]}, "2014.trec_conference-2014.18": {"snippet": "...ding relevant inforamtion using pseudorelevance feedback and Google search engine results) to retrieve more relevant tweets, then extracted several effective semantic features (e.g., Jansen-Shannon Di...", "weights": [[8, 16, 1.0], [85, 92, 1.0], [111, 119, 1.0]]}, "2019.tist_journal-ir0volumeA10A1.6": {"snippet": "Learning Facial Expressions with 3D Mesh Convolutional Neural Network\n\n\n Making machines understand human expressions enables various useful applications in human-machine interaction. In this article,...", "weights": [[0, 8, 1.0], [163, 170, 1.0]]}, "2018.sigirconf_conference-2018.80": {"snippet": "...arch results and the probability of user satisfaction, from mobile click logs. With this information, we can estimate the quality of different vertical results and improve the ranking of heterogeneous...", "weights": [[8, 15, 1.0], [155, 162, 1.0]]}, "D14-1068": {"snippet": "...ffer relevant results. We present a machine learning approach to correcting these errors, based largely on character-level n-gram features. We demonstrate superior performance over rule-based methods,...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [39, 46, 1.0], [47, 55, 1.0]]}, "2005.cikm_conference-2005.105": {"snippet": "...some relevant pages are not retrieved. More relevant pages can be obtained by adding the active ingredient of the drug to the query. In order to eliminate irrelevant pages, we propose a machine learni...", "weights": [[8, 16, 1.0], [47, 55, 1.0], [189, 196, 1.0], [197, 205, 1.0]]}, "2017.wsdm_conference-2017.57": {"snippet": "Machine Learning at Amazon\n\n\n ABSTRACTIn this talk I will give an introduction into the field of machine learning and discuss why it is a crucial technology for Amazon.Machine learning is the science ...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [97, 104, 1.0], [105, 113, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "1999.trec_conference-1999.68": {"snippet": "...onal machine learning tasks where the system begins with a large sample of evaluated training documents. This report describes the track, presents the evaluation results in graphical format, and provi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [165, 172, 1.0]]}, "N06-1051": {"snippet": "... how machine learning techniques can fit this task. This leads to smaller pools than traditional round robin pooling, thus reduces significantly the manual assessment workload. Experimental results on...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [193, 200, 1.0]]}, "2022.hcinlp-1.6": {"snippet": "...tive machine learning method, machine teaching (MT), for the text-based emotion prediction task. We compare this method experimentally against a more well-studied technique, active learning (AL). Resu...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [33, 40, 1.0], [184, 192, 1.0], [199, 206, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.3": {"snippet": "... are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling metho...", "weights": [[8, 16, 1.0], [38, 46, 1.0]]}, "2017.ipm_journal-ir0volumeA53A4.5": {"snippet": "...nt a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [187, 194, 1.0], [195, 203, 1.0]]}, "2018.sigirconf_workshop-2018profs.3": {"snippet": "...tive Learning (CAL) is a technique where a human reviewer works in loop with a machine learning model; the model presents a set of documents likely to be relevant and the reviewer provides relevance f...", "weights": [[8, 16, 1.0], [82, 89, 1.0], [90, 98, 1.0], [157, 165, 1.0]]}, "2011.cikm_conference-2011.248": {"snippet": "...o be relevant; if the top-ranked document is deemed most relevant, similar documents are also likely to be relevant, discouraging diversity. At the other extreme, n = 1 encourages diversity since only...", "weights": [[8, 16, 1.0], [60, 68, 1.0], [110, 118, 1.0]]}, "2009.cikm_conference-2009.190": {"snippet": "...arch results play a crucial role in guiding a user's search process. We present a machine-learned algorithm that trains a boosted tree to pick the most relevant title for a Web search result. We compa...", "weights": [[8, 15, 1.0], [85, 92, 1.0], [155, 163, 1.0]]}, "2015.sigirconf_conference-2015.13": {"snippet": "Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Measures", "weights": [[0, 8, 1.0]]}, "2010.cikm_conference-2010.120": {"snippet": "...es a machine learning algorithm to classify and select live web feeds, and generates\"previews\" of new web pages based on the link descriptions available in these feeds. The search engine can then inde...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A5.3": {"snippet": "...ised learning to rank with pseudo labels\n\n\n Abstract Semi-supervised learning is a machine learning paradigm that can be applied to create pseudo labels from unlabeled data for learning a ranking mode...", "weights": [[8, 16, 1.0], [72, 80, 1.0], [86, 93, 1.0], [94, 102, 1.0], [180, 188, 1.0]]}, "W01-1603": {"snippet": "...of a Machine Learnable Discourse Tagging Tool\n\n\n We have developed a discourse level tagging tool for spoken dialogue corpus using machine learning methods. As discourse level information, we focused ...", "weights": [[8, 15, 1.0], [134, 141, 1.0], [142, 150, 1.0]]}, "2012.sigirconf_conference-2012.192": {"snippet": "...anic results such as ads, embedded image results, news or local results, etc. -thereby recreating a realistic search experience for the subjects. Paid annotators rated every search result for relevanc...", "weights": [[8, 15, 1.0], [44, 51, 1.0], [67, 74, 1.0]]}, "1998.jasis_journal-ir0volumeA49A8.2": {"snippet": "A Machine Learning Approach to Inductive Query by Examples: An Experiment Using Relevance Feedback, ID3, Genetic Algorithms, and Simulated Annealing", "weights": [[2, 9, 1.0], [10, 18, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...Each machine finds the set of results in its sub-index that satisfy the query, scores these results using locally available features, and sends the top-k results to a result aggregation machine, which...", "weights": [[8, 15, 1.0], [33, 40, 1.0], [95, 102, 1.0], [157, 164, 1.0], [189, 196, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.7": {"snippet": "...s to machine learning", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2007.sigirconf_conference-2007.39": {"snippet": "... for learning ranking functions for improving relevance of search engines serving diverse streams of user queries. We explore supervised learning methodology from machine learning, and we distinguish ...", "weights": [[8, 16, 1.0], [140, 148, 1.0], [166, 173, 1.0], [174, 182, 1.0]]}, "2017.ictir_workshop-2017learner.8": {"snippet": "...s on Learning to Rank\n\n\n Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of documents an...", "weights": [[8, 16, 1.0], [28, 36, 1.0], [71, 78, 1.0], [79, 87, 1.0]]}, "N13-2003": {"snippet": "A Machine Learning Approach to Automatic Term Extraction using a Rich Feature Set\n\n\n In this paper we propose an automatic term extraction approach that uses machine learning incorporating varied and ...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [158, 165, 1.0], [166, 174, 1.0]]}, "2012.sigirconf_conference-2012.180": {"snippet": "...emed relevant iff its subtopic ti matches query subtopic t:We next define R k = P k i=1 ri, where R k is the number of relevant items from the first k selections. Reading R k \u2265 n as I[R k \u2265 n], we exp...", "weights": [[8, 16, 1.0], [122, 130, 1.0]]}, "2010.wsdm_conference-2010.16": {"snippet": "...rch, machine learning, and information retrieval. Recent work on ranking focused on a number of different paradigms, namely, pointwise, pairwise, and list-wise approaches. Each of those paradigms focu...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2010.cikm_conference-2010.288": {"snippet": "...ovel machine learning approach for regression based on the idea of combining bagging and boosting that we call BagBoo. Our BagBoo model borrows its high accuracy potential from Friedman's gradient boo...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2021.ipm_journal-ir0volumeA58A4.9": {"snippet": "A machine learning, bias-free approach for predicting business success using Crunchbase data", "weights": [[2, 9, 1.0], [10, 18, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "... are relevant; the coverage of a system is measured by recall, the proportion of relevant documents that are retrieved.) A second system configuration is used to produce a second set of retrieval resu...", "weights": [[8, 16, 1.0], [84, 92, 1.0], [199, 206, 1.0]]}, "2012.amta-papers.12": {"snippet": "...ated Results on Translingual Information Retrieval\n\n\n In an ideal cross-lingual information retrieval (CLIR) system, a user query would generate a search over documents in a different language and the...", "weights": [[8, 15, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "...arch results. Even with state-of-the-art search engines users are often dissatisfied with the returned results and have to manually alter their query in order to retrieve relevant documents. Despite t...", "weights": [[8, 15, 1.0], [106, 113, 1.0], [174, 182, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.10": {"snippet": "...onal machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing import...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [149, 156, 1.0], [157, 165, 1.0]]}, "2007.sigirconf_conference-2007.13": {"snippet": "...ndem learning using an \"oracle\" results in much better performance than learning on only features or only instances. We find that humans can emulate the oracle to an extent that results in performance...", "weights": [[8, 16, 1.0], [35, 42, 1.0], [75, 83, 1.0], [181, 188, 1.0]]}, "2016.jasis_journal-ir0volumeA67A9.5": {"snippet": "A machine-learning approach to negation and speculation detection for sentiment analysis", "weights": [[2, 9, 1.0], [10, 18, 1.0]]}, "2008.sigirconf_conference-2008.37": {"snippet": "...arch results. The problem has been the focus of much of the research at the intersection of Web search and machine learning, and learning ranking functions from preference data in particular has recen...", "weights": [[8, 15, 1.0], [110, 117, 1.0], [118, 126, 1.0], [132, 140, 1.0]]}, "2020.acl-main.689": {"snippet": "Learning a Multi-Domain Curriculum for Neural Machine Translation\n\n\n Most data selection research in machine translation focuses on improving a single domain. We perform data selection for multiple do...", "weights": [[0, 8, 1.0], [46, 53, 1.0], [101, 108, 1.0]]}, "W00-1316": {"snippet": "A Machine Learning Approach to Answering Questions for Reading Comprehension Tests\n\n\n In this paper, we report results on answering questions for the reading comprehension task, using a machine learni...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [111, 118, 1.0], [186, 193, 1.0], [194, 202, 1.0]]}, "2020.cikm_conference-2020.489": {"snippet": "...able Machine Learning and Artificial Intelligence\n\n\n Searching on the Web has become an essential part of learning, and bookmarking is a way to remember relevant and interesting sites. Even though boo...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [109, 117, 1.0], [156, 164, 1.0]]}, "2020.wsdm_conference-2020.110": {"snippet": "...ible Machine Learning and Knowledge Discovery Boosted by Visual Means\n\n\n Intelligible machine learning and knowledge discovery are important for modeling individual and social behavior, user activity,...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [89, 96, 1.0], [97, 105, 1.0]]}, "2019.wsdm_conference-2019.114": {"snippet": "...s in machine learning systems. We will motivate the need for adopting a \"fairness-first\" approach (as opposed to viewing algorithmic bias / fairness considerations as an afterthought), when developing...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2018.cikm_conference-2018.299": {"snippet": "...se a machine learned model with words as features and target clicks or relevance labels. Contrary to these approaches, we build a parameterized model to determine the weights of popular title terms fo...", "weights": [[8, 15, 1.0]]}, "2011.wsdm_conference-2011.79": {"snippet": "...ical results within blocks of textual web results challenging: how do we represent, assess, and compare the relevance of these heterogeneous entities?In this paper we present a machine-learning framew...", "weights": [[8, 15, 1.0], [45, 52, 1.0], [180, 187, 1.0], [188, 196, 1.0]]}, "2016.trec_conference-2016.26": {"snippet": "...of a Machine Learning Method to Rank PubMed Central Articles For Clinical Relevancy: NCH at TREC 2016 Clinical Decision Support Track", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2013.ntcir_conference-2013.2": {"snippet": "...step Machine Learning Approach to Link Detection in Wikipedia: NTCIR Crosslink-2 Experiments at KSLP\n\n\n This study describes a link detection method to find relevant cross-lingual links from Korean Wi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [160, 168, 1.0]]}, "2012.sigirconf_conference-2012.5": {"snippet": "...se a machine learning approach to learn the probability that a user may find a follow-up query both useful and relevant, given his initial query. Our approach is based on a machine learning model whic...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [114, 122, 1.0], [176, 183, 1.0], [184, 192, 1.0]]}, "2016.sigirconf_workshop-2016sal.9": {"snippet": "...arch Results for Educational Goals: Incorporating Keyword Density as a Retrieval Objective\n\n\n While past research has shown that learning outcomes can be influenced by the amount of e\u21b5ort students inv...", "weights": [[8, 15, 1.0], [132, 140, 1.0]]}, "2018.sigirconf_conference-2018.219": {"snippet": "...sly, machine learning methods have been exploited to address the problem, which learns a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning h...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [150, 158, 1.0], [193, 201, 1.0]]}, "2018.ecir_conference-2018.17": {"snippet": "...arch results by either the explicit relevance annotations from external assessors or implicit behavior signals from users. Because the mobile search is different from the desktop search in terms of th...", "weights": [[8, 15, 1.0]]}, "2018.cikm_conference-2018.68": {"snippet": "...task Learning of Information Retrieval and Reading Comprehension\n\n\n ABSTRACTThis study considers the task of machine reading at scale (MRS) wherein, given a question, a system first performs the infor...", "weights": [[8, 16, 1.0], [112, 119, 1.0]]}, "2011.iwslt-evaluation.20": {"snippet": "... for machine translation\n\n\n We discuss learning latent annotations for synchronous context-free grammars (SCFG) for the purpose of improving machine translation. We show that learning annotations for ...", "weights": [[8, 15, 1.0], [42, 50, 1.0], [144, 151, 1.0], [178, 186, 1.0]]}, "2007.ipm_journal-ir0volumeA43A4.3": {"snippet": "...r of relevant documents. The proposed method achieved 131%, 34%, 12% improvements in micro-averaged F 1 for 25, 46, and 58 topics with less than 10, 30, and 50 relevant documents in learning, respecti...", "weights": [[8, 16, 1.0], [163, 171, 1.0], [185, 193, 1.0]]}, "2011.sigirconf_conference-2011.12": {"snippet": "Learning to rank for freshness and relevance\n\n\n ABSTRACTFreshness of results is important in modern web search. Failing to recognize the temporal aspect of a query can negatively affect the user exper...", "weights": [[0, 8, 1.0], [69, 76, 1.0]]}, "2018.wwwconf_conference-2018c.298": {"snippet": "...I, a learning search engine for largescale structured data that uses automatically generated machine learning classifiers and Unified Famous Objects (UFOs)  to return the most relevant search results ...", "weights": [[8, 16, 1.0], [96, 103, 1.0], [104, 112, 1.0], [179, 187, 1.0], [195, 202, 1.0]]}, "2011.tist_journal-ir0volumeA2A3.7": {"snippet": "...cale machine learning\n\n\n With the dramatic increase in computing power, the field of machine learning has undergone tremendous changes in recent years. This change can be attributed to a deeper unders...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [88, 95, 1.0], [96, 104, 1.0]]}, "2012.wsdm_conference-2012.45": {"snippet": "...arch results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms using either direct human relevance judgments or indirect judgments obtai...", "weights": [[8, 15, 1.0], [102, 109, 1.0], [110, 118, 1.0]]}, "W05-0406": {"snippet": "...nt a machine learning system for identifying non-referential it. Types of non-referential it are examined to determine relevant linguistic patterns. The patterns are incorporated as features in a mach...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [122, 130, 1.0], [199, 206, 1.0]]}, "2010.wsdm_conference-2010.19": {"snippet": "...This results in a model based on intrinsic relevance, as opposed to perceived relevance. We use the model to predict document relevance and then use this as feature for a \"Learning to Rank\" machine le...", "weights": [[8, 15, 1.0], [175, 183, 1.0], [193, 200, 1.0], [201, 209, 1.0]]}, "1992.trec_conference-1992.31": {"snippet": "Machine Learning and Relevance Feedback", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2010.cikm_conference-2010.163": {"snippet": "...arch results. We propose a machine learning approach to adaptively optimizing the diversification of search results for each query so as to optimize the overall utility in an entire session. Experimen...", "weights": [[8, 15, 1.0], [30, 37, 1.0], [38, 46, 1.0], [111, 118, 1.0]]}, "2016.cikm_conference-2016.93": {"snippet": "...aged machine learning to ameliorate these weaknesses, and in the process created novel experiences. In this talk, I will give three examples of machine learning improving the email experience.The firs...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [147, 154, 1.0], [155, 163, 1.0]]}, "P19-1488": {"snippet": "...y of machine learning (ML) models becomes more relevant with their increasing adoption. In this work, we address the interpretability of ML based question answering (QA) models on a combination of kno...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [50, 58, 1.0]]}, "1997.sigirconf_conference-97.4": {"snippet": "... non-relevant corpus. Results show that simple strategies to select non-relevant articles for learning the feedback queries yield significantly better feedback queries. The rest of this study ia organ...", "weights": [[8, 16, 1.0], [25, 32, 1.0], [75, 83, 1.0], [97, 105, 1.0]]}, "2020.emnlp-main.692": {"snippet": "...from Machine Learning Papers\n\n\n Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. In this paper, we present AXCELL, an automati...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [56, 63, 1.0], [64, 72, 1.0]]}, "2011.wwwconf_conference-2011.40": {"snippet": "Learning to rank with multiple objective functions\n\n\n ABSTRACTWe investigate the problem of learning to rank for document retrieval from the perspective of learning with multiple objective functions. ...", "weights": [[0, 8, 1.0], [92, 100, 1.0], [156, 164, 1.0]]}, "2014.sigirconf_conference-2014.17": {"snippet": "...tive learning, require substantially and significantly less human review effort (P < 0.01) to achieve any given level of recall, than passive learning, in which the machine-learning algorithm plays no...", "weights": [[8, 16, 1.0], [145, 153, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "I08-1068": {"snippet": "...that learning these user profiles using snippets surrounding the results for a query gives better performance than learning from entire document collection.", "weights": [[8, 16, 1.0], [68, 75, 1.0], [118, 126, 1.0]]}, "2019.cikm_conference-2019.385": {"snippet": "Machine Learning on Graphs with Kernels", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2015.wwwconf_conference-2015c.287": {"snippet": "...sing results with regards to the baseline according to all used metrics. By this paper, we wish to provide a novel viewpoint to address the BR relevance problem, different from the dominating machine ", "weights": [[8, 15, 1.0], [195, 202, 1.0]]}, "2012.tois_journal-ir0volumeA30A4.0": {"snippet": "...line Learning Framework for Refining Recency Search Results with User Click Feedback\n\n\n Traditional machine-learned ranking systems for Web search are often trained to capture stationary relevance of ...", "weights": [[8, 16, 1.0], [55, 62, 1.0], [103, 110, 1.0]]}, "2009.wwwconf_conference-2009.1": {"snippet": "...n of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can also provide an important source of implic...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2015.tist_journal-ir0volumeA7A1.0": {"snippet": "Learning to Rank from Noisy Data\n\n\n Learning to rank, which learns the ranking function from training data, has become an emerging research area in information retrieval and machine learning. Most exi...", "weights": [[0, 8, 1.0], [36, 44, 1.0], [174, 181, 1.0], [182, 190, 1.0]]}, "2010.cikm_conference-2010.189": {"snippet": "...line learning for recency search ranking using real-time user feedback\n\n\n ABSTRACTTraditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance...", "weights": [[8, 16, 1.0], [97, 104, 1.0]]}, "W97-1002": {"snippet": "...t of relevant items. Given the recent success of empirical or corpusbased approaches in other areas of natural language processing, machine learning has the potential to significantly aid the developm...", "weights": [[8, 16, 1.0], [135, 142, 1.0], [143, 151, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.1": {"snippet": "...arch results to users. Instead, they rely on the user of the system to estimate which items are relevant, partially relevant, or not relevant. The user of the system is given the task of distinguishin...", "weights": [[8, 15, 1.0], [99, 107, 1.0], [119, 127, 1.0], [136, 144, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "...asic machine learning concepts and tools; in the second session, we will introduce more advanced topics in machine learning, and will present recent developments in machine learning and its applicatio...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [110, 117, 1.0], [118, 126, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "2017.wwwconf_conference-2017c.159": {"snippet": "...uted Machine Learning: Foundations, Trends, and Practices\n\n\n ABSTRACTIn recent years, artificial intelligence has achieved great success in many important applications. Both novel machine learning alg...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [183, 190, 1.0], [191, 199, 1.0]]}, "2016.airs_conference-2016.27": {"snippet": "...ovel machine learning framework for ranking, which depends on two new samples. The first sample is one-group constructed of one document with higher relevance judgment and a group of documents with lo...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2021.semeval-1.157": {"snippet": "...onal Machine Learning for Humorous and Offensive Tasks enough in 2021?\n\n\n This paper describes the approach that was developed for SemEval 2021 Task 7 Hahackathon: Incorporating Demographic Factors in...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2019.tist_journal-ir0volumeA10A2.1": {"snippet": "...shot learning. First of all, we provide an overview of zero-shot learning. According to the data utilized in model optimization, we classify zero-shot learning into three learning settings. Second, we...", "weights": [[8, 16, 1.0], [68, 76, 1.0], [154, 162, 1.0], [174, 182, 1.0]]}, "L00-1159": {"snippet": "...sing Machine Learning Methods to Improve Quality of Tagged Corpora and Learning Models", "weights": [[8, 15, 1.0], [16, 24, 1.0], [74, 82, 1.0]]}}}
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Web crawling: a survey\n\n\n Abstract Deep Web crawling refers to the problem of traversing the collection of pages in a deep Web site, which are dynamically generated in response to a particular query ...", "weights": [[8, 16, 1.0], [48, 56, 1.0]]}, "2014.cikm_conference-2014.104": {"snippet": "...m of crawling structured data embedded inside HTML pages. We describe Anthelion, the first focused crawler addressing this task. We propose new methods of focused crawling specifically designed for co...", "weights": [[8, 16, 1.0], [166, 174, 1.0]]}, "2006.wwwconf_conference-2006.195": {"snippet": "...cale crawling through website analysis\n\n\n ABSTRACTThe web crawler space is often delimited into two general areas: full-web crawling and focused crawling. We present netSifter, a crawler system which ...", "weights": [[8, 16, 1.0], [127, 135, 1.0], [148, 156, 1.0]]}, "L18-1599": {"snippet": "... Web crawling is an efficient way for compiling the monolingual, parallel and/or domain-specific corpora needed for machine translation and other HLT applications. These corpora can be automatically p...", "weights": [[8, 16, 1.0], [119, 126, 1.0]]}, "2013.wwwconf_conference-2013.41": {"snippet": "... al. using on-page and anchor text features . Tang, et al. studied homepage acquisition from search engine results using researcher names as queries . 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Sin...", "weights": [[8, 16, 1.0], [75, 83, 1.0], [125, 130, 1.0], [131, 139, 1.0]]}, "2001.tois_journal-ir0volumeA19A3.3": {"snippet": "...used crawling which is able to crawl particular topical portions of the World Wide Web quickly, without having to explore all web pages. In this paper, we propose the novel concept of intelligent craw...", "weights": [[8, 16, 1.0], [199, 207, 1.0]]}, "2020.wwwconf_conference-2020.37": {"snippet": "... for Crawling Commercial Web Content\n\n\n The rapid growth of commercial web content has driven the development of shopping search services to help users find product offers. Due to the dynamic nature o...", "weights": [[8, 16, 1.0]]}, "2012.wwwconf_conference-2012c.4": {"snippet": "...orum crawling problem to a URL type recognition problem and show how to learn accurate and effective regular expression patterns of implicit navigation paths from an automatically created training set...", "weights": [[8, 16, 1.0]]}, "2020.wwwconf_conference-2020.16": {"snippet": "...ffic websites and actual browsing behaviour measured from an opt-in sample of over 50,000 users of the Firefox Web browser. Our analysis reveals differences between the treatment of stateless crawling...", "weights": [[8, 16, 1.0], [195, 203, 1.0]]}, "2005.tois_journal-ir0volumeA23A4.2": {"snippet": "Learning to crawl: Comparing classification schemes\n\n\n Topical crawling is a young and creative area of research that holds the promise of benefiting from several sophisticated data mining techniques....", "weights": [[0, 8, 1.0], [63, 71, 1.0]]}, "2020.lrec-1.455": {"snippet": "...ural Machine Translation Quality?\n\n\n We introduce a machine translation dataset for three pairs of languages in the legal domain with post-edited high-quality neural machine translation and independen...", "weights": [[8, 15, 1.0], [55, 62, 1.0], [169, 176, 1.0]]}, "2010.tois_journal-ir0volumeA28A4.0": {"snippet": "... web crawling\n\n\n When crawling resources, for example, number of machines, crawl-time, and so on, are limited, so a crawler has to decide an optimal order in which to crawl and recrawl Web pages. Idea...", "weights": [[8, 16, 1.0], [25, 33, 1.0]]}, "2007.wwwconf_conference-2007.46": {"snippet": "...es a crawling, i.e. builds and crawls the graph at the same time. The graphs generated have lot of known properties of Web crawls. Our model is simpler than most random Web graph models, but captures ...", "weights": [[8, 16, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A5.3": {"snippet": "...ised learning to rank with pseudo labels\n\n\n Abstract Semi-supervised learning is a machine learning paradigm that can be applied to create pseudo labels from unlabeled data for learning a ranking mode...", "weights": [[8, 16, 1.0], [72, 80, 1.0], [86, 93, 1.0], [94, 102, 1.0], [180, 188, 1.0]]}, "2010.wwwconf_conference-2010.62": {"snippet": "...h to learning URL normalization rules\n\n\n ABSTRACTDuplicate URLs have brought serious troubles to the whole pipeline of a search engine, from crawling, indexing, to result serving. 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We choose four English-Portuguese corpora ...", "weights": [[8, 16, 1.0], [132, 139, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.7": {"snippet": "...s to machine learning", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "...asic machine learning concepts and tools; in the second session, we will introduce more advanced topics in machine learning, and will present recent developments in machine learning and its applicatio...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [110, 117, 1.0], [118, 126, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "2019.tweb_journal-ir0volumeA13A1.0": {"snippet": "...ing, machine learning, and natural language processing to interpret privacy policies at scale, thus producing annotations for use in interfaces that inform Internet users of salient policy details. 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We have experimented with several...", "weights": [[8, 13, 1.0], [135, 140, 1.0], [141, 148, 1.0], [149, 157, 1.0]]}, "2000.ir_journal-ir0volumeA3A2.2": {"snippet": "...with Machine Learning", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2011.eamt-1.40": {"snippet": "...ards Using Web-Crawled Data for Domain Adaptation in Statistical Machine Translation\n\n\n This paper reports on the ongoing work focused on domain adaptation of statistical machine translation using dom...", "weights": [[8, 13, 1.0], [68, 75, 1.0], [174, 181, 1.0], [194, 199, 1.0]]}, "2011.wwwconf_conference-2011.23": {"snippet": "...ures using machine-learning techniques applied to labeled datasets.To demonstrate the effectiveness and efficiency of Prophiler, we crawled and collected millions of pages, which we analyzed for malic...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 30, 1.0]]}, "2013.wwwconf_conference-2013.49": {"snippet": "...rize websites in terms of the semantics of the queries that lead to them by linking queries to large knowledge bases on the Web. 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Learning the models jointly with the proposed method is very useful in two ways:(1) learning a local model for a website can be effectively influenced by labeled and unlabeled ...", "weights": [[8, 16, 1.0], [27, 35, 1.0], [111, 119, 1.0]]}, "2013.cikm_conference-2013.354": {"snippet": "...gent crawling and archiving of web applications\n\n\n ABSTRACTWe demonstrate here a new approach to Web archival crawling, based on an application-aware helper that drives crawls of Web applications acco...", "weights": [[8, 16, 1.0], [113, 121, 1.0]]}, "L08-1442": {"snippet": "...ries Using Pattern-Based and Machine Learning Techniques", "weights": [[8, 13, 1.0], [32, 39, 1.0], [40, 48, 1.0]]}, "2008.airs_conference-2008.26": {"snippet": "...used Crawling Based on Ontology", "weights": [[8, 16, 1.0]]}, "2010.cikm_conference-2010.192": {"snippet": "...used crawling using navigational rank\n\n\n ABSTRACTThe goal of focused crawling is to use limited resources to effectively discover web pages related to a specific topic rather than downloading all acce...", "weights": [[8, 16, 1.0], [17, 22, 1.0], [72, 80, 1.0]]}, "2013.cikm_conference-2013.177": {"snippet": "...date websites from initial seed objects. 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In 1996, at the first of a series of workshops on collaborative filtering, it first ...", "weights": [[8, 13, 1.0], [112, 117, 1.0]]}, "2018.tist_journal-ir0volumeA9A6.0": {"snippet": "...fies users' personal interests, temporal cyclic patterns, and the sequential influence of recently visited locations in a principled manner.In light of the above challenges, we propose a Temporal Pers...", "weights": [[8, 13, 1.0], [80, 89, 1.0]]}, "2017.ipm_journal-ir0volumeA53A5.11": {"snippet": "...sing users' community membership as a source of personalized recommendations for individual users. The first part of the paper focuses on feasibility. It attempts to assess to what extent the interest...", "weights": [[8, 13, 1.0], [95, 100, 1.0]]}, "2011.wwwconf_conference-2011c.51": {"snippet": "...over users' interests using Latent Dirichlet Allocation (LDA), a linguistic topic model that represents users as mixtures of topics. 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There are several debiasing me...", "weights": [[8, 13, 1.0], [83, 88, 1.0]]}, "2020.ipm_journal-ir0volumeA57A2.10": {"snippet": "...cial influence in recommender systems: A study of bribery in collaborative hybrid algorithms", "weights": [[8, 17, 1.0]]}, "2008.tweb_journal-ir0volumeA2A3.1": {"snippet": "...many users over an extended time period, and their collective behavior provides a basis for computing source authority. They are drawn from the end of users' postquery browse trails where users may ce...", "weights": [[8, 13, 1.0], [154, 159, 1.0], [191, 196, 1.0]]}, "2013.riao_conference-2013.23": {"snippet": "Exploiting the diversity of user preferences for recommendation", "weights": []}, "2020.wsdm_conference-2020.75": {"snippet": "...nary users and items, ignoring the fact that many real-world communities are notably influenced by a small group of key opinion leaders, whose feedback on items wields outsize influence. With importan...", "weights": [[8, 13, 1.0], [179, 188, 1.0]]}, "2018.wwwconf_conference-2018.80": {"snippet": "...TWeb users are increasingly turning to ad blockers to avoid ads, which are often perceived as annoying or an invasion of privacy. While there has been significant research into the factors driving ad ...", "weights": [[8, 13, 1.0]]}, "2018.cikm_conference-2018.98": {"snippet": "...cial influence on users' exposure, i.e. social knowledge influence and social consumption influence, into basic EXMF model for better recommendation performance. Furthermore, SoEXBMF uses Bernoulli di...", "weights": [[8, 17, 1.0], [21, 26, 1.0], [60, 69, 1.0], [93, 102, 1.0]]}, "2020.sigirconf_conference-2020.337": {"snippet": "...fits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper items based on user interests. However, significant effo...", "weights": [[8, 13, 1.0], [129, 134, 1.0]]}, "2002.cikm_conference-2002.7": {"snippet": "...help users find and evaluate items of interest. They do so by connecting users with information regarding the content of recommended items or the opinions of other individuals. Such systems have becom...", "weights": [[8, 13, 1.0], [76, 81, 1.0]]}, "2013.wwwconf_conference-2013.98": {"snippet": "... 237 users, in which we show explanations with varying levels of social information and analyze their effect on users' decisions. We distinguish between two key decisions: the likelihood of checking o...", "weights": [[8, 13, 1.0], [115, 120, 1.0]]}, "2021.wsdm_conference-2021.143": {"snippet": "...what users want beyond what users like, which can better capture dynamic user intention and have attracted a surge of interest. However, user intention modeling is non-trivial because it is generally ...", "weights": [[8, 13, 1.0], [31, 36, 1.0]]}, "2014.tois_journal-ir0volumeA32A4.1": {"snippet": "...ween users. For instance, in Epinions, the concepts of personal \"web of trust\" and personal \"block list\" allow users to categorize their friends based on the quality of reviews into trusted and distru...", "weights": [[8, 13, 1.0], [114, 119, 1.0]]}, "2017.wwwconf_conference-2017.164": {"snippet": "...wing influence of social networks to our daily life. The intuition of social recommendation is that users tend to show affinities with items favored by their social ties due to social influence. Despi...", "weights": [[8, 17, 1.0], [103, 108, 1.0], [187, 196, 1.0]]}, "2013.cikm_conference-2013.77": {"snippet": "...ilar users is important for in-town users while social influence becomes more important for out-of-town users.", "weights": [[8, 13, 1.0], [39, 44, 1.0], [58, 67, 1.0], [107, 112, 1.0]]}, "2011.sigirconf_conference-2011.35": {"snippet": "...cial influence and geographical influence for POI recommendations. In addition to deriving user preference based on user-based collaborative filtering and exploring social influence from friends, we p...", "weights": [[8, 17, 1.0], [35, 44, 1.0], [175, 184, 1.0]]}, "2020.ecir_conference-20201.14": {"snippet": "...ical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobilit...", "weights": [[8, 17, 1.0], [66, 71, 1.0]]}, "2015.airs_conference-2015.9": {"snippet": "... for users to be completely free of others' influences and thus to some extent their opinions become affected and biased. In the field of recommender systems, prior research on biased opinions has att...", "weights": [[8, 13, 1.0]]}, "2018.chiir_conference-2018.62": {"snippet": "...e. e users' subjective evaluations show significant improvement in many metrics. I further show that the users explored a diverse set of recommended items while experiencing an increase in overall use...", "weights": [[8, 13, 1.0], [108, 113, 1.0]]}, "2016.wwwconf_conference-2016.102": {"snippet": "...work recommenders, the system relies on a \"friend-of-friend\"-style algorithm, which we show generally results in users being recommended proportional to their degree. Second, we find that the baseline...", "weights": [[8, 20, 1.0], [116, 121, 1.0]]}, "2014.wwwconf_conference-2014c.235": {"snippet": "...ased recommenders\n\n\n ABSTRACTCritiquing based recommenders are very commonly used to help users navigate through the product space to find the required product by tweaking/critiquing one or more featu...", "weights": [[8, 20, 1.0], [49, 61, 1.0], [93, 98, 1.0]]}, "2017.jasis_journal-ir0volumeA68A3.1": {"snippet": "...cial influence", "weights": [[8, 17, 1.0]]}, "2011.wsdm_conference-2011.57": {"snippet": "...cial influence. However, we also discover that this does not hold true across all trusted users. A trust relation does not guarantee that two users have similar preferences, implying that personalised...", "weights": [[8, 17, 1.0], [93, 98, 1.0], [145, 150, 1.0]]}, "2017.wwwconf_conference-2017c.241": {"snippet": "...lly, users' desktop context has been modeled on data about the users' browsing and searching activities. With the shift towards mobile information access, recommendations need to be delivered to users...", "weights": [[8, 13, 1.0], [66, 71, 1.0], [198, 203, 1.0]]}, "2016.wwwconf_conference-2016.11": {"snippet": "...ting influence on recommenders in generating conversion.We first confirm that the use of recommenders increases the baseline conversion rate by 5.9%. We find that the recommenders act as substitutes f...", "weights": [[8, 17, 1.0], [21, 33, 1.0], [92, 104, 1.0], [170, 182, 1.0]]}, "2013.cikm_workshop-2013ueo.2": {"snippet": "...ween users and books, and a user-follower network. We first investigate in general, if one's recommendations have impacts on her followers' decisions. We then analyze the correlation between one's inf...", "weights": [[8, 13, 1.0], [200, 209, 1.0]]}, "2009.sigirconf_conference-2009.27": {"snippet": "...with users who have made very few ratings.(2) The traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections among u...", "weights": [[8, 13, 1.0], [105, 110, 1.0], [202, 207, 1.0]]}, "2016.tist_journal-ir0volumeA7A3.16": {"snippet": "...s of users. It is found that in contradiction to common assumptions, not all users suffer as expected from the data sparsity problem. In fact, the group of users that receive the most accurate recomme...", "weights": [[8, 13, 1.0], [80, 85, 1.0], [159, 164, 1.0]]}, "2016.wwwconf_conference-2016.120": {"snippet": "...lect users' interaction with previously seen recommendations (up to 15% enhancement on all users and 34% improvement on heavy users).", "weights": [[8, 13, 1.0], [94, 99, 1.0], [129, 134, 1.0]]}, "2012.tist_journal-ir0volumeA3A4.15": {"snippet": "...tive influence can be beneficial while negative influence may cause-negative impact on other users of the same community. In this article, a research framework was proposed to study user influence wit...", "weights": [[8, 17, 1.0], [51, 60, 1.0], [96, 101, 1.0], [190, 199, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.4": {"snippet": "...ated influence in location recommendation\n\n\n Abstract Point-Of-Interest (POI) recommendation aims to mine a user's visiting history and find her/his potentially preferred places. Although location rec...", "weights": [[8, 17, 1.0]]}, "2013.sigirconf_conference-2013.39": {"snippet": "...mong users over time. The results of experiments on a real world dataset demonstrate that RTM-based recommendation outperforms the state-ofthe-art recommendation methods. Case studies also show that R...", "weights": [[8, 13, 1.0]]}, "2016.cikm_conference-2016.3": {"snippet": "...ical influence, temporal cyclic effect and semantic effect in a unified way by embedding the four corresponding relational graphs (POI-POI, POI-Region, POI-Time and POI-Word) into a shared low dimensi...", "weights": [[8, 17, 1.0]]}, "2013.spire_conference-2013.6": {"snippet": "...tual Influence to Improve Recommendations", "weights": [[8, 17, 1.0]]}, "2013.cikm_conference-2013.194": {"snippet": "...meet users' individualities, especially for experienced users. Moreover, for cold start users, the interpersonal interest similarity and interpersonal influence can enhance the intrinsic link among fe...", "weights": [[8, 13, 1.0], [59, 64, 1.0], [91, 96, 1.0], [154, 163, 1.0]]}, "2019.tois_journal-ir0volumeA37A3.3": {"snippet": "...such users.We face the challenge that the users on whom we focus have not interacted with the recommender system before, which is a common scenario that every recommender system encounters when new us...", "weights": [[8, 13, 1.0], [45, 50, 1.0], [201, 206, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A4.0": {"snippet": "...cial influence diffusion in the network to find the global and local influence nodes and then embed this dual influence data into a", "weights": [[8, 17, 1.0], [72, 81, 1.0], [113, 122, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A4.12": {"snippet": "...work users equally, which assume that the effect of recommendation on a user is decided by the user's own preferences and social influence. However, a user's own knowledge in a field has not been cons...", "weights": [[8, 13, 1.0], [132, 141, 1.0]]}, "2017.cikm_conference-2017.302": {"snippet": "...mous users. In this paper, we propose a list-wise deep neural network based architecture to model the limited user behaviors within each session. To train the model efficiently, we first design a sess...", "weights": [[8, 13, 1.0]]}, "2014.ir_journal-ir0volumeA17A2.1": {"snippet": "...s by influence analysis", "weights": [[8, 17, 1.0]]}, "2020.wwwconf_conference-2020.301": {"snippet": "Influence Function based Data Poisoning Attacks to Top-N Recommender Systems", "weights": [[0, 9, 1.0]]}, "2015.cikm_conference-2015.167": {"snippet": "...s of Users in Recommender Systems", "weights": [[8, 13, 1.0]]}, "2019.wwwconf_conference-2019.264": {"snippet": "...ds): Influence Neighborhoods for Social Recommendations", "weights": [[8, 17, 1.0]]}, "2014.wwwconf_conference-2014c.239": {"snippet": "...tial users. We believe that insight about influence can be gained from analyzing the affective content of the text as well as affect intensity.We employ text mining to extract the emotionality of 68,0...", "weights": [[8, 13, 1.0], [45, 54, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.33": {"snippet": "...tial influence logs based on the community-level topic influence diffusion to infer the latent topic distribution over users and items. Our model enables tracking the time-varying distributions of top...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [122, 127, 1.0]]}, "2017.wwwconf_conference-2017.53": {"snippet": "...Food recommenders have the potential to positively influence the eating habits of users. To achieve this, however, we need to understand how healthy recommendations are and the factors which influence...", "weights": [[8, 20, 1.0], [54, 63, 1.0], [85, 90, 1.0], [194, 203, 1.0]]}, "2015.tist_journal-ir0volumeA7A1.10": {"snippet": "...tial Influence Modeling for Location Recommendations: A Gravity-based Approach\n\n\n Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which ...", "weights": [[8, 17, 1.0], [101, 106, 1.0]]}, "2020.wsdm_conference-2020.60": {"snippet": "... the users' preference to some extent, the lack of such clicks does not necessarily indicate a negative response from the users, as it is possible that the users were not exposed to the items (positiv...", "weights": [[8, 13, 1.0], [125, 130, 1.0], [159, 164, 1.0]]}, "2020.wwwconf_conference-2020c.184": {"snippet": "...mend users to other users in a personalised manner, in scenarios where both sides of the preference relation must be considered. Existing RRS approaches based on collaborative filtering or content-bas...", "weights": [[8, 13, 1.0], [23, 28, 1.0]]}, "2018.tist_journal-ir0volumeA9A6.6": {"snippet": "...help users find preferential items among a large amount of products so that users' time is saved and sellers' profits are increased. Cross-domain recommender systems aim to recommend items based on us...", "weights": [[8, 13, 1.0], [79, 84, 1.0], [201, 206, 1.0]]}, "2013.sigirconf_conference-2013.129": {"snippet": "... can influence users to select events that result in tighter and more compact routes, thus allowing users to spend less time travelling and more time visiting events.", "weights": [[8, 17, 1.0], [18, 23, 1.0], [103, 108, 1.0]]}, "2012.sigirconf_conference-2012.107": {"snippet": "...l of influence. The opinion influence can be modeled and inferred from the time-stamped reading and writing behaviors between users. The opinion diffusion can be observed by a serial of opinion influe...", "weights": [[8, 17, 1.0], [31, 40, 1.0], [129, 134, 1.0], [197, 206, 1.0]]}, "2018.sigirconf_conference-2018.21": {"snippet": "...ween users' internal interests and the social influence from the social network drives the evolution of users' preferences over time. Nevertheless, traditional approaches either neglected the social n...", "weights": [[8, 13, 1.0], [49, 58, 1.0], [107, 112, 1.0]]}, "2020.wwwconf_conference-2020c.114": {"snippet": "...work Influence Ranking via Embedding Network Interactions for User Recommendation\n\n\n Within social networks user influence may be modelled based on user interactions. Further, it is typical to recomme...", "weights": [[8, 17, 1.0], [116, 125, 1.0]]}, "2012.sigirconf_conference-2012.69": {"snippet": "...cial influence between linked friends and the personal preferences of users through statistical inference. To address the challenges arising from multiple layers of hidden factors in SIS, we develop a...", "weights": [[8, 17, 1.0], [73, 78, 1.0]]}, "2011.sigirconf_conference-2011.135": {"snippet": "...cial influence for item recommendation\n\n\n ABSTRACTSocial networking services possess two features: (1) capturing the social relationships among people, represented by the social network, and (2) allow...", "weights": [[8, 17, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A1.3": {"snippet": "...y of users opinions, we apply the uncertain set to acquire users' sentiment polarity, and the uncertain variable to determine users' sentiment strength through sentiment analysis. To more accurately i...", "weights": [[8, 13, 1.0], [62, 67, 1.0], [129, 134, 1.0]]}, "2017.wwwconf_conference-2017c.165": {"snippet": "...rix, users and movies are usually connected to other objects via different relationships. For example, users are linked to other users via friendship links, and movies are 941 linked to actors/actress...", "weights": [[8, 13, 1.0], [106, 111, 1.0], [132, 137, 1.0]]}, "2020.sigirconf_conference-2020.270": {"snippet": "...s of users' sequential subjective sentiments on their behaviors-and sometimes the temporal changes in human sentiment patterns plays a decisive role in users' final preferences. To investigate the inf...", "weights": [[8, 13, 1.0], [155, 160, 1.0], [200, 209, 1.0]]}, "2011.wwwconf_conference-2011c.33": {"snippet": "...zing users' context. However, incorporating context into recommender systems is not a simple task in that context can influence users' item preferences in various ways depending on the application. In...", "weights": [[8, 13, 1.0], [121, 130, 1.0], [131, 136, 1.0]]}, "2020.wwwconf_conference-2020.114": {"snippet": "...d by users in the next 24 hours. As check-in data is very sparse, it is challenging to accurately capture user preferences in temporal patterns. To this end, we propose a category-aware deep model Cat...", "weights": [[8, 13, 1.0]]}, "2016.tist_journal-ir0volumeA7A4.15": {"snippet": "...text influence. STCAPLRS can make accurate recommendation and facilitate people's local visiting and new location exploration by exploiting the context information of user behavior, associations betwe...", "weights": [[8, 17, 1.0]]}, "2018.tois_journal-ir0volumeA36A4.8": {"snippet": "...ware Influence Maximization over Dynamic Social Streams\n\n\n Influence maximization (IM), which selects a set of k seed users (a.k.a., a seed set) to maximize the influence spread over a social network,...", "weights": [[8, 17, 1.0], [62, 71, 1.0], [121, 126, 1.0], [164, 173, 1.0]]}, "2021.wsdm_conference-2021.48": {"snippet": "...ince users' interests are influenced by their friends, recommender systems can leverage social networks to better understand their users' preferences and thus provide more accurate recommendations. Ho...", "weights": [[8, 13, 1.0], [134, 139, 1.0]]}, "2007.tois_journal-ir0volumeA26A1.3": {"snippet": "... the users strongly influences the accuracy of the recommendations. CF works better when users look for specific information than when they search for general information. The accuracy drops significa...", "weights": [[8, 13, 1.0], [92, 97, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.10": {"snippet": "...alze users' preference on locations. We try to model the muti-tag, social and geographical influences separately form three layers of LBSN. We first model the mutli-tag influences via extracting a use...", "weights": [[8, 13, 1.0]]}, "2014.wwwconf_conference-2014.62": {"snippet": "...t on users in these terms. This paper examines the longitudinal impacts of a collaborative filtering-based recommender system on users. To the best of our knowledge, it is the first paper to measure t...", "weights": [[8, 13, 1.0], [132, 137, 1.0]]}, "2020.wwwconf_conference-2020.249": {"snippet": "...cial Influence in Recommender Networks\n\n\n People's ability to influence others' opinion on matters of taste varies greatly-both offline and in recommender systems. What are the mechanisms underlying t...", "weights": [[8, 17, 1.0], [65, 74, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.6": {"snippet": "...pant influence. However, participant influence is not well studied by previous works. In this article, we propose an event recommendation model that considers participant influence, and exploits the i...", "weights": [[8, 17, 1.0], [40, 49, 1.0], [174, 183, 1.0], [202, 211, 1.0]]}, "2018.sigirconf_workshop-2018birndl.3": {"snippet": "... new users and items into the system. In this work, we present a time-aware recommender system that accounts for the concept-drift in user interest. By computing user-specific conceptdrift score, our ...", "weights": [[8, 13, 1.0]]}, "2011.tist_journal-ir0volumeA2A3.11": {"snippet": "... the users are independent and identically distributed; this assumption ignores the connections among users, which is not consistent with the real-world observations where we always turn to our truste...", "weights": [[8, 13, 1.0], [105, 110, 1.0]]}, "2016.tist_journal-ir0volumeA8A1.9": {"snippet": "...ture users' preferences accurately. Geographical influence can help alleviate this problem and have a large impact on the final recommendation result. By studying users' moving patterns, we find that ...", "weights": [[8, 13, 1.0], [52, 61, 1.0], [166, 171, 1.0]]}, "W12-0608": {"snippet": "...from users' reviews about products, in order to improve a recommendation system. While typical recommender systems compare a user profile with some reference characteristics to rate unseen items, they...", "weights": [[8, 13, 1.0]]}, "2017.tweb_journal-ir0volumeA11A3.1": {"snippet": "...ween users and items characterized by a single relation. In many cases, such as social networks, users and items are joined in a complex web of relations, not readily reduced to a single value. Recent...", "weights": [[8, 13, 1.0], [100, 105, 1.0]]}, "2012.wsdm_conference-2012.59": {"snippet": "... can influence users' posterior evaluation on the products or services recommended to them, the answer to which is critical to estimating user satisfaction when proposing a word-of-mouth marketing str...", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "2016.cikm_conference-2016.202": {"snippet": "...cial influence, an implicit source of information in social networks, is seldomly considered, even though it often drives recommendations in social networks. In this paper, we introduce a new global a...", "weights": [[8, 17, 1.0]]}, "2018.tweb_journal-ir0volumeA12A1.3": {"snippet": "...atch users' interests, but also have high chances to be selected by the users, because the users can find suitable partners to attend the corresponding activities together. We conduct experiments on r...", "weights": [[8, 13, 1.0], [75, 80, 1.0], [94, 99, 1.0]]}, "2013.airs_conference-2013.37": {"snippet": "...d on Users' Preference", "weights": [[8, 13, 1.0]]}}}
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For example, most of thes...", "weights": [[8, 15, 1.0], [169, 176, 1.0]]}, "2011.wwwconf_workshop-2011ldow.11": {"snippet": "...n of caching strategies based on these assumptions. In this paper we evaluate the potential of caching to improve efficiency and result completeness in link traversal based query execution systems. We...", "weights": [[8, 15, 1.0], [98, 105, 1.0]]}, "2008.wwwconf_conference-2008.40": {"snippet": "...list caching in search engines\n\n\n ABSTRACTDue to the rapid growth in the size of the web, web search engines are facing enormous performance challenges. The larger engines in particular have to be abl...", "weights": [[8, 15, 1.0], [19, 25, 1.0], [97, 103, 1.0]]}, "2015.cikm_conference-2015.183": {"snippet": "...pid\" Search Components on User Interaction with Search Engines", "weights": [[8, 14, 1.0], [51, 57, 1.0]]}, "2011.jasis_journal-ir0volumeA62A4.7": {"snippet": "... and caching in Web search engines", "weights": [[8, 15, 1.0], [23, 29, 1.0]]}, "2013.cikm_conference-2013.145": {"snippet": "...list caching in flash-based web search engines\n\n\n ABSTRACTCaching is a widely used technique to boost the performance of search engines. Based on the observation that the speed gap between the random ...", "weights": [[8, 15, 1.0], [35, 41, 1.0], [124, 130, 1.0]]}, "2011.ecir_conference-2011.12": {"snippet": "Caching for Realtime Search\n\n\n Abstract. Modern search engines feature real-time indices, which incorporate changes to content within seconds. As search engines also cache search results for reducing ...", "weights": [[0, 7, 1.0], [21, 27, 1.0], [48, 54, 1.0], [146, 152, 1.0], [172, 178, 1.0]]}, "2006.tois_journal-ir0volumeA24A1.1": {"snippet": "... Web search engines: Caching and prefetching query results by exploiting historical usage data\n\n\n This article discusses efficiency and effectiveness issues in caching the results of queries submitted...", "weights": [[8, 14, 1.0], [24, 31, 1.0], [163, 170, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.8": {"snippet": "...uted search engines\n\n\n AbstractWeb search engines have to deal with a rapidly increasing amount of information, high query loads and tight performance constraints. The success of a search engine depen...", "weights": [[8, 14, 1.0], [38, 44, 1.0], [184, 190, 1.0], [191, 197, 1.0]]}, "2008.sigirconf_conference-2008.19": {"snippet": "...ults caching and index pruning for high-performance web search engines\n\n\n ABSTRACTResults caching is an efficient technique for reducing the query processing load, hence it is commonly used in real se...", "weights": [[8, 15, 1.0], [59, 65, 1.0], [93, 100, 1.0], [201, 207, 1.0]]}, "2019.ecir_conference-20192.20": {"snippet": "... for Search Engine Result Caching\n\n\n Abstract.We investigate the impact of the size of query frequency history and its compact representation in memory for search result caching.", "weights": [[8, 14, 1.0], [15, 21, 1.0], [29, 36, 1.0], [159, 165, 1.0], [173, 180, 1.0]]}, "2003.wwwconf_conference-2003p.6": {"snippet": "... for Caching Web Search Engine Results", "weights": [[8, 15, 1.0], [20, 26, 1.0], [27, 33, 1.0]]}, "2003.wwwconf_conference-2003.3": {"snippet": "... for caching search results, that is based on a probabilistic model of search engine users. We then use a trace of over seven million queries submitted to the search engine AltaVista to evaluate PDC, ...", "weights": [[8, 15, 1.0], [16, 22, 1.0], [74, 80, 1.0], [81, 87, 1.0], [162, 168, 1.0], [169, 175, 1.0]]}, "2011.sigirconf_conference-2011.100": {"snippet": "... web search engines", "weights": [[8, 14, 1.0]]}, "2012.sigirconf_conference-2012.66": {"snippet": "... web search\n\n\n ABSTRACTCaches of results are critical components of modern Web search engines, since they enable lower response time to frequent queries and reduce the load to the search engine backen...", "weights": [[8, 14, 1.0], [82, 88, 1.0], [183, 189, 1.0], [190, 196, 1.0]]}, "2007.sigirconf_conference-2007.26": {"snippet": "...ient caching systems for Web search engines. We explore the impact of different approaches, such as static vs. dynamic caching, and caching query results vs. caching posting lists. Using a query log s...", "weights": [[8, 15, 1.0], [32, 38, 1.0], [122, 129, 1.0], [135, 142, 1.0], [161, 168, 1.0]]}, "2014.tois_journal-ir0volumeA32A4.5": {"snippet": "...ased Search Engine Architectures: An Experimental Study\n\n\n Caching is an important optimization in search engine architectures. Existing caching techniques for search engine optimization are mostly bi...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [62, 69, 1.0], [102, 108, 1.0], [109, 115, 1.0], [140, 147, 1.0], [163, 169, 1.0], [170, 176, 1.0]]}, "2003.sigirconf_conference-2003.102": {"snippet": "... the search engine.(3) Search Engine Discovery: If a user submits a URL that does not have a search engine on the Webpage, this component will crawl Webpages \"nearby\" to find search engine interfaces....", "weights": [[8, 14, 1.0], [15, 21, 1.0], [26, 32, 1.0], [33, 39, 1.0], [96, 102, 1.0], [103, 109, 1.0], [178, 184, 1.0], [185, 191, 1.0]]}, "2015.sigirconf_conference-2015.80": {"snippet": "...sult Caching for Meta-Search Engines\n\n\n ABSTRACTOur goal in this paper is to design cost-aware result caching approaches for meta-search engines. We introduce different levels of eviction, namely, que...", "weights": [[8, 15, 1.0], [25, 31, 1.0], [105, 112, 1.0], [133, 139, 1.0]]}, "2010.wwwconf_conference-2010.114": {"snippet": "Caching search engine results over incremental indices", "weights": [[0, 7, 1.0], [8, 14, 1.0], [15, 21, 1.0]]}, "2011.tweb_journal-ir0volumeA5A2.3": {"snippet": "...sult Caching in Web Search Engines\n\n\n Search engines and large-scale IR systems need to cache query results for efficiency and scalability purposes. Static and dynamic caching techniques (as well as t...", "weights": [[8, 15, 1.0], [23, 29, 1.0], [41, 47, 1.0], [171, 178, 1.0]]}, "2005.wwwconf_conference-2005.28": {"snippet": "...evel caching for efficient query processing in large Web search engines\n\n\n ABSTRACTLarge web search engines have to answer thousands of queries per second with interactive response times. Due to the s...", "weights": [[8, 15, 1.0], [60, 66, 1.0], [96, 102, 1.0]]}, "2010.sigirconf_conference-2010.12": {"snippet": "Caching search engine results over incremental indices", "weights": [[0, 7, 1.0], [8, 14, 1.0], [15, 21, 1.0]]}, "2015.sigirconf_conference-2015.149": {"snippet": "... for search engine storage, the bottleneck of snippet generation shifts from I/O to computation. We propose a simple, but effective method for exploiting this trend, which we call fragment caching: in...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [192, 199, 1.0]]}, "2012.ipm_journal-ir0volumeA48A5.2": {"snippet": "... web search engines\n\n\n a b s t r a c tCaching is a crucial performance component of large-scale web search engines, as it greatly helps reducing average query response times and query processing workl...", "weights": [[8, 14, 1.0], [103, 109, 1.0]]}, "2013.sigirconf_conference-2013.71": {"snippet": "...e on search engine cache management\n\n\n ABSTRACTCaching is an important optimization in search engine architectures. Existing caching techniques for search engine optimization are mostly biased towards...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [90, 96, 1.0], [97, 103, 1.0], [128, 135, 1.0], [151, 157, 1.0], [158, 164, 1.0]]}, "2010.wwwconf_conference-2010.19": {"snippet": "...e of search engine caching\n\n\n ABSTRACTCommercial Web search engines have to process user queries over huge Web indexes under tight latency constraints. In practice, to achieve low latency, large resul...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 29, 1.0], [56, 62, 1.0]]}, "2011.jasis_journal-ir0volumeA62A7.5": {"snippet": "...y of search engine results", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2007.sigirconf_conference-2007.19": {"snippet": "...d by search engines has become an expectation of search engine users. In this paper we explore the algorithms and data structures required as part of a search engine to allow efficient generation of q...", "weights": [[8, 14, 1.0], [52, 58, 1.0], [59, 65, 1.0], [155, 161, 1.0], [162, 168, 1.0]]}, "2017.jasis_journal-ir0volumeA68A8.3": {"snippet": "The effects of credibility cues on the selection of search engine results", "weights": [[4, 11, 1.0], [52, 58, 1.0], [59, 65, 1.0]]}, "2001.sigirconf_conference-2001.7": {"snippet": "...evel Caching for Scalable Search Engines", "weights": [[8, 15, 1.0], [29, 35, 1.0]]}, "2019.cikm_conference-2019.305": {"snippet": "Caching Scores for Faster Query Processing with Dynamic Pruning in Search Engines", "weights": [[0, 7, 1.0], [67, 73, 1.0]]}, "2012.wwwconf_conference-2012.18": {"snippet": "...ting search engine overload, and implement the two-level cache scheme on a very busy web server. Our experimental results show that the proposed caching framework can effectively reduce the impact of ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [148, 155, 1.0]]}, "2012.spire_conference-2012.17": {"snippet": "... Web Search Engine Result Caches", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2019.ipm_journal-ir0volumeA56A1.7": {"snippet": "... web search. Such impact is reflected both in users' subjective perception of the usability of a search engine and in their interaction with the search engine in terms of the number of search results ...", "weights": [[8, 14, 1.0], [100, 106, 1.0], [107, 113, 1.0], [148, 154, 1.0], [155, 161, 1.0], [188, 194, 1.0]]}, "2008.cikm_workshop-2008lsdsir.7": {"snippet": "...osed caching techniques can have a significant impact on performance, like caching on text queries has been proved effective for traditional Web search engines.", "weights": [[8, 15, 1.0], [78, 85, 1.0], [148, 154, 1.0]]}, "2008.wwwconf_conference-2008.179": {"snippet": "...sult caching revisited\n\n\n ABSTRACTQuery result caching is an important mechanism for search engine efficiency. In this study, we first review several query features that are used to determine the cont...", "weights": [[8, 15, 1.0], [50, 57, 1.0], [88, 94, 1.0], [95, 101, 1.0]]}, "2007.spire_conference-2007.7": {"snippet": "...s of Search Engine Results", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2007.wwwconf_conference-MenasceA07.0": {"snippet": "...ased Caching\n\n\n Abstract Online auction sites have very specific workloads and user behavior characteristics. Previous studies on workload characterization conducted by the authors showed that (1) bid...", "weights": [[8, 15, 1.0]]}, "2009.jasis_journal-ir0volumeA60A8.3": {"snippet": "...n of search engine performance", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2020.ipm_journal-ir0volumeA57A3.20": {"snippet": "...sult caching in web search engines", "weights": [[8, 15, 1.0], [23, 29, 1.0]]}, "2009.wwwconf_conference-2009.44": {"snippet": "...sult caching in web search engines\n\n\n ABSTRACTQuery processing is a major cost factor in operating large web search engines. In this paper, we study query result caching, one of the main techniques us...", "weights": [[8, 15, 1.0], [23, 29, 1.0], [112, 118, 1.0], [165, 172, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.4": {"snippet": "...n of search engine query logs, and estimate the effect of statistical characteristics of search engine queries on new topic identification. By applying multiple linear regression and multi-factor ANOV...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [92, 98, 1.0], [99, 105, 1.0]]}, "2009.ecir_conference-2009.68": {"snippet": "... and Caching Effects", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2013.cikm_conference-2013.248": {"snippet": "...cial search engine, we explore the idea of a caching scheme based on pairs of terms, rather than individual terms (which is the typical approach used by search engines today). We propose an inverted l...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [48, 55, 1.0], [156, 162, 1.0]]}, "2014.tweb_journal-ir0volumeA8A2.1": {"snippet": "...in a search engine's ranking function, provide guidelines for SEOs and Web masters to optimize their Web pages, validate or disprove new ranking features, and evaluate search engine ranking results fo...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [171, 177, 1.0], [178, 184, 1.0]]}, "2013.tweb_journal-ir0volumeA8A1.2": {"snippet": "...sult Caching and Prefetching in Search Engines\n\n\n Web search engines are known to cache the results of previously issued queries. The stored results typically contain the document summaries and some d...", "weights": [[8, 15, 1.0], [35, 41, 1.0], [57, 63, 1.0]]}, "2011.ecir_conference-2011.51": {"snippet": "...sult Caching in Search Engines\n\n\n Abstract. Result caches are vital for efficiency of search engines. In this work, we propose a novel caching strategy in which a dynamic result cache is split into tw...", "weights": [[8, 15, 1.0], [19, 25, 1.0], [89, 95, 1.0], [138, 145, 1.0]]}, "2012.sigirconf_conference-2012.203": {"snippet": "...ware caching and prefetching query results in web search engines\n\n\n ABSTRACTQuery results caching is an efficient technique for Web search engines. In this paper we present User-Aware Cache, a novel a...", "weights": [[8, 15, 1.0], [53, 59, 1.0], [93, 100, 1.0], [135, 141, 1.0]]}, "2005.wwwconf_conference-2005si.46": {"snippet": "... the search options further consists of five categories. Performance of a search engine includes three aspects with the quality of results divided into two more sub-subgroups. For many search engine u...", "weights": [[8, 14, 1.0], [77, 83, 1.0], [84, 90, 1.0], [188, 194, 1.0], [195, 201, 1.0]]}, "2013.sigirconf_conference-2013.184": {"snippet": "... web search engines . In particular, the tutorial provides an in-depth architectural overview of a web search engine, mainly focusing on the web crawling, indexing, query processing, and caching compo...", "weights": [[8, 14, 1.0], [106, 112, 1.0], [113, 119, 1.0], [190, 197, 1.0]]}, "2006.wwwjournals_journal-ir0volumeA9A4.1": {"snippet": "...ther caching levels. We also observe that a careful selection of cache admission and eviction policies is crucial for best overall performance.Keywords web search . search engine architecture . search...", "weights": [[8, 15, 1.0], [159, 165, 1.0], [168, 174, 1.0], [175, 181, 1.0], [197, 203, 1.0]]}}}
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behavior as communication markets, and consumers learn product quality information both passively (reading online reviews) and act...", "weights": [[8, 15, 1.0], [128, 135, 1.0], [187, 194, 1.0]]}, "2014.wwwconf_conference-2014c.66": {"snippet": "...n of product recommendations\n\n\n ABSTRACTBecause of the important role of product reviews during users' decision process, we propose a novel explanation interface that particularly fuses the feature se...", "weights": [[8, 15, 1.0], [76, 83, 1.0], [84, 91, 1.0]]}, "2016.tois_journal-ir0volumeA35A2.3": {"snippet": "...ion, product recommendation under a given unexplored category, and product recommendation under generic unexplored categories.", "weights": [[8, 15, 1.0], [70, 77, 1.0]]}, "2005.wwwconf_conference-2005.38": {"snippet": "...n of consumer opinions on these products, which helps him/her to decide which product to buy. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product ...", "weights": [[8, 16, 1.0], [81, 88, 1.0], [103, 110, 1.0], [195, 202, 1.0]]}, "H05-1043": {"snippet": "...ting Product Features and Opinions from Reviews\n\n\n Consumers are often forced to wade through many on-line reviews in order to make an informed product choice. This paper introduces OPINE, an unsuperv...", "weights": [[8, 15, 1.0], [43, 50, 1.0], [110, 117, 1.0], [147, 154, 1.0]]}, "W13-4205": {"snippet": "...line Consumer Reviews as a Classification Problem\n\n\n The paper aims to address an opinion mining problem: to find the helpful reviews from online consumer reviews before mining the detail. This task c...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [129, 136, 1.0], [149, 157, 1.0], [158, 165, 1.0]]}, "2016.ecir_conference-2016.53": {"snippet": "...hase Consumer Reviews", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "2012.ecir_conference-2012.71": {"snippet": "...n of Product Reviews", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2012.wwwconf_conference-2012.20": {"snippet": "...s in consumer reviews\n\n\n ABSTRACTOpinionated social media such as product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit o...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [69, 76, 1.0], [77, 84, 1.0]]}, "2021.hcinlp-1.9": {"snippet": "...Fake Reviews? User Analysis and Assistance Tool for Fake Review Detection\n\n\n Customer reviews are useful in providing an indirect, secondhand experience of a product. People often use reviews written ...", "weights": [[8, 15, 1.0], [89, 96, 1.0], [161, 168, 1.0], [187, 194, 1.0]]}, "H05-2017": {"snippet": "...line reviews in order to make an informed product choice. We introduce OPINE, an unsupervised, high-precision information extraction system which mines product reviews in order to build a model of pro...", "weights": [[8, 15, 1.0], [45, 52, 1.0], [155, 162, 1.0], [163, 170, 1.0], [200, 207, 1.0]]}, "2020.ipm_journal-ir0volumeA57A6.89": {"snippet": "Consumer health information needs: A systematic review of instrument development", "weights": [[0, 8, 1.0]]}, "2018.sigirconf_workshop-2018ecom.2": {"snippet": "...cant Product Aspects from e-Commerce Product Reviews\n\n\n Product reviews increasingly influence buying decisions on e-commerce websites. Reviewers share their experiences of using a product and provide...", "weights": [[8, 15, 1.0], [40, 47, 1.0], [48, 55, 1.0], [59, 66, 1.0], [67, 74, 1.0], [184, 191, 1.0]]}, "2001.jasis_journal-ir0volumeA52A4.7": {"snippet": "... for consumer products, Eric Bergman, editor", "weights": [[8, 16, 1.0]]}, "2013.cikm_workshop-2013pikm.6": {"snippet": "...om), product reviews have become an indispensable part of online shopping; shoppers nowadays will not purchase a product without reading a product review. Unfortunately, many of the reviews they read ...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [116, 123, 1.0], [142, 149, 1.0], [185, 192, 1.0]]}, "2021.ipm_journal-ir0volumeA58A3.52": {"snippet": "...line consumer reviews: A multi-perspective approach", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "2015.ipm_journal-ir0volumeA51A1.3": {"snippet": "... for consumer decision making. Product aspect extraction is fundamental to online review mining. This research is aimed to improve the performance of aspect extraction from online consumer reviews. To...", "weights": [[8, 16, 1.0], [34, 41, 1.0], [183, 191, 1.0], [192, 199, 1.0]]}, "2012.wwwconf_conference-2012.21": {"snippet": "...line reviews based on economic signaling theory , in which consumer reviews diminish the inherent information asymmetry between consumers and producers, by acting as a signal to a product's true, unkn...", "weights": [[8, 15, 1.0], [62, 70, 1.0], [71, 78, 1.0], [183, 190, 1.0]]}, "W10-0207": {"snippet": "...from product reviews\n\n\n This paper describes methods aimed at solving the novel problem of automatically discovering 'wishes' from (English) documents such as reviews or customer surveys. These wishes...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [162, 169, 1.0]]}, "2020.ipm_journal-ir0volumeA57A2.15": {"snippet": "Consumer health information needs: A systematic review of measures", "weights": [[0, 8, 1.0]]}, "2020.ipm_journal-ir0volumeA57A6.19": {"snippet": "...line Consumer Reviews on Perceived Helpfulness", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "R15-1019": {"snippet": "...l of Product Properties, Aspects and Ratings for Online Reviews\n\n\n Product review mining is an important task that can benefit both businesses and consumers. Lately a number of models combining collab...", "weights": [[8, 15, 1.0], [59, 66, 1.0], [70, 77, 1.0]]}, "P11-1150": {"snippet": "...tant Product Aspects from Online Consumer Reviews\n\n\n In this paper, we dedicate to the topic of aspect ranking, which aims to automatically identify important product aspects from online consumer revi...", "weights": [[8, 15, 1.0], [36, 44, 1.0], [45, 52, 1.0], [162, 169, 1.0], [190, 198, 1.0], [199, 206, 1.0]]}, "2010.riao_conference-2010.34": {"snippet": "...pful product reviews", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2015.sigirconf_conference-2015.41": {"snippet": "... the reviews are all written by consumers who have bought and used a product, there are generally very few or even no reviews available for a new product or an unpopular product. We study the novel pr...", "weights": [[8, 15, 1.0], [72, 79, 1.0], [121, 128, 1.0], [149, 156, 1.0], [173, 180, 1.0]]}, "2010.jasis_journal-ir0volumeA61A1.1": {"snippet": "Consumer health information on the Web: The relationship of visual design and perceptions of credibility", "weights": [[0, 8, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A6.3": {"snippet": "...line reviews based on topic frequency and order\n\n\n Abstract Large volume of online review data can reveal consumers' major interests on domain product, which attracts great research interests from the...", "weights": [[8, 15, 1.0], [146, 153, 1.0]]}, "O17-2003": {"snippet": "...ract Product Features from {C}hinese Consumer Reviews and Establish Product Feature Structure Tree\n\n\n With the progress of e-commerce and web technology, a large volume of consumer reviews for product...", "weights": [[8, 15, 1.0], [40, 48, 1.0], [49, 56, 1.0], [71, 78, 1.0], [175, 183, 1.0], [184, 191, 1.0]]}, "2017.sigirconf_workshop-2017ecom.6": {"snippet": "...into consumer attitudes. When studying consumer behavior, the existing literature is primarily concerned with product reviews. In this paper we posit that textual product descriptions are also importa...", "weights": [[8, 16, 1.0], [42, 50, 1.0], [113, 120, 1.0], [121, 128, 1.0], [166, 173, 1.0]]}, "L14-1003": {"snippet": "...s in Product Reviews", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "I17-1079": {"snippet": "...on a product? Make a purchase? etc.). Natural language approaches that model information like product reviews have proved to be incredibly useful in improving the performance of such methods, as revie...", "weights": [[8, 15, 1.0], [97, 104, 1.0], [105, 112, 1.0], [198, 205, 1.0]]}, "O14-3002": {"snippet": "...line Consumer Reviews as a Classification Problem\n\n\n The paper addresses an opinion mining problem: how to find the helpful reviews from online consumer reviews via the quality of the content. Since t...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [127, 134, 1.0], [147, 155, 1.0], [156, 163, 1.0]]}, "I17-1040": {"snippet": "...s in Consumer Product Reviews\n\n\n In this paper we introduce the problem of identifying usage expression sentences in a consumer product review. We create a human-annotated gold standard dataset of 565...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [25, 32, 1.0], [122, 130, 1.0], [131, 138, 1.0]]}, "2019.ipm_journal-ir0volumeA56A4.16": {"snippet": "...line reviews need to be investigated. Although the criteria and viewpoints for judging the value of these reviews may differ from one person to another, the existing studies on online reviews have not...", "weights": [[8, 15, 1.0], [109, 116, 1.0], [187, 194, 1.0]]}, "2019.ipm_journal-ir0volumeA56A4.3": {"snippet": "...fake reviews that has been evaluated in the consumer electronics domain. The contributions are fourfold: (i) Construction of a dataset for classifying fake reviews in the consumer electronics domain i...", "weights": [[8, 15, 1.0], [47, 55, 1.0], [159, 166, 1.0], [174, 182, 1.0]]}, "D11-1013": {"snippet": "... the product specifications), as well as consumer reviews. Based on the derived hierarchy, we generate a hierarchical organization of consumer reviews on various product aspects and aggregate consumer...", "weights": [[8, 15, 1.0], [44, 52, 1.0], [53, 60, 1.0], [137, 145, 1.0], [146, 153, 1.0], [165, 172, 1.0], [195, 203, 1.0]]}, "2016.wwwconf_conference-2016.11": {"snippet": "...s of Product Attributes and Consumer Reviews on Recommender Performance\n\n\n ABSTRACTWe investigate the moderating effect of product attributes and consumer reviews on the efficacy of a collaborative fi...", "weights": [[8, 15, 1.0], [31, 39, 1.0], [40, 47, 1.0], [126, 133, 1.0], [149, 157, 1.0], [158, 165, 1.0]]}, "2018.jasis_journal-ir0volumeA69A11.1": {"snippet": "...ful\" reviews? The interaction effect of review valence and review usefulness on consumers' purchase decisions", "weights": [[8, 15, 1.0]]}, "2009.ecir_conference-2009.41": {"snippet": "...g of Product Reviews\n\n\n Abstract. Online product reviews are becoming increasingly available, and are being used more and more frequently by consumers in order to choose among competing products. Tool...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [44, 51, 1.0], [52, 59, 1.0]]}, "2017.ir_journal-ir0volumeA20A6.1": {"snippet": "Product review summarization through question retrieval and diversification", "weights": [[0, 7, 1.0]]}, "2016.sigirconf_conference-2016.41": {"snippet": "...ze a Product Review\n\n\n ABSTRACTProduct reviews have become an important resource for customers before they make purchase decisions. However, the abundance of reviews makes it difficult for customers t...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [161, 168, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A2.8": {"snippet": "...ding reviews. However, such extracted summaries are lack of immediacy, and could not keep the reviews' narrative structures. Aiming at enhancing the diversity of reviews and eliminating the above meth...", "weights": [[8, 15, 1.0], [97, 104, 1.0], [165, 172, 1.0]]}, "2016.ipm_journal-ir0volumeA52A6.8": {"snippet": "...rent product information to find matches for the customer's preferences. Some studies have also shown that sellers can use specifically designed techniques to alter consumer behavior. This study propo...", "weights": [[8, 15, 1.0], [168, 176, 1.0]]}, "2010.wwwconf_conference-2010.191": {"snippet": "...s of consumer reviews is more and more desirable with the evolutionary development of online review systems. Existing helpfulness assessment methodologies make use of the positive vote fraction as a b...", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "2009.airs_conference-2009.9": {"snippet": "...y of Product Reviews", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2013.wwwconf_conference-2013.77": {"snippet": "...line reviews\n\n\n ABSTRACTRecommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend ...", "weights": [[8, 15, 1.0]]}, "2016.wwwconf_conference-2016.57": {"snippet": "...tive Product-Related Queries with Customer Reviews\n\n\n ABSTRACTOnline reviews are often our first port of call when considering products and purchases online. When evaluating a potential purchase, we m...", "weights": [[8, 15, 1.0], [46, 53, 1.0], [72, 79, 1.0]]}, "D12-1036": {"snippet": "...n of Consumer Reviews\n\n\n This paper proposes to generate appropriate answers for opinion questions about products by exploiting the hierarchical organization of consumer reviews. The hierarchy organiz...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [164, 172, 1.0], [173, 180, 1.0]]}, "2010.tweb_journal-ir0volumeA4A2.1": {"snippet": "...line reviews have become increasingly popular as a way to judge the quality of various products and services. However, recent work demonstrates that the absence of reporting incentives leads to a bias...", "weights": [[8, 15, 1.0]]}, "2012.wwwjournals_journal-ir0volumeA15A3.2": {"snippet": "...uy a product. In real life, when obtaining a service, many consumers depend on the user reviews. 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Based on the derived hierarchy, we generate a hierarchical organization of the consumer reviews based on various aspects of the product, and aggregate consumer opinions on the a...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [105, 113, 1.0], [114, 121, 1.0], [154, 161, 1.0], [177, 185, 1.0]]}, "2018.jasis_journal-ir0volumeA69A3.9": {"snippet": "...line consumer reviews and sales: Examining the chicken-egg relationships", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A2.0": {"snippet": "...ning Product Reviews in Web Forums", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2012.tist_journal-ir0volumeA3A4.3": {"snippet": "...esty reviews; a store is more reliable if it has more positive reviews from trustworthy reviewers; and a review is more honest if many other honest reviews support it. This is the first time such intr...", "weights": [[8, 15, 1.0], [66, 73, 1.0], [151, 158, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.5": {"snippet": "...etic reviews\n\n\n A B S T R A C TConsumers evaluate products through online reviews, in addition to sharing their product experiences. Online reviews affect product marketing, and companies use online r...", "weights": [[8, 15, 1.0], [77, 84, 1.0], [115, 122, 1.0], [143, 150, 1.0], [158, 165, 1.0], [202, 209, 1.0]]}, "2019.wwwconf_conference-2019.125": {"snippet": "...ting Product Descriptions from User Reviews", "weights": [[8, 15, 1.0], [39, 46, 1.0]]}}}
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1.0], [142, 149, 1.0], [150, 158, 1.0]]}, "2020.peoples-1.13": {"snippet": "Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations\n\n\n One of the major downsides of Deep Learning is its supposed need for vast amounts of tra...", "weights": [[0, 8, 1.0], [70, 78, 1.0], [97, 108, 1.0], [147, 155, 1.0]]}, "1986.sigirconf_conference-86.28": {"snippet": "A Machine Learning Approach to Information Retrieval", "weights": [[2, 9, 1.0], [10, 18, 1.0]]}, "P96-1050": {"snippet": "...s of Learning to Recognize Names Across Languages\n\n\n The development of natural language processing (NLP) systems that perform machine translation (MT) and information retrieval (IR) has highlighted t...", "weights": [[8, 16, 1.0], [130, 137, 1.0]]}, "W01-0501": {"snippet": "Limitations of Co-Training for Natural Language Learning from Large Datasets", "weights": [[0, 11, 1.0], [48, 56, 1.0]]}, "2019.sigirconf_workshop-2019birndl.15": {"snippet": "Why Machines Cannot Learn Mathematics, Yet", "weights": []}, "2007.cikm_conference-2007.109": {"snippet": "...ture learning on limited data\n\n\n ABSTRACTIn recent years, Bagging method has been applied to learn Bayesian networks (BNs), especially on limited datasets. However, the BNs learned using Bagging metho...", "weights": [[8, 16, 1.0]]}, "2015.wwwconf_conference-2015.79": {"snippet": "...onal machine learning (RML) is an excellent framework for these problems as it jointly models the user labels given their attributes and the relational structure. Further, semi-supervised learning met...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [191, 199, 1.0]]}, "W91-0302": {"snippet": "...onal machine translation: Where is the limit?) [In {N}orwegian]", "weights": [[8, 15, 1.0]]}, "2021.paclic-1.37": {"snippet": "Machine Learning Approach for Depression Detection in {J}apanese\n\n\n In this paper, we describe the results of our research aimed at detecting depression in Japanese sentences. In the United States and...", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "L16-1427": {"snippet": "...bust machine learning classifiers and less reliable evaluation results. In this paper we present a dataset balancing algorithm that minimizes the sample selection bias by eliminating irrelevant system...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2018.sigirconf_conference-2018.238": {"snippet": "...rain machine learning algorithms where there is no large-scale or highquality data in hand. Therefore, considering the quick progress in development of machine learning models, this is an ideal time f...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [155, 162, 1.0], [163, 171, 1.0]]}, "2017.sigirconf_conference-2017.229": {"snippet": "Machine Learning Powered A/B Testing\n\n\n ABSTRACTOnline search evaluation, and A/B testing in particular, is an irreplaceable tool for modern search engines. Typically, online experiments last for seve...", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2011.tois_journal-ir0volumeA29A4.1": {"snippet": "...s\n\n\n Machine-learned ranking functions have shown successes in Web search engines. With the increasing demands on developing effective ranking functions for different search domains, we have seen a bi...", "weights": [[8, 15, 1.0]]}, "2022.findings-acl.94": {"snippet": "Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming\n\n\n A critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is ex...", "weights": [[0, 8, 1.0], [126, 133, 1.0], [134, 142, 1.0]]}, "2020.sigirjournals_journal-ir0volumeA54A1.14": {"snippet": "Machine learning applied in natural language processing", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2006.coria_conference-2006.2": {"snippet": "...e of Machine Learning in Information Retrieval Research", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2014.wwwconf_conference-2014c.108": {"snippet": "...hese limitations with a computational approach and a large dataset of online news. Overall, we demonstrate how to carry out a large-scale computational research of agenda setting with online news data...", "weights": [[8, 19, 1.0]]}, "2017.wsdm_conference-2017.57": {"snippet": "Machine Learning at Amazon\n\n\n ABSTRACTIn this talk I will give an introduction into the field of machine learning and discuss why it is a crucial technology for Amazon.Machine learning is the science ...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [97, 104, 1.0], [105, 113, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "2002.spire_conference-2002.14": {"snippet": "Machine Learning Approach for Homepage Finding Task", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2011.tist_journal-ir0volumeA2A3.3": {"snippet": "...tric learning approach for portfolio selection\n\n\n Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this ...", "weights": [[8, 16, 1.0], [53, 60, 1.0], [61, 69, 1.0]]}, "2013.cikm_conference-2013.3": {"snippet": "...y in machine learning at scale with graphlab\n\n\n ABSTRACTToday, machine learning (ML) methods play a central role in industry and science. The growth of the Web and improvements in sensor data collecti...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [66, 73, 1.0], [74, 82, 1.0]]}, "2020.wwwconf_conference-2020c.108": {"snippet": "...Deep Learning for Temporal Forecasting of User Activity on Social Media: Challenges and Limitations\n\n\n The recent advances in neural network-based machine learning algorithms promise a revolution in p...", "weights": [[8, 16, 1.0], [91, 102, 1.0], [150, 157, 1.0], [158, 166, 1.0]]}, "D13-1173": {"snippet": "...ited Machine Translation\n\n\n We introduce dependency relations into deciphering foreign languages and show that dependency relations help improve the state-ofthe-art deciphering accuracy by over 500%. ...", "weights": [[8, 15, 1.0]]}, "2016.cikm_conference-2016.306": {"snippet": "...s of Machine Learning with Dataflow", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2007.cikm_conference-2007.3": {"snippet": "...ised machine learning recognize mentions of entities and relationships of predefined types in different kinds of sources, which can then be used to answer some useful types of queries. However, superv...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "W13-5103": {"snippet": "...ts a machine learning deidentification system for clinical free text in Dutch, relying on best practices from the state of the art in de-identification of English-language texts. We combine string and...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A5.3": {"snippet": "...ised learning to rank with pseudo labels\n\n\n Abstract Semi-supervised learning is a machine learning paradigm that can be applied to create pseudo labels from unlabeled data for learning a ranking mode...", "weights": [[8, 16, 1.0], [72, 80, 1.0], [86, 93, 1.0], [94, 102, 1.0], [180, 188, 1.0]]}, "D18-2008": {"snippet": "...hout machine learning experience as well as model developers. To address these limitations, we present DERE, a novel framework for declarative specification and compilation of templatebased informatio...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [82, 93, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.7": {"snippet": "...s to machine learning", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2020.wwwconf_conference-2020c.134": {"snippet": "...\n As Machine Learning (ML) systems becomes more ubiquitous, ensuring the fair and equitable application of their underlying algorithms is of paramount importance. We argue that one way to achieve this...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "P15-1166": {"snippet": "...ural machine translation model which generalizes machine translation as a sequence learning problem. We extend the neural machine translation to a multi-task learning framework which shares source lan...", "weights": [[8, 15, 1.0], [52, 59, 1.0], [86, 94, 1.0], [125, 132, 1.0], [161, 169, 1.0]]}, "2021.ipm_journal-ir0volumeA58A4.9": {"snippet": "A machine learning, bias-free approach for predicting business success using Crunchbase data", "weights": [[2, 9, 1.0], [10, 18, 1.0]]}, "2022.acl-long.390": {"snippet": "...ural Machine Translation with Phrase-Level Universal Visual Representations\n\n\n Multimodal machine translation (MMT) aims to improve neural machine translation (NMT) with additional visual information,...", "weights": [[8, 15, 1.0], [93, 100, 1.0], [142, 149, 1.0]]}, "2020.acl-srw.11": {"snippet": "...ural Machine Translation by Character Tokenization and Phoneme Decomposition\n\n\n The primary limitation of North Korean to English translation is the lack of a parallel corpus; therefore, high translat...", "weights": [[8, 15, 1.0]]}, "2019.tweb_journal-ir0volumeA13A2.1": {"snippet": "What Web Template Extractor Should I Use? A Benchmarking and Comparison for Five Template Extractors\n\n\n A Web template is a resource that implements the structure and format of a website, making it re...", "weights": []}, "2014.amta-workshop.2": {"snippet": "...tive machine learning paradigms, namely online and multi-task learning, measuring their capability to overcome the limitations of current batch methods. The results of our experiments, which are carri...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [65, 73, 1.0], [118, 129, 1.0]]}, "2018.wsdm_conference-2018.3": {"snippet": "...s to Machine Learning With Seven Sparks from the Causal Revolution\n\n\n ABSTRACTCurrent machine learning systems operate, almost exclusively, in a statistical, or model-blind mode, which entails severe ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [89, 96, 1.0], [97, 105, 1.0]]}, "2018.cikm_conference-2018.112": {"snippet": "...ming machine learning tasks with large data within the devices is challenging because of their limited memory resources. For this reason, diskbased machine learning methods have been actively research...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [151, 158, 1.0], [159, 167, 1.0]]}, "U07-1020": {"snippet": "...s to Machine-learning based Gene Normalisation", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2020.wsdm_conference-2020.26": {"snippet": "...ther machine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a heterogeneous network have several limitations, such as discovering ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [170, 181, 1.0]]}, "2002.amta-papers.1": {"snippet": "...rule learning for resource-limited {MT}", "weights": [[8, 16, 1.0]]}, "P16-1075": {"snippet": "...Task Learning for Automated Essay Scoring\n\n\n Supervised machine learning models for automated essay scoring (AES) usually require substantial task-specific training data in order to make accurate pred...", "weights": [[8, 16, 1.0], [59, 66, 1.0], [67, 75, 1.0]]}, "2021.acl-tutorials.6": {"snippet": "...e in machine learning.", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2003.cikm_conference-2003.36": {"snippet": "...ctor machine (SVM) learning algorithms focus on finding the hyperplane that maximizes the margin (the distance from the separating hyperplane to the nearest examples) since this criterion provides a g...", "weights": [[8, 15, 1.0], [22, 30, 1.0]]}, "W15-3802": {"snippet": "...ised machine learning techniques have proved to be the most effective approach to this problem. However, they require labelled training data which may not be available in sufficient quantity (or at al...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2021.wsdm_conference-2021.78": {"snippet": "...ther machine learning models, GNNs may make predictions biased on protected sensitive attributes, e.g., skin color and gender. Because machine learning algorithms including GNNs are trained to reflect...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [138, 145, 1.0], [146, 154, 1.0]]}, "2022.acl-tutorials.5": {"snippet": "Learning with Limited Text Data", "weights": [[0, 8, 1.0]]}, "2011.tist_journal-ir0volumeA2A3.7": {"snippet": "...cale machine learning\n\n\n With the dramatic increase in computing power, the field of machine learning has undergone tremendous changes in recent years. This change can be attributed to a deeper unders...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [88, 95, 1.0], [96, 104, 1.0]]}, "2015.tc-1.14": {"snippet": "...tion machine translation\n\n\n We have reached the theoretical limits of what can be achieved through the application of Statistical, Rule based and Transfer based machine translation technology. The lim...", "weights": [[8, 15, 1.0], [164, 171, 1.0]]}, "N07-2043": {"snippet": "...ised machine learning approaches to IE customization pose too high a burden on the user. Semisupervised learning approaches may in principle offer a more resource effective solution but are still insu...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [107, 115, 1.0]]}, "2016.cikm_conference-2016.93": {"snippet": "...aged machine learning to ameliorate these weaknesses, and in the process created novel experiences. In this talk, I will give three examples of machine learning improving the email experience.The firs...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [147, 154, 1.0], [155, 163, 1.0]]}, "W08-0305": {"snippet": "Learning Performance of a Machine Translation System: a Statistical and Computational Analysis\n\n\n We present an extensive experimental study of a Statistical Machine Translation system, Moses (Koehn e...", "weights": [[0, 8, 1.0], [26, 33, 1.0], [158, 165, 1.0]]}, "2018.ir_journal-ir0volumeA21A1.1": {"snippet": "Machine learning techniques for XML (co-)clustering by structure-constrained phrases", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2018.cikm_conference-2018.291": {"snippet": "...cale machine learning (ML) is becoming ubiquitous in various domains ranging from business intelligence to self-driving cars. Many companies are building ML pipelines in a unified data processing envi...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2009.ipm_journal-ir0volumeA45A3.2": {"snippet": "...in a machine learning model (in our case, Neural Network). In the data labeling, the method also makes use of a traditional IR model (in our case, BM25). A stopping criterion based on machine learning...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [187, 194, 1.0], [195, 203, 1.0]]}, "C14-1040": {"snippet": "Machine Translation Quality Estimation Across Domains\n\n\n Machine Translation (MT) Quality Estimation (QE) aims to automatically measure the quality of MT system output without reference translations. ...", "weights": [[0, 7, 1.0], [57, 64, 1.0]]}, "2020.findings-emnlp.399": {"snippet": "... for learning language representations across multiple linguistics tasks by Multi-Task Learning. LIMIT-BERT includes five key linguistics tasks: Part-Of-Speech (POS) tags, constituent and dependency s...", "weights": [[8, 16, 1.0], [90, 98, 1.0]]}, "2021.wsdm_conference-2021.156": {"snippet": "...p on Machine Reasoning: International Machine Reasoning Conference (MRC 2021)\n\n\n Recent years have witnessed the success of machine learning and especially deep learning in many research areas such as...", "weights": [[8, 15, 1.0], [41, 48, 1.0], [127, 134, 1.0], [135, 143, 1.0], [164, 172, 1.0]]}, "2020.sigirconf_conference-2020.4": {"snippet": "...s of Machine Learning: A Meta Theory\n\n\n Machine learning (ML) has been run and applied by premising a series of presuppositions, which contributes both the great success of AI and the bottleneck of fu...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [43, 50, 1.0], [51, 59, 1.0]]}, "2010.tois_journal-ir0volumeA28A4.0": {"snippet": "Clustering-based incremental web crawling\n\n\n When crawling resources, for example, number of machines, crawl-time, and so on, are limited, so a crawler has to decide an optimal order in which to crawl...", "weights": []}, "2020.wsdm_conference-2020.42": {"snippet": "...shot learning-based speaker identification framework (AFEASI) to develop robust speaker identification models with only a limited number of training instances. We first employ metric learning-based fe...", "weights": [[8, 16, 1.0], [186, 194, 1.0]]}, "1981.tc-1.21": {"snippet": "...n in machine translation", "weights": [[8, 15, 1.0]]}, "2017.tist_journal-ir0volumeA8A2.15": {"snippet": "...sual Learning: Image Recognition via Dissipative Learning Model\n\n\n According to consciousness involvement, human's learning can be roughly classified into explicit learning and implicit learning. Cont...", "weights": [[8, 16, 1.0], [52, 60, 1.0], [118, 126, 1.0], [167, 175, 1.0], [189, 197, 1.0]]}, "P16-1155": {"snippet": "...sfer learning have let go the limitations of traditional supervised machine learning algorithms for being dependent on annotated training data for training new models for every new domain. However, se...", "weights": [[8, 16, 1.0], [33, 44, 1.0], [71, 78, 1.0], [79, 87, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A5.15": {"snippet": "...reme learning machine with errors in variables\n\n\n Abstract Extreme learning machine (ELM) is widely used in training single-hidden layer feedforward neural networks (SLFNs) because of its good general...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [70, 78, 1.0], [79, 86, 1.0]]}, "2021.sustainlp-1.3": {"snippet": "Limitations of Knowledge Distillation for Zero-shot Transfer Learning\n\n\n Pretrained transformer-based encoders such as BERT have been demonstrated to achieve state-of-the-art performance on numerous N...", "weights": [[0, 11, 1.0], [61, 69, 1.0]]}, "P14-1031": {"snippet": "...ting machine learning approaches suffer from limitations in the modeling of complex linguistic structures across sentences and often fail to capture nonlocal contextual cues that are important for sen...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [48, 59, 1.0]]}, "2010.cikm_conference-2010.189": {"snippet": "...line learning for recency search ranking using real-time user feedback\n\n\n ABSTRACTTraditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance...", "weights": [[8, 16, 1.0], [97, 104, 1.0]]}, "2018.cikm_conference-2018.277": {"snippet": "...deep learning models if needed, we find that often simple linear n-gram models perform on par with deep learning methods at a much lower operational cost. The proposed method learns all parameters of ...", "weights": [[8, 16, 1.0], [107, 115, 1.0]]}, "C08-1065": {"snippet": "... and learning, and show robustness of a memory-based learning approach in doing authorship attribution and verification with many authors and limited training data when compared to eager learning meth...", "weights": [[8, 16, 1.0], [56, 64, 1.0], [190, 198, 1.0]]}, "2022.bionlp-1.26": {"snippet": "...g of machine learning performance: Evidence from a comprehensive set of simulations\n\n\n It is commonly claimed that inter-annotator agreement (IAA) is the ceiling of machine learning (ML) performance, ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "Y10-1067": {"snippet": "...of a machine learning framework that exploit syntactic information in the recognition of biomedical terms and present the limits of machine learning in generating a novel term candidate list. Conditio...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [135, 142, 1.0], [143, 151, 1.0]]}, "2002.sigirconf_conference-2002.15": {"snippet": "...ised learning for text summarization\n\n\n ABSTRACTWith the huge amount of information available electronically, there is an increasing demand for automatic text summarization systems. 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{"2018.clef_conference-2018.5": {"snippet": "...ical Medicine\n\n\n Abstract. The constantly expanding medical libraries contain immense amounts of information, including evidence from healthcare research. Gathering and interpreting this evidence can ...", "weights": [[8, 16, 1.0], [148, 156, 1.0]]}, "1995.jasis_journal-ir0volumeA46A10.5": {"snippet": "...s in Medicine", "weights": [[8, 16, 1.0]]}, "2012.jasis_journal-ir0volumeA63A8.15": {"snippet": "... the research in the research literature?", "weights": [[8, 16, 1.0], [24, 32, 1.0]]}, "2017.trec_conference-2017.10": {"snippet": "...sion Medicine\n\n\n The TREC 2017 Precision Medicine Track aims at building systems providing meaningful precision medicine-related information to clinicians in the field of oncology. The track includes ...", "weights": [[8, 16, 1.0], [44, 52, 1.0], [115, 123, 1.0], [124, 131, 1.0]]}, "2003.trec_conference-2003.8": {"snippet": "Recognizing Gene and Protein Function in MEDLINE Abstracts\n\n\n Identification of genes and proteins that affect biological function in humans and other organisms is a critical step in the discovery of ...", "weights": []}, "J07-1005": {"snippet": "...ical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which empl...", "weights": [[8, 16, 1.0], [131, 139, 1.0]]}, "2018.trec_conference-2018.44": {"snippet": "... IMS Research Group at TREC 2018 Precision Medicine Track\n\n\n We report on the participation of the Information Management System (IMS) Research Group of the University of Padua in the second task of t...", "weights": [[8, 16, 1.0], [46, 54, 1.0], [138, 146, 1.0]]}, "2014.sigirconf_conference-2014.16": {"snippet": "Interactions between health searchers and search engines\n\n\n ABSTRACTThe Web is an important resource for understanding and diagnosing medical conditions. Based on exposure to online content, people ma...", "weights": []}, "2020.wsdm_conference-2020.30": {"snippet": "...This research direction draws increasing attention in research areas such as economics, healthcare, and education. For example, we can study how a medicine (the treatment) causally affects the health ...", "weights": [[8, 16, 1.0], [57, 65, 1.0], [150, 158, 1.0]]}, "1976.jasis_journal-ir0volumeA27A5.12": {"snippet": "...ions research models to libraries: A Case Study of the Use of Monographs in the Francis A. Countway Library of Medicine, Harvard University. Ching-chih Chen. Cambridge, MA: MIT Press (1976)", "weights": [[8, 16, 1.0], [114, 122, 1.0]]}, "2021.naacl-main.51": {"snippet": "...NLP} research\n\n\n Preregistration refers to the practice of specifying what you are going to do, and what you expect to find in your study, before carrying out the study. This practice is increasingly ...", "weights": [[8, 16, 1.0]]}, "W17-2321": {"snippet": "...sion Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations\n\n\n The Precision Medicine Track in BioCreative VI aims to bring together the BioNLP commun...", "weights": [[8, 16, 1.0], [130, 138, 1.0]]}, "1987.jasis_journal-ir0volumeA38A1.6": {"snippet": "...y of Medicine: The view at 150 years", "weights": [[8, 16, 1.0]]}, "2010.sigirconf_conference-2010.98": {"snippet": "...ical research and decision-making.The objective of our research is to develop text analytics tools that are capable of parsing clinical medical data so that predefined search subjects that correspond ...", "weights": [[8, 16, 1.0], [58, 66, 1.0]]}, "2006.sigirconf_conference-2006.14": {"snippet": "...ical medicine based on the principles of evidence-based medicine (EBM). Experiments show that an EBM-based scoring algorithm dramatically outperforms a state-of-the-art baseline that employs only term...", "weights": [[8, 16, 1.0], [59, 67, 1.0]]}, "2005.jasis_journal-ir0volumeA56A5.11": {"snippet": "...y of Medicine", "weights": [[8, 16, 1.0]]}, "1991.jasis_journal-ir0volumeA42A4.5": {"snippet": "...text research project", "weights": [[8, 16, 1.0]]}, "2021.wsdm_conference-2021.3": {"snippet": "...ized Medicine\n\n\n The recent availability of diverse health data resources on large cohorts of human individuals presents many challenges and opportunities. I will present our work aimed at developing ...", "weights": [[8, 16, 1.0]]}, "2015.wsdm_conference-2015.53": {"snippet": "... and Medicine from Internet Data\n\n\n ABSTRACTSurveys show that around 70% of US Internet users consult the Internet when they require medical information. People seek this information using both tradit...", "weights": [[8, 16, 1.0]]}, "2019.trec_conference-2019.16": {"snippet": "...sion Medicine Track\n\n\n This paper describes the system developed for the TREC 2019 Precision Medicine Track by the Team DUTIR from Dalian University of Technology. In the system, we applied a hybrid m...", "weights": [[8, 16, 1.0], [96, 104, 1.0]]}, "P17-5001": {"snippet": "...sion Medicine\n\n\n We will introduce precision medicine and showcase the vast opportunities for NLP in this burgeoning field with great societal impact. We will review pressing NLP problems, state-of-th...", "weights": [[8, 16, 1.0], [48, 56, 1.0]]}, "1983.sigirconf_conference-83.4": {"snippet": "Intelligent Information Systems\n\n\n ABSTRACTNatural language processing techniques developed for Artificial Intelligence programs can aid in constructing powerful information retrieval systems in at le...", "weights": []}, "1983.sigirconf_conference-83.0": {"snippet": "Research and Development in Information Retrieval, Sixth Annual International ACM SIGIR Conference, National Library of Medicine, Bethesda, Maryland, USA, June 6-8, 1983", "weights": [[0, 8, 1.0], [120, 128, 1.0]]}, "W12-2422": {"snippet": "...ased Medicine (EBM) is the practice of using the knowledge gained from the best medical evidence to make decisions in the effective care of patients. This medical evidence is extracted from medical do...", "weights": [[8, 16, 1.0]]}, "2018.trec_conference-2018.34": {"snippet": "...sion Medicine Track\n\n\n This paper describes the approach developed by the MedIER team-a collaboration between the University of Michigan and the University of Cincinnati-for the TREC 2018 Precision Me...", "weights": [[8, 16, 1.0], [201, 209, 1.0]]}, "2014.sigirconf_workshop-2014medir.9": {"snippet": "Exploring Clustering Based Knowledge Discovery towards Improved Medical Diagnosis\n\n\n We propose to develop a framework for an intelligent reasoner with capabilities that support complex decision makin...", "weights": []}, "2020.sigirconf_conference-2020.179": {"snippet": "...sion Medicine Literature Search\n\n\n In professional search tasks such as precision medicine literature search, queries often involve multiple aspects. To assess the relevance of a document, a searcher ...", "weights": [[8, 16, 1.0], [85, 93, 1.0]]}, "2018.trec_conference-2018.14": {"snippet": "...sion Medicine Track\n\n\n Nowadays, the growing amount of biomedical scientific literature that can be accessed online represents a valuable source of information useful to tailor medical decisions to a ...", "weights": [[8, 16, 1.0]]}, "D12-1053": {"snippet": "...ased Medicine Categories\n\n\n Evidence-based medicine is an approach whereby clinical decisions are supported by the best available findings gained from scientific research. This requires efficient acce...", "weights": [[8, 16, 1.0], [46, 54, 1.0], [165, 173, 1.0]]}, "1956.earlymt-1.4": {"snippet": "...t on Research", "weights": [[8, 16, 1.0]]}, "U11-1012": {"snippet": "...ased Medicine Summarisation\n\n\n In this paper we introduce some of the key NLP-related problems related to the practice of Evidence Based Medicine and propose the task of multi-document query-focused s...", "weights": [[8, 16, 1.0], [81, 88, 1.0], [98, 105, 1.0], [140, 148, 1.0]]}, "2014.sigirconf_conference-2014.215": {"snippet": "...tive research approaches, so that when evaluating research reports participants can consider the relationship among the research goals, approaches and the researcher's choices, as opposed to whether t...", "weights": [[8, 16, 1.0], [53, 61, 1.0], [123, 131, 1.0]]}, "2016.wwwconf_conference-2016c.136": {"snippet": "Research Collaborations in Multidisciplinary Institutions: a Case Study of iSchools\n\n\n ABSTRACTAlthough closely related, multidisciplinarity and interdisciplinarity are different. The former indicates...", "weights": [[0, 8, 1.0], [112, 119, 1.0]]}, "1972.ipm_journal-ir0volumeA8A6.0": {"snippet": "...y of medicine and international medlars cooperation", "weights": [[8, 16, 1.0]]}, "2016.sigirconf_conference-2016.230": {"snippet": "...alth-related content is one of the most searched-for topics on the internet, and as such this is an important domain for research in information retrieval.Medical information is of interest to a wide ...", "weights": [[8, 15, 1.0], [124, 132, 1.0]]}, "2018.trec_conference-2018.13": {"snippet": "...sion Medicine Track\n\n\n The TREC-PM challenge aims for advances in the field of information retrieval applied to precision medicine. Here we describe our experimental setup and the achieved results in ...", "weights": [[8, 16, 1.0], [125, 133, 1.0]]}, "J80-1004": {"snippet": "...s in Medicine", "weights": [[8, 16, 1.0]]}, "2021.emnlp-main.486": {"snippet": "...OVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public", "weights": [[8, 16, 1.0], [118, 126, 1.0]]}, "2020.findings-emnlp.78": {"snippet": "... and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83 % for AI papers and 95 % in the field of medicine. It is open source 1 and available for t...", "weights": [[8, 16, 1.0], [154, 162, 1.0]]}, "2014.sigirconf_conference-2014.214": {"snippet": "...tive research approaches, so that when evaluating research reports participants can consider the relationship among the research goals, approaches and the researcher's choices, as opposed to whether t...", "weights": [[8, 16, 1.0], [53, 61, 1.0], [123, 131, 1.0]]}, "2017.trec_conference-2017.1": {"snippet": "...sion Medicine-related Biomedical Article Retrieval and Clinical Trial Matching", "weights": [[8, 16, 1.0], [17, 24, 1.0]]}, "2019.sigirconf_conference-2019.213": {"snippet": "...sion Medicine", "weights": [[8, 16, 1.0]]}, "2015.cikm_workshop-2015dtmbio.16": {"snippet": "...drug research trend is recognized to minimize risk of new drug testing and understand the anti-cancer drug research area. The purpose of this paper is to identify the landscape of pancreatic cancer dr...", "weights": [[8, 16, 1.0], [110, 118, 1.0]]}, "2012.ipm_journal-ir0volumeA48A3.0": {"snippet": "...lems related with engineering, industrial applications, medicine, finances, etc. In this special issue we present a set of seven papers that report original research about the use of SC techniques to ...", "weights": [[8, 15, 1.0], [59, 67, 1.0], [160, 168, 1.0]]}, "2017.trec_conference-2017.5": {"snippet": "...sion Medicine Track\n\n\n This paper describes the participation of the CBNU team at the TREC Precision Medicine Track 2017. We have constructed cancer-centered document clusters using cancer-gene relati...", "weights": [[8, 16, 1.0], [104, 112, 1.0]]}, "2020.trec_conference-2020.13": {"snippet": "...sion Medicine 2020", "weights": [[8, 16, 1.0]]}, "2013.tist_journal-ir0volumeA4A2.13": {"snippet": "Research commentary: Intelligent systems and technology for integrative and predictive medicine: An ACP approach\n\n\n One of the principal goals in medicine is to determine and implement the best treatm...", "weights": [[0, 8, 1.0], [87, 95, 1.0], [146, 154, 1.0]]}, "2017.trec_conference-2017.60": {"snippet": "...sion Medicine Track\n\n\n In this paper, we describe the system designed for the TREC 2017 Precision Medicine track by the University of Texas at Dallas (UTD) Human Language Technology Research Institute...", "weights": [[8, 16, 1.0], [101, 109, 1.0], [185, 193, 1.0]]}, "2008.cikm_conference-2008.16": {"snippet": "...ests related medical phrases to help the user quickly digest search results and refine the query. We evaluated MedSearch using medical questions posted on medical discussion forums. The results show t...", "weights": [[8, 15, 1.0]]}, "2013.sigirconf_conference-2013.198": {"snippet": "...cing medicine\n\n\n ABSTRACTThis workshop brings together researchers and practitioners from industry and academia to discuss search and discovery in the medical domain. The event focuses on ways to make...", "weights": [[8, 16, 1.0]]}, "2018.trec_conference-2018.19": {"snippet": "...sion Medicine track\n\n\n Precision medicine aims to provide the most accurate course of treatment, personalized for each patient. The 2018 Precision Medicine (PM) track aims to build systems, that sift ...", "weights": [[8, 16, 1.0], [36, 44, 1.0], [150, 158, 1.0]]}, "2015.cikm_workshop-2015dtmbio.15": {"snippet": "...ical research (COSAMED)\n\n\n ABSTRACTAnalyzing true effect of medicine or functional food is major issue in associated research area. In order to achieve that, gathering of massive clinical trial data i...", "weights": [[8, 16, 1.0], [63, 71, 1.0], [120, 128, 1.0]]}, "2019.trec_conference-2019.17": {"snippet": "...sion Medicine Track\n\n\n he paper describes the system presented by the University of L'Aquila in collaboration with the University of Havana-team named UNIVAQ-to the TREC 2019 Precision Medicine Track....", "weights": [[8, 16, 1.0], [188, 196, 1.0]]}, "D19-6202": {"snippet": "...d of medicine as they do in others. In this work, we use intrinsic methods to evaluate embeddings from the various fields of medicine as defined by their ICD-9 systems. We find significant differences...", "weights": [[8, 16, 1.0], [128, 136, 1.0]]}, "1983.sigirconf_conference-83.19": {"snippet": "A Network Organization Used for Document Retrieval\n\n\n AbstractA network organization for implementing a document retrieval system is proposed.This organization has significant advantages in terms of t...", "weights": []}, "1983.sigirconf_conference-83.9": {"snippet": "Evaluation of The 2-Poisson Model as a Basis for Using Term Frequency Data in Searching\n\n\n ABSTRACTThe early work on the probabilistic", "weights": []}, "2018.sigirconf_conference-2018.226": {"snippet": ".... IR research has much to offer here by developing new tools and techniques specific to this domain .The range of health information available (primary research sources, secondary research sources, pa...", "weights": [[8, 16, 1.0], [155, 163, 1.0], [183, 191, 1.0]]}, "1983.sigirconf_conference-83.6": {"snippet": "Progress Report on Project Information Bridge", "weights": []}, "C14-1045": {"snippet": "...from research articles in three domains (medicine, terminology, and science) in the framework of Rhetorical Structure Theory (RST). The results help to establish a new criteria to be used in RST-based...", "weights": [[8, 16, 1.0], [44, 52, 1.0]]}, "E17-1110": {"snippet": "...sion medicine show that our approach can learn an accurate cross-sentence extractor, using only a small existing knowledge base and unlabeled text from biomedical research articles. Compared to the ex...", "weights": [[8, 16, 1.0], [166, 174, 1.0]]}, "2018.clef_conference-2018w.48": {"snippet": "...ical Medicine\n\n\n The 2018 CLEF eHealth Task 2 has two sub-tasks in order to write a systematic review of evidence-based medicine. Researchers are required to retrieve relevant documents given by medic...", "weights": [[8, 16, 1.0], [123, 131, 1.0]]}, "2009.cikm_conference-2009.57": {"snippet": "...nese medicine book search engine\n\n\n ABSTRACTLearning traditional Chinese medicine knowledge from the digital library is becoming more and more important these days in China. 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However, most of the currently Chinese...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [68, 75, 1.0], [76, 84, 1.0], [85, 95, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.12": {"snippet": "...atic processing of temporal expressions and events in natural language\n\n\n a b s t r a c tThis paper addresses the problem of the automatic recognition and classification of temporal expressions and ev...", "weights": [[8, 18, 1.0], [57, 64, 1.0], [65, 73, 1.0]]}, "1996.trec_conference-1996.17": {"snippet": "...able Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "P14-6001": {"snippet": "... for Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "1986.jasis_journal-ir0volumeA37A4.2": {"snippet": "Natural language processing in information retrieval", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "1990.ipm_journal-ir0volumeA26A1.3": {"snippet": "Natural language processing and information retrieval", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2013.tist_journal-ir0volumeA4A3.2": {"snippet": "...ious natural language processing tasks, including statistical machine translation. A commonly used method for paraphrase generation is pivoting , which benefits from linguistic knowledge implicit in t...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2001.jasis_journal-ir0volumeA52A13.20": {"snippet": "Natural language processing: Word recognition without segmentation", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2016.fire_conference-2016w.55": {"snippet": "...sing Natural Language Processing Techniques & Semantic Similarity Computations\n\n\n The paper reports the approaches utilized and results achieved for our system in the shared task (in FIRE-2016) for pa...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "J91-2013": {"snippet": "...eech Processing presents extensive reviews of current thinking on psycholinguistic and computational topics in speech recognition and natural language processing, along with a substantial body of new ...", "weights": [[8, 18, 1.0], [137, 144, 1.0], [145, 153, 1.0], [154, 164, 1.0]]}, "H94-1119": {"snippet": "...h in Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "C86-1052": {"snippet": "...n to Natural Language Processing\n\n\n Semantic processing is one of the important tasks for natural language processing. Basic to semantic processing is descriptions of lexical items. The most frequentl...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [48, 58, 1.0], [93, 100, 1.0], [101, 109, 1.0], [110, 120, 1.0], [140, 150, 1.0]]}, "H90-1078": {"snippet": "...tive Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "W17-8106": {"snippet": "Natural Language Processing in Political Campaigns", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2005.clef_workshop-2005w.8": {"snippet": "...ight Natural Language Processing to Ad-Hoc Cross Language Information Retrieval\n\n\n In the CLEF 2005 Ad-Hoc Track we experimented with language-specific morphosyntactic processing and light Natural Lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [52, 60, 1.0], [137, 145, 1.0], [171, 181, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "I05-2043": {"snippet": "...nese Natural Language Processing Studies over the Last Decade\n\n\n Using natural language processing, we carried out a trend survey on Japanese natural language processing studies that have been done ov...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [74, 81, 1.0], [82, 90, 1.0], [91, 101, 1.0], [145, 152, 1.0], [153, 161, 1.0], [162, 172, 1.0]]}, "J88-3020": {"snippet": "... for natural language processing and the fact that one must consider what control structures or procedures must be in place to process language. These issues are never related to language as a process...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [138, 146, 1.0], [182, 190, 1.0]]}, "2018.trec_conference-2018.31": {"snippet": "...d on Natural Language Pre-processing (NLP) tools and Word2Vec embedding models. Using the title of TREC topics, we select semantically related terms to the query. Our approach is composed of four step...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [29, 39, 1.0]]}, "2016.sigirconf_conference-2016.1": {"snippet": "...uman Language: Can NLP and Deep Learning Help?\n\n\n ABSTRACTThere is a lot of overlap between the core problems of information retrieval (IR) and natural language processing (NLP). An IR system gains fr...", "weights": [[8, 16, 1.0], [147, 154, 1.0], [155, 163, 1.0], [164, 174, 1.0]]}, "H93-1101": {"snippet": "...h in Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.5": {"snippet": "... for natural language generation\n\n\n a b s t r a c tReferring expression generation is the part of natural language generation that decides how to refer to the entities appearing in an automatically ge...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [101, 108, 1.0], [109, 117, 1.0]]}, "C86-1103": {"snippet": "... for Natural Language Processing\n\n\n The paper describes the design of a fair sized lexical database that is to be used with a natural language based expert system with German as the language of intera...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [129, 136, 1.0], [137, 145, 1.0], [185, 193, 1.0]]}, "2010.wwwjournals_journal-ir0volumeA13A3.3": {"snippet": "...work Language Generation based on a Semantic Networks Serialization Grammar\n\n\n Abstract This paper studies the Semantic Network Language Generation (SNLG), which is used to generate natural language f...", "weights": [[8, 16, 1.0], [131, 139, 1.0], [185, 192, 1.0], [193, 201, 1.0]]}, "P92-1014": {"snippet": "...bust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effectiveness of traditional key-wor...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [113, 120, 1.0], [121, 129, 1.0], [130, 140, 1.0]]}, "2007.wwwconf_conference-2007.6": {"snippet": "...of a natural-language-processing system and an intelligent form system that observes an assistant processing requests. The intelligent form system then generates a labeled training corpus by interpret...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [101, 111, 1.0]]}, "1984.tois_journal-ir0volumeA2A1.0": {"snippet": "...ased Natural Language Processor for Office Environments\n\n\n During the 1970s, a number of systems providing limited English-language processing capabilities were developed to permit computer access by ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [126, 134, 1.0], [135, 145, 1.0]]}, "R09-1050": {"snippet": "...s of natural language technology in areas as diverse as natural language generation, natural language understanding, machine translation, dialog system etc. Here a mathematical exploration of the issu...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [59, 66, 1.0], [67, 75, 1.0], [88, 95, 1.0], [96, 104, 1.0]]}, "W03-0807": {"snippet": "... for Natural Language Processing\n\n\n In Natural Language Processing (NLP), research results from software engineering and software technology have often been neglected. This paper describes some factor...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [42, 49, 1.0], [50, 58, 1.0], [59, 69, 1.0]]}, "1990.ipm_journal-ir0volumeA26A1.5": {"snippet": "...a in natural language processing and information retrieval", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2022.acl-tutorials.8": {"snippet": "Natural Language Processing for Multilingual Task-Oriented Dialogue", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "N09-2014": {"snippet": "...ards Natural Language Understanding of Partial Speech Recognition Results in Dialogue Systems\n\n\n We investigate natural language understanding of partial speech recognition results to equip a dialogue...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [115, 122, 1.0], [123, 131, 1.0]]}, "H91-1021": {"snippet": "...oken language systems, Unisys natural language coupled with MIT-Summit speech recognition, Unisys natural language coupled wish MIT-Lincoln Labs speech recognition and Unisys natural language coupled ...", "weights": [[8, 16, 1.0], [33, 40, 1.0], [41, 49, 1.0], [101, 108, 1.0], [109, 117, 1.0], [178, 185, 1.0], [186, 194, 1.0]]}, "C88-1008": {"snippet": "... for Natural Language Processing\n\n\n This paper presents the most important characteristic of the new formalism used in Eurotra, the E-Framework. It is a formalism for natural language processing withi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [170, 177, 1.0], [178, 186, 1.0], [187, 197, 1.0]]}, "P05-3017": {"snippet": "... for Natural Language Processing\n\n\n We demonstrate a system for flexible querying against text that has been annotated with the results of NLP processing. The system supports self-overlapping and para...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [146, 156, 1.0]]}, "2008.wwwconf_conference-2008.233": {"snippet": "...ols. Natural language processing (NLP) is one of the key technologies for solving the above Web usability problems. Almost all the Web page provide with the essential information in the form of natura...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [197, 204, 1.0]]}, "2004.ipm_journal-ir0volumeA40A6.4": {"snippet": "... the natural language processing (NLP) system for improving the system. Therefore, the necessity of co-occurrence word information in the natural language processing system increases further and vario...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [141, 148, 1.0], [149, 157, 1.0], [158, 168, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.23": {"snippet": "... for natural language conversation\n\n\n AbstractAs an important task of artificial intelligence, natural language conversation has attracted wide attention of researchers in natural language processing....", "weights": [[8, 15, 1.0], [16, 24, 1.0], [98, 105, 1.0], [106, 114, 1.0], [175, 182, 1.0], [183, 191, 1.0], [192, 202, 1.0]]}, "2020.wsdm_conference-2020.120": {"snippet": "...p on Natural Language Processing for Recommendations\n\n\n Natural language processing is becoming more and more important in recommender systems. This half day workshop explores challenges and potential...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [59, 66, 1.0], [67, 75, 1.0], [76, 86, 1.0]]}, "2021.winlp-1.4": {"snippet": "Natural language processing as a tool to identify the {R}eddit particularities of cancer survivors around the time of diagnosis and remission: A pilot study", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2001.ir_journal-ir0volumeA4A1.4": {"snippet": "...ical Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "W17-3704": {"snippet": "...that Natural Language is a suitable tool for describing human perceptions, building selfexplanatory computational systems ready to communicate with humans in Natural Language becomes a hot challenge. ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [161, 168, 1.0], [169, 177, 1.0]]}, "J86-2001": {"snippet": "...y, a natural language interface should be constructed in accord with principles of integrated processing: processing syntax and semantics at the same time, processing syntax and semantics using the sa...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [97, 107, 1.0], [109, 119, 1.0], [159, 169, 1.0]]}, "2020.acl-tutorials.4": {"snippet": "...wing Natural Language Processing Research", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "C92-2121": {"snippet": "...cale Natural Language Processing\n\n\n This paper demonstrates the utility of the Semantic Network Array Processor (SNAP) as a massively parallel platform for high performance and large-scale natural lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "C69-4101": {"snippet": "...ated Language Processing\n\n\n This paper is concerned with natural language, computers, and two groups of people interested in natural language: ]inguists, and persons engaged in computer processing of ...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [60, 67, 1.0], [68, 76, 1.0], [128, 135, 1.0], [136, 144, 1.0], [189, 199, 1.0]]}, "1985.tmi-1.17": {"snippet": "... and Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "J75-4013": {"snippet": "...nd a Natural-Language Interface for Interactive Data Management\n\n\n We describe a natural-language recognition system having both applied and theoretical relevance. A t the applications level, the prwr...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [84, 91, 1.0], [92, 100, 1.0]]}, "2014.wsdm_conference-2014.44": {"snippet": "...ting natural language processing algorithms were designed for processing monolingual information, mixed multilingual texts cannot be well analyzed by them. Hence, it is of critical importance to prepr...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [65, 75, 1.0]]}, "1985.tois_journal-ir0volumeA3A2.1": {"snippet": "...LSP) natural language processing system has been developed as a domain-independent natural language processing system. Initially utilized for processing sets of medical messages and other texts in the...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [86, 93, 1.0], [94, 102, 1.0], [103, 113, 1.0], [145, 155, 1.0]]}, "J95-3006": {"snippet": "...ews: Natural Language Processing: A Paninian Perspective\n\n\n This book is an elementary introduction to natural language processing, but it is a very unusual one for two reasons. First, its subject lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [106, 113, 1.0], [114, 122, 1.0], [123, 133, 1.0]]}, "W04-0609": {"snippet": "...ased Natural Language Processing\n\n\n Conceptualising a domain has long been recognised as a prerequisite for understanding that domain and processing information about it. Ontologies are explicit speci...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [141, 151, 1.0]]}, "2021.teachingnlp-1.3": {"snippet": "Natural Language Processing 4 All ({NLP}4{A}ll): A New Online Platform for Teaching and Learning {NLP} Concepts\n\n\n Natural Language Processing offers new insights into language data across almost all ...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [115, 122, 1.0], [123, 131, 1.0], [132, 142, 1.0], [168, 176, 1.0]]}, "2016.coria_conference-2016.23": {"snippet": "Processing Natural Language Queries to Disambiguate Named Entities and Extract Users' Goals : Application to e-Tourism", "weights": [[0, 10, 1.0], [11, 18, 1.0], [19, 27, 1.0]]}, "J01-2007": {"snippet": "...ews: Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language\n\n\n The strong connection between knowledge representation and reasoning and natural lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [66, 74, 1.0], [107, 115, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "W05-0705": {"snippet": "...ng a Natural Language Processing System for {E}uropean Languages to Treat {A}rabic in Information Processing and Information Retrieval Applications\n\n\n The goal of many natural language processing plat...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [101, 111, 1.0], [171, 178, 1.0], [179, 187, 1.0], [188, 198, 1.0]]}, "2020.rocling-1.36": {"snippet": "Natural Language Processing for Digital Humanities", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2019.sigirconf_workshop-2019birndl.0": {"snippet": "... and Natural Language Processing for Digital Libraries (BIRNDL 2019) co-located with the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), Pari...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "J06-1006": {"snippet": "... and Language Processing by John Coleman represents a bold effort to educate students in speech science about some of the important methods used in speech and natural language processing (NLP). This b...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [162, 169, 1.0], [170, 178, 1.0], [179, 189, 1.0]]}, "2001.ipm_journal-ir0volumeA37A6.0": {"snippet": "Natural language processing in support of decision-making: phrases and part-of-speech tagging", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "C92-4190": {"snippet": "...h in natural language processing, however, has not dealt seriously with adverbs, though linguists have done significant work on this word class. The current paper draws on this linguistic research to ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "E14-2001": {"snippet": "...nt a natural language processing (NLP) platform, namely the \"ITU Turkish NLP Web Service\" by the natural language processing group of Istanbul Technical University. The platform (available at tools.nl...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [100, 107, 1.0], [108, 116, 1.0], [117, 127, 1.0]]}, "2007.sigirconf_conference-2007.3": {"snippet": "...NLP -natural language processing -or NLIP -processing information conveyed in natural language. At that time, most researchers thought primarily of the efficiency gains that could be Copyright is held...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [46, 56, 1.0], [81, 88, 1.0], [89, 97, 1.0]]}, "W12-1009": {"snippet": "...uery processing is an essential part of a range of applications in the social sciences and cultural heritage do main. However, outof-the-box natural language processing tools originally developed for ...", "weights": [[8, 18, 1.0], [144, 151, 1.0], [152, 160, 1.0], [161, 171, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.5": {"snippet": "Natural language processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "N03-5001": {"snippet": "...ical Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2020.sigirconf_conference-2020.365": {"snippet": "...ires processing and understanding such information effectively and efficiently, where natural language processing (NLP) technologies are widely deployed. Natural language data are represented as a seq...", "weights": [[8, 18, 1.0], [89, 96, 1.0], [97, 105, 1.0], [106, 116, 1.0], [157, 164, 1.0], [165, 173, 1.0]]}, "M95-1021": {"snippet": "...UNO} Natural Language Processing System as Used for {MUC}-6\n\n\n The UNO natural language processing (NLP) system implements a Boolean algebra computational model o f natural language [Iwadska, 1992 ] [...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [74, 81, 1.0], [82, 90, 1.0], [91, 101, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "1992.trec_conference-1992.29": {"snippet": "...e of Natural Language Processing at TREC", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "J89-3012": {"snippet": "...ews: Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2021.acl-long.570": {"snippet": "...s of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom att...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [64, 71, 1.0], [72, 80, 1.0], [81, 91, 1.0], [167, 175, 1.0], [176, 186, 1.0]]}, "1971.sigirconf_conference-71.2": {"snippet": "...ated processing of natural language is a realistic goal. It should not be neglected, despite the fact that it is difficult to predict when this goal will be achieved.\nKEY WORDS AND PHRASESnatural lang...", "weights": [[8, 18, 1.0], [22, 29, 1.0], [30, 38, 1.0], [199, 207, 1.0]]}, "J81-4005": {"snippet": "Natural Language Processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2018.sigirconf_workshop-2018birndl.0": {"snippet": "... and Natural Language Processing for Digital Libraries (BIRNDL 2018) co-located with the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), Ann ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "L18-1025": {"snippet": "...s in natural language processing papers, can account for a variety of types of research reports. These dimensions are reproducibility of a conclusion, of a finding, and of a value. Three biomedical na...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [201, 208, 1.0]]}, "2017.wwwconf_conference-2017c.152": {"snippet": "...uent natural language questions.\nBACKGROUND AND MOTIVATIONAs more and more RDF Question/Answering (Q/A) systems over RDF knowledge graphs are designed, which allow users to express queries in natural ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [195, 202, 1.0]]}, "P86-1002": {"snippet": "...ging Natural Language Processing to the Microcomputer Market: The Story of {Q{\\&}A}", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2007.ecir_conference-2007.46": {"snippet": "Natural Language Processing for Usage Based Indexing of Web Resources", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "C80-1059": {"snippet": "Processing of Syntax and Semantics of Natural Language by Predicate Logic of Predicate Logic\n\n\n The syntax and semantic analyses of natural language are described from the standpoint of manmachine com...", "weights": [[0, 10, 1.0], [38, 45, 1.0], [46, 54, 1.0], [132, 139, 1.0], [140, 148, 1.0]]}, "2002.ntcir_workshop-2002.5": {"snippet": "...n of Natural Language Processing (NLP) techniques such as summarization, information extraction, and text mining. One of the challenges with these technologies is developing an accurate Question and A...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2020.cikm_conference-2020.67": {"snippet": "...d in natural language and convert it into forms that can be more easily and precisely processed by computers. Knowledge Graphs (KGs) offer offer a good solution for such processing. Natural Language P...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [173, 183, 1.0], [185, 192, 1.0], [193, 201, 1.0], [202, 212, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...t of natural language processing techniques. Different groups focused on different aspects of the overall problem, ranging from the development of language-independent tools such as stemmers to much w...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [150, 158, 1.0]]}, "Y99-1019": {"snippet": "...oken Language Systems - Technical Challenges for Speech and Natural Language Processing\n\n\n Speech is the most natural means of communication among humans. It is also believed that spoken language proc...", "weights": [[8, 16, 1.0], [63, 70, 1.0], [71, 79, 1.0], [80, 90, 1.0], [113, 120, 1.0], [190, 198, 1.0], [199, 209, 1.0]]}, "1995.sigirjournals_journal-ir0volumeA29A2.2": {"snippet": "...ion: Natural Language Processing and Information Retrieval Group Information Access and User Interfaces Division National Institute of Standards and Technology\n\n\n OverviewThe Natural Language Processi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [178, 185, 1.0], [186, 194, 1.0], [195, 205, 1.0]]}, "Y12-1005": {"snippet": "...e of Natural Language Processing?\n\n\n In this talk, I will outline some of the myriad of challenges and opportunities that social media offer for natural language processing. 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In this paper, we focus on the impact of pairwise preferences on the diversity of recommendations. We propose a novel graph-based ranking oriented recommendation algorithm that exploits bo...", "weights": [[8, 13, 1.0], [133, 138, 1.0], [139, 144, 1.0], [145, 152, 1.0]]}, "2019.ipm_journal-ir0volumeA56A4.5": {"snippet": "...ised graph-based rank aggregation for improved retrieval\n\n\n A R T I C L E I N F O\nKeywords:Rank aggregation Content-based retrieval Multimodal retreival Graph-based fusion A B S T R A C T This paper p...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [119, 124, 1.0], [156, 161, 1.0], [162, 167, 1.0]]}, "2015.sigirconf_workshop-2015gsb.1": {"snippet": "...onal graph. 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Given an arbitrary directed graph with edge and node labels, we present a new flow-based model and an efficient method to dynamically rank the nod...", "weights": [[8, 15, 1.0], [85, 90, 1.0], [140, 145, 1.0]]}, "2008.ecir_conference-2008.70": {"snippet": "Graph-Based Profile Similarity Calculation Method and Evaluation", "weights": [[0, 5, 1.0], [6, 11, 1.0]]}, "2018.cikm_conference-2018.276": {"snippet": "Deep Graph Embedding for Ranking Optimization in E-commerce", "weights": [[5, 10, 1.0], [25, 32, 1.0]]}, "2020.wsdm_conference-2020.23": {"snippet": "...s on graph data and demonstrated that graph embedding techniques are also vulnerable to adversarial attacks. Fake users on social media and fake product reviews are examples of perturbations in graph ...", "weights": [[8, 13, 1.0], [41, 46, 1.0], [197, 202, 1.0]]}, "2016.ijmir_journal-ir0volumeA5A1.4": {"snippet": "... and graph-based ranking", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0]]}, "2009.cikm_conference-2009.103": {"snippet": "...odel-based ranking for queries on RDF-graphs", "weights": [[8, 13, 1.0], [14, 21, 1.0]]}, "2008.airs_conference-2008.25": {"snippet": "...ised Graph-Ranking for Text Retrieval", "weights": [[8, 13, 1.0], [14, 21, 1.0]]}, "2017.wsdm_conference-2017.83": {"snippet": "...ised Ranking using Graph Structures and Node Attributes\n\n\n ABSTRACTPageRank has been the signature unsupervised ranking model for ranking node importance in a graph. One potential drawback of PageRank...", "weights": [[8, 15, 1.0], [22, 27, 1.0], [115, 122, 1.0], [133, 140, 1.0], [162, 167, 1.0]]}, "2007.wwwconf_conference-2007.194": {"snippet": "...hted graph model k-UCM graph to reveal the implicit relationship between authors hidden behind the citations among their articles. Furthermore, we design a graph-based ranking algorithm, the Credit Au...", "weights": [[8, 13, 1.0], [26, 31, 1.0], [159, 164, 1.0], [165, 170, 1.0], [171, 178, 1.0]]}, "2009.cikm_conference-2009.121": {"snippet": "... and ranking, feature weighting methods developed for classification cannot be readily applied to feature weighting for ranking. A state of the art feature selection method for ranking, called GAS, ha...", "weights": [[8, 15, 1.0], [123, 130, 1.0], [180, 187, 1.0]]}, "2009.cikm_conference-2009.258": {"snippet": "...raph based semi-supervised sentence ranking for query-oriented summarization\n\n\n ABSTRACTGraph based sentence ranking algorithms such as PageRank and HITS have been successfully used in query-oriented ...", "weights": [[8, 13, 1.0], [39, 46, 1.0], [97, 102, 1.0], [112, 119, 1.0]]}, "2017.ictir_conference-2017.31": {"snippet": "...best graph-based vertical ranking methods substantially and statistically signi cantly outperform a competitive baseline that ranks verticals based on click counts.", "weights": [[8, 13, 1.0], [14, 19, 1.0], [29, 36, 1.0], [145, 150, 1.0]]}, "S15-1036": {"snippet": "...ovel graph-based coherence features based on frequent subgraphs and compare their ability to assess the readability of Wall Street Journal articles. In contrast to Pitler and Nenkova (2008) some of ou...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [39, 44, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...link-based ranking features can be implemented in large-scale search engines; and we identify promising avenues for future research.\nKeywords:Web graph, link analysis, ranking, PageRank, HITS, SALSA.\n...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [149, 154, 1.0], [171, 178, 1.0]]}, "2008.cikm_conference-2008.7": {"snippet": "... for ranking purposes than popular query-independent link-based ranking algorithms such as PageRank. Unfortunately, this requires significant effort at query-time, to access the link graph and compute...", "weights": [[8, 15, 1.0], [61, 66, 1.0], [67, 74, 1.0], [186, 191, 1.0]]}, "2017.wwwconf_conference-2017.131": {"snippet": "...ntic Ranking for Academic Search via Knowledge Graph Embedding\n\n\n ABSTRACTThis paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analy...", "weights": [[8, 15, 1.0], [50, 55, 1.0], [117, 124, 1.0], [138, 145, 1.0], [181, 186, 1.0]]}, "2008.cikm_conference-2008.116": {"snippet": "...this graph using a simple union of the Web's hyperlink and click graphs. The hyperlink graph expresses link structure among Web pages, while the click graph is a bipartite graph of queries and documen...", "weights": [[8, 13, 1.0], [90, 95, 1.0], [154, 159, 1.0], [175, 180, 1.0]]}, "2020.cikm_conference-2020.329": {"snippet": "Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation", "weights": [[0, 5, 1.0], [29, 36, 1.0]]}, "P04-3020": {"snippet": "Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization\n\n\n This paper presents an innovative unsupervised method for automatic sentence extraction using graphbased rankin...", "weights": [[0, 5, 1.0], [6, 11, 1.0], [12, 19, 1.0], [194, 201, 1.0]]}, "2020.iwpt-1.22": {"snippet": "...is a graph-based parser with secondorder inference. For the low-resource Tamil corpus, we specially mixed the training data of Tamil with other languages and significantly improved the performance of ...", "weights": [[8, 13, 1.0], [14, 19, 1.0]]}, "2012.cikm_conference-2012.397": {"snippet": "...rove graph-based ranking using wikipedia", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0]]}, "2012.sigirconf_conference-2012.77": {"snippet": "...ing, ranking and evaluation\n\n\n ABSTRACTIn this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes ...", "weights": [[8, 15, 1.0], [144, 151, 1.0]]}, "2018.sigirconf_conference-2018.16": {"snippet": "... for Ranking Refinement\n\n\n ABSTRACTLearning to rank has been intensively studied and widely applied in information retrieval. Typically, a global ranking function is learned from a set of labeled data...", "weights": [[8, 15, 1.0], [149, 156, 1.0]]}, "2009.wsdm_conference-2009.25": {"snippet": "...d by graph-based machine learning algorithms, we propose a novel and general framework to model the re-ranking algorithm, by regularizing the smoothness of ranking scores over the graph, along with a ...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [106, 113, 1.0], [159, 166, 1.0], [183, 188, 1.0]]}, "2018.sigirconf_workshop-2018profs.8": {"snippet": "...edge graph (KG) and propose a general approach for increasing the interpretability of recommendation systems. We demonstrate the usefulness of applying a variational autoencoder to the Entity Set Expa...", "weights": [[8, 13, 1.0]]}, "2021.wwwconf_conference-2021.223": {"snippet": "Graph Neural Networks for Friend Ranking in Large-scale Social Platforms", "weights": [[0, 5, 1.0], [33, 40, 1.0]]}, "W04-3252": {"snippet": "...k -a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications. In particular, we propose two innovative unsupervised methods for...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0]]}, "2015.sigirconf_conference-2015.119": {"snippet": "...ment Ranking with Rank-based Features\n\n\n ABSTRACTLearning to Rank (LtR) is an effective machine learning methodology for inducing high-quality document ranking functions. Given a query and a candidate...", "weights": [[8, 15, 1.0], [26, 31, 1.0], [155, 162, 1.0]]}, "2014.jasis_journal-ir0volumeA65A4.8": {"snippet": "...iden Ranking graphs", "weights": [[8, 15, 1.0]]}, "N19-1104": {"snippet": "...lize graph patterns in a knowledge graph to overcome such problems. Our proposed model, the graph pattern entity ranking model (GRank), constructs an entity ranking system for each graph pattern and e...", "weights": [[8, 13, 1.0], [38, 43, 1.0], [95, 100, 1.0], [116, 123, 1.0], [160, 167, 1.0], [184, 189, 1.0]]}, "2012.amta-papers.13": {"snippet": "...icit ranking approach, and show that the graph-based strategy: 1) is considerably faster, and 2) produces consistently more reliable annotations.", "weights": [[8, 15, 1.0], [44, 49, 1.0], [50, 55, 1.0]]}, "D08-1079": {"snippet": "...t on Graph-Based Multi-Document Summarization\n\n\n The graph-based ranking algorithm has been recently exploited for multi-document summarization by making only use of the sentence-to-sentence relations...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [56, 61, 1.0], [62, 67, 1.0], [68, 75, 1.0]]}, "2004.wwwconf_conference-2004at.94": {"snippet": "...Link-based ranking algorithms are used for ordering results of search queries passed to search engines. More precisely, each document is assigned some ranking score which is computed by a ranking algo...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [154, 161, 1.0], [191, 198, 1.0]]}, "2015.sigirconf_workshop-2015gsb.4": {"snippet": "...n in Graph-Based Ranking for Query-Biased Summarization\n\n\n We propose a graph-based ranking method for query-biased summarization in a three-layer graph model consisting of document, sentence and word...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0], [75, 80, 1.0], [81, 86, 1.0], [87, 94, 1.0], [150, 155, 1.0]]}, "2017.sigirconf_workshop-2017kg4ir.3": {"snippet": "... the ranking process. The ranking model identifies and ranks the most important attributes (facts) of an entity. In order to experiment with our approach, we initially collected a dataset by exploitin...", "weights": [[8, 15, 1.0], [29, 36, 1.0]]}, "P14-2013": {"snippet": "Graph Ranking for Collective Named Entity Disambiguation\n\n\n Named Entity Disambiguation (NED) refers to the task of mapping different named entity mentions in running text to their correct interpretat...", "weights": [[0, 5, 1.0], [6, 13, 1.0]]}, "2018.sigirconf_workshop-2018profs.1": {"snippet": "...ion, based on feedback about whether the candidate was SELECTED or REJECTED. The TEAC system is deployed in a large multinational IT services organization.", "weights": [[8, 13, 1.0]]}, "2017.sigirconf_workshop-2017kg4ir.6": {"snippet": "Enhancing Categorization of Computer Science Research Papers using Knowledge Bases\n\n\n Automatic categorization of computer science research papers using just the abstracts, is a hard problem to solve....", "weights": []}, "2018.sigirjournals_journal-ir0volumeA52A2.26": {"snippet": "...ntic Ranking approach that soft matches the query and documents with continuous knowledge graph embeddings. The entity-based text representations are then combined with word-based representations in a...", "weights": [[8, 15, 1.0], [93, 98, 1.0], [122, 127, 1.0], [177, 182, 1.0]]}, "2020.wsdm_conference-2020.70": {"snippet": "...item ranking under learned neural network based ranking measures is largely still an open question. In this paper, we formulate ranking under neural network based measures as a generic ranking task, O...", "weights": [[8, 15, 1.0], [45, 50, 1.0], [51, 58, 1.0], [131, 138, 1.0], [160, 165, 1.0], [188, 195, 1.0]]}, "2014.cikm_conference-2014.13": {"snippet": "...path-based ranking features to derive the final ranking scores for candidate cited papers. Experimental results with ACM full-text corpus show that meta-path-based ranking with PRF on the new graph si...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [51, 58, 1.0], [161, 166, 1.0], [167, 174, 1.0], [195, 200, 1.0]]}, "2013.cikm_workshop-2013ueo.4": {"snippet": "...ts a graph-based ranking model to the context of the problem of improving the process of serving advertisements to users. We transform the ad-based clickstream data into a heterogeneous graph model wh...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0], [145, 150, 1.0], [189, 194, 1.0]]}, "2010.wwwconf_conference-2010.18": {"snippet": "...se a ranking scheme for ontology sub-graphs on top of approximately matched token q-grams. The ranking leverages the graph-structure of the ontology to incorporate not explicitly mentioned concepts. I...", "weights": [[8, 15, 1.0], [98, 105, 1.0], [120, 125, 1.0]]}, "P18-1223": {"snippet": "...ural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval\n\n\n This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs ...", "weights": [[8, 15, 1.0], [53, 58, 1.0], [147, 154, 1.0]]}, "2012.cikm_conference-2012.75": {"snippet": "...ues. Graph-based algorithms have been proposed to rank papers based on networks formed by citation and co-author relationships. This paper proposes a new graph-based ranking framework MutualRank that ...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [65, 70, 1.0], [157, 162, 1.0], [163, 168, 1.0], [169, 176, 1.0]]}, "2018.cikm_workshop-2018w.24": {"snippet": "... the ranking qualities, this paper explores possibilities of graph analytical methods. LD is regarded as a large graph, graph analytical approaches are therefore appropriate for this purpose. Since qu...", "weights": [[8, 15, 1.0], [64, 69, 1.0], [116, 121, 1.0], [123, 128, 1.0]]}, "2018.wwwconf_conference-2018c.293": {"snippet": "...oned graph data models through the foundational RDF standard, the recent hype around industry-driven initiativessuch as Google's Knowledge Graph, Facebook's Graph API or the Open Graph Protocol -indic...", "weights": [[8, 13, 1.0], [142, 147, 1.0], [160, 165, 1.0], [182, 187, 1.0]]}, "2015.mir_conference-2015.27": {"snippet": "... and Graph-based Ranking Algorithms", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0]]}, "2021.ipm_journal-ir0volumeA58A2.4": {"snippet": "Rank-based self-training for graph convolutional networks", "weights": [[5, 10, 1.0], [29, 34, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.40": {"snippet": "...page ranking algorithm based on link structure analysis of a seed set, but ranking given by them has not yet been efficient. In this paper, we propose a variant of SALSA to give sNorm(p) for the effic...", "weights": [[8, 15, 1.0], [26, 31, 1.0], [78, 85, 1.0]]}, "2016.tist_journal-ir0volumeA7A4.21": {"snippet": "...nity Graph for Image Segmentation and Image Annotation\n\n\n This article investigates a low-rank representation-based graph, which can used in graph-based vision tasks including image segmentation and i...", "weights": [[8, 13, 1.0], [113, 118, 1.0], [119, 124, 1.0], [144, 149, 1.0], [150, 155, 1.0]]}, "I13-1062": {"snippet": "...k, a graph-based keyphrase extraction method that relies on a topical representation of the document. Candidate keyphrases are clustered into topics and used as vertices in a complete graph. A graph-b...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [187, 192, 1.0], [196, 201, 1.0], [202, 207, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A3.5": {"snippet": "...g of graph similarity search\n\n\n Abstract A graph similarity search is to find a set of graphs from a graph database that are similar to a given query graph. Existing works solve this problem by first ...", "weights": [[8, 13, 1.0], [46, 51, 1.0], [104, 109, 1.0], [153, 158, 1.0]]}, "2010.wwwconf_conference-2010.87": {"snippet": "...t\" a graph model to a real social graph, extract a set of model parameters, and use them to generate multiple synthetic graphs statistically similar to the original graph. While numerous graph models ...", "weights": [[8, 13, 1.0], [37, 42, 1.0], [168, 173, 1.0], [190, 195, 1.0]]}, "2008.wwwconf_conference-2008.42": {"snippet": "...n in ranking of the documents. This paper addresses the issue and formulates it as a novel learning problem, referred to as, 'learning to rank relational objects'. In the new learning task, the rankin...", "weights": [[8, 15, 1.0], [197, 204, 1.0]]}, "2009.wsdm_conference-2009.28": {"snippet": "...link-based ranking algorithms such as HITS, MAX and SALSA. All these ranking algorithms view the results of a query as nodes in the web graph, expand the result set to include neighboring nodes, and c...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [72, 79, 1.0], [139, 144, 1.0]]}, "2020.ecir_conference-20202.79": {"snippet": "...ploy graph database management systems to implement existing and novel graph-based models for information retrieval. For this a unifying mapping from a graph query language to graph based retrieval mo...", "weights": [[8, 13, 1.0], [74, 79, 1.0], [80, 85, 1.0], [155, 160, 1.0], [179, 184, 1.0], [185, 190, 1.0]]}, "2016.tist_journal-ir0volumeA7A4.20": {"snippet": "...ting graph-based ranking algorithms mostly focus on ranking the current importance of literature. For researchers who enter an emerging research area, they might be more interested in new papers and y...", "weights": [[8, 13, 1.0], [14, 19, 1.0], [20, 27, 1.0], [55, 62, 1.0]]}, "2006.sigirconf_conference-2006.12": {"snippet": "...ment ranking wherein we utilize cluster information within a graph-based framework. The main idea is to perform re-ranking based on centrality within bipartite graphs of documents (on one side) and cl...", "weights": [[8, 15, 1.0], [64, 69, 1.0], [70, 75, 1.0], [118, 125, 1.0], [126, 131, 1.0]]}, "2011.cikm_workshop-2011pikm.3": {"snippet": "...ship graph, it can incorporate diverse semantics existing in the graph. Utilizing this kind of graph-based data models has been recognized as a reasonable way for dealing with heterogeneous data. Howe...", "weights": [[8, 13, 1.0], [68, 73, 1.0], [98, 103, 1.0], [104, 109, 1.0]]}, "2011.sigirconf_conference-2011.55": {"snippet": "...fold ranking for image retrieval\n\n\n ABSTRACTManifold Ranking (MR), a graph-based ranking algorithm, has been widely applied in information retrieval and shown to have excellent performance and feasibi...", "weights": [[8, 15, 1.0], [56, 63, 1.0], [72, 77, 1.0], [78, 83, 1.0], [84, 91, 1.0]]}, "2014.trec_conference-2014.12": {"snippet": "...lity based Document Ranking\n\n\n In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place ed...", "weights": [[8, 13, 1.0], [23, 30, 1.0], [75, 82, 1.0], [119, 124, 1.0], [185, 190, 1.0]]}}}
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A study was conducted on information retrieval using a commercial hypertext-...", "weights": [[8, 19, 1.0], [114, 125, 1.0], [152, 163, 1.0], [164, 173, 1.0]]}, "2011.clef_workshop-2011w.42": {"snippet": "...011: Medical Retrieval Task\n\n\n In this paper, we reported some experiments conducted by our members in the SIG team at the IRIT laboratory in the University of Toulouse within the context of the medic...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [198, 205, 1.0]]}, "2007.clef_workshop-2007w.42": {"snippet": "... the medical and photo retrieval tasks and discriminative models for the object retrieval and the medical automatic annotation task. For the photo and medical retrieval task, we apply a maximum entrop...", "weights": [[8, 15, 1.0], [26, 35, 1.0], [83, 92, 1.0], [101, 108, 1.0], [154, 161, 1.0], [162, 171, 1.0]]}, "2008.sigirconf_conference-2008.3": {"snippet": "...gent medical search\n\n\n ABSTRACTSearching for medical information on the Web has become highly popular, but it remains a challenging task because searchers are often uncertain about their exact medical...", "weights": [[8, 15, 1.0], [48, 55, 1.0], [56, 67, 1.0], [196, 203, 1.0]]}, "2013.clef_conference-2013w.44": {"snippet": "...ased Medical Document Retrieval: THCIB at CLEF eHealth Lab 2013 Task 3\n\n\n We describe our participation in the task 3 of ShARe/CLEF eHealth Lab 2013: information retrieval to address questions patient...", "weights": [[8, 15, 1.0], [25, 34, 1.0], [153, 164, 1.0], [165, 174, 1.0]]}, "2014.sigirconf_workshop-2014medir.2": {"snippet": "...here Information Retrieval Meets Data Mining", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2010.clef_workshop-2010w.65": {"snippet": "Medical Case-based Retrieval by Leveraging Medical Ontology and Physician Feedback: UIUC-IBM at ImageCLEF 2010\n\n\n This paper reports the experiment results of the UIUC-IBM team in participating in the...", "weights": [[0, 7, 1.0], [19, 28, 1.0], [43, 50, 1.0]]}, "2013.cikm_conference-2013.34": {"snippet": "...what retrieval model best suits the information need behind the query. The main challenge in medical image retrieval is to cope the semantic gap between user information needs and retrieval models. In...", "weights": [[8, 17, 1.0], [39, 50, 1.0], [96, 103, 1.0], [110, 119, 1.0], [161, 172, 1.0], [183, 192, 1.0]]}, "2016.sigirconf_conference-2016.232": {"snippet": "...ving Information Retrieval\nMOTIVATIONInformation retrieval and information privacy/security are two fast-growing computer science disciplines. Information retrieval provides a set of information seeki...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [52, 61, 1.0], [66, 77, 1.0], [146, 157, 1.0], [158, 167, 1.0], [186, 197, 1.0]]}, "2017.fire_conference-2017w.43": {"snippet": "... The medical knowledge have the potential to impact considerably on the quality of care provided by clinicians. Medical field has various types of queries: short questions, medical case reports, medic...", "weights": [[8, 15, 1.0], [115, 122, 1.0], [176, 183, 1.0], [198, 205, 1.0]]}, "2013.sigirconf_conference-2013.99": {"snippet": "... for medical records search\n\n\n ABSTRACTThe complexity of medical terminology raises challenges when searching medical records. For example, 'cancer', 'tumour', and 'neoplasms', which are synonyms, may...", "weights": [[8, 15, 1.0], [60, 67, 1.0], [113, 120, 1.0]]}, "2014.sigirconf_conference-2014.131": {"snippet": "... for information retrieval in the medical domain", "weights": [[8, 19, 1.0], [20, 29, 1.0], [37, 44, 1.0]]}, "1989.sigirconf_conference-89.24": {"snippet": "...onic information systems but at the potential risk of becoming lost in the network of interconnected pieces of information. A study was conducted on information retrieval using a commercial hypertextb...", "weights": [[8, 19, 1.0], [114, 125, 1.0], [152, 163, 1.0], [164, 173, 1.0]]}, "2011.jeptalnrecital-demonstration.12": {"snippet": "...SI} (Medical information extraction at {LIMSI})", "weights": [[8, 15, 1.0], [16, 27, 1.0]]}, "2006.jasis_journal-ir0volumeA57A8.8": {"snippet": "...mall medical journal: A case study of the Croatian Medical Journal", "weights": [[8, 15, 1.0], [54, 61, 1.0]]}, "2007.clef_workshop-2007.76": {"snippet": "...rove Medical Information Retrieval\n\n\n Abstract. This paper describes the SINAI team participation in the ImageCLEFmed campaign. The SINAI research group has participated in the multilingual image retr...", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0], [199, 208, 1.0]]}, "1994.ipm_journal-ir0volumeA30A2.3": {"snippet": "Information Retrieval Evaluation in Practice: A Case Study Approach", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2010.sigirconf_conference-2010.98": {"snippet": "...eful information from the vast amount of such semi-structured data is a challenge faced by medical professionals. Without proper tools, the rich information and knowledge buried in the medical health ...", "weights": [[8, 19, 1.0], [94, 101, 1.0], [148, 159, 1.0], [188, 195, 1.0]]}, "W18-4905": {"snippet": "...plex Medical Terms: Evidence from a Clinical Corpus\n\n\n We conduct a corpus study to investigate the structure of multi-word expressions (MWEs) in the clinical domain. Based on an existing medical taxo...", "weights": [[8, 15, 1.0], [191, 198, 1.0]]}, "2011.sigirconf_conference-2011.139": {"snippet": "...ting medical information retrieval\n\n\n ABSTRACTThis paper presents a framework for evaluating information retrieval of medical records. We use the BLULab corpus, a large collection of real-world de-ide...", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0], [96, 107, 1.0], [108, 117, 1.0], [121, 128, 1.0], [141, 144, 1.0]]}, "2018.tist_journal-ir0volumeA9A2.0": {"snippet": "... for Medical Question Answering\n\n\n Answering medical questions related to complex medical cases, as required in modern Clinical Decision Support (CDS) systems, imposes (1) access to vast medical knowl...", "weights": [[8, 15, 1.0], [48, 55, 1.0], [85, 92, 1.0], [190, 197, 1.0]]}, "2010.clef_workshop-2010w.66": {"snippet": "Medical Image Retrieval: ISSR at CLEF 2010\n\n\n This is the second participation of Institute of Statistical Studies and Research (ISSR) group in CLEF 2010-Medical image retrieval track. This paper desc...", "weights": [[0, 7, 1.0], [14, 23, 1.0], [107, 114, 1.0], [154, 161, 1.0], [168, 177, 1.0]]}, "2004.clef_workshop-2004w.1": {"snippet": "... the Medical Retrieval Task", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2014.clef_conference-2014.8": {"snippet": "...ised Medical Literature Search", "weights": [[8, 15, 1.0]]}, "2011.trec_conference-2011.6": {"snippet": "...2011 Medical Track: A Concept-Based Information Retrieval Approach\n\n\n recently participated in the TREC 2011 Medical Records Track. This paper reports on our methods, results and experience using a co...", "weights": [[8, 15, 1.0], [39, 50, 1.0], [51, 60, 1.0], [112, 119, 1.0]]}, "U12-1004": {"snippet": "...thin medical documents. Medical terminologies contain a rich source of semantic information critical to a number of techniques in medical informatics, including medical information retrieval. Recent r...", "weights": [[8, 15, 1.0], [27, 34, 1.0], [83, 94, 1.0], [133, 140, 1.0], [164, 171, 1.0], [172, 183, 1.0], [184, 193, 1.0]]}, "2009.clef_workshop-2009w.82": {"snippet": "Medical Image Retrieval: ISSR at CLEF 2009\n\n\n This paper represents the first participation of the Institute of Statistical Studies and Research at Cairo University group in CLEF 2009-Medical image re...", "weights": [[0, 7, 1.0], [14, 23, 1.0], [124, 131, 1.0], [184, 191, 1.0], [198, 207, 1.0]]}, "2015.wsdm_conference-2015.53": {"snippet": "...uire medical information. People seek this information using both traditional search engines and via social media. The information created using the search process offers an unprecedented opportunity ...", "weights": [[8, 15, 1.0], [16, 27, 1.0], [46, 57, 1.0], [122, 133, 1.0]]}, "2006.airs_conference-2006.36": {"snippet": "... for Medical Image Retrieval Using the Insight Toolkit (ITK)\n\n\n Abstract. Visual information retrieval is an emerging domain in the medical field as it has been in computer vision for more than ten ye...", "weights": [[8, 15, 1.0], [22, 31, 1.0], [84, 95, 1.0], [96, 105, 1.0], [135, 142, 1.0]]}, "2007.clef_workshop-2007w.79": {"snippet": "...2007 Medical Retrieval and Annotation Tasks\n\n\n This paper describes the medical image retrieval and medical image annotation tasks of ImageCLEF 2007. Separate sections describe each of the two tasks, ...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [75, 82, 1.0], [89, 98, 1.0], [103, 110, 1.0]]}, "W17-6943": {"snippet": "...tory medical guidelines; the system uses a combination of text analysis and information retrieval modules. We also report positive evaluation results on a small corpus of contradictory medical recomme", "weights": [[8, 15, 1.0], [79, 90, 1.0], [91, 100, 1.0], [188, 195, 1.0]]}, "2017.airs_conference-2017.4": {"snippet": "... for Medical Domain Retrieval\n\n\n Abstract. Search is important in medical domain. For example, physicians need to search for literature to support their decisions when they diagnose the patients, espe...", "weights": [[8, 15, 1.0], [23, 32, 1.0], [69, 76, 1.0]]}, "2011.trec_conference-2011.45": {"snippet": "... for Medical Record Retrieval\n\n\n In TREC 2011, we are motivated to participate in the medical record retrieval task, namely TRECMed. Our research focused on the evaluation of term weighting models and...", "weights": [[8, 15, 1.0], [23, 32, 1.0], [89, 96, 1.0], [104, 113, 1.0]]}, "W02-0310": {"snippet": "...ture retrieval\n\n\n We describe the use of clinical data present in the medical record to determine the relevance of research evidence from literature databases. We studied the effect of using automated...", "weights": [[8, 17, 1.0], [37, 40, 1.0], [73, 80, 1.0]]}, "2011.clef_workshop-2011w.4": {"snippet": "...011: Medical Retrieval Task", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2018.trec_conference-2018.14": {"snippet": "...e of information useful to tailor medical decisions to a specific clinical case. With this respect, Information Retrieval tools play an essential role in enabling physicians to automatically analyze h...", "weights": [[8, 19, 1.0], [37, 44, 1.0], [103, 114, 1.0], [115, 124, 1.0]]}, "2019.mir_conference-2019.11": {"snippet": "...n in Medical Image Retrieval Systems using Spread Spectrum Steganography", "weights": [[8, 15, 1.0], [22, 31, 1.0]]}, "1995.sigirconf_conference-95.20": {"snippet": "...s of Information Retrieval Evaluation\n\n\n AbstractAll of the methods cumently used to evaluate information retrieval (Ill) systems have limitations in their ability to measure how well users are able t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "2015.ecir_conference-2015.59": {"snippet": "...ving Medical Literature for Clinical Decision Support", "weights": [[8, 15, 1.0]]}, "2014.jasis_journal-ir0volumeA65A3.4": {"snippet": "... the retrieval effectiveness of collaborative tags and author keywords in different retrieval environments: An experimental study on medical collections", "weights": [[8, 17, 1.0], [87, 96, 1.0], [136, 143, 1.0]]}, "W19-2606": {"snippet": "...lth: Information Extraction to Support New Interfaces for Accessing Medical Evidence\n\n\n Standard paradigms for search do not work well in the medical context. Typical information needs, such as retrie...", "weights": [[8, 19, 1.0], [71, 78, 1.0], [145, 152, 1.0], [170, 181, 1.0]]}, "2014.sigirconf_conference-2014.225": {"snippet": "...R14: medical information retrieval workshop", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0]]}, "2014.cikm_workshop-2014esair.13": {"snippet": "...into retrieval (in particular for medical information retrieval) as well as previous attempts that have been made so far with mixed success. Medical information retrieval is a fertile ground for testi...", "weights": [[8, 17, 1.0], [37, 44, 1.0], [45, 56, 1.0], [57, 66, 1.0], [144, 151, 1.0], [152, 163, 1.0], [164, 173, 1.0]]}, "2016.sigirconf_conference-2016.230": {"snippet": "...h in medical information retrieval to provide enhanced search services for all users with interest in medical information search. This workshop aims to bring together researchers interested in medical...", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0], [105, 112, 1.0], [113, 124, 1.0], [196, 203, 1.0]]}, "1999.jasis_journal-ir0volumeA50A2.3": {"snippet": "... the Use of Variables in Information Retrieval User Studies", "weights": [[8, 11, 1.0], [28, 39, 1.0], [40, 49, 1.0], [55, 62, 1.0]]}, "2018.cikm_workshop-2018w.5": {"snippet": "...from medical documents can be employed to improve the retrieval of medical literature. Semantic relations explicitly represent relatedness between concepts and carry high informative power that can be...", "weights": [[8, 15, 1.0], [57, 66, 1.0], [70, 77, 1.0]]}, "2011.trec_conference-2011.60": {"snippet": "Medical-Miner at TREC 2011 Medical Records Track\n\n\n This paper presents work done at Medical Minner Project on the TREC-2011 Medical Records Track. The paper proposes four models for medical informati...", "weights": [[0, 7, 1.0], [27, 34, 1.0], [85, 92, 1.0], [125, 132, 1.0], [183, 190, 1.0], [191, 202, 1.0]]}, "2014.sigirconf_workshop-2014medir.1": {"snippet": "...p on Medical Information Retrieval (MedIR)\n\n\n The workshop on Medical Information Retrieval took place at SIGIR 2014 in Gold Coast, Australia on July 11. The workshop included eight oral presentations...", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0], [65, 72, 1.0], [73, 84, 1.0], [85, 94, 1.0]]}, "2021.emnlp-main.486": {"snippet": "...e in Medical Research Literature\n\n\n Health and medical researchers often give clinical and policy recommendations to inform health practice and public health policy. However, no current health informa...", "weights": [[8, 15, 1.0], [50, 57, 1.0], [196, 207, 1.0]]}, "2004.clef_workshop-2004.74": {"snippet": "...h to Medical Image Retrieval\n\n\n Abstract. We present a probabilistic approach to the medical retrieval task. We experimented with the Westerveld method [1] to obtain our results for ImageCLEF. In addi...", "weights": [[8, 15, 1.0], [22, 31, 1.0], [88, 95, 1.0], [96, 105, 1.0]]}, "2014.sigirconf_workshop-2014medir.7": {"snippet": "... for Medical Image Retrieval\n\n\n Medical image retrieval can assist physicians in finding information supporting their diagnosis. Systems that allow searching for medical images need to provide tools f...", "weights": [[8, 15, 1.0], [22, 31, 1.0], [35, 42, 1.0], [49, 58, 1.0], [92, 103, 1.0], [165, 172, 1.0]]}, "2006.clef_workshop-2006.72": {"snippet": "...2006 Medical Retrieval and Medical Annotation Tasks", "weights": [[8, 15, 1.0], [16, 25, 1.0], [30, 37, 1.0]]}, "2005.clef_workshop-2005w.4": {"snippet": "... for Medical Image Indexing and Retrieval\n\n\n Medical images are critical assets for medical diagnosis, research, and teaching. To facilitate automatic indexing and retrieval of large medical image dat...", "weights": [[8, 15, 1.0], [35, 44, 1.0], [48, 55, 1.0], [87, 94, 1.0], [167, 176, 1.0], [186, 193, 1.0]]}, "2014.clef_conference-2014w.23": {"snippet": "...h in Medical Information Retrieval\n\n\n In our participation in the CLEF 2014 eHealth task 3a, we investigate the effectiveness of concept-based retrieval techniques on medical IR. Concepts are determin...", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0], [146, 155, 1.0], [170, 177, 1.0]]}, "2013.sigirconf_conference-2013.198": {"snippet": "... for medical retrieval (including query expansion, UMLS, concept-hierarchies, etc.); \u2022 Visualization and exploration of medical information; \u2022 Privacy issues on the storage and retrieval of medical co...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [123, 130, 1.0], [131, 142, 1.0], [180, 189, 1.0], [193, 200, 1.0]]}, "2020.ccl-1.107": {"snippet": "...onic Medical Records (Trial) (China's Ministry of Health, 2010) by the National Health and Family Planning Medical Affairs Commission in 2010, medical institutions have accumulated many obstetric Elec...", "weights": [[8, 15, 1.0], [110, 117, 1.0], [146, 153, 1.0]]}, "2013.cikm_conference-2013.176": {"snippet": "...e in medical records search\n\n\n ABSTRACTNegated language is frequently used by medical practitioners to indicate that a patient does not have a given medical condition. Traditionally, information retri...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [152, 159, 1.0], [186, 197, 1.0], [198, 207, 1.0]]}, "2016.ir_journal-ir0volumeA19A12.1": {"snippet": "Information retrieval as semantic inference: a Graph Inference model applied to medical search", "weights": [[0, 11, 1.0], [12, 21, 1.0], [80, 87, 1.0]]}, "2012.wwwconf_conference-2012c.128": {"snippet": "...y on medical searches\n\n\n ABSTRACTIn this paper, we analyze medical searching behavior performed by a typical medical searcher. We broadly classify a typical medical searcher as: non-medical profession...", "weights": [[8, 15, 1.0], [62, 69, 1.0], [112, 119, 1.0], [160, 167, 1.0], [185, 192, 1.0]]}, "1995.ipm_journal-ir0volumeA31A4.3": {"snippet": "...s of Medical Librarianship", "weights": [[8, 15, 1.0]]}, "2014.sigirconf_workshop-2014medir.6": {"snippet": "...ical retrieval task is cognitively demanding. Previous research has highlighted the challenges and issues information retrieval systems are faced with when determining the relevance of documents in th...", "weights": [[8, 17, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "2016.ir_journal-ir0volumeA19A12.0": {"snippet": "Medical information retrieval: introduction to the special issue", "weights": [[0, 7, 1.0], [8, 19, 1.0], [20, 29, 1.0]]}, "2003.spire_conference-2003.15": {"snippet": "...Text Retrieval in Medical Collections Through Automatic Categorization\n\n\n Abstract.A current and important research issue is the retrieval of relevant medical information. In fact, while the medical k...", "weights": [[8, 17, 1.0], [21, 28, 1.0], [132, 141, 1.0], [154, 161, 1.0], [162, 173, 1.0], [194, 201, 1.0]]}, "2012.trec_conference-2012.83": {"snippet": "... for Medical Records Search: An Experiment in Identifying Cohorts for Comparative Effectiveness Research\n\n\n This paper describes an experiment performed on a medical record data set, using an informat...", "weights": [[8, 15, 1.0], [161, 168, 1.0], [195, 206, 1.0]]}, "2015.trec_conference-2015.4": {"snippet": "... the medical records of the patients. Due to the large volume of the existing literature and the diversity in the biomedical field, this is a very challenging task. This paper describes the two medica...", "weights": [[8, 15, 1.0], [197, 204, 1.0]]}, "2013.sigirconf_conference-2013.147": {"snippet": "... for medical record search\n\n\n ABSTRACTIn this paper, we present a medical record search system which is useful for identifying cohorts required in clinical studies. In particular, we propose a query-a...", "weights": [[8, 15, 1.0], [69, 76, 1.0], [159, 166, 1.0]]}, "W08-0619": {"snippet": "...able retrieval and analysis of the clinical notes. We have created an algorithm that maps medical expressions in clinical notes into a medical terminology. This algorithm indexes medical terms into an...", "weights": [[8, 17, 1.0], [93, 100, 1.0], [138, 145, 1.0], [182, 189, 1.0]]}, "2014.trec_conference-2014.36": {"snippet": "...ical Information Retrieval\n\n\n With fast development of medical information systems and software, clinical decision support (CDS) systems continue to develop new methods to deal with diverse informatio...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [58, 65, 1.0], [66, 77, 1.0], [193, 204, 1.0]]}, "2015.clef_conference-2015w.148": {"snippet": "...y of information retrieval systems when searching for web medical documents. In this task, our approach is to integrate a retrieval of medical concepts into the preprocessing of corpora. This means th...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [61, 68, 1.0], [125, 134, 1.0], [138, 145, 1.0]]}, "2007.clef_workshop-2007w.95": {"snippet": "... the medical task. The experiments accomplished in both cases result from very different approaches. For the ad hoc task the main Information Retrieval (IR) system used combines the document lists ret...", "weights": [[8, 15, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2012.sigirconf_conference-2012.29": {"snippet": "Studies of the onset and persistence of medical concerns in search logs\n\n\n ABSTRACTThe Web provides a wealth of information about medical symptoms and disorders. Although this content is often valuabl...", "weights": [[0, 7, 1.0], [40, 47, 1.0], [112, 123, 1.0], [130, 137, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A2.12": {"snippet": "...stic information retrieval study: an investigation of methods for enhanced searching of distributed data objects exploiting bidirectional relevance feedback\n\n\n The primary aim of this research is to i...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "P13-2145": {"snippet": "...line medical support community. A temporal tagger retrieves and normalizes dates mentioned informally in social media to actual month and year referents. Building on this, an event date extraction sys...", "weights": [[8, 15, 1.0]]}, "2018.sigirconf_conference-2018.243": {"snippet": "...PTS\u2022 Information systems \u2192 Document representation; Information extraction; Specialized information retrieval; \nKEYWORDS\nRESEARCH DIRECTIONClinical Decision Support (CDS) systems are designed to assis...", "weights": [[8, 19, 1.0], [55, 66, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "P14-1057": {"snippet": "... for Medical Records Search\n\n\n An important search task in the biomedical domain is to find medical records of patients who are qualified for a clinical trial. One commonly used approach is to apply N...", "weights": [[8, 15, 1.0], [95, 102, 1.0]]}, "2017.ijmir_journal-ir0volumeA6A4.0": {"snippet": "...ased medical image retrieval", "weights": [[8, 15, 1.0], [22, 31, 1.0]]}, "2022.findings-acl.110": {"snippet": "...d on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge\n\n\n Medical code prediction from clinical notes aims at automatically associating medical codes with...", "weights": [[8, 15, 1.0], [57, 64, 1.0], [107, 114, 1.0], [185, 192, 1.0]]}, "2012.trec_conference-2012.78": {"snippet": "...2012 Medical Records track, which fosters research in the retrieval of electronic health records using free text fields. Our contributions to this track investigate several problem areas in the retrie...", "weights": [[8, 15, 1.0], [61, 70, 1.0], [197, 206, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2014.cikm_conference-2014.164": {"snippet": "... the medical domain, information retrieval systems can be used for identifying cohorts (i.e. patients) required for clinical studies. However, a challenge faced by such search systems is to retrieve t...", "weights": [[8, 15, 1.0], [24, 35, 1.0], [36, 45, 1.0], [128, 135, 1.0]]}, "2007.clef_workshop-2007.59": {"snippet": "...2007 Medical Retrieval and Medical Annotation Tasks", "weights": [[8, 15, 1.0], [16, 25, 1.0], [30, 37, 1.0]]}, "2008.ipm_journal-ir0volumeA44A3.9": {"snippet": "...sing information gain to improve multi-modal information retrieval systems\n\n\n AbstractNowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval ...", "weights": [[8, 19, 1.0], [48, 59, 1.0], [60, 69, 1.0], [109, 120, 1.0], [193, 202, 1.0]]}, "2012.clef_workshop-2012w.1": {"snippet": "...ased Medical Image Retrieval", "weights": [[8, 15, 1.0], [22, 31, 1.0]]}, "2008.clef_workshop-2008.87": {"snippet": "...s in Medical Image Retrieval", "weights": [[8, 15, 1.0], [22, 31, 1.0]]}, "2010.ipm_journal-ir0volumeA46A6.2": {"snippet": "... for information and collaborative grounding of information. This study investigates breakdowns in collaborative information seeking through analyses of the use of the electronic medication record ado...", "weights": [[8, 19, 1.0], [51, 62, 1.0], [116, 127, 1.0], [160, 163, 1.0]]}, "2014.ipm_journal-ir0volumeA50A1.1": {"snippet": "...alth information retrieval systems: An empirical user study\n\n\n a b s t r a c tWhen consumers search for health information, a major obstacle is their unfamiliarity with the medical terminology. 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The performance of transliteration from a source language to a target language builds the ground work in support of proper name Cross Lingual Information Retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [92, 100, 1.0], [113, 121, 1.0], [185, 196, 1.0], [197, 206, 1.0]]}, "2004.ir_journal-ir0volumeA7A12.7": {"snippet": "...gual Information Retrieval Using Machine Translation, Relevance Feedback and Decompounding", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.17": {"snippet": "A Language Modeling Approach to Information Retrieval", "weights": [[2, 10, 1.0], [32, 43, 1.0], [44, 53, 1.0]]}, "1995.ipm_journal-ir0volumeA31A3.9": {"snippet": "...ural Language Information Retrieval", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0]]}, "1986.jasis_journal-ir0volumeA37A4.2": {"snippet": "...ural language processing in information retrieval", "weights": [[8, 16, 1.0], [31, 42, 1.0], [43, 52, 1.0]]}, "1990.ipm_journal-ir0volumeA26A1.3": {"snippet": "...ural language processing and information retrieval", "weights": [[8, 16, 1.0], [32, 43, 1.0], [44, 53, 1.0]]}, "2016.clef_conference-2016w.4": {"snippet": "...ding Sources for Medical Information Retrieval\n\n\n This paper describes the participation of MRIM team in Task 3: Patient-Centered Information Retrieval-IRTask 1: Ad-hoc search of CLEF eHealth Evaluati...", "weights": [[8, 15, 1.0], [28, 39, 1.0], [40, 49, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2000.riao_conference-2000.60": {"snippet": "Language-Based Multimedia Information Retrieval", "weights": [[0, 8, 1.0], [26, 37, 1.0], [38, 47, 1.0]]}, "2002.clef_workshop-2002w.32": {"snippet": "...gual Information Access\n\n\n The third Cross-Language Evaluation Forum workshop (CLEF-2002) provides the unprecedented opportunity to evaluate retrieval in eight different languages using a uniform set ...", "weights": [[8, 19, 1.0], [46, 54, 1.0], [144, 153, 1.0], [163, 172, 1.0]]}, "I08-6012": {"snippet": "...urce language into an equivalent query in the language of the target document collection. The relevant documents are then retrieved using a Language Modeling based retrieval algorithm. On CLEF 2007 da...", "weights": [[8, 16, 1.0], [49, 57, 1.0], [143, 151, 1.0], [167, 176, 1.0]]}, "2015.fire_workshop-2015w.4": {"snippet": "...ross Language Source Code Reuse Detection Using Intermediate Language\n\n\n In today's digital era information access is just a click away. so computer science students also have easy access to all the s...", "weights": [[8, 16, 1.0], [64, 72, 1.0], [99, 110, 1.0]]}, "W16-6315": {"snippet": "Meaning Matters: Senses of Words are More Informative than Words for Cross-domain Sentiment Analysis\n\n\n Getting labeled data in each domain is always an expensive and a time consuming task. Hence, cro...", "weights": []}, "2020.findings-emnlp.81": {"snippet": "...iple sources. Previous work simply concatenates all input sources or averages information from different input sources. In this work, we study dialogue models with multiple input sources adapted from ...", "weights": [[8, 15, 1.0], [61, 68, 1.0], [81, 92, 1.0], [98, 107, 1.0], [114, 121, 1.0], [182, 189, 1.0]]}, "2002.clef_workshop-2002.29": {"snippet": "Information Retrieval with Language Knowledge", "weights": [[0, 11, 1.0], [12, 21, 1.0], [27, 35, 1.0]]}, "O08-2009": {"snippet": "...urce language to the target language. Currently, this system provides the functions of the monolingual and cross-language patent retrieval in English and Japanese. The users can input the queries and ...", "weights": [[8, 16, 1.0], [31, 39, 1.0], [116, 124, 1.0], [132, 141, 1.0]]}, "2005.sigirconf_conference-2005.70": {"snippet": "...ross-language information retrieval\n\n\n ABSTRACTThe bottleneck for dictionary-based cross-language information retrieval is the lack of comprehensive dictionaries, in particular for many different lang...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [92, 100, 1.0], [101, 112, 1.0], [113, 122, 1.0], [189, 198, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.16": {"snippet": "... and information pathways. The former denotes the ways information sources are mapped in preference order in an imaginary field, while information pathways refers to the sequences in which sources pla...", "weights": [[8, 19, 1.0], [58, 69, 1.0], [70, 77, 1.0], [138, 149, 1.0], [192, 199, 1.0]]}, "2013.clef_conference-2013w.193": {"snippet": "...urce Retrieval Notebook for PAN at CLEF 2013\n\n\n The source retrieval task for plagiarism detection involves the use of a search engine to retrieve candidate sources of plagiarism for a suspicious docu...", "weights": [[8, 17, 1.0], [62, 71, 1.0], [160, 167, 1.0]]}, "2019.icon-1.7": {"snippet": "...ross-Language Information Retrieval\n\n\n This paper describes our first experiment on Neural Machine Translation (NMT) based query translation for Amharic-Arabic Cross-Language Information Retrieval (CL...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [169, 177, 1.0], [178, 189, 1.0], [190, 199, 1.0]]}, "2004.clef_workshop-2004.60": {"snippet": "...ross-Language Image Retrieval\n\n\n Abstract. For many cross-language retrieval tasks, the predominant approach is to translate the query into the language of the document collection (target language). T...", "weights": [[8, 16, 1.0], [23, 32, 1.0], [61, 69, 1.0], [70, 79, 1.0], [147, 155, 1.0], [191, 199, 1.0]]}, "1994.jasis_journal-ir0volumeA45A7.4": {"snippet": "...g in Information Retrieval and Library Searching? Eleven Sources of Search Information", "weights": [[8, 19, 1.0], [20, 29, 1.0], [60, 67, 1.0], [78, 89, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [43, 51, 1.0], [91, 102, 1.0], [103, 112, 1.0], [148, 156, 1.0]]}, "N15-3023": {"snippet": "...ural language (NL) questions and respond to keywords. We use multiple information sources including curated knowledge base, raw text, auto-generated triples, and NL processing results. We develop open...", "weights": [[8, 16, 1.0], [73, 84, 1.0], [85, 92, 1.0]]}, "2019.ecir_conference-20192.54": {"snippet": "... the different elements of narratives which are often spread among different sources. In the second edition of the workshop, we foster the discussion of recent advances in the link between Information...", "weights": [[8, 17, 1.0], [70, 79, 1.0], [80, 87, 1.0], [192, 203, 1.0]]}, "2020.sigirconf_conference-2020.120": {"snippet": "...rnal Sources for Representation Learning in Conversational Search\n\n\n Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various infor...", "weights": [[8, 15, 1.0], [198, 209, 1.0]]}, "2005.sigirconf_conference-2005.101": {"snippet": "...gual information retrieval using on-line translation systems\n\n\n ABSTRACTTypical cross language retrieval requires special linguistic resources, such as bilingual dictionaries and parallel corpus. In t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [89, 97, 1.0], [98, 107, 1.0]]}, "C12-1057": {"snippet": "... use information from documents in the source language. We overcome this limitation by using information from retrieved documents in the source language to improve the retrieval quality of the target ...", "weights": [[8, 19, 1.0], [49, 57, 1.0], [96, 107, 1.0], [147, 155, 1.0], [171, 180, 1.0]]}, "1999.cikm_conference-99.41": {"snippet": "... for Information Retrieval\n\n\n ABSTRACTStatistical language modeling has been successfully used for speech recognition, part-of-speech tagging, and syntactic parsing. Recently, it has also been applied...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 61, 1.0]]}, "2017.wsdm_conference-2017.98": {"snippet": "...port Retrieval-Based Applications\n\n\n ABSTRACTAdvances in text retrieval do not apply directly to source code retrieval because of the difference in characteristics of source code when compared to text...", "weights": [[8, 17, 1.0], [65, 74, 1.0], [112, 121, 1.0]]}, "2004.clef_workshop-2004.47": {"snippet": "...urce Language in Bilingual Question Answering\n\n\n Abstract. This paper presents a bilingual question answering system that has Finnish as its source language and English as its target language. The sys...", "weights": [[8, 16, 1.0], [151, 159, 1.0], [186, 194, 1.0]]}, "2007.clef_workshop-2007w.69": {"snippet": "...ical retrieval task (i.e. ImageCLEFmed). The main idea in this paper is to incorporate medical knowledge in the language modeling approach to information retrieval (IR). Our model makes use of the tex...", "weights": [[8, 17, 1.0], [115, 123, 1.0], [145, 156, 1.0], [157, 166, 1.0]]}, "2011.fire_workshop-2011.30": {"snippet": "Information Retrieval with Hindi, Bengali, and Marathi Languages: Evaluation and Analysis\n\n\n Abstract. Our first objective in participating in FIRE evaluation campaigns is to analyze the retrieval eff...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [187, 196, 1.0]]}, "2001.clef_workshop-2001.4": {"snippet": "...gual Information Retrieval Using English and Chinese Queries", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2022.findings-acl.255": {"snippet": "... and retrieval from the knowledge in the source language. The retrieved knowledge is then translated into the target language and integrated into a pre-trained multilingual language model via visible ...", "weights": [[8, 17, 1.0], [51, 59, 1.0], [120, 128, 1.0], [176, 184, 1.0]]}, "2001.ir_journal-ir0volumeA4A34.2": {"snippet": "...ross-Language Information Retrieval: Problems, Methods, and Research Findings", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.13": {"snippet": "...e in information retrieval research, yet is also a fundamentally important task, because an improved general retrieval model would benefit every search engine. The language modeling approach to inform...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [112, 121, 1.0], [167, 175, 1.0], [197, 208, 1.0]]}, "2019.ecir_conference-20191.33": {"snippet": "...gual Information Retrieval\n\n\n Abstract. We present a method for automatic query expansion for cross-lingual information retrieval in the medical domain. The method employs machine translation of sourc...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [111, 122, 1.0], [123, 132, 1.0]]}, "2001.clef_workshop-2000w.8": {"snippet": "...ross-Language Information Retrieval using Dutch Query Translation\n\n\n This paper describes a hastily carried out, rather limited bilingual information retrieval experiment. The experiment takes Dutch t...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [141, 152, 1.0], [153, 162, 1.0]]}, "1999.trec_conference-1999.18": {"snippet": "...many different languages. Compared to the usual definition of cross-language information retrieval, where systems work with a single language pair, retrieving documents in a language L1 using queries ...", "weights": [[8, 17, 1.0], [71, 79, 1.0], [80, 91, 1.0], [92, 101, 1.0], [136, 144, 1.0], [177, 185, 1.0]]}, "2016.sigirconf_conference-2016.230": {"snippet": "...ical information search refers to methodologies and technologies that seek to improve access to medical information archives via a process of information retrieval (IR). Such information is now potent...", "weights": [[8, 19, 1.0], [107, 118, 1.0], [145, 156, 1.0], [157, 166, 1.0], [178, 189, 1.0]]}, "2002.trec_conference-2002.13": {"snippet": "...uery-language term weights from document-language evidence were tried. A new technique that exploits translation probability information was found to outperform a comparable technique in which that in...", "weights": [[8, 16, 1.0], [44, 52, 1.0], [128, 139, 1.0], [201, 212, 1.0]]}, "2005.clef_workshop-2005w.82": {"snippet": "...GIRT Information Retrieval\n\n\n For our fifth participation in the CLEF evaluation campaigns, the first objective was to propose an effective and general stopword list along with a light stemming proced...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.wwwconf_conference-2011.26": {"snippet": "... the sources. We compute the agreement between the sources as the agreement of the answers returned. While computing the agreement, we also measure and compensate for possible collusion between the so...", "weights": [[8, 15, 1.0], [54, 61, 1.0], [201, 208, 1.0]]}, "2021.icnlsp-1.29": {"snippet": "Domain and Task-Informed Sample Selection for Cross-Domain Target-based Sentiment Analysis\n\n\n A challenge for target-based sentiment analysis is that most datasets are domain-specific and thus buildin...", "weights": []}, "1999.trec_conference-1999.12": {"snippet": "...ross-language information retrieval (CLIR) track, we adopted the approach of using machine translation to prepare a source-language query for use in a target-language retrieval task. We empirically ev...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [126, 134, 1.0], [161, 169, 1.0], [170, 179, 1.0]]}, "O12-1015": {"snippet": "...ment retrieval model for cross-language document retrieval. Given a certain document in the source language, it will be translated into the target language of statistical machine translation model. Th...", "weights": [[8, 17, 1.0], [34, 42, 1.0], [52, 61, 1.0], [102, 110, 1.0], [150, 158, 1.0]]}, "2016.iwslt-1.8": {"snippet": "...urce language affect the efficiency of the MLAN schemes. We experiment with three different scenarios using, i) French, as a source language uncorrelated to the target language, ii) Ukrainian, as a so...", "weights": [[8, 16, 1.0], [85, 94, 1.0], [135, 143, 1.0], [171, 179, 1.0]]}, "2001.ntcir_workshop-2001.12": {"snippet": "...lish retrieval and in cross-language retrieval using each language as a source language for the other. We found that 6-grams performed comparably with English words and that 2-grams and 3-grams perfor...", "weights": [[8, 17, 1.0], [31, 39, 1.0], [40, 49, 1.0], [61, 69, 1.0], [82, 90, 1.0]]}, "2016.airs_conference-2016.10": {"snippet": "...ning different query expansion methods with different text fields weighting strategies of different resources. Experiments on TREC data sets showed that our combination of multiple information sources...", "weights": [[8, 17, 1.0], [47, 56, 1.0], [93, 102, 1.0], [184, 195, 1.0], [196, 203, 1.0]]}, "2006.clef_workshop-2006.4": {"snippet": "...ross Language Information Retrieval (CLIR) experiments of Language Technologies Research Centre (LTRC, IIIT-Hyderabad) as part of our participation in the ad-hoc track of CLEF 2006. This is our first ...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [61, 69, 1.0]]}, "2005.cikm_conference-2005.76": {"snippet": "...tric language models\n\n\n ABSTRACTWe investigates language models for informational and navigational web search. Retrieval on the web is a task that differs substantially from ordinary ad hoc retrieval....", "weights": [[8, 16, 1.0], [51, 59, 1.0], [114, 123, 1.0], [193, 202, 1.0]]}, "W03-1120": {"snippet": "...ross-Language Information Retrieval Based on Category Matching Between Language Versions of a Web Directory\n\n\n Since the Web consists of documents in various domains or genres, the method for Cross-La...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [74, 82, 1.0], [201, 209, 1.0]]}, "2007.cikm_conference-2007.30": {"snippet": "...-hoc retrieval\n\n\n ABSTRACTThe term frequency normalisation parameter sensitivity is an important issue in the probabilistic model for Information Retrieval. A high parameter sensitivity indicates that...", "weights": [[8, 17, 1.0], [137, 148, 1.0], [149, 158, 1.0]]}, "2002.clef_workshop-2002w.29": {"snippet": "...ross-language information retrieval, this problem of choice is mitigated since more than one translation alternative can be retained in the target query. Between choosing just one word as a translatio...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0]]}, "2009.cikm_conference-2009.261": {"snippet": "...ting information sources by analyzing static information of available information sources that is sampled in the offline manner. On the other hand, most prior research ignored a large amount of valuab...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [48, 59, 1.0], [73, 84, 1.0], [85, 92, 1.0]]}, "N06-5003": {"snippet": "...ural language processing and information retrieval. However, most of the times, they are perceived as different disciplines, with different algorithms, different applications, and different potential ...", "weights": [[8, 16, 1.0], [32, 43, 1.0], [44, 53, 1.0], [105, 114, 1.0], [133, 142, 1.0], [155, 164, 1.0], [183, 192, 1.0]]}, "1988.riao_conference-1988.72": {"snippet": "...gual Information Retrieval Mechanism Using VLSI. Requirements and Approaches for Information Retrieval Systems in the Computer-Aided Software Engineering and Document Processing Environment", "weights": [[8, 19, 1.0], [20, 29, 1.0], [84, 95, 1.0], [96, 105, 1.0]]}, "2020.coling-main.232": {"snippet": "...tive Retrieval over Multiple Knowledge Sources\n\n\n In order to facilitate natural language understanding, the key is to engage commonsense or background knowledge. However, how to engage commonsense ef...", "weights": [[8, 17, 1.0], [42, 49, 1.0], [84, 92, 1.0]]}, "2003.cikm_conference-2003.21": {"snippet": "...ross-language retrieval\n\n\n ABSTRACTMost cross language information retrieval research concentrates on language pairs for which direct, rich, and often multiple translation resources already exist. How...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [49, 57, 1.0], [58, 69, 1.0], [70, 79, 1.0], [105, 113, 1.0]]}, "2007.clef_workshop-2007.9": {"snippet": "...ross-Language Retrieval with Wikipedia", "weights": [[8, 16, 1.0], [17, 26, 1.0]]}, "2007.wwwconf_conference-2007.9": {"snippet": "...bute information extracted from different sources, we may not be able to achieve satisfactory retrieval performance. In this paper, we propose several language models for Web object retrieval, namely ...", "weights": [[8, 19, 1.0], [35, 44, 1.0], [45, 52, 1.0], [97, 106, 1.0], [154, 162, 1.0], [185, 194, 1.0]]}, "2011.cikm_conference-2011.235": {"snippet": "... the language barrier in cross language information retrieval (CLIR) in general is machine translation (MT). High quality MT systems are becoming widely available for many language pairs and generally...", "weights": [[8, 16, 1.0], [34, 42, 1.0], [43, 54, 1.0], [55, 64, 1.0], [175, 183, 1.0]]}, "2006.sigirjournals_journal-ir0volumeA40A2.8": {"snippet": "...urce Information Retrieval systems (OSIR06)", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1998.amta-tutorials.5": {"snippet": "...ross language information retrieval", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0]]}, "2007.clef_workshop-2007w.23": {"snippet": "...urce language into an equivalent query in the language of the target document collection. The relevant documents are then retrieved using a Language Modeling based retrieval algorithm. On CLEF 2007 da...", "weights": [[8, 16, 1.0], [49, 57, 1.0], [143, 151, 1.0], [167, 176, 1.0]]}, "I05-1003": {"snippet": "...ross-Language Information Retrieval\n\n\n For cross-language text retrieval systems that rely on bilingual dictionaries for bridging the language gap between the source query language and the target docu...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [52, 60, 1.0], [66, 75, 1.0], [137, 145, 1.0], [175, 183, 1.0]]}, "2007.tois_journal-ir0volumeA25A1.3": {"snippet": "...ross-language information retrieval\n\n\n We present a method for creating a comparable text corpus from two document collections in different languages. The collections can be very different in origin. ...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [133, 142, 1.0], [182, 191, 1.0]]}, "2012.sigirconf_conference-2012.84": {"snippet": "...eous information sources. A mixture probabilistic model is thus proposed to learn more appropriate combination weights with respect to different types of information sources with some training data. A...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [138, 147, 1.0], [157, 168, 1.0], [169, 176, 1.0]]}, "2010.sigirconf_conference-2010.208": {"snippet": "...ross-language information retrieval are not enough. In fact, if the analysed source and target languages are related in some way (common linguistic ancestors or technical vocabulary), a simple compari...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0]]}, "C12-3040": {"snippet": "...tion Retrieval System\n\n\n We introduce a translation retrieval system THUTR, which casts translation as a retrieval problem. Translation retrieval aims at retrieving a list of target-language translati...", "weights": [[8, 17, 1.0], [55, 64, 1.0], [108, 117, 1.0], [139, 148, 1.0], [185, 193, 1.0]]}, "2009.clef_workshop-2009w.33": {"snippet": "... for different languages which is combined with a conceptbased retrieval approach based on Explicit Semantic Analysis. In order to create the language-specific indices for each language, a language de...", "weights": [[8, 17, 1.0], [66, 75, 1.0], [145, 153, 1.0], [180, 188, 1.0], [192, 200, 1.0]]}, "A97-1049": {"snippet": "...tic) information browsing and retrieval system, which is aimed at monolingual users who are interested in information from multiple language sources. The system takes advantage of information extracti...", "weights": [[8, 19, 1.0], [33, 42, 1.0], [109, 120, 1.0], [135, 143, 1.0], [144, 151, 1.0], [183, 194, 1.0]]}, "N04-4023": {"snippet": "...dio, language, video, and image features. In this paper, we investigate the correlation between automatically-derived multimodal features and story boundaries in seven different broadcast news sources...", "weights": [[8, 16, 1.0], [171, 180, 1.0], [196, 203, 1.0]]}, "2008.sigirconf_conference-2008.190": {"snippet": "...gual information retrieval, which can handle tens of languages and millions of documents. Functioning of the system is demonstrated on corpus of European Legislation (22 languages, more than 400,000 d...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2017.chiir_conference-2017.28": {"snippet": "...ixed Language Queries in Different Search Intents\n\n\n ABSTRACTWith the increasing number of multilingual resources on the Internet, cross-language information retrieval has become an important research...", "weights": [[8, 16, 1.0], [28, 37, 1.0], [140, 148, 1.0], [149, 160, 1.0], [161, 170, 1.0]]}, "2015.clef_conference-2015.10": {"snippet": "...e in different languages. The lack of material in individual languages means that cross-language information retrieval (CLIR) within these collections is required to satisfy the user's information nee...", "weights": [[8, 17, 1.0], [91, 99, 1.0], [100, 111, 1.0], [112, 121, 1.0], [188, 199, 1.0]]}, "2020.ecir_conference-20202.78": {"snippet": "...ured information sources such as knowledge bases. One opportunity that remains open is the research of unified frameworks for the representation and retrieval of heterogeneous information sources. The...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [152, 161, 1.0], [179, 190, 1.0], [191, 198, 1.0]]}, "2008.cikm_conference-2008.178": {"snippet": "...iple sources.Example 1. Consider a system with four information sources and a mediator. Query Q = \u03a0B,C \u03c3P (r 1 s) involves relations r(A, B) and s(A, C) in the mediator schema. Assume XML data sources...", "weights": [[8, 15, 1.0], [55, 66, 1.0], [67, 74, 1.0], [196, 203, 1.0]]}, "2008.wwwconf_workshop-2008l.9": {"snippet": "... web sources\n\n\n ABSTRACTVernacular place names are names that are commonly in use to refer to geographical places. For purposes of effective information retrieval, the spatial extent associated with t...", "weights": [[8, 15, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "2022.naacl-main.15": {"snippet": "...uire different types of knowledge sources. To address the above challenges, we present PLUG 1 , a language model that homogenizes different knowledge sources to a unified knowledge representation for ...", "weights": [[8, 17, 1.0], [37, 44, 1.0], [101, 109, 1.0], [133, 142, 1.0], [153, 160, 1.0]]}, "2016.sigirconf_conference-2016.27": {"snippet": "...rget language, and the classification performance is far from satisfactory because of the language gap between the source language and the translated target language.In this paper, to address the abov...", "weights": [[8, 16, 1.0], [93, 101, 1.0], [125, 133, 1.0], [160, 168, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...odic information to isolate sarcasm. -Language models. ASR systems generally contain a language model that provides word sequence probabilities. Some information retrieval systems also rely on languag...", "weights": [[8, 19, 1.0], [41, 49, 1.0], [90, 98, 1.0], [153, 164, 1.0], [165, 174, 1.0], [196, 204, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.5": {"snippet": "...tive language as one index unit. For Cross-Language Information Retrieval (CLIR), one of the major hindrances to achieving retrieval performance at the level of Mono-Lingual Information Retrieval (MLI...", "weights": [[8, 16, 1.0], [46, 54, 1.0], [55, 66, 1.0], [67, 76, 1.0], [126, 135, 1.0], [177, 188, 1.0], [189, 198, 1.0]]}, "2018.wwwconf_conference-2018c.22": {"snippet": "...ving Information from Multiple Sources\n\n\n ABSTRACTThe Web has several information sources on which an ongoing event is discussed. To get a complete picture of the event, it is important to retrieve in...", "weights": [[8, 19, 1.0], [34, 41, 1.0], [73, 84, 1.0], [85, 92, 1.0], [201, 212, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [42, 53, 1.0], [54, 63, 1.0], [92, 103, 1.0], [104, 113, 1.0], [122, 130, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A1.16": {"snippet": "...s on language modeling for information retrieval", "weights": [[8, 16, 1.0], [30, 41, 1.0], [42, 51, 1.0]]}, "2004.riao_conference-2004.5": {"snippet": "...gual Information Retrieval with Asian Languages", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2015.fire_conference-2015.9": {"snippet": "... the language of reused document differs from the source document. In this paper we present a comparative study of different translation approaches used to map the language barrier for cross-lingual r...", "weights": [[8, 16, 1.0], [118, 127, 1.0], [167, 175, 1.0], [202, 211, 1.0]]}, "I05-1075": {"snippet": "...ngle-language cases, but in fact, mixed-case performance always exceeds the maximum of the single-language cases. We observed this effect in all six experiments we ran, involving three different sourc...", "weights": [[8, 16, 1.0], [101, 109, 1.0], [188, 197, 1.0]]}, "P11-1132": {"snippet": "...ehow different than non-translated text, scholars have emphasized two distinct bases for such differences. Some have noted interference from the source language spilling over into translation in a sou...", "weights": [[8, 17, 1.0], [155, 163, 1.0]]}, "2009.eamt-1.32": {"snippet": "...urce language contextinformed features. These features enable us to exploit source similarity in addition to target similarity, as modelled by the language model. In our experiments two kinds of super...", "weights": [[8, 16, 1.0], [150, 158, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.9": {"snippet": "...ross-language information retrieval (CLIR). However, technical terms and proper names in different languages often share the same Latin or Greek origin, being thus spelling variants of each other. In ...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [92, 101, 1.0]]}, "2014.fire_conference-2014.5": {"snippet": "...: An Information Retrieval Approach for Source Code Plagiarism Detection\n\n\n ABSTRACTWe investigate an information retrieval (IR) based approach to source code plagiarism detection. The standard method...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [105, 116, 1.0], [117, 126, 1.0]]}, "2010.ecir_conference-2010.45": {"snippet": "...elog Retrieval", "weights": [[8, 17, 1.0]]}, "1999.ipm_journal-ir0volumeA35A4.0": {"snippet": "Information retrieval with Asian languages: an introduction", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2002.cikm_conference-2002.1": {"snippet": "...able information retrieval systems\n\n\n AbstractImplementing scalable information retrieval systems requires the design and development of efficient methods to ingest data from multiple sources, search ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [71, 82, 1.0], [83, 92, 1.0], [187, 194, 1.0]]}, "2013.tweb_journal-ir0volumeA7A2.6": {"snippet": "...lity sources and ranking the results from the selected sources. Though there are existing methods for both the steps, they assess the relevance of the sources and the results using the query-result si...", "weights": [[8, 15, 1.0], [58, 65, 1.0], [154, 161, 1.0]]}, "2004.riao_conference-2004.39": {"snippet": "...Code Retrieval using Conceptual Similarity", "weights": [[8, 17, 1.0]]}, "U05-1011": {"snippet": "...erse Information Sources to Retrieve Samples of Low Density Languages\n\n\n Language samples are useful as an object of study for a diverse range of people. Samples of low-density languages in particular...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [76, 84, 1.0]]}, "2020.lrec-1.269": {"snippet": "...ural Language Descriptions\n\n\n Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retri...", "weights": [[8, 16, 1.0], [186, 197, 1.0], [198, 207, 1.0]]}, "2008.cikm_workshop-2008inews.5": {"snippet": "...ross-language information retrieval using compressed word format mapping algorithm\n\n\n ABSTRACTTransliteration of named entities in user queries is a vital step in any Cross-Language Information Retrie...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [176, 184, 1.0], [185, 196, 1.0], [197, 206, 1.0]]}, "2015.fire_workshop-2015w.1": {"snippet": "...ross-Language SOurce COde Re-use\n\n\n The detection of source code re-use is an important research field for both software industry and academia fields. This paper summarizes the goals, organization and...", "weights": [[8, 16, 1.0]]}, "2003.clef_workshop-2003w.21": {"snippet": "...gual Information Retrieval\n\n\n This paper reports on the participation of ITC-irst in the Cross Language Evaluation Forum 2003; in particular, in the monolingual, bilingual, small multilingual, and spo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [98, 106, 1.0]]}, "2014.clef_conference-2014w.104": {"snippet": "...urce Retrieval\n\n\n Source retrieval involves making use of a search engine to retrieve candidate sources of plagiarism for a given suspicious document so that more accurate comparisons can be made. We ...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [99, 106, 1.0]]}, "O12-5002": {"snippet": "...ross-Language Document Retrieval\n\n\n This paper proposed an integrated approach for Cross-Language Information Retrieval (CLIR), which integrated with four statistical models: Translation model, Query ...", "weights": [[8, 16, 1.0], [26, 35, 1.0], [92, 100, 1.0], [101, 112, 1.0], [113, 122, 1.0]]}, "2009.tois_journal-ir0volumeA27A3.0": {"snippet": "...ers, language models, and ad hoc information retrieval\n\n\n The language-modeling approach to information retrieval provides an effective statistical framework for tackling various problems and often ac...", "weights": [[8, 16, 1.0], [36, 47, 1.0], [48, 57, 1.0], [65, 73, 1.0], [95, 106, 1.0], [107, 116, 1.0]]}, "2017.cikm_conference-2017.230": {"snippet": "...urce Retrieval for Web-Scale Text Reuse Detection\n\n\n ABSTRACT e rst step of text reuse detection addresses the source retrieval problem: given a suspicious document, a set of candidate sources from wh...", "weights": [[8, 17, 1.0], [121, 130, 1.0], [188, 195, 1.0]]}, "2008.clef_workshop-2008w.21": {"snippet": "... XML retrieval system, to evaluate image retrieval based on textual evidence in the context of the wikipediaMM task at ImageCLEF 2008. We employed a language modelling framework and found that the tex...", "weights": [[8, 17, 1.0], [44, 53, 1.0], [152, 160, 1.0]]}, "2009.ipm_journal-ir0volumeA45A1.0": {"snippet": "A language modeling framework for expert finding\n\n\n a b s t r a c tStatistical language models have been successfully applied to many information retrieval tasks, including expert finding: the process...", "weights": [[2, 10, 1.0], [79, 87, 1.0], [134, 145, 1.0], [146, 155, 1.0]]}, "2012.wsdm_conference-2012.46": {"snippet": "...iple information sources\n\n\n ABSTRACTMost standard information retrieval models use a single source of information (e.g., the retrieval corpus) for query formulation tasks such as term and phrase weigh...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [53, 64, 1.0], [65, 74, 1.0], [105, 116, 1.0], [128, 137, 1.0]]}, "2014.cikm_conference-2014.187": {"snippet": "...ross-Language Information Retrieval (CLIR) addresses the problem of finding documents in some language that are relevant to a question posed in a different language. Retrieving answers to questions wr...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [97, 105, 1.0], [149, 158, 1.0], [159, 167, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.6": {"snippet": "...d by different language models (i.e., Dirichlet, Jelinek-Mercer and two-stage models). These language models are generally used in information retrieval to represent the relationship between documents...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [96, 104, 1.0], [134, 145, 1.0], [146, 155, 1.0]]}, "2018.ir_journal-ir0volumeA21A1.0": {"snippet": "Retrieving and classifying instances of source code plagiarism", "weights": []}}}
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Such metasearch provides a mechanism for locating documents on t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [52, 60, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.5": {"snippet": "...uage information retrieval\n\n\n AbstractQuery translation is a viable method for cross-language information retrieval (CLIR), but it suffers from translation ambiguities caused by multiple translations ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [97, 108, 1.0], [109, 118, 1.0], [181, 189, 1.0]]}, "2016.ictir_conference-2016.17": {"snippet": "... for information retrieval evaluation with multiple test collections. Given a topic-by-run raw score matrix in terms of some evaluation measure, each score can be standardised using the topic's sample...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 54, 1.0]]}, "2017.ipm_journal-ir0volumeA53A1.4": {"snippet": "...emic papers. Given a tested paper from a given corpus of 10,099 peer-reviewed scientific papers, a two-stage process was activated. During the first stage, most of the papers were filtered out using a...", "weights": [[8, 14, 1.0], [92, 98, 1.0], [171, 177, 1.0]]}, "O12-2004": {"snippet": "...y of Methods for Topic Modeling in Spoken Document Retrieval\n\n\n Topic modeling for information retrieval (IR) has attracted significant attention and demonstrated good performance in a wide variety of...", "weights": [[8, 15, 1.0], [54, 63, 1.0], [86, 97, 1.0], [98, 107, 1.0]]}, "2020.clssts-1.6": {"snippet": "...ised Methods for Ad hoc Cross-Lingual Document Retrieval\n\n\n We address the problem of linking related documents across languages in a multilingual collection. We evaluate three diverse unsupervised me...", "weights": [[8, 15, 1.0], [50, 59, 1.0], [201, 208, 1.0]]}, "2015.clef_conference-2015w.87": {"snippet": "Information Retrieval Features for Personality Traits\n\n\n This paper describes the methods employed to solve the Author Profiling task at PAN-2015. The main goal was to test the use of features derived...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [82, 89, 1.0]]}, "P10-2060": {"snippet": "...from multiple review texts according to two scores that represent the informativeness and readability of the sentence order. The informativeness score is defined by the number of sentiment expressions...", "weights": [[8, 16, 1.0]]}, "2014.trec_conference-2014.41": {"snippet": "...e of multiple information retrieval techniques: retrieval functions, re-ranking, query expansion and classification of medical articles into question categories. The best performing run was based on a...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [51, 60, 1.0]]}, "2018.cikm_conference-2018.74": {"snippet": "... for Retrieval Experiments Involving Multiple Test Collections\n\n\n ABSTRACTTraditional practice recommends that information retrieval experiments be run over multiple test collections, to support, if n...", "weights": [[8, 17, 1.0], [40, 48, 1.0], [114, 125, 1.0], [126, 135, 1.0], [160, 168, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords Information Retrieval (IR) solutions to retrieve passages matching a set of terms extracted from the question.There is a fundamental disconnect between the capabilities of the bag-of-words retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [197, 206, 1.0]]}, "2014.sigirconf_conference-2014.107": {"snippet": "...stic retrieval systems\n\n\n ABSTRACTThe use of sampling, randomized algorithms, or training based on the unpredictable inputs of users in Information Retrieval often leads to non-deterministic outputs. ...", "weights": [[8, 17, 1.0], [139, 150, 1.0], [151, 160, 1.0]]}, "2008.clef_workshop-2008w.72": {"snippet": "Methods for combining Content-based and Textual-based Approaches in Medical Image Retrieval\n\n\n This paper describes our participation in the Medical Image Retrieval task of Image CLEF 2008. Our aim wa...", "weights": [[0, 7, 1.0], [82, 91, 1.0], [155, 164, 1.0]]}, "2012.cikm_conference-2012.269": {"snippet": "...op-k retrieval using conditional preference networks\n\n\n ABSTRACTThis paper considers top-k retrieval using Conditional Preference Network (CP-Net). As a model for expressing user preferences on multip...", "weights": [[8, 17, 1.0], [94, 103, 1.0], [197, 205, 1.0]]}, "2007.sigirconf_conference-2007.77": {"snippet": "...n in information retrieval\n\n\n ABSTRACTResearch in Information Retrieval usually shows performance improvement when many sources of evidence are combined to produce a ranking of documents (e.g., texts,...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 64, 1.0], [65, 74, 1.0]]}, "2005.ntcir_workshop-2005.23": {"snippet": "...sion Methods for Text Retrieval\n\n\n This paper describes our work at the fifth NTCIR workshop on the subtasks of single language information retrieval (SLIR). Several automatic global query expansion s...", "weights": [[8, 15, 1.0], [25, 34, 1.0], [131, 142, 1.0], [143, 152, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.7": {"snippet": "...ated information retrieval system generally contains multiple databases that are inconsistent in terms of their content and indexing. This paper proposes a rough set-based transfer (RST) model for int...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [56, 64, 1.0]]}, "I08-1024": {"snippet": "...ning multiple resources use simple methods such as linear combination. In this paper, we drew up a comparative study between linear combination and confidence measures to combine multiple translation ...", "weights": [[8, 16, 1.0], [38, 45, 1.0], [182, 190, 1.0]]}, "W98-1103": {"snippet": "...ious methods of incorporating a cross-linguistic capability to existing information retrieval (IR) systems. Our results indicate that translating queries with off-theshelf machine translation systems ...", "weights": [[8, 15, 1.0], [75, 86, 1.0], [87, 96, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.1": {"snippet": "...uage information retrieval: a review\n\n\n AbstractThis paper reviews state-of-the-art techniques and methods for enhancing effectiveness of cross-language information retrieval (CLIR). The following res...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [102, 109, 1.0], [156, 167, 1.0], [168, 177, 1.0]]}, "2020.coling-main.565": {"snippet": "...over Multiple Dependency Sub-graphs for Relation Extraction\n\n\n We propose in this paper a contextualised graph convolution network over multiple dependency sub-graphs for relation extraction. A novel ...", "weights": [[8, 16, 1.0], [139, 147, 1.0]]}, "2013.cikm_conference-2013.86": {"snippet": "...tial methods. Our extensive experimental evaluation shows the scalability exhibited by our proposed methods under various workload settings.", "weights": [[8, 15, 1.0], [103, 110, 1.0]]}, "2002.ntcir_workshop-2002.58": {"snippet": "...ging Multiple Results from Information Retrieval System\n\n\n We participated in Web retrieval tasks in order to investigate the integration of multiple retrieval results. We also examined the possibilit...", "weights": [[8, 16, 1.0], [30, 41, 1.0], [42, 51, 1.0], [85, 94, 1.0], [144, 152, 1.0], [153, 162, 1.0]]}, "2021.emnlp-main.322": {"snippet": "...tain multiple comparisons. The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences. To address these issues, in this work we first introduce a...", "weights": [[8, 16, 1.0]]}, "2008.ntcir_workshop-2008.26": {"snippet": "... the methods of phrase based statistical machine translation and OOV translation methods based on search engine, and a document retrieval component which combines outputs of multiple IR models with a ...", "weights": [[8, 15, 1.0], [84, 91, 1.0], [131, 140, 1.0], [177, 185, 1.0]]}, "2020.ictir_conference-2020.7": {"snippet": "...ment Retrieval with Multiple Queries\n\n\n The merits of using multiple queries representing the same information need to improve retrieval effectiveness have recently been demonstrated in several studie...", "weights": [[8, 17, 1.0], [23, 31, 1.0], [63, 71, 1.0], [102, 113, 1.0], [130, 139, 1.0]]}, "2007.sigirconf_conference-2007.38": {"snippet": "...with multiple hyperplanes\n\n\n ABSTRACTThe central problem for many applications in Information Retrieval is ranking and learning to rank is considered as a promising approach for addressing the issue. ...", "weights": [[8, 16, 1.0], [85, 96, 1.0], [97, 106, 1.0]]}, "2016.clef_conference-2016.5": {"snippet": "...gual Information Retrieval\n\n\n Abstract. Machine Translation (MT) systems employed to translate queries for Cross-Lingual Information Retrieval typically produce a single translation with maximum trans...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [124, 135, 1.0], [136, 145, 1.0]]}, "2006.sigirconf_conference-2006.58": {"snippet": "...racy retrieval with multiple nested ranker\n\n\n ABSTRACTHigh precision at the top ranks has become a new focus of research in information retrieval. This paper presents the multiple nested ranker approa...", "weights": [[8, 17, 1.0], [23, 31, 1.0], [127, 138, 1.0], [139, 148, 1.0], [174, 182, 1.0]]}, "2001.clef_workshop-2000w.9": {"snippet": "...uage Retrieval for the CLEF Collections - Comparing Multiple Methods of Retrieval\n\n\n For our participation in CLEF, the Berkeley group participated in the monolingual, multilingual and GIRT tasks. To ...", "weights": [[8, 17, 1.0], [55, 63, 1.0], [64, 71, 1.0], [75, 84, 1.0]]}, "2002.trec_conference-2002.13": {"snippet": "...lish Retrieval\n\n\n The focus of the experiments reported in this paper was techniques for combining evidence for crosslanguage retrieval, searching Arabic documents using English queries. Evidence from...", "weights": [[8, 17, 1.0], [129, 138, 1.0]]}, "2011.wwwconf_conference-2011.44": {"snippet": "...ther retrieval measures.", "weights": [[8, 17, 1.0]]}, "2012.ipm_journal-ir0volumeA48A2.11": {"snippet": "...opic retrieval methods: Clustering versus diversification of search results\n\n\n a b s t r a c tTo address the inability of current ranking systems to support subtopic retrieval, two main post-processin...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [169, 178, 1.0]]}, "2002.jasis_journal-ir0volumeA53A1.3": {"snippet": "...sual information retrieval methodologies case study: Comparative analysis of textual, icon, graphical, and spring displays", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.19": {"snippet": "...s of information retrieval based on measuring the divergence from randomness. ACMTransactions on Information Systems 20(4), 357-389], which result in 170% enhancements in mean average precision when c...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [100, 111, 1.0]]}, "2016.cikm_conference-2016.73": {"snippet": "...ring Information Retrieval Systems", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2015.tois_journal-ir0volumeA33A2.4": {"snippet": "...ased methods for multimodal information fusion in content-based multimedia information retrieval. Additional Key Words and Phrases: Content-based multimedia information retrieval, information fusion, ...", "weights": [[8, 15, 1.0], [31, 42, 1.0], [78, 89, 1.0], [90, 99, 1.0], [160, 171, 1.0], [172, 181, 1.0], [183, 194, 1.0]]}, "2006.spire_conference-2006.20": {"snippet": "A Multiple Criteria Approach for Information Retrieval", "weights": [[2, 10, 1.0], [33, 44, 1.0], [45, 54, 1.0]]}, "2013.cikm_conference-2013.200": {"snippet": "... the multiple views. An advantage of the proposed method over existing methods is that the tuning parameters can be determined effectively from the given data. Through experiments on synthetic and ben...", "weights": [[8, 16, 1.0], [74, 81, 1.0]]}, "2016.airs_conference-2016.10": {"snippet": "...tent Retrieval Based on Multiple Information Resources\n\n\n Abstract. Query expansion methods have been proven to be effective to improve the average performance of patent retrieval, and most of query e...", "weights": [[8, 17, 1.0], [27, 35, 1.0], [36, 47, 1.0], [87, 94, 1.0], [173, 182, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2007.ecir_conference-2007.68": {"snippet": "...l of Multiple Retrieval Methods with the Friedman Test", "weights": [[8, 16, 1.0], [17, 26, 1.0], [27, 34, 1.0]]}, "C16-1134": {"snippet": "...ring methods as long as they output multiple preordering candidates, and it is trivial to employ existing preordering methods into our system. In our experiments for translating diverse 11 languages i...", "weights": [[8, 15, 1.0], [39, 47, 1.0], [121, 128, 1.0]]}, "W03-1120": {"snippet": "...uage Information Retrieval Based on Category Matching Between Language Versions of a Web Directory\n\n\n Since the Web consists of documents in various domains or genres, the method for Cross-Language In...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [201, 212, 1.0]]}, "2005.ir_journal-ir0volumeA8A3.3": {"snippet": "...tion Methods for Data Fusion in Information Retrieval", "weights": [[8, 15, 1.0], [35, 46, 1.0], [47, 56, 1.0]]}, "2001.sigirconf_conference-2001.52": {"snippet": "...s of Multiple-Evidence Combinations for Retrieval Strategies", "weights": [[8, 16, 1.0], [43, 52, 1.0]]}, "2006.sigirconf_conference-2006.43": {"snippet": "...ning multiple retrieval sources\n\n\n ABSTRACTCombining the output from multiple retrieval sources over the same document collection is of great importance to a number of retrieval tasks such as multimed...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [72, 80, 1.0], [81, 90, 1.0], [171, 180, 1.0]]}, "2010.sigirconf_conference-2010.34": {"snippet": "... for multiple documents\n\n\n ABSTRACTCombining multiple documents to represent an information object is well-known as an effective approach for many Information Retrieval tasks. For example, passages ca...", "weights": [[8, 16, 1.0], [48, 56, 1.0], [83, 94, 1.0], [150, 161, 1.0], [162, 171, 1.0]]}, "2002.spire_conference-2002.20": {"snippet": "Multiple Example Queries in Content-Based Image Retrieval", "weights": [[0, 8, 1.0], [48, 57, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.18": {"snippet": "...y of information retrieval metrics based on graded relevance\n\n\n AbstractThis paper compares 14 information retrieval metrics based on graded relevance, together with 10 traditional metrics based on bi...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "2005.ntcir_workshop-2005.47": {"snippet": "...tent Retrieval Experiments at Hitachi\n\n\n In NTCIR-5, we used five retrieval methods proposed in NTCIR-4: (1) query term weighting using only document frequency, (2) stopword deletion, (3) two-stage pa...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [79, 86, 1.0]]}, "2002.tois_journal-ir0volumeA20A3.0": {"snippet": "...ving retrieval feedback with multiple term-ranking function combination\n\n\n In this article we consider methods for automatic query expansion from top retrieved documents (i.e., retrieval feedback) tha...", "weights": [[8, 17, 1.0], [32, 40, 1.0], [106, 113, 1.0], [180, 189, 1.0]]}, "2015.clef_conference-2015.33": {"snippet": "...ixed-Methods for Evaluating Information Access Systems\n\n\n Abstract. The evaluation of information access systems is increasingly making use of multiple evaluation methods. While such studies represent...", "weights": [[8, 15, 1.0], [31, 42, 1.0], [89, 100, 1.0], [146, 154, 1.0], [166, 173, 1.0]]}, "2002.ntcir_workshop-2002.6": {"snippet": "...ying Multiple Characteristics and Techniques to Obtain High Levels of Performance in Information Retrieval\n\n\n Our information retrieval system which achieves its goals by taking advantage of numerous ...", "weights": [[8, 16, 1.0], [88, 99, 1.0], [100, 109, 1.0], [117, 128, 1.0], [129, 138, 1.0]]}, "2012.sigirconf_conference-2012.84": {"snippet": "...zing multiple centralized retrieval algorithms. One simple approach is to learn a set of combination weights for multiple centralized retrieval algorithms (e.g., logistic regression) to compute compar...", "weights": [[8, 16, 1.0], [29, 38, 1.0], [116, 124, 1.0], [137, 146, 1.0]]}, "2018.clef_conference-2018w.86": {"snippet": "...d by Multiple Query Variants Using Data Fusion\n\n\n In this paper, we describe the methods deployed in the different runs submitted for our participation to the CLEF eHealth2018 Task 3: Consumer Health ...", "weights": [[8, 16, 1.0], [84, 91, 1.0]]}, "2008.clef_workshop-2008.72": {"snippet": "...sity Methods for Hybrid Text and Image Retrieval Approaches", "weights": [[8, 15, 1.0], [42, 51, 1.0]]}, "2004.ntcir_workshop-2004.67": {"snippet": "... for Information Retrieval\n\n\n This paper presents a genre-independent method of generating a single summary from a set of the retrieved documents for information retrieval. The proposed method generat...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [153, 164, 1.0], [165, 174, 1.0]]}, "2009.clef_workshop-2009w.33": {"snippet": "...gual Information Retrieval based on Multiple Indexes\n\n\n In this paper we present the technical details of the retrieval system with which we participated at the CLEF09 Ad-hoc TEL task. We present a re...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [39, 47, 1.0], [113, 122, 1.0], [201, 210, 1.0]]}, "1998.sigirconf_conference-98.21": {"snippet": "...tive information retrieval (IR) systems on a shared problem: a question-answering task, 6 statements of information need, and a collection of 210,158 articles from the Financial Times of London 1991-1...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [107, 118, 1.0]]}, "1997.sigirconf_conference-97.30": {"snippet": "...s in retrieval performance can be achieved by combining multiple representations of an information need.However, little effort has been made to understand the reason why combining multiple sources of ...", "weights": [[8, 17, 1.0], [59, 67, 1.0], [90, 101, 1.0], [183, 191, 1.0]]}, "A97-1049": {"snippet": "...gual Information Browsing and Retrieval System Using Information Extraction\n\n\n In this paper, we describe our multilingual (or cross-linguistic) information browsing and retrieval system, which is aim...", "weights": [[8, 19, 1.0], [33, 42, 1.0], [56, 67, 1.0], [148, 159, 1.0], [173, 182, 1.0]]}, "N09-1060": {"snippet": "...ting methods on a broad range of tests. We also demonstrate how it can be used to improve existing applications in information retrieval and summarization.", "weights": [[8, 15, 1.0], [118, 129, 1.0], [130, 139, 1.0]]}, "2013.sigirconf_conference-2013.147": {"snippet": "...e in multiple medical reports (from different hospital departments or from different tests within the same department) of a patient. Furthermore, we explore several informative features for learning o", "weights": [[8, 16, 1.0]]}, "2008.airs_conference-2008.56": {"snippet": "...ting Multiple News Sites", "weights": [[8, 16, 1.0]]}, "2004.ntcir_workshop-2004.7": {"snippet": "...ying Multiple Characteristics and Techniques to Obtain High Levels of Performance in Information Retrieval at NTCIR-4\n\n\n Our information retrieval system takes advantage of numerous characteristics of...", "weights": [[8, 16, 1.0], [88, 99, 1.0], [100, 109, 1.0], [128, 139, 1.0], [140, 149, 1.0]]}, "2008.clef_workshop-2008w.23": {"snippet": "...r to compare retrieval based on text description, to image-only retrieval, and to merged retrieval, and to compare results for the different clustering methods.", "weights": [[8, 15, 1.0], [16, 25, 1.0], [67, 76, 1.0], [92, 101, 1.0], [110, 117, 1.0], [155, 162, 1.0]]}, "2022.naacl-srw.7": {"snippet": "Retrieval-augmented Generation across Heterogeneous Knowledge\n\n\n Retrieval-augmented generation (RAG) methods have been receiving increasing attention from the NLP community and achieved stateof-the-a...", "weights": [[0, 9, 1.0], [65, 74, 1.0], [102, 109, 1.0]]}, "1985.sigirconf_conference-85.5": {"snippet": "...ling Multiple Databases in Document Retrieval", "weights": [[8, 16, 1.0], [39, 48, 1.0]]}, "2009.cikm_conference-2009.133": {"snippet": "...some retrieval situations, a system must search across multiple collections. This task, referred to as federated search, occurs for example when searching a distributed index or aggregating content fo...", "weights": [[8, 17, 1.0], [58, 66, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2018.wwwconf_conference-2018c.22": {"snippet": "...ving Information from Multiple Sources\n\n\n ABSTRACTThe Web has several information sources on which an ongoing event is discussed. To get a complete picture of the event, it is important to retrieve in...", "weights": [[8, 19, 1.0], [25, 33, 1.0], [73, 84, 1.0], [201, 212, 1.0]]}, "2016.ntcir_workshop-2016evia.1": {"snippet": "...n of Information Retrieval Systems (IRSs) using publically available test collections has become an established practice in Information Retrieval (IR). By means of the popular Cranfield evaluation par...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [127, 138, 1.0], [139, 148, 1.0]]}, "1995.ipm_journal-ir0volumeA31A3.11": {"snippet": "...e of Multiple Query Representations for Information Retrieval", "weights": [[8, 16, 1.0], [43, 54, 1.0], [55, 64, 1.0]]}, "2008.cikm_workshop-2008dtmbio.9": {"snippet": "...e of multiple parameter sets. Preliminary results for a knowledge discovery application such as homology detection reveal that using multiple parameter sets for pairwise statistical significance estim...", "weights": [[8, 16, 1.0], [136, 144, 1.0]]}, "2013.ictir_conference-2013.12": {"snippet": "...rank information retrieval (IR) output with the objective of improving ad hoc search effectiveness. Unlike existing work, our methods exploit recommender techniques to extract the correlation between ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [129, 136, 1.0]]}, "2006.trec_conference-2006.13": {"snippet": "...omic Information Retrieval\n\n\n We participated in the passage retrieval and aspect retrieval subtasks of the TREC 2006 Genomics Track. This paper describes the methods developed for these two subtasks....", "weights": [[8, 19, 1.0], [20, 29, 1.0], [64, 73, 1.0], [85, 94, 1.0], [162, 169, 1.0]]}, "2018.sigirconf_conference-2018.224": {"snippet": "...t in Information Retrieval. The goal of fusion methods is to merge different sources of information so as to address a retrieval task. For example, in the adhoc retrieval setting, fusion methods have ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 57, 1.0], [91, 102, 1.0], [122, 131, 1.0], [164, 173, 1.0], [190, 197, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ased retrieval problem. Similarly to the distribution of future wins, the retrieval framework considers multiple possible concept-based document representations for each document resulting in multiple...", "weights": [[8, 17, 1.0], [77, 86, 1.0], [107, 115, 1.0], [195, 203, 1.0]]}, "2013.sigirconf_conference-2013.68": {"snippet": "... for information retrieval\n\n\n ABSTRACTIn many domains of information retrieval, system estimates of document relevance are based on multidimensional quality criteria that have to be accommodated in a ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [60, 71, 1.0], [72, 81, 1.0]]}, "2017.ictir_conference-2017.18": {"snippet": "Text Retrieval based on Least Information Measurement\n\n\n ABSTRACTWe developed a new information retrieval framework based on the Least Information (LI) metric. We derived multiple term weighting schem...", "weights": [[5, 14, 1.0], [30, 41, 1.0], [84, 95, 1.0], [96, 105, 1.0], [135, 146, 1.0], [171, 179, 1.0]]}, "2016.tist_journal-ir0volumeA7A4.21": {"snippet": "...uses multiple types of image features in a framework named multitask low-rank affinity pursuit. Given the image patches described with multiple types of features, we aim at inferring a unified affinit...", "weights": [[8, 16, 1.0], [138, 146, 1.0]]}, "2004.sigirconf_conference-2004.34": {"snippet": "Information retrieval using word senses: root sense tagging approach\n\n\n ABSTRACTInformation retrieval using word senses is emerging as a good research challenge on semantic information retrieval. 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Vector Space Models\n\n\n One of the major challenges nowadays is Information retrieval from social media platforms. Most of the informa...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [133, 144, 1.0], [145, 154, 1.0], [160, 166, 1.0], [167, 172, 1.0], [196, 207, 1.0]]}, "2021.jasis_journal-ir0volumeA72A1.6": {"snippet": "...ving social media data: Understanding data management practices among social media researchers and their implications for archives", "weights": [[8, 14, 1.0], [15, 20, 1.0], [73, 79, 1.0], [80, 85, 1.0]]}, "2016.cikm_conference-2016.99": {"snippet": "... App Retrieval for Social Media Users via Inference of Implicit Intent in Social Media Text", "weights": [[8, 17, 1.0], [22, 28, 1.0], [29, 34, 1.0], [77, 83, 1.0], [84, 89, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.15": {"snippet": "...s in social media\n\n\n a b s t r a c tSocial media is increasingly becoming a significant fraction of the content retrieved daily by Web users. However, the potential lack of quality of user generated c...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [46, 51, 1.0]]}, "W19-3021": {"snippet": "...t on Social Media: {USI}-{UPF} at the {CLP}sych 2019 Shared Task\n\n\n This paper describes the participation of the USI-UPF team at the shared task of the 2019 Computational Linguistics and Clinical Psy...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2012.wwwconf_conference-2012c.203": {"snippet": "...rent information systems for crisis management are increasingly incorporating information obtained from citizens transmitted via social media and SMS. This information proves particularly useful at th...", "weights": [[8, 19, 1.0], [81, 92, 1.0], [132, 138, 1.0], [139, 144, 1.0], [159, 170, 1.0]]}, "1989.tois_journal-ir0volumeA7A3.4": {"snippet": "...onic information systems but at the potential risk of becoming lost in the network of interconnected pieces of information. A study was conducted on information retrieval using a commercial hypertext-...", "weights": [[8, 19, 1.0], [114, 125, 1.0], [152, 163, 1.0], [164, 173, 1.0]]}, "2019.clef_conference-2019w.78": {"snippet": "...a on social media in CLEF eRisk 2019. Our models make use of different kinds of deep neural networks to classify the users in a danger situation. We show the effectiveness of our models by using the v...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2017.wsdm_conference-2017.64": {"snippet": "...evel media buying; and running a campaign is no different than an investment of acquiring new customers in return for obtaining additional converted sales. Thus, how to optimally bid on an ad impressi...", "weights": [[8, 13, 1.0]]}, "2010.jasis_journal-ir0volumeA61A12.18": {"snippet": "Social Information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively", "weights": [[0, 6, 1.0], [7, 18, 1.0], [19, 28, 1.0]]}, "2018.clef_conference-2018w.113": {"snippet": "...onal Information for Depression and Anorexia Detection\n\n\n The early detection of risks behaviors can significantly contribute to prevent serious psychological and health consequences. This paper repor...", "weights": [[8, 19, 1.0], [84, 89, 1.0]]}, "2020.clef_conference-2020w.59": {"snippet": "...n in social media users. The second task focused on automatically filling a depression questionnaire given user writing history. The RELAI team participated in both tasks, and addressed them using top...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2021.ranlp-1.160": {"snippet": "...alse information in social media. However, most of these machine learning models focus only on the language they were trained on. Given the fact that social media platforms are being used in different...", "weights": [[8, 19, 1.0], [23, 29, 1.0], [30, 35, 1.0], [153, 159, 1.0], [160, 165, 1.0]]}, "2014.sigirconf_conference-2014.228": {"snippet": "...014: social media retrieval and analysis workshop\n\n\n ABSTRACTThe SoMeRA workshop targets cutting edge research from all fields of retrieval, recommendation, and browsing in social media, as well as th...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 30, 1.0], [133, 142, 1.0], [176, 182, 1.0], [183, 188, 1.0]]}, "2016.sigirconf_conference-2016.232": {"snippet": "...ving Information Retrieval\nMOTIVATIONInformation retrieval and information privacy/security are two fast-growing computer science disciplines. Information retrieval provides a set of information seeki...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [52, 61, 1.0], [66, 77, 1.0], [146, 157, 1.0], [158, 167, 1.0], [186, 197, 1.0]]}, "2018.wwwconf_conference-2018c.196": {"snippet": "... and social computing; Collaborative and social computing systems and tools; Social Networking Sites KEYWORDS smart cities; smart people; smart living; online social networks; privacy threats; securit...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [80, 86, 1.0], [162, 168, 1.0]]}, "2020.acl-main.723": {"snippet": "...from social media as a ranking problem whose goal is maximizing detection of severely at-risk individuals given the time available. Building on measures developed for resource-bounded document retriev...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [196, 205, 1.0]]}, "2014.sigirconf_conference-2014.4": {"snippet": "...n of retrieval systems\n\n\n ABSTRACTThe aim of risk-sensitive evaluation is to measure when a given information retrieval (IR) system does not perform worse than a corresponding baseline system for any ...", "weights": [[8, 17, 1.0], [101, 112, 1.0], [113, 122, 1.0]]}, "1989.sigirconf_conference-89.24": {"snippet": "...onic information systems but at the potential risk of becoming lost in the network of interconnected pieces of information. A study was conducted on information retrieval using a commercial hypertextb...", "weights": [[8, 19, 1.0], [114, 125, 1.0], [152, 163, 1.0], [164, 173, 1.0]]}, "2017.wwwconf_conference-2017c.190": {"snippet": "...mic, social and medical burden coupled with increased morbidity of the aforementioned diseases makes their prevention an active research area. Currently, the traditional method of MetS diagnosis is ba...", "weights": [[8, 14, 1.0]]}, "W14-6202": {"snippet": "Information Extraction for Social Media\n\n\n The rapid growth in IT in the last two decades has led to a growth in the amount of information available online. A new style for sharing information is soci...", "weights": [[0, 11, 1.0], [27, 33, 1.0], [34, 39, 1.0], [127, 138, 1.0], [181, 192, 1.0], [196, 202, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.24": {"snippet": "... for Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2017.fire_conference-2017w.40": {"snippet": "...ays, Social media has become a major part to transfer the message that must be shared with the people ideas and express the information globally in our day-today life. Through social media can able to...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [127, 138, 1.0], [179, 185, 1.0], [186, 191, 1.0]]}, "2019.clef_conference-2019w.50": {"snippet": "...e of social media facilities and proliferation in the fame of online social media websites such as Twitter, Facebook, LinkedIn, etc. directed to the growth of unwanted social bots as automatic social ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [72, 78, 1.0], [79, 84, 1.0], [171, 177, 1.0], [196, 202, 1.0]]}, "2020.sigirconf_conference-2020.49": {"snippet": "...view information to make purchase decisions. With this information, they can infer the quality of products to reduce the risk of purchase. Specifically, items with high rating scores and good reviews ...", "weights": [[8, 19, 1.0], [58, 69, 1.0]]}, "2019.chiir_conference-2019.20": {"snippet": "...mage Retrieval Approach to Searching for Images on Social Media\n\n\n ABSTRACTSearching for images posted within social media services such as Twitter relies on matching textual queries to the contents o...", "weights": [[8, 17, 1.0], [54, 60, 1.0], [61, 66, 1.0], [113, 119, 1.0], [120, 125, 1.0]]}, "2019.tois_journal-ir0volumeA37A2.10": {"snippet": "...s in Information Retrieval. Risk-sensitive L2R is a subarea of L2R that tries to learn models that are good on average while at the same time reducing the risk of performing poorly in a few but import...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "W10-0504": {"snippet": "An Information-Retrieval Approach to Language Modeling: Applications to Social Data\n\n\n In this paper we propose the IR-LM (Information Retrieval Language Model) which is an approach to carrying out la...", "weights": [[3, 14, 1.0], [15, 24, 1.0], [72, 78, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2019.clef_conference-2019w.108": {"snippet": "...a in Social Media through Shared Personal Information\n\n\n Detecting mental-health risk behaviours at primary stages is crucial to bring help to people and to avoid undesired consequences. In this paper...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [45, 56, 1.0]]}, "2012.ecir_conference-2012.53": {"snippet": "...ople Retrieval in Social Media with Multiple Roles", "weights": [[8, 17, 1.0], [21, 27, 1.0], [28, 33, 1.0]]}, "2021.tist_journal-ir0volumeA12A1.11": {"snippet": "...ring Media Bias via Social Network Learning", "weights": [[8, 13, 1.0], [23, 29, 1.0]]}, "2008.wwwconf_conference-2008.73": {"snippet": "...ring social annotations for information retrieval\n\n\n ABSTRACTSocial annotation has gained increasing popularity in many Web-based applications, leading to an emerging research area in text analysis an...", "weights": [[8, 14, 1.0], [31, 42, 1.0], [43, 52, 1.0]]}, "P10-4010": {"snippet": "...t of risks pertaining to a business area or entity. We argue that by combining Web mining and Information Extraction (IE) techniques, risks can be detected automatically before they materialize, thus ...", "weights": [[8, 13, 1.0], [97, 108, 1.0], [137, 142, 1.0]]}, "2017.clef_conference-2017w.46": {"snippet": "...t in social media. Several approaches based on supervised learning and information retrieval methods were used to estimate the risk of depression for a user given the content of its posts in reddit. A...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [74, 85, 1.0], [86, 95, 1.0]]}, "2018.ecir_workshop-2018brodyn.4": {"snippet": "Social Media based Analysis of Refugees in Turkey\n\n\n In this paper, we propose a method to find out public social media accounts of refugees, and trace them back to infer temporal and spatial events.T...", "weights": [[0, 6, 1.0], [7, 12, 1.0], [107, 113, 1.0], [114, 119, 1.0]]}, "2009.cikm_conference-2009.102": {"snippet": "... and information begins to emerge as an interesting problem. In this paper, we investigate the problem of information retrieval in a mobile peer-to-peer network. The prevailing approach to information...", "weights": [[8, 19, 1.0], [109, 120, 1.0], [121, 130, 1.0], [192, 203, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.17": {"snippet": "...s of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar s...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [60, 66, 1.0], [67, 72, 1.0], [110, 116, 1.0], [117, 122, 1.0], [202, 208, 1.0]]}, "2014.cikm_workshop-2014psbd.5": {"snippet": "...vacy Risks in User Search Histories\n\n\n ABSTRACTThis paper proposes a new model of user-centric, global, probabilistic privacy, geared for today's challenges of helping users to manage their privacy-se...", "weights": [[8, 13, 1.0]]}, "W14-3201": {"snippet": "...t-in social media postings by patients using social media websites, e.g., Facebook. We describe the software infrastructure that we have completed for this phase two system. During phase three we will...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [48, 54, 1.0], [55, 60, 1.0]]}, "2011.mir_conference-2011.33": {"snippet": "Social media driven image retrieval", "weights": [[0, 6, 1.0], [7, 12, 1.0], [26, 35, 1.0]]}, "2012.wwwconf_conference-2012c.121": {"snippet": "...y in social networks\n\n\n ABSTRACTCurrent online social networking (OSN) sites pose severe risks to their users' privacy. Facebook in particular is capturing more and more of a user's past activities, s...", "weights": [[8, 14, 1.0], [50, 56, 1.0], [92, 97, 1.0]]}, "2016.sigirconf_conference-2016.54": {"snippet": "...that retrieval performance highly varies among IR systems across topics. Thus, a single system would in general fail in providing enough information about the real baseline performance for every topic...", "weights": [[8, 17, 1.0], [140, 151, 1.0]]}, "2012.ir_journal-ir0volumeA15A34.2": {"snippet": "...sing social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2017.clef_conference-2017w.64": {"snippet": "...s in social media relying on various features that we detail in this paper. We submitted 4 models which differences are also detailed in this paper. Best results were obtained when using a combination...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2013.sigirconf_conference-2013.159": {"snippet": "...ed!: social media search system requirements for emergency management professionals\n\n\n ABSTRACTSocial media provides a new and potentially rich source of information for emergency management services....", "weights": [[8, 14, 1.0], [15, 20, 1.0], [105, 110, 1.0], [157, 168, 1.0]]}, "2013.wwwconf_conference-2013c.216": {"snippet": "...sion retrieval from twitter\n\n\n ABSTRACTWhile social media receive a lot of attention from the scientific community in general, there is little work on high recall retrieval of messages relevant to a d...", "weights": [[8, 17, 1.0], [48, 54, 1.0], [55, 60, 1.0], [166, 175, 1.0]]}, "2021.wsdm_conference-2021.6": {"snippet": "...n on Social Media\n\n\n The rising ubiquity of social media presents a platform for individuals to express suicide ideation, instead of traditional, formal clinical settings. While neural methods for ass...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [47, 53, 1.0], [54, 59, 1.0]]}, "2021.ranlp-1.24": {"snippet": "...n in Social Media\n\n\n In this work, we provide an extensive part-ofspeech analysis of the discourse of social media users with depression. Research in psychology revealed that depressed users tend to b...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [105, 111, 1.0], [112, 117, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.2": {"snippet": "... for information retrieval\n\n\n AbstractThis paper presents a probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [77, 88, 1.0], [89, 98, 1.0], [122, 131, 1.0]]}, "2013.cikm_conference-2013.270": {"snippet": "...e of social networks on social media content providers. Additionally, we propose several document expansion methods, which leverage the content generated by the social networks of the authors of socia...", "weights": [[8, 14, 1.0], [27, 33, 1.0], [34, 39, 1.0], [164, 170, 1.0], [198, 204, 1.0]]}, "2020.emnlp-main.198": {"snippet": "...lyze social media for detecting the suicidal risk of military personnel, which is especially crucial for countries with compulsory military service such as the Republic of Korea. From a widely-used Ko...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2018.ecir_conference-2018.79": {"snippet": "...n on Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2014.sigirconf_conference-2014.226": {"snippet": "...when information retrieval meets privacy and security\n\n\n ABSTRACTInformation retrieval (IR) and information privacy/security are two fast-growing computer science disciplines. There are many synergies...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 89, 1.0], [99, 110, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A2.7": {"snippet": "...s on social media data\n\n\n Abstract Social media sites contain a considerable amount of data for natural calamities events, such as earthquakes, snowstorms, mud-rock flows. With the increasing amount o...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [38, 44, 1.0], [45, 50, 1.0]]}, "2020.sigirjournals_journal-ir0volumeA54A1.16": {"snippet": "Information extraction from digital social trace data with applications to social media and scholarly communication data", "weights": [[0, 11, 1.0], [36, 42, 1.0], [75, 81, 1.0], [82, 87, 1.0]]}, "2020.sigirconf_conference-2020.312": {"snippet": "... for Social Media Users to Explore Their Digital Footprints\n\n\n Small pieces of data that are shared online, over time and across multiple social networks, have the potential to reveal more cumulativel...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [141, 147, 1.0]]}, "2010.sigirconf_conference-2010.212": {"snippet": "...e of information available in social media, ways of accessing this information intelligently are needed; this is the scope of my research.Why should we care about information in social media? Here are...", "weights": [[8, 19, 1.0], [33, 39, 1.0], [40, 45, 1.0], [70, 81, 1.0], [166, 177, 1.0], [181, 187, 1.0], [188, 193, 1.0]]}, "2019.ecir_conference-20192.47": {"snippet": "... for Social Media Retrieval\n\n\n Abstract. The huge amount of information that is available in social media today has resulted in a pressing demand for information retrieval models to support users in d...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 30, 1.0], [63, 74, 1.0], [96, 102, 1.0], [103, 108, 1.0], [153, 164, 1.0], [165, 174, 1.0]]}, "2017.sigirconf_conference-2017.239": {"snippet": "...line Social Media (OSM) in providing actionable situational information. However, in such media, the crucial information is typically obscured by a lot of insignificant information (e.g., personal opi...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [63, 74, 1.0], [93, 98, 1.0], [112, 123, 1.0], [172, 183, 1.0]]}, "2016.tist_journal-ir0volumeA8A1.3": {"snippet": "...nown risks. In order to be proactive for handling the unknown risks, the above workforce must be scheduled dynamically so the system is adaptive to meet the day-to-day stochastic demands on its workfo...", "weights": [[8, 13, 1.0], [65, 70, 1.0]]}, "2021.wsdm_conference-2021.133": {"snippet": "...s in social media and intercepts the outgoing text messages from a user or a digital assistant, if they impose potential privacy leakage risks. Such messages are redirected to authorized users for app...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [140, 145, 1.0]]}, "2018.ecir_conference-2018.76": {"snippet": "... the Retrieval of Related Tweets to News Articles\n\n\n Abstract. Nowadays, social media users react in real-time to local and global events. Therefore, social media can be used to measure the impact of ...", "weights": [[8, 17, 1.0], [76, 82, 1.0], [83, 88, 1.0], [153, 159, 1.0], [160, 165, 1.0]]}, "2019.sigirconf_conference-2019.260": {"snippet": "...Text Retrieval", "weights": [[8, 17, 1.0]]}, "2013.sigirconf_conference-2013.152": {"snippet": "...n in social media\n\n\n ABSTRACTIt is well recognized that users rely on social media (e.g. Twitter or Digg) to fulfill two common needs (i.e. social need and informational need ) that is to keep in touc...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [73, 79, 1.0], [80, 85, 1.0], [143, 149, 1.0]]}, "W18-0603": {"snippet": "...d on social media postings. We also introduce and demonstrate the value of a new, detailed rubric for assessing suicide risk, compare crowdsourced with expert performance, and present baseline predict...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2009.ecir_conference-2009.5": {"snippet": "...ware Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2022.acl-short.70": {"snippet": "...t on Social Media\n\n\n Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings. With advances in Natur...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [55, 61, 1.0], [62, 67, 1.0]]}, "2019.clef_conference-2019w.31": {"snippet": "...into Social Media Texts for the Early Detection of Mental and Food Disorders\n\n\n This paper describes the participation of the Bioinformatics group of the Institute of Electronics and Engineering Infor...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "S19-2113": {"snippet": "...ning social media language to discover offensive language\n\n\n User's content share through social media has reached huge proportions nowadays. However, along with the free expression of thoughts on soc...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [93, 99, 1.0], [100, 105, 1.0], [200, 206, 1.0]]}, "W18-5601": {"snippet": "...from Social Media Activity\n\n\n This work explores the detection of individuals' risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity. Our approach extends a deep ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [142, 148, 1.0], [149, 154, 1.0]]}, "2018.clef_conference-2018w.7": {"snippet": "...a on Social Media Text\n\n\n In recent years, people actively write text messages on social media platforms like Twitter and Reddit. The text shared on social media drives various applications including ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [85, 91, 1.0], [92, 97, 1.0], [152, 158, 1.0], [159, 164, 1.0]]}, "2013.sigirconf_conference-2013.210": {"snippet": "...e in social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "W19-3020": {"snippet": "{CLP}sych2019 Shared Task: Predicting Suicide Risk Level from {R}eddit Posts on Multiple Forums\n\n\n We aimed to predict an individual suicide risk level from longitudinal posts on Reddit discussion for...", "weights": []}, "2018.wwwconf_conference-2018c.268": {"snippet": "...l on social media. Unfortunately, certain social media users go to extreme lengths to click selfies, which puts their lives at risk. Two hundred and sixteen individuals have died since March 2014 unti...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [45, 51, 1.0], [52, 57, 1.0]]}, "2012.tois_journal-ir0volumeA30A2.2": {"snippet": "...Peer Information Retrieval: An Overview\n\n\n Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been develo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [157, 168, 1.0], [169, 178, 1.0]]}, "W19-3003": {"snippet": "...d on social media postings, using data from Reddit to identify users at no, low, moderate, or severe risk. Two variations of the task focused on users whose posts to the r/SuicideWatch subreddit indic...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "N18-1110": {"snippet": "...n of risks in social media sources. The goal is to effectively identify the potential risk using as little evidence as possible and with as much anticipation as possible. MulR allows us to generate mu...", "weights": [[8, 13, 1.0], [17, 23, 1.0], [24, 29, 1.0]]}, "2016.wsdm_conference-2016.88": {"snippet": "...ting Social Media Icebergs by Their Tips: Rumors, Persuasion Campaigns, and Information Needs\n\n\n ABSTRACTOnline activities of more than one billion social media users all over the world form a resourc...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [79, 90, 1.0], [151, 157, 1.0], [158, 163, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A4.17": {"snippet": "...tric information shown in medical records. However, methods of systematically summarizing medical records into risk factors have not been evaluated to support such a patient-centric focus in healthcar...", "weights": [[8, 19, 1.0]]}, "2013.ictir_conference-2013.10": {"snippet": "...ead; social media and information interaction in many forms are increasingly used in daily life, etc. Simultaneously, the information experiences of users and use of information has become increasingl...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [25, 36, 1.0], [125, 136, 1.0], [169, 180, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.14": {"snippet": "...sing information in social media: (i) the people creating the social media and (ii) their individual utterances (e.g., blog posts, tweets). These entry points act as a doorway to the information in so...", "weights": [[8, 19, 1.0], [23, 29, 1.0], [30, 35, 1.0], [65, 71, 1.0], [72, 77, 1.0], [186, 197, 1.0], [201, 207, 1.0]]}, "2012.ir_journal-ir0volumeA15A34.0": {"snippet": "...e on information retrieval for social media", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 40, 1.0], [41, 46, 1.0]]}, "2020.emnlp-main.619": {"snippet": "...n on Social Media\n\n\n Social media's ubiquity fosters a space for users to exhibit suicidal thoughts outside of traditional clinical settings. Understanding the build-up of such ideation is critical fo...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [24, 30, 1.0], [31, 36, 1.0]]}, "2018.wwwconf_conference-2018c.305": {"snippet": "... for social media applications. In essence it is completely at the discretion of the social network provider how the user's data is used and what content is delivered. 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Similar to actual ripples propagating...", "weights": [[8, 17, 1.0], [124, 133, 1.0], [177, 183, 1.0]]}, "2021.ccl-1.43": {"snippet": "\u57fa\u4e8e\u8bcd\u6c47\u94fe\u5f3a\u5316\u8868\u5f81\u7684\u7bc7\u7ae0\u4fee\u8f9e\u7ed3\u6784\u5206\u6790\u7814\u7a76(Lexical Chain Based Strengthened Representation for Discourse Rhetorical Structure Parsing)", "weights": []}, "1975.jasis_journal-ir0volumeA26A6.1": {"snippet": "...s on theoretical research in information science", "weights": [[8, 19, 1.0]]}, "1987.sigirjournals_journal-ir0volumeA21A12.6": {"snippet": "... New Theoretical Framework for Information Retrieval", "weights": [[8, 19, 1.0]]}, "P19-1101": {"snippet": "...mple Theoretical Model of Importance for Summarization\n\n\n Research on summarization has mainly been driven by empirical approaches, crafting systems to perform well on standard datasets with the notio...", "weights": [[8, 19, 1.0]]}, "2019.wsdm_conference-2019.112": {"snippet": "...tory Experiments Properly with Statistical Tools: An Easy Hands-On Tutorial", "weights": [[8, 19, 1.0]]}, "2020.sigirconf_conference-2020.2": {"snippet": "Proof by Experimentation? Towards Better IR Research", "weights": []}, "P10-1148": {"snippet": "...vity knowledge comprising valuable information such as facts, opinions and personal experiences. In this paper, we propose a method for mining personal experiences from a large set of weblogs. 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The laboratories allow students to perform exercises a...", "weights": [[8, 19, 1.0]]}, "1981.sigirconf_conference-81.18": {"snippet": "...s of Knowledge: Validity and Applications to Information Science\n\n\n AbstractThis research examined the relationship between subjects' knowledge of an area and their similarity ratings of pairs of conc...", "weights": [[8, 17, 1.0], [137, 146, 1.0]]}, "P86-1036": {"snippet": "...oded knowledge and the limited inference-making capability of a taxonomic knowledge representation system to reduce the constituent structure of logical expressions. The specific application is to the...", "weights": [[8, 17, 1.0], [77, 86, 1.0]]}, "2020.sigirconf_conference-2020.32": {"snippet": "...over Knowledge Graphs\n\n\n Knowledge graphs have been widely adopted to improve recommendation accuracy. The multi-hop user-item connections on knowledge graphs also endow reasoning about why an item is...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [145, 154, 1.0]]}, "2010.clef_conference-2010.1": {"snippet": "IR between Science and Engineering, and the Role of Experimentation\n\n\n Abstract. Evaluation has always played a major role in IR research, as a means for judging about the quality of competing models....", "weights": []}, "W12-0603": {"snippet": "... the knowledge about unlabeled data. The efficacy of semi-supervised learning is established by our extensive experiments, which also show that transductive SVM is more suitable than graph-based label...", "weights": [[8, 17, 1.0], [113, 124, 1.0]]}, "2006.sigirconf_conference-2006.14": {"snippet": "...s of knowledge that are important for information seeking. We instantiate this general framework in the domain of clinical medicine based on the principles of evidence-based medicine (EBM). Experiment...", "weights": [[8, 17, 1.0], [193, 204, 1.0]]}, "P05-3004": {"snippet": "... the experiments were extended into text summarization. Based on these experiments, The Knowledge Management System (KMS) was developed to combine these two capabilities and to serve as a unified basi...", "weights": [[8, 19, 1.0], [74, 85, 1.0], [91, 100, 1.0]]}, "2018.iwslt-1.4": {"snippet": "...s of knowledge distillation, few have discussed the actual reasons why it works, especially in the context of neural MT. In this paper, we conduct several experiments aimed at understanding why and ho...", "weights": [[8, 17, 1.0], [55, 61, 1.0], [158, 169, 1.0]]}, "P14-5011": {"snippet": "...ning Experiments on Textual Data\n\n\n We present DKPro TC, a framework for supervised learning experiments on textual data. The main goal of DKPro TC is to enable researchers to focus on the actual rese...", "weights": [[8, 19, 1.0], [96, 107, 1.0], [192, 198, 1.0]]}, "2014.wwwconf_conference-2014c.276": {"snippet": "Strengthening collaborative data analysis and decision making in web communities", "weights": []}, "P14-1001": {"snippet": "...with theoretical guarantees for learning accurate ensembles of several structured prediction rules for which no prior knowledge is assumed. This includes a number of randomized and deterministic algor...", "weights": [[8, 19, 1.0], [121, 130, 1.0]]}, "C92-3146": {"snippet": "...ical Knowledge Base\n\n\n This paper describes a project to construct a terminological knowledge base, called COGNITERM. First, we position our research framework in relationship to recent developments i...", "weights": [[8, 17, 1.0], [87, 96, 1.0]]}, "D19-1590": {"snippet": "...ound knowledge to our classifiers. Since BERT models are pre-trained with a large corpus, some sort of background knowledge for event causality may be learned during pre-training. Our experiments with...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [187, 198, 1.0]]}, "2021.blackboxnlp-1.2": {"snippet": "...stic knowledge has focused on interpolation tasks. In this paper, we instead analyse probes in an extrapolation setting, where the inputs at test time are deliberately chosen to be 'harder' than the t...", "weights": [[8, 17, 1.0]]}, "W14-5418": {"snippet": "... Our experiments show that the concept topologies in the two modalities share similarities, results that strengthen our conjecture.", "weights": [[8, 19, 1.0], [108, 118, 1.0]]}, "L18-1635": {"snippet": "...e\n\n\n Theoretical studies on the Information Structure-prosody interface argue that the content packaged in terms of theme and rheme correlates with the intonation of the corresponding sentence. Howeve...", "weights": [[8, 19, 1.0]]}, "2018.sigirconf_conference-2018.78": {"snippet": "...n in Knowledge Bases: Max-K Criterion and Prediction Protocols\n\n\n ABSTRACTBuilding knowledge base embedding models for link prediction has achieved great success. We however argue that the conventiona...", "weights": [[8, 17, 1.0], [86, 95, 1.0]]}, "2016.ipm_journal-ir0volumeA52A5.14": {"snippet": "... the knowledge gap. First, domain knowledge is extracted as the concept map, which provides a basis of comparing user background knowledge and target knowledge. Then, the knowledge gap is defined with...", "weights": [[8, 17, 1.0], [37, 46, 1.0], [132, 141, 1.0], [153, 162, 1.0], [174, 183, 1.0]]}, "2011.cikm_conference-2011.115": {"snippet": "...tial experiments on extensive UCI data sets verify the theoretical conclusions. In addition, experiments of clustering based on the derived dissimilarity metrics show a significant performance improve", "weights": [[8, 19, 1.0], [58, 69, 1.0], [96, 107, 1.0]]}, "2010.tois_journal-ir0volumeA28A3.1": {"snippet": "...on a theoretical and numerical comparison of the two approaches. Numerical experiments illustrate the effectiveness of our theoretical results.", "weights": [[8, 19, 1.0], [78, 89, 1.0], [126, 137, 1.0]]}, "2013.wsdm_conference-2013.1": {"snippet": "...oral experiments on a scale and speed that would have been hard to imagine just a decade ago. To illustrate the point, I describe some recent experiments that showcase the advantages of virtual over t...", "weights": [[8, 19, 1.0], [145, 156, 1.0]]}, "2021.acl-long.429": {"snippet": "... for Knowledge Graph Embedding\n\n\n In knowledge graph embedding, the theoretical relationship between the softmax crossentropy and negative sampling loss functions has not been investigated. This makes...", "weights": [[8, 17, 1.0], [40, 49, 1.0], [71, 82, 1.0]]}, "2013.wsdm_conference-2013.9": {"snippet": "... and strengthen our theoretical claims by performing exhaustive experiments on realworld commerce search data, and show that our algorithm outperforms state-of-the-art tiered indexing techniques that ...", "weights": [[8, 18, 1.0], [23, 34, 1.0], [67, 78, 1.0]]}, "2013.ipm_journal-ir0volumeA49A5.6": {"snippet": "...that knowledge was correlated with users' perceptions of task attributes and accomplishment. In addition, task type was found to affect several aspects of knowledge levels and knowledge change. These ...", "weights": [[8, 17, 1.0], [158, 167, 1.0], [179, 188, 1.0]]}, "1984.ipm_journal-ir0volumeA20A12.19": {"snippet": "...ning experiments in information science", "weights": [[8, 19, 1.0]]}, "Y10-1014": {"snippet": "Decision Theory and Discourse Particles: A Case Study from a Large {J}apanese Sentiment Corpus\n\n\n The distribution and use of the Japanese particle yo is examined using a large annotated sentiment cor...", "weights": []}, "P02-1012": {"snippet": "...been theoretical rather than applied in nature. Frequently, such methods are based on Centering Theory, which deals with the resolution of anaphoric pronouns. But it is not clear that complex theoreti...", "weights": [[8, 19, 1.0], [195, 206, 1.0]]}, "2021.adaptnlp-1.3": {"snippet": "...ther experiments demonstrate that CAN has a good ability to generalize learned knowledge to unseen domains.", "weights": [[8, 19, 1.0], [82, 91, 1.0]]}, "2021.findings-acl.139": {"snippet": "...ured knowledge (e.g. reviews and documents). In this paper, we formulate a task of modeling TOD grounded on both structured and unstructured knowledge. To address this task, we propose a TOD system wi...", "weights": [[8, 17, 1.0], [144, 153, 1.0]]}, "2020.sigirconf_conference-2020.258": {"snippet": "...se a knowledge-enhanced recommendation model ACAM, which incorporates item attributes distilled from knowledge graphs (KGs) as side information, and is built with a co-attention mechanism on attribute...", "weights": [[8, 17, 1.0], [104, 113, 1.0]]}, "2011.wsdm_conference-2011.80": {"snippet": "...ires knowledge of the structure of the queries and the results space, as well as knowledge of the input query distribution. We formulate the problem under the framework of stochastic optimization; the...", "weights": [[8, 17, 1.0], [84, 93, 1.0]]}, "2022.acl-long.407": {"snippet": "...duct experiments to demonstrate the existence of each logic trap. Through both theoretical and experimental analysis, we hope to increase attention on the inaccurate evaluation of attribution scores. ...", "weights": [[8, 19, 1.0], [82, 93, 1.0]]}, "2020.sigirconf_conference-2020.19": {"snippet": "...sive experiments in three real-world datasets, Chorus gains significant improvements compared to state-of-the-art baseline methods. Furthermore, the time-related parameters are highly interpretable an...", "weights": [[8, 19, 1.0]]}, "2018.sigirconf_conference-2018.191": {"snippet": "...here theoretical advances are empirically demonstrated and validated, often across a large set of collections, tasks and domains. Experiments often consist of glueing together a number of tools for in...", "weights": [[8, 19, 1.0], [133, 144, 1.0]]}, "2020.coling-main.489": {"snippet": "Knowledge Base Embedding By Cooperative Knowledge Distillation\n\n\n Knowledge bases are increasingly exploited as gold standard data sources which benefit various knowledge-driven NLP tasks. In this pap...", "weights": [[0, 9, 1.0], [40, 49, 1.0], [66, 75, 1.0], [161, 170, 1.0]]}, "2022.acl-long.55": {"snippet": "... and strengthen the current model. Experiments on four tasks show PRBOOST outperforms state-of-the-art WSL baselines up to 7.1%, and bridges the gaps with fully supervised models.Our Implementation is...", "weights": [[8, 18, 1.0], [38, 49, 1.0]]}, "2020.emnlp-main.17": {"snippet": "...ides theoretical inspirations on the not-well-understood questions: why and how one uses multi-head attention. Extensive experiments on various attention models and applications demonstrate that the p...", "weights": [[8, 19, 1.0], [124, 135, 1.0]]}, "W00-1406": {"snippet": "... its actual effect, determined on a model of the system's beliefs. We consider three main rebuttal strategies: refute the user's rejoinder, strengthen the argument goal, and dismiss the user's line of", "weights": [[8, 14, 1.0], [143, 153, 1.0]]}, "2008.jasis_journal-ir0volumeA59A5.4": {"snippet": "The Effect of training on biologists acceptance of bioinformatics tools: A field experiment", "weights": []}, "2001.tois_journal-ir0volumeA19A4.0": {"snippet": "....e., theoretical) evaluation of IR systems has been proposed to circumvent the controversies of the experimental methods. Several studies have adopted the inductive approach, but they mostly focus on ...", "weights": [[8, 19, 1.0]]}, "2020.semeval-1.214": {"snippet": "... the actual meaning of the message to the readers. Emphasis selection is the task of choosing candidate words for emphasis, it helps in automatically designing posters and other media contents with wr...", "weights": [[8, 14, 1.0]]}, "2020.cikm_conference-2020.260": {"snippet": "...ting knowledge graph entities in a continuous vector space. In this paper, we investigate the effect of materializing implicit A-box axioms induced by subproperties, as well as symmetric and transitiv...", "weights": [[8, 17, 1.0]]}, "L00-1271": {"snippet": "...LE}: Theoretical Observations", "weights": [[8, 19, 1.0]]}, "2014.wwwconf_conference-2014c.9": {"snippet": "...ough theoretical resource to formalize such phenomena, economics and social sciences lack adequate technology to properly operationalize their theoretical insights, thereby demanding an integrative so...", "weights": [[8, 19, 1.0], [146, 157, 1.0]]}, "2017.wwwconf_conference-2017.58": {"snippet": "...ogs. Experiments on real-world logs demonstrate the effectiveness of the proposed model on both applications.", "weights": [[8, 19, 1.0]]}, "2019.sigirconf_conference-2019.88": {"snippet": "Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval", "weights": []}, "2021.eacl-main.212": {"snippet": "...udes theoretical and empirical evidence as well as practical experiments to support the effectiveness of our Annealing-KD method. We did a comprehensive set of experiments on different tasks such as i...", "weights": [[8, 19, 1.0], [64, 75, 1.0], [163, 174, 1.0]]}, "1997.jasis_journal-ir0volumeA48A6.4": {"snippet": "...ng a Theoretical Framework", "weights": [[8, 19, 1.0]]}, "2010.jasis_journal-ir0volumeA61A6.8": {"snippet": "...tive theoretical model of knowledge-sharing behavior in the context of Wikipedia", "weights": [[8, 19, 1.0], [29, 38, 1.0]]}, "2018.sigirconf_conference-2018.183": {"snippet": "Theoretical Analysis of Interdependent Constraints in Pseudo-Relevance Feedback\n\n\n ABSTRACTAxiomatic analysis is a well-defined theoretical framework for analytical evaluation of information retrieval...", "weights": [[0, 11, 1.0], [128, 139, 1.0]]}, "2021.findings-acl.140": {"snippet": "...ring knowledge from three aspects, i.e., domain, language and task, and strengthening connections among them. Specifically, we propose four practical guidelines to guide knowledge transfer and task fi...", "weights": [[8, 17, 1.0], [173, 182, 1.0]]}, "2015.wwwconf_conference-2015.58": {"snippet": "Events and Controversies: Influences of a Shocking News Event on Information Seeking\n\n\n ABSTRACTIt has been suggested that online search and retrieval contributes to the intellectual isolation of user...", "weights": []}, "2021.eacl-main.133": {"snippet": "... our theoretical relationship. We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. Using the derived objective, accurate KGEs are learnt f...", "weights": [[8, 19, 1.0], [83, 94, 1.0], [131, 140, 1.0]]}, "2020.emnlp-main.11": {"snippet": "... the actual task. This work proposes Knowledge Triplet Learning (KTL), a self-supervised task over knowledge graphs. We propose heuristics to create synthetic graphs for commonsense and scientific kno...", "weights": [[8, 14, 1.0], [40, 49, 1.0], [102, 111, 1.0], [200, 209, 1.0]]}, "2004.airs_conference-2004.22": {"snippet": "...gh a theoretical analysis, and then verify the above claim through experiments by actually ranking the systems submitted to the NTCIR-3 CLIR Task. Our experiments confirm that the Q-measure ranking is...", "weights": [[8, 19, 1.0], [70, 81, 1.0], [154, 165, 1.0]]}, "2017.wwwconf_conference-2017.48": {"snippet": "...this theoretical algorithm, and test it on a large class of test graphs. In all cases, Tur\u00e1n-shadow has less than 2% error, and runs in a fraction of the time used by well-tuned exact algorithms. We d...", "weights": [[8, 19, 1.0]]}, "2009.wsdm_workshop-2009wscd.7": {"snippet": "...s to strengthen the original formulation by introducing a new global objective function, and thus formalising the problem as a combinatorial optimisation one. Such a reformulation defines a common fra...", "weights": [[8, 18, 1.0]]}, "2017.jasis_journal-ir0volumeA68A6.5": {"snippet": "...g on theoretical models and crowdsourced knowledge", "weights": [[8, 19, 1.0], [44, 53, 1.0]]}, "2013.sigirconf_conference-2013.122": {"snippet": "...that actual error rates seem to be lower than the theoretically expected 5%, further confirming that we may actually be underestimating significance.", "weights": [[8, 14, 1.0]]}, "H86-1008": {"snippet": "...oded knowledge and the limited inference-making capability of a taxonomic knowledge representation system to reduce the constituent structure of logical expressions. The specific application is to the...", "weights": [[8, 17, 1.0], [77, 86, 1.0]]}, "2020.emnlp-main.575": {"snippet": "...m to strengthen the answer-explanation coupling as well as two evaluation scores to quantify the coupling. We conduct experiments on the HOTPOTQA benchmark data set and perform a user study. The user ...", "weights": [[8, 18, 1.0], [121, 132, 1.0]]}, "W15-0104": {"snippet": "... the experiments on theoretical grounds and employ GermaNet, Topic Models and Singular Value Decomposition for generalization, to compensate for data sparseness. Evaluating against three human-rated g...", "weights": [[8, 19, 1.0], [23, 34, 1.0]]}, "L16-1243": {"snippet": "...ion, theoretical linguistics, etc. To strengthen the automatic processing of the Vietnamese language, a Vietnamese treebank has been built. However, the quality of this treebank is not satisfactory an...", "weights": [[8, 19, 1.0], [41, 51, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.6": {"snippet": "...lude theoretical and empirical results, as well as the actual code of each algorithm. An extensive bibliography is also included.", "weights": [[8, 19, 1.0], [58, 64, 1.0]]}, "2005.jasis_journal-ir0volumeA56A14.6": {"snippet": "...s of Knowledge hypothesis in a real-life problem situation: A study of history and psychology undergraduates seeking information for a course essay", "weights": [[8, 17, 1.0]]}, "2015.wwwconf_conference-2015c.338": {"snippet": "...line experiments. First, we review basic concepts in causal inference and motivate the need for experiments. Then we will discuss basic statistical tools to help plan experiments: exploratory analysis...", "weights": [[8, 19, 1.0], [99, 110, 1.0], [170, 181, 1.0]]}, "2021.cinlp-1.1": {"snippet": "Causal Augmentation for Causal Sentence Classification\n\n\n Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification....", "weights": []}, "2014.wwwconf_conference-2014.28": {"snippet": "...ield experiments\n\n\n ABSTRACTOnline experiments are widely used to compare specific design alternatives, but they can also be used to produce generalizable knowledge and inform strategic decision makin...", "weights": [[8, 19, 1.0], [38, 49, 1.0], [158, 167, 1.0]]}, "2020.acl-main.420": {"snippet": "...code knowledge related to the task. A commonly held belief is that using simpler models as probes is better; the logic is that simpler models will identify linguistic structure, but not learn the task...", "weights": [[8, 17, 1.0]]}, "2018.ictir_conference-2018.20": {"snippet": "...ical experiments suggest that it captures quality aspects from traditional metrics, while the reverse is not true. Our work also advances the understanding of theoretical foundations of the empiricall...", "weights": [[8, 19, 1.0], [162, 173, 1.0]]}, "2021.wsdm_conference-2021.32": {"snippet": "... the theoretical discoveries, we conduct exploratory experiments that supports our theoretical insights for the product embeddings.", "weights": [[8, 19, 1.0], [56, 67, 1.0], [86, 97, 1.0]]}, "2021.trustnlp-1.3": {"snippet": "...rong theoretical guarantees on its utility. We support our theoretical proofs with empirical experiments on multiple word embedding models and NLP datasets, achieving in some cases more than 10% gains...", "weights": [[8, 19, 1.0], [62, 73, 1.0], [96, 107, 1.0]]}, "2006.tois_journal-ir0volumeA24A4.1": {"snippet": "...nd a theoretical point of view. Our experimental analysis compares a structure-ignorant with a structure-aware retrieval approach using the test suite of the INEX XML Retrieval Evaluation Initiative. ...", "weights": [[8, 19, 1.0]]}, "2021.wwwconf_conference-2021.106": {"snippet": "Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels", "weights": []}, "1981.jasis_journal-ir0volumeA32A3.3": {"snippet": "... and experiments. I. Basic theoretical and conceptual development", "weights": [[8, 19, 1.0], [30, 41, 1.0]]}, "2015.wwwconf_conference-2015.6": {"snippet": "... Our experiments reinforce the theoretical findings by demonstrating that our algorithms generally outperform previous approaches, both in terms of solution cost and reconstruction of an underlying gr...", "weights": [[8, 19, 1.0], [34, 45, 1.0]]}, "P12-2053": {"snippet": "...s to strengthen the views' consistency for target data with source training data by identifying the correlations of domain-specific features from different domains. We present an Information-theoretic...", "weights": [[8, 18, 1.0]]}, "J12-1002": {"snippet": "A Context-Theoretic Framework for Compositionality in Distributional Semantics\n\n\n Formalizing \"meaning as context\" mathematically leads to a new, algebraic theory of meaning, in which composition is b...", "weights": []}, "L08-1425": {"snippet": "Strengthening the {E}stonian Language Technology", "weights": []}, "O02-2006": {"snippet": "...t on Knowledge Extraction from an Encyclopedia Based on Lexicon Semantics) [In {C}hinese]\n\n\n The typical approaches to extracting text knowledge are sentential parsing and pattern matching. Theoretica...", "weights": [[8, 17, 1.0], [138, 147, 1.0]]}, "2016.ecir_conference-2016.88": {"snippet": "Living Labs for Online Evaluation: From Theory to Practice", "weights": []}, "2013.cikm_conference-2013.230": {"snippet": "...onal knowledge from the encyclopedic information and then align the knowledge with a WordNet-like ontology. Finally as a demonstration, a Chinese semantic knowledge repository named JNet is constructe...", "weights": [[8, 17, 1.0], [71, 80, 1.0], [158, 167, 1.0]]}, "2017.wwwconf_conference-2017c.74": {"snippet": "...ized Experiments\n\n\n ABSTRACTWe propose a nonparametric sequential test that aims to address two practical problems pertinent to online randomized experiments: (i) how to do a hypothesis test for compl...", "weights": [[8, 19, 1.0], [149, 160, 1.0]]}, "2014.wwwconf_conference-2014.57": {"snippet": "...lled experiments of human participants in a continuous sequence of ad auctions, similar to those used by Internet companies. The goal of the research was to understand users' strategies in making bids...", "weights": [[8, 19, 1.0]]}, "2015.tois_journal-ir0volumeA33A3.1": {"snippet": "... our theoretical findings, we experimentally show the versatility of our approach by considering two important cases in the context of Web search. In the first case, we favor the retrieval of document...", "weights": [[8, 19, 1.0]]}, "2020.conll-1.10": {"snippet": "...y in theoretical linguistic literature, but computational linguistics research on the phenomenon is sparse. Not only that, the long standing linguistic controversy between the determinative and the no...", "weights": [[8, 19, 1.0]]}, "C18-1277": {"snippet": "...over Knowledge Bases\n\n\n Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most impo...", "weights": [[8, 17, 1.0], [51, 60, 1.0], [164, 173, 1.0]]}, "2011.sigirconf_conference-2011.96": {"snippet": "...sfer knowledge across languages. With these recognitions, we attempt to transfer knowledge from the auxiliary language, in which abundant labeled Web pages are available, to target languages, in which...", "weights": [[8, 17, 1.0], [84, 93, 1.0]]}, "1986.sigirconf_conference-86.23": {"snippet": "... New Theoretical Framework for Information Retrieval", "weights": [[8, 19, 1.0]]}, "A00-1036": {"snippet": "...stic Knowledge can Improve Information Retrieval\n\n\n This paper describes the results of some experiments using a new approach to information access that combines techniques from natural language proce...", "weights": [[8, 17, 1.0], [96, 107, 1.0]]}, "2006.jasis_journal-ir0volumeA57A1.6": {"snippet": "...h: A theoretical framework", "weights": [[8, 19, 1.0]]}, "2008.jasis_journal-ir0volumeA59A1.17": {"snippet": "Evaluations of individual scientists and research institutions", "weights": []}, "Y03-1001": {"snippet": "...ical Knowledge Base for Causality Reasoning\n\n\n The A virtually linked knowledge base is designed to utilize a pre-constructed knowledge base in a dynamic mode when it is in actual use. An algorithm is...", "weights": [[8, 17, 1.0], [73, 82, 1.0], [129, 138, 1.0], [176, 182, 1.0]]}, "2017.cikm_conference-2017.110": {"snippet": "...hich strengthen their influences. We incorporate the concept of hyper questions into existing answer aggregation methods. The results of our experiments conducted using both synthetic datasets and rea...", "weights": [[8, 18, 1.0], [144, 155, 1.0]]}}}
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Character N-Grams\n\n\n The authors of fake news often use facts from verified news sources and mix them with misinformation to create confusion and provoke ...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [19, 28, 1.0], [82, 86, 1.0], [87, 91, 1.0], [122, 126, 1.0]]}, "2019.wwwconf_conference-2019.283": {"snippet": "... for Fake News Detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2020.ipm_journal-ir0volumeA57A2.1": {"snippet": "...line fake news: Characterization, detection, and discussion", "weights": [[8, 12, 1.0], [13, 17, 1.0], [37, 46, 1.0]]}, "2020.fire_conference-2020w.62": {"snippet": "Urdu Fake News Detection using Generalized Autoregressors", "weights": [[5, 9, 1.0], [10, 14, 1.0], [15, 24, 1.0]]}, "2020.cikm_conference-2020.431": {"snippet": "...old: Fake News Detection Using User Reactions on Reddit", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2020.clef_conference-2020w.20": {"snippet": "...t of fake news on politics, economy and even 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Internet users must face a high amount of false information in digital media such as rumours, fake news, and extremel...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [180, 184, 1.0], [185, 189, 1.0]]}, "W17-4214": {"snippet": "Fake news stance detection using stacked ensemble of classifiers\n\n\n Fake news has become a hotly debated topic in journalism. In this paper, we present our entry to the 2017 Fake News Challenge which ...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [17, 26, 1.0], [68, 72, 1.0], [73, 77, 1.0], [174, 178, 1.0], [179, 183, 1.0]]}, "2020.nlp4if-1.1": {"snippet": "...}-19 Fake News Detection and Fact Checking\n\n\n The rapid advancement of technology in online communication via social media platforms has led to a prolific rise in the spread of misinformation and fake...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [199, 203, 1.0]]}, "2021.acl-srw.32": {"snippet": "... for fake news detection is confirmed, firstly, by manual experiment based on a set of known true and fake news. Besides, we compared our fake news classification system based on the proposed feature ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [105, 109, 1.0], [110, 114, 1.0], [141, 145, 1.0], [146, 150, 1.0]]}, "I17-2043": {"snippet": "Fake News Detection Through Multi-Perspective Speaker Profiles\n\n\n Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very l...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0], [76, 80, 1.0], [81, 85, 1.0], [86, 95, 1.0]]}, "2020.rdsm-1.5": {"snippet": "...with fake news detection better. We investigated the nature of fake news by dividing it into two non-overlapping classes: satire and fake news. As a result, we obtained the set of models for fake news...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [66, 70, 1.0], [71, 75, 1.0], [136, 140, 1.0], [141, 145, 1.0], [194, 198, 1.0], [199, 203, 1.0]]}, "2021.ecir_conference-20212.9": {"snippet": "...s of News Title and Body to Detect Fake News: A Reproducibility Study\n\n\n Fake news, a deliberately designed news to mislead others, is becoming a big societal threat with its fast dissemination over t...", "weights": [[8, 12, 1.0], [38, 42, 1.0], [43, 47, 1.0], [76, 80, 1.0], [81, 85, 1.0], [111, 115, 1.0]]}, "2012.wwwconf_conference-2012.20": {"snippet": "...ting fake reviews and individual fake reviewers. However, a fake reviewer group (a group of reviewers who work collaboratively to write fake reviews) is even more damaging as they can take total contr...", "weights": [[8, 12, 1.0], [36, 40, 1.0], [63, 67, 1.0], [139, 143, 1.0]]}, "2021.triton-1.16": {"snippet": "Fake News Detection for {P}ortuguese with Deep Learning\n\n\n The exponential growth of the internet and social media in the past decade gave way to the increase in dissemination of false or misleading i...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0]]}, "C18-1131": {"snippet": "...lass Fake News Detection\n\n\n Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent yea...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [31, 35, 1.0], [36, 40, 1.0], [145, 149, 1.0], [150, 154, 1.0]]}, "2020.fire_conference-2020w.65": {"snippet": "..._NLP@Fake news detection in the Urdu language (UrduFake) 2020", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2021.wnut-1.21": {"snippet": "...s in Fake News Detection Dataset\n\n\n Fake news causes significant damage to society. To deal with these fake news, several studies on building detection models and arranging datasets have been conducte...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [39, 43, 1.0], [44, 48, 1.0], [106, 110, 1.0], [111, 115, 1.0], [145, 154, 1.0]]}, "2020.lrec-1.309": {"snippet": "... for Fake News Detection in the {U}rdu Language\n\n\n The task of fake news detection is to distinguish legitimate news articles that describe real facts from those which convey deceiving and fictitious ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [66, 70, 1.0], [71, 75, 1.0], [76, 85, 1.0], [115, 119, 1.0]]}, "2017.cikm_conference-2017.84": {"snippet": "... for Fake News Detection\n\n\n ABSTRACT e topic of fake news has drawn a ention both from the public and the academic communities. Such misinformation has the potential of a ecting public opinion, provid...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [51, 55, 1.0], [56, 60, 1.0]]}, "2020.clef_conference-2020w.169": {"snippet": "...utes fake news, as in the PAN @ CLEF 2020 competition Profiling Fake News Spreaders on Twitter. This competition asks for systems that identify possible fake news spreaders on social media as a first ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [67, 71, 1.0], [72, 76, 1.0], [156, 160, 1.0], [161, 165, 1.0]]}, "2018.wwwconf_conference-2018c.270": {"snippet": "... for Fake News Stance Identification\n\n\n ABSTRACTIdentifying the veracity of a news article is an interesting problem while automating this process can be a challenging task. Detection of a news articl...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [81, 85, 1.0], [177, 186, 1.0], [192, 196, 1.0]]}, "2020.coling-main.165": {"snippet": "...shot fake news detection, alleviating the need of large scale training data to train fake news detection models. Experimental results on FakenewsNet, a benchmark dataset for fake news detection, demon...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [88, 92, 1.0], [93, 97, 1.0], [98, 107, 1.0], [177, 181, 1.0], [182, 186, 1.0], [187, 196, 1.0]]}, "2019.wsdm_conference-2019.115": {"snippet": "Fake News: Fundamental Theories, Detection Strategies and Challenges\n\n\n ABSTRACTThe explosive growth of fake news and its erosion to democracy, justice, and public trust increased the demand for fake ...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [33, 42, 1.0], [104, 108, 1.0], [109, 113, 1.0], [195, 199, 1.0]]}, "2019.icon-1.27": {"snippet": "...ving fake news detection problem in online news contents of multiple domains. We evaluate our techniques on the two recently released datasets, namely Fake-News AMT and Celebrity for fake news detecti...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [46, 50, 1.0], [154, 158, 1.0], [159, 163, 1.0], [186, 190, 1.0], [191, 195, 1.0], [196, 205, 1.0]]}, "2020.wwwconf_conference-2020c.36": {"snippet": "...tter Fake News Detection on Mobile", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2021.internlp-1.7": {"snippet": "...n of fake news, text that is published with an intent to spread misinformation and sway beliefs. Detecting fake news is important to prevent misinformation and maintain a healthy society. While prior ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [110, 114, 1.0], [115, 119, 1.0]]}, "2020.clef_conference-2020w.24": {"snippet": "... for Detection of Fake Profiles\n\n\n The explosive growth of fake news on social networks has aroused great interest from researchers in different disciplines. To achieve efficient and effective detecti...", "weights": [[8, 17, 1.0], [21, 25, 1.0], [62, 66, 1.0], [67, 71, 1.0], [196, 205, 1.0]]}, "N19-1347": {"snippet": "... for Fake News Detection\n\n\n On the one hand, nowadays, fake news articles are easily propagated through various online media platforms and have become a grand threat to the trustworthiness of informat...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [58, 62, 1.0], [63, 67, 1.0]]}, "2019.cikm_conference-2019.376": {"snippet": "...able Fake News Detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2020.mir_conference-2020.82": {"snippet": "Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0]]}, "2020.clef_conference-2020w.54": {"snippet": "...ting Fake News Spreaders in Social Networks via Linguistic and Personality Features\n\n\n This paper addresses the problem of automatically detecting fake news spreaders in social networks such as Twitte...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [150, 154, 1.0], [155, 159, 1.0]]}, "2019.rocling-1.20": {"snippet": "...{AI} Detection:Evidence From {T}aiwan News Report)\n\n\n In recent years, because the Internet acts as a medium, fake news can be quickly spread. Many countries have been seriously affected by fake news....", "weights": [[8, 17, 1.0], [41, 45, 1.0], [113, 117, 1.0], [118, 122, 1.0], [193, 197, 1.0], [198, 202, 1.0]]}, "2021.acl-long.62": {"snippet": "...ural Fake News Detection with External Knowledge\n\n\n Nowadays, fake news detection, which aims to verify whether a news document is trusted or fake, has become urgent and important. Most existing metho...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [65, 69, 1.0], [70, 74, 1.0], [75, 84, 1.0], [117, 121, 1.0], [145, 149, 1.0]]}, "2021.ipm_journal-ir0volumeA58A1.24": {"snippet": "... for fake news detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2019.wwwconf_conference-2019c.232": {"snippet": "Fake News Detection: An Interdisciplinary Research", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0]]}, "2018.wsdm_conference-2018.78": {"snippet": "...like fake news and rumor detection, it is very challenging, if not impossible, to identify useful features from content. For example, intentional spreaders of fake news may manipulate the content to m...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [28, 37, 1.0], [162, 166, 1.0], [167, 171, 1.0]]}, "P18-1022": {"snippet": "... and Fake News\n\n\n We report on a comparative style analysis of hyperpartisan (extremely one-sided) news and fake news. A corpus of 1,627 articles from 9 political publishers, three each from the mains...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [102, 106, 1.0], [111, 115, 1.0], [116, 120, 1.0]]}, "2020.fire_conference-2020w.61": {"snippet": "Fake News Detection in the Urdu Language using CharCNN-RoBERTa", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0]]}, "2020.clef_conference-2020w.66": {"snippet": "...in a Fake News Detection Approach\n\n\n Misinformation is a growing problem around the web. The spread of such a phenomenon may impact public opinions. Hence fake news detection is indispensable. The fir...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [158, 162, 1.0], [163, 167, 1.0], [168, 177, 1.0]]}, "2020.clef_conference-2020w.161": {"snippet": "...ling Fake News Spreaders on Twitter\n\n\n Automatic detection of fake news in social media has become a prominent research topic due to its widespread, adverse effect on not only the society and public h...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [52, 61, 1.0], [65, 69, 1.0], [70, 74, 1.0]]}, "P19-2050": {"snippet": "...ting Fake News Corpus for Automated Fact Checking\n\n\n Popular fake news articles spread faster than mainstream articles on the same topic which renders manual fact checking inefficient. At the same tim...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [64, 68, 1.0], [69, 73, 1.0]]}, "2020.clef_conference-2020w.102": {"snippet": "...s of Fake News Spreaders on Twitter\n\n\n Fake news detection on social medial has attracted a huge body of research as one of the most important tasks of social analysis in recent years. In this task, g...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [42, 46, 1.0], [47, 51, 1.0], [52, 61, 1.0]]}, "2022.acl-long.97": {"snippet": "...ging fake news detection tasks show that using inference operators leads to a better understanding of the social media framework enabling fake news spread, resulting in improved performance.", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [141, 145, 1.0], [146, 150, 1.0]]}, "2020.cikm_conference-2020.206": {"snippet": "... for Fake News Detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2020.cikm_conference-2020.118": {"snippet": "... for Fake News Detection Using Graph Representation", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2020.wwwconf_conference-2020c.117": {"snippet": "...able Detection of Fake News and Cyberbullying on Social Media", "weights": [[8, 17, 1.0], [21, 25, 1.0], [26, 30, 1.0]]}, "2021.wwwconf_conference-2021.305": {"snippet": "... for Fake News Detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "C18-1287": {"snippet": "...n of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collec...", "weights": [[8, 12, 1.0], [31, 35, 1.0], [121, 125, 1.0], [126, 130, 1.0], [131, 140, 1.0], [167, 171, 1.0]]}, "2022.fever-1.4": {"snippet": "...atic Fake News Detection: Are current models {``}fact-checking{''} or{``}gut-checking{''}?\n\n\n Automatic fake news detection models are ostensibly based on logic, where the truth of a claim made in a h...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [107, 111, 1.0], [112, 116, 1.0], [117, 126, 1.0]]}, "2018.wwwconf_conference-2018c.140": {"snippet": "Fake News Detection in Social Networks via Crowd Signals\n\n\n ABSTRACTOur work considers leveraging crowd signals for detecting fake news and is motivated by tools recently introduced by Facebook that e...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0], [126, 130, 1.0], [131, 135, 1.0]]}, "2021.case-1.10": {"snippet": "...s of news articles and tweets on those articles. Therefore, it is extremely important to stop the spread of fake news before it reaches a mass level, which is a big challenge. We propose a novel fake ...", "weights": [[8, 12, 1.0], [111, 115, 1.0], [116, 120, 1.0], [198, 202, 1.0]]}, "2021.eacl-main.287": {"snippet": "...ased Fake News Detection using a Summarization Technique\n\n\n Nowadays, fake news is spreading in various ways, and this fake information is causing a lot of social damages. Thus the need to detect fake...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [73, 77, 1.0], [78, 82, 1.0], [122, 126, 1.0], [199, 203, 1.0]]}, "2020.clef_conference-2020w.200": {"snippet": "...Such news is termed as fake news and detecting the fake news spreader is drawing more attention these days as fake news can manipulate communities' minds and also social trust. Until date, many studie...", "weights": [[8, 12, 1.0], [26, 30, 1.0], [31, 35, 1.0], [54, 58, 1.0], [59, 63, 1.0], [113, 117, 1.0], [118, 122, 1.0]]}, "W17-4215": {"snippet": "...t to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles\n\n\n We present a system for the detection of the stance of headlines with regard to their corresponding arti...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [127, 136, 1.0]]}, "2020.tist_journal-ir0volumeA11A1.6": {"snippet": "...bust Fake News Detection Over Time and Attack", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "W18-5510": {"snippet": "...ance Detection in Fake News A Combined Feature Representation\n\n\n With the uncontrolled increasing of fake news and rumors over the Web, different approaches have been proposed to address the problem. ...", "weights": [[8, 17, 1.0], [21, 25, 1.0], [26, 30, 1.0], [104, 108, 1.0], [109, 113, 1.0]]}, "2020.lrec-1.747": {"snippet": "...d of fake news. Thus, the effect of fake news has been growing, sometimes extending to the offline world and threatening public safety. Given the massive amount of Web content, automatic fake news det...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [39, 43, 1.0], [44, 48, 1.0], [190, 194, 1.0], [195, 199, 1.0], [200, 209, 1.0]]}, "2020.fire_conference-2020w.66": {"snippet": "...Urdu Fake News Detection", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0]]}, "2022.acl-long.311": {"snippet": "...rve: News Environment Perception for Fake News Detection\n\n\n Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones...", "weights": [[8, 12, 1.0], [40, 44, 1.0], [45, 49, 1.0], [50, 59, 1.0], [63, 67, 1.0], [68, 72, 1.0], [73, 82, 1.0], [179, 183, 1.0], [184, 188, 1.0]]}, "2020.clef_conference-2020w.52": {"snippet": "...read fake news or not. We have built models for two languages-English and Spanish. We have achieved 72% accuracy in this fake news spreader detection task.", "weights": [[8, 12, 1.0], [13, 17, 1.0], [124, 128, 1.0], [129, 133, 1.0], [143, 152, 1.0]]}, "2019.wsdm_conference-2019.40": {"snippet": "...yond News Contents: The Role of Social Context for Fake News Detection", "weights": [[8, 12, 1.0], [54, 58, 1.0], [59, 63, 1.0], [64, 73, 1.0]]}, "2018.sigirconf_conference-2018.30": {"snippet": "...mbat Fake News\n\n\n ABSTRACTA large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. De...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [102, 106, 1.0], [107, 111, 1.0], [170, 174, 1.0], [175, 179, 1.0]]}, "2014.wwwconf_conference-2014c.229": {"snippet": "...t as fake or real. We created a data corpus of tweets around big events focusing on the ones linking to images (fake or real) of which the reliability could be verified by independent online sources. ...", "weights": [[8, 12, 1.0], [115, 119, 1.0]]}, "2021.jasis_journal-ir0volumeA72A1.0": {"snippet": "...ting fake news stories via multimodal analysis", "weights": [[8, 12, 1.0], [13, 17, 1.0]]}, "2020.rdsm-1.2": {"snippet": "...h to Fake News Detection Through Interpretable Features and {BRNN}\n\n\n Fake news' -succinctly defined as false or misleading information masquerading as legitimate news -is a ubiquitous phenomenon and ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [73, 77, 1.0], [78, 82, 1.0], [166, 170, 1.0]]}, "2019.wwwconf_conference-2019.387": {"snippet": "...able Fake News Detector with Visualizations", "weights": [[8, 12, 1.0], [13, 17, 1.0]]}, "P17-2067": {"snippet": "... for Fake News Detection\n\n\n Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approache...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 27, 1.0], [41, 45, 1.0], [46, 50, 1.0], [51, 60, 1.0], [99, 108, 1.0]]}, "N19-1141": {"snippet": "Fake News Detection using Deep {M}arkov Random Fields\n\n\n Deep-learning-based models have been successfully applied to the problem of detecting fake news on social media. While the correlations among n...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [10, 19, 1.0], [143, 147, 1.0], [148, 152, 1.0], [199, 203, 1.0]]}, "2020.emnlp-main.621": {"snippet": "...d of Fake News\n\n\n Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but us...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [103, 107, 1.0], [108, 112, 1.0]]}, "2020.coling-main.475": {"snippet": "...arly Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning\n\n\n The dissemination of fake news significantly affects personal reputation ...", "weights": [[8, 17, 1.0], [21, 25, 1.0], [26, 30, 1.0], [63, 67, 1.0], [151, 155, 1.0], [156, 160, 1.0]]}, "2021.wsdm_conference-2021.62": {"snippet": "...city detection, i.e. explaining why a particular piece of text is factually correct or incorrect. In this work, we seek to bridge this gap by proposing a technique, FACE-KEG, to automatically perform ...", "weights": [[8, 17, 1.0]]}, "2020.clef_conference-2020w.111": {"snippet": "...gual Detection of Fake News Spreaders via Sparse Matrix Factorization\n\n\n Fake news is an emerging problem in online news and social media. Efficient detection of fake news spreaders and spurious accou...", "weights": [[8, 17, 1.0], [21, 25, 1.0], [26, 30, 1.0], [76, 80, 1.0], [81, 85, 1.0], [119, 123, 1.0], [152, 161, 1.0], [165, 169, 1.0], [170, 174, 1.0]]}}}
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As highlevel logic for retrieval of hypermedia documents, we...", "weights": [[8, 17, 1.0], [21, 31, 1.0], [121, 131, 1.0], [132, 141, 1.0], [166, 175, 1.0]]}, "1992.sigirjournals_journal-ir0volumeA26A2.3": {"snippet": "... for Retrieval of Data on Multimedia Documents\n\n\n AbstractTraditionally, two paradigms are discerned with respect to data retrieval :1. A query mechanism enables the user to express conditions that ch...", "weights": [[8, 17, 1.0], [29, 39, 1.0], [125, 134, 1.0]]}, "1986.tois_journal-ir0volumeA4A4.3": {"snippet": "...nted multimedia information system that provides integrated facilities for creating and managing complex multimedia objects. In this paper the model for multimedia documents supported by MINOS and its...", "weights": [[8, 18, 1.0], [108, 118, 1.0], [156, 166, 1.0]]}, "2005.wwwconf_conference-2005si.39": {"snippet": "...ther multimedia objects, such as images and video. In this project, we propose a modified Multimedia PageRank algorithm to support the searching of multimedia objects in the Web.", "weights": [[8, 18, 1.0], [93, 103, 1.0], [151, 161, 1.0]]}, "2019.clef_conference-2019w.9": {"snippet": "A Multimedia Modular Approach to Lifelog Moment Retrieval\n\n\n Lifelogging offers a mean for people to record their day to day life. However, without proper tools for retrieving specific moments, the la...", "weights": [[2, 12, 1.0], [48, 57, 1.0]]}, "2012.sigirconf_conference-2012.50": {"snippet": "...s of multimedia items for making personalized recommendations\n\n\n ABSTRACTExisting multimedia recommenders suggest a specific type of multimedia items rather than items of different types personalized ...", "weights": [[8, 18, 1.0], [85, 95, 1.0], [136, 146, 1.0]]}, "2016.ijmir_journal-ir0volumeA5A4.0": {"snippet": "...s in multimedia information retrieval", "weights": [[8, 18, 1.0], [31, 40, 1.0]]}, "2010.wwwconf_conference-2010.84": {"snippet": "...s of multimedia presentations in the web with the multimedia metadata ontology\n\n\n ABSTRACTThe semantics of rich multimedia presentations in the web such as SMIL, SVG, and Flash cannot or only to a ver...", "weights": [[8, 18, 1.0], [53, 63, 1.0], [115, 125, 1.0]]}, "2013.sigirconf_conference-2013.1": {"snippet": "... and retrieval, including IBM Multimedia Analysis and Retrieval System (IMARS), and show recent results for semanticbased classification and retrieval. We conclude with future directions for improving...", "weights": [[8, 17, 1.0], [33, 43, 1.0], [57, 66, 1.0], [144, 153, 1.0]]}, "2008.sigirjournals_journal-ir0volumeA42A1.1": {"snippet": "Multimedia retrieval at INEX 2007", "weights": [[0, 10, 1.0], [11, 20, 1.0]]}, "2015.sigirconf_conference-2015.193": {"snippet": "...usic Retrieval and Recommendation: A Tutorial Overview\n\n\n ABSTRACTIn this tutorial, we give an introduction to the field of and state of the art in music information retrieval (MIR). The tutorial part...", "weights": [[8, 17, 1.0], [169, 178, 1.0]]}, "1999.cikm_conference-99.67": {"snippet": "...n of Multimedia Data\n\n\n AbstractThe management of multimedia information poses special requirements for multimedia information systems. Both representation and retrieval of the complex and multifacete...", "weights": [[8, 18, 1.0], [53, 63, 1.0], [107, 117, 1.0], [163, 172, 1.0]]}, "2008.sigirconf_conference-2008.134": {"snippet": "... for multimedia search\n\n\n ABSTRACTWith the rapid increase in online video services, video retrieval systems are becoming increasingly important search tools to many users in many different fields. In ...", "weights": [[8, 18, 1.0], [93, 102, 1.0]]}, "2011.mir_conference-2011.72": {"snippet": "Multimedia retrieval in social networks for photo book creation", "weights": [[0, 10, 1.0], [11, 20, 1.0]]}, "2014.ijmir_journal-ir0volumeA3A2.0": {"snippet": "Multimedia information retrieval: best papers and expanding frontiers", "weights": [[0, 10, 1.0], [23, 32, 1.0]]}, "2012.mir_conference-2012.69": {"snippet": "...tive multimedia retrieval system", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2014.mir_workshop-2014somus.0": {"snippet": "...cial Multimedia and Storytelling co-located with ACM International Conference on Multimedia Retrieval (ICMR 2014), Glasgow, Scotland, UK, April 1, 2014", "weights": [[8, 18, 1.0], [84, 94, 1.0], [95, 104, 1.0]]}, "2009.ecir_conference-2009.16": {"snippet": "XML Multimedia Retrieval: From Relevant Textual Information to Relevant Multimedia Fragments", "weights": [[4, 14, 1.0], [15, 24, 1.0], [72, 82, 1.0]]}, "2007.cikm_conference-2007.40": {"snippet": "...n to multimedia retrieval\n\n\n ABSTRACTAs one of the most effective query expansion approaches, local feedback is able to automatically discover new query terms and improve retrieval accuracy for differ...", "weights": [[8, 18, 1.0], [19, 28, 1.0], [174, 183, 1.0]]}, "2020.clef_conference-2020.22": {"snippet": "...020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.20": {"snippet": "...t in multimedia Web search, those few Web search engines offering multimedia Web search, provide limited multimedia search functionality. Keywords are still the only means of multimedia retrieval, whi...", "weights": [[8, 18, 1.0], [69, 79, 1.0], [108, 118, 1.0], [178, 188, 1.0], [189, 198, 1.0]]}, "2002.sigirconf_conference-2002.91": {"snippet": "...stic multimedia retrieval\n\n\n ABSTRACTWe present a framework in which probabilistic models for textual and visual information retrieval can be integrated seamlessly. The framework facilitates searching...", "weights": [[8, 18, 1.0], [19, 28, 1.0], [128, 137, 1.0]]}, "2015.tois_journal-ir0volumeA33A2.4": {"snippet": "...ased multimedia information retrieval. Additional Key Words and Phrases: Content-based multimedia information retrieval, information fusion, graph-based methods, cross-media similarity, random walk, V...", "weights": [[8, 18, 1.0], [31, 40, 1.0], [90, 100, 1.0], [113, 122, 1.0]]}, "2012.mir_conference-2012.46": {"snippet": "...mage retrieval", "weights": [[8, 17, 1.0]]}, "2016.mir_conference-2016.60": {"snippet": "Retrieval of Multimedia Objects by Fusing Multiple Modalities", "weights": [[0, 9, 1.0], [13, 23, 1.0]]}, "2007.mir_workshop-2007.35": {"snippet": "...ized multimedia retrieval: the new trend?", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2009.wwwconf_workshop-2009ldow.10": {"snippet": "...king Multimedia: How to Apply Linked Data Principles to Multimedia Fragments\n\n\n In this paper, we introduce interlinking multimedia (iM), a pragmatic way to apply the linked data principles to fragmen...", "weights": [[8, 18, 1.0], [59, 69, 1.0], [124, 134, 1.0]]}, "2012.mir_conference-2012.63": {"snippet": "...tion retrieval", "weights": [[8, 17, 1.0]]}, "2020.acl-demos.11": {"snippet": "...ined Multimedia Knowledge Extraction System\n\n\n We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multime...", "weights": [[8, 18, 1.0], [98, 108, 1.0], [196, 206, 1.0]]}, "2014.mir_conference-2014.34": {"snippet": "... for multimedia retrieval", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2008.mir_conference-2008.33": {"snippet": "Multimedia information retrieval: watershed events", "weights": [[0, 10, 1.0], [23, 32, 1.0]]}, "2013.ipm_journal-ir0volumeA49A6.5": {"snippet": "...tion retrieval, only a few works investigated its interest in multimedia retrieval. More precisely, the task we are interested in this paper is to retrieve multimedia fragments (i.e. XML elements havi...", "weights": [[8, 17, 1.0], [65, 75, 1.0], [76, 85, 1.0], [159, 169, 1.0]]}, "P07-2003": {"snippet": "Multimedia Blog Creation System using Dialogue with Intelligent Robot\n\n\n A multimedia blog creation system is described that uses Japanese dialogue with an intelligent robot. Although multimedia blogs...", "weights": [[0, 10, 1.0], [75, 85, 1.0], [184, 194, 1.0]]}, "2007.sigirconf_conference-2007.221": {"snippet": "...n of retrieval expert coefficients for multimedia retrieval\n\n\n ABSTRACTContent-based Multimedia Information Retrieval can be defined as the task of matching a multi-modal information need against vari...", "weights": [[8, 17, 1.0], [42, 52, 1.0], [53, 62, 1.0], [88, 98, 1.0], [111, 120, 1.0]]}, "2007.ipm_journal-ir0volumeA43A5.17": {"snippet": "... for multimedia analysis and retrieval.Part two focuses on fundamental multimedia content analysis technologies in five respective chapters. 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There are other problems in and around IR where ASR is still lik...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [128, 137, 1.0]]}, "1997.cikm_workshop-97.16": {"snippet": "... for Retrieval of Documents and Images\n\n\n AbstractThis paper describes a new method of multimedia information retrieval which shows multimedia information on a screen in a multi-directional way, using...", "weights": [[8, 17, 1.0], [90, 100, 1.0], [113, 122, 1.0], [135, 145, 1.0]]}, "2014.sigirconf_conference-2014.197": {"snippet": "...cale multimedia retrieval\n\n\n ABSTRACTThe tremendous increase of multimedia data in recent years has heightened the need for systems that not only allow to search with keywords, but that also support c...", "weights": [[8, 18, 1.0], [19, 28, 1.0], [67, 77, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ased multimedia retrieval\n\n\n AbstractRepresenting multimedia documents by means of concepts labels attached to parts of these documents has great potential for improving retrieval performance. The rea...", "weights": [[8, 18, 1.0], [19, 28, 1.0], [53, 63, 1.0], [173, 182, 1.0]]}, "2012.ijmir_journal-ir0volumeA1A1.0": {"snippet": "Multimedia information retrieval in the twenty-first century", "weights": [[0, 10, 1.0], [23, 32, 1.0]]}, "2013.mir_conference-2013.23": {"snippet": "...odal retrieval", "weights": [[8, 17, 1.0]]}, "2013.mir_conference-2013.1": {"snippet": "Retrieving geo-location of videos with a divide & conquer hierarchical multimodal approach", "weights": []}, "2014.mir_conference-2014.90": {"snippet": "...ased Multimedia Retrieval", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2001.cikm_conference-2001.65": {"snippet": "...ased Retrieval of MP3 Music Objects\n\n\n ABSTRACTIn recent years, the searching and indexing techniques for multimedia data are getting more attention in the area of multimedia databases. As many resear...", "weights": [[8, 17, 1.0], [109, 119, 1.0], [167, 177, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A1.6": {"snippet": "...n of multimedia retrieval", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2000.sigirconf_conference-2000.53": {"snippet": "...tion retrieval from multimedia data streams which occur in the presentation recording scenario.\nAOF as an Example for Presentation RecordingWith the Authoring on the Fly (AOF) approach multimedia pres...", "weights": [[8, 17, 1.0], [23, 33, 1.0], [188, 198, 1.0]]}, "2005.mir_workshop-2005.3": {"snippet": "Multimedia information retrieval: what is it, and why isn't anyone using it?", "weights": [[0, 10, 1.0], [23, 32, 1.0]]}, "2017.chiir_conference-2017.72": {"snippet": "...ideo retrieval tools and methods, which allow for automatic search in videos, still the performance of automatic video retrieval is far from optimal.At the same time, the organization of personal data...", "weights": [[8, 17, 1.0], [122, 131, 1.0]]}, "2013.mir_conference-2013.32": {"snippet": "A general Framework of video segmentation to logical unit based on conditional random fields", "weights": []}, "2010.sigirconf_conference-2010.200": {"snippet": "Multimedia information retrieval\n\n\n Abstract:This tutorial is concerned with creating the best possible multimedia search experience. The intriguing bit here is that the query itself can be a multimed...", "weights": [[0, 10, 1.0], [23, 32, 1.0], [104, 114, 1.0], [192, 202, 1.0]]}, "2013.mir_conference-2013.4": {"snippet": "...mage retrieval", "weights": [[8, 17, 1.0]]}, "2005.spire_conference-2005.24": {"snippet": "XML Multimedia Retrieval", "weights": [[4, 14, 1.0], [15, 24, 1.0]]}, "2013.mir_conference-2013.21": {"snippet": "...edia retrieval", "weights": [[8, 17, 1.0]]}, "2019.clef_conference-2019.28": {"snippet": "...019: Multimedia Retrieval in Medicine, Lifelogging, Security and Nature", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2012.mir_conference-2012.1": {"snippet": "...ideo retrieval: towards multimedia summarization", "weights": [[8, 17, 1.0], [27, 37, 1.0]]}, "2009.clef_workshop-20092.21": {"snippet": "...t in Multimedia Information Retrieval", "weights": [[8, 18, 1.0], [31, 40, 1.0]]}, "2010.sigirconf_conference-2010.82": {"snippet": "...tion retrieval (MIR) holds great promise as a technology for managing large music archives. One of the key components of MIR that has been actively researched into is music tagging. While significant ...", "weights": [[8, 17, 1.0]]}}}
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An IR system gains fr...", "weights": [[8, 16, 1.0], [116, 127, 1.0], [128, 137, 1.0], [147, 154, 1.0], [155, 163, 1.0], [164, 174, 1.0]]}, "2007.spire_conference-2007.2": {"snippet": "...ided Natural Language Text Compression\n\n\n Abstract. We describe a novel compression technique for natural language text collections which takes advantage of the information provided by edges when a gr...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [101, 108, 1.0], [109, 117, 1.0], [164, 175, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.5": {"snippet": "... for natural language generation\n\n\n a b s t r a c tReferring expression generation is the part of natural language generation that decides how to refer to the entities appearing in an automatically ge...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [101, 108, 1.0], [109, 117, 1.0]]}, "I05-3013": {"snippet": "...rmal language (NIL) expressions. For example, \" (ou3)\" is used to replace \" (wo3)\" in Chinese ICQ. Without unconventional resource, knowledge and techniques, the existing natural language processing a...", "weights": [[8, 16, 1.0], [174, 181, 1.0], [182, 190, 1.0], [191, 201, 1.0]]}, "H91-1068": {"snippet": "...Text Processing for Information Retrieval\n\n\n We describe an advanced text processing system for information retrieval from natural language document collections. We use both syntactic processing as we...", "weights": [[8, 18, 1.0], [23, 34, 1.0], [35, 44, 1.0], [77, 87, 1.0], [99, 110, 1.0], [111, 120, 1.0], [126, 133, 1.0], [134, 142, 1.0], [187, 197, 1.0]]}, "F13-4002": {"snippet": "Information Retrieval and Natural Language Processing (Recherche d{'}Information et Traitement Automatique des Langues Naturelles) [in {F}rench]", "weights": [[0, 11, 1.0], [12, 21, 1.0], [26, 33, 1.0], [34, 42, 1.0], [43, 53, 1.0], [69, 80, 1.0]]}, "A97-1046": {"snippet": "...g\n\n\n Information Retrieval (IR) is an important application area of Natural Language Processing (NLP) where one encounters the genuine challenge of processing large quantities of unrestricted natural ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [71, 78, 1.0], [79, 87, 1.0], [88, 98, 1.0], [151, 161, 1.0], [195, 202, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [35, 42, 1.0], [43, 51, 1.0], [52, 62, 1.0], [91, 102, 1.0], [103, 112, 1.0], [140, 147, 1.0], [148, 156, 1.0], [157, 167, 1.0]]}, "2007.wwwconf_conference-2007.6": {"snippet": "...n of natural-language-processing techniques can be used to build an intelligent form interface to semi-automatically process information-intent request messages. However, high performance parsers are ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [128, 139, 1.0]]}, "1988.sigirconf_conference-88.5": {"snippet": "Natural Language Techniques for Intelligent Information Retrieval", "weights": [[0, 7, 1.0], [8, 16, 1.0], [44, 55, 1.0], [56, 65, 1.0]]}, "1983.sigirconf_conference-83.4": {"snippet": "...gent Information Systems\n\n\n ABSTRACTNatural language processing techniques developed for Artificial Intelligence programs can aid in constructing powerful information retrieval systems in at least, tw...", "weights": [[8, 19, 1.0], [47, 55, 1.0], [56, 66, 1.0], [158, 169, 1.0], [170, 179, 1.0]]}, "2018.ictir_conference-2018.13": {"snippet": "...to a natural language processing task, probing the internal representations for information.Using this approach, we analyze the information relevant to retrieving answer passages from the perspective ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [83, 94, 1.0], [131, 142, 1.0]]}, "2017.sigirconf_workshop-2017birndl.0": {"snippet": "...nced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017) co-located with the 40th International ACM SIGIR Conference on Research and Development in Information Re...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 41, 1.0], [42, 50, 1.0], [51, 61, 1.0], [189, 200, 1.0], [201, 210, 1.0]]}, "A94-1028": {"snippet": "...Text Processing in Automated Information Retrieval\n\n\n We report on the results of a series of experiments with a prototype text retrieval system which uses relatively advanced natural language process...", "weights": [[8, 18, 1.0], [32, 43, 1.0], [44, 53, 1.0], [131, 140, 1.0], [179, 186, 1.0], [187, 195, 1.0], [196, 206, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.13": {"snippet": "...e in information retrieval research, yet is also a fundamentally important task, because an improved general retrieval model would benefit every search engine. The language modeling approach to inform...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [112, 121, 1.0], [167, 175, 1.0], [197, 208, 1.0]]}, "2004.clef_workshop-2004.6": {"snippet": "...act. Information Retrieval systems can benefit from advanced linguistic resources when carrying out tasks such as word-stemming or query translation. The main goal of our experiments has been the deve...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1990.ipm_journal-ir0volumeA26A1.5": {"snippet": "...a in natural language processing and information retrieval", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [40, 51, 1.0], [52, 61, 1.0]]}, "2001.clef_workshop-2000w.8": {"snippet": "...ross-Language Information Retrieval using Dutch Query Translation\n\n\n This paper describes a hastily carried out, rather limited bilingual information retrieval experiment. The experiment takes Dutch t...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [141, 152, 1.0], [153, 162, 1.0]]}, "2012.cikm_conference-2012.183": {"snippet": "...lled natural language\n\n\n ABSTRACTInformal Mathematical Discourse (IMD) is characterized by the mixture of natural language and symbolic expressions in the context of textbooks, publications in mathema...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [109, 116, 1.0], [117, 125, 1.0]]}, "2004.ipm_journal-ir0volumeA40A6.4": {"snippet": "...word information in the natural language processing system increases further and various researches using co-occurrence word information are done. Moreover, in the natural language processing, diction...", "weights": [[8, 19, 1.0], [27, 34, 1.0], [35, 43, 1.0], [44, 54, 1.0], [128, 139, 1.0], [167, 174, 1.0], [175, 183, 1.0], [184, 194, 1.0]]}, "2018.spire_conference-2018.27": {"snippet": "...ting Natural Language into Denotations", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2020.wsdm_conference-2020.120": {"snippet": "...p on Natural Language Processing for Recommendations\n\n\n Natural language processing is becoming more and more important in recommender systems. This half day workshop explores challenges and potential...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [59, 66, 1.0], [67, 75, 1.0], [76, 86, 1.0]]}, "2003.jasis_journal-ir0volumeA54A2.1": {"snippet": "...ying Natural Language Processing to Information Retrieval", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [39, 50, 1.0], [51, 60, 1.0]]}, "C92-2121": {"snippet": "...cale Natural Language Processing\n\n\n This paper demonstrates the utility of the Semantic Network Array Processor (SNAP) as a massively parallel platform for high performance and large-scale natural lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "2010.ecir_conference-2010.4": {"snippet": "... and Natural Language Processing for Multimedia Information Retrieval", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [51, 62, 1.0], [63, 72, 1.0]]}, "2017.sigirconf_workshop-2017birndl.1": {"snippet": "...nced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL) at SIGIR 2017", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 41, 1.0], [42, 50, 1.0], [51, 61, 1.0]]}, "1985.tois_journal-ir0volumeA3A2.1": {"snippet": "...LSP) natural language processing system has been developed as a domain-independent natural language processing system. Initially utilized for processing sets of medical messages and other texts in the...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [86, 93, 1.0], [94, 102, 1.0], [103, 113, 1.0], [145, 155, 1.0]]}, "1992.sigirconf_conference-92.3": {"snippet": "...of a Retrieval Process", "weights": [[8, 17, 1.0]]}, "W04-0609": {"snippet": "... for information management and for the presentation of information. Both accessing and presenting information are mediated via natural language and the ontologies are coupled with the lexicon used in...", "weights": [[8, 19, 1.0], [59, 70, 1.0], [102, 113, 1.0], [131, 138, 1.0], [139, 147, 1.0]]}, "J01-2007": {"snippet": "...ews: Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language\n\n\n The strong connection between knowledge representation and reasoning and natural lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [66, 74, 1.0], [107, 115, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "W05-0705": {"snippet": "...ng a Natural Language Processing System for {E}uropean Languages to Treat {A}rabic in Information Processing and Information Retrieval Applications\n\n\n The goal of many natural language processing plat...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [89, 100, 1.0], [101, 111, 1.0], [116, 127, 1.0], [128, 137, 1.0], [171, 178, 1.0], [179, 187, 1.0], [188, 198, 1.0]]}, "2019.sigirconf_workshop-2019birndl.0": {"snippet": "...nced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) co-located with the 42nd International ACM SIGIR Conference on Research and Development in Information Re...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 41, 1.0], [42, 50, 1.0], [51, 61, 1.0], [189, 200, 1.0], [201, 210, 1.0]]}, "J06-1006": {"snippet": "... and Language Processing by John Coleman represents a bold effort to educate students in speech science about some of the important methods used in speech and natural language processing (NLP). This b...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [162, 169, 1.0], [170, 178, 1.0], [179, 189, 1.0]]}, "C92-2122": {"snippet": "...y of Natural Language Customisation: The Practical Effects of World Knowledge\n\n\n This paper proposes a methodology for the eustomisation of natural language interfaces to information retrieval applica...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [143, 150, 1.0], [151, 159, 1.0], [174, 185, 1.0], [186, 195, 1.0]]}, "O98-4002": {"snippet": "...text processing technology of information extraction (IE) whose function is to extract information about a pre-specified set of entities, relations or events from natural language textsand to record t...", "weights": [[8, 18, 1.0], [33, 44, 1.0], [90, 101, 1.0], [166, 173, 1.0], [174, 182, 1.0]]}, "N06-5003": {"snippet": "... for Natural Language Processing and Information Retrieval\n\n\n Graph theory is a well studied discipline, and so are the fields of natural language processing and information retrieval. However, most o...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [40, 51, 1.0], [52, 61, 1.0], [133, 140, 1.0], [141, 149, 1.0], [150, 160, 1.0], [165, 176, 1.0], [177, 186, 1.0]]}, "2007.sigirconf_conference-2007.3": {"snippet": "...with information. One of these areas was NLP -natural language processing -or NLIP -processing information conveyed in natural language. At that time, most researchers thought primarily of the efficie...", "weights": [[8, 19, 1.0], [49, 56, 1.0], [57, 65, 1.0], [66, 76, 1.0], [87, 97, 1.0], [98, 109, 1.0], [122, 129, 1.0], [130, 138, 1.0]]}, "2016.sigirconf_conference-2016.219": {"snippet": "... for Information Retrieval\n\n\n ABSTRACTRecent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being successfully appl...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [94, 105, 1.0], [106, 115, 1.0], [120, 127, 1.0], [128, 136, 1.0], [137, 147, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.5": {"snippet": "Natural language processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2020.sigirconf_conference-2020.365": {"snippet": "...ires processing and understanding such information effectively and efficiently, where natural language processing (NLP) technologies are widely deployed. Natural language data are represented as a seq...", "weights": [[8, 18, 1.0], [42, 53, 1.0], [89, 96, 1.0], [97, 105, 1.0], [106, 116, 1.0], [157, 164, 1.0], [165, 173, 1.0]]}, "W93-0302": {"snippet": "...Text Processing in Automated Information Retrieval\n\n\n This paper outlines a prototype text retrieval system which uses relatively advanced natural language processing techniques in order to enhance th...", "weights": [[8, 18, 1.0], [32, 43, 1.0], [44, 53, 1.0], [94, 103, 1.0], [142, 149, 1.0], [150, 158, 1.0], [159, 169, 1.0]]}, "2001.sigirconf_workshop-2001w1.2": {"snippet": "...text processing techniques rely on proper capitalization, and people can more easily read mixed case text. Proper capitalization, however, is often absent in a number of text sources, including automa...", "weights": [[8, 18, 1.0]]}, "P96-1003": {"snippet": "... for Information Retrieval\n\n\n Information retrieval is an important application area of natural-language processing where one encounters the genuine challenge of processing large quantities of unrestr...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [33, 44, 1.0], [45, 54, 1.0], [91, 98, 1.0], [99, 107, 1.0], [108, 118, 1.0], [165, 175, 1.0]]}, "1971.sigirconf_conference-71.2": {"snippet": "...ated processing of natural language is a realistic goal. It should not be neglected, despite the fact that it is difficult to predict when this goal will be achieved.\nKEY WORDS AND PHRASESnatural lang...", "weights": [[8, 18, 1.0], [22, 29, 1.0], [30, 38, 1.0], [199, 207, 1.0]]}, "J81-4005": {"snippet": "Natural Language Processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2008.cikm_conference-2008.27": {"snippet": "...k on Natural Language Processing for Information Retrieval has shown the inadequateness of semantic and syntactic structures for both document retrieval and categorization. The main reason is the high...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [40, 51, 1.0], [52, 61, 1.0], [146, 155, 1.0]]}, "1973.sigirconf_conference-73.4": {"snippet": "... for Information Retrieval\n\n\n AbstractNatural language query formulations exhibit advantages over artificial language statements since they permit the user to approach the retrieval environment withou...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [49, 57, 1.0], [112, 120, 1.0], [175, 184, 1.0]]}, "2017.wwwconf_conference-2017c.152": {"snippet": "...uent natural language questions.\nBACKGROUND AND MOTIVATIONAs more and more RDF Question/Answering (Q/A) systems over RDF knowledge graphs are designed, which allow users to express queries in natural ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [195, 202, 1.0]]}, "X96-1030": {"snippet": "Natural Language Information Retrieval: {TIPSTER}-2 Final Report\n\n\n We report on the joint GE/NYU natural language information retrieval project as related to the Tipster Phase 2 research conducted in...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 28, 1.0], [29, 38, 1.0], [98, 105, 1.0], [106, 114, 1.0], [115, 126, 1.0], [127, 136, 1.0]]}, "2015.fire_workshop-2015w.24": {"snippet": "...s of Natural Language Processing and is regarded as the subtask of information retrieval. NER is the process to detect Named Entities (NEs) in a document and to categorize them into certain Named enti...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [70, 81, 1.0], [82, 91, 1.0]]}, "H92-1040": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a fully automated Information Retrieval System which uses advanced natural language processing techniques to enhance the ...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [35, 42, 1.0], [43, 51, 1.0], [52, 62, 1.0], [97, 108, 1.0], [109, 118, 1.0], [146, 153, 1.0], [154, 162, 1.0], [163, 173, 1.0]]}, "2020.cikm_conference-2020.67": {"snippet": "...d in natural language and convert it into forms that can be more easily and precisely processed by computers. Knowledge Graphs (KGs) offer offer a good solution for such processing. Natural Language P...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [173, 183, 1.0], [185, 192, 1.0], [193, 201, 1.0], [202, 212, 1.0]]}, "2021.rocling-1.22": {"snippet": "... the natural language processing tasks have made great progresses by leveraging the bidirectional encoder representations from Transformers (BERT). The goal of information retrieval is to search the m...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [163, 174, 1.0], [175, 184, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...odic information to isolate sarcasm. -Language models. ASR systems generally contain a language model that provides word sequence probabilities. Some information retrieval systems also rely on languag...", "weights": [[8, 19, 1.0], [41, 49, 1.0], [90, 98, 1.0], [153, 164, 1.0], [165, 174, 1.0], [196, 204, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [42, 53, 1.0], [54, 63, 1.0], [92, 103, 1.0], [104, 113, 1.0], [122, 130, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.0": {"snippet": "...m of language identification of noisy texts, which could represent the primary step of many natural language processing or information retrieval tasks. Language identification is the task of automatic...", "weights": [[8, 16, 1.0], [95, 102, 1.0], [103, 111, 1.0], [112, 122, 1.0], [126, 137, 1.0], [138, 147, 1.0], [155, 163, 1.0]]}, "1995.sigirjournals_journal-ir0volumeA29A2.2": {"snippet": "...ion: Natural Language Processing and Information Retrieval Group Information Access and User Interfaces Division National Institute of Standards and Technology\n\n\n OverviewThe Natural Language Processi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [40, 51, 1.0], [52, 61, 1.0], [68, 79, 1.0], [178, 185, 1.0], [186, 194, 1.0], [195, 205, 1.0]]}, "1994.riao_conference-1994.11": {"snippet": "... and Retrieval using Natural Language Processing", "weights": [[8, 17, 1.0], [24, 31, 1.0], [32, 40, 1.0], [41, 51, 1.0]]}, "Y09-1039": {"snippet": "...nal) Natural Language Processing: A Cognitive Perspective\n\n\n In this paper I aim at sketching out in bare outline a new model/framework of language processing with its implications for natural languag...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [142, 150, 1.0], [151, 161, 1.0], [188, 195, 1.0], [196, 204, 1.0]]}, "2000.cikm_conference-2000.57": {"snippet": "Retrieval from Captioned Image Databases Using Natural Language Processing\n\n\n ABSTRACTA t rst sight, it might appear that natural language processing should improve the accuracy of information retriev...", "weights": [[0, 9, 1.0], [47, 54, 1.0], [55, 63, 1.0], [64, 74, 1.0], [122, 129, 1.0], [130, 138, 1.0], [139, 149, 1.0], [181, 192, 1.0], [193, 202, 1.0]]}, "A92-1033": {"snippet": "...w of Natural Language Processing of Captions for Retrieving Multimedia Data\n\n\n This paper briefly describes the current implementation status of an intelligent information retrieval system, MARIE, tha...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [163, 174, 1.0], [175, 184, 1.0]]}, "W15-5955": {"snippet": "...r in natural language. The emphasis of this paper is on the natural language processing(NLP) techniques used to retrieve the relevant information from the English word problem. The system shows improv...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [63, 70, 1.0], [71, 79, 1.0], [80, 90, 1.0], [137, 148, 1.0]]}, "A97-1044": {"snippet": "...s In Natural Language Information Retrieval\n\n\n In this paper we report on our natural language information retrieval (NLIR) project as related to the recently concluded 5th Text Retrieval Conference (...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 36, 1.0], [37, 46, 1.0], [81, 88, 1.0], [89, 97, 1.0], [98, 109, 1.0], [110, 119, 1.0], [181, 190, 1.0]]}, "2015.fire_workshop-2015w.20": {"snippet": "...s\n\n\n Natural Language processing (NLP) in its pure sense, is a platform that provides the ability for transforming natural language text to useful information. Named Entity Recognition (NER) is a key ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [118, 125, 1.0], [126, 134, 1.0], [150, 161, 1.0]]}, "1991.riao_conference-1991.40": {"snippet": "Processing natural language for an expert system using a sub-language approach", "weights": [[0, 10, 1.0], [11, 18, 1.0], [19, 27, 1.0], [61, 69, 1.0]]}, "2018.cikm_conference-2018.119": {"snippet": "...ween natural language queries and web documents, if we directly use natural language queries as inputs for retrieval, the results are usually unsatisfactory. In this work, we formulated the task as a ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [71, 78, 1.0], [79, 87, 1.0], [110, 119, 1.0]]}, "2000.riao_conference-2000.38": {"snippet": "Natural Language Processing for Text Mining and Decision Making", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2018.fire_conference-2018.12": {"snippet": "...krit language by embedding character level features\n\n\n ABSTRACTPart-of-Speech (POS) tagging is an important task in Natural Language Processing and numerous taggers have been developed for POS tagging...", "weights": [[8, 16, 1.0], [119, 126, 1.0], [127, 135, 1.0], [136, 146, 1.0]]}, "H92-1115": {"snippet": "...h in Natural Language Processing\n\n\n Our general objective has been the enhancement of techniques for extracting information and retrieving documents from natural language text. Our focus has been on m...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [115, 126, 1.0], [157, 164, 1.0], [165, 173, 1.0]]}, "2007.cikm_workshop-2007pikm.1": {"snippet": "Natural language processing for information retrieval: the time is ripe (again)", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [32, 43, 1.0], [44, 53, 1.0]]}, "U06-1026": {"snippet": "Natural Language Processing and {XML} Retrieval\n\n\n XML information retrieval (XML-IR) systems respond to user queries with results more specific than documents. XML-IR queries contain both content and...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [38, 47, 1.0], [55, 66, 1.0], [67, 76, 1.0]]}, "1985.tois_journal-ir0volumeA3A2.5": {"snippet": "... The Natural Language Processing Project in the AI Program at MCC\n\n\n We discuss a recently launched, long-term project in natural language processing, the primary concern of which is that natural lang...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [125, 132, 1.0], [133, 141, 1.0], [142, 152, 1.0], [191, 198, 1.0], [199, 207, 1.0]]}, "1982.sigirjournals_journal-ir0volumeA17A3.3": {"snippet": "...3\n\n\n Natural language processing techniques developed for Artificial Intelligence programs can aid in constructing powerful information retrieval systems in at least two areas. Automatic construction ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [127, 138, 1.0], [139, 148, 1.0]]}, "2001.sigirconf_workshop-2001w1.9": {"snippet": "...Text Retrieval: Using Target IR Collections for Statistical Language Model Adaptation in Speech Recognition", "weights": [[8, 17, 1.0], [63, 71, 1.0]]}, "1996.tois_journal-ir0volumeA14A3.0": {"snippet": "Natural-Language Retrieval of Images Based on Descriptive Captions\n\n\n We describe a prototype intelligent information retrieval system that uses natural-language understanding to efficiently locate ca...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 26, 1.0], [106, 117, 1.0], [118, 127, 1.0], [145, 152, 1.0], [153, 161, 1.0]]}, "1987.sigirconf_conference-87.5": {"snippet": "...h to Natural Language Processing for Document Retrieval\n\n\n AbstractDocument retrieval systems have been restricted, by the nature of tile task, to techniques that can be used with large numbers of doc...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [49, 58, 1.0], [79, 88, 1.0]]}, "H89-2078": {"snippet": "...r on Natural Language Processing\n\n\n I. Scope Major Challenges We take the ultimate goal of natural language processing (NLP) to be the ability to use natural languages as effectively as humans do. Nat...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [94, 101, 1.0], [102, 110, 1.0], [111, 121, 1.0], [153, 160, 1.0], [200, 207, 1.0]]}, "A00-1036": {"snippet": "...from natural language processing and knowledge representation with a penaltybased technique for relevance estimation and passage retrieval. Unlike many attempts to combine natural language processing ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [132, 141, 1.0], [175, 182, 1.0], [183, 191, 1.0], [192, 202, 1.0]]}, "2018.sigirconf_conference-2018.88": {"snippet": "...ces. Natural language interfaces, exemplified by virtual assistants like Apple Siri and Microsoft Cortana, are widely believed to be a promising direction. 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"doc_id": "2020.emnlp-main.654"}]}], "docs": {"2017.sigirconf_workshop-2017ecom.12": {"snippet": "...nder systems (RSs) are useful in libraries, schools and e-commerce applications. To our knowledge, no book RS exploits social networks other than book-cataloguing websites. We propose a recommendation...", "weights": [[8, 15, 1.0], [189, 203, 1.0]]}, "2015.wsdm_conference-2015.2": {"snippet": "...nder Systems\n\n\n ABSTRACTRecommender systems are an integral part of how we experience the Web today and they have become so ubiquitous that we do not even notice them anymore. However, today's recomme...", "weights": [[8, 15, 1.0], [39, 46, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.6": {"snippet": "...ideo Recommendation\n\n\n With the availability of abundant online multi-relational video information, recommender systems that can effectively exploit these sorts of data and suggest creatively interest...", "weights": [[8, 22, 1.0], [115, 122, 1.0]]}, "2014.tist_journal-ir0volumeA5A4.4": {"snippet": "...d of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and no...", "weights": [[8, 22, 1.0], [23, 30, 1.0], [112, 126, 1.0]]}, "D13-1200": {"snippet": "...gued Recommendation: Adaptive Models Based on Words in Recommender Systems\n\n\n Recommendation systems (RS) take advantage of products and users information in order to propose items to consumers. Colla...", "weights": [[8, 22, 1.0], [70, 77, 1.0], [81, 95, 1.0], [96, 103, 1.0]]}, "2005.tois_journal-ir0volumeA23A3.0": {"snippet": "...nder systems", "weights": [[8, 15, 1.0]]}, "2005.cikm_conference-2005.115": {"snippet": "...ased recommendation system\n\n\n ABSTRACTThe explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems-a personalized information filtering ...", "weights": [[8, 22, 1.0], [158, 165, 1.0]]}, "2021.wsdm_conference-2021.138": {"snippet": "...nder Systems Utilizing Side Information\n\n\n Recommendation Systems (RS) are designed to assist users in decision making by recommending the most appropriate information or products for them. Nonetheles...", "weights": [[8, 15, 1.0], [46, 60, 1.0], [61, 68, 1.0]]}, "2004.tois_journal-ir0volumeA22A1.0": {"snippet": "...hese systems, originally referred to as collaborative filtering systems, were developed to address two challenges that could not be addressed by existing keyword-based information filtering systems. F...", "weights": [[8, 15, 1.0], [67, 74, 1.0], [193, 200, 1.0]]}, "2008.wwwconf_conference-2008.21": {"snippet": "...ased recommendation systems: an axiomatic approach\n\n\n ABSTRACTHigh-quality, personalized recommendations are a key feature in many online systems. Since these systems often have explicit knowledge of ...", "weights": [[8, 22, 1.0], [23, 30, 1.0], [141, 148, 1.0], [162, 169, 1.0]]}, "2007.wwwconf_conference-2007.195": {"snippet": "...nder systems based on maximum entropy\n\n\n ABSTRACTWe propose a model for user purchase behavior in online stores that provide recommendation services. We model the purchase probability given recommenda...", "weights": [[8, 15, 1.0], [128, 142, 1.0]]}, "2014.wwwconf_conference-2014.32": {"snippet": "...tart recommendation\n\n\n ABSTRACTRecommendation systems have been widely used in E-commerce sites, social networks, etc. One of the core tasks in recommendation systems is to predict the users' ratings ...", "weights": [[8, 22, 1.0], [49, 56, 1.0], [147, 161, 1.0], [162, 169, 1.0]]}, "2017.wsdm_conference-2017.56": {"snippet": "...nder systems have become ubiquitous in online applications where companies personalize the user experience based on explicit or inferred user preferences. Most modern recommender systems concentrate o...", "weights": [[8, 15, 1.0], [182, 189, 1.0]]}, "2020.wwwconf_conference-2020c.22": {"snippet": "...-art recommendation algorithms. A focus of this repository is on best practices in development of recommendation systems. We have also incorporated learnings from our experience with recommendation sy...", "weights": [[8, 22, 1.0], [101, 115, 1.0], [116, 123, 1.0], [186, 200, 1.0], [201, 208, 1.0]]}, "2019.wsdm_conference-2019.31": {"snippet": "...nder Systems\n\n\n ABSTRACTThe increasing role of recommender systems in many aspects of society makes it essential to consider how such systems may impact social good. Various modifications to recommend...", "weights": [[8, 15, 1.0], [62, 69, 1.0], [137, 144, 1.0], [194, 208, 1.0]]}, "2020.wsdm_conference-2020.1": {"snippet": "...s of Recommendation\n\n\n The challenge of building a good recommendation system is deeply connected to missing data-unknown features and labels to suggest the most \"valuable\" items to the user. The myst...", "weights": [[8, 22, 1.0], [59, 73, 1.0]]}, "2013.sigirconf_conference-2013.32": {"snippet": "...nder systems in various scenarios, including the zero-query pull-based recommendation scenario (e.g. recommendation on an e-commerce web site) and a proactive push-based promotion scenario (e.g. email...", "weights": [[8, 15, 1.0], [74, 88, 1.0], [104, 118, 1.0]]}, "2016.ipm_journal-ir0volumeA52A6.8": {"snippet": "...ice, recommendation systems are widely used by ecommerce practitioners and have become an important research topic in the field of information sciences and decision support systems (Liang et al., 2008...", "weights": [[8, 22, 1.0], [23, 30, 1.0], [176, 183, 1.0]]}, "2015.iir_workshop-2015.5": {"snippet": "...nder Systems", "weights": [[8, 15, 1.0]]}, "2011.wwwconf_conference-2011c.117": {"snippet": "...nder systems\n\n\n ABSTRACTThe goal of this tutorial is to expose participants to the current research on social recommender systems (i.e., recommender systems for the social web). Participants will beco...", "weights": [[8, 15, 1.0], [125, 132, 1.0], [152, 159, 1.0]]}, "2014.ecir_conference-2014.6": {"snippet": "...ised Recommendation System for Context-Aware Suggestions", "weights": [[8, 22, 1.0]]}, "2020.wsdm_conference-2020.75": {"snippet": "...s in Recommendation Systems: Opinion Elicitation and Diffusion\n\n\n Recommendation systems typically rely on the interactions between a crowd of ordinary users and items, ignoring the fact that many rea...", "weights": [[8, 22, 1.0], [23, 30, 1.0], [69, 83, 1.0], [84, 91, 1.0]]}, "2021.wwwconf_conference-2021.339": {"snippet": "Bidirectional Distillation for Top-K Recommender System", "weights": []}, "2020.emnlp-main.654": {"snippet": "...able Recommendation Dialog Systems\n\n\n In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. However, this is a challenge in developing a s...", "weights": [[8, 22, 1.0], [30, 37, 1.0], [44, 58, 1.0]]}, "2020.wsdm_conference-2020.8": {"snippet": "... the recommendation process. Experiments show that the proposed model both preserves the quality of recommendation service and protects users against private-attribute inference attacks. CCS CONCEPTS ...", "weights": [[8, 22, 1.0], [103, 117, 1.0]]}, "2002.cikm_conference-2002.7": {"snippet": "Meta-recommendation systems: user-controlled integration of diverse recommendations\n\n\n ABSTRACTIn a world where the number of choices can be overwhelming, recommender systems help users find and evalu...", "weights": [[5, 19, 1.0], [20, 27, 1.0], [167, 174, 1.0]]}, "2018.iir_workshop-2018.6": {"snippet": "...edia Recommendation Systems: Definition and Application Domains", "weights": [[8, 22, 1.0], [23, 30, 1.0]]}, "2019.ipm_journal-ir0volumeA56A1.11": {"snippet": "...ring systems -such as recommender systems -to provide personalized recommendations. However, most of them work with users or items as vectors, by applying different types of mathematical operations be...", "weights": [[8, 15, 1.0], [37, 44, 1.0]]}, "2019.tois_journal-ir0volumeA37A3.4": {"snippet": "...e of recommendation. In this article, we consider the knowledge graph (KG) as the source of side information. To address the limitations of existing embedding-based and path-based methods for KG-aware...", "weights": [[8, 22, 1.0]]}, "2015.clef_conference-2015w.55": {"snippet": "...nder systems, able to deliver recommendations in real time, is a challenging task. In contrast to traditional recommender systems, recommending news entails additional requirements. These requirements...", "weights": [[8, 15, 1.0], [125, 132, 1.0]]}, "2015.cikm_conference-2015.150": {"snippet": "...nder systems by exploiting the anticipated social-network information diffusion and withholding recommendation of items that are expected to reach a user through sharing/reposting. We devise a novel r...", "weights": [[8, 15, 1.0], [99, 113, 1.0]]}, "2019.cikm_conference-2019.156": {"snippet": "Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling", "weights": []}, "2016.fire_conference-2016.2": {"snippet": "...ware Recommendation System\n\n\n ABSTRACTThe effectiveness of recommendation systems is improving with the incorporation of richer context. The frequentist recommendation methods such as Markov models ar...", "weights": [[8, 22, 1.0], [62, 76, 1.0], [77, 84, 1.0], [156, 170, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A4.5": {"snippet": "...nder systems in tackling cold start problem and dealing with sparse rating datasets. Our system also enjoys improved recommendation accuracy compared with several major existing recommendation approac", "weights": [[8, 15, 1.0], [120, 134, 1.0], [181, 195, 1.0]]}, "2021.tist_journal-ir0volumeA12A2.6": {"snippet": "...nder Systems", "weights": [[8, 15, 1.0]]}, "2008.airs_conference-2008.57": {"snippet": "...usic Recommendation\n\n\n Abstract. We present a music recommendation system that incorporates both collaborative filtering and mood-based recommendations. The benefits of incorporating mood-based recomm...", "weights": [[8, 22, 1.0], [55, 69, 1.0]]}, "2011.tois_journal-ir0volumeA29A4.3": {"snippet": "Recommendation systems with complex constraints: A course recommendation perspective\n\n\n We study the problem of making recommendations when the objects to be recommended must also satisfy constraints ...", "weights": [[0, 14, 1.0], [15, 22, 1.0], [58, 72, 1.0]]}, "2017.wwwconf_conference-2017.42": {"snippet": "...nder Systems\n\n\n ABSTRACTThe increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming s...", "weights": [[8, 15, 1.0], [79, 86, 1.0], [178, 185, 1.0]]}, "2016.ismir_conference-2016.11": {"snippet": "...usic Recommendation Systems: Do Demographic, Profiling, and Contextual Features Improve Their Performance?", "weights": [[8, 22, 1.0], [23, 30, 1.0]]}, "L16-1312": {"snippet": "... and Recommendation System\n\n\n In this paper, we present a music retrieval and recommendation system using machine learning techniques. We propose a query by humming system for music retrieval that use...", "weights": [[8, 22, 1.0], [81, 95, 1.0]]}, "2018.sigirconf_conference-2018.231": {"snippet": "...able Recommendation and Search, under the background of Explainable AI in a more general sense. Early recommendation and search systems adopted intuitive yet easily explainable models to generate reco...", "weights": [[8, 22, 1.0], [105, 119, 1.0], [131, 138, 1.0], [199, 213, 1.0]]}, "2012.cikm_conference-2012.302": {"snippet": "...brid recommendation\n\n\n ABSTRACTMost existing recommender systems can be classified into two categories: collaborative filtering and content-based filtering. Hybrid recommender systems combine the adva...", "weights": [[8, 22, 1.0], [60, 67, 1.0], [179, 186, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A6.3": {"snippet": "...ased recommendation system for online social networks\n\n\n Abstract Recommendation systems are popular both commercially and in the research community. For example, Online in Social Networks (OSNs) like...", "weights": [[8, 22, 1.0], [69, 83, 1.0], [84, 91, 1.0]]}, "2007.wwwjournals_journal-ir0volumeA10A4.3": {"snippet": "...nder systems that engage some MCDM method. Such systems, which we refer to as multi-criteria recommender systems, have early demonstrated the potential of applying MCDM methods to facilitate recommend...", "weights": [[8, 15, 1.0], [51, 58, 1.0], [108, 115, 1.0], [194, 208, 1.0]]}, "2016.wwwconf_conference-2016.11": {"snippet": "...nder Systems Work the Best?: The Moderating Effects of Product Attributes and Consumer Reviews on Recommender Performance\n\n\n ABSTRACTWe investigate the moderating effect of product attributes and cons...", "weights": [[8, 15, 1.0]]}, "2014.tist_journal-ir0volumeA5A4.1": {"snippet": "...nder Systems\n\n\n Recommender systems are quickly becoming ubiquitous in applications such as e-commerce, social media channels, and content providers, among others, acting as an enabling mechanism desi...", "weights": [[8, 15, 1.0], [31, 38, 1.0]]}, "2009.sigirconf_conference-2009.27": {"snippet": "...nder systems suffer the following problems: (1) The large-scale and sparse data of the user-item matrix seriously affect the recommendation quality. As a result, most of the recommender systems cannot...", "weights": [[8, 15, 1.0], [128, 142, 1.0], [189, 196, 1.0]]}, "2017.ecir_workshop-2017bir.2": {"snippet": "...nder Systems for Academia: The Pain and Gain in Building, Operating, and Researching them\n\n\n Research on recommender systems is a challenging task, as is building and operating such systems. Major cha...", "weights": [[8, 15, 1.0], [120, 127, 1.0], [185, 192, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A2.6": {"snippet": "...nder systems\n\n\n AbstractIn recent years, immense progress has been made in the development of recommendation, retrieval, and personalisation techniques. The evaluation of these systems is still based ...", "weights": [[8, 15, 1.0], [97, 111, 1.0], [180, 187, 1.0]]}, "2001.wwwconf_conference-2001p.25": {"snippet": "...nder systems are new types of internet-based software tools, designed to help users find their way through today's complex on-line shops and entertainment websites. Here we provide an overview of curr...", "weights": [[8, 15, 1.0]]}, "2020.sigirconf_conference-2020.355": {"snippet": "...nder systems. CCS CONCEPTS \u2022 Information systems \u2192 Users and interactive retrieval; Recommender systems; Personalization; \u2022 Human-centered computing \u2192 Interactive systems and tools.", "weights": [[8, 15, 1.0], [44, 51, 1.0], [99, 106, 1.0], [166, 173, 1.0]]}, "2011.tweb_journal-ir0volumeA5A1.0": {"snippet": "...nder systems", "weights": [[8, 15, 1.0]]}, "2008.cikm_workshop-2008wicow.4": {"snippet": "...ring recommendation method for use with recently popular online social tagging systems. Combining the information provided by tagging systems with the effective recommendation abilities given by colla...", "weights": [[8, 22, 1.0], [82, 89, 1.0], [137, 144, 1.0], [164, 178, 1.0]]}, "2017.clef_conference-2017w.8": {"snippet": "...nder systems have been developed for supporting users in finding the most interesting items in vast collections of available items. In contrast to traditional recommender systems, news recommender sys...", "weights": [[8, 15, 1.0], [174, 181, 1.0], [200, 207, 1.0]]}, "2011.wsdm_conference-2011.36": {"snippet": "...nder systems with social regularization\n\n\n ABSTRACTAlthough Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In th...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [176, 183, 1.0]]}, "2019.tois_journal-ir0volumeA37A3.3": {"snippet": "... for recommendation than historical ones.\nCCS Concepts: \u2022 Information systems \u2192 Recommender systems;X. Li is now at the National University of Defense Technology, Changsha, China. This research was pa...", "weights": [[8, 22, 1.0], [73, 80, 1.0], [95, 102, 1.0]]}, "2017.iir_workshop-2017.13": {"snippet": "...nder Systems", "weights": [[8, 15, 1.0]]}, "2017.wsdm_workshop-2017swm.4": {"snippet": "...nder systems for scholarly information\n\n\n ABSTRACT e depth and breadth of research now being published is overwhelming for an individual researcher to keep track of, let alone consume. Recommender sys...", "weights": [[8, 15, 1.0], [200, 207, 1.0]]}, "2016.ipm_journal-ir0volumeA52A3.6": {"snippet": "... the recommendation for a group of size one (i.e. single user recommendation). Obtained results, support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation...", "weights": [[8, 22, 1.0], [65, 79, 1.0], [189, 203, 1.0]]}, "2020.wwwjournals_journal-ir0volumeA23A3.1": {"snippet": "Recommender system for marketing optimization", "weights": []}, "2021.wsdm_conference-2021.16": {"snippet": "...onal recommendation systems are hard to provide the continuous extended reading function of these relevant items, since the main recommendation results should be diversified. In this paper, we propose...", "weights": [[8, 22, 1.0], [23, 30, 1.0], [132, 146, 1.0]]}, "2017.ecir_conference-2017.73": {"snippet": "... Set Recommendation in Group Recommender Systems", "weights": [[8, 22, 1.0], [44, 51, 1.0]]}, "2021.wsdm_conference-2021.141": {"snippet": "...ware Recommendation in Multi-Sided Platforms\n\n\n Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important ...", "weights": [[8, 22, 1.0], [112, 119, 1.0]]}, "2013.ecir_conference-2013.35": {"snippet": "...nder Systems\n\n\n Abstract.Recommender systems suggest new items to users to try or buy based on their previous preferences or behavior. Many times the information used to recommend these items is limit...", "weights": [[8, 15, 1.0], [40, 47, 1.0]]}, "2010.cikm_conference-2010.178": {"snippet": "...ding recommendation systems using peer-to-peer shared content\n\n\n ABSTRACTPeer-to-Peer (p2p) networks are used for sharing content by millions of users. Often, meta-data used for searching is missing o...", "weights": [[8, 22, 1.0], [23, 30, 1.0]]}, "2017.wsdm_conference-2017.97": {"snippet": "...nder Systems: Research Direction\n\n\n ABSTRACTRecommender systems are omnipresent on the web. They aim to address the problem of information overload. Recommender systems personalizes the content to eac...", "weights": [[8, 15, 1.0], [59, 66, 1.0], [164, 171, 1.0]]}, "2018.wwwconf_conference-2018c.51": {"snippet": "...nder Systems\n\n\n ABSTRACTRecent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-proces...", "weights": [[8, 15, 1.0], [43, 57, 1.0], [58, 65, 1.0], [137, 151, 1.0]]}, "2017.iir_workshop-2017.15": {"snippet": "...nder Systems in R", "weights": [[8, 15, 1.0]]}, "2018.cikm_workshop-2018w.4": {"snippet": "...nder Systems\n\n\n Chat-based recommender systems are getting more and more attention in recent time given their natural interaction with the user. Indeed, chat-based recommender systems implement a para...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [179, 186, 1.0]]}, "2004.tois_journal-ir0volumeA22A1.5": {"snippet": "...op-N recommendation algorithms\n\n\n The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems-a personalized information filtering tech...", "weights": [[8, 22, 1.0], [154, 161, 1.0]]}, "2013.cikm_workshop-2013livinglab.3": {"snippet": "...nder systems have become a ubiquitous feature in the online world. Research on systems and algorithms in this area has flourished, leading to novel techniques for personalization and recommendation. T...", "weights": [[8, 15, 1.0], [82, 89, 1.0], [186, 200, 1.0]]}, "2017.chiir_conference-2017.68": {"snippet": "...uery recommendation, using an approximation of need.\nCCS Concepts\u2022Information systems \u2192 Query suggestion; Personalization; Document topic models; Retrieval effectiveness;\nKeywordsSearch session analys...", "weights": [[8, 22, 1.0], [81, 88, 1.0]]}, "R19-2009": {"snippet": "...nder Systems for Books\n\n\n Recommender systems are an essential part of today's largest websites. Without them, it would be hard for users to find the right products and content. One of the most popula...", "weights": [[8, 15, 1.0], [41, 48, 1.0]]}, "2007.ismir_conference-2007.47": {"snippet": "...usic Recommendation Systems", "weights": [[8, 22, 1.0], [23, 30, 1.0]]}, "D19-1189": {"snippet": "... the recommendation system by introducing knowledge-grounded information about users' preferences, and the recommender system can improve that of the dialog generation system by providing recommendati...", "weights": [[8, 22, 1.0], [191, 205, 1.0]]}, "2012.wwwconf_conference-2012.2": {"snippet": "...nder systems\n\n\n ABSTRACTThe aim of a web-based recommender system is to provide highly accurate and up-to-date recommendations to its users; in practice, it will hope to retain its users over time. Ho...", "weights": [[8, 15, 1.0]]}, "2018.sigirconf_conference-2018.55": {"snippet": "...nder systems today are designed in the context of an offline setting. Compared with the traditional recommender systems, large-volume and high-velocity are posing severe challenges for streaming recom...", "weights": [[8, 15, 1.0], [115, 122, 1.0]]}, "2013.sigirconf_conference-2013.168": {"snippet": "...term recommendation systems", "weights": [[8, 22, 1.0], [23, 30, 1.0]]}, "2018.ipm_journal-ir0volumeA54A4.13": {"snippet": "...nder systems. Selecting suitable neighbors set for the users leads to improve the accuracy of ratings prediction in recommendation process. In this paper, a novel social recommendation method is propo...", "weights": [[8, 15, 1.0], [119, 133, 1.0], [173, 187, 1.0]]}, "2017.tois_journal-ir0volumeA35A4.8": {"snippet": "...nder systems have been proposed and deployed for the App domain. However, existing work on App recommendation has largely focused on one single platform (e.g., smartphones), while it ignores the rich ...", "weights": [[8, 15, 1.0], [98, 112, 1.0]]}, "2014.wwwconf_conference-2014.11": {"snippet": "...ized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering approaches to recommendation systems ...", "weights": [[8, 22, 1.0], [23, 30, 1.0], [180, 194, 1.0], [195, 202, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.11": {"snippet": "...nder systems: Alternatives and performance\n\n\n a b s t r a c tIn collaborative filtering recommender systems recommendations can be made to groups of users. There are four basic stages in the collabora...", "weights": [[8, 15, 1.0], [103, 110, 1.0]]}, "2017.wwwconf_conference-2017c.165": {"snippet": "...rich recommendation.\nKeywordsRecommender Systems; Information Network Mining; contexrich recommendation; Spatio-temporal recommendation\nORGANIZERSYizhou Sun (Ph.D., UIUC), is an assistant professor at...", "weights": [[8, 22, 1.0], [44, 51, 1.0], [92, 106, 1.0], [124, 138, 1.0]]}, "2014.sigirconf_conference-2014.212": {"snippet": "...nder systems and information retrieval\n\n\n ABSTRACTThe development and evaluation of Information Retrieval and Recommender Systems has traditionally focused on the relevance and accuracy of retrieved d...", "weights": [[8, 15, 1.0], [125, 132, 1.0]]}, "2020.ecnlp-1.8": {"snippet": "...nder systems have become an indispensable part of helping users explore the available inventory. In this work, we present a novel approach for item-based collaborative filtering, by leveraging BERT to...", "weights": [[8, 15, 1.0]]}, "2004.wwwconf_conference-2004.40": {"snippet": "...nder systems for fun and profit\n\n\n ABSTRACTRecommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application ...", "weights": [[8, 15, 1.0], [58, 65, 1.0]]}, "2014.wwwconf_conference-2014c.275": {"snippet": "...onal recommendation\n\n\n ABSTRACTProduct recommendation is an important aspect of many e-commerce systems. It provides an effective way to help users navigate complex product spaces. In this paper, we f...", "weights": [[8, 22, 1.0], [42, 56, 1.0], [99, 106, 1.0]]}, "2013.sigirconf_conference-2013.127": {"snippet": "...nder systems\n\n\n ABSTRACTPersonalized recommender systems aim to push only the relevant items and information directly to the users without requiring them to browse through millions of web resources. T...", "weights": [[8, 15, 1.0], [52, 59, 1.0]]}, "2018.cikm_conference-2018.138": {"snippet": "...nder systems and deep neural network. And deep learning methods have been shown to achieve state-of-the-art on many recommendation tasks. For example, a recent model, NeuMF, first projects users and i...", "weights": [[8, 15, 1.0], [119, 133, 1.0]]}, "2007.tois_journal-ir0volumeA26A1.3": {"snippet": "...size recommendation system fit all? the effectiveness of collaborative filtering based recommendation systems across different domains and search modes\n\n\n Collaborative filtering (CF) is a personaliza...", "weights": [[8, 22, 1.0], [90, 104, 1.0], [105, 112, 1.0]]}, "2018.wwwconf_conference-2018.174": {"snippet": "...,200 recommendation requests per second with 60 millisecond latency. Today, systems backed by Pixie contribute to more than 80% of all user engagement on Pinterest.", "weights": [[8, 22, 1.0], [79, 86, 1.0]]}, "2010.sigirconf_conference-2010.28": {"snippet": "...nder systems\n\n\n ABSTRACTCollaborative Filtering (CF) algorithms, used to build webbased recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current ...", "weights": [[8, 15, 1.0], [103, 110, 1.0]]}, "2021.ijirr_journal-ir0volumeA11A2.0": {"snippet": "...ased Recommendation System for Effective Information Computing", "weights": [[8, 22, 1.0]]}, "2019.sigirjournals_journal-ir0volumeA53A1.7": {"snippet": "...nder systems", "weights": [[8, 15, 1.0]]}, "2021.wsdm_conference-2021.147": {"snippet": "...onal systems will have a major impact on human-computer interaction, and specifically, the IR/DM/RecSys communities have begun to explore Conversational Recommendation Systems. Conversational recommen...", "weights": [[8, 15, 1.0], [156, 170, 1.0], [171, 178, 1.0], [195, 209, 1.0]]}, "2004.tois_journal-ir0volumeA22A3.3": {"snippet": "...nder systems using collaborative filtering are a popular technique for reducing information overload and finding products to purchase. One limitation of current recommenders is that they are not porta...", "weights": [[8, 15, 1.0]]}, "2012.wwwconf_conference-2012c.20": {"snippet": "...s of recommendation requests are very important. In this paper, we take a graph-based approach to accomplishing such requirements in recommender systems and present a generic graph-based multidimensio...", "weights": [[8, 22, 1.0], [148, 155, 1.0]]}, "2009.wwwconf_conference-2009.70": {"snippet": "...ized recommendation on dynamic content using predictive bilinear models\n\n\n ABSTRACTIn Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely i...", "weights": [[8, 22, 1.0], [164, 171, 1.0]]}, "2014.ipm_journal-ir0volumeA50A6.2": {"snippet": "... and recommendation\n\n\n a b s t r a c tIn this paper, we present ViGOR (Video Grouping, Organisation and Recommendation), an exploratory video retrieval system. Exploratory video retrieval tasks are ha...", "weights": [[8, 22, 1.0], [107, 121, 1.0]]}, "2016.sigirconf_conference-2016.34": {"snippet": "...onal recommendation systems (RSs) aim to recommend items that are relevant to the user's interest. Unfortunately, the recommended items will soon become too familiar to the user and hence fail to arou...", "weights": [[8, 22, 1.0], [23, 30, 1.0]]}, "2017.tweb_journal-ir0volumeA11A3.1": {"snippet": "...onal Recommendation in Heterogeneous Networks\n\n\n Recommender systems are key components in information-seeking contexts where personalization is sought. However, the dominant framework for recommendat...", "weights": [[8, 22, 1.0], [64, 71, 1.0], [192, 206, 1.0]]}, "W12-0608": {"snippet": "...ve a recommendation system. While typical recommender systems compare a user profile with some reference characteristics to rate unseen items, they rarely make use of the content of reviews users have...", "weights": [[8, 22, 1.0], [57, 64, 1.0]]}, "2020.ir_journal-ir0volumeA23A4.0": {"snippet": "...nder systems", "weights": [[8, 15, 1.0]]}, "2021.wwwconf_conference-2021c.67": {"snippet": "...nder Systems", "weights": [[8, 15, 1.0]]}, "2019.wwwconf_conference-2019.299": {"snippet": "...nder Systems with Heterogeneous Side Information", "weights": [[8, 15, 1.0]]}, "2018.tweb_journal-ir0volumeA12A1.3": {"snippet": "...y of recommendation significantly.\nCCS Concepts: \u2022 Information systems \u2192 Social recommendation;Additional Key Words and Phrases: Recommendation system, Location-based social network Permission to make...", "weights": [[8, 22, 1.0], [66, 73, 1.0], [83, 97, 1.0], [132, 146, 1.0]]}}}
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This has led to the use of a tiered index, where the searc...", "weights": [[8, 14, 1.0], [28, 34, 1.0], [198, 204, 1.0]]}, "2019.sigirconf_conference-2019.142": {"snippet": "...erce Search", "weights": [[8, 14, 1.0]]}, "2010.airs_conference-2010.3": {"snippet": "...duct Search - Exemplified in a Chinese E-commerce Portal", "weights": [[8, 14, 1.0]]}, "2012.airs_conference-2012.25": {"snippet": "... and Search in E-Commerce", "weights": [[8, 14, 1.0]]}, "2015.sigirconf_workshop-2015sps.4": {"snippet": "Finding Intermediary Topics Between People of Opposing Views: A Case Study\n\n\n In micro-blogging platforms, people can connect with others and have conversations on a wide variety of topics. However, b...", "weights": []}, "2018.sigirconf_workshop-2018ecom.10": {"snippet": "...erce Search\n\n\n In this paper, we address the problem of evaluating whether results served by an e-commerce search engine for a query are good or not. is is a critical question in evaluating any e-comm...", "weights": [[8, 14, 1.0], [110, 116, 1.0]]}, "2020.cikm_conference-2020.248": {"snippet": "...Shop Search in E-commerce", "weights": [[8, 14, 1.0]]}, "2015.sigirconf_workshop-2015sps.7": {"snippet": "... web search logs to present a large-scale study of such \"on behalf of\" searches. We develop an exploratory technique for recognizing such searches, and present information to describe and understand t...", "weights": [[8, 14, 1.0]]}, "2020.chiir_conference-2020.72": {"snippet": "Third Workshop on Evaluation of Personalisation in Information Retrieval (WEPIR 2020): In Memoriam Seamus Lawless", "weights": []}, "2021.emnlp-main.280": {"snippet": "...onal Search for Online Shopping with Utterance Transfer\n\n\n Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. How...", "weights": [[8, 14, 1.0], [88, 94, 1.0]]}, "2018.sigirconf_workshop-2018ecom.29": {"snippet": "...erce Search and Discovery\n\n\n E-Commerce (E-Com) search is an emerging problem with multiple new challenges. One of the primary challenges constitutes optimizing multiple objectives involving business ...", "weights": [[8, 14, 1.0], [51, 57, 1.0]]}, "2014.sigirconf_workshop-2014pir.6": {"snippet": "... Web Search: Exploring Search Query Parameters and User Information Privacy Implications-The Case of Google\n\n\n Personalised search adapts search results to the needs and interests of users. This is do...", "weights": [[8, 14, 1.0], [26, 32, 1.0], [114, 126, 1.0], [127, 133, 1.0], [141, 147, 1.0]]}, "2020.cikm_conference-2020.95": {"snippet": "Personalized Re-ranking with Item Relationships for E-commerce", "weights": []}, "2004.wwwconf_conference-2004at.126": {"snippet": "...erce search engines\n\n\n ABSTRACTIn this paper, we sketch a method for clustering e-commerce search engines by the type of products/services they sell. This method utilizes the special features of inter...", "weights": [[8, 14, 1.0], [94, 100, 1.0]]}, "2020.wsdm_conference-2020.5": {"snippet": "...duct Search on E-Commerce Platforms\n\n\n Product search forms an indispensable component of any e-commerce service, and helps customers find products of their interest from a large catalog on these webs...", "weights": [[8, 14, 1.0], [50, 56, 1.0]]}, "2003.ecir_conference-2003.9": {"snippet": "Attaining Fast and Successful Searches in E-commerce Environments", "weights": []}, "2004.ir_journal-ir0volumeA7A34.6": {"snippet": "OSGS - A Personalized Online Store for E-Commerce Environments", "weights": []}, "2020.sigirconf_conference-2020.143": {"snippet": "...ized Search\n\n\n Personalized search is a task to tailor the general document ranking list based on user interests to better satisfy the user's information need. Many personalized search models have bee...", "weights": [[8, 14, 1.0], [31, 37, 1.0], [181, 187, 1.0]]}, "2009.wsdm_conference-2009.5": {"snippet": "...ized search\n\n\n ABSTRACTPersonalized Web search takes advantage of information about an individual to identify the most relevant results for that person. A challenge for personalization lies in collect...", "weights": [[8, 14, 1.0], [43, 49, 1.0]]}, "2012.ipm_journal-ir0volumeA48A4.6": {"snippet": "...y of Personalised Information Retrieval and Adaptive Hypermedia techniques\n\n\n a b s t r a c tA key driver for next generation web information retrieval systems is becoming the degree to which a user's...", "weights": [[8, 20, 1.0]]}, "2020.wsdm_conference-2020.122": {"snippet": "... Web Search and Conversational Agents\n\n\n User intent understanding is a crucial step in designing both conversational agents and search engines. Detecting or inferring user intent is challenging, sinc...", "weights": [[8, 14, 1.0], [132, 138, 1.0]]}, "2018.sigirconf_conference-2018.26": {"snippet": "...dhoc search engine or traditional multi round dialog system. They usually only utilize user inputs in the current session, ignoring users' long term preferences. On the other hand, it is well known th...", "weights": [[8, 14, 1.0]]}, "2014.wsdm_conference-2014.80": {"snippet": "... for search and for other domains. This is increasingly the case as devices enable more forms of data to be gathered, are always on/connected and are always with users. This workshop brings researcher...", "weights": [[8, 14, 1.0]]}, "2019.ir_journal-ir0volumeA22A5.0": {"snippet": "... and search", "weights": [[8, 14, 1.0]]}, "2010.wsdm_conference-2010.36": {"snippet": "...ored search\n\n\n ABSTRACTSponsored search is a multi-billion dollar business that generates most of the revenue for search engines. Predicting the probability that users click on ads is crucial to spons...", "weights": [[8, 14, 1.0], [36, 42, 1.0], [117, 123, 1.0]]}, "2018.sigirconf_workshop-2018ecom.18": {"snippet": "...s to search and buy products. Identifying attributes in a query is a critical component in connecting users to relevant items. However, in many cases, the queries have multiple attributes, and some of...", "weights": [[8, 14, 1.0]]}, "2021.naacl-industry.19": {"snippet": "...erce Search\n\n\n Nowadays, with many e-commerce platforms conducting global business, e-commerce search systems are required to handle product retrieval under multilingual scenarios. Moreover, comparing...", "weights": [[8, 14, 1.0], [98, 104, 1.0]]}, "2011.wsdm_conference-2011.84": {"snippet": "...very search engine. It helps search engine users narrow or broaden their searches. Published work on query suggestion methods has mainly focused on the web domain. But, the module is also popular in t...", "weights": [[8, 14, 1.0], [32, 38, 1.0]]}, "2014.sigirconf_conference-2014.117": {"snippet": "...ving search personalisation with dynamic group formation\n\n\n ABSTRACTRecent research has shown that the performance of search engines can be improved by enriching a user's personal profile with informa...", "weights": [[8, 14, 1.0], [121, 127, 1.0]]}, "2019.wwwconf_conference-2019c.63": {"snippet": "...erce Search Data using Topic Models", "weights": [[8, 14, 1.0]]}, "2019.sigirconf_conference-2019.200": {"snippet": "...tive Search Experience in E-commerce Platforms", "weights": [[8, 14, 1.0]]}, "2014.wwwconf_conference-2014c.42": {"snippet": "...s in search and ranking ranges from traditional relational databases to document systems to web search and, more recently, vertical applications like product search in e-commerce or vertical search ap...", "weights": [[8, 14, 1.0], [99, 105, 1.0], [161, 167, 1.0], [194, 200, 1.0]]}, "2019.tois_journal-ir0volumeA37A2.5": {"snippet": "...duct search. In fact, personalized search has been widely studied in literature over the past few years. Thereinto, the study of personalized web search incorporating the user preference is the most r...", "weights": [[8, 14, 1.0], [38, 44, 1.0], [149, 155, 1.0]]}, "2016.wwwconf_conference-2016.10": {"snippet": "...n or search in practice. The performance metrics of recommender and search systems currently tend to focus on just one side of the match, in some cases the consumers (e.g. satisfaction) and in other c...", "weights": [[8, 14, 1.0], [71, 77, 1.0]]}, "2012.wwwconf_conference-2012c.137": {"snippet": "...ajor search engines develop different types of personalisation of search results. Personalisation includes deriving user's long-term preferences, query disambiguation etc. User sessions provide very p...", "weights": [[8, 14, 1.0], [69, 75, 1.0]]}, "2018.sigirconf_workshop-2018ecom.19": {"snippet": "...uent search, recommendations and related personalized services. E-commerce product classification is challenging due to the large scale and complexity of the product information and categories. In the...", "weights": [[8, 14, 1.0]]}, "2008.wwwconf_conference-2008.49": {"snippet": "...eted search\n\n\n ABSTRACTFaceted search is becoming a popular method to allow users to interactively search and navigate complex information spaces. A faceted search system presents users with keyvalue ...", "weights": [[8, 14, 1.0], [34, 40, 1.0], [102, 108, 1.0], [160, 166, 1.0]]}, "2015.sigirconf_workshop-2015sps.3": {"snippet": "Improving Contextual Suggestions using Open Web Domain Knowledge\n\n\n Contextual suggestion aims at recommending items to users given their current context, such as location-based tourist recommendation...", "weights": []}, "2012.wsdm_conference-2012.45": {"snippet": "... web search\n\n\n ABSTRACTWe present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms ...", "weights": [[8, 14, 1.0], [74, 80, 1.0], [135, 141, 1.0]]}, "2010.sigirconf_conference-2010.49": {"snippet": "... the search experience to the user-specific needs has become evident. In this study we examine ways of personalising affective models trained on facial expression data. Using personalised data we adap...", "weights": [[8, 14, 1.0], [178, 190, 1.0]]}, "2007.wwwconf_conference-2007.59": {"snippet": "...ized search has significant improvement over common web search on some queries but it has little effect on other queries (e.g., queries with small click entropy). It even harms search accuracy under s...", "weights": [[8, 14, 1.0], [59, 65, 1.0], [180, 186, 1.0]]}, "2020.wsdm_conference-2020.115": {"snippet": "... and Search\n\n\n Conversational systems have improved dramatically recently, and are receiving increasing attention in academic literature. These systems are also becoming adapted in E-Commerce due to i...", "weights": [[8, 14, 1.0]]}, "2015.sigirconf_workshop-2015sps.5": {"snippet": "Analysis of Sentiment Communities in Online Networks\n\n\n This article reports our experience in developing a recommender system (RS) able to suggest relevant people to the target user. Such a RS relies...", "weights": []}, "2013.cikm_conference-2013.323": {"snippet": "...vide personalised ranked lists of documents which improve significantly over a non-personalised baseline. Further examination shows that the performance of the personalised system is particularly good...", "weights": [[8, 20, 1.0], [86, 98, 1.0], [163, 175, 1.0]]}, "2018.sigirconf_workshop-2018ecom.31": {"snippet": "...erce Search\n\n\n Query Understanding is a semantic search method that can classify tokens in a customer's search query to entities such as Product, Brand, etc. This method can overcome the limitations o...", "weights": [[8, 14, 1.0], [52, 58, 1.0], [107, 113, 1.0]]}, "2018.sigirconf_conference-2018.223": {"snippet": "...s of search (exploratory search vs. lookup tasks), recommender systems, and natural language processing in e-commerce portals. Recently, the explosive popularity of e-commerce sites has made research ...", "weights": [[8, 14, 1.0], [28, 34, 1.0]]}, "2015.sigirconf_workshop-2015sps.6": {"snippet": "Retrieving Relevant Conversations for Q&A on Twitter\n\n\n Community Question and Answering (Q&A) sites provide special features for asking questions and receiving answers from users on the Web. Neverthe...", "weights": []}, "2016.ntcir_workshop-2016evia.1": {"snippet": "...n of Personalised Search\n\n\n Comparative evaluation of Information Retrieval Systems (IRSs) using publically available test collections has become an established practice in Information Retrieval (IR)....", "weights": [[8, 20, 1.0], [21, 27, 1.0]]}, "2014.wwwconf_conference-2014.11": {"snippet": "... web search, and more. Collaborative filtering approaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers) is lowrank. In this paper, we examine an al...", "weights": [[8, 14, 1.0]]}, "2015.sigirconf_workshop-2015sps.2": {"snippet": "... the search or recent actions of the searcher such as queries issued, results clicked, and pages viewed. In this talk, I will describe a recent framework for personalized search which improves the qua...", "weights": [[8, 14, 1.0], [174, 180, 1.0]]}, "2015.ecir_conference-2015.67": {"snippet": "... for Search Personalisation", "weights": [[8, 14, 1.0]]}, "2019.sigirjournals_journal-ir0volumeA53A1.3": {"snippet": "Report on the CHIIR 2019 Second Workshop on Evaluation of Personalisation in Information Retrieval (WEPIR 2019)", "weights": []}, "2007.sigirconf_conference-2007.105": {"snippet": "... for personalised search systems is the ability to disambiguate queries based on some knowledge of a user's interests. An analysis of log files from three search providers, covering a range of scenari...", "weights": [[8, 20, 1.0], [21, 27, 1.0], [158, 164, 1.0]]}, "2021.wsdm_conference-2021.144": {"snippet": "Personalization in Practice: Methods and Applications\n\n\n Personalization is one of the key applications in machine learning with widespread usage across e-commerce, entertainment, production, healthca...", "weights": []}, "2017.sigirconf_conference-2017.67": {"snippet": "...duct Search\n\n\n ABSTRACTProduct search is an important part of online shopping. In contrast to many search tasks, the objectives of product search are not con ned to retrieving relevant products. Inste...", "weights": [[8, 14, 1.0], [34, 40, 1.0], [102, 108, 1.0], [142, 148, 1.0]]}, "2014.wsdm_conference-2014.26": {"snippet": "Taxonomy discovery for personalized recommendation\n\n\n ABSTRACTPersonalized recommender systems based on latent factor models are widely used to increase sales in e-commerce. Such systems use the past ...", "weights": []}, "2011.wsdm_conference-2011.9": {"snippet": "... web search using long term browsing history\n\n\n ABSTRACTPersonalizing web search results has long been recognized as an avenue to greatly improve the search experience. We present a personalization ap...", "weights": [[8, 14, 1.0], [77, 83, 1.0], [153, 159, 1.0]]}, "2017.chiir_conference-2017.32": {"snippet": "...ng a personalised query suggestion framework for Intranet search. For each search session, we construct two temporal user profiles: a click user profile using the user's clicked documents and a query ...", "weights": [[8, 20, 1.0], [61, 67, 1.0], [78, 84, 1.0]]}, "2013.cikm_conference-2013.157": {"snippet": "...erce search\n\n\n ABSTRACTCommerce search engines allow users to discover products, learn about them, and, importantly, make purchases. Commerce search is a challenging problem -one that is very differen...", "weights": [[8, 14, 1.0], [35, 41, 1.0], [145, 151, 1.0]]}, "2020.wwwconf_conference-2020c.44": {"snippet": "...erce Search\n\n\n We address the problem of personalization in the context of eCommerce search. Specifically, we develop personalization ranking features that use in-session context to augment a generic ...", "weights": [[8, 14, 1.0], [88, 94, 1.0]]}, "2020.sigirconf_conference-2020.139": {"snippet": "...erce search. We present the first large-scale and in-depth study of query reformulations performed by users of e-commerce search; the study is based on the query logs of eBay's search engine. We analy...", "weights": [[8, 14, 1.0], [125, 131, 1.0], [180, 186, 1.0]]}, "2020.sigirconf_conference-2020.352": {"snippet": "...erce Search via Embedding Learning\n\n\n Nowadays e-commerce search has become an integral part of many people's shopping routines. Two critical challenges stay in today's e-commerce search: how to retri...", "weights": [[8, 14, 1.0], [61, 67, 1.0], [183, 189, 1.0]]}, "2020.ecir_conference-20201.43": {"snippet": "...erce search engines consists of infrequent queries and identifying user intent in such tail queries is critical in retrieving relevant products. The intent of a query is defined as a labelling of its ...", "weights": [[8, 14, 1.0]]}, "2019.sigirconf_workshop-2019ecom.4": {"snippet": "... web search, query classification (QC) is used to map a query to a user's search intent. In the e-commerce domain, user's product search queries can be broadly categorised into product specific querie...", "weights": [[8, 14, 1.0], [77, 83, 1.0], [133, 139, 1.0]]}, "2016.clef_conference-2016.14": {"snippet": "...erce Search\n\n\n Abstract. During the online shopping process, users search for interesting products in order to quickly access those that fit with their needs among a long tail of similar or closely re...", "weights": [[8, 14, 1.0], [70, 76, 1.0]]}, "2017.clef_conference-2017w.51": {"snippet": "...n of personalised information retrieval (PIR). The PIR-CLEF 2017 Lab provides a preliminary pilot edition of a Lab task dedicated to personalised search, while the workshop at the conference is intend...", "weights": [[8, 20, 1.0], [136, 148, 1.0], [149, 155, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A1.7": {"snippet": "Report on the CHIIR 2018 Workshop on Evaluation of Personalisation in Information Retrieval (WEPIR 2018)", "weights": []}, "2018.sigirconf_conference-2018.182": {"snippet": "...erce Search\n\n\n ABSTRACTUnderstanding the search tasks and search behavior of users is necessary for optimizing search engine results. While much work has been done on understanding the users in Web se...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [61, 67, 1.0], [114, 120, 1.0], [201, 207, 1.0]]}, "2009.wwwconf_conference-2009.130": {"snippet": "Rare item detection in e-commerce site", "weights": []}, "2021.chiir_workshop-2021birds.10": {"snippet": "Information Retrieval Evaluation in Knowledge Acquisition Tasks", "weights": []}, "2018.wwwconf_conference-2018.189": {"snippet": "...ored Search\n\n\n ABSTRACTOn most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding ...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [135, 141, 1.0]]}, "2013.wwwconf_conference-2013c.143": {"snippet": "Context mining and integration into predictive web analytics\n\n\n ABSTRACTPredictive Web Analytics is aimed at understanding behavioural patterns of users of various web-based applications: e-commerce, ...", "weights": []}, "2020.wwwconf_conference-2020.244": {"snippet": "Review-guided Helpful Answer Identification in E-commerce", "weights": []}, "2009.cikm_conference-2009.278": {"snippet": "...ween search sessions to support multi-session search tasks. This approach to supporting users with their multi-session search tasks is evaluated in a between-subjects multiple time-series study with t...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [122, 128, 1.0]]}, "2018.sigirconf_conference-2018.39": {"snippet": "...duct search. There are several intrinsic diferences between web search and product search that make the direct application of traditional search ranking algorithms to EC search platforms diicult. Firs...", "weights": [[8, 14, 1.0], [67, 73, 1.0], [86, 92, 1.0], [141, 147, 1.0], [173, 179, 1.0]]}, "2018.chiir_conference-2018.70": {"snippet": "...n of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmi...", "weights": [[8, 20, 1.0], [108, 114, 1.0], [148, 160, 1.0]]}, "2018.cikm_conference-2018.21": {"snippet": "...onal Search and Recommendation: System Ask, User Respond\n\n\n ABSTRACTConversational search and recommendation based on user-system dialogs exhibit major differences from conventional search and recomme...", "weights": [[8, 14, 1.0], [86, 92, 1.0], [185, 191, 1.0]]}}}
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Ensemble learning can be employed to obtain more ...", "weights": [[8, 17, 1.0], [61, 69, 1.0]]}, "2020.lrec-1.617": {"snippet": "...ased Sentiment Analysis (ABSA) is an area of sentiment analysis which deals with sentiment at a finer level. ABSA classifies sentiment with respect to each aspect to gain greater insights into the sen...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [48, 57, 1.0], [58, 66, 1.0], [84, 93, 1.0], [128, 137, 1.0], [200, 209, 1.0]]}, "O16-1020": {"snippet": "...ting Sentiment Scores of Verb Phrases for {V}ietnamese\n\n\n Sentiment analysis is an emerging research field. One of the major tasks of sentiment analysis is building sentiment lexicons and calculating ...", "weights": [[8, 17, 1.0], [61, 70, 1.0], [71, 79, 1.0], [137, 146, 1.0], [147, 155, 1.0], [168, 177, 1.0]]}, "2014.cikm_workshop-2014dtmbio.21": {"snippet": "...sion analysis or network analysis. Meanwhile, the activity in dryexperiment rather than expression in wet-experiment have been proposed to effectively estimate the actual regulation power of such impo...", "weights": [[8, 16, 1.0], [28, 36, 1.0]]}, "L16-1182": {"snippet": "...weet sentiment analysis in particular. One existing and two newly created corpora are used, and the performance of four different sentiment analysis systems is reported; we make our annotated datasets...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [133, 142, 1.0], [143, 151, 1.0]]}, "2020.sigirconf_conference-2020.131": {"snippet": "...pect-Sentiment Analysis with Minimal User Guidance\n\n\n Aspect-based sentiment analysis is a substantial step towards text understanding which benefits numerous applications. Since most existing algorit...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [70, 79, 1.0], [80, 88, 1.0]]}, "2021.emnlp-main.22": {"snippet": "...icit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training\n\n\n Aspect-based sentiment analysis aims to identify the sentiment polarity of a specific aspect in product re...", "weights": [[8, 17, 1.0], [34, 43, 1.0], [44, 52, 1.0], [110, 119, 1.0], [120, 128, 1.0], [150, 159, 1.0]]}, "P12-1105": {"snippet": "... for Sentiment Dictionaries\n\n\n Polarity classification of words is important for applications such as Opinion Mining and Sentiment Analysis. A number of sentiment word/sense dictionaries have been man...", "weights": [[8, 17, 1.0], [124, 133, 1.0], [134, 142, 1.0], [156, 165, 1.0]]}, "2012.ecir_conference-2012.36": {"snippet": "...s of Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2018.mir_workshop-2018mmart.0": {"snippet": "...orks Analysis and Attractiveness Computing in Multimedia, MMArt&ACM@ICMR 2018, Yokohama, Japan, June 11, 2018", "weights": [[8, 16, 1.0]]}, "2014.sigirconf_workshop-2014somera.9": {"snippet": "...usic analysis and creation within the giantsteps project\n\n\n ABSTRACTGiantSteps is an EU-funded project that aims at developing the next generation of music composition tools for the creative industrie...", "weights": [[8, 16, 1.0]]}, "W13-5502": {"snippet": "... for Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2016.ipm_journal-ir0volumeA52A1.1": {"snippet": "... for sentiment analysis of Twitter\n\n\n a b s t r a c tSentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this pa...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [66, 74, 1.0]]}, "E17-3019": {"snippet": "...tif: Sentiment Analysis for the Digital Humanities\n\n\n Lingmotif is a lexicon-based, linguistically-motivated, user-friendly, GUI-enabled, multi-platform, Sentiment Analysis desktop application. Lingmo...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [157, 166, 1.0], [167, 175, 1.0]]}, "S13-2069": {"snippet": "... for Sentiment Analysis in {T}witter\n\n\n This paper presents the contribution of our team at task 2 of SemEval 2013: Sentiment Analysis in Twitter. We submitted a constrained run for each of the two su...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [119, 128, 1.0], [129, 137, 1.0]]}, "W17-7515": {"snippet": "...ets) sentiment text when sentiment classes are limited to two/three. Furthermore, when the sentiment analysis task turn into a kind of multi-class classifiction instead of binary on large corpora, dee...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [94, 103, 1.0], [104, 112, 1.0]]}, "W12-3701": {"snippet": "...odal Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "I11-1131": {"snippet": "... for Sentiment Analysis\n\n\n Existing research on sentiment analysis mainly uses sentiment words and phrases to determine sentiments expressed in documents and sentences. Techniques have also been devel...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [51, 60, 1.0], [61, 69, 1.0], [82, 91, 1.0]]}, "N15-2007": {"snippet": "...evel Sentiment Detection and Inference\n\n\n Most of the work in sentiment analysis and opinion mining focuses on extracting explicit sentiments. Opinions may be expressed implicitly via inference rules ...", "weights": [[8, 17, 1.0], [65, 74, 1.0], [75, 83, 1.0]]}, "2012.wwwconf_conference-2012c.82": {"snippet": "...s of sentiment analysis in text require an understanding of what kinds of sentiment-bearing language are generally used to describe specific topics. Thus, fine-grained sentiment analysis requires both...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [77, 86, 1.0], [171, 180, 1.0], [181, 189, 1.0]]}, "S13-2081": {"snippet": "... for sentiment classification\n\n\n We evaluate a naive machine learning approach to sentiment classification focused on Twitter in the context of the sentiment analysis task of SemEval-2013. We employ a...", "weights": [[8, 17, 1.0], [85, 94, 1.0], [151, 160, 1.0], [161, 169, 1.0]]}, "2011.ipm_journal-ir0volumeA47A4.10": {"snippet": "... for sentiment analysis\n\n\n a b s t r a c tSentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [55, 63, 1.0], [105, 114, 1.0]]}, "L10-1623": {"snippet": "Sentiment Analysis in the News", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2020.ccl-1.61": {"snippet": "...ased sentiment analysis. This work discussed and analyzed the appearance, characteristics, and sentiment marking of repetition in text, with a focus on the analysis of the repetition of words and repe...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [98, 107, 1.0], [159, 167, 1.0]]}, "W15-1829": {"snippet": "Sentiment analysis on conversational texts\n\n\n This paper describes ongoing work related to the analysis of spoken utterance transcripts and estimating the speaker's attitude towards the whole dialogue...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [95, 103, 1.0]]}, "2018.wwwconf_conference-2018c.330": {"snippet": "...just sentiment analysis techniques to automatically detect customer emotion in on-line service interactions of multiple business domains. Then we use the adjusted sentiment analysis tool to report ins...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [166, 175, 1.0], [176, 184, 1.0]]}, "2015.cikm_workshop-2015dtmbio.14": {"snippet": "... and Analysis of Medical Entity Co-occurrences in Twitter\n\n\n ABSTRACTTwitter is an attractive source of data for public health surveillance, as it is less hindered by the legal and technical obstacles...", "weights": [[8, 16, 1.0]]}, "W16-0411": {"snippet": "...efit Sentiment Analysis? A Holistic Approach to Sentiment Analysis\n\n\n Existing opinion analysis techniques rely on the clues within the sentence that focus on the sentiment analysis task itself. Howev...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [51, 60, 1.0], [61, 69, 1.0], [90, 98, 1.0], [166, 175, 1.0], [176, 184, 1.0]]}, "P11-4009": {"snippet": "Dr Sentiment Knows Everything!\n\n\n Sentiment analysis is one of the hot demanding research areas since last few decades. Although a formidable amount of research have been done, the existing reported s...", "weights": [[3, 12, 1.0], [34, 43, 1.0], [44, 52, 1.0]]}, "2012.coria_conference-2012.23": {"snippet": "...n in Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2018.wwwconf_conference-2018c.379": {"snippet": "...cial Sentiment Analysis using Deep Learning\n\n\n ABSTRACTAspect based sentiment analysis aims to detect an aspect (i.e. features) in a given text and then perform sentiment analysis of the text with res...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [71, 80, 1.0], [81, 89, 1.0], [164, 173, 1.0], [174, 182, 1.0]]}, "2022.findings-acl.109": {"snippet": "Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with {ASR} Errors\n\n\n Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed...", "weights": [[0, 9, 1.0], [58, 67, 1.0], [68, 76, 1.0], [109, 118, 1.0], [119, 127, 1.0]]}, "2018.ecir_workshop-2018brodyn.4": {"snippet": "...ased Analysis of Refugees in Turkey\n\n\n In this paper, we propose a method to find out public social media accounts of refugees, and trace them back to infer temporal and spatial events.These data will...", "weights": [[8, 16, 1.0]]}, "J16-3008": {"snippet": "...dly. Sentiment analysis, the topic studying such subjective feelings expressed in text, has attracted significant attention from both the research community and industry. Although we have known sentim...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [197, 206, 1.0]]}, "2020.ecir_workshop-2020text2story.7": {"snippet": "...dict sentiment of narrative text however despite much work being carried out to date on sentiment analysis of social media and user feedback. In this paper, we present an approach to sentiment analysi...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [101, 109, 1.0], [186, 195, 1.0], [196, 204, 1.0]]}, "2018.ecir_workshop-2018brodyn.2": {"snippet": "... the sentiment analysis around specific aspects of a resource. Firstly, certain aspects are identified, according to the distributions of the emotional terms in the vicinity of the most frequent names...", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "W10-3111": {"snippet": "...n in sentiment analysis. We will present various computational approaches modeling negation in sentiment analysis. We will, in particular, focus on aspects, such as level of representation used for se...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [98, 107, 1.0], [108, 116, 1.0], [201, 210, 1.0]]}, "2008.ntcir_workshop-2008.27": {"snippet": "...nion Analysis Task\n\n\n The paper presents our work in the multilingual opinion analysis task in NTCIR7 in Simplified Chinese. In detecting opinionated sentences, an EM algorithm was proposed to extract...", "weights": [[8, 16, 1.0], [81, 89, 1.0]]}, "W19-3717": {"snippet": "Sentiment Analysis for Multilingual Corpora\n\n\n The paper presents a generic approach to the supervised sentiment analysis of social media content in foreign languages. The method proposes translating ...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [103, 112, 1.0], [113, 121, 1.0]]}, "C16-3002": {"snippet": "...ring sentiment and opinions are one of the major categories of user generated data in social media. Sentiment analysis is the main technology to quickly capture what people think from these text data,...", "weights": [[8, 17, 1.0], [103, 112, 1.0], [113, 121, 1.0]]}, "2018.sigirconf_conference-2018.20": {"snippet": "Sentiment Analysis of Peer Review Texts for Scholarly Papers\n\n\n ABSTRACTSentiment analysis has been widely explored in many text domains, including product reviews, movie reviews, tweets, and so on. H...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [82, 90, 1.0]]}, "2022.acl-srw.39": {"snippet": "...eted sentiment analysis. The results demonstrate that the proposed models outperform the traditional sentiment analysis models for the targeted sentiment analysis task.", "weights": [[8, 17, 1.0], [18, 26, 1.0], [104, 113, 1.0], [114, 122, 1.0], [147, 156, 1.0], [157, 165, 1.0]]}, "R15-1002": {"snippet": "...ased Sentiment Analysis for {P}ersian Text", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "O12-5003": {"snippet": "...rity Analysis for Sentiment Classification) [In {C}hinese]\n\n\n The researches of sentiment analysis aim at exploring the emotional state of writers. The analysis highly depends on the application domai...", "weights": [[8, 16, 1.0], [21, 30, 1.0], [83, 92, 1.0], [93, 101, 1.0], [155, 163, 1.0]]}, "W14-2619": {"snippet": "... for Sentiment Analysis\n\n\n This paper illustrates the use of deep semantic processing for sentiment analysis. Existing methods for sentiment analysis use supervised approaches which take into account ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [93, 102, 1.0], [103, 111, 1.0], [134, 143, 1.0], [144, 152, 1.0]]}, "2013.cikm_workshop-2013unlp.7": {"snippet": "Sentiment analysis of sentences with modalities\n\n\n ABSTRACTThis paper is concerned with sentiment analysis of sentences with modality. Modality is a commonly occuring linguistic phenomenon. Due to its...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [88, 97, 1.0], [98, 106, 1.0]]}, "N18-1171": {"snippet": "Sentiment Analysis: It{'}s Complicated!\n\n\n Sentiment analysis is used as a proxy to measure human emotion, where the objective is to categorize text according to some predefined notion of sentiment. S...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [43, 52, 1.0], [53, 61, 1.0], [188, 197, 1.0], [199, 208, 1.0]]}, "W19-4802": {"snippet": "Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification\n\n\n Neural methods for sentiment analysis have led to quantitative improvements over previous approaches, but these adva...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [56, 65, 1.0], [103, 112, 1.0], [113, 121, 1.0]]}, "D09-1019": {"snippet": "Sentiment Analysis of Conditional Sentences\n\n\n This paper studies sentiment analysis of conditional sentences. The aim is to determine whether opinions expressed on different topics in a conditional s...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [66, 75, 1.0], [76, 84, 1.0]]}, "S13-2078": {"snippet": "... for Sentiment Analysis in {T}witter\n\n\n Sentiment analysis refers to automatically extracting the sentiment present in a given natural language text. We present our participation to the SemEval2013 co...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [43, 52, 1.0], [53, 61, 1.0], [101, 110, 1.0]]}, "2014.cikm_conference-2014.109": {"snippet": "...pect/Sentiment Model for Sentiment Analysis\n\n\n ABSTRACTSentiment analysis in various languages has been a research hotspot with many applications. However, sentiment resources (e.g., labeled corpora, ...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [38, 46, 1.0], [68, 76, 1.0], [159, 168, 1.0]]}, "2011.airs_conference-2011.33": {"snippet": "Sentiment Analysis for Online Reviews Using an Author-Review-Object Model", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2018.sigirconf_conference-2018.170": {"snippet": "... for Sentiment Analysis\n\n\n ABSTRACTAnalyzing public opinions towards products, services and social events is an important but challenging task. Despite the remarkable successes of deep neural networks...", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "D14-1053": {"snippet": "Sentiment Analysis on the People{'}s Daily\n\n\n We propose a semi-supervised bootstrapping algorithm for analyzing China's foreign relations from the People's Daily. Our approach addresses sentiment tar...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [187, 196, 1.0]]}, "2018.sigirconf_conference-2018.103": {"snippet": "...nts, sentiment analysis as a core area of social media analytics has gone beyond traditional text-based analysis. Multimodal sentiment analysis has become an important research topic in recent years. ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [107, 115, 1.0], [128, 137, 1.0], [138, 146, 1.0]]}, "2012.wsdm_conference-2012.65": {"snippet": "...cale sentiment analysis for Yahoo! answers", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2015.ipm_journal-ir0volumeA51A4.6": {"snippet": "Sentiment analysis meets social media - Challenges and solutions of the field in view of the current information sharing context", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2013.tweb_journal-ir0volumeA8A1.5": {"snippet": "...epth analysis of Web search queries for controversial topics, focusing on query sentiment. To this end, we conduct extensive user assessments and discriminative term analyses, as well as a sentiment a...", "weights": [[8, 16, 1.0], [83, 92, 1.0], [192, 201, 1.0], [202, 210, 1.0]]}, "P09-1029": {"snippet": "... for Sentiment Analysis\n\n\n This paper describes an approach to utilizing term weights for sentiment analysis tasks and shows how various term weighting schemes improve the performance of sentiment ana...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [93, 102, 1.0], [103, 111, 1.0], [190, 199, 1.0], [200, 208, 1.0]]}, "2015.wwwconf_conference-2015c.41": {"snippet": "... the sentiment of posts in online health communities enables the investigation of various factors such as what affects the sentiment change and discovery of sentiment change patterns. Since each write...", "weights": [[8, 17, 1.0], [126, 135, 1.0], [160, 169, 1.0]]}, "S16-1054": {"snippet": "...ased Sentiment Analysis\n\n\n This paper gives the description of the TGB system submitted to the Aspect Based Sentiment Analysis Task of SemEval-2016 (Task 5). The system is built on linear binary class...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [111, 120, 1.0], [121, 129, 1.0]]}, "2016.tois_journal-ir0volumeA35A1.6": {"snippet": "...s in Sentiment Analysis\n\n\n This article introduces a sentiment analysis approach that adopts the way humans read, interpret, and extract sentiment from text. Our motivation builds on the assumption th...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [56, 65, 1.0], [66, 74, 1.0], [140, 149, 1.0]]}, "C18-1085": {"snippet": "...e of sentiment analysis. Our proposed method is based on a convolutional neural network and an external sentiment lexicon. Experiments on four popular sentiment analysis datasets show that this method...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [107, 116, 1.0], [154, 163, 1.0], [164, 172, 1.0]]}, "2017.ijirr_journal-ir0volumeA7A4.0": {"snippet": "...line Sentiment Analysis: Issues With Sentiment Analysis of Online Micro-Texts", "weights": [[8, 17, 1.0], [18, 26, 1.0], [40, 49, 1.0], [50, 58, 1.0]]}, "2015.ipm_journal-ir0volumeA51A2.12": {"snippet": "...form sentiment analysis, there is no publicly available interactive on-line platform for dynamic adaptive sentiment analysis, which would be able to handle changes in data streams and adapt its behavi...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [109, 118, 1.0], [119, 127, 1.0]]}, "2020.aacl-srw.12": {"snippet": "...tes, sentiment analysis has gained more importance. Aspect Based Sentiment Analysis (ABSA) is a task of identifying the sentiment at aspect level. It has more importance than sentiment analysis from c...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [68, 77, 1.0], [78, 86, 1.0], [123, 132, 1.0], [178, 187, 1.0], [188, 196, 1.0]]}, "W14-3010": {"snippet": "...s in Sentiment Analysis\n\n\n Sentiment Analysis, an important area of Natural Language Understanding, often relies on the assumption that lexemes carry inherent sentiment values, as reflected in special...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [30, 39, 1.0], [40, 48, 1.0], [162, 171, 1.0]]}, "2016.wsdm_conference-2016.2": {"snippet": "...like sentiment, self-disclosure, and responsiveness) to address these dynamics. In the prediction phase, a tweet posted by a user is deemed a query and the predicted repliers/retweeters are retrieved ...", "weights": [[8, 17, 1.0]]}, "R17-1084": {"snippet": "...n of Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2021.ranlp-1.14": {"snippet": "...ased Sentiment Analysis\n\n\n Recently, the majority of sentiment analysis researchers focus on target-based sentiment analysis because it delivers in-depth analysis with more accurate results as compare...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [56, 65, 1.0], [66, 74, 1.0], [109, 118, 1.0], [119, 127, 1.0], [157, 165, 1.0]]}, "L18-1100": {"snippet": "...ated Sentiment Analysis in {T}elugu (a low resource language) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction\n\n\n Understanding the polarity or sentiment of a text is an importa...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [116, 125, 1.0], [170, 179, 1.0]]}, "L16-1008": {"snippet": "Sentiment Analysis in Social Networks through Topic modeling\n\n\n In this paper, we analyze the sentiments derived from the conversations that occur in social networks. Our goal is to identify the senti...", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2009.jasis_journal-ir0volumeA60A12.7": {"snippet": "Sentiment analysis of Chinese documents: From sentence to document level", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2020.acl-main.374": {"snippet": "...EP}: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis\n\n\n Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge...", "weights": [[8, 17, 1.0], [54, 63, 1.0], [64, 72, 1.0], [86, 95, 1.0], [96, 104, 1.0], [184, 193, 1.0]]}, "L10-1603": {"snippet": "...ards Sentiment Analysis of Financial Texts in {C}roatian", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "D08-1058": {"snippet": "...nese Sentiment Analysis\n\n\n It is a challenging task to identify sentiment polarity of Chinese reviews because the resources for Chinese sentiment analysis are limited. Instead of leveraging only monol...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [67, 76, 1.0], [139, 148, 1.0], [149, 157, 1.0]]}, "S15-2129": {"snippet": "...evel sentiment analysis (Pontiki et al., 2015) , which saw participation from over 25 teams. In Aspectbased Sentiment Analysis, the aim is to identify the aspects of entities and the sentiment express...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [111, 120, 1.0], [121, 129, 1.0], [186, 195, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A4.11": {"snippet": "... and sentiment at message level, is addressed by proposing a novel fully unsupervised generative model. The experimental results show that the proposes sentiment analysis models grounded on Approval N...", "weights": [[8, 17, 1.0], [155, 164, 1.0], [165, 173, 1.0]]}, "W11-1707": {"snippet": "...s of Sentiment in Text Based on Commonsense Knowledge\n\n\n Sentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. Most existing approaches are based on word-level...", "weights": [[8, 17, 1.0], [60, 69, 1.0], [70, 78, 1.0]]}, "2015.sigirconf_workshop-2015sps.5": {"snippet": "Analysis of Sentiment Communities in Online Networks\n\n\n This article reports our experience in developing a recommender system (RS) able to suggest relevant people to the target user. Such a RS relies...", "weights": [[0, 8, 1.0], [12, 21, 1.0]]}, "2016.sigirconf_conference-2016.170": {"snippet": "... for sentiment prediction or to detect the possible reasons of a sentiment change. Understanding topic and sentiment evolution allows enterprises or government to capture negative sentiment and act pr...", "weights": [[8, 17, 1.0], [68, 77, 1.0], [110, 119, 1.0], [183, 192, 1.0]]}, "2021.findings-acl.320": {"snippet": "...in a Sentiment Lexicon\n\n\n Sentiment lexicons are instrumental for sentiment analysis. One can use a set of sentiment words provided in a sentiment lexicon and a lexicon-based classifier to perform sen...", "weights": [[8, 17, 1.0], [29, 38, 1.0], [69, 78, 1.0], [79, 87, 1.0], [110, 119, 1.0], [140, 149, 1.0], [200, 209, 1.0]]}, "2020.mir_workshop-2020mmart.7": {"snippet": "...orks Analysis", "weights": [[8, 16, 1.0]]}, "2009.cikm_conference-2009.41": {"snippet": "...oint sentiment/topic model for sentiment analysis\n\n\n ABSTRACTSentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings...", "weights": [[8, 17, 1.0], [34, 43, 1.0], [44, 52, 1.0], [74, 82, 1.0]]}, "2019.ipm_journal-ir0volumeA56A2.5": {"snippet": "...abic sentiment analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2014.sigirconf_conference-2014.136": {"snippet": "...evel sentiment labeling with review-level sentiment classification\n\n\n ABSTRACTCurrent approaches for contextual sentiment lexicon construction in phrase-level sentiment analysis assume that the numeri...", "weights": [[8, 17, 1.0], [45, 54, 1.0], [115, 124, 1.0], [162, 171, 1.0], [172, 180, 1.0]]}, "J15-2004": {"snippet": "... for sentiment analysis, we develop a statistical parser to directly analyze the sentiment structure of a sentence. We show that complicated phenomena in sentiment analysis (e.g., negation, intensific...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [84, 93, 1.0], [157, 166, 1.0], [167, 175, 1.0]]}, "W18-3306": {"snippet": "...s of Sentiment Analysis\n\n\n Current multimodal sentiment analysis frames sentiment score prediction as a general Machine Learning task. However, what the sentiment score actually represents has often b...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [49, 58, 1.0], [59, 67, 1.0], [75, 84, 1.0], [156, 165, 1.0]]}, "P17-1081": {"snippet": "...dent Sentiment Analysis in User-Generated Videos\n\n\n Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research consider...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [66, 75, 1.0], [76, 84, 1.0]]}, "W13-2403": {"snippet": "...ting Sentiment Analysis Systems in {R}ussian\n\n\n In this paper we describe our experience in conducting the first open sentiment analysis evaluations in Russian in 2011-2012. These initiatives took par...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [121, 130, 1.0], [131, 139, 1.0]]}, "2015.wsdm_conference-2015.62": {"snippet": "...evel Sentiment Analysis\n\n\n ABSTRACTIn this paper, we propose a representation learning research framework for document-level sentiment analysis. Given a document as the input, document-level sentiment...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [128, 137, 1.0], [138, 146, 1.0], [194, 203, 1.0]]}, "W11-0705": {"snippet": "Sentiment Analysis of {T}witter Data\n\n\n We examine sentiment analysis on Twitter data. The contributions of this paper are: (1) We introduce POS-specific prior polarity features. (2) We explore the us...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [51, 60, 1.0], [61, 69, 1.0]]}, "2016.wsdm_conference-2016.3": {"snippet": "...tual sentiment analysis techniques. We first fine-tune a convolutional neural network (CNN) for image sentiment analysis and train a paragraph vector model for textual sentiment analysis. On top of th...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [105, 114, 1.0], [115, 123, 1.0], [171, 180, 1.0], [181, 189, 1.0]]}, "W19-6120": {"snippet": "...ased Sentiment Analysis using {BERT}\n\n\n Sentiment analysis has become very popular in both research and business due to the vast amount of opinionated text currently produced by Internet users. Standa...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [43, 52, 1.0], [53, 61, 1.0]]}, "W15-4202": {"snippet": "...odal Sentiment and Emotion Analysis\n\n\n The number of tools and services for sentiment analysis is increasing rapidly. Unfortunately, the lack of standard formats hinders interoperability. To tackle th...", "weights": [[8, 17, 1.0], [30, 38, 1.0], [79, 88, 1.0], [89, 97, 1.0]]}, "L08-1619": {"snippet": "Sentiment Analysis Based on Probabilistic Models Using Inter-Sentence Information", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2006.sigirconf_conference-2006.144": {"snippet": "... of 'sentiment analysis' seeks to characterize such aspects of natural language text, as opposed to just the bare facts. We suggest that 'appraisal expression extraction' should be viewed as a fundame...", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "S14-2042": {"snippet": "... and sentiment analysis. In this paper, we describe our approaches used for sentiment analysis in twitter (task 9) organized in SemEval 2014. This task tries to determine whether the sentiment orienta...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [79, 88, 1.0], [89, 97, 1.0], [186, 195, 1.0]]}, "W10-2919": {"snippet": "...evel sentiment analysis. The aim is to determine whether a sentence expresses a positive, negative or neutral sentiment, as well as its intensity. The method performs WSD over the words in the sentenc...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [113, 122, 1.0]]}, "2014.wwwconf_conference-2014c.41": {"snippet": "...evel sentiment analysis: a world wide web conference 2014 tutorial", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2012.jasis_journal-ir0volumeA63A12.12": {"snippet": "Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "R17-1041": {"snippet": "...e on Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "L12-1239": {"snippet": "...zing Sentiment Analysis on a User Forum", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2012.sigirconf_conference-2012.101": {"snippet": "...a of Sentiment Analysis . An important task is the classification of documents according to the overall sentiment, i.e. whether positive or negative. A common behaviour among reviewers is to summarise...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [107, 116, 1.0]]}, "2013.cikm_conference-2013.122": {"snippet": "...tent sentiment explanations for aspect-level review analysis\n\n\n ABSTRACTIn sentiment analysis, aspect-level review analysis has been an important task because it can catalogue, aggregate, or summarize...", "weights": [[8, 17, 1.0], [55, 63, 1.0], [78, 87, 1.0], [88, 96, 1.0], [118, 126, 1.0]]}, "W16-0426": {"snippet": "...eper Sentiment Analysis", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "L08-1395": {"snippet": "Sentiment Analysis and the Use of Extrinsic Datasets in Evaluation", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2017.ipm_journal-ir0volumeA53A3.2": {"snippet": "...ised sentiment analysis in multilingual environments", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2014.sigirconf_workshop-2014somera.10": {"snippet": "...irst analysis within the PHENICX project: \"performances as highly enriched aNd interactive concert eXperiences\"\n\n\n ABSTRACTIn the ongoing EU-FP7 project \"Performances as Highly Enriched aNd Interactiv...", "weights": [[8, 16, 1.0]]}, "P12-4004": {"snippet": "... and Sentiment Analysis\n\n\n Subjectivity and sentiment analysis focuses on the automatic identification of private states, such as opinions, emotions, sentiments, evaluations, beliefs, and speculations...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [47, 56, 1.0], [57, 65, 1.0]]}, "2020.rocling-1.35": {"snippet": "...base sentiment analysis (ABSA) has been proposed to explore the sentiment categories of a target in different aspects. In this paper, we explore the idea of ABSA for sentiment analysis of singers on s...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [67, 76, 1.0], [169, 178, 1.0], [179, 187, 1.0]]}, "2020.sigirconf_conference-2020.212": {"snippet": "...ring Sentiment Analysis\n\n\n Question-answering sentiment analysis (QASA) is a novel but meaningful sentiment analysis task based on question-answering online reviews. Existing neural network-based mode...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [49, 58, 1.0], [59, 67, 1.0], [101, 110, 1.0], [111, 119, 1.0]]}, "2017.ipm_journal-ir0volumeA53A4.1": {"snippet": "...tter sentiment analysis using hybrid cuckoo search method\n\n\n a b s t r a c tSentiment analysis is one of the prominent fields of data mining that deals with the identification and analysis of sentimen...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [89, 97, 1.0], [183, 191, 1.0]]}, "D17-1115": {"snippet": "...odal Sentiment Analysis\n\n\n Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [41, 50, 1.0], [51, 59, 1.0], [162, 171, 1.0], [172, 180, 1.0]]}, "2020.rocling-1.26": {"snippet": "...ars. Sentiment analysis is a useful technique to accomplish the investment psychology. In sentiment analysis, there are two main approaches to emotion state representation: categorical and dimensional...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [93, 102, 1.0], [103, 111, 1.0]]}, "2011.cikm_conference-2011.128": {"snippet": "...ment analysis has applications in many areas and the exploration of its potential has only just begun. We propose Pathos, a framework which performs document sentiment analysis (partly) based on a doc...", "weights": [[8, 16, 1.0], [161, 170, 1.0], [171, 179, 1.0]]}, "2020.ecnlp-1.4": {"snippet": "... for Sentiment Analysis of Service Calls\n\n\n Sentiment analysis is crucial for the advancement of artificial intelligence (AI). Sentiment understanding can help AI to replicate human language and disco...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [47, 56, 1.0], [57, 65, 1.0], [130, 139, 1.0]]}, "2016.ecir_conference-2016.74": {"snippet": "...edia Sentiment Analysis Says About City Liveability", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2017.cikm_conference-2017.307": {"snippet": "...odal Sentiment Analysis\n\n\n ABSTRACTWith the prevalence of more diverse and multiform user-generated content in social networking sites, multimodal sentiment analysis has become an increasingly importa...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [150, 159, 1.0], [160, 168, 1.0]]}, "L14-1406": {"snippet": "...tter Sentiment Analysis Tools", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2021.emnlp-main.551": {"snippet": "...icit Sentiment Analysis with Event-centered Text Representation\n\n\n Implicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive resear...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [79, 88, 1.0], [89, 97, 1.0], [123, 132, 1.0], [155, 164, 1.0]]}}}
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Using world knowledge to inform a model, and ...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [92, 98, 1.0], [99, 107, 1.0], [158, 163, 1.0]]}, "2017.ismir_conference-2017.5": {"snippet": "...usic Using Convolutional Neural Networks", "weights": [[8, 13, 1.0], [28, 34, 1.0], [35, 43, 1.0]]}, "1991.sigirconf_conference-91.21": {"snippet": "Using the Cosine Measure in a Neural Network for Document\n\n\n AbstractThe task of document retrieval systems is to match one natural language query against a large number of natural language documents....", "weights": [[0, 5, 1.0], [30, 36, 1.0], [90, 99, 1.0]]}, "2018.cikm_conference-2018.120": {"snippet": "...tity retrieval from knowledge graphs indicates superior retrieval accuracy of the proposed neural architecture relative to both state-of-the-art neural architectures for ad-hoc document retrieval and ...", "weights": [[8, 17, 1.0], [59, 68, 1.0], [94, 100, 1.0], [148, 154, 1.0], [189, 198, 1.0]]}, "W10-3505": {"snippet": "... for using crowd-sourcing techniques to label informative and non-informative information regarding the sentiment expressed in a sentence. We also show that pruning non-informative information using n...", "weights": [[8, 13, 1.0], [196, 201, 1.0]]}, "2016.mir_conference-2016.44": {"snippet": "...mage Retrieval with Top-Weighted Convolutional Neural Networks", "weights": [[8, 17, 1.0], [50, 56, 1.0], [57, 65, 1.0]]}, "2019.ecir_conference-20192.26": {"snippet": "Deep Neural Networks for Query Expansion Using Word Embeddings\n\n\n Abstract. Query expansion is a method for alleviating the vocabulary mismatch problem present in information retrieval tasks. Previous...", "weights": [[5, 11, 1.0], [12, 20, 1.0], [41, 46, 1.0], [175, 184, 1.0]]}, "2020.cikm_conference-2020.432": {"snippet": "...tors using Semantically Informed Neural Networks", "weights": [[8, 13, 1.0], [36, 42, 1.0], [43, 51, 1.0]]}, "1995.jasis_journal-ir0volumeA46A3.2": {"snippet": "...tion Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms", "weights": [[8, 17, 1.0], [19, 25, 1.0], [26, 34, 1.0]]}, "W15-2915": {"snippet": "...onal Neural Network\n\n\n Sentiment lexicons and other linguistic knowledge proved to be beneficial in polarity classification. This paper introduces a linguistically informed Convolutional Neural Networ...", "weights": [[8, 14, 1.0], [190, 196, 1.0]]}, "1989.sigirconf_conference-89.4": {"snippet": "A Neural Network for Probabilistic Information Retrieval", "weights": [[2, 8, 1.0], [47, 56, 1.0]]}, "2021.ecir_conference-20211.22": {"snippet": "Using the Hammer only on Nails: A Hybrid Method for Representation-Based Evidence Retrieval for Question Answering\n\n\n Evidence retrieval is a key component of explainable question answering (QA). We a...", "weights": [[0, 5, 1.0], [82, 91, 1.0], [127, 136, 1.0]]}, "2018.fire_conference-2018w.9": {"snippet": "...logy using convolutional neural networks. Convolutional neural networks are a class of neural networks that have proven very effective in areas such as pattern recognition and classification. They are...", "weights": [[8, 13, 1.0], [28, 34, 1.0], [35, 43, 1.0], [59, 65, 1.0], [66, 74, 1.0], [90, 96, 1.0], [97, 105, 1.0]]}, "1995.ipm_journal-ir0volumeA31A6.0": {"snippet": "...nd a Neural Network for Intelligent Information Retrieval", "weights": [[8, 14, 1.0], [51, 60, 1.0]]}, "2015.wwwconf_conference-2015c.329": {"snippet": "...s of neural networks, and their structures. Then we explain the training algorithm via backpropagation, which is a common method of training artificial neural networks including deep neural networks. ...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [155, 161, 1.0], [162, 170, 1.0], [186, 192, 1.0], [193, 201, 1.0]]}, "Q19-1028": {"snippet": "...rent Neural Network Architecture for Multilingual Readability Assessment\n\n\n We present a multiattentive recurrent neural network architecture for automatic multilingual readability assessment. This ar...", "weights": [[8, 14, 1.0], [117, 123, 1.0]]}, "2003.ismir_conference-2003.13": {"snippet": "... and retrieval of melodies using a self-organizing neural network", "weights": [[8, 17, 1.0], [30, 35, 1.0], [54, 60, 1.0]]}, "C16-1084": {"snippet": "... new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention...", "weights": [[8, 14, 1.0], [55, 61, 1.0], [108, 114, 1.0], [115, 123, 1.0], [139, 145, 1.0], [146, 154, 1.0], [174, 180, 1.0]]}, "2017.wwwconf_conference-2017.133": {"snippet": "...deep neural networks, one that matches the query and the document using a local representation, and another that matches the query and the document using learned distributed representations. The two n...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [69, 74, 1.0], [151, 156, 1.0], [202, 210, 1.0]]}, "2016.sigirconf_conference-2016.47": {"snippet": "Hierarchical Random Walk Inference in Knowledge Graphs\n\n\n ABSTRACTRelational inference is a crucial technique for knowledge base population. The central problem in the study of relational inference is...", "weights": []}, "2017.sigirconf_conference-2017.244": {"snippet": "...rch, neural networks have resurfaced as a popular machine learning paradigm in many other directions of research as well, including information retrieval. Recent years have seen neural networks being ...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [147, 156, 1.0], [181, 187, 1.0], [188, 196, 1.0]]}, "2018.sigirconf_conference-2018.13": {"snippet": "Neural Query Performance Prediction using Weak Supervision from Multiple Signals\n\n\n ABSTRACTPredicting the performance of a search engine for a given query is a fundamental and challenging task in inf...", "weights": [[0, 6, 1.0], [36, 41, 1.0]]}, "2005.sigirconf_conference-2005.47": {"snippet": "Using term informativeness for named entity detection\n\n\n ABSTRACTInformal communication (e-mail, bulletin boards) poses a difficult learning environment because traditional grammatical and lexical inf...", "weights": [[0, 5, 1.0]]}, "2018.ictir_conference-2018.13": {"snippet": "...r of Neural Retrieval Models Using NLP Tasks\n\n\n ABSTRACTThe ease of constructing effective neural networks has resulted in a large number of varying architectures iteratively improving performance on ...", "weights": [[8, 14, 1.0], [15, 24, 1.0], [32, 37, 1.0], [94, 100, 1.0], [101, 109, 1.0]]}, "2021.ecir_conference-20211.34": {"snippet": "...h to Neural Multi-label Hierarchical Patent Classification Using Transformers\n\n\n With the aim of facilitating internal processes as well as search applications, patent offices categorize documents int...", "weights": [[8, 14, 1.0], [62, 67, 1.0]]}, "2017.sigirconf_conference-2017.9": {"snippet": "... and using di erent input representations (i.e., from encoding querydocument pairs into dense/sparse vectors to using word embedding representation). We train our networks using tens of millions of tr...", "weights": [[8, 13, 1.0], [115, 120, 1.0], [166, 174, 1.0], [175, 180, 1.0]]}, "2021.ecir_conference-20212.82": {"snippet": "...mely using neural networks algorithms. However, the incorporation of semantics is still scarce. This project proposes that using external semantic sources of knowledge along with the latest state-of-t...", "weights": [[8, 13, 1.0], [14, 20, 1.0], [21, 29, 1.0], [126, 131, 1.0]]}, "2019.wsdm_conference-2019.78": {"snippet": "Neural Demographic Prediction using Search Query\n\n\n ABSTRACTDemographics of online users such as age and gender play an important role in personalized web applications. However, it is difficult to dir...", "weights": [[0, 6, 1.0], [30, 35, 1.0]]}, "2019.rocling-1.5": {"snippet": "...ment Retrieval based on Neural Network and Document Concept Graph)\n\n\n If the search results can consider topics or similar situations, we can find results that are more in line with user's expectation...", "weights": [[8, 17, 1.0], [27, 33, 1.0]]}, "2020.wsdm_conference-2020.126": {"snippet": "...Deep Neural Networks: Challenges and Opportunities\n\n\n One crucial aspect that yet remains fairly unknown while can inform us about the behavior of deep neural networks is their decision boundaries. Tr...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [155, 161, 1.0], [162, 170, 1.0]]}, "2020.wsdm_conference-2020.62": {"snippet": "...cial networks; \u2022 Computing methodologies \u2192 Neural networks;", "weights": [[8, 16, 1.0], [46, 52, 1.0], [53, 61, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.19": {"snippet": "...onal neural network is an effective method to extract high-level semantic features for image database retrieval. To overcome the problem of over-fitting when the number of training samples in dataset ...", "weights": [[8, 14, 1.0], [105, 114, 1.0]]}, "2020.wwwconf_conference-2020c.182": {"snippet": "...onal Neural Network Models for Retrieval-based Question Answering System in E-commerce\n\n\n A key solution to the Information Retrieval (IR)-based question answering (QA) models is to retrieve the most ...", "weights": [[8, 14, 1.0], [34, 43, 1.0], [127, 136, 1.0]]}, "2015.sigirconf_conference-2015.39": {"snippet": "...Deep Neural Networks\n\n\n ABSTRACTLearning a similarity function between pairs of objects is at the core of learning to rank approaches. In information retrieval tasks we typically deal with query-docum...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [153, 162, 1.0]]}, "2018.ictir_conference-2018.23": {"snippet": "...tion Retrieval\n\n\n ABSTRACTNeural network approaches have recently shown to be effective in several information retrieval (IR) tasks. However, neural approaches often require large volumes of training ...", "weights": [[8, 17, 1.0], [114, 123, 1.0], [145, 151, 1.0]]}, "2018.tist_journal-ir0volumeA9A6.8": {"snippet": "...ired Neural Networks\n\n\n Neural networks have become very popular in recent years, because of the astonishing success of deep learning in various domains such as image and speech recognition. In many o...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [27, 33, 1.0], [34, 42, 1.0]]}, "2017.wwwconf_conference-2017c.234": {"snippet": "...rent Neural Networks (RNN) with selfdeveloped association rules in getting the most out of the data for training and evaluation. We aim to test the performance of our approach using human-induced terr...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [179, 184, 1.0]]}, "N15-1092": {"snippet": "...ning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval\n\n\n Methods of deep neural networks (DNNs) have recently demonstrated superior performance on a number o...", "weights": [[8, 13, 1.0], [30, 36, 1.0], [37, 45, 1.0], [90, 99, 1.0], [119, 125, 1.0], [126, 134, 1.0]]}, "1992.sigirconf_conference-92.3": {"snippet": "...of a Retrieval Process", "weights": [[8, 17, 1.0]]}, "2017.sigirconf_conference-2017.187": {"snippet": "...onal Neural Network Models for Answer Selection\n\n\n ABSTRACTIn recent years, neural networks have been applied to many text processing problems. One example is learning a similarity function between pa...", "weights": [[8, 14, 1.0], [79, 85, 1.0], [86, 94, 1.0]]}, "2012.cikm_workshop-2012webkr.1": {"snippet": "...edge retrieval\n\n\n ABSTRACTWeb-scale knowledge retrieval can be enabled by distributed information retrieval; clustering Web clients to a large-scale computing infrastructure for knowledge discovery fr...", "weights": [[8, 17, 1.0], [49, 58, 1.0], [101, 110, 1.0]]}, "1996.cikm_conference-96.18": {"snippet": "...to a Neural Network Model for Information Retrieval", "weights": [[8, 14, 1.0], [45, 54, 1.0]]}, "2020.mir_conference-2020.25": {"snippet": "...n of Neural Network", "weights": [[8, 14, 1.0]]}, "D17-3005": {"snippet": "...nted Neural Networks for Natural Language Processing\n\n\n Memory Augmented Neural Networks (MANNs) can store and read information from an external memory. While the traditional machine learning algorith...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [76, 82, 1.0], [83, 91, 1.0]]}, "2018.wsdm_conference-2018.14": {"snippet": "Neural Graph Learning: Training Neural Networks Using Graphs\n\n\n ABSTRACTLabel propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, h...", "weights": [[0, 6, 1.0], [32, 38, 1.0], [39, 47, 1.0], [48, 53, 1.0], [163, 169, 1.0], [170, 178, 1.0]]}, "2016.sigirconf_conference-2016.219": {"snippet": "...tion retrieval, such as word embedding, recurrent neural networks, and convolutional neural networks. In the second part, we explain how deep learning, particularly representation learning techniques,...", "weights": [[8, 17, 1.0], [53, 59, 1.0], [60, 68, 1.0], [88, 94, 1.0], [95, 103, 1.0]]}, "2019.chiir_conference-2019.21": {"snippet": "...ased Neural Network Model for Click Modeling", "weights": [[8, 14, 1.0]]}, "2018.chiir_conference-2018.2": {"snippet": "...s in informational search sessions. Using crowdsourcing, we recruited 500 distinct users and orchestrated real-world search sessions spanning 10 different topics and information needs. By using scient...", "weights": [[8, 21, 1.0], [39, 44, 1.0], [191, 196, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...mage retrieval using a form of text based representation. Presumably for commercial reasons, it is not possible to find how these representations were generated or how effective the retrieval methods ...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [185, 194, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.4": {"snippet": "...deep neural network-based approach that relies on coarse-grained sentence modelling using a convolutional neural network (CNN) and a recurrent neural network (RNN) model, combined with a specific fine...", "weights": [[8, 14, 1.0], [87, 92, 1.0], [109, 115, 1.0], [146, 152, 1.0]]}, "2020.clef_conference-2020w.22": {"snippet": "...ment Retrieval Using Deep Neural Ranking Models\n\n\n Conversational argument retrieval is the problem of ranking argumentative texts in a collection of focused arguments in order of their relevance to a...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [29, 35, 1.0], [78, 87, 1.0]]}, "2005.clef_workshop-2005.109": {"snippet": "Using Semantic Networks for Geographic Information Retrieval", "weights": [[0, 5, 1.0], [15, 23, 1.0], [51, 60, 1.0]]}, "2020.sigirconf_conference-2020.375": {"snippet": "... and Retrieval with Neural Networks\n\n\n Relevance in ad-hoc retrieval is a fundamental problem of text understanding. Developing neural network methods for this foundational task of Information Retriev...", "weights": [[8, 17, 1.0], [23, 29, 1.0], [30, 38, 1.0], [62, 71, 1.0], [131, 137, 1.0], [196, 205, 1.0]]}, "2016.sigirconf_conference-2016.231": {"snippet": "...p on Neural Information Retrieval\n\n\n KeywordsNeural networks; deep learning; information retrieval Website: http://research.microsoft.com/neuir2016\nMOTIVATIONIn recent years, deep neural networks have...", "weights": [[8, 14, 1.0], [27, 36, 1.0], [55, 63, 1.0], [92, 101, 1.0], [183, 189, 1.0], [190, 198, 1.0]]}, "I05-2024": {"snippet": "...tion Retrieval Capable of Visualization and High Precision\n\n\n We present a neural-network based selforganizing approach that enables visualization of the information retrieval while at the same time i...", "weights": [[8, 17, 1.0], [78, 84, 1.0], [169, 178, 1.0]]}, "2017.ijirr_journal-ir0volumeA7A2.2": {"snippet": "...tion Retrieval Model using Uncertain Confidence's Network", "weights": [[8, 17, 1.0], [24, 29, 1.0]]}, "1997.trec_conference-1997.27": {"snippet": "...tion Retrieval Using Term Relationship Networks", "weights": [[8, 17, 1.0], [18, 23, 1.0], [42, 50, 1.0]]}, "2019.sigirconf_conference-2019.125": {"snippet": "...y of Neural Retrieval Models using DeepSHAP", "weights": [[8, 14, 1.0], [15, 24, 1.0], [32, 37, 1.0]]}, "2012.cikm_conference-2012.221": {"snippet": "...blog retrieval. The results based on the TREC Microblog track show that the proposed quality model, combined with a variety of retrieval models, can improve retrieval performance and is competitive wi...", "weights": [[8, 17, 1.0], [130, 139, 1.0], [160, 169, 1.0]]}, "2021.ecir_conference-20211.24": {"snippet": "...onal neural network (GCN). The medical KG is used to model hierarchy and causality relationships between diseases and symptoms. We have evaluated our proposed method using realistic patient cases. The...", "weights": [[8, 14, 1.0], [169, 174, 1.0]]}, "2015.tois_journal-ir0volumeA34A1.3": {"snippet": "...ding Using Morphological Knowledge\n\n\n Neural network techniques are widely applied to obtain high-quality distributed representations of words (i.e., word embeddings) to address text mining, informati...", "weights": [[8, 13, 1.0], [41, 47, 1.0]]}, "2018.wwwconf_conference-2018c.225": {"snippet": "...with Neural Question-to-Answer Translation for FAQ-based Question Answering\n\n\n ABSTRACTWe propose a novel Frequently Asked Question (FAQ) retrieval technique with a neural query expansion model. With ...", "weights": [[8, 14, 1.0], [141, 150, 1.0], [168, 174, 1.0]]}, "2020.sigirconf_conference-2020.249": {"snippet": "...raph neural network to capture the user-news, user-user and news-news relatedness in the user-news bipartite graphs by modeling the interactions between different users and news. In addition, we propo...", "weights": [[8, 14, 1.0]]}, "2021.ecir_conference-20211.19": {"snippet": "...rned using deep neural networks, for figures. We learn representations using both text and image data and compare different model architectures and loss functions for the task. Furthermore, to overcom...", "weights": [[8, 13, 1.0], [19, 25, 1.0], [26, 34, 1.0], [74, 79, 1.0]]}, "P18-1123": {"snippet": "...eved using a traditional TF-IDF based retrieval model. The retrieved responses are used to create exemplar vectors that are used by the decoder to generate the response. The contribution of each retri...", "weights": [[8, 13, 1.0], [41, 50, 1.0]]}, "2017.ictir_conference-2017.48": {"snippet": "...tion retrieval methods (e.g., TF-IDF) to complex systems that adopt query expansion, knowledge bases and deep neural networks. e goal is to o er an overview of some promising approaches to tackle this", "weights": [[8, 17, 1.0], [113, 119, 1.0], [120, 128, 1.0]]}, "2021.wwwconf_conference-2021.21": {"snippet": "...ment Retrieval using Deep Neural Networks", "weights": [[8, 17, 1.0], [18, 23, 1.0], [29, 35, 1.0], [36, 44, 1.0]]}, "2016.sigirconf_conference-2016.8": {"snippet": "...Deep Neural Networks for Retrieval-Based Human-Computer Conversation System\n\n\n ABSTRACTTo establish an automatic conversation system between humans and computers is regarded as one of the most hardcor...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [28, 37, 1.0]]}, "2013.sigirconf_conference-2013.152": {"snippet": "... the informational utility, which stands for the degree to which a friend satisfies the target user's unfulfilled informational need, called informational friend recommendation. In order to capture us...", "weights": [[8, 21, 1.0], [117, 130, 1.0], [144, 157, 1.0]]}, "2016.iir_workshop-2016.6": {"snippet": "...onal Neural Networks for Image Retrieval", "weights": [[8, 14, 1.0], [15, 23, 1.0], [34, 43, 1.0]]}, "2017.ismir_conference-2017.47": {"snippet": "...onal Neural Networks", "weights": [[8, 14, 1.0], [15, 23, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...eech Retrieval Systems: A Study Design,\" proposes a user study whose goal is to determine the user expectation in this emerging field.The paper \"Speech-Driven Text Retrieval: Using Target IR Collectio...", "weights": [[8, 17, 1.0], [167, 176, 1.0], [178, 183, 1.0]]}, "P15-2033": {"snippet": "...onal Neural Networks for Automatic Resolution of Crossword Puzzles\n\n\n Automatic resolution of Crossword Puzzles (CPs) heavily depends on the quality of the answer candidate lists produced by a retriev...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [196, 205, 1.0]]}, "2020.wsdm_conference-2020.105": {"snippet": "...-End Neural Ad Hoc Retrieval\n\n\n We present Capreolus, a toolkit designed to facilitate end-to-end ad hoc retrieval experiments with neural networks by providing implementations of prominent neural ran...", "weights": [[8, 14, 1.0], [22, 31, 1.0], [108, 117, 1.0], [135, 141, 1.0], [142, 150, 1.0], [193, 199, 1.0]]}, "R19-1116": {"snippet": "...tion retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architec...", "weights": [[8, 17, 1.0], [118, 124, 1.0], [125, 133, 1.0]]}, "2017.cikm_conference-2017.312": {"snippet": "...deep neural networks becomes feasible. We develop and evaluate two neural networks, deep autoencoder and convolutional neural network, in our fraud detection framework. Experimental results on a real ...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [70, 76, 1.0], [77, 85, 1.0], [122, 128, 1.0]]}, "X96-1031": {"snippet": "...tion Retrieval Technology\n\n\n Over the past few years, HNC has developed a neural network based, vector space approach to text retrieval. This approach, embodied in a system called MatchPlus, allows th...", "weights": [[8, 17, 1.0], [77, 83, 1.0], [129, 138, 1.0]]}, "2014.wsdm_conference-2014.10": {"snippet": "... the neural network in RankNet: one of these methods regularize the error back-propagation via a truncated gradient approach, while the other method limits the depth of the back propagation when adapt...", "weights": [[8, 14, 1.0]]}, "2000.sigirconf_conference-2000.40": {"snippet": "...n on neural networks using only positive examples\n\n\n AbstractIn this paper, we show how a simple feedforward neural network can be trained to filter documents when only positive information is availab...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [24, 29, 1.0], [112, 118, 1.0]]}, "2021.ecir_conference-20211.5": {"snippet": "... and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits\n\n\n We study the utility of the lexical translation model (IBM Model 1) for En...", "weights": [[8, 14, 1.0], [58, 67, 1.0]]}, "D08-1108": {"snippet": "... web using a search engine, generate hypotheses of formal equivalents via this data, and rank the hypotheses using a conditional log-linear model. In the log-linear model, we incorporate as feature fu...", "weights": [[8, 13, 1.0], [112, 117, 1.0]]}, "2018.ictir_conference-2018.25": {"snippet": "...rent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), have been widely utilized in sequence representation. However, RNNs neglect variational information a...", "weights": [[8, 14, 1.0], [15, 23, 1.0]]}, "N16-1007": {"snippet": "Neural Network-Based Abstract Generation for Opinions and Arguments\n\n\n We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model...", "weights": [[0, 6, 1.0], [180, 186, 1.0]]}, "2017.fire_conference-2017.1": {"snippet": "...tion Retrieval using a Novel Tripartite Neural Network based Approach\n\n\n ABSTRACTCollective intelligence of the crowds is distilled together in various Community estion Answering (CQA) Services such a...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [43, 49, 1.0]]}, "2017.ecir_workshop-2017bir.7": {"snippet": "...thor networks, citation networks and text models. The three types of information are mixed by building network embeddings and sentence embeddings, which rely on the network topology and context-driven...", "weights": [[8, 16, 1.0], [27, 35, 1.0]]}, "2018.wwwconf_conference-2018c.187": {"snippet": "...cial Networks\n\n\n ABSTRACTSocial networks have been a popular choice of study, given the surge of online data on friendship networks, communication networks, collaboration networks etc. This popularity...", "weights": [[8, 16, 1.0], [35, 43, 1.0], [126, 134, 1.0], [150, 158, 1.0], [174, 182, 1.0]]}, "2017.tois_journal-ir0volumeA35A3.10": {"snippet": "...e in Neural Networks for Sentiment Classification\n\n\n Phrase/Sentence representation is one of the most important problems in natural language processing. Many neural network models such as Convolution...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [162, 168, 1.0]]}, "2020.emnlp-main.535": {"snippet": "...able neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. The model is first pretrained on the huge general-domain data, then finetuned on our corpus. We show ...", "weights": [[8, 14, 1.0], [91, 100, 1.0]]}, "N19-1330": {"snippet": "Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models\n\n\n Recurrent neural network language models (RNNLM) form a valuable foundation for many NLP systems, but training the m...", "weights": [[0, 5, 1.0], [58, 64, 1.0], [94, 100, 1.0]]}, "2014.cikm_conference-2014.187": {"snippet": "...tion Retrieval (CLIR) addresses the problem of finding documents in some language that are relevant to a question posed in a different language. Retrieving answers to questions written using formal vo...", "weights": [[8, 17, 1.0], [188, 193, 1.0]]}, "2018.wsdm_conference-2018.102": {"snippet": "Neural Networks for Information Retrieval\n\n\n AbstractMachine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day resear...", "weights": [[0, 6, 1.0], [7, 15, 1.0], [32, 41, 1.0]]}}}
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We first use query logs to identify important contexts associated with terms belonging to a ...", "weights": [[8, 13, 1.0], [99, 104, 1.0], [124, 129, 1.0]]}, "2007.jasis_journal-ir0volumeA58A2.2": {"snippet": "...oral analysis of a very large topically categorized Web query log", "weights": [[8, 16, 1.0], [59, 64, 1.0], [65, 68, 1.0]]}, "1999.sigirjournals_journal-ir0volumeA33A1.1": {"snippet": "Analysis of a Very Large Web Search Engine Query Log\n\n\n AbstractIn this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search re...", "weights": [[0, 8, 1.0], [43, 48, 1.0], [49, 52, 1.0], [92, 100, 1.0], [131, 136, 1.0], [137, 140, 1.0]]}, "2017.wwwconf_conference-2017.58": {"snippet": "... and Query Suggestion\n\n\n ABSTRACTContextual data plays an important role in modeling search engine users' behaviors on both query auto-completion (QAC) log and normal query (click) log. User's recent ...", "weights": [[8, 13, 1.0], [127, 132, 1.0], [155, 158, 1.0], [170, 175, 1.0], [184, 187, 1.0]]}, "2009.wsdm_workshop-2009wscd.2": {"snippet": "Analysis of long queries in a large scale search log\n\n\n ABSTRACTWe propose to use the search log to study long queries, in order to understand the types of information needs that are behind them, and ...", "weights": [[0, 8, 1.0], [49, 52, 1.0], [93, 96, 1.0]]}, "2016.wsdm_conference-2016.3": {"snippet": "...ment Analysis of Social Multimedia\n\n\n ABSTRACTSentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentime...", "weights": [[8, 16, 1.0], [59, 67, 1.0]]}, "2016.tois_journal-ir0volumeA35A2.0": {"snippet": "...of a query log and make the first attempt to cross the language barrier in query logs. We propose a novel model named Cross-Lingual Query Log Topic Model (CL-QLTM) to analyze query logs from a cross-l...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [78, 83, 1.0], [135, 140, 1.0], [141, 144, 1.0], [178, 183, 1.0]]}, "2004.sigirconf_conference-2004.42": {"snippet": "...urly analysis of a very large topically categorized web query log\n\n\n ABSTRACTWe review a query log of hundreds of millions of queries that constitute the total query traffic for an entire week of a ge...", "weights": [[8, 16, 1.0], [59, 64, 1.0], [65, 68, 1.0], [92, 97, 1.0], [98, 101, 1.0], [163, 168, 1.0]]}, "2012.cikm_conference-2012.196": {"snippet": "...heir analysis has proven critical to improving search engine functionality. Yet, despite the deluge of information, query log analysis often suffers from the sparsity of the query space. Based on the ...", "weights": [[8, 16, 1.0], [119, 124, 1.0], [125, 128, 1.0], [129, 137, 1.0], [177, 182, 1.0]]}, "2006.wwwconf_conference-2006.194": {"snippet": "...gine query logs for query recommendation\n\n\n ABSTRACTThis paper presents a simple and intuitive method for mining search engine query logs to get fast query recommendations on a large scale industrial-...", "weights": [[8, 13, 1.0], [23, 28, 1.0], [130, 135, 1.0], [153, 158, 1.0]]}, "2007.sigirjournals_journal-ir0volumeA41A2.17": {"snippet": "Query log analysis: social and technological challenges\n\n\n AbstractAnalysis of search engine query logs is an important tool for developers and researchers.However, the potentially personal content of...", "weights": [[0, 5, 1.0], [6, 9, 1.0], [10, 18, 1.0], [93, 98, 1.0]]}, "2013.ir_journal-ir0volumeA16A6.3": {"snippet": "... use query suggestion? 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The motivating assumption o...", "weights": [[8, 13, 1.0], [53, 61, 1.0], [83, 88, 1.0], [158, 163, 1.0]]}, "2013.wsdm_conference-2013.82": {"snippet": "... the query covering approach. The general idea is to populate the cache with those documents that contribute to the result pages of a large number of queries, as opposed to caching the top documents o...", "weights": [[8, 13, 1.0]]}, "2003.sigirconf_conference-2003.86": {"snippet": "...om a query can help a user to refine an overly specific query to one which has more coverage. Most query reformulation research focuses on query expansion (eg. ). Our research on query term deletion p...", "weights": [[8, 13, 1.0], [59, 64, 1.0], [102, 107, 1.0], [142, 147, 1.0], [182, 187, 1.0]]}, "2009.sigirconf_conference-2009.157": {"snippet": "...a: a query log analysis", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0]]}, "2008.sigirconf_conference-2008.121": {"snippet": "Web query translation via web log mining", "weights": [[4, 9, 1.0], [30, 33, 1.0]]}, "2010.sigirconf_conference-2010.169": {"snippet": "Query log analysis in the context of information retrieval for children\n\n\n ABSTRACTIn this paper we analyze queries and sessions intended to satisfy children's information needs using a large-scale qu...", "weights": [[0, 5, 1.0], [6, 9, 1.0], [10, 18, 1.0], [198, 203, 1.0]]}, "2002.wwwconf_conference-2002.32": {"snippet": "... web query logs. In this study, we propose a new method for query expansion based on query logs. The central idea is to extract probabilistic correlations between query terms and document terms by ana...", "weights": [[8, 13, 1.0], [63, 68, 1.0], [88, 93, 1.0], [166, 171, 1.0]]}, "2009.sigirjournals_journal-ir0volumeA43A2.10": {"snippet": "...CLEF query log analysis workshop 2009", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0]]}, "2019.ir_journal-ir0volumeA22A5.4": {"snippet": "...n of query auto completion logs for anonymity-preserving analysis", "weights": [[8, 13, 1.0], [60, 68, 1.0]]}, "2018.chiir_conference-2018.55": {"snippet": "... log analysis is an unobtrusive technique used to better understand user behavior in search systems . It can be used to evaluate search algorithms or user interfaces, or to (re-)design systems.Traditi...", "weights": [[8, 16, 1.0]]}, "E12-2018": {"snippet": "Query log analysis with {L}ang{L}og\n\n\n This article describes GALATEAS LangLog, a system performing Search Log Analysis. LangLog illustrates how NLP technologies can be a powerful support tool for mar...", "weights": [[0, 5, 1.0], [6, 9, 1.0], [10, 18, 1.0], [107, 110, 1.0], [111, 119, 1.0]]}, "2011.sigirconf_conference-2011.7": {"snippet": "...ple: analysis of a people search engine log\n\n\n ABSTRACTRecent years show an increasing interest in vertical search: searching within a particular type of information. Understanding what people search ...", "weights": [[8, 16, 1.0], [43, 46, 1.0]]}, "2008.sigirconf_conference-2008.122": {"snippet": "...d on analysis of Web data resources, including anchor text, click log and query log. By combining evidence from multiple sources, we are able to accurately disambiguate the abbreviation in queries. Ex...", "weights": [[8, 16, 1.0], [69, 72, 1.0], [77, 82, 1.0], [83, 86, 1.0]]}, "2005.ecir_conference-2005.24": {"snippet": "An Analysis of Query Similarity in Collaborative Web Search\n\n\n Abstract. Web search logs provide an invaluable source of information regarding the search behaviour of users. This information can be re...", "weights": [[3, 11, 1.0], [15, 20, 1.0]]}, "2018.wwwconf_conference-2018c.78": {"snippet": "...Deep Analysis of SPARQL Queries\n\n\n ABSTRACTIn this demonstration, we showcase DARQL, the first tool for deep, large-scale analysis of SPARQL queries. We have harvested a large corpus of query logs wit...", "weights": [[8, 16, 1.0], [125, 133, 1.0], [189, 194, 1.0]]}, "2013.wsdm_conference-2013.89": {"snippet": "...ets. Query logs have also revolutionized query assistance tools such as related queries (recommended queries after the search was issued) or query completions (suggested queries as the query is being ...", "weights": [[8, 13, 1.0], [44, 49, 1.0], [144, 149, 1.0], [188, 193, 1.0]]}, "2008.tweb_journal-ir0volumeA2A4.0": {"snippet": "...e on query log analysis: Technology and ethics", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0]]}, "2014.wsdm_conference-2014.22": {"snippet": "...arch log data, which records users' search behaviors when interacting with search engines, provides a valuable source of information about users' search intents. Therefore, properly characterizing the...", "weights": [[8, 11, 1.0]]}, "2005.jasis_journal-ir0volumeA56A13.1": {"snippet": "Analysis of the query logs of a Web site search engine", "weights": [[0, 8, 1.0], [16, 21, 1.0]]}, "2016.wsdm_conference-2016.55": {"snippet": "...ence analysis is an important problem in social network analysis due to its impact on viral marketing and targeted advertisements. Most of the existing influence analysis methods determine the influen...", "weights": [[8, 16, 1.0], [59, 67, 1.0], [165, 173, 1.0]]}, "2013.wsdm_conference-2013.51": {"snippet": "...tent analysis of those documents depends primarily on the text. Although automated discovery of semantically related words from text improves free text query understanding, translating videos into tex...", "weights": [[8, 16, 1.0], [155, 160, 1.0]]}, "2008.cikm_conference-2008.64": {"snippet": "... the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more.In this paper we introduce the query-flow ...", "weights": [[8, 13, 1.0], [61, 66, 1.0], [67, 70, 1.0], [71, 79, 1.0], [130, 135, 1.0], [192, 197, 1.0]]}, "2009.sigirconf_workshop-2009uiir.7": {"snippet": "...e of log analysis has grown, log data constitute a relevant aspect in the evaluation process of the quality of a digital library system. In this paper, we address the problem of log analysis for compl...", "weights": [[8, 11, 1.0], [12, 20, 1.0], [32, 35, 1.0], [181, 184, 1.0], [185, 193, 1.0]]}, "2008.spire_conference-2008.19": {"snippet": "...ique Analysis of Query Log Graphs", "weights": [[8, 16, 1.0], [20, 25, 1.0], [26, 29, 1.0]]}, "2017.wsdm_conference-2017.96": {"snippet": "...om a query log or 2) make incremental changes to queries in a user's current session. While such approaches have been effective in improving retrieval, they either are limited to suggesting queries in...", "weights": [[8, 13, 1.0], [14, 17, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.7": {"snippet": "...sive analysis of a web search engine voice query log. -We combine a semantic analysis using novel methods, such as analyzing a broad set of triggered cards, with an in-depth syntactic analysis, to she...", "weights": [[8, 16, 1.0], [46, 51, 1.0], [52, 55, 1.0], [80, 88, 1.0], [187, 195, 1.0]]}, "2010.wsdm_conference-2010.5": {"snippet": "...tary query logs and selected samples of queries. In this paper, we suggest that anchor text, which is readily available, can be an effective substitute for a query log and study the effectiveness of a...", "weights": [[8, 13, 1.0], [161, 166, 1.0], [167, 170, 1.0]]}, "2011.clef_workshop-2011w.50": {"snippet": "...gual Log File Analysis: Language Identification, Query Classification, and Success of a Query\n\n\n Since 2009 LogCLEF has been the initiative within the Cross-Language Evaluation Forum which aims at sti...", "weights": [[8, 11, 1.0], [17, 25, 1.0], [52, 57, 1.0], [91, 96, 1.0]]}, "2006.spire_conference-2006.18": {"snippet": "...l of Query Log Generation", "weights": [[8, 13, 1.0], [14, 17, 1.0]]}, "2009.clef_workshop-2009.64": {"snippet": "...d on Query Logs, and Search Log Analysis by Automatic Language Identification", "weights": [[8, 13, 1.0], [31, 34, 1.0], [35, 43, 1.0]]}, "2017.chiir_conference-2017.68": {"snippet": "...k to Query Recommendation and Evaluation\n\n\n ABSTRACTIn practice, query recommenders in Web search typically recommend queries directly from a query log or iteratively refine a user's current context t...", "weights": [[8, 13, 1.0], [68, 73, 1.0], [145, 150, 1.0], [151, 154, 1.0]]}, "2010.wsdm_conference-2010.33": {"snippet": "... the query, url and position, neglecting some other important attributes in click log data, such as the local time. Obviously, the click through rate is different between daytime and midnight. In this...", "weights": [[8, 13, 1.0], [85, 88, 1.0]]}, "2014.wsdm_conference-2014.24": {"snippet": "...y: a query log analysis of within-session learning", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0]]}, "2011.wsdm_conference-2011.46": {"snippet": "... the analysis of log data. To handle a huge amount of log data, we develop distributed algorithms for learning model parameters efficiently. We also devise approaches to computing a topic-concept cube...", "weights": [[8, 16, 1.0], [20, 23, 1.0], [57, 60, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.14": {"snippet": "...cale log analysis of their search actions at a national audiovisual broadcast archive. Our analysis characterizes not only the searches of media professionals, but also their purchasing behavior. In o...", "weights": [[8, 11, 1.0], [12, 20, 1.0], [94, 102, 1.0]]}, "2014.sigirconf_conference-2014.114": {"snippet": "...ng a query log via crowdsourcing\n\n\n ABSTRACTA query log is a key asset in a commercial search engine. Everyday millions of users rely on search engines to find information on the Web by entering a few...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [49, 54, 1.0], [55, 58, 1.0]]}, "2011.wwwconf_conference-2011c.8": {"snippet": "...TERM-QUERY GRAPH MODELLet Q be a query-log, i.e. a sequence of queries q1, . . . , qn . Moreover, let Q be the set of queries in the query-log. Each query is made up of terms from a vocabulary T , wit...", "weights": [[8, 13, 1.0], [36, 41, 1.0], [42, 45, 1.0], [136, 141, 1.0], [142, 145, 1.0], [152, 157, 1.0]]}, "2020.sigirconf_conference-2020.290": {"snippet": "...nes. Query logs record users' activities along with timestamps and additional search-related information. The analysis of those chronological query logs enables the modeling of search tasks from user ...", "weights": [[8, 13, 1.0], [113, 121, 1.0], [145, 150, 1.0]]}, "2011.wwwconf_conference-2011c.32": {"snippet": "...from query logs instead of web corpora has several advantages, in view of query-log-based application . Along this direction, we present a completely unsupervised (i.e. we do not need seeds) and domai...", "weights": [[8, 13, 1.0], [77, 82, 1.0], [83, 86, 1.0]]}}}
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{"snippet": "...amed Entity Recognition", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2011.trec_conference-2011.54": {"snippet": "...2011 Entity Track: Entity List Completion Using Contextual Unsupervised Scores for Candidate Entities Ranking", "weights": [[8, 14, 1.0], [22, 28, 1.0]]}, "D15-1104": {"snippet": "...amed entity recognition (NER) model to extract entity names first, then run an entity linking model to link extracted names to a knowledge base. NER and linking models are usually trained separately, ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [50, 56, 1.0], [82, 88, 1.0]]}, "W02-2035": {"snippet": "...amed Entity Recognition", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2021.acl-long.275": {"snippet": "...amed Entity Recognition via Explicitly Excluding the Influence of the Best Path\n\n\n This paper presents a novel method for nested named entity recognition. As a layered method, our method extends the p...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [138, 144, 1.0], [145, 156, 1.0]]}, "2020.sigirconf_conference-2020.184": {"snippet": "...ting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants\n\n\n We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken quer...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [51, 62, 1.0]]}, "2006.clef_workshop-2006w.105": {"snippet": "...t of Entity Recognition in Answer Validation\n\n\n The Answer Validation Exercise (AVE) 2006 is aimed at developing systems able to decide whether the answer of a Question Answering (QA) system is correc...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2014.sigirconf_conference-2014.223": {"snippet": "...'14: entity recognition and disambiguation challenge", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2015.wwwconf_conference-2015.49": {"snippet": "... for Entity Recognition\n\n\n ABSTRACTRecognizing entity instances in documents according to a knowledge base is a fundamental problem in many data mining applications. The problem is extremely challengi...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [50, 56, 1.0]]}, "2019.wwwconf_conference-2019.343": {"snippet": "...amed Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2018.ecir_workshop-2018newsir.8": {"snippet": "...amed Entity Recognition for Telugu News Articles using Na\u00efve Bayes Classifier\n\n\n The Named Entity Recognition (NER) is identifying name of Person, Location, Organization etc. in a given sentence or a ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [94, 100, 1.0], [101, 112, 1.0]]}, "2020.acl-main.519": {"snippet": "...amed Entity Recognition\n\n\n The task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not. Models are usually separat...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [48, 54, 1.0], [55, 66, 1.0]]}, "2021.emnlp-demo.35": {"snippet": "...amed Entity Recognition\n\n\n Named Entity Recognition is a fundamental task in information extraction and is an essential element for various Natural Language Processing pipelines. Adversarial attacks h...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [36, 42, 1.0], [43, 54, 1.0]]}, "E14-3003": {"snippet": "...amed entity recognition within one clinical sub-domain as well as on the task of adapting a named entity recognition model to a new clinical sub-domain. Four named entity types, all very relevant for ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [101, 107, 1.0], [108, 119, 1.0], [167, 173, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.16": {"snippet": "...amed entity recognition with multiple segment representations\n\n\n a b s t r a c tNamed entity recognition (NER) is mostly formalized as a sequence labeling problem in which segments of named entities a...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [89, 95, 1.0], [96, 107, 1.0]]}, "2020.clef_conference-2020w.210": {"snippet": "...amed Entity Recognition in Chemical Reactions from Patents\n\n\n This paper describes our VinAI system for the ChEMU task 1 of named entity recognition (NER) in chemical reactions. Our system employs a B...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [133, 139, 1.0], [140, 151, 1.0]]}, "2013.tist_journal-ir0volumeA4A1.2": {"snippet": "...amed entity recognition for tweets\n\n\n Two main challenges of Named Entity Recognition (NER) for tweets are the insufficient information in a tweet and the lack of training data. We propose a novel met...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [70, 76, 1.0], [77, 88, 1.0]]}, "2016.fire_conference-2016w.68": {"snippet": "...ixed Entity Recognition\n\n\n Entity Recognition is an essential part of Information Extraction, where explicitly available information and relations are extracted from the entities within the text. Plet...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [30, 36, 1.0], [37, 48, 1.0]]}, "W12-6327": {"snippet": "...amed Entity Recognition and Disambiguation System\n\n\n In this paper we describe an integrated approach for named entity recognition and disambiguation in Chinese. The proposed method relies on named en...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [115, 121, 1.0], [122, 133, 1.0], [201, 207, 1.0]]}, "W03-0420": {"snippet": "...amed Entity Recognition\n\n\n In this paper, we describe a system that applies maximum entropy (ME) models to the task of named entity recognition (NER). Starting with an annotated corpus and a set of fe...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [128, 134, 1.0], [135, 146, 1.0]]}, "2011.cikm_conference-2011.338": {"snippet": "...amed entity recognition using a modified Pegasos algorithm\n\n\n ABSTRACTIn this paper, we describe a named entity recognition using a modified Pegasos algorithm for structural SVMs. We show the modified...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [108, 114, 1.0], [115, 126, 1.0]]}, "W12-6321": {"snippet": "...amed Entity Recognition and Disambiguation in {C}hinese Bakeoff\n\n\n The CIPS-SIGHAN 2012 Chinese Named Entity Recognition and Disambiguation (NERD) bake-off was held in the summer of 2012. Named entity...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [105, 111, 1.0], [112, 123, 1.0], [197, 203, 1.0]]}, "W04-1221": {"snippet": "...amed Entity Recognition using Conditional Random Fields and Rich Feature Sets", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2022.semeval-1.217": {"snippet": "...amed Entity Recognition\n\n\n Named Entity Recognition (NER), an essential subtask in NLP that identifies text belonging to predefined semantics such as a person, location, organization, drug, time, clin...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [36, 42, 1.0], [43, 54, 1.0]]}, "S15-1028": {"snippet": "...icit entity mentions and introduce the problem of implicit entity recognition (IER) in clinical documents. We propose a solution to IER that leverages entity definitions from a knowledge base to creat...", "weights": [[8, 14, 1.0], [62, 68, 1.0], [69, 80, 1.0], [154, 160, 1.0]]}, "2013.clef_conference-2013.32": {"snippet": "Entity Recognition in Parallel Multi-lingual Biomedical Corpora: The CLEF-ER Laboratory Overview", "weights": [[0, 6, 1.0], [7, 18, 1.0]]}, "2020.ecir_conference-20202.48": {"snippet": "...amed-Entity Recognition: An Empirical Study on Generalization\n\n\n Contextualized embeddings use unsupervised language model pretraining to compute word representations depending on their context. This ...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2009.sigirconf_conference-2009.35": {"snippet": "...amed entity recognition in query\n\n\n ABSTRACTThis paper addresses the problem of Named Entity Recognition in Query (NERQ), which involves detection of the named entity in a given query and classificati...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [89, 95, 1.0], [96, 107, 1.0], [163, 169, 1.0]]}, "2009.jasis_journal-ir0volumeA60A8.9": {"snippet": "...amed Entity Recognition for Arabic", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "N09-1037": {"snippet": "...amed Entity Recognition\n\n\n For many language technology applications, such as question answering, the overall system runs several independent processors over the data (such as a named entity recognize...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [187, 193, 1.0]]}, "2016.fire_conference-2016w.69": {"snippet": "...amed Entity Recognition for Code Mixing in Indian Languages using Hybrid Approach\n\n\n Automating the process of Named Entity Recognition has received a lot of attention over past few years in Social Me...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [120, 126, 1.0], [127, 138, 1.0]]}, "2014.sigirconf_workshop-2014erd.12": {"snippet": "UBC entity recognition and disambiguation at ERD 2014", "weights": [[4, 10, 1.0], [11, 22, 1.0]]}, "2014.sigirjournals_journal-ir0volumeA48A2.4": {"snippet": "...'14: Entity Recognition and Disambiguation Challenge\n\n\n AbstractIn this paper we overview the 2014 Entity Recognition and Disambiguation Challenge (ERD'14), which took place from March to June 2014 an...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [102, 108, 1.0], [109, 120, 1.0]]}, "2014.sigirconf_workshop-2014erd.0": {"snippet": "...p on Entity Recognition & Disambiguation, July 11, 2014, Gold Coast, Queensland, Australia", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2014.sigirconf_workshop-2014erd.17": {"snippet": "... for entity recognition and disambiguation\n\n\n ABSTRACTWe present a system for entity recognition and disambiguation (ERD) in short text, aiming at identifying all text fragments referring to an entity...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [81, 87, 1.0], [88, 99, 1.0], [197, 203, 1.0]]}, "2011.jasis_journal-ir0volumeA62A4.8": {"snippet": "...amed entity recognition using semisupervised learning and conditional random fields", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "C12-1004": {"snippet": "...amed Entity Recognition Pipeline\n\n\n Online content analysis employs algorithmic methods to identify entities in unstructured text. Both machine learning and knowledge-base approaches lie at the founda...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2019.cikm_conference-2019.256": {"snippet": "...tive Entity Recognition in Low Resource Settings", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "R17-1021": {"snippet": "...amed Entity Recognition", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2022.findings-acl.329": {"snippet": "...shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework\n\n\n Entity recognition is a fundamental task in understanding document images. Traditional sequence labeling fra...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [95, 101, 1.0], [102, 113, 1.0]]}, "W07-0803": {"snippet": "...Name Entity Recognition for {A}rabic\n\n\n Named entity recognition (NER) is nowadays an important task, which is responsible for the identification of proper names in text and their classification as di...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [49, 55, 1.0], [56, 67, 1.0]]}, "2014.airs_conference-2014.24": {"snippet": "...amed entity recognition from the newspaper articles. Thus, this study designs and develops a crime named entity recognition based on machine learning approaches that extract nationalities, weapons, an...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [108, 114, 1.0], [115, 126, 1.0]]}, "2009.eamt-1.13": {"snippet": "...amed Entity Recognition and Translation\n\n\n We present an environment for the recognition and translation of Named Entities (NEs). The environment consists of a new formalism for the Named Entity Recog...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [80, 91, 1.0], [191, 197, 1.0], [198, 209, 1.0]]}, "2022.acl-long.67": {"snippet": "...amed Entity Recognition\n\n\n Named entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructin...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [36, 42, 1.0], [43, 54, 1.0], [140, 146, 1.0], [147, 158, 1.0]]}, "E99-1001": {"snippet": "...amed Entity Recognition without Gazetteers\n\n\n It is often claimed that Named Entity recognition systems need extensive gazetteers--lists of names of people, organisations, locations, and other named e...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [80, 86, 1.0], [87, 98, 1.0]]}, "W19-3644": {"snippet": "...amed Entity Recognition with Commonsense Knowledge", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2015.fire_workshop-2015w.19": {"snippet": "...-IL: Entity Extraction from Social Media Text for Indian Languages @ FIRE 2015 - An Overview\n\n\n Entity recognition is a very important sub task of Information extraction and find its applications in i...", "weights": [[8, 14, 1.0], [99, 105, 1.0], [106, 117, 1.0]]}, "W17-1413": {"snippet": "...amed Entity Recognition\n\n\n In the paper we present an adaptation of Liner2 framework to solve the BSNLP 2017 shared task on multilingual named entity recognition. The tool is tuned to recognize and le...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [146, 152, 1.0], [153, 164, 1.0]]}, "2015.clef_conference-2015w.167": {"snippet": "...amed Entity Recognition Based on CRF\n\n\n Named entity recognition of biomedical text is the shared task 1b of the 2015 CLEF eHealth evaluation lab, which focuses on making biomedical text easier to und...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [49, 55, 1.0], [56, 67, 1.0]]}, "I13-1101": {"snippet": "...amed Entity Recognizer for Open-Domain Search Queries\n\n\n Named entity recognition in queries is the task of identifying sequences of terms in search queries that refer to a unique concept. This proble...", "weights": [[8, 14, 1.0], [66, 72, 1.0], [73, 84, 1.0]]}, "C18-1168": {"snippet": "... for Entity Recognition of Novel Classes\n\n\n In this reproduction paper, we replicate and extend several past studies on transfer learning for entity recognition. In particular, we are interested in en...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [145, 151, 1.0], [152, 163, 1.0], [201, 207, 1.0]]}, "L14-1328": {"snippet": "...amed Entity Recognition on {T}urkish Tweets", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2011.trec_conference-2011.79": {"snippet": "...2011 Entity Track: Related Entity Finding and Entity List Completion", "weights": [[8, 14, 1.0], [30, 36, 1.0], [49, 55, 1.0]]}, "W07-1712": {"snippet": "...amed Entity Recognition for {U}krainian: A Resource-Light Approach\n\n\n Named entity recognition (NER) is a subtask of information extraction (IE) which can be used further on for different purposes. In...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [79, 85, 1.0], [86, 97, 1.0]]}, "W18-3221": {"snippet": "...tity Recognition with Embedding Attention\n\n\n We describe our work for the CALCS 2018 shared task on named entity recognition on code-switched data. Our system ranked first place for MS Arabic-Egyptian...", "weights": [[8, 19, 1.0], [109, 115, 1.0], [116, 127, 1.0]]}, "P01-1041": {"snippet": "...amed Entity Recognition based on a Simple Rule Generator and Decision Tree Learning\n\n\n Named entity (NE) recognition is a task in which proper nouns and numerical information in a document are detecte...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [96, 102, 1.0], [108, 119, 1.0]]}, "W06-1660": {"snippet": "...amed Entity Recognition Model across Domains\n\n\n When a machine learning-based named entity recognition system is employed in a new domain, its performance usually degrades. In this paper, we provide a...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [87, 93, 1.0], [94, 105, 1.0]]}, "2006.airs_conference-2006.49": {"snippet": "...amed entity recognizer and fine-grained named entity dictionary. In this paper, we describe a fine-grained Named Entity Recognition using Conditional Random Fields (CRFs) for question answering. We us...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [116, 122, 1.0], [123, 134, 1.0]]}, "2013.ntcir_conference-2013.12": {"snippet": "...ical Entity Recognition Using Cost-Sensitive Structured Perceptron for NTCIR-10 MedNLP\n\n\n This paper reports on our approach to the NTCIR-10 MedNLP task, which aims at identifying personal and medical...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2020.clef_conference-2020w.119": {"snippet": "...amed entity recognition, entity mention detection and entity linking. For the task of named entity recognition we used an ensemble of fine-tuned BERT models; entity linking was approached by three dif...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [28, 34, 1.0], [57, 63, 1.0], [95, 101, 1.0], [102, 113, 1.0], [161, 167, 1.0]]}, "W02-2010": {"snippet": "...amed Entity Recognition as a House of Cards: Classifier Stacking", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "L18-1705": {"snippet": "...amed Entity Recognition using Conditional Random Fields and Local Grammars\n\n\n Named Entity Recognition involves automatically identifying and classifying entities such as persons, places, and organiza...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [87, 93, 1.0], [94, 105, 1.0]]}, "2017.cikm_conference-2017.242": {"snippet": "...amed-Entity Recognition System Using Syllable Bigram Embedding with Eojeol Prefix Information\n\n\n ABSTRACTKorean named-entity recognition (NER) systems have been developed mainly on the morphological-l...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [121, 127, 1.0], [128, 139, 1.0]]}, "D19-1652": {"snippet": "... for Entity Recognition\n\n\n The task of entity recognition has traditionally been modelled as a sequence labelling task. However, this usually requires a large amount of fine-grained data annotated at ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [42, 48, 1.0], [49, 60, 1.0]]}, "D17-1282": {"snippet": "...amed Entity Recognition with Bidirectional Recursive Neural Networks\n\n\n In this paper, we utilize the linguistic structures of texts to improve named entity recognition by BRNN-CNN, a special bidirect...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [153, 159, 1.0], [160, 171, 1.0]]}, "2020.aacl-main.22": {"snippet": "...amed Entity Recognition in Multi-level Contexts\n\n\n Named entity recognition is a critical task in the natural language processing field. Most existing methods for this task can only exploit contextual...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [60, 66, 1.0], [67, 78, 1.0]]}, "W03-1121": {"snippet": "...amed entity type. Therefore we focus on named entity recognition model in Korean. Korean named entity recognition is difficult since each word of named entity has not specific features such as the cap...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [56, 67, 1.0], [98, 104, 1.0], [105, 116, 1.0], [155, 161, 1.0]]}, "2021.wwwconf_workshop-2021cleopatra.2": {"snippet": "...amed-Entity Recognition English corpus", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2007.sigirconf_conference-2007.145": {"snippet": "...amed entity recognition and relation extraction for question answering", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2014.wwwconf_conference-2014.38": {"snippet": "...amed entity recognition for idiosyncratic web collections\n\n\n ABSTRACTNamed Entity Recognition (NER) plays an important role in a variety of online information management tasks including text categoriz...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [78, 84, 1.0], [85, 96, 1.0]]}, "2012.cikm_workshop-2012dtmbio.6": {"snippet": "...ical entity recognition using structural support vector machines with rich features\n\n\n ABSTRACTNamed entity recognition (NER) is an important task for natural language processing (NLP) of clinical tex...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [104, 110, 1.0], [111, 122, 1.0]]}, "W14-1309": {"snippet": "...amed entity recognition on Turkish tweets, using two different annotated data sets. In these experiments, starting with a baseline named entity recognition system, we adapt its recognition rules and r...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [140, 146, 1.0], [147, 158, 1.0], [180, 191, 1.0]]}, "2014.fire_conference-2014.17": {"snippet": "...amed Entity Recognition for Indian Languages using Rich Features\n\n\n ABSTRACTThis paper aims at implementing Named Entity Recognition (NER) for four languages such as English, Tamil, Hindi and Malayala...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [117, 123, 1.0], [124, 135, 1.0]]}, "O05-5010": {"snippet": "...amed Entity Categorization Based on the People{'}s Daily Corpus) [In {C}hinese]\n\n\n Named entity recognition is a very important part of information retrieval and information extraction. Classification...", "weights": [[8, 14, 1.0], [92, 98, 1.0], [99, 110, 1.0]]}, "2012.amta-caas14.4": {"snippet": "...amed entity recognition (ARNE), which includes tokenization, morphological analysis, Buckwalter transliteration, part of speech tagging and named entity recognition of person, location and organisatio...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [149, 155, 1.0], [156, 167, 1.0]]}, "2019.sigirconf_conference-2019.185": {"snippet": "Name Entity Recognition with Policy-Value Networks", "weights": [[5, 11, 1.0], [12, 23, 1.0]]}, "2006.clef_workshop-2006.57": {"snippet": "...t of Entity Recognition on Answer Validation", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2020.clef_conference-2020.18": {"snippet": "...amed Entity Recognition and Event Extraction of Chemical Reactions from Patents", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "L08-1253": {"snippet": "...amed Entity Recognition for Digitised Historical Texts", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2017.clef_conference-2017w.49": {"snippet": "Entity Recognition and Language Identification with FELTS", "weights": [[0, 6, 1.0], [7, 18, 1.0]]}, "R11-1023": {"snippet": "...amed entity recognition and vice versa, how noun compound detection is supported by identified named entities. Our experiments demonstrate that already known noun compounds yield better performance in...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "W12-0508": {"snippet": "...amed Entity Recognition and Entity Linking System\n\n\n We present a joint system for named entity recognition (NER) and entity linking (EL), allowing for named entities mentions extracted from textual d...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [31, 37, 1.0], [92, 98, 1.0], [99, 110, 1.0], [121, 127, 1.0]]}, "R17-1101": {"snippet": "...amed Entity Recognition", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "L12-1212": {"snippet": "...amed-entity recognition framework", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2014.cikm_workshop-2014dtmbio.3": {"snippet": "...amed Entity Recognition Based on the Combination of Regional and Global Text Features\n\n\n ABSTRACTThe biomedical information extraction, especially Named Entity Recognition (NER), is a primary task in ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [156, 162, 1.0], [163, 174, 1.0]]}, "L00-1128": {"snippet": "...amed Entity Recognition in {G}reek Texts", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "D09-1015": {"snippet": "...amed Entity Recognition\n\n\n Many named entities contain other named entities inside them. Despite this fact, the field of named entity recognition has almost entirely ignored nested named entity recogn...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [130, 136, 1.0], [137, 148, 1.0], [190, 196, 1.0], [197, 208, 1.0]]}, "2013.fire_conference-2013.14": {"snippet": "...amed-Entity Recognition in Bengali\n\n\n ABSTRACTThis paper 1 describes two systems for Named Entity Recognition (NER) and performance of two systems has been compared. The first system is a rule-based o...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [94, 100, 1.0], [101, 112, 1.0]]}, "2015.clef_conference-2015w.42": {"snippet": "...lain entity recognition, 0.711 for normalized entity recognition and 0.872 for entity normalization.", "weights": [[8, 14, 1.0], [15, 26, 1.0], [49, 55, 1.0], [56, 67, 1.0], [82, 88, 1.0]]}, "W03-0423": {"snippet": "...amed Entity Recognition with a Maximum Entropy Approach", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "W12-6324": {"snippet": "...amed Entity Recognition and Disambiguation\n\n\n In this paper, we briefly report our system for Chinese Named Entity Recognition and Disambiguation task in CIPS-SIGHAN joint conference. We first present...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [111, 117, 1.0], [118, 129, 1.0]]}, "2014.fire_conference-2014.16": {"snippet": "...amed Entity Recognition for Indian Languages\n\n\n ABSTRACTThis paper describes the approaches we have adopted to identify the classes of named entities and annotate them with proper tags. Named Entity R...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [195, 201, 1.0], [202, 213, 1.0]]}, "2016.clef_conference-2016w.3": {"snippet": "... For entity recognition, the highest performance was achieved on the EMEA corpus, with an overall F-measure of 0.702 for plain entities recognition and 0.529 for normalized entity recognition. For ent...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [139, 150, 1.0], [176, 182, 1.0], [183, 194, 1.0], [200, 206, 1.0]]}, "2015.wwwconf_conference-2015c.71": {"snippet": "...ards Entity Correctness, Completeness and Emergence for Entity Recognition\n\n\n ABSTRACTLinking words or phrases in unstructured text to entities in knowledge bases is the problem of entity recognition ...", "weights": [[8, 14, 1.0], [59, 65, 1.0], [66, 77, 1.0], [184, 190, 1.0], [191, 202, 1.0]]}, "2014.sigirconf_workshop-2014erd.9": {"snippet": "...h to entity recognition: magnetic and IISAS team at ERD challenge\n\n\n ABSTRACTERD 2014 was a research challenge focused on the task of recognition and disambiguation of knowledge base entities in short...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [137, 148, 1.0]]}, "2020.clef_conference-2020w.120": {"snippet": "...amed Entity Recognition in Chemical Patents using Ensemble of Contextual Language Models\n\n\n Chemical patent documents describe a broad range of applications holding key reaction and compound informati...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2020.jasis_journal-ir0volumeA71A5.5": {"snippet": "...amed Entity Recognition in User Comments via Parameterized Label Propagation", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2021.triton-1.10": {"snippet": "...amed Entity Recognition via {F}ast{A}lign: a Case Study\n\n\n Named Entity Recognition is an essential task in natural language processing to detect entities and classify them into predetermined categori...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [68, 74, 1.0], [75, 86, 1.0]]}, "2018.fire_conference-2018w.19": {"snippet": "...amed Entity Recognition System for Indian Languages\n\n\n Named Entity Recognition is the process of classifying the elementary units in a text document into meaningful categories such as person, locatio...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [64, 70, 1.0], [71, 82, 1.0]]}, "E06-3004": {"snippet": "...amed Entity Recognition with Automatically Generated Gazetteer Lists\n\n\n Current Named Entity Recognition systems suffer from the lack of hand-tagged data as well as degradation when moving to other do...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [89, 95, 1.0], [96, 107, 1.0]]}, "D19-1034": {"snippet": "...amed Entity Recognition\n\n\n In natural language processing, it is common that many entities contain other entities inside them. Most existing works on named entity recognition (NER) only deal with flat...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [159, 165, 1.0], [166, 177, 1.0]]}, "2020.acl-main.722": {"snippet": "...amed Entity Recognition\n\n\n Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [48, 54, 1.0], [55, 66, 1.0]]}, "W18-3215": {"snippet": "...amed Entity Recognition on Code-Switched Data Using Conditional Random Fields\n\n\n Named Entity Recognition is an important information extraction task that identifies proper names in unstructured texts...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [90, 96, 1.0], [97, 108, 1.0]]}, "2004.sigirconf_conference-2004.37": {"snippet": "...amed entity recognition using machine learning\n\n\n ABSTRACTIn this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2017.fire_conference-2017.4": {"snippet": "...amed Entity Recognition for Telugu\n\n\n ABSTRACTIn this paper, we apply three classification learning algorithms to Telugu Named Entity Recognition (NER) task and we present a comparative study between ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [130, 136, 1.0], [137, 148, 1.0]]}, "2021.acl-long.216": {"snippet": "...amed Entity Recognition\n\n\n Named entity recognition (NER) is a wellstudied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [36, 42, 1.0], [43, 54, 1.0]]}, "2015.clef_conference-2015w.168": {"snippet": "...amed Entity Recognition Based on CRF\n\n\n Named entity recognition of biomedical text is the shared task 1b of the 2015 CLEF eHealth evaluation lab, which focuses on making biomedical text easier to und...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [49, 55, 1.0], [56, 67, 1.0]]}}}
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Relevation! is web-based, fully configurable and expandable; it ...", "weights": [[8, 17, 1.0], [80, 89, 1.0]]}, "2010.ntcir_workshop-2010evia.5": {"snippet": "...hout Relevance Assessments: Revisited\n\n\n We reexamine the problem of ranking retrieval systems without relevance assessments in the context of collaborative evaluation forums such as TREC and NTCIR. 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In particular, we as...", "weights": [[8, 17, 1.0]]}, "2012.clef_conference-2012.14": {"snippet": "...y of Relevance Assessments with Students", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "1979.jasis_journal-ir0volumeA30A5.3": {"snippet": "Relevance", "weights": [[0, 9, 1.0]]}, "2009.cikm_conference-2009.270": {"snippet": "...from relevance assessments has been proposed as an effective means for improving retrieval. We introduce a novel language modeling method which uses information from each assessed document and their a...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2017.sigirconf_conference-2017.233": {"snippet": "Relevance Judgments: Preferences, Scores and Ties", "weights": [[0, 9, 1.0]]}, "2018.sigirconf_conference-2018.138": {"snippet": "...ment relevance judgments, and query-independent assessments of document readability. In this paper we study the lexical and aesthetic features of web documents that may underlie this relationship. Lev...", "weights": [[8, 17, 1.0], [51, 62, 1.0]]}, "2016.chiir_conference-2016.45": {"snippet": "...aded Relevance Assessments", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2012.trec_conference-2012.84": {"snippet": "... for relevance on the same document set, and use this training set to build strategies. We apply machine approaches, including clustering, to order documents for each topic, and then ask crowdworkers ...", "weights": [[8, 17, 1.0]]}, "2013.wsdm_conference-2013.12": {"snippet": "...able relevance judgments than non-owners, presumably due to the background knowledge associated with their queries. We quantify the benefit of using owner assessments versus non-owner assessments in t...", "weights": [[8, 17, 1.0], [158, 169, 1.0], [187, 198, 1.0]]}, "2018.sigirconf_conference-2018.163": {"snippet": "... for relevance assessment based on the results of prior assessments. Unlike static and dynamic pooling methods that are commonly used to compile relevance assessments for the creation of information r...", "weights": [[8, 17, 1.0], [59, 70, 1.0], [148, 157, 1.0], [158, 169, 1.0]]}, "2016.sigirconf_conference-2016.126": {"snippet": "...d to relevance sampling. The effect is substantial and significant [0.54 vs. 0.42, p<0.0002] across a population of documents including both relevant and non-relevant documents, for several definition...", "weights": [[8, 17, 1.0]]}, "2011.wwwconf_conference-2011.26": {"snippet": "...ased relevance assessment. When applied to the deep web these methods have two deficiencies. First is that the methods are agnostic to the correctness (trustworthiness) of the sources. Secondly, the q...", "weights": [[8, 17, 1.0]]}, "2008.tois_journal-ir0volumeA27A1.0": {"snippet": "... and relevance assessments. Obtaining relevance assessments that are as sound and complete as possible is crucial for the comparison of retrieval approaches. In XML retrieval, the problem of obtaining...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [41, 50, 1.0], [51, 62, 1.0]]}, "2002.sigirconf_conference-2002.44": {"snippet": "...eral relevance criteria of TREC -: counting on negligible documents?\n\n\n ABSTRACTMost test collections (like TREC and CLEF) for experimental research in information retrieval apply binary relevance ass...", "weights": [[8, 17, 1.0], [190, 199, 1.0], [200, 211, 1.0]]}, "2018.cikm_conference-2018.66": {"snippet": "Relevance Estimation with Multiple Information Sources on Search Engine Result Pages\n\n\n ABSTRACTRelevance estimation is among the most important tasks in the ranking of search results. Current relevan...", "weights": [[0, 9, 1.0], [193, 202, 1.0]]}, "2012.trec_conference-2012.49": {"snippet": "...text relevance assessing task (TRAT) and an image relevance assessing task (IRAT). This track overview describes the track and provides analysis of the track's results.", "weights": [[8, 17, 1.0], [53, 62, 1.0]]}, "2007.cikm_workshop-2007pikm.21": {"snippet": "... for relevance assessment\n\n\n ABSTRACTThe major question raised in information retrieval on semistructured documents relates to the manner of effectively handling the structure and the contents of the ...", "weights": [[8, 17, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...s in relevance assessments across system types is unknown.To verify that the TREC collections are reliable laboratory tools, NIST investigated the effect changes in the relevance assessments have on t...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [172, 181, 1.0], [182, 193, 1.0]]}, "2019.sigirconf_conference-2019.99": {"snippet": "...ster Relevance Assessing", "weights": [[8, 17, 1.0]]}, "2014.sigirjournals_journal-ir0volumeA48A2.0": {"snippet": "... for Relevance Assessments", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "1975.ipm_journal-ir0volumeA11A12.4": {"snippet": "... the relevance assessment of documents", "weights": [[8, 17, 1.0]]}, "1985.jasis_journal-ir0volumeA36A5.14": {"snippet": "Relevance?", "weights": [[0, 9, 1.0]]}, "2017.trec_conference-2017.29": {"snippet": "...t of relevance assessments (\"qrels\") comparable to the official Common Core Track qrels (cf. [12, 2]). To this end, we adapted for live, human-in-the-loop use, the AutoTAR CAL implementation, 1 which ...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2014.sigirconf_conference-2014.101": {"snippet": "...d in relevance assessments for images\n\n\n ABSTRACTHow assessors and end users judge the relevance of images has been studied in information science and information retrieval for a considerable time. Th...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [90, 99, 1.0]]}, "2009.sigirconf_conference-2009.59": {"snippet": "...l of relevance assessments\n\n\n ABSTRACTGrowing interest in online collections of digital books and video content motivates the development and optimization of adequate retrieval systems. However, tradi...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2014.sigirconf_conference-2014.224": {"snippet": "...ient assessments of relevance (GEAR)\n\n\n ABSTRACTEvaluation is a fundamental part of Information Retrieval, and in the conventional Cranfield evaluation paradigm, sets of relevance assessments are a fu...", "weights": [[8, 19, 1.0], [23, 32, 1.0], [173, 182, 1.0], [183, 194, 1.0]]}, "2019.ecir_conference-20191.50": {"snippet": "...stic Relevance for Crowdsourcing\n\n\n Abstract. It has been recently proposed to consider relevance assessment as a stochastic process where relevance judgements are modeled as binomial random variables...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [142, 151, 1.0]]}, "2020.ecir_conference-20201.30": {"snippet": "...h to relevance. To consider semantic matching between query and document, we propose an unsupervised semantic matching model by simulating a user who makes relevance decisions. The primary goal of the...", "weights": [[8, 17, 1.0], [159, 168, 1.0]]}, "2014.sigirconf_workshop-2014medir.6": {"snippet": "...sing Relevance in Medical IR is Demanding\n\n\n This study investigates if and why assessing relevance of clinical records for a clinical retrieval task is cognitively demanding. Previous research has hi...", "weights": [[8, 17, 1.0], [93, 102, 1.0]]}, "2019.sigirconf_conference-2019.153": {"snippet": "...ment Relevance", "weights": [[8, 17, 1.0]]}, "2002.jasis_journal-ir0volumeA53A13.5": {"snippet": "...aded relevance assessments in IR evaluation", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2000.ir_journal-ir0volumeA2A1.6": {"snippet": "...iple Relevance Assessments", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2003.clef_workshop-2003.45": {"snippet": "...uage Relevance Assessment", "weights": [[8, 17, 1.0]]}, "2008.jasis_journal-ir0volumeA59A3.10": {"snippet": "...eudo-relevance feedback using graded relevance assessments", "weights": [[8, 17, 1.0], [40, 49, 1.0], [50, 61, 1.0]]}, "2016.ir_journal-ir0volumeA19A4.0": {"snippet": "...sing relevance judgments", "weights": [[8, 17, 1.0]]}, "2008.sigirconf_conference-2008.86": {"snippet": "...t in relevance judgements between these three groups. We report on experiments to determine if this is sufficient to invalidate the use of a test collection for measuring system performance when relev...", "weights": [[8, 17, 1.0], [198, 207, 1.0]]}, "2017.trec_conference-2017.30": {"snippet": "...t of relevance assessments (\"qrels\") comparable to the official Common Core Track qrels (cf. [12, 2]). To this end, we adapted for live, human-in-the-loop use, the AutoTAR CAL implementation, 1 which ...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2012.ipm_journal-ir0volumeA48A6.2": {"snippet": "...TREC relevance assessment\n\n\n a b s t r a c tCrowdsourcing has recently gained a lot of attention as a tool for conducting different kinds of relevance evaluations. At a very high level, crowdsourcing ...", "weights": [[8, 17, 1.0], [144, 153, 1.0]]}, "1996.jasis_journal-ir0volumeA47A1.3": {"snippet": "...s in Relevance Assessments and the Measurement of Retrieval Effectiveness", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2004.cikm_conference-2004.59": {"snippet": "...tive relevance assessments for XML retrieval evaluation\n\n\n ABSTRACTComparing retrieval approaches requires test collections, which consist of documents, queries and relevance assessments. Obtaining co...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [168, 177, 1.0], [178, 189, 1.0]]}, "2012.sigirconf_conference-2012.89": {"snippet": "...rced relevance assessments\n\n\n ABSTRACTCrowdsourcing is a market of steadily-growing importance upon which both academia and industry increasingly rely. However, this market appears to be inherently in...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2001.wwwconf_conference-2001p.73": {"snippet": "Relevance Evaluation of Search Engines' Query Results", "weights": [[0, 9, 1.0]]}, "2012.sigirconf_conference-2012.115": {"snippet": "...rced relevance assessments with terrier\n\n\n ABSTRACTInformation retrieval (IR) systems rely on document relevance assessments for queries to gauge their effectiveness for a variety of tasks, e.g. Web r...", "weights": [[8, 17, 1.0], [18, 29, 1.0], [106, 115, 1.0], [116, 127, 1.0]]}, "2006.sigirconf_conference-2006.98": {"snippet": "...aded relevance judgments\n\n\n ABSTRACTWe present rpref ; our generalization of the bpref evaluation metric for assessing the quality of search engine results, given graded rather than binary user releva", "weights": [[8, 17, 1.0], [197, 206, 1.0]]}, "2008.ir_journal-ir0volumeA11A5.3": {"snippet": "...lete relevance assessments", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2016.clef_conference-2016.20": {"snippet": "... the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content bet...", "weights": [[8, 17, 1.0], [74, 83, 1.0]]}, "2007.ecir_conference-2007.26": {"snippet": "...lete Relevance Assessments", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2017.sigirconf_conference-2017.155": {"snippet": "...y of Relevance Assessments using Inter-Rater Agreement", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2010.sigirconf_conference-2010.126": {"snippet": "...icit relevance judgments which are expensive to collect. Relevance assessments inferred from implicit feedback such as click-through data can be collected inexpensively, but may be less reliable. We c...", "weights": [[8, 17, 1.0], [60, 69, 1.0], [70, 81, 1.0]]}, "2017.chiir_conference-2017.44": {"snippet": "...ment Relevance in Exploratory Search User Studies\n\n\n ABSTRACTEvaluation is crucial in assessing the e\u21b5ectiveness of new information retrieval and human computer interaction techniques and systems. Rel...", "weights": [[8, 17, 1.0], [200, 209, 1.0]]}, "2004.ecir_conference-2004.22": {"snippet": "...t of Relevance for the INEX'02 Test Collection", "weights": [[8, 17, 1.0]]}, "1996.ipm_journal-ir0volumeA32A3.1": {"snippet": "Relevance Judgments for Assessing Recall", "weights": [[0, 9, 1.0]]}, "2006.wwwconf_conference-2006.34": {"snippet": "Addressing the testing challenge with a web-based e-assessment system that tutors as it assesses\n\n\n ABSTRACTSecondary teachers across the country are being asked to use formative assessment data to in...", "weights": []}, "2013.tweb_journal-ir0volumeA7A2.6": {"snippet": "...sing relevance and trust of the deep web sources and results based on inter-source agreement\n\n\n Deep web search engines face the formidable challenge of retrieving high-quality results from the vast c...", "weights": [[8, 17, 1.0]]}, "2017.ictir_conference-2017.13": {"snippet": "...g to relevance and/or credibility , no single measure currently exists for evaluating the accuracy or precision (and more generally effectiveness) of both the relevance and the credibility of retrieve...", "weights": [[8, 17, 1.0], [162, 171, 1.0]]}, "2006.cikm_conference-2006.13": {"snippet": "...ning relevance assessments is a costly task. In XML retrieval this problem is exacerbated as the elements of the document must also be assessed with respect to the exhaustivity and specificity dimensi...", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2020.birws_workshop-2020.10": {"snippet": "...ween Relevance Assessments, Bibliometrics and Altmetrics", "weights": [[8, 17, 1.0], [18, 29, 1.0]]}, "2002.clef_workshop-2002w.5": {"snippet": "...uage Relevance Assessment and Task Context\n\n\n An experiment on how users assess relevance in a foreign language they know well is reported. Results show that relevance assessment in a foreign language...", "weights": [[8, 17, 1.0], [83, 92, 1.0], [161, 170, 1.0]]}, "1968.ipm_journal-ir0volumeA4A4.2": {"snippet": "Relevance assessments and retrieval system evaluation", "weights": [[0, 9, 1.0], [10, 21, 1.0]]}}}
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Deep Neural architectures like Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRU) were applied and compared agai...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [49, 55, 1.0], [56, 64, 1.0], [118, 124, 1.0], [125, 133, 1.0]]}, "2017.clef_conference-2017w.4": {"snippet": "...odal Deep Neural Network approach to Bird-song Identication\n\n\n We present a multi-modal Deep Neural Network (DNN) approach for bird song identification. The presented approach takes both audio samples...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [91, 95, 1.0], [96, 102, 1.0]]}, "2017.clef_conference-2017w.45": {"snippet": "Deep Neural Architecture for News Recommendation\n\n\n Deep neural networks have yielded immense success in speech recognition, computer vision and natural language processing. However, the exploration o...", "weights": [[0, 4, 1.0], [5, 11, 1.0], [52, 56, 1.0], [57, 63, 1.0], [64, 72, 1.0]]}, "E17-1104": {"snippet": "Very Deep Convolutional Networks for Text Classification\n\n\n The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, the...", "weights": [[5, 9, 1.0], [24, 32, 1.0], [115, 121, 1.0], [122, 130, 1.0], [171, 177, 1.0], [178, 186, 1.0]]}, "D16-1173": {"snippet": "Deep Neural Networks with Massive Learned Knowledge\n\n\n Regulating deep neural networks (DNNs) with human structured knowledge has shown to be of great benefit for improved accuracy and interpretabilit...", "weights": [[0, 4, 1.0], [5, 11, 1.0], [12, 20, 1.0], [66, 70, 1.0], [71, 77, 1.0], [78, 86, 1.0]]}, "2020.wsdm_conference-2020.126": {"snippet": "...y of Deep Neural Networks: Challenges and Opportunities\n\n\n One crucial aspect that yet remains fairly unknown while can inform us about the behavior of deep neural networks is their decision boundarie...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [155, 159, 1.0], [160, 166, 1.0], [167, 175, 1.0]]}, "2017.fire_conference-2017w.22": {"snippet": "...sing Deep Neural Network\n\n\n This paper is based on finding and extracting important key phrases (catchphrase) from a document from which the the document can be summarized. This is important as this w...", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2017.cikm_conference-2017.271": {"snippet": "Deep Neural Networks for News Recommendations\n\n\n ABSTRACTA fundamental role of news websites is to recommend articles that are interesting to read. The key challenge of news recommendation is to recom...", "weights": [[0, 4, 1.0], [5, 11, 1.0], [12, 20, 1.0]]}, "2019.wsdm_conference-2019.119": {"snippet": "...ity, deep learning has also attracted much attention, and researchers have proposed a large number of deep matching models to tackle the matching problem for different IR applications. Despite the fac...", "weights": [[8, 12, 1.0], [105, 109, 1.0]]}, "2021.scil-1.51": {"snippet": "Deep neural networks easily learn unnatural infixation and reduplication patterns", "weights": [[0, 4, 1.0], [5, 11, 1.0], [12, 20, 1.0]]}, "2016.mir_conference-2016.43": {"snippet": "...odal Deep Convolutional Neural Network for Audio-Visual Emotion Recognition", "weights": [[8, 12, 1.0], [27, 33, 1.0]]}, "2018.tist_journal-ir0volumeA9A6.8": {"snippet": "...ired Neural Networks\n\n\n Neural networks have become very popular in recent years, because of the astonishing success of deep learning in various domains such as image and speech recognition. In many o...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [27, 33, 1.0], [34, 42, 1.0], [123, 127, 1.0]]}, "N15-1092": {"snippet": "...Task Deep Neural Networks for Semantic Classification and Information Retrieval\n\n\n Methods of deep neural networks (DNNs) have recently demonstrated superior performance on a number of natural languag...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [97, 101, 1.0], [102, 108, 1.0], [109, 117, 1.0]]}, "2017.wsdm_conference-2017.46": {"snippet": "...nt a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks co...", "weights": [[8, 12, 1.0], [115, 119, 1.0], [132, 138, 1.0], [139, 147, 1.0], [185, 191, 1.0], [192, 200, 1.0]]}, "2012.iwslt-keynotes.3": {"snippet": "...{--} deep neural network of {G}aussian mixture model\n\n\n Recently we have shown that the context-dependent deep neural network (DNN) hidden Markov model (CD-DNN-HMM) can do surprisingly well for large ...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [109, 113, 1.0], [114, 120, 1.0]]}, "2017.mir_conference-2017.50": {"snippet": "...task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2019.cikm_conference-2019.114": {"snippet": "...zing Deep Neural Networks by Ensemble-based Low-Level Sample-Variances Method", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "L18-1063": {"snippet": "...sing deep neural networks. In this paper, we describe Sanaphor++, which is an improvement of a top-level deep neural network system for coreference resolution-namely Stanford deep-coref-through the ad...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [108, 112, 1.0], [113, 119, 1.0], [178, 182, 1.0]]}, "2018.wwwconf_conference-2018c.381": {"snippet": "...onal Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with a ridge regression and a voting strategy for sentiment and aspect predictions, and therefore, does not rely on any handcrafted fea...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [45, 51, 1.0], [52, 60, 1.0]]}, "2018.sigirconf_workshop-2018ecom.33": {"snippet": "...ding Deep Neural Networks in CTR Prediction\n\n\n Although deep learning techniques have been successfully applied to many tasks, interpreting deep neural network models is still a big challenge to us. R...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [59, 63, 1.0], [143, 147, 1.0], [148, 154, 1.0]]}, "2017.cikm_conference-2017.151": {"snippet": "...king Deep Neural Network\n\n\n ABSTRACTFriend recommendation is a critical task in social networks. In this paper, we propose a Bayesian Personalized Ranking Deep Neural Network (BayDNN) model for friend...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [90, 98, 1.0], [158, 162, 1.0], [163, 169, 1.0]]}, "D14-1080": {"snippet": "...with Deep Recurrent Neural Networks\n\n\n Recurrent neural networks (RNNs) are connectionist models of sequential data that are naturally applicable to the analysis of natural language. Recently, \"depth ...", "weights": [[8, 12, 1.0], [23, 29, 1.0], [30, 38, 1.0], [52, 58, 1.0], [59, 67, 1.0]]}, "P17-2059": {"snippet": "A Deep Network with Visual Text Composition Behavior\n\n\n While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep netwo...", "weights": [[2, 6, 1.0], [120, 126, 1.0], [190, 194, 1.0]]}, "C18-1182": {"snippet": "...sing deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approach...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [120, 124, 1.0], [125, 131, 1.0]]}, "2016.sigirconf_conference-2016.231": {"snippet": "...p on Neural Information Retrieval\n\n\n KeywordsNeural networks; deep learning; information retrieval Website: http://research.microsoft.com/neuir2016\nMOTIVATIONIn recent years, deep neural networks have...", "weights": [[8, 14, 1.0], [55, 63, 1.0], [65, 69, 1.0], [178, 182, 1.0], [183, 189, 1.0], [190, 198, 1.0]]}, "2019.cikm_conference-2019.187": {"snippet": "...gh a Deep Neural Network", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2020.wwwconf_conference-2020c.107": {"snippet": "...with Deep Neural Networks", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "2017.mir_conference-2017.38": {"snippet": "...into Deep Neural Networks", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "2020.jasis_journal-ir0volumeA71A11.3": {"snippet": "...fied deep neural network for segmentation and labeling of multipanel biomedical figures", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2018.wwwconf_conference-2018.46": {"snippet": "...rent networks to have different sizes, to be (un)weighted and (un)directed. It leverages multiple networks via cross-network relationships between nodes in different networks, which may form many-to-m...", "weights": [[8, 16, 1.0], [101, 109, 1.0], [169, 177, 1.0]]}, "2020.wsdm_conference-2020.4": {"snippet": "Can Deep Learning Only Be Neural Networks?\n\n\n The word \"deep learning\" is generally regarded as a synonym of \"deep neural networks (DNNs)\". In this talk, we will discuss on essentials in deep learning...", "weights": [[4, 8, 1.0], [26, 32, 1.0], [33, 41, 1.0], [56, 60, 1.0], [110, 114, 1.0], [115, 121, 1.0], [122, 130, 1.0], [187, 191, 1.0]]}, "S17-2052": {"snippet": "...k 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering\n\n\n We describe deep neural networks frameworks in this paper to address the community question answering (cQA) ...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [107, 111, 1.0], [112, 118, 1.0], [119, 127, 1.0]]}, "R17-2007": {"snippet": "...ying Deep Neural Network to Retrieve Relevant Civil Law Articles", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2018.ismir_conference-2018.37": {"snippet": "Deep Neural Networks with Voice Entry Estimation Heuristics for Voice Separation in Symbolic Music Representations", "weights": [[0, 4, 1.0], [5, 11, 1.0], [12, 20, 1.0]]}, "2019.tois_journal-ir0volumeA37A2.13": {"snippet": "...s of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction.However, most existing deep learning solutions are not optimized toward the targ...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [58, 62, 1.0], [146, 150, 1.0]]}, "W12-2703": {"snippet": "...yer. Deep neural networks (DNNs) with more hidden layers have been shown to capture higher-level discriminative information about input features, and thus produce better networks. Motivated by the suc...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [173, 181, 1.0]]}, "2018.ijmir_journal-ir0volumeA7A1.1": {"snippet": "... for deep neural networks", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "S17-1006": {"snippet": "...rent neural network, and convolutional neural networks. In experimental results we show that convolutional neural networks are able to outperform the previous state-of-the-art for MWE identification, ...", "weights": [[8, 14, 1.0], [42, 48, 1.0], [49, 57, 1.0], [110, 116, 1.0], [117, 125, 1.0]]}, "2018.ijclclp-2.1": {"snippet": "...onal Neural Network)\u53ca\u6df1\u5c64\u985e\u795e\u7d93\u7db2\u8def(Deep Neural Network)\u5efa\u69cb\u51fa CLDNN \u6a21 \u578b\u3002 \u8a13\u7df4\u8a9e\u6599\u90e8\u5206\uff0c\u672c\u7814\u7a76\u4f7f\u7528\u4e86 TCC300(24 \u5c0f\u6642)\u3001AIShell(162 \u5c0f\u6642)\u3001NER(111 \u5c0f\u6642)\uff0c\u4e26\u52a0\u5165\u8a9e\u8a00\u6a21\u578b\u5efa\u7acb\u5927\u8fad\u5f59\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\uff0c\u70ba\u4e86\u6aa2\u6e2c\u7cfb\u7d71\u5f37\u5065\u5ea6 (Robustness)\uff0c\u4f7f\u7528\u4e09\u7a2e\u4e0d\u540c\u74b0\u5883\u4e4b\u6e2c\u8a66\u8a9e\u6599\uff0c\u5206\u5225\u70ba TCC300(2.4 \u5c0f\u6642\uff0c\u6717\u8b80", "weights": [[8, 14, 1.0], [32, 36, 1.0], [37, 43, 1.0]]}, "2017.ismir_workshop-2017dlfm.4": {"snippet": "...sing Deep Neural Networks", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "2020.ipm_journal-ir0volumeA57A6.78": {"snippet": "A deep neural network model for speakers coreference resolution in legal texts", "weights": [[2, 6, 1.0], [7, 13, 1.0]]}, "2017.wwwconf_conference-2017c.98": {"snippet": "...cted Deep Learning Framework for Industrial-scale Recommender Systems\n\n\n ABSTRACTIn this work, we propose a locally connected deep learning framework for recommender systems, which reduces the complex...", "weights": [[8, 12, 1.0], [129, 133, 1.0]]}, "D18-1092": {"snippet": "...ning Neural Networks for On-Device Short Text Classification\n\n\n Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applic...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [67, 71, 1.0], [72, 78, 1.0], [79, 87, 1.0]]}, "P17-1052": {"snippet": "Deep Pyramid Convolutional Neural Networks for Text Categorization\n\n\n This paper proposes a low-complexity word-level deep convolutional neural network (CNN) architecture for text categorization that ...", "weights": [[0, 4, 1.0], [27, 33, 1.0], [34, 42, 1.0], [118, 122, 1.0], [137, 143, 1.0]]}, "C16-1138": {"snippet": "...n by deep recurrent neural networks with data augmentation\n\n\n Nowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, rese...", "weights": [[8, 12, 1.0], [23, 29, 1.0], [30, 38, 1.0], [75, 81, 1.0], [82, 90, 1.0], [177, 183, 1.0]]}, "2018.wwwconf_conference-2018.180": {"snippet": "...se a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate predictio...", "weights": [[8, 12, 1.0], [141, 145, 1.0]]}, "2020.tweb_journal-ir0volumeA14A2.2": {"snippet": "...sing Deep Neural Networks", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "2017.cikm_conference-2017.312": {"snippet": "...ning deep neural networks becomes feasible. We develop and evaluate two neural networks, deep autoencoder and convolutional neural network, in our fraud detection framework. Experimental results on a ...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0], [75, 81, 1.0], [82, 90, 1.0], [92, 96, 1.0], [127, 133, 1.0]]}, "C18-1116": {"snippet": "Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction\n\n\n Wikipedia provides an invaluable source of parallel multilingual data, which are in high demand for various sorts of...", "weights": [[0, 4, 1.0], [5, 11, 1.0], [12, 20, 1.0]]}, "P18-2003": {"snippet": "Deep {RNN}s Encode Soft Hierarchical Syntax\n\n\n We present a set of experiments to demonstrate that deep recurrent neural networks (RNNs) learn internal representations that capture soft hierarchical n...", "weights": [[0, 4, 1.0], [99, 103, 1.0], [114, 120, 1.0], [121, 129, 1.0]]}, "2018.tois_journal-ir0volumeA36A4.4": {"snippet": "...with Deep Neural Networks", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "W18-4411": {"snippet": "...y of deep neural network models of varying complexity. Our results reveal that deep neural network models require more data points to do better than an NBSVM linear baseline based on character n-grams...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [82, 86, 1.0], [87, 93, 1.0]]}, "2018.mir_conference-2018.42": {"snippet": "...with Deep Neural Network", "weights": [[8, 12, 1.0], [13, 19, 1.0]]}, "2018.wwwconf_conference-2018c.377": {"snippet": "...with Deep Neural Networks\n\n\n ABSTRACTAspect-based financial sentiment analysis, which aims to classify the text instance into a pre-defined aspect class and predict the sentiment score for the mention...", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "D18-1105": {"snippet": "...ning Neural Networks for On-Device Short Text Classification\n\n\n Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applic...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [67, 71, 1.0], [72, 78, 1.0], [79, 87, 1.0]]}, "2020.wwwconf_conference-2020c.40": {"snippet": "...onal Neural Networks (PPCNN)", "weights": [[8, 14, 1.0], [15, 23, 1.0]]}, "O16-1028": {"snippet": "...\u5075\u6e2c\u7cfb\u7d71(Deep Neural Networks for Audio Event Detection)[In {C}hinese]", "weights": [[8, 12, 1.0], [13, 19, 1.0], [20, 28, 1.0]]}, "L16-1330": {"snippet": "...dard Neural Networks look like, limiting the possibility to use standard Neural Networks frameworks. This work presents NNBlocks, a new framework written in Python to build and train Neural Networks t...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [76, 82, 1.0], [83, 91, 1.0], [186, 192, 1.0], [193, 201, 1.0]]}, "2018.wsdm_conference-2018.102": {"snippet": "Neural Networks for Information Retrieval\n\n\n AbstractMachine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day resear...", "weights": [[0, 6, 1.0], [7, 15, 1.0], [125, 129, 1.0]]}}}
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It is intrinsically related to retrieval performance because a document needs to be retrieved before it can b...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [125, 134, 1.0]]}, "1968.ipm_journal-ir0volumeA4A2.6": {"snippet": "...icit information retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "C94-2169": {"snippet": "...mple Retrieval by Generating Retrieval Queries from Similarities\n\n\n In example-based NLP, the problem of eoml)utational cost of example retrieval is severe, since the retrieval time increases in propo...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [139, 148, 1.0], [170, 179, 1.0]]}, "2008.ntcir_workshop-2008.48": {"snippet": "...IR-7 Information Retrieval for Question Answering\n\n\n This paper presents an information retrieval system for the NTCIR-7 information retrieval for question answering task. This system is composed by t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [79, 90, 1.0], [91, 100, 1.0], [124, 135, 1.0], [136, 145, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.22": {"snippet": "... the information available to us has been notably increasing. In this overload of information, Information Retrieval (IR) has been playing a prominent role by developing systems capable of separating ...", "weights": [[8, 19, 1.0], [85, 96, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "2015.sigirconf_conference-2015.71": {"snippet": "Information Retrieval as Card Playing: A Formal Model for Optimizing Interactive Retrieval Interface", "weights": [[0, 11, 1.0], [12, 21, 1.0], [81, 90, 1.0]]}, "2012.tist_journal-ir0volumeA3A4.10": {"snippet": "...edia retrieval. The first three studies deal primarily with comment-targeting retrieval, while the last one deals with commentexploiting retrieval. Throughout the article, connections to information r...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [140, 149, 1.0], [190, 201, 1.0], [202, 211, 1.0]]}, "2000.jasis_journal-ir0volumeA51A11.5": {"snippet": "...ling information retrieval system", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1986.sigirjournals_journal-ir0volumeA20A14.4": {"snippet": "... the retrieval processes of document retrieval systems\u00b0 Two basic categories of models that have been employed in information retrieval are the vector processing models and the Boolean retrieval model", "weights": [[8, 17, 1.0], [40, 49, 1.0], [117, 128, 1.0], [129, 138, 1.0], [188, 197, 1.0]]}, "2001.cikm_conference-2001.24": {"snippet": "...uted Information Retrieval\n\n\n ABSTRACTQuery expansion has been shown effective for both single database retrieval and for distributed information retrieval where complete collection information is ava...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [107, 116, 1.0], [137, 148, 1.0], [149, 158, 1.0], [185, 196, 1.0]]}, "2012.wsdm_conference-2012.2": {"snippet": "...ject retrieval\n\n\n ABSTRACTThe success of image object retrieval systems relies on the visual bag-of-words paradigm, which allows image retrieval systems to adopt a retrieval strategy analogous to text...", "weights": [[8, 17, 1.0], [57, 66, 1.0], [138, 147, 1.0], [167, 176, 1.0]]}, "2015.ictir_conference-2015.2": {"snippet": "...y of Retrieval: The Retrievability of Information\n\n\n ABSTRACTRetrievability is an important and interesting indicator that can be used in a number of ways to analyse Information Retrieval systems and ...", "weights": [[8, 17, 1.0], [41, 52, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2014.clef_conference-2014.5": {"snippet": "An Information Retrieval Ontology for Information Retrieval Nanopublications", "weights": [[3, 14, 1.0], [15, 24, 1.0], [38, 49, 1.0], [50, 59, 1.0]]}, "2017.cikm_conference-2017.301": {"snippet": "...thin Information Retrieval. Long has it been known that certain algorithms favour particular documents due to a ributes of these documents that are not directly related to relevance. e evaluation of b...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2005.sigirconf_conference-2005.7": {"snippet": "...back information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several contextsensitive retrieval ...", "weights": [[8, 19, 1.0], [65, 76, 1.0], [89, 98, 1.0], [126, 137, 1.0], [138, 147, 1.0], [193, 202, 1.0]]}, "2007.cikm_conference-2007.76": {"snippet": "...tive retrieval\n\n\n ABSTRACTIt has been shown that using phrases properly in the document retrieval leads to higher retrieval effectiveness. In this paper, we define four types of noun phrases and prese...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [117, 126, 1.0]]}, "2005.clef_workshop-2005w.54": {"snippet": "...ical information retrieval techniques cause to information retrieval. The baseline approach is based on the development of named entity recognition and geospatial information retrieval tools and on it...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0], [166, 177, 1.0], [178, 187, 1.0]]}, "2004.sigirconf_conference-2004.25": {"snippet": "...ased retrieval using language models\n\n\n ABSTRACTPrevious research on cluster-based retrieval has been inconclusive as to whether it does bring improved retrieval effectiveness over document-based retr...", "weights": [[8, 17, 1.0], [86, 95, 1.0], [155, 164, 1.0], [199, 208, 1.0]]}, "2004.clef_workshop-2004.30": {"snippet": "...gual information. While monolingual information retrieval was improved by using the analyzer, bi-lingual information retrieval still has room for improvement.", "weights": [[8, 19, 1.0], [39, 50, 1.0], [51, 60, 1.0], [108, 119, 1.0], [120, 129, 1.0]]}, "1991.sigirconf_conference-91.24": {"snippet": "...ssor Information Retrieval Systems\n\n\n Abstract.Information retrieval is the selection of documents that are potentially relevant to a user's information need. Given the vast volume of data stored in m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0], [144, 155, 1.0]]}, "2010.ntcir_workshop-2010.21": {"snippet": "... for information retrieval for question answering (IR4QA) of NTCIR-8 tasks. For improving information retrieval performance, we focus mostly on the document re-ranking technique, which locates between...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [93, 104, 1.0], [105, 114, 1.0]]}, "2002.clef_workshop-2002.29": {"snippet": "Information Retrieval with Language Knowledge", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.8": {"snippet": "...cale information retrieval systems. Distributing different aspects of search (e.g., crawling, indexing, and query processing) is essential to achieve scalability in large-scale information retrieval s...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "L16-1113": {"snippet": "... Web information, namely parsing content to find relevant documents on the basis of a user's query, represents a crucial step to guarantee fast and accurate Information Retrieval (IR). Generally, an a...", "weights": [[8, 19, 1.0], [160, 171, 1.0], [172, 181, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords Information Retrieval (IR) solutions to retrieve passages matching a set of terms extracted from the question.There is a fundamental disconnect between the capabilities of the bag-of-words retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [197, 206, 1.0]]}, "2014.cikm_conference-2014.9": {"snippet": "...ween Retrieval Bias and Retrieval Performance\n\n\n ABSTRACTRetrievability provides an alternative way to assess an Information Retrieval (IR) system by measuring how easily documents can be retrieved. R...", "weights": [[8, 17, 1.0], [27, 36, 1.0], [116, 127, 1.0], [128, 137, 1.0]]}, "2008.sigirconf_conference-2008.48": {"snippet": "...ment retrieval (SDR) have made use of speech lattices: speech lattices contain information about alternative speech transcription hypotheses other than the 1-best transcripts, and this information can...", "weights": [[8, 17, 1.0], [82, 93, 1.0], [188, 199, 1.0]]}, "2015.jasis_journal-ir0volumeA66A11.9": {"snippet": "...tive information retrieval behavior to inform design", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...n of retrieval models particularly for recall oriented retrieval domains, where the focus is on retrieving all relevant information but not just retrieving a subset of relevant information. Available ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [123, 134, 1.0], [180, 191, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "W98-0704": {"snippet": "...l in information retrieval applications. Experiments using several standard information retrieval test collections show that our method results in a significant improvement of information retrieval pe", "weights": [[8, 19, 1.0], [20, 29, 1.0], [79, 90, 1.0], [91, 100, 1.0], [179, 190, 1.0], [191, 200, 1.0]]}, "2003.sigirconf_conference-2003.73": {"snippet": "XML retrieval: what to retrieve?\n\n\n ABSTRACTThe fundamental difference between standard information retrieval and XML retrieval is the unit of retrieval. In traditional IR, the unit of retrieval is fi...", "weights": [[4, 13, 1.0], [88, 99, 1.0], [100, 109, 1.0], [118, 127, 1.0], [143, 152, 1.0], [185, 194, 1.0]]}, "2022.deelio-1.3": {"snippet": "...ense Retrieval\n\n\n Dense retrieval aims at searching for the most relevant documents to the given query by encoding texts in the embedding space, requiring a large amount of query-document pairs to tra...", "weights": [[8, 17, 1.0], [27, 36, 1.0]]}, "2006.jasis_journal-ir0volumeA57A10.13": {"snippet": "Information retrieval design: Principles and options for information description, organization, display, and access in information retrieval databases, digital libraries, catalogs, and indexes", "weights": [[0, 11, 1.0], [12, 21, 1.0], [57, 68, 1.0], [119, 130, 1.0], [131, 140, 1.0]]}, "N07-1012": {"snippet": "Information Retrieval On Empty Fields\n\n\n We explore the problem of retrieving semi-structured documents from a realworld collection using a structured query. We formally develop Structured Relevance M...", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "1998.sigirconf_conference-98.31": {"snippet": "...-Hoc Retrieval for Short Queries\n\n\n AbstractShort queries in an ad-hoc retrieval environment are difftcult but unavoidable.We present several methods to try to improve our current strategy of 2-stage ...", "weights": [[8, 17, 1.0], [74, 83, 1.0]]}, "A00-1003": {"snippet": "...edia Information Retrieval\n\n\n Simple measures can achieve high-accuracy cross-language retrieval in carefully chosen applications. Image retrieval is one of those applications, with results ranging fr...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [90, 99, 1.0], [140, 149, 1.0]]}, "2004.clef_workshop-2004w.54": {"snippet": "...gual information retrieval. The performance of cross-lingual information retrieval was poor and that of monolingual information retrieval was not good. We need to check our modules and procedures.", "weights": [[8, 19, 1.0], [20, 29, 1.0], [64, 75, 1.0], [76, 85, 1.0], [119, 130, 1.0], [131, 140, 1.0]]}, "Y05-1024": {"snippet": "...Text Retrieval System for Cellular Phones with Web Browsing Capability\n\n\n This paper describes a text retrieval system for cellular phones with Web browsing capability, which accepts spoken queries ov...", "weights": [[8, 17, 1.0], [105, 114, 1.0]]}, "2006.clef_workshop-2006.108": {"snippet": "...Page Retrieval", "weights": [[8, 17, 1.0]]}, "2003.sigirconf_conference-2003.58": {"snippet": "...g of retrieval systems in imperfect environments\n\n\n ABSTRACTThe empirical investigation of the effectiveness of information retrieval (IR) systems requires a test collection, a set of query topics, an...", "weights": [[8, 17, 1.0], [115, 126, 1.0], [127, 136, 1.0]]}, "2002.ntcir_workshop-2002.59": {"snippet": "...s-DB Retrieval\n\n\n In cross-DB retrieval, the domain of queries differs from the retrieval target in the distribution of that of term occurrences. This causes incorrect term weighting in the retrieval ...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [83, 92, 1.0], [193, 202, 1.0]]}, "1990.jasis_journal-ir0volumeA41A2.10": {"snippet": "...s of information retrieval and automatic text processing: The transformation analysis, and retrieval of information by computer", "weights": [[8, 19, 1.0], [20, 29, 1.0], [94, 103, 1.0], [107, 118, 1.0]]}, "2002.ir_journal-ir0volumeA5A23.8": {"snippet": "Information Retrieval: Algorithms and Heuristics", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2015.ecir_conference-2015.22": {"snippet": "... and Retrieval Bias: A Comparison of Inequality Measures", "weights": [[8, 17, 1.0]]}, "2017.fire_conference-2017w.24": {"snippet": "...017: Information Retrieval From Legal Documents\n\n\n This paper details the approach of implementing the Catchphrase Extraction and Precedence Retrieval tasks to be presented at Information Retrieval fr...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [144, 153, 1.0], [179, 190, 1.0], [191, 200, 1.0]]}, "2020.sigirconf_conference-2020.367": {"snippet": "... for Information Retrieval: Fundamentals and Advances\n\n\n Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users' information needs by suggesting...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [60, 71, 1.0], [72, 81, 1.0], [172, 183, 1.0]]}, "W09-4632": {"snippet": "...e\n\n\n Information retrieval is a field of research reaching from computer and information science to lingusitics. As a linguist in the information retrieval field, I leave the quest for effective searc...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [137, 148, 1.0], [149, 158, 1.0]]}, "2017.fire_conference-2017w.19": {"snippet": "... the Information Retrieval task. The distributed representation of text attained 3rd position in subtask 1 and attained satisfactory score in subtask 2. CCS CONCEPTS \u2022 Information systems \u2192 Informatio...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [171, 182, 1.0], [193, 204, 1.0]]}, "2004.jasis_journal-ir0volumeA55A10.2": {"snippet": "...tive retrieval strategies in the same information retrieval system", "weights": [[8, 17, 1.0], [41, 52, 1.0], [53, 62, 1.0]]}, "2002.cikm_conference-2002.59": {"snippet": "Information retrieval on the semantic web", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2004.ntcir_workshop-2004.57": {"snippet": "...onal Retrieval Task 2 that was conducted from 2003 to 2004 as a subtask of the WEB Task at the Fourth NTCIR Workshop ('NTCIR-4 WEB'). In the Informational Retrieval Task, we attempted to assess the re...", "weights": [[8, 17, 1.0], [158, 167, 1.0], [201, 210, 1.0]]}, "2017.trec_conference-2017.21": {"snippet": "...ular information retrieval application is web search engine such as Google [2] , Baidu [3] , Bing [4] and Sogou [5]. These application will return top-N best retrieval result to users. Information Ret...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [161, 170, 1.0], [188, 199, 1.0], [200, 209, 1.0]]}, "T78-1026": {"snippet": "... for retrieval only, and not for retrieval and updating simultaneously. A related assumption (or constraint) that in questions descriptions are used referentially only (i.e., a question cannot be used...", "weights": [[8, 17, 1.0], [36, 45, 1.0]]}, "2020.sigirconf_conference-2020.327": {"snippet": "...n an Information Retrieval course. CCS CONCEPTS \u2022 Information systems \u2192 Search engine architectures and scalability.", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 64, 1.0]]}, "2020.clef_conference-2020w.68": {"snippet": "...ment Retrieval with Transformers\n\n\n We report on our recent efforts to employ transformer-based models as part of an information retrieval pipeline, using argument retrieval as a benchmark. Transforme...", "weights": [[8, 17, 1.0], [120, 131, 1.0], [132, 141, 1.0], [167, 176, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2007.ntcir_workshop-2007.11": {"snippet": "...nese Information Retrieval Based on Document Expansion\n\n\n This paper describes our work at the sixth NTCIR workshop on the subtasks of monolingual information retrieval (CLIR). This is the second time...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [150, 161, 1.0], [162, 171, 1.0]]}, "1977.sigirjournals_journal-ir0volumeA12A2.0": {"snippet": "Information Storage and Retrieval, A Survey and Functional Description", "weights": [[0, 11, 1.0], [24, 33, 1.0]]}, "2012.ipm_journal-ir0volumeA48A1.9": {"snippet": "... and information science, the same cannot be said about the subfield of information retrieval. We address that in this work by sketching the information retrieval intellectual landscape through visual...", "weights": [[8, 19, 1.0], [75, 86, 1.0], [87, 96, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "2016.sigirconf_conference-2016.89": {"snippet": "...This information retrieval test collection is the first to combine multilingual search, passage retrieval, and informal online genre text.", "weights": [[8, 19, 1.0], [20, 29, 1.0], [99, 108, 1.0]]}, "2020.sigirconf_conference-2020.5": {"snippet": "... for Information Retrieval\n\n\n Information retrieval (IR) is the science of search, the search of user query relevant pieces of information from a collection of unstructured resources. Information in t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [33, 44, 1.0], [45, 54, 1.0], [130, 141, 1.0], [187, 198, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.10": {"snippet": "... XML retrieval. XML retrieval deals with retrieving those document parts that specifically answer a query. It is concerned with using the document structure to improve the retrieval of information fro...", "weights": [[8, 17, 1.0], [23, 32, 1.0], [175, 184, 1.0], [188, 199, 1.0]]}, "1992.sigirconf_conference-92.3": {"snippet": "...of a Retrieval Process", "weights": [[8, 17, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2016.ictir_conference-2016.20": {"snippet": "...t of retrieval systems that performance and efficiency measures do not. Retrievability is often used to calculate the retrievability bias, an indication of how accessible a system makes all the docume...", "weights": [[8, 17, 1.0]]}, "2010.wwwconf_conference-2010.11": {"snippet": "...dard information retrieval (IR). Recently, there has been a great deal of research into developing advanced match ranking algorithms. However, no previous research has addressed the ad indexing proble...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2004.tois_journal-ir0volumeA22A3.4": {"snippet": "...uted information retrieval, which aims to reduce the network traffic of query passing in the network. We carry out experiments to show the distributed image retrieval system and the Firework informati...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [160, 169, 1.0], [194, 205, 1.0]]}, "1999.jasis_journal-ir0volumeA50A11.6": {"snippet": "Information Retrieval and the Virtual Document", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2001.ntcir_workshop-2001.29": {"snippet": "On a Retrieval Support System by Suggesting Terms to a User\n\n\n We propose a support method for information retrieval. This method automatically suggests terms relevant to a query, to a user, and when ...", "weights": [[5, 14, 1.0], [95, 106, 1.0], [107, 116, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.2": {"snippet": "... for information retrieval\n\n\n AbstractThis paper presents a probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [77, 88, 1.0], [89, 98, 1.0], [122, 131, 1.0]]}, "2010.jasis_journal-ir0volumeA61A4.16": {"snippet": "...n to Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1995.jasis_journal-ir0volumeA46A9.12": {"snippet": "Information Retrieval: New Systems and Current Research. Proceedings of the 15th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, edited by Ruben Leon", "weights": [[0, 11, 1.0], [12, 21, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2005.spire_conference-2005.25": {"snippet": "Retrieval Status Values in Information Retrieval Evaluation", "weights": [[0, 9, 1.0], [27, 38, 1.0], [39, 48, 1.0]]}, "1998.ecir_conference-1998.4": {"snippet": "...ning Information Retrieval with Information Extraction for Efficient Retrieval of Calls for Papers", "weights": [[8, 19, 1.0], [20, 29, 1.0], [35, 46, 1.0], [72, 81, 1.0]]}, "2017.sigirconf_workshop-2017birndl.8": {"snippet": "... of \"Information Retrieval\" - A Tale of Three Databases\n\n\n Coverage is an important criterion when evaluating information systems. This exploratory study investigates this issue by submitting the same...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [113, 124, 1.0]]}, "2018.airs_conference-2018.0": {"snippet": "Information Retrieval Technology - 14th Asia Information Retrieval Societies Conference, AIRS 2018, Taipei, Taiwan, November 28-30, 2018, Proceedings", "weights": [[0, 11, 1.0], [12, 21, 1.0], [45, 56, 1.0], [57, 66, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "2005.ntcir_workshop-2005.47": {"snippet": "...tent Retrieval Experiments at Hitachi\n\n\n In NTCIR-5, we used five retrieval methods proposed in NTCIR-4: (1) query term weighting using only document frequency, (2) stopword deletion, (3) two-stage pa...", "weights": [[8, 17, 1.0], [69, 78, 1.0]]}, "2002.ntcir_workshop-2002.1": {"snippet": "...run\" retrieval topics of the task. In this report we will give a brief description about our basic method for web information retrieval, our web information retrieval system and some retrieval experim", "weights": [[8, 17, 1.0], [117, 128, 1.0], [129, 138, 1.0], [148, 159, 1.0], [160, 169, 1.0], [186, 195, 1.0]]}, "2009.cikm_conference-2009.134": {"snippet": "...opic retrieval\n\n\n ABSTRACTTraditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [63, 72, 1.0], [167, 178, 1.0], [194, 203, 1.0]]}, "2005.clef_workshop-2005w.119": {"snippet": "... All retrieval results rely heavily on two retrieval systems: for visual retrieval we use the GNU Image Finding Tool (GIFT), and for textual retrieval the EasyIR retrieval system. For the ad-hoc retri...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [76, 85, 1.0], [144, 153, 1.0], [165, 174, 1.0], [198, 207, 1.0]]}, "2016.airs_conference-2016.0": {"snippet": "Information Retrieval Technology - 12th Asia Information Retrieval Societies Conference, AIRS 2016, Beijing, China, November 30 - December 2, 2016, Proceedings", "weights": [[0, 11, 1.0], [12, 21, 1.0], [45, 56, 1.0], [57, 66, 1.0]]}, "W03-1115": {"snippet": "...resh information. However, fast indexing alone is not enough to retrieve fresh information, as support for temporal information based retrieval is also required. In this paper, we describe temporal in...", "weights": [[8, 19, 1.0], [82, 93, 1.0], [119, 130, 1.0], [137, 146, 1.0], [201, 212, 1.0]]}, "2002.ntcir_workshop-2002.16": {"snippet": "...iven retrieval, which facilitates retrieving information with spoken queries, we organized the speech-driven retrieval subtask in the NTCIR-3 Web retrieval task. Search topics for the Web retrieval ma...", "weights": [[8, 17, 1.0], [48, 59, 1.0], [112, 121, 1.0], [149, 158, 1.0], [191, 200, 1.0]]}, "1987.sigirjournals_journal-ir0volumeA21A34.1": {"snippet": "... for Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "I05-2024": {"snippet": "Information Retrieval Capable of Visualization and High Precision\n\n\n We present a neural-network based selforganizing approach that enables visualization of the information retrieval while at the same...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [161, 172, 1.0], [173, 182, 1.0]]}, "1980.sigirconf_conference-80.0": {"snippet": "Information Retrieval Research, Proc. Joint ACM/BCS Symposium in Information Storage and Retrieval, Cambridge, UK, June 1980", "weights": [[0, 11, 1.0], [12, 21, 1.0], [65, 76, 1.0], [89, 98, 1.0]]}, "1990.jasis_journal-ir0volumeA41A2.13": {"snippet": "... and information retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1990.sigirjournals_journal-ir0volumeA24A3.10": {"snippet": "Information Retrieval Abstracts", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2017.clef_conference-2017w.74": {"snippet": "...ered Information Retrieval Task-1: Improving Medical Document Retrieval with Query Expansion\n\n\n In this report, we describe our retrieval framework for participating in CLEF eHealth 2017 Patient-Cente...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [65, 74, 1.0], [131, 140, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.22": {"snippet": "...ased retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic i...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [202, 213, 1.0]]}, "1989.sigirconf_conference-89.7": {"snippet": "...ated Information Retrieval in a Knowledge Worker Support System\n\n\n AbstractThis paper describes the design of the information retrieval facilities of an integrated information system called EUROMATH. ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [117, 128, 1.0], [129, 138, 1.0], [167, 178, 1.0]]}, "2004.tois_journal-ir0volumeA22A2.4": {"snippet": "...d on information retrieval concepts\n\n\n XIRQL (\"circle\") is an XML query language that incorporates imprecision and vagueness for both structural and content-oriented query conditions. The correspondin...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...n of Information Retrieval Models\n\n\n Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is nece...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [61, 70, 1.0], [118, 129, 1.0], [130, 139, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A3.1": {"snippet": "...bout Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.12": {"snippet": "...line retrieval", "weights": [[8, 17, 1.0]]}, "2019.ictir_conference-2019.39": {"snippet": "...sage Retrieval for Entity Retrieval", "weights": [[8, 17, 1.0], [29, 38, 1.0]]}, "2005.sigirconf_conference-2005.64": {"snippet": "...s to information retrieval\n\n\n ABSTRACTExisting retrieval models generally do not offer any guarantee for optimal retrieval performance. Indeed, it is even difficult, if not impossible, to predict a mo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 59, 1.0], [116, 125, 1.0]]}, "2012.ecir_conference-2012.15": {"snippet": "...eech Retrieval", "weights": [[8, 17, 1.0]]}, "2010.jasis_journal-ir0volumeA61A11.15": {"snippet": "Information Retrieval: Searching in the 21st Century; Human Information Retrieval", "weights": [[0, 11, 1.0], [12, 21, 1.0], [60, 71, 1.0], [72, 81, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "1981.sigirconf_conference-81.7": {"snippet": "...stic Retrieval\n\n\n ABSTRACTThe objective is to relate the effectiveness of retrieval, the fuzzy set concept and the processing of Boolean query. The use of a probabilistic retrieval scheme is motivated...", "weights": [[8, 17, 1.0], [77, 86, 1.0], [174, 183, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.5": {"snippet": "...tive Information Retrieval in an information-intensive domain\n\n\n AbstractIn this article we investigate the expressions of collaborative activities within information seeking and retrieval processes (...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [36, 47, 1.0], [158, 169, 1.0], [182, 191, 1.0]]}, "2020.ictir_conference-2020.28": {"snippet": "...s on Retrieval Bias and Effectiveness\n\n\n Prior work on using retrievability measures in the evaluation of information retrieval (IR) systems has laid out the foundations for investigating the relation...", "weights": [[8, 17, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "1987.jasis_journal-ir0volumeA38A2.2": {"snippet": "...ment retrieval: II. The retrieval process", "weights": [[8, 17, 1.0], [27, 36, 1.0]]}, "2008.sigirconf_conference-2008.170": {"snippet": "...ment retrieval has shown to be a competitive approach in XML element retrieval, which is counter-intuitive since the element retrieval task requests all and only relevant document parts to be retrieve...", "weights": [[8, 17, 1.0], [72, 81, 1.0], [128, 137, 1.0]]}, "2016.ictir_conference-2016.14": {"snippet": "...y of Information Retrieval Models\n\n\n ABSTRACTDeveloping effective information retrieval models has been a long standing challenge in Information Retrieval (IR), and significant progresses have been ma...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [69, 80, 1.0], [81, 90, 1.0], [136, 147, 1.0], [148, 157, 1.0]]}, "2008.ntcir_workshop-2008.28": {"snippet": "Information Retrieval Using PU Learning Based Re-ranking\n\n\n In this paper, we describe our approach for information retrieval for question answering (IR4QA) on simple Chinese language of NTCIR-7 tasks...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [104, 115, 1.0], [116, 125, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ment retrieval. The shot retrieval and the video segment model re-use the probability of relevance and a language modeling ranking function respectively, basing themselves on decades of text retrieval...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [194, 203, 1.0]]}, "2014.sigirconf_conference-2014.222": {"snippet": "... for Information Retrieval, where his research interests include: (i) Models for the retrieval of documents, sentences, experts and other information objects ; (ii) Probabilistic models of user intera...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [88, 97, 1.0], [141, 152, 1.0]]}, "2010.clef_workshop-2010w.79": {"snippet": "...edia Retrieval Task at ImageCLEF 2010\n\n\n ImageCLEF's Wikipedia Retrieval task provides a testbed for the system-oriented evaluation of multimedia information retrieval from a collection of Wikipedia i...", "weights": [[8, 17, 1.0], [66, 75, 1.0], [149, 160, 1.0], [161, 170, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.13": {"snippet": "...y of retrieval modalities for video shot retrieval", "weights": [[8, 17, 1.0], [44, 53, 1.0]]}, "2018.ictir_conference-2018.10": {"snippet": "...ment Retrieval\n\n\n ABSTRACTIn this paper, we study jointly query reformulation and document relevance estimation, the two essential aspects of information retrieval (IR). Their interactions are modelle...", "weights": [[8, 17, 1.0], [145, 156, 1.0], [157, 166, 1.0]]}, "1991.jasis_journal-ir0volumeA42A1.7": {"snippet": "...ased information retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2004.sigirconf_conference-2004.8": {"snippet": "...y of information retrieval heuristics\n\n\n ABSTRACTEmpirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [73, 84, 1.0], [85, 94, 1.0], [118, 127, 1.0], [181, 190, 1.0]]}, "2004.jasis_journal-ir0volumeA55A7.1": {"snippet": "Information retrieval by metabrowsing", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2009.sigirconf_conference-2009.203": {"snippet": "...tive retrieval: study of emotion in adaptive retrieval", "weights": [[8, 17, 1.0], [48, 57, 1.0]]}, "2005.jasis_journal-ir0volumeA56A2.7": {"snippet": "Information representation and retrieval in the digital age", "weights": [[0, 11, 1.0], [31, 40, 1.0]]}, "2019.sigirjournals_journal-ir0volumeA53A2.10": {"snippet": "Information retrieval and its sister disciplines", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2008.ipm_journal-ir0volumeA44A3.9": {"snippet": "...sing information gain to improve multi-modal information retrieval systems\n\n\n AbstractNowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval ...", "weights": [[8, 19, 1.0], [48, 59, 1.0], [60, 69, 1.0], [109, 120, 1.0], [193, 202, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ical information retrieval\n\n\n AbstractIn this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 153, 1.0], [154, 163, 1.0]]}, "1999.sigirconf_conference-99.73": {"snippet": "Information Retrieval Library (IRLIB) (demonstration abstract)\n\n\n ABSTRACTThe IRLIB is a full-text web searchable digital library, whose contents are publications and documents relative to the field o...", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "1987.sigirconf_conference-87.21": {"snippet": "...near Information Retrieval Models\n\n\n AbstractLinear decision (retrieval)functions have been widely adopted in information retrieval systems such as in Boolean, vector, and probabilistic", "weights": [[8, 19, 1.0], [20, 29, 1.0], [65, 74, 1.0], [113, 124, 1.0], [125, 134, 1.0]]}, "2001.sigirconf_conference-2001.84": {"snippet": "...main Information Servers using features of the standard Z39.50 information retrieval protocol[Z39.50]. The system is currently being used to build and search distributed indexes for databases with dis...", "weights": [[8, 19, 1.0], [66, 77, 1.0], [78, 87, 1.0]]}, "2011.iir_workshop-2011.22": {"snippet": "Web Information Retrieval for Complex Not-Informational Intents: An Introduction to User Intent Analysis", "weights": [[4, 15, 1.0], [16, 25, 1.0]]}, "2010.sigirconf_conference-2010.138": {"snippet": "...uted information retrieval system . However, most post-retrieval query performance predictors are expensive to compute, and not well-suited for the web.The key idea behind RAPP is to use retrieval sco...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [58, 67, 1.0], [190, 199, 1.0]]}, "2002.cikm_conference-2002.1": {"snippet": "... the Information Retrieval Laboratory at the Illinois Institute of Technology. His research area is scalable information systems spanning communication networks, parallel processing, and information r...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [112, 123, 1.0], [190, 201, 1.0], [202, 211, 1.0]]}, "2005.ecir_conference-2005.37": {"snippet": "...rier Information Retrieval Platform\n\n\n Abstract. Terrier is a modular platform for the rapid development of large-scale Information Retrieval (IR) applications. It can index various document collectio...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2021.emnlp-main.297": {"snippet": "...rase Retrieval Learns Passage Retrieval, Too\n\n\n Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems. Among them, dense phrase retrieval-the most f...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [57, 66, 1.0], [112, 121, 1.0], [183, 192, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A2.6": {"snippet": "Information retrieval in context: IRiX", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "L08-1002": {"snippet": "...eech Information Retrieval\n\n\n In this article we present a method for combining different information retrieval models in order to increase the retrieval performance in a Speech Information Retrieval ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [93, 104, 1.0], [105, 114, 1.0], [147, 156, 1.0], [181, 192, 1.0], [193, 202, 1.0]]}, "2009.trec_conference-2009.7": {"snippet": "...atic retrieval models on the large data collection. Axiomatic approaches to information retrieval have been recently proposed and studied. The basic idea is to search for retrieval functions that can ...", "weights": [[8, 17, 1.0], [79, 90, 1.0], [91, 100, 1.0], [174, 183, 1.0]]}, "1992.jasis_journal-ir0volumeA43A2.6": {"snippet": "...gent Information Retrieval: An Introduction", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1988.riao_conference-1988.41": {"snippet": "...Text Retrieval for Legal Information Retrieval Systems", "weights": [[8, 17, 1.0], [28, 39, 1.0], [40, 49, 1.0]]}, "2001.clef_workshop-2001w.21": {"snippet": "...gual Information Retrieval\n\n\n This paper reports the work of NTU on bilingual-retrieval task at CLEF 2001. We proposed five models. Model 1 used co-occurrence information to disambiguate translation e...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [81, 90, 1.0], [162, 173, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.20": {"snippet": "Information Retrieval as Statistical Translation", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "1976.sigirjournals_journal-ir0volumeA11A3.3": {"snippet": "Information Retrieval at Cornell", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.8": {"snippet": "...tive Information Retrieval: Using a Connectionist Representation to Retrieve and Leasrn about Documents", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1988.sigirconf_conference-88.34": {"snippet": "... for Information Retrieval Systems\n\n\n This paper is a contribution to the construction of a general model for information retrieval.As in the paper of Van Rijsbergen ([RIJSG]), the implicit base in al...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [113, 124, 1.0], [125, 134, 1.0]]}, "2014.cikm_conference-2014.187": {"snippet": "...uage Information Retrieval (CLIR) addresses the problem of finding documents in some language that are relevant to a question posed in a different language. Retrieving answers to questions written usi...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2007.wwwconf_conference-2007.133": {"snippet": "... for information retrieval\n\n\n ABSTRACTA number of existing information retrieval systems propose the notion of query context to combine the knowledge of query and user into retrieval to reveal the mos...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [62, 73, 1.0], [74, 83, 1.0], [176, 185, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.31": {"snippet": "... and Information  Retrieval\" (1960). This was the first paper to present a probabilistic approach to information retrieval, and perhaps the first paper on ranked retrieval. Although it is one of the m...", "weights": [[8, 19, 1.0], [21, 30, 1.0], [104, 115, 1.0], [116, 125, 1.0], [165, 174, 1.0]]}}}
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By examining and generalizing existing CF approaches, an expressive SIF search space is designed and represented as a structured multi-...", "weights": [[8, 14, 1.0], [26, 32, 1.0], [140, 146, 1.0]]}, "2020.ir_journal-ir0volumeA23A2.0": {"snippet": "...e on eCommerce search and recommendation", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "2019.cikm_conference-2019.276": {"snippet": "Neural Review Rating Prediction with User and Product Memory", "weights": [[0, 6, 1.0], [46, 53, 1.0]]}, "2012.cikm_conference-2012.362": {"snippet": "...each product item based on its transaction history. By incorporating such bestselling prediction with relevance, we propose a new ranking model for product search, in which we rank higher the product ...", "weights": [[8, 15, 1.0], [133, 140, 1.0], [151, 158, 1.0], [159, 165, 1.0], [195, 202, 1.0]]}, "2011.sigirconf_conference-2011.10": {"snippet": "...call ecommerce queries\n\n\n ABSTRACTUser expectation and experience for web search and eCommerce (product) search are quite different. Product descriptions are concise as compared to typical web documen...", "weights": [[8, 17, 1.0], [77, 83, 1.0], [88, 97, 1.0], [99, 106, 1.0], [108, 114, 1.0], [136, 143, 1.0]]}, "2017.ir_journal-ir0volumeA20A2.1": {"snippet": "Neural Semantic Personalized Ranking for item cold-start recommendation", "weights": [[0, 6, 1.0], [29, 36, 1.0]]}, "2019.wsdm_conference-2019.50": {"snippet": "...of a search engine, and one of the main challenges is the vocabulary gap between user queries and documents. This problem is more serious in ecommerce, because language in product titles is more profe...", "weights": [[8, 14, 1.0], [144, 153, 1.0], [175, 182, 1.0]]}, "2018.sigirconf_workshop-2018ecom.9": {"snippet": "...onal Neural Network and Bidirectional LSTM Based Taxonomy Classification Using External Dataset at SIGIR eCom Data Challenge\n\n\n In eCommerce websites, products are annotated with various metadata such...", "weights": [[8, 14, 1.0], [134, 143, 1.0]]}, "2017.cikm_conference-2017.302": {"snippet": "...user search behaviors such as clicks and views. Based on the learnt session representation, we further propose a list-wise ranking model to generate the recommendation result for each anonymous user s...", "weights": [[8, 14, 1.0], [126, 133, 1.0]]}, "2020.wwwconf_conference-2020.259": {"snippet": "...zing Neural Search Ranking Models\n\n\n Neural search ranking models have been not only actively studied in the information retrieval community, but also widely adopted in real-world industrial applicati...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 29, 1.0], [40, 46, 1.0], [47, 53, 1.0], [54, 61, 1.0]]}, "2020.sigirconf_conference-2020.364": {"snippet": "...ar's eCommerce IR workshop is integrating recommendations into search for eCommerce. In addition to the focus on recommender systems in eCommerce search, Rakuten France is sponsoring a data challenge ...", "weights": [[8, 17, 1.0], [66, 72, 1.0], [77, 86, 1.0], [139, 148, 1.0], [149, 155, 1.0]]}, "2020.emnlp-main.188": {"snippet": "...g in Product Question Answering\n\n\n Product-related question answering platforms nowadays are widely employed in many Ecommerce sites, providing a convenient way for potential customers to address thei...", "weights": [[8, 15, 1.0], [38, 45, 1.0], [120, 129, 1.0]]}, "2018.sigirconf_workshop-2018ecom.5": {"snippet": "... for eCommerce-scale Product Catalogs, we propose an approach based on deep convolutional neural networks to predict product taxonomies using their descriptions. The classification performance of the ...", "weights": [[8, 17, 1.0], [24, 31, 1.0], [93, 99, 1.0], [120, 127, 1.0]]}, "2017.cikm_conference-2017.229": {"snippet": "...Item Ranking\n\n\n ABSTRACTPersonalized item ranking for recommending top-N items of interest to a user is an interesting and challenging problem in ecommerce. Researchers and practitioner are continuous...", "weights": [[8, 15, 1.0], [45, 52, 1.0], [149, 158, 1.0]]}, "2006.cikm_conference-2006.64": {"snippet": "...inal ranking score for each tuple. Tuples with the top ranking scores are presented to the user first. Our ranking method is domain independent and requires no user feedback. Experimental results demo...", "weights": [[8, 15, 1.0], [58, 65, 1.0], [110, 117, 1.0]]}, "2012.airs_conference-2012.25": {"snippet": "... and Search in E-Commerce", "weights": [[8, 14, 1.0]]}, "N18-2095": {"snippet": "...onal Neural Networks with Auxiliary Domain Discriminators\n\n\n With the growing amount of reviews in ecommerce websites, it is critical to assess the helpfulness of reviews and recommend them accordingl...", "weights": [[8, 14, 1.0], [102, 111, 1.0]]}, "2020.acl-main.614": {"snippet": "...erce Product Search\n\n\n Showing items that do not match search query intent degrades customer experience in ecommerce. These mismatches result from counterfactual biases of the ranking algorithms towar...", "weights": [[8, 15, 1.0], [16, 22, 1.0], [58, 64, 1.0], [110, 119, 1.0], [179, 186, 1.0]]}, "2020.sigirconf_conference-2020.65": {"snippet": "...that product reviews usually provide useful information for a given question, and thus can assist the ranking process. In this paper, we investigate the answer ranking problem for product-related ques...", "weights": [[8, 15, 1.0], [105, 112, 1.0], [163, 170, 1.0], [183, 190, 1.0]]}, "2019.ictir_conference-2019.18": {"snippet": "...wise Neural Ranking Models", "weights": [[8, 14, 1.0], [15, 22, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.12": {"snippet": "...s of search engines in response to ecommerce queries by comparing the engines\u00d5 quality of ecommerce links using topical relevancy ratings. This research employs 100 ecommerce queries, five major searc...", "weights": [[8, 14, 1.0], [38, 47, 1.0], [93, 102, 1.0], [168, 177, 1.0], [198, 204, 1.0]]}, "2020.sigirjournals_journal-ir0volumeA54A1.1": {"snippet": "...s in eCommerce search and recommendations", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "2016.sigirconf_conference-2016.50": {"snippet": "...ance ranking both positively influence the shopping experience of millions of customers and significantly impact revenue. In the past, Amazon's product search engine consisted of several handtuned ran...", "weights": [[8, 15, 1.0], [147, 154, 1.0], [155, 161, 1.0], [200, 207, 1.0]]}, "2019.sigirconf_workshop-2019ecom.19": {"snippet": "Ranking Sentences from Product Description & Bullets for Better Search\n\n\n Products in an ecommerce catalog contain information-rich fields like description and bullets that can be useful to extract en...", "weights": [[0, 7, 1.0], [23, 30, 1.0], [64, 70, 1.0], [89, 98, 1.0]]}, "2020.wwwconf_conference-2020c.44": {"snippet": "...ized Ranking in eCommerce Search\n\n\n We address the problem of personalization in the context of eCommerce search. Specifically, we develop personalization ranking features that use in-session context ...", "weights": [[8, 15, 1.0], [19, 28, 1.0], [29, 35, 1.0], [99, 108, 1.0], [109, 115, 1.0], [158, 165, 1.0]]}, "2018.sigirconf_workshop-2018ecom.10": {"snippet": "...erce Search\n\n\n In this paper, we address the problem of evaluating whether results served by an e-commerce search engine for a query are good or not. is is a critical question in evaluating any e-comm...", "weights": [[8, 14, 1.0], [110, 116, 1.0]]}, "2008.wwwconf_workshop-2008l.7": {"snippet": "...oved search for local businesses. As a result, major search companies have invested significant resources into geographic search technologies, also often called local search.This paper studies geograp...", "weights": [[8, 14, 1.0], [56, 62, 1.0], [125, 131, 1.0], [170, 176, 1.0]]}, "2018.sigirconf_workshop-2018ecom.14": {"snippet": "...-End Neural Ranking for eCommerce Product Search: an Application of Task Models and Textual Embeddings\n\n\n Yan. 2018. End-to-End Neural Ranking for eCommerce Product Search: An application of task mode...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [27, 36, 1.0], [37, 44, 1.0], [45, 51, 1.0], [131, 137, 1.0], [138, 145, 1.0], [150, 159, 1.0], [160, 167, 1.0], [168, 174, 1.0]]}, "2019.cikm_conference-2019.314": {"snippet": "...time Ranking with Reinforcement Learning in E-commerce Sponsored Search Advertising", "weights": [[8, 15, 1.0], [68, 74, 1.0]]}, "2019.wwwconf_conference-2019c.66": {"snippet": "Neural Product Retrieval at Walmart.com", "weights": [[0, 6, 1.0], [7, 14, 1.0]]}, "2009.cikm_conference-2009.143": {"snippet": "... C2C ecommerce sites\n\n\n ABSTRACTOnline ecommerce has been booming for a decade. For instance, as the largest online C2C marketplace (eBay), millions of new items are listed daily. Due to the overwhelm...", "weights": [[8, 17, 1.0], [42, 51, 1.0]]}, "2019.wwwconf_conference-2019.217": {"snippet": "Neural IR Meets Graph Embedding: A Ranking Model for Product Search", "weights": [[0, 6, 1.0], [35, 42, 1.0], [53, 60, 1.0], [61, 67, 1.0]]}, "2018.sigirconf_workshop-2018ecom.12": {"snippet": "Ecommerce Product Title Classification\n\n\n E-commerce catalogs include a continuously growing number of products that are constantly updated. Each item in a catalog is characterized by several attribut...", "weights": [[0, 9, 1.0], [10, 17, 1.0]]}, "2019.sigirconf_conference-2019.33": {"snippet": "...ment Ranking with Neural Signals", "weights": [[8, 15, 1.0], [21, 27, 1.0]]}, "2018.sigirconf_workshop-2018ecom.23": {"snippet": "...rent Neural Networks\n\n\n We present a neural network for predicting purchasing intent in an Ecommerce setting. Our main contribution is to address the significant investment in feature engineering that...", "weights": [[8, 14, 1.0], [40, 46, 1.0], [94, 103, 1.0]]}, "2014.wwwconf_conference-2014c.42": {"snippet": "... web search and contrasts them with approaches specifically designed for e-commerce product search.\u2022 e-commerce product search: More specifically in commerce search top-k plays an important role as pr...", "weights": [[8, 14, 1.0], [87, 94, 1.0], [95, 101, 1.0], [115, 122, 1.0], [123, 129, 1.0], [161, 167, 1.0], [201, 208, 1.0]]}, "2019.spire_conference-2019.4": {"snippet": "...k in eCommerce Search", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "E17-2101": {"snippet": "...erce Product Listings.\n\n\n In this paper we study the impact of using images to machine-translate user-generated ecommerce product listings. We study how a multi-modal Neural Machine Translation (NMT) ...", "weights": [[8, 15, 1.0], [115, 124, 1.0], [125, 132, 1.0], [170, 176, 1.0]]}, "2005.wwwconf_conference-2005si.24": {"snippet": "...ance product search for on-line shopping\n\n\n AbstractThe growing eCommerce business requires an advanced way of searching for products. Buyers today are not only using the web to accomplish transaction...", "weights": [[8, 15, 1.0], [16, 22, 1.0], [67, 76, 1.0]]}, "2022.ecnlp-1.6": {"snippet": "...y in Product Search using High-Precision Query-Product Semantic Similarity\n\n\n Ensuring relevance quality in product search is a critical task as it impacts the customer's ability to find intended prod...", "weights": [[8, 15, 1.0], [16, 22, 1.0], [50, 57, 1.0], [111, 118, 1.0], [119, 125, 1.0]]}, "2016.clef_conference-2016.14": {"snippet": "...er a product feature-based user-centric model for product search involving, in addition to product characteristics, the user engagement toward the product. This model has been evaluated through the pr...", "weights": [[8, 15, 1.0], [53, 60, 1.0], [61, 67, 1.0], [94, 101, 1.0], [150, 157, 1.0], [201, 208, 1.0]]}, "2019.tois_journal-ir0volumeA37A2.5": {"snippet": "...ized product search. To this end, we propose a novel Attentive Long Short-Term Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to lear...", "weights": [[8, 15, 1.0], [16, 22, 1.0], [134, 141, 1.0], [142, 148, 1.0], [171, 177, 1.0]]}, "N18-3002": {"snippet": "... for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advant...", "weights": [[8, 15, 1.0], [69, 75, 1.0], [87, 93, 1.0], [125, 132, 1.0], [145, 152, 1.0]]}, "2020.ecnlp-1.3": {"snippet": "...tive Product Recommendations at Scale\n\n\n Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find th...", "weights": [[8, 15, 1.0], [93, 102, 1.0], [156, 163, 1.0]]}, "2011.cikm_conference-2011.281": {"snippet": "...y in eCommerce search. Our work focuses on bridging this gap. As little work has been done around diversity in eCommerce search, we study and describe the role of relevance and diversity in eCommerce ...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [114, 123, 1.0], [124, 130, 1.0], [193, 202, 1.0]]}, "2019.cikm_conference-2019.288": {"snippet": "Neural Review Summarization Leveraging User and Product Information", "weights": [[0, 6, 1.0], [48, 55, 1.0]]}, "2013.sigirconf_conference-2013.164": {"snippet": "...ased product retrieval for e-commerce\n\n\n ABSTRACTThe retrieval results of online products information in ecommerce web sites are often difficult for users to use because of different descriptions for ...", "weights": [[8, 15, 1.0], [108, 117, 1.0]]}, "2020.ipm_journal-ir0volumeA57A6.2": {"snippet": "...into neural ranking models for information retrieval", "weights": [[8, 14, 1.0], [15, 22, 1.0]]}, "2021.emnlp-main.191": {"snippet": "...ient Neural Architecture Search by Pairwise Ranking\n\n\n This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem. 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For example, in eCommerce search there may be no hypertext links between documents (products); there is a click stream, but mor...", "weights": [[8, 17, 1.0], [92, 101, 1.0], [102, 108, 1.0]]}, "2018.sigirconf_conference-2018.39": {"snippet": "...d to product search. There are several intrinsic diferences between web search and product search that make the direct application of traditional search ranking algorithms to EC search platforms diicu...", "weights": [[8, 15, 1.0], [16, 22, 1.0], [75, 81, 1.0], [86, 93, 1.0], [94, 100, 1.0], [149, 155, 1.0], [156, 163, 1.0], [181, 187, 1.0]]}, "2020.wsdm_conference-2020.13": {"snippet": "...oice Product Search?: On the Relation between Product Relevance and Customer Satisfaction in eCommerce\n\n\n One emerging benefit of voice assistants is to facilitate product search experience, allowing ...", "weights": [[8, 15, 1.0], [16, 22, 1.0], [49, 56, 1.0], [96, 105, 1.0], [167, 174, 1.0], [175, 181, 1.0]]}, "2006.jasis_journal-ir0volumeA57A14.8": {"snippet": "...ring ecommerce Web searching", "weights": [[8, 17, 1.0]]}, "2011.wwwconf_workshop-2011ldow.14": {"snippet": "...ured eCommerce data benefit more due to structured data being more readily search engine indexable, but the lack of available product ontologies and product master datasheets is impeding the creation ...", "weights": [[8, 17, 1.0], [78, 84, 1.0], [129, 136, 1.0], [152, 159, 1.0]]}, "2017.sigirconf_conference-2017.251": {"snippet": "...opic.eCommerce IR is ripe for research and has a unique set of problems. For example, in eCommerce search there may be no hypertext links between documents (products); there is a click stream, but mor...", "weights": [[8, 17, 1.0], [92, 101, 1.0], [102, 108, 1.0]]}, "2021.ecnlp-1.8": {"snippet": "...l as ecommerce product search to show that the proposed approach provides the best performance with 64% coverage and 87% accuracy on a large real-life data-set. A system based on the proposed techniqu...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [26, 32, 1.0]]}, "2021.wwwconf_conference-2021.223": {"snippet": "...raph Neural Networks for Friend Ranking in Large-scale Social Platforms", "weights": [[8, 14, 1.0], [35, 42, 1.0]]}}}
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These spam we...", "weights": [[8, 17, 1.0], [21, 24, 1.0], [25, 30, 1.0], [128, 131, 1.0], [132, 137, 1.0], [166, 169, 1.0], [201, 204, 1.0]]}, "2005.wwwconf_workshop-2004webdyn.11": {"snippet": "... the Evolution of Related Web Pages\n\n\n WebRelievo is a system for browsing and analyzing the evolution of the web graph structure based on link analysis. This system enables us to answer historical qu...", "weights": [[8, 17, 1.0], [29, 32, 1.0], [33, 38, 1.0], [96, 105, 1.0], [113, 116, 1.0]]}, "2021.wwwconf_conference-2021c.80": {"snippet": "...opic Evolution", "weights": [[8, 17, 1.0]]}, "2010.sigirconf_conference-2010.181": {"snippet": "...from web user maintenance activities and incorporate this feature into web search. 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Cafe is based on techniques used in text re...", "weights": [[8, 13, 1.0], [40, 45, 1.0]]}, "2013.clef_workshop-2013bioasq.5": {"snippet": "Large-Scale Semantic Indexing of Biomedical Publications\n\n\n Automated annotation of scientific publications in real-world digital libraries requires dealing with challenges such as large number of con...", "weights": []}, "2015.tois_journal-ir0volumeA33A4.1": {"snippet": "Selective Search: Efficient and Effective Search of Large Textual Collections\n\n\n The traditional search solution for large collections divides the collection into subsets (shards), and processes the q...", "weights": []}, "2002.jasis_journal-ir0volumeA53A11.7": {"snippet": "...xing exhaustivity and term distributions on a document space", "weights": [[8, 20, 1.0]]}, "2017.airs_conference-2017.2": {"snippet": "A Boosted Supervised Semantic Indexing for Reranking", "weights": []}, "2004.cikm_conference-2004.59": {"snippet": "Providing consistent and exhaustive relevance assessments for XML retrieval evaluation\n\n\n ABSTRACTComparing retrieval approaches requires test collections, which consist of documents, queries and rele...", "weights": []}, "2020.ecir_workshop-2020siirh.6": {"snippet": "Report of the First International Workshop on Semantic Indexing and Information Retrieval for Health from heterogeneous content types and languages", "weights": []}, "2020.ecir_workshop-2020siirh.1": {"snippet": "BioASQ: The challenge and the Community of Biomedical Semantic Indexing and Question Answering - Abstract\n\n\n The BioASQ challenge on large-scale biomedical semantic indexing and question answering is ...", "weights": []}, "2017.cikm_conference-2017.299": {"snippet": "Non-Exhaustive, Overlapping Co-Clustering\n\n\n ABSTRACT e goal of co-clustering is to simultaneously identify a clustering of the rows as well as the columns of a two dimensional data matrix. Most exist...", "weights": []}, "2011.wwwconf_conference-2011c.106": {"snippet": "A user-tunable approach to marketplace search\n\n\n ABSTRACTThe notion of relevance is key to the performance of search engines as they interpret the user queries and respond with matching results. Onlin...", "weights": []}, "1963.earlymt-1.29": {"snippet": "Path economization in exhaustive left-to-right syntactic analysis", "weights": []}, "2016.sigirjournals_journal-ir0volumeA50A1.10": {"snippet": "Improved Indexing & Searching Throughput", "weights": []}, "2011.sigirconf_conference-2011.102": {"snippet": "...rted index structure called a block-max index. Essentially, this is a structure that stores the maximum impact score for each block of a compressed inverted list in uncompressed form, thus enabling us...", "weights": [[8, 13, 1.0], [43, 48, 1.0]]}}}
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[[0, 12, 1.0]]}, "2001.cikm_conference-2001.29": {"snippet": "...ased optimization of cost and coverage can unfortunately lead to expensive planning as well as highly inefficient plans. In this paper we present techniques for joint optimization of cost and coverage...", "weights": [[8, 20, 1.0], [170, 182, 1.0]]}, "2012.cikm_workshop-2012clouddb.10": {"snippet": "...port query optimization, and histograms are of particular interest. Naturally, histograms could be used to support query optimization and efficient utilization of computing resources in the cloud. His...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [118, 123, 1.0], [124, 136, 1.0]]}, "1985.tois_journal-ir0volumeA3A1.2": {"snippet": "...uted query optimization algorithms are presented here for two types of local area networks: address ring networks and broadcast networks. Optimal algorithms are designed for simple queries. Optimizati...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [193, 205, 1.0]]}, "1995.ipm_journal-ir0volumeA31A6.2": {"snippet": "Query Evaluation: Strategies and Optimizations", "weights": [[0, 5, 1.0]]}, "2014.cikm_conference-2014.105": {"snippet": "...king Optimization with Constraints", "weights": [[8, 20, 1.0]]}, "1993.cikm_conference-93.47": {"snippet": "...iple Query Optimization with Depth-First Branch-and-Bound and Dynamic Query Ordering\n\n\n AbstractIn certain database applications such as deductive databases, batch query processing, and recursive quer...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [73, 78, 1.0], [167, 172, 1.0], [199, 204, 1.0]]}, "2009.cikm_conference-2009.88": {"snippet": "...k of query expansion via robust constrained optimization\n\n\n ABSTRACTWe introduce a new theoretical derivation, evaluation methods, and extensive empirical analysis for an automatic query expansion fra...", "weights": [[8, 13, 1.0], [47, 59, 1.0], [184, 189, 1.0]]}, "2010.wwwconf_conference-2010.91": {"snippet": "... the query logs, it is much more difficult to efficiently suggest relevant queries to a rare query. In this paper, we propose an optimal rare query suggestion framework by leveraging implicit feedback...", "weights": [[8, 13, 1.0], [96, 101, 1.0], [145, 150, 1.0]]}, "2013.cikm_workshop-2013ueo.7": {"snippet": "...ific query expansion\n\n\n ABSTRACTQueries submitted to a search engine are usually short and ambiguous. Query expansion is an effective way to resolve the ambiguity of a query. We attempt to make a cons...", "weights": [[8, 13, 1.0], [105, 110, 1.0], [171, 176, 1.0]]}, "2018.wsdm_conference-2018.21": {"snippet": "...hing query and document n-grams, Conv-KNRM uses Convolutional Neural Networks to represent ngrams of various lengths and so matches them in a uni ed embedding space. e n-gram so matches are then utili...", "weights": [[8, 13, 1.0]]}, "2019.wsdm_conference-2019.8": {"snippet": "...same query in the training data are used as the input. We propose a novel ranking method, referred to as WassRank, under which the problem of listwise document ranking boils down to the task of learni...", "weights": [[8, 13, 1.0]]}, "2008.tweb_journal-ir0volumeA2A1.5": {"snippet": "...vice query algebra and optimization\n\n\n We present a query algebra that supports optimized access of Web services through service-oriented queries. The service query algebra is defined based on a forma...", "weights": [[8, 13, 1.0], [26, 38, 1.0], [55, 60, 1.0], [162, 167, 1.0]]}, "2017.wwwconf_conference-2017.83": {"snippet": "...ased query optimization for RDF graph pattern queries. The approach comprises of (i) a novel type system for RDF data induced from data and ontologies and (ii) a query optimization and evaluation fram...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [165, 170, 1.0], [171, 183, 1.0]]}, "1993.cikm_conference-93.78": {"snippet": "Query Optimization and Processing in Federated Database Systems", "weights": [[0, 5, 1.0], [6, 18, 1.0]]}, "2006.cikm_conference-2006.71": {"snippet": "...ng a query, and (2) algorithms to rewrite a query using usable views.Semantic information, such as knowledge of the key of a view, can be used to further optimize a rewritten query. Within our general...", "weights": [[8, 13, 1.0], [47, 52, 1.0], [178, 183, 1.0]]}, "2010.wwwconf_conference-2010.5": {"snippet": "...ARQL query language also closely follows SQL syntax. As a natural consequence, most of the SPARQL query processing techniques are based on database query processing and optimization techniques. For SP...", "weights": [[8, 13, 1.0], [101, 106, 1.0], [151, 156, 1.0], [172, 184, 1.0]]}, "2011.cikm_conference-2011.397": {"snippet": "... are query optimization methods specifically conceived in the database theory community to efficiently answer (near-)acyclic queries. We propose to demonstrate H-DB, an SQL query optimizer that combin...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [176, 181, 1.0]]}, "2000.ipm_journal-ir0volumeA36A5.2": {"snippet": "...s to query optimization in document retrieval", "weights": [[8, 13, 1.0], [14, 26, 1.0]]}, "1985.sigirconf_conference-85.13": {"snippet": "Optimization of Inverted Vector Searches", "weights": [[0, 12, 1.0]]}, "2007.cikm_conference-2007.41": {"snippet": "...oute query classes. We prove the correctness of the unified algorithm. We then turn our attention to the optimization of the proposed algorithm. Several pruning and optimization techniques are derived...", "weights": [[8, 13, 1.0], [108, 120, 1.0], [168, 180, 1.0]]}, "2008.sigirconf_conference-2008.202": {"snippet": "...ured query, and represents it as a Mediated Query. The mediated query is an intermediate query in structured form to bridge the gap of structural differences between unstructured query and structured ...", "weights": [[8, 13, 1.0], [47, 52, 1.0], [67, 72, 1.0], [92, 97, 1.0], [182, 187, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...t of query operators on Web search engine results\n\n\n Research has reported that about 10% of Web searchers utilize advanced query operators, with the other 90% using extremely simple queries. It is of...", "weights": [[8, 13, 1.0], [127, 132, 1.0]]}, "2008.ipm_journal-ir0volumeA44A6.3": {"snippet": "...good query terms can be seen as an optimization problem where the objective function to be optimized is based on the effectiveness of a query to retrieve relevant material. Some characteristics of thi...", "weights": [[8, 13, 1.0], [38, 50, 1.0], [139, 144, 1.0]]}, "2008.wwwconf_conference-2008.134": {"snippet": "...or a query can be organized into a few subtopics about the query, each subtopic representing a specific aspect of the query. We believe that the subtopics in the search results for a query can reflect...", "weights": [[8, 13, 1.0], [62, 67, 1.0], [121, 126, 1.0], [186, 191, 1.0]]}, "2018.cikm_conference-2018.168": {"snippet": "...tive Query Expansion for Web Search\n\n\n ABSTRACTQuery Expansion (QE) techniques expand the user queries with additional terms, e.g., synonyms and acronyms, to enhance the system recall. State-of-the-ar...", "weights": [[8, 13, 1.0]]}, "2004.cikm_conference-2004.33": {"snippet": "...e of query access patterns. We extend this further and propose a query restructuring scheme and a multi-query optimization. Queries are restructured and regrouped based on their table access patterns ...", "weights": [[8, 13, 1.0], [68, 73, 1.0], [107, 112, 1.0], [113, 125, 1.0]]}, "2009.wwwconf_conference-2009.41": {"snippet": "... and query processing with optimized document ordering\n\n\n ABSTRACTWeb search engines use highly optimized compression schemes to decrease inverted index size and improve query throughput, and many ind...", "weights": [[8, 13, 1.0], [173, 178, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A3.4": {"snippet": "...ting query performance. In order to compare our approach with an approach which also can support the optimization of every query on the schema, we implemented an improvement of a state-of-the-art appr...", "weights": [[8, 13, 1.0], [104, 116, 1.0], [126, 131, 1.0]]}, "2001.sigirconf_conference-2001.63": {"snippet": "Query Optimization for Vector Space Problems\n\n\n ABSTRACTWe present performance measurement results for a parallel SQL based information retrieval system implemented on a PC cluster system. We used the...", "weights": [[0, 5, 1.0], [6, 18, 1.0]]}, "1991.jasis_journal-ir0volumeA42A4.2": {"snippet": "... the optimization of search logic for the MEDLINE database", "weights": [[8, 20, 1.0]]}, "2018.wsdm_conference-2018.104": {"snippet": "... and Optimization\n\n\n In defining the essence of professional teamwork, Hackman and Katz  stated that teams function as 'purposive social systems', defined as people who are readily identifiable to eac...", "weights": [[8, 20, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "... on. Query-dependent or \"dynamic\" features take the query into account and thus can only be computed at query time, whereas query-independent or \"static\" features do not rely on the query and thus can...", "weights": [[8, 13, 1.0], [55, 60, 1.0], [107, 112, 1.0], [127, 132, 1.0], [185, 190, 1.0]]}, "2015.cikm_conference-2015.117": {"snippet": "... the Optimization of Aggregate Continuous Queries", "weights": [[8, 20, 1.0]]}, "2010.wsdm_conference-2010.17": {"snippet": "An optimization framework for query recommendation\n\n\n ABSTRACTQuery recommendation is an integral part of modern search engines. The goal of query recommendation is to facilitate users while searching...", "weights": [[3, 15, 1.0], [30, 35, 1.0], [141, 146, 1.0]]}, "1993.cikm_conference-93.23": {"snippet": "...tive Query Optimization in a Deductive Database System", "weights": [[8, 13, 1.0], [14, 26, 1.0]]}, "2011.cikm_conference-2011.345": {"snippet": "...rial optimization problem. We begin with a database, abstracted as a collection of strings. From this data set we generate a query workload. The input to the optimization problem is the database and t...", "weights": [[8, 20, 1.0], [128, 133, 1.0], [161, 173, 1.0]]}, "2005.cikm_conference-2005.33": {"snippet": "Optimizing continuous multijoin queries over distributed streams", "weights": []}, "2019.wsdm_conference-2019.83": {"snippet": "... for optimization of the TL model, and the performance of the TL model can provide \"rewards\" in turn to update the selector. We build the reinforced data selector based on the actor-critic framework a...", "weights": [[8, 20, 1.0]]}, "1993.cikm_conference-93.5": {"snippet": "...onal query optimization usually minimizes the total time for complete evaluation of a query. We argue that this criterion may not be the only or even necessary requirement. In some interactive environ...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [89, 94, 1.0]]}, "2019.wsdm_conference-2019.47": {"snippet": "...m an optimization (objective function) perspective. We demonstrate that matrix factorization is equivalent to optimizing two objectives: one is for bringing together the embeddings of similar nodes; t...", "weights": [[8, 20, 1.0]]}, "2014.cikm_conference-2014.186": {"snippet": "...rase Query Optimization on Inverted Indexes\n\n\n ABSTRACTPhrase queries are a key functionality of modern search engines. Beyond that, they increasingly serve as an important building block for applicat...", "weights": [[8, 13, 1.0], [14, 26, 1.0]]}, "1995.cikm_conference-95.26": {"snippet": "...ible Query Optimizer for an Objectbase Management System", "weights": [[8, 13, 1.0]]}, "2010.airs_conference-2010.49": {"snippet": "...tive Optimization Algorithm for Query Model and Ranking Refinement", "weights": [[8, 20, 1.0], [35, 40, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.8": {"snippet": "...ined query processing, which as we show can improve the query processing performance and/or the quality of results. Then, we extend one of these methods with intra-query parallelism, which as we show ...", "weights": [[8, 13, 1.0], [59, 64, 1.0], [167, 172, 1.0]]}, "2016.ictir_conference-2016.37": {"snippet": "...ires query analysis to identify the most important query concepts as well as query transformation by adding new concepts to a query, which can be extracted from the top retrieved documents or medical ...", "weights": [[8, 13, 1.0], [54, 59, 1.0], [80, 85, 1.0], [129, 134, 1.0]]}, "2003.sigirconf_conference-2003.86": {"snippet": "...om a query can help a user to refine an overly specific query to one which has more coverage. Most query reformulation research focuses on query expansion (eg. ). Our research on query term deletion p...", "weights": [[8, 13, 1.0], [59, 64, 1.0], [102, 107, 1.0], [142, 147, 1.0], [182, 187, 1.0]]}, "2020.cikm_conference-2020.5": {"snippet": "Optimization of Answer Set Programs for Consistent Query Answering by Means of First-Order Rewriting", "weights": [[0, 12, 1.0], [51, 56, 1.0]]}, "2019.wsdm_conference-2019.37": {"snippet": "Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs\n\n\n ABSTRACTWe tackle Attitude Detection, which we define as the task of extracting the replier's attitude, i.e.,...", "weights": []}, "2011.cikm_conference-2011.391": {"snippet": "...tive query languages, views and multi-query optimization. Users create personalized feeds by defining and composing content-based filtering and aggregation queries on collections of RSS feeds. These q...", "weights": [[8, 13, 1.0], [41, 46, 1.0], [47, 59, 1.0]]}, "1999.cikm_conference-99.48": {"snippet": "... the optimization of techniques for query processing in order to exploit these new alternatives. Accordingly, this paper introduces an improved query optimization technique. which is capable of assess...", "weights": [[8, 20, 1.0], [39, 44, 1.0], [147, 152, 1.0], [153, 165, 1.0]]}, "2013.cikm_conference-2013.311": {"snippet": "... two query optimization modes: offline periodic optimization and online optimization. We devise efficient algorithms for both offline and online cases followed by a competitive analysis to show the po...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [51, 63, 1.0], [75, 87, 1.0]]}, "2016.wsdm_conference-2016.15": {"snippet": "...ting query suggestions for leading users to more relevant search results. The optimization is based on a probabilistic model of users who click on query suggestions in accordance with their intents, a...", "weights": [[8, 13, 1.0], [81, 93, 1.0], [150, 155, 1.0]]}, "1995.sigirconf_conference-95.41": {"snippet": "On the Reuse of Past Optimal Queries", "weights": []}, "2008.cikm_conference-2008.56": {"snippet": "... and query evaluation support for XQuery. By building a hybrid system, the designers of DB2 9 were able to use the existing SQL query evaluation and optimization techniques to develop similar methods ...", "weights": [[8, 13, 1.0], [131, 136, 1.0], [152, 164, 1.0]]}, "2011.cikm_conference-2011.343": {"snippet": "...ntal query, namely, reachability query, have been proposed. The structures and performances of these indexes on different graphs have known to be very different. Worst still, deriving an accurate cost...", "weights": [[8, 13, 1.0], [36, 41, 1.0]]}, "2008.cikm_conference-2008.178": {"snippet": "...e . \nQUERY-PROCESSING ALGORITHMSThis section outlines our basic query-processing algorithms for information integration. (Please see [1] for the details.) All our algorithms, with the exception of sem...", "weights": [[8, 13, 1.0], [67, 72, 1.0]]}, "1995.sigirconf_conference-95.5": {"snippet": "...osed optimization techniques. Optimizing execution, however, is even more important in the context of large document collections. We present a new structured query optimization technique which we have...", "weights": [[8, 20, 1.0], [161, 166, 1.0], [167, 179, 1.0]]}, "2016.cikm_conference-2016.262": {"snippet": "...ring Query Optimization", "weights": [[8, 13, 1.0], [14, 26, 1.0]]}, "2008.cikm_conference-2008.67": {"snippet": "...join query processing. The pipelined execution of these queries forces the operations in the query plan to be processed concurrently. Making a wrong decision regarding the amount of memory allocated f...", "weights": [[8, 13, 1.0], [96, 101, 1.0]]}, "2005.cikm_conference-2005.150": {"snippet": "...tive query optimization\n\n\n ABSTRACTModern query optimizers select an efficient join ordering for a physical execution plan based essentially on the average join selectivity factors among the reference...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [45, 50, 1.0]]}, "2010.cikm_conference-2010.156": {"snippet": "Query optimization for ontology-based information integration\n\n\n ABSTRACTIn recent years, there has been an explosion of publicly available RDF and OWL data sources. In order to effectively and quickl...", "weights": [[0, 5, 1.0], [6, 18, 1.0]]}, "2011.cikm_conference-2011.182": {"snippet": "...uery optimization based on program slicing\n\n\n ABSTRACTXQuery has become the standard query language for XML. 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Multimedia documents are information objects containing formatted data, text, image, ...", "weights": [[0, 5, 1.0], [52, 57, 1.0]]}, "2019.wsdm_conference-2019.33": {"snippet": "Federated Online Learning to Rank with Evolution Strategies\n\n\n ABSTRACTOnline Learning to Rank is a powerful paradigm that allows to train a ranking model using only online feedback from its users. In...", "weights": []}, "2001.cikm_conference-2001.28": {"snippet": "...rall query optimization. For example, a result contains an interesting sort order. Such information should be passed as a hint to the FDBS query processor to avoid further sort operations.\nQUERY PROCE...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [142, 147, 1.0], [192, 197, 1.0]]}, "2006.cikm_conference-2006.119": {"snippet": "...ulti-query optimization of sliding window aggregates by schedule synchronization", "weights": [[8, 13, 1.0], [14, 26, 1.0]]}, "2020.cikm_conference-2020.96": {"snippet": "...ized Query Processing Framework", "weights": [[8, 13, 1.0]]}}}
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implies high probability of similarity between documents, which is different from high similarity of the documents. But how similar two documents are is what we really need...", "weights": [[8, 18, 1.0], [59, 69, 1.0], [118, 128, 1.0]]}, "2004.airs_conference-2004.20": {"snippet": "...ment Similarity\n\n\n Abstract. Measuring pairwise document similarity is critical to various text retrieval and mining tasks. The most popular measure for document similarity is the Cosine measure in Ve...", "weights": [[8, 18, 1.0], [60, 70, 1.0], [165, 175, 1.0], [183, 189, 1.0], [201, 207, 1.0]]}, "C16-1222": {"snippet": "...this vector space, and the query-document similarity score depends on the length of the vectors and the relationships between them. Introduction Vector-space in text retrieval is old news. Cosine simi...", "weights": [[8, 14, 1.0], [45, 55, 1.0], [148, 154, 1.0], [192, 198, 1.0], [199, 209, 1.0]]}, "W18-4507": {"snippet": "...tual similarity (cosine similarity on tf-idf vectors or topic vectors) are also compared to new strategies that strive to anchor textual measurement in a social context.", "weights": [[8, 18, 1.0], [20, 26, 1.0], [27, 37, 1.0]]}, "2004.ntcir_workshop-2004.61": {"snippet": "...as a vector that is composed of vectors associated with words included in the document. The word vectors and document vectors are positioned in an identical vector space and the relevant degree of sim...", "weights": [[8, 14, 1.0], [160, 166, 1.0], [200, 210, 1.0]]}, "2003.tois_journal-ir0volumeA21A1.2": {"snippet": "...tive similarity scores. We extend our similarity measures to provide appropriate results in the presence of multisets (also handled unsatisfactorily by traditional measures), for example, to correctly...", "weights": [[8, 18, 1.0], [41, 51, 1.0]]}, "2006.jasis_journal-ir0volumeA57A9.3": {"snippet": "...rlap vector and n-overlap similarity theory", "weights": [[8, 14, 1.0], [29, 39, 1.0]]}, "N19-1181": {"snippet": "Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing\n\n\n The prevalent way to estimate the similarity of two documents based on word embeddings is to apply the co...", "weights": [[5, 15, 1.0], [129, 139, 1.0], [198, 204, 1.0]]}, "W11-0329": {"snippet": "...onal vector space. Our approach operates by finding the optimal matrix to minimize the loss of the pre-selected similarity function (e.g., cosine) of the projected vectors, and is able to efficiently ...", "weights": [[8, 14, 1.0], [115, 125, 1.0], [142, 148, 1.0]]}, "2022.insights-1.17": {"snippet": "...sing Cosine Similarity (DV-ngrams-cosine) proposed in their paper and the Bag-of-Ngrams (BON) vectors scaled by Naive Bayesian weights. While large pre-trained Transformerbased models have shown SOTA ...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [37, 43, 1.0]]}, "2013.clef_conference-2013w.34": {"snippet": "...mage vector, such that the resulting vector contain the maximal similarity to each concept of the intermediate space. The 96/116 concepts to identify are represented in the same space and their simila...", "weights": [[8, 14, 1.0], [40, 46, 1.0], [67, 77, 1.0], [197, 207, 1.0]]}, "W00-1310": {"snippet": "...uced vector space model calculated from co-occurrences of word pairs provides word co-occurrence vectors. The sum of word cooccurrence vectors represents tile context of a document, and the cosine sim...", "weights": [[8, 14, 1.0], [193, 199, 1.0], [200, 210, 1.0]]}, "D19-1008": {"snippet": "...like cosine similarity is used to compare individual word vectors, we introduce a novel application of the centered kernel alignment (CKA) as a natural generalisation of squared cosine similarity for ...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [181, 187, 1.0], [188, 198, 1.0]]}, "2017.wwwconf_conference-2017.47": {"snippet": "...wise cosine similarity among these vectors defines user similarity. The predominant task for user similarity applications is to discover all similar pairs that have a pairwise cosine similarity value ...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [59, 69, 1.0], [101, 111, 1.0], [179, 185, 1.0], [186, 196, 1.0]]}, "2007.wwwconf_conference-2007.14": {"snippet": "...airs similarity search\n\n\n ABSTRACTGiven a large collection of sparse vector data in a high dimensional space, we investigate the problem of finding all pairs of vectors whose similarity score (as dete...", "weights": [[8, 18, 1.0], [72, 78, 1.0], [178, 188, 1.0]]}, "2013.ntcir_conference-2013.74": {"snippet": "...k by cosine similarity and the Yahoo! Japan Web search results, and 0.86 in the Japanese task by cosine similarity and the Bing search results.", "weights": [[8, 14, 1.0], [15, 25, 1.0], [100, 106, 1.0], [107, 117, 1.0]]}, "2004.cikm_conference-2004.55": {"snippet": "...ning similarity measures in non-orthogonal space\n\n\n ABSTRACTMany machine learning and data mining algorithms crucially rely on the similarity metrics. The Cosine similarity, which calculates the inner...", "weights": [[8, 18, 1.0], [134, 144, 1.0], [158, 164, 1.0], [165, 175, 1.0]]}, "2017.ijmir_journal-ir0volumeA6A2.2": {"snippet": "...NOVA Cosine Similarity Image Recommendation in vertical search", "weights": [[8, 14, 1.0], [15, 25, 1.0]]}, "2004.cikm_conference-2004.52": {"snippet": "...m of similarity-search in PDNs, and propose a family of distributed access methods, termed Small-World Access Methods (SWAM), for efficient execution of various similarity-search queries, namely exact...", "weights": [[8, 18, 1.0], [164, 174, 1.0]]}, "2013.wsdm_conference-2013.72": {"snippet": "...heir similarity. Query-query and document-document similarities can also be naturally defined as dot products in the latent space. We formalize the learning of similarity functions as learning of the ...", "weights": [[8, 18, 1.0], [163, 173, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.0": {"snippet": "...y of similarity predicate committee\n\n\n A B S T R A C TIn the last decades, many similarity measures are proposed, such as Jaccard coefficient, cosine similarity, BM25, language model, etc. Despite the...", "weights": [[8, 18, 1.0], [83, 93, 1.0], [146, 152, 1.0], [153, 163, 1.0]]}, "2017.wwwconf_conference-2017.109": {"snippet": "...data similarity are the cosine and the resemblance (the latter is for binary data). In this study, we develop theoretical results for both the cosine and the GMM (generalized min-max) kernel , which i...", "weights": [[8, 18, 1.0], [27, 33, 1.0], [146, 152, 1.0]]}, "N19-1100": {"snippet": "...d to similarity measures between these embeddings, with cosine similarity being used unquestionably in the majority of cases. In this work, we illustrate that for all common word vectors, cosine simil...", "weights": [[8, 18, 1.0], [59, 65, 1.0], [66, 76, 1.0], [191, 197, 1.0], [198, 208, 1.0]]}, "P18-2088": {"snippet": "...with vector cosine being typically used as the only similarity metric. In this paper, we report experiments with a rank-based metric for WE, which performs comparably to vector cosine in similarity es...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [55, 65, 1.0], [173, 179, 1.0], [180, 186, 1.0], [190, 200, 1.0]]}, "1999.sigirconf_conference-99.9": {"snippet": "... the cosine similarity measure. Both the document-document similarity matrix and the term-term similarity matrix naturally arise from the maximum likelihood estimation of the model parameters, and the...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [62, 72, 1.0], [98, 108, 1.0]]}, "P19-2057": {"snippet": "...ngth vector. Document embedding models map each document to a dense, lowdimensional vector in continuous vector space. This paper proposes training document embeddings using cosine similarity instead ...", "weights": [[8, 14, 1.0], [87, 93, 1.0], [108, 114, 1.0], [177, 183, 1.0], [184, 194, 1.0]]}, "2022.acl-short.45": {"snippet": "...with Cosine as a Measure of Embedding Similarity for High Frequency Words\n\n\n Cosine similarity of contextual embeddings is used in many NLP tasks (e.g., QA, IR, MT) and metrics (e.g., BERTScore). Here...", "weights": [[8, 14, 1.0], [41, 51, 1.0], [80, 86, 1.0], [87, 97, 1.0]]}, "L16-1723": {"snippet": "... and Vector Cosine in the {ESL} and {TOEFL} Datasets\n\n\n In this paper, we claim that Vector Cosine -which is generally considered one of the most efficient unsupervised measures for identifying word s...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [88, 94, 1.0], [95, 101, 1.0], [202, 212, 1.0]]}, "2016.tweb_journal-ir0volumeA10A2.0": {"snippet": "...User similarity is traditionally calculated by cosine similarity or the Pearson correlation coefficient. However, both of these measures consider only the direction of rating vectors, and suffer from ...", "weights": [[8, 18, 1.0], [50, 56, 1.0], [57, 67, 1.0]]}, "2016.cikm_conference-2016.204": {"snippet": "Personalized Semantic Word Vectors", "weights": []}, "2005.cikm_conference-2005.161": {"snippet": "...sing vector space bases\n\n\n ABSTRACTIn this paper, context is modeled by vector space bases and its evolution is modeled by linear transformations from one base to another. Each document or query can b...", "weights": [[8, 14, 1.0], [75, 81, 1.0]]}, "2009.jasis_journal-ir0volumeA60A2.1": {"snippet": "...ween similarity measures for vectors based on vector norms", "weights": [[8, 18, 1.0], [49, 55, 1.0]]}, "2014.cikm_workshop-2014esair.8": {"snippet": "...evel vector, such as a vector of Wikipedia concepts . It is also considered a human-friendly way to annotate text -it generates concept vector that can be easily interpreted by human. We propose an ap...", "weights": [[8, 14, 1.0], [26, 32, 1.0], [139, 145, 1.0]]}, "2016.cikm_conference-2016.18": {"snippet": "...tent Vector Spaces for Product Search", "weights": [[8, 14, 1.0]]}, "2002.cikm_conference-2002.16": {"snippet": "...d to similarity search in databases. We investigate the vulnerability of such systems against users who try to copy the database by sending automated queries. We analyze two models for similarity sear...", "weights": [[8, 18, 1.0], [188, 198, 1.0]]}, "2013.dir_workshop-2013.3": {"snippet": "...tive similarity measure. We show that this new similarity measure is superior to both the Jensen-Shannon divergence and the cosine similarity in a task, in which pairs of words, taken from Wordnet, ha...", "weights": [[8, 18, 1.0], [50, 60, 1.0], [127, 133, 1.0], [134, 144, 1.0]]}, "2000.cikm_conference-2000.28": {"snippet": "Vector Approximation based Indexing for Non-uniform High Dimensional Data Sets\n\n\n AbstractWith the proliferation of multimedia data, there is increasing need to support the indexing and searching of h...", "weights": [[0, 6, 1.0]]}, "P06-2110": {"snippet": "...s of similarity, i.e., taxonomic similarity and associative similarity. The result of the comparison was that the dictionary-based word vectors better reflect taxonomic similarity, while the LSAbased ...", "weights": [[8, 18, 1.0], [36, 46, 1.0], [63, 73, 1.0], [172, 182, 1.0]]}, "D17-1193": {"snippet": "...ring Vector Spaces for Semantic Relations\n\n\n Word embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods a...", "weights": [[8, 14, 1.0]]}, "2020.lt4hala-1.10": {"snippet": "...onal similarity. To this end, we apply established algorithms to find connections between headwords based on existing lexicons and other digital resources. For semantic similarity, we utilize the cosi...", "weights": [[8, 18, 1.0], [172, 182, 1.0], [199, 205, 1.0]]}, "W16-4118": {"snippet": "...that cosine similarity relates better to human ranks than the Dice coefficient. We also saw a tendency that including syntactic context to the TF-IDF vector space model is beneficial for this kind of ...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [153, 159, 1.0]]}, "2013.clef_conference-2013w.98": {"snippet": "...port Vector Machines\n\n\n In this paper we present two systems that address the issues of disorder recognition and normalization submitted by the authors as defined by the CLEF/ShARe Evaluation Lab. The...", "weights": [[8, 14, 1.0]]}, "2013.cikm_conference-2013.169": {"snippet": "...ling similarity measures outperform alternative measures in both experiments. This outcome strongly recommends the adoption of these measures, replacing cosine similarity in future work.", "weights": [[8, 18, 1.0], [156, 162, 1.0], [163, 173, 1.0]]}, "W17-7542": {"snippet": "...fied Cosine-Similarity based Log Kernel for Support Vector Machines in the Domain of Text Classification\n\n\n The popularity of the internet is increasing day-by-day, which makes tough for the end-user ...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [55, 61, 1.0]]}, "D09-1083": {"snippet": "... for Similarity Measures\n\n\n Measuring the similarity between two texts is a fundamental problem in many NLP and IR applications. Among the existing approaches, the cosine measure of the term vectors r...", "weights": [[8, 18, 1.0], [45, 55, 1.0], [167, 173, 1.0]]}, "2018.cikm_conference-2018.125": {"snippet": "...etic Similarity Measure\n\n\n ABSTRACTDetermining the similarity between two objects is pertinent to many applications. When the basis for similarity is a set of objectto-object relationships, it is natu...", "weights": [[8, 18, 1.0], [54, 64, 1.0], [139, 149, 1.0]]}, "Y16-2021": {"snippet": "...inst Vector Cosine on Similarity Estimation\n\n\n In Distributional Semantic Models (DSMs), Vector Cosine is widely used to estimate similarity between word vectors, although this measure was noticed to ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [25, 35, 1.0], [92, 98, 1.0], [99, 105, 1.0], [133, 143, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.0": {"snippet": "...rent similarity search techniques and real-life datasets demonstrate that the secondary filtering can speed-up similarity search several times. \nINTRODUCTIONAn efficient similarity search presents a s...", "weights": [[8, 18, 1.0], [114, 124, 1.0], [173, 183, 1.0]]}, "W16-2503": {"snippet": "... for vector-space semantic models: it is considered desirable for a space to represent semantic relations as consistent vector offsets. We show that the method's reliance on cosine similarity conflate...", "weights": [[8, 14, 1.0], [123, 129, 1.0], [177, 183, 1.0], [184, 194, 1.0]]}, "1992.sigirconf_conference-92.14": {"snippet": "...s of vector-based models for information retrieval from the computational geometry point of view. It is shown that both the pseudo-cosine and the standard vector space models can be viewed as special ...", "weights": [[8, 14, 1.0], [134, 140, 1.0], [158, 164, 1.0]]}, "2017.clef_conference-2017w.10": {"snippet": "...sine Similarity\n\n\n The 2017 Microblog Cultural Contextualization task consists in three challenges: (1) Content Analysis, (2) Microblog search, and (3) TimeLine illustration. This paper describes the ...", "weights": [[8, 18, 1.0]]}, "P16-1214": {"snippet": "....g., vector normalization achieves an efficient search with cosine similarity.", "weights": [[8, 14, 1.0], [63, 69, 1.0], [70, 80, 1.0]]}, "2014.cikm_workshop-2014esair.12": {"snippet": "...ased Similarity Measure for Concept Hierarchies and Ontologies\n\n\n ABSTRACTDespite the popularity of concept hierarchies and ontologies, such as Yahoo! Directory, a similarity measure that considers bo...", "weights": [[8, 18, 1.0], [167, 177, 1.0]]}, "I05-2026": {"snippet": "...tent similarity evaluation employs ngrams of lexical chains and measures similarity using the cosine of vectors of n-grams of lexical chains, vectors of tf*idfweighted keywords, and vectors of unweigh...", "weights": [[8, 18, 1.0], [76, 86, 1.0], [97, 103, 1.0]]}, "2018.cikm_conference-2018.190": {"snippet": "...Soft Cosine Measure\n\n\n ABSTRACTThe standard bag-of-words vector space model (vsm) is efficient, and ubiquitous in information retrieval, but it underestimates the similarity of documents with the same...", "weights": [[8, 14, 1.0], [60, 66, 1.0], [166, 176, 1.0]]}, "2016.clef_conference-2016w.23": {"snippet": "...ture vector was built for each training set code by merging all the statements found for this code in the training data. Given a new statement, we ranked candidate codes with Cosine similarity. Featur...", "weights": [[8, 14, 1.0], [178, 184, 1.0], [185, 195, 1.0]]}, "2010.cikm_workshop-2010smuc.11": {"snippet": "...ased similarity measures. Based on the weighted similarity model, we present weighted variations of several existing measures like Dice and Cosine similarity measures. We evaluate the proposed similar...", "weights": [[8, 18, 1.0], [51, 61, 1.0], [143, 149, 1.0], [150, 160, 1.0], [196, 206, 1.0]]}, "2015.airs_conference-2015.33": {"snippet": ".... In vector space model, different types of term weighting schemes are used to adjust bag-of-words document vectors in order to improve the performance of the most widely used cosine distance. Even th...", "weights": [[8, 14, 1.0], [179, 185, 1.0]]}, "2015.cikm_conference-2015.100": {"snippet": "...uery similarity functions. In this paper, we propose new algorithms that facilitate and increase the effectiveness of this propagation. Given a query similarity function and a query-document relevance...", "weights": [[8, 18, 1.0], [153, 163, 1.0]]}, "2013.ipm_journal-ir0volumeA49A2.10": {"snippet": "...ific similarity are still limited to vector space models wherein there is no connection to probabilistic approaches. We suggest a probabilistic framework that defines query-sensitive similarity based ...", "weights": [[8, 18, 1.0], [40, 46, 1.0], [186, 196, 1.0]]}, "L18-1167": {"snippet": "... one vector. This has a negative effect particularly when computing similarity scores between words using standard vector-based similarity measures such as cosine similarity. We argue that minor sense...", "weights": [[8, 14, 1.0], [71, 81, 1.0], [118, 124, 1.0], [131, 141, 1.0], [159, 165, 1.0], [166, 176, 1.0]]}}}
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One begins with basic roots, neatly separated, and a \"machine translation* must m show how they are put together. The Chinese written language, being compos...", "weights": [[8, 15, 1.0]]}, "2022.semeval-1.10": {"snippet": "...lish Reverse Dictionary?\n\n\n We present the Uppsala University system for SemEval-2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE). We explore the performance of multilingual reverse di...", "weights": [[8, 15, 1.0], [193, 200, 1.0]]}, "1974.jasis_journal-ir0volumeA25A4.4": {"snippet": "Indexing and automatic significance analysis", "weights": [[0, 8, 1.0]]}, "2010.sigirconf_conference-2010.215": {"snippet": "...se a Reverse Annotation framework , that labels word-images by their equivalent text label in the offline phase. Reverse Annotation applies a retrieval based approach to recognition. Unlike traditiona...", "weights": [[8, 15, 1.0], [116, 123, 1.0]]}, "2011.trec_conference-2011.99": {"snippet": "...y in reverse chronological order. The approach utilizes a learning-to-rank (L2R) algorithm that has been increasingly used for information retrieval (IR). Generally, L2R algorithms require features wh...", "weights": [[8, 15, 1.0]]}, "W11-4631": {"snippet": "...ndom Indexing Re-Hashed\n\n\n This paper introduces a modified version of Random Indexing, a technique for dimensionality reduction based on random projections. We here describe how RI can be efficiently...", "weights": [[8, 16, 1.0], [81, 89, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A6.1": {"snippet": "...p-by reverse skyline computation\n\n\n Abstract The reverse skyline query is very useful in many decision making applications. Given a multi-dimensional dataset P and a query point q, the reverse skyline...", "weights": [[8, 15, 1.0], [52, 59, 1.0], [188, 195, 1.0]]}, "2011.wsdm_conference-2011.81": {"snippet": "Learning to re-rank web search results with multiple pairwise features\n\n\n ABSTRACTWeb search ranking functions are typically learned to rank search results based on features of individual documents, i...", "weights": []}, "2015.ijirr_journal-ir0volumeA5A1.1": {"snippet": "Indexing Outline Shapes Using Simple Polygons", "weights": [[0, 8, 1.0]]}, "2012.wwwconf_conference-2012.41": {"snippet": "...ulti-indexing: bringing order to social search\n\n\n ABSTRACTTo answer search queries on a social network rich with usergenerated content, it is desirable to give a higher ranking to content that is clos...", "weights": [[8, 16, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.3": {"snippet": "...ient indexing. In this work, we investigate distributed indexing paradigms, in particular within the auspices of the MapReduce programming framework. In particular, we describe two indexing approaches...", "weights": [[8, 16, 1.0], [59, 67, 1.0], [184, 192, 1.0]]}, "2020.cikm_conference-2020.233": {"snippet": "An Index Advisor Using Deep Reinforcement Learning", "weights": []}, "1994.jasis_journal-ir0volumeA45A8.4": {"snippet": "...h in Indexing", "weights": [[8, 16, 1.0]]}, "1980.jasis_journal-ir0volumeA31A4.12": {"snippet": "Indexing and behavior", "weights": [[0, 8, 1.0]]}, "2010.cikm_conference-2010.109": {"snippet": "...rted indexing for feedback and expansion\n\n\n ABSTRACTTraditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a list...", "weights": [[8, 16, 1.0]]}, "1988.jasis_journal-ir0volumeA39A6.9": {"snippet": "...ring indexing", "weights": [[8, 16, 1.0]]}, "2015.ictir_conference-2015.38": {"snippet": "...rted indexing. In this paper, we explore a radical proposition: What if we abandon document inversion and instead adopt an architecture based on brute force scans of document representations? In such ...", "weights": [[8, 16, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.9": {"snippet": "... the reverse-time sequence, in general, cannot be used to derive similar properties of the (difficult to obtain) forward time sequence. We show that both sequences are the same if and only if the auth...", "weights": [[8, 15, 1.0]]}, "2013.cikm_conference-2013.240": {"snippet": "...tion indexing: fast approximate retrieval from large corpora\n\n\n ABSTRACTInverted indexing is a ubiquitous technique used in retrieval systems including web search. Despite its popularity, it has a dra...", "weights": [[8, 16, 1.0], [84, 92, 1.0]]}, "2003.wwwconf_conference-2003.11": {"snippet": "Dynamic maintenance of web indexes using landmarks\n\n\n ABSTRACTRecent work on incremental crawling has enabled the indexed document collection of a search engine to be more synchronized with the changi...", "weights": []}, "2019.ecir_conference-20191.22": {"snippet": "... and indexing frameworks. Furthermore, we make our implementation publicly available to help promote further research in this space.", "weights": [[8, 16, 1.0]]}, "2015.spire_conference-2015.17": {"snippet": "Prefix and Suffix Reversals on Strings", "weights": []}, "2009.cikm_conference-2009.172": {"snippet": "Inverted indexes vs. bitmap indexes in decision support systems\n\n\n ABSTRACTBitmap indexes are widely used in Decision Support Systems (DSSs) to improve query performance. In this paper, we evaluate th...", "weights": []}, "2012.ipm_journal-ir0volumeA48A5.5": {"snippet": "...like indexing, and the suitability of the per-posting list MapReduce indexing strategy, in particular for indexing at a terabyte-scale. Hence, we conclude that MapReduce is a suitable framework for th...", "weights": [[8, 16, 1.0], [72, 80, 1.0], [109, 117, 1.0]]}, "2005.cikm_conference-2005.157": {"snippet": "Fast on-line index construction by geometric partitioning\n\n\n ABSTRACTInverted index structures are the mainstay of modern text retrieval systems. They can be constructed quickly using off-line mergeba...", "weights": []}, "W05-1623": {"snippet": "Reversibility and Re-usability of Resources in {NLG} and Natural Language Dialog Systems\n\n\n Reversibility is a key to efficient and maintainable NLG systems. In this paper, we present a formal definit...", "weights": []}, "2020.emnlp-demos.23": {"snippet": "...line Reverse Dictionary System\n\n\n A reverse dictionary takes descriptions of words as input and outputs words semantically matching the input descriptions. Reverse dictionaries have great practical va...", "weights": [[8, 15, 1.0], [39, 46, 1.0], [159, 166, 1.0]]}, "1977.ipm_journal-ir0volumeA13A6.3": {"snippet": "...y of indexing", "weights": [[8, 16, 1.0]]}, "2008.sigirconf_conference-2008.179": {"snippet": "...that reverse natural graphs tend to have low indegree while maintaining fast PageRank convergence. It follows that calculating Reverse PageRank locally is frequently more feasible than computing PageR...", "weights": [[8, 15, 1.0], [130, 137, 1.0]]}, "2020.acl-main.382": {"snippet": "...ide. REVERSE HYPOTHESIS: The guy is outside. PREDICTED LABEL: contradiction REVERSE EXPLANATION: Snowboarding is not done outside. PREMISE: A man talks to two guards as he holds a drink. ORIGINAL HYPO...", "weights": [[8, 15, 1.0], [79, 86, 1.0]]}, "2010.jasis_journal-ir0volumeA61A3.14": {"snippet": "Relationship of the h-index, g-index, and e-index", "weights": []}, "2007.tois_journal-ir0volumeA26A1.4": {"snippet": "...when indexing and searching using n-grams. Also, reversing error correction models to perform query garbling in conjunction with weighted structured queries yields improved retrieval effectiveness. La...", "weights": [[8, 16, 1.0]]}, "2009.sigirconf_conference-2009.174": {"snippet": "A unified inverted index for an efficient image and text retrieval", "weights": []}, "2010.wsdm_conference-2010.16": {"snippet": "IntervalRank: isotonic regression with listwise and pairwise constraints\n\n\n ABSTRACTRanking a set of retrieved documents according to their relevance to a given query has become a popular problem at t...", "weights": []}, "2017.jasis_journal-ir0volumeA68A7.14": {"snippet": "...mage indexing?", "weights": [[8, 16, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.4": {"snippet": "PP-Index: Using Permutation Prefixes for Efficient and Scalable Approximate Similarity Search\n\n\n We present the Permutation Prefix Index (PP-Index), an index data structure that allows to perform effi...", "weights": []}, "2020.ecir_workshop-2020siirh.1": {"snippet": "...ntic Indexing and Question Answering - Abstract\n\n\n The BioASQ challenge on large-scale biomedical semantic indexing and question answering is running for the 8th time in 2020. In these 8 years, a larg...", "weights": [[8, 16, 1.0], [110, 118, 1.0]]}, "W91-0104": {"snippet": "Inherently Reversible Grammars, Logic Programming and Computability\n\n\n This paper attempts to clarify two distinct notions of \"reversibility\": (i) Uniformity of implementation of parsing and generatio...", "weights": []}, "L08-1336": {"snippet": "An Inverted Index for Storing and Retrieving Grammatical Dependencies", "weights": []}, "2009.cikm_conference-2009.33": {"snippet": "...ense indexing is possible. The hash function uses the algebraic signatures of n-grams. Use of hashing provides for constant index access time for arbitrarily long patterns, unlike other structures who...", "weights": [[8, 16, 1.0]]}, "2011.sigirconf_conference-2011.58": {"snippet": "Inverted indexes for phrases and strings\n\n\n ABSTRACTInverted indexes are the most fundamental and widely used data structures in information retrieval. For each unique word occurring in a document col...", "weights": []}, "1993.sigirconf_conference-93.3": {"snippet": "...atic Indexing Based on Bayesian Inference Networks\n\n\n AbstractIn this paper, aBayesian inference network model for automatic indexing with index terms (descriptors) from a prescribed vocabulary is pre...", "weights": [[8, 16, 1.0], [128, 136, 1.0]]}, "2020.wanlp-1.30": {"snippet": "... and Reverse Translation Corpus Extension System\n\n\n In this paper, we present our work for the NADI Shared Task (Abdul-Mageed et al., 2020): Nuanced Arabic Dialect Identification for Subtask-1: countr...", "weights": [[8, 15, 1.0]]}, "2008.spire_conference-2008.15": {"snippet": "Indexed Hierarchical Approximate String Matching", "weights": []}, "2005.cikm_conference-2005.78": {"snippet": "...ble.\nINDEXING ALGORITHMOur indexing algorithm is based on a re-merge indexupdate strategy, which Lester et al.  found to have good performance. In the re-merge strategy, new documents are added to a d...", "weights": [[8, 16, 1.0], [30, 38, 1.0]]}, "1998.spire_conference-1998.13": {"snippet": "Reversal and Transposition Distance of Linear Chromosomes", "weights": []}, "2015.ecir_workshop-2015mrdm.9": {"snippet": "... for Indexing and Retrieval\n\n\n Abstract. Annotation of MEDLINE citations with controlled vocabulary terms improves the quality of retrieval results. Due to variety in descriptions of similar clinical ...", "weights": [[8, 16, 1.0]]}, "1974.jasis_journal-ir0volumeA25A5.3": {"snippet": "...atic indexing", "weights": [[8, 16, 1.0]]}, "2019.wsdm_conference-2019.6": {"snippet": "Fast Dictionary-Based Compression for Inverted Indexes\n\n\n ABSTRACTDictionary-based compression schemes provide fast decoding operation, typically at the expense of reduced compression effectiveness co...", "weights": []}, "1979.jasis_journal-ir0volumeA30A4.7": {"snippet": "...h of indexing", "weights": [[8, 16, 1.0]]}, "2001.ntcir_workshop-2001.15": {"snippet": "...Term Indexing: an Evaluation\n\n\n Retrieval effectiveness depends on how terms are extracted and indexed. For Chinese text (and others like Japanese and Korean), there are no space to delimit words. Ind...", "weights": [[8, 16, 1.0], [200, 208, 1.0]]}, "1978.ipm_journal-ir0volumeA14A6.10": {"snippet": "...rase indexing", "weights": [[8, 16, 1.0]]}, "2011.tweb_journal-ir0volumeA5A4.1": {"snippet": "Efficient Search Engine Measurements\n\n\n We address the problem of externally measuring aggregate functions over documents indexed by search engines, like corpus size, index freshness, and density of d...", "weights": []}, "2017.wwwjournals_journal-ir0volumeA20A5.1": {"snippet": "Reverse direction-based surrounder queries for mobile recommendations\n\n\n Abstract This paper proposes a new spatial query called a reverse direction-based surrounder (RDBS) query, which retrieves a us...", "weights": [[0, 7, 1.0], [131, 138, 1.0]]}, "C90-2019": {"snippet": "Generating {F}rench with a Reversible Unification Grammar\n\n\n ISSCO 54 rte des Acacias, CH-1227 Geneva O. Intr~cluction In this paper, we describe the linguistic solutions to some of the problems encou...", "weights": []}, "2020.findings-emnlp.388": {"snippet": "...gual Reverse Dictionary\n\n\n Reverse dictionary is the task to find the proper target word given the word description. In this paper, we tried to incorporate BERT into this task. However, since BERT is ...", "weights": [[8, 15, 1.0], [30, 37, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.3": {"snippet": "...ment indexing: a concept-based approach to term weight estimation\n\n\n AbstractTraditional index weighting approaches for information retrieval from texts depend on the term frequency based analysis of ...", "weights": [[8, 16, 1.0]]}, "P11-2034": {"snippet": "Reversible Stochastic Attribute-Value Grammars\n\n\n An attractive property of attribute-value grammars is their reversibility. Attribute-value grammars are usually coupled with separate statistical comp...", "weights": []}, "C16-1263": {"snippet": "...ng a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture\n\n\n In this paper, we outline an approach to build graph-based reverse dictionaries using word definitions. A reverse...", "weights": [[8, 15, 1.0], [149, 156, 1.0], [196, 203, 1.0]]}, "2017.jasis_journal-ir0volumeA68A9.16": {"snippet": "...ds\": Reverse image lookup and digital library assessment", "weights": [[8, 15, 1.0]]}, "2003.jasis_journal-ir0volumeA54A3.8": {"snippet": "Simplifying the Reversed Duplicate Removal Procedur", "weights": []}, "C90-2052": {"snippet": "Reversible Unification Based Machine Translation\n\n\n n this paper it will be shown how unification grammars can be used to build a reversible machine translation system. Unification grammars are often ...", "weights": []}, "L14-1216": {"snippet": "...n of Reverse Associations", "weights": [[8, 15, 1.0]]}, "2012.ipm_journal-ir0volumeA48A5.6": {"snippet": "Use of permutation prefixes for efficient and scalable approximate similarity search\n\n\n a b s t r a c tWe present the Permutation Prefix Index (this work is a revised and extended version of , present...", "weights": []}, "2004.wwwconf_conference-2004at.94": {"snippet": "Random surfer with back step\n\n\n ABSTRACTWe present a novel link-based ranking algorithm RBS, which may be viewed as an extension of PageRank by back-step feature.\nCategories and Subject Descriptors\nH....", "weights": []}, "2013.clef_workshop-2013bioasq.8": {"snippet": "...ntic Indexing Challenge\n\n\n This paper describes our participation in the BioASQ semantic indexing challenge with two hierarchical text categorization systems. Both systems originated from previous res...", "weights": [[8, 16, 1.0], [92, 100, 1.0]]}, "2021.findings-acl.276": {"snippet": "...the \"reverse\" (target to source) direction, yielding equivalent (or better) performance to standard unidirectional translation models where the source and target language is flipped. We also show how ...", "weights": [[8, 15, 1.0]]}, "2014.wsdm_conference-2014.79": {"snippet": "...uted indexing and retrieval of dynamic information sources\u2022 Caching for search engines\u2022 Retrieval models for distributed IR\u2022 Standards and benchmarks for distributed IR\u2022 Evaluation of the impact of se...", "weights": [[8, 16, 1.0]]}, "2008.jasis_journal-ir0volumeA59A9.12": {"snippet": "An empirical investigation of the g-index for 26 physicists in comparison with the h-index, the A-index, and the R-index", "weights": []}, "2009.cikm_conference-2009.47": {"snippet": "On-line index maintenance using horizontal partitioning\n\n\n ABSTRACTIn this paper, we propose a new merge-based index maintenance strategy for Information Retrieval systems. The new model is based on p...", "weights": []}, "2008.cikm_conference-2008.69": {"snippet": "...dard indexing method for supporting search queries in a variety of content-based applications. Examples of such applications include enterprise document management, e-mail, web search, and social netw...", "weights": [[8, 16, 1.0]]}, "2010.wsdm_conference-2010.38": {"snippet": "Revisiting globally sorted indexes for efficient document retrieval\n\n\n ABSTRACTThere has been a large amount of research on efficient document retrieval in both IR and web search areas. One important ...", "weights": []}, "2012.cikm_conference-2012.98": {"snippet": "...atic reverse nearest neighbor queries on land surfaces\n\n\n ABSTRACTFinding reverse nearest neighbors (RNNs) is an important operation in spatial databases. The problem of evaluating RNN queries has alr...", "weights": [[8, 15, 1.0], [77, 84, 1.0]]}, "2011.iir_workshop-2011.18": {"snippet": "...ndom Indexing for Content-Based Recommender Systems", "weights": [[8, 16, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.9": {"snippet": "Combination Forecasting Reversion Strategy for Online Portfolio Selection\n\n\n Machine learning and artificial intelligence techniques have been applied to construct online portfolio selection strategie...", "weights": []}, "1988.riao_conference-1988.22": {"snippet": "... for Indexing and Retrieving", "weights": [[8, 16, 1.0]]}, "C98-1094": {"snippet": "...atic indexing through concept combination. The increase in the volume of indexing is 10.5% for free indexing and 52.3% for controlled indexing. The resulting structure of the indexed corpus is a parti...", "weights": [[8, 16, 1.0], [76, 84, 1.0], [103, 111, 1.0], [137, 145, 1.0]]}, "2015.ecir_workshop-2015mrdm.7": {"snippet": "...odal Indexing and Retrieval Using an LSA-Based Kernel\n\n\n Abstract. This article proposes a Latent Semantic Analysis (LSA) based kernel function which effectively integrates low-level visual features w...", "weights": [[8, 16, 1.0]]}, "1979.jasis_journal-ir0volumeA30A2.13": {"snippet": "Indexing", "weights": [[0, 8, 1.0]]}, "2008.wwwjournals_journal-ir0volumeA11A4.0": {"snippet": "...with reverse axes which are either ancestor or parent. To evaluate such queries in the stream-based context, some rewriting rules have been proposed to transform the paths with reverse axes into equiv...", "weights": [[8, 15, 1.0], [180, 187, 1.0]]}, "2011.cikm_conference-2011.142": {"snippet": "... and reverse navigation when compared to the state of the art. In social networks, splitting the graph to achieve a good decomposition is not easy. For this case, we explore a new proposal for indexin...", "weights": [[8, 15, 1.0], [196, 204, 1.0]]}, "2011.spire_conference-2011.14": {"snippet": "Indexing with Gaps", "weights": [[0, 8, 1.0]]}, "2005.cikm_conference-2005.126": {"snippet": "...ient reverse k-nearest neighbors retrieval with local kNN-distance estimation\n\n\n ABSTRACTThe Reverse k-Nearest Neighbors (RkNN) queries are important in profile-based marketing, information retrieval,...", "weights": [[8, 15, 1.0], [96, 103, 1.0]]}, "2009.ecir_conference-2009.81": {"snippet": "...ntic Indexing", "weights": [[8, 16, 1.0]]}, "2015.tois_journal-ir0volumeA33A4.3": {"snippet": "...nant indexing method in information retrieval systems. It enables fast return of the list of all documents containing a given query term. However, for retrieval schemes involving query expansion, as i...", "weights": [[8, 16, 1.0]]}, "2016.wwwconf_conference-2016.24": {"snippet": "...stem reverse engineer the desired query from the examples. This approach has been heavily used in the setting of relational databases. We provide here an investigation of the reverse engineering probl...", "weights": [[8, 15, 1.0], [178, 185, 1.0]]}, "1997.sigirjournals_journal-ir0volumeA31A1.5": {"snippet": "...atic Indexing\n\n\n Abstract:This note summarizes some of the currently available insights in automatic indexing. The emphasis is on aspects that are expected to be useful in practical automatic indexing...", "weights": [[8, 16, 1.0], [104, 112, 1.0], [195, 203, 1.0]]}, "1991.jasis_journal-ir0volumeA42A7.7": {"snippet": "...n to indexing and abstracting", "weights": [[8, 16, 1.0]]}, "W06-1654": {"snippet": "...ndom Indexing using Statistical Weight Functions\n\n\n Random Indexing is a vector space technique that provides an efficient and scalable approximation to distributional similarity problems. We present ...", "weights": [[8, 16, 1.0], [62, 70, 1.0]]}, "1977.jasis_journal-ir0volumeA28A1.5": {"snippet": "On indexing, retrieval and the meaning of about", "weights": [[3, 11, 1.0]]}, "2007.sigirconf_conference-2007.86": {"snippet": "...ient indexing for this operation is known to be a hard problem for arbitrary data distributions. However, text corpora used in Information Retrieval applications often have convenient power-law constr...", "weights": [[8, 16, 1.0]]}, "2015.wsdm_conference-2015.8": {"snippet": "Optimal Space-time Tradeoffs for Inverted Indexes\n\n\n ABSTRACTInverted indexes are usually represented by dividing posting lists into constant-sized blocks and representing them with an encoder for seq...", "weights": []}, "P98-1097": {"snippet": "...atic indexing through concept combination. The increase in the volume of indexing is 10.5% for free indexing and 52.3% for controlled indexing. The resulting structure of the indexed corpus is a parti...", "weights": [[8, 16, 1.0], [76, 84, 1.0], [103, 111, 1.0], [137, 145, 1.0]]}, "2011.spire_conference-2011.26": {"snippet": "...Text Indexing with Wildcards", "weights": [[8, 16, 1.0]]}, "2016.ipm_journal-ir0volumeA52A3.7": {"snippet": "A query term re-weighting approach using document similarity\n\n\n a b s t r a c tPseudo-relevance feedback is the basis of a category of automatic query modification techniques. Pseudo-relevance feedbac...", "weights": []}, "2011.cikm_conference-2011.42": {"snippet": "Factorization-based lossless compression of inverted indices\n\n\n ABSTRACTMany large-scale Web applications that require ranked top-k retrieval are implemented using inverted indices. An inverted index ...", "weights": []}, "2013.clef_workshop-2013bioasq.5": {"snippet": "...ntic Indexing of Biomedical Publications\n\n\n Automated annotation of scientific publications in real-world digital libraries requires dealing with challenges such as large number of concepts and traini...", "weights": [[8, 16, 1.0]]}, "C67-1003": {"snippet": "Programming of Reversible Systems in Computational Linguistics", "weights": []}, "2020.ecir_workshop-2020siirh.6": {"snippet": "...ntic Indexing and Information Retrieval for Health from heterogeneous content types and languages", "weights": [[8, 16, 1.0]]}, "2008.cikm_conference-2008.30": {"snippet": "...and, reverse natural graphs tend to have low in-degree while maintaining fast PageRank convergence. It follows that calculating Reverse PageRank locally is frequently more feasible than computing Page...", "weights": [[8, 15, 1.0], [131, 138, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A1.3": {"snippet": "...nked reverse boolean spatial keyword nearest neighbors search\n\n\n Abstract Recently, Reverse k Nearest Neighbors (RkNN) queries, returning every answer for which the query is one of its k nearest neigh...", "weights": [[8, 15, 1.0], [87, 94, 1.0]]}, "2016.cikm_conference-2016.133": {"snippet": "...Data Indexing for Parallelizing Dynamic and Reverse Skylines", "weights": [[8, 16, 1.0], [47, 54, 1.0]]}}}
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A large body of research has been built in the last 30 years around gap compression, including theoretical modeling of the gap distribution, specialized instantaneous codes suitable f...", "weights": [[8, 18, 1.0], [92, 103, 1.0]]}, "2010.cikm_conference-2010.128": {"snippet": "...ease index size.We propose new index compression techniques for versioned document collections that achieve reductions in index size over previous methods. 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It is known that this can significantly improve index compression compared to a random document ordering.We study index compression and query processing techniques for suc...", "weights": [[8, 19, 1.0], [20, 30, 1.0], [80, 85, 1.0], [86, 97, 1.0], [146, 151, 1.0], [152, 163, 1.0], [185, 195, 1.0]]}, "2000.tois_journal-ir0volumeA18A2.0": {"snippet": "... The compression and decompression speeds and the amount of compression achieved are very good when compared to well-known algorithms in the literature .The compression scheme presented in this articl...", "weights": [[8, 19, 1.0], [63, 74, 1.0], [160, 171, 1.0]]}, "2008.wwwconf_conference-2008.201": {"snippet": "...gine.Index Compression: Compression of inverted indexes greatly reduces disk space use as well as disk and main memory accesses, resulting in faster query evaluation. There are many index compression ...", "weights": [[8, 13, 1.0], [14, 25, 1.0], [27, 38, 1.0], [185, 190, 1.0], [191, 202, 1.0]]}, "2019.wsdm_conference-2019.6": {"snippet": "... and compression efficiency can be achieved. Our observations are supported by experiments using the document-level inverted index data for two large text collections, and a wide range of other index ...", "weights": [[8, 19, 1.0], [128, 133, 1.0], [197, 202, 1.0]]}, "2011.cikm_conference-2011.57": {"snippet": "...ased compression of the differential inverted lists, instead of gap-encoding them as is the usual practice. 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Us...", "weights": [[8, 13, 1.0], [14, 25, 1.0], [137, 148, 1.0]]}, "2014.ecir_conference-2014.30": {"snippet": "...rted Index Compression for Search Engine Efficiency\n\n\n Abstract. Efficient access to the inverted index data structure is a key aspect for a search engine to achieve fast response times to users' quer...", "weights": [[8, 13, 1.0], [14, 25, 1.0], [101, 106, 1.0]]}, "2015.airs_conference-2015.2": {"snippet": "... for compression-based archiving, provided that random-access to small fragments of the original data can be achieved without needing to decompress everything. The recent RLZ (relative Lempel Ziv) com...", "weights": [[8, 19, 1.0], [200, 211, 1.0]]}, "2005.ir_journal-ir0volumeA8A1.6": {"snippet": "...rted Index Compression Using Word-Aligned Binary Codes", "weights": [[8, 13, 1.0], [14, 25, 1.0]]}, "2008.wsdm_conference-2008.12": {"snippet": "...raph compression with communities\n\n\n ABSTRACTA link server is a system designed to support efficient implementations of graph computations on the web graph. In this work, we present a compression sche...", "weights": [[8, 19, 1.0], [187, 198, 1.0]]}, "2011.wwwconf_conference-2011.52": {"snippet": "...oned index size. We show that there exists a tradeoff between the compression of a partitioned index and the distribution of documents from the same host across the index partitions (i.e., host distri...", "weights": [[8, 13, 1.0], [69, 80, 1.0], [98, 103, 1.0], [168, 173, 1.0]]}, "2017.spire_conference-2017.10": {"snippet": "...ient Compression and Indexing of Trajectories", "weights": [[8, 19, 1.0]]}, "2007.wwwconf_conference-2007.159": {"snippet": "... for index compression\n\n\n ABSTRACTSequential patterns of d-gaps exist pervasively in inverted lists of Web document collection indices due to the cluster property. In this paper the information of d-g...", "weights": [[8, 13, 1.0], [14, 25, 1.0]]}, "2015.airs_conference-2015.3": {"snippet": "Large Scale Sentiment Analysis with Locality Sensitive BitHash\n\n\n Abstract. 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We also gain further insight about the compression and querying performance by st...", "weights": [[8, 19, 1.0], [161, 172, 1.0]]}, "2005.wwwconf_conference-2005.26": {"snippet": "...less compression methods, while keeping the loss in retrieval precision to a minimum. When compared to the indices size with no compression at all, the compression rate is higher than 88%, i.e., less ...", "weights": [[8, 19, 1.0], [131, 142, 1.0], [155, 166, 1.0]]}, "2019.ecir_conference-20191.23": {"snippet": "...y of Index Compression and DAAT Query Processing Methods\n\n\n Abstract. In the last two decades, the IR community has seen numerous advances in top-k query processing and inverted index compression tech...", "weights": [[8, 13, 1.0], [14, 25, 1.0], [181, 186, 1.0], [187, 198, 1.0], [199, 209, 1.0]]}, "2017.ir_journal-ir0volumeA20A3.2": {"snippet": "...e of index compression in score-at-a-time query evaluation", "weights": [[8, 13, 1.0], [14, 25, 1.0]]}, "2019.iir_workshop-2019.10": {"snippet": "...raph Index Compression Techniques", "weights": [[8, 13, 1.0], [14, 25, 1.0], [26, 36, 1.0]]}, "2011.spire_conference-2011.26": {"snippet": "Compressed Text Indexing with Wildcards", "weights": []}, "2014.sigirconf_conference-2014.207": {"snippet": "...data compression\n\n\n ABSTRACTWe propose a two-stage lossless compression approach on large scale RDF data. Our approach exploits both Representation Compression and Component Compression techniques to ...", "weights": [[8, 19, 1.0], [63, 74, 1.0], [151, 162, 1.0], [177, 188, 1.0], [189, 199, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.26": {"snippet": "...brid compression of inverted lists for reordered document collections\n\n\n Keywords: Index compression for information retrieval Reordered document collections A B S T R A C TText search engines are a f...", "weights": [[8, 19, 1.0], [86, 91, 1.0], [92, 103, 1.0]]}, "2020.ecir_workshop-2020siirh.1": {"snippet": "BioASQ: The challenge and the Community of Biomedical Semantic Indexing and Question Answering - Abstract\n\n\n The BioASQ challenge on large-scale biomedical semantic indexing and question answering is ...", "weights": []}, "2012.ipm_journal-ir0volumeA48A5.1": {"snippet": "...self-index (i.e., an index that compresses the text and is able to extract arbitrary parts of it) can be competitive with an inverted index if we consider the whole query process, which includes index...", "weights": [[8, 13, 1.0], [24, 29, 1.0], [137, 142, 1.0], [198, 203, 1.0]]}, "2007.cikm_conference-2007.81": {"snippet": "Index compression is good, especially for random access\n\n\n ABSTRACTIndex compression techniques are known to substantially decrease the storage requirements of a text retrieval system. 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The framework is made of two main modules: Query Similarity Learning (QSL) and Graph-based Query Clustering (GQC). The former is dev...", "weights": [[8, 14, 1.0], [52, 58, 1.0], [59, 65, 1.0], [114, 119, 1.0], [162, 167, 1.0]]}, "2004.cikm_conference-2004.14": {"snippet": "...t on search performance over the naive query log mining algorithm as well as the baseline search engine.", "weights": [[8, 14, 1.0], [42, 47, 1.0], [93, 99, 1.0], [100, 106, 1.0]]}, "2011.wwwconf_conference-2011c.9": {"snippet": "...n of search results using query logs\n\n\n ABSTRACTWe study the problem of diversifying search results by exploiting the knowledge mined from query logs. Our proposal exploits the presence of different \"...", "weights": [[8, 14, 1.0], [29, 34, 1.0], [35, 39, 1.0], [88, 94, 1.0], [142, 147, 1.0], [148, 152, 1.0]]}, "2007.sigirjournals_journal-ir0volumeA41A2.17": {"snippet": "Query log analysis: social and technological challenges\n\n\n AbstractAnalysis of search engine query logs is an important tool for developers and researchers.However, the potentially personal content of...", "weights": [[0, 5, 1.0], [79, 85, 1.0], [86, 92, 1.0], [93, 98, 1.0], [99, 103, 1.0]]}, "2018.sigirconf_conference-2018.167": {"snippet": "... Job Search: Study of Users' Search Behavior using Search Engine Query Logs\n\n\n ABSTRACTOver the last few years, an increasing number of user's and enterprises on the internet has generated a global ma...", "weights": [[8, 14, 1.0], [32, 38, 1.0], [54, 60, 1.0], [61, 67, 1.0], [68, 73, 1.0], [74, 78, 1.0]]}, "2017.wsdm_conference-2017.19": {"snippet": "...ocal search differs from traditional web-search because to correctly capture users' click behavior, the estimation of relevance between query and candidate results must be integrated with geographical...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [139, 144, 1.0]]}, "2009.clef_workshop-2009w.5": {"snippet": "A Search Engine based on Query Logs and Search Log Analysis at the University of Sunderland\n\n\n This work describes a variation on the traditional Information Retrieval paradigm, where instead of text ...", "weights": [[2, 8, 1.0], [9, 15, 1.0], [25, 30, 1.0], [31, 35, 1.0], [40, 46, 1.0]]}, "2020.wwwconf_conference-2020.278": {"snippet": "... for Search Engine Optimization", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 34, 1.0]]}, "2009.sigirconf_workshop-2009uiir.5": {"snippet": "... web search and implemented the technology in a browser extension that can dynamically modify search engine results pages (Google, Yahoo!, and Live Search). A combination of explicit (queries, reformu...", "weights": [[8, 14, 1.0], [97, 103, 1.0], [104, 110, 1.0], [151, 157, 1.0]]}, "2010.wwwconf_conference-2010.91": {"snippet": "... the query-URL bipartite graphs. Consequently, our model is capable of scaling up to the need of commercial search engines. Experimental results on one-month query logs from a large commercial search ...", "weights": [[8, 13, 1.0], [111, 117, 1.0], [161, 166, 1.0], [167, 171, 1.0], [196, 202, 1.0]]}, "2013.cikm_workshop-2013ueo.7": {"snippet": "...to a search engine are usually short and ambiguous. Query expansion is an effective way to resolve the ambiguity of a query. We attempt to make a consolidated study of the effectiveness of query logs ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [55, 60, 1.0], [121, 126, 1.0], [192, 197, 1.0], [198, 202, 1.0]]}, "2017.wsdm_conference-2017.15": {"snippet": "...ware Query Auto-Completion via Hawkes Processes\n\n\n ABSTRACTQuery auto completion (QAC) is a prominent feature in modern search engines. High quality QAC substantially improves search experiences by he...", "weights": [[8, 13, 1.0], [123, 129, 1.0], [179, 185, 1.0]]}, "2013.wsdm_conference-2013.89": {"snippet": "...ets. Query logs have also revolutionized query assistance tools such as related queries (recommended queries after the search was issued) or query completions (suggested queries as the query is being ...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [44, 49, 1.0], [122, 128, 1.0], [144, 149, 1.0], [188, 193, 1.0]]}, "2005.jasis_journal-ir0volumeA56A13.1": {"snippet": "... the query logs of a Web site search engine", "weights": [[8, 13, 1.0], [14, 18, 1.0], [33, 39, 1.0], [40, 46, 1.0]]}, "2002.tois_journal-ir0volumeA20A1.3": {"snippet": "Query clustering using user logs\n\n\n Query clustering is a process used to discover frequently asked questions or most popular topics on a search engine. This process is crucial for search engines base...", "weights": [[0, 5, 1.0], [28, 32, 1.0], [36, 41, 1.0], [138, 144, 1.0], [145, 151, 1.0], [181, 187, 1.0]]}, "2009.sigirconf_conference-2009.76": {"snippet": "...zing search engine revenue in sponsored search\n\n\n ABSTRACTDisplaying sponsored ads alongside the search results is a key monetization strategy for search engine companies. Since users are more likely ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [43, 49, 1.0], [100, 106, 1.0], [150, 156, 1.0], [157, 163, 1.0]]}, "2008.cikm_conference-2008.64": {"snippet": "... the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more.In this paper we introduce the query-flow ...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [61, 66, 1.0], [130, 135, 1.0], [192, 197, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.7": {"snippet": "...oice search is still in its infancy, it is gradually becoming more widespread. In this article, we report a comprehensive voice search query log analysis of a commercial web search engine's mobile app...", "weights": [[8, 14, 1.0], [131, 137, 1.0], [138, 143, 1.0], [177, 183, 1.0], [184, 190, 1.0]]}, "2010.wsdm_conference-2010.5": {"snippet": "...d on query logs have been studied as a method of capturing user intent and improving retrieval effectiveness. The evaluation of these techniques has primarily, however, focused on proprietary query lo...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [195, 200, 1.0], [201, 205, 1.0]]}, "2009.clef_workshop-2009.64": {"snippet": "A Search Engine Based on Query Logs, and Search Log Analysis by Automatic Language Identification", "weights": [[2, 8, 1.0], [9, 15, 1.0], [25, 30, 1.0], [31, 35, 1.0], [41, 47, 1.0]]}, "2006.wwwconf_conference-2006.146": {"snippet": "...from search engine query logs\n\n\n ABSTRACTIn this work we propose a method that retrieves a list of related queries given an initial input query. The related queries are based on the query log of previ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [28, 32, 1.0], [141, 146, 1.0], [185, 190, 1.0]]}, "2011.cikm_conference-2011.87": {"snippet": "...king query recommendations which not only consider relevance to the original query but also take into account efficiency of a query at accomplishing a user search task at hand. We formalize the notion...", "weights": [[8, 13, 1.0], [80, 85, 1.0], [129, 134, 1.0], [159, 165, 1.0]]}, "2015.wwwconf_conference-2015c.217": {"snippet": "... Web search, any change to a component of the search engine may result in a different search result page for the same query, but we normally cannot infer reliably from search log how users would react...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [56, 62, 1.0], [89, 95, 1.0], [121, 126, 1.0], [171, 177, 1.0]]}, "2009.sigirconf_workshop-2009uiir.4": {"snippet": "...dual query but also the complete search process is necessary. Only in this way it is possible to satisfy an information need.", "weights": [[8, 13, 1.0], [36, 42, 1.0]]}, "2019.wsdm_conference-2019.110": {"snippet": "...mage Search\n\n\n ABSTRACTWeb-based image search engines di er from Web search engines greatly. The intents or goals behind human interactions with image search engines are di erent. In image search, use...", "weights": [[8, 14, 1.0], [42, 48, 1.0], [72, 78, 1.0], [154, 160, 1.0], [192, 198, 1.0]]}, "2005.ecir_conference-2005.2": {"snippet": "...site logs where queries and later actions are recorded from search engine referrers or from an internal search box. In this paper we present two applications based in analyzing and clustering queries....", "weights": [[8, 12, 1.0], [63, 69, 1.0], [70, 76, 1.0], [107, 113, 1.0]]}, "2008.wwwconf_conference-2008.7": {"snippet": "... the query logs of the search engine. We identify queries related to a campaign by exploiting the associations between queries and URLs as they are captured by the user's clicks. These queries form go...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [26, 32, 1.0], [33, 39, 1.0]]}, "2020.sigirconf_conference-2020.290": {"snippet": "...ting Search Query Logs by Learning to Detect Search Task Boundaries\n\n\n To fulfill their information needs, users submit sets of related queries to available search engines. Query logs record users' ac...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 25, 1.0], [48, 54, 1.0], [160, 166, 1.0], [176, 181, 1.0], [182, 186, 1.0]]}, "2008.wwwconf_conference-2008.134": {"snippet": "... the search results for a query can be organized into a few subtopics about the query, each subtopic representing a specific aspect of the query. We believe that the subtopics in the search results fo...", "weights": [[8, 14, 1.0], [29, 34, 1.0], [83, 88, 1.0], [142, 147, 1.0], [186, 192, 1.0]]}, "2021.ecir_conference-20211.26": {"snippet": "...able search engines. The mining of query logs from search engines enables the automatic extraction of search tasks by clustering related queries into groups representing search tasks. The extraction o...", "weights": [[8, 14, 1.0], [38, 43, 1.0], [44, 48, 1.0], [54, 60, 1.0], [105, 111, 1.0], [173, 179, 1.0]]}, "2009.wsdm_workshop-2009wscd.2": {"snippet": "...ture search, and they have been studied to some extent using TREC data. They are also, however, quite common in web search, as can be seen by looking at the distribution of query lengths in a large sc...", "weights": [[8, 14, 1.0], [119, 125, 1.0], [176, 181, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.13": {"snippet": "... Web search engine data logs\n\n\n AbstractThe analysis of contextual information in search engine query logs enhances the understanding of Web users' search patterns. Obtaining contextual information on...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [27, 31, 1.0], [85, 91, 1.0], [92, 98, 1.0], [99, 104, 1.0], [105, 109, 1.0], [151, 157, 1.0]]}, "2012.wwwconf_conference-2012c.106": {"snippet": "... the search engine query stream\n\n\n ABSTRACTSearch engines record a large amount of metadata each time a user issues a query. While efficiently mining this data can be challenging, the results can be u...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [121, 126, 1.0]]}, "2020.sigirconf_conference-2020.291": {"snippet": "...able search engines. Query logs registered by search engines enable the automatic identification of the search tasks that users perform to fulfill their information needs. Search engine logs contain q...", "weights": [[8, 14, 1.0], [24, 29, 1.0], [30, 34, 1.0], [49, 55, 1.0], [107, 113, 1.0], [175, 181, 1.0], [182, 188, 1.0], [189, 193, 1.0]]}, "2009.wsdm_workshop-2009wscd.10": {"snippet": "...sing query logs and click data to create improved document descriptions\n\n\n ABSTRACTLogfiles of search engines are a promising resource for data mining, since they provide raw data associated to users ...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [98, 104, 1.0]]}, "2011.sigirconf_conference-2011.7": {"snippet": "... the search engines themselves. In this paper we analyze the search logs of one particular vertical: people search engines. Based on an extensive analysis of the logs of a search engine geared towards...", "weights": [[8, 14, 1.0], [64, 70, 1.0], [71, 75, 1.0], [111, 117, 1.0], [165, 169, 1.0], [175, 181, 1.0], [182, 188, 1.0]]}, "2009.cikm_conference-2009.281": {"snippet": "... the search engine recorded in query logs.A recent query-log mining approach for query recommendation is based on Query Flow Graphs (QFG). In this paper we propose an evaluation of the effects of time...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [34, 39, 1.0], [40, 44, 1.0], [54, 59, 1.0], [84, 89, 1.0], [117, 122, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "... on. Query-dependent or \"dynamic\" features take the query into account and thus can only be computed at query time, whereas query-independent or \"static\" features do not rely on the query and thus can...", "weights": [[8, 13, 1.0], [55, 60, 1.0], [107, 112, 1.0], [127, 132, 1.0], [185, 190, 1.0]]}, "2008.cikm_conference-2008.50": {"snippet": "...from search engine logs. These two patterns can be used to address the mis-specification and under-specification problems of ineffective queries. Experiment results on real search engine logs show tha...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 26, 1.0], [176, 182, 1.0], [183, 189, 1.0], [190, 194, 1.0]]}, "2009.sigirconf_workshop-2009uiir.7": {"snippet": "...s of search query logs or Web logs is not sufficient to study users and interpret their preferences. In fact the combination of implicitly and explicitly collected data improves understanding of behav...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 25, 1.0], [33, 37, 1.0]]}, "2017.wsdm_conference-2017.96": {"snippet": "...yond Query Logs: Recommendation and Evaluation\n\n\n ABSTRACTQuery recommendation in Web search is typically manifested in algorithms that 1) recommend previously issued queries from a query log or 2) ma...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [89, 95, 1.0], [185, 190, 1.0]]}, "2010.wsdm_conference-2010.43": {"snippet": "...cale search bot detection\n\n\n ABSTRACTIn this paper, we study search bot traffic from search engine query logs at a large scale. Although bots that generate search traffic aggressively can be easily de...", "weights": [[8, 14, 1.0], [64, 70, 1.0], [88, 94, 1.0], [95, 101, 1.0], [102, 107, 1.0], [108, 112, 1.0], [159, 165, 1.0]]}, "2007.jasis_journal-ir0volumeA58A14.18": {"snippet": "...g of search engine logs", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 26, 1.0]]}, "2011.wwwconf_conference-2011c.32": {"snippet": "... web search query logs\n\n\n ABSTRACTQuery logs of a Web search engine have been increasingly used as a vital source for data mining. This paper presents a study on largescale domain-independent entity e...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 25, 1.0], [43, 47, 1.0], [57, 63, 1.0], [64, 70, 1.0]]}, "2006.cikm_conference-2006.127": {"snippet": "...iven query by constructing the query context from search query logs. Evaluation results show that Q-Rank gains a considerable advantage over the current ranking system of a large-scale commercial Web ...", "weights": [[8, 13, 1.0], [34, 39, 1.0], [53, 59, 1.0], [60, 65, 1.0], [66, 70, 1.0]]}, "2016.ipm_journal-ir0volumeA52A5.1": {"snippet": "... new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targ...", "weights": [[8, 13, 1.0], [89, 94, 1.0], [147, 152, 1.0], [153, 157, 1.0], [180, 186, 1.0], [187, 193, 1.0]]}, "2016.ipm_journal-ir0volumeA52A6.12": {"snippet": "...sing search logs by (semi-)automatically identifying user search tactics from logged events, allowing large-scale analysis that is comparable across search systems. In addition, as the resulting analy...", "weights": [[8, 14, 1.0], [15, 19, 1.0], [61, 67, 1.0], [152, 158, 1.0]]}, "2009.cikm_conference-2009.161": {"snippet": "...n of query logs\n\n\n ABSTRACTUser search query logs have proven to be very useful, but have vast potential for misuse. Several incidents have shown that simple removal of identifiers is insufficient to ...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [35, 41, 1.0], [42, 47, 1.0], [48, 52, 1.0]]}, "2011.wsdm_conference-2011.48": {"snippet": "...om a search engine. We model the conditional probability of a searchable web site being relevant to a given query in term of three main components: the language model of the query, the language model ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [111, 116, 1.0], [177, 182, 1.0]]}, "2011.sigirconf_conference-2011.217": {"snippet": "... web search by mining search and browse logs\n\n\n ABSTRACTHuge amounts of search log data have been accumulated in various search engines. Currently, a commercial search engine receives billions of quer...", "weights": [[8, 14, 1.0], [25, 31, 1.0], [43, 47, 1.0], [75, 81, 1.0], [124, 130, 1.0], [164, 170, 1.0], [171, 177, 1.0]]}, "2017.wwwconf_conference-2017.58": {"snippet": "...ring Query Auto-Completion and Click Logs for Contextual-Aware Web Search and Query Suggestion\n\n\n ABSTRACTContextual data plays an important role in modeling search engine users' behaviors on both que...", "weights": [[8, 13, 1.0], [40, 44, 1.0], [70, 76, 1.0], [81, 86, 1.0], [161, 167, 1.0], [168, 174, 1.0], [200, 205, 1.0]]}, "2011.sigirconf_conference-2011.82": {"snippet": "...y on search engine query logs to determine possible query suggestions. However, for customized search systems in the enterprise domain, intranet search, or personalized search such as email or desktop...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [28, 32, 1.0], [55, 60, 1.0], [98, 104, 1.0], [148, 154, 1.0], [172, 178, 1.0]]}, "2011.cikm_conference-2011.15": {"snippet": "... the search sessions is of great importance for providing a good search experience. Most search assistance methods in the literature that involve query generation, query expansion and other techniques...", "weights": [[8, 14, 1.0], [68, 74, 1.0], [92, 98, 1.0], [149, 154, 1.0], [167, 172, 1.0]]}, "2006.wwwconf_conference-2006.194": {"snippet": "...ning search engine query logs for query recommendation\n\n\n ABSTRACTThis paper presents a simple and intuitive method for mining search engine query logs to get fast query recommendations on a large sca...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [28, 32, 1.0], [37, 42, 1.0], [130, 136, 1.0], [137, 143, 1.0], [144, 149, 1.0], [150, 154, 1.0], [167, 172, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.8": {"snippet": "...uted search engines\n\n\n AbstractWeb search engines have to deal with a rapidly increasing amount of information, high query loads and tight performance constraints. The success of a search engine depen...", "weights": [[8, 14, 1.0], [38, 44, 1.0], [120, 125, 1.0], [184, 190, 1.0], [191, 197, 1.0]]}, "2012.wwwconf_conference-2012c.167": {"snippet": "...n of query suggestions using query logs\n\n\n ABSTRACTIn this work we are studying actualization techniques for building an up-to-date query suggestions model using query logs. The performance of the pro...", "weights": [[8, 13, 1.0], [32, 37, 1.0], [38, 42, 1.0], [135, 140, 1.0], [165, 170, 1.0], [171, 175, 1.0]]}, "2003.sigirconf_conference-2003.86": {"snippet": "...sWeb search, query reformulation, query modeling Spink et al. [4]  found that reformulations of queries constituted 40% to 52% of queries. Other research using web search query logs has sought to char...", "weights": [[8, 14, 1.0], [16, 21, 1.0], [37, 42, 1.0], [167, 173, 1.0], [174, 179, 1.0], [180, 184, 1.0]]}, "2003.sigirconf_conference-2003.102": {"snippet": "... the search engine.(3) Search Engine Discovery: If a user submits a URL that does not have a search engine on the Webpage, this component will crawl Webpages \"nearby\" to find search engine interfaces....", "weights": [[8, 14, 1.0], [15, 21, 1.0], [26, 32, 1.0], [33, 39, 1.0], [96, 102, 1.0], [103, 109, 1.0], [178, 184, 1.0], [185, 191, 1.0]]}, "2002.wwwconf_conference-2002.32": {"snippet": "... web query logs. In this study, we propose a new method for query expansion based on query logs. The central idea is to extract probabilistic correlations between query terms and document terms by ana...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [63, 68, 1.0], [88, 93, 1.0], [94, 98, 1.0], [166, 171, 1.0]]}, "2016.wsdm_conference-2016.15": {"snippet": "...zing search result presentation for queries with diverse intents, by selectively presenting query suggestions for leading users to more relevant search results. The optimization is based on a probabil...", "weights": [[8, 14, 1.0], [95, 100, 1.0], [148, 154, 1.0], [168, 180, 1.0]]}, "2006.sigirconf_conference-2006.124": {"snippet": "...user query logs\n\n\n ABSTRACTThe exponential growth of the Web and the increasing ability of web search engines to index data have led to a problem of plenty. The number of results returned per query is...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [98, 104, 1.0], [195, 200, 1.0]]}, "2013.tois_journal-ir0volumeA31A3.3": {"snippet": "...ough search engines. First, we identify user tasks from individual user sessions stored in search engine query logs. In our vision, a user task is a set of possibly noncontiguous queries (within a use...", "weights": [[8, 14, 1.0], [94, 100, 1.0], [101, 107, 1.0], [108, 113, 1.0], [114, 118, 1.0]]}, "2012.sigirconf_conference-2012.213": {"snippet": "...ets. Query logs have also revolutionized query assistance tools such as related queries (recommended queries after the search was issued) or query completions (suggested queries as the query is being ...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [44, 49, 1.0], [122, 128, 1.0], [144, 149, 1.0], [188, 193, 1.0]]}, "2010.wsdm_conference-2010.19": {"snippet": "...ough logs of a web search engine\n\n\n ABSTRACTWe propose a new model to interpret the clickthrough logs of a web search engine. This model is based on explicit assumptions on the user behavior. In parti...", "weights": [[8, 12, 1.0], [22, 28, 1.0], [29, 35, 1.0], [100, 104, 1.0], [114, 120, 1.0], [121, 127, 1.0]]}, "2014.sigirconf_conference-2014.114": {"snippet": "...ng a query log via crowdsourcing\n\n\n ABSTRACTA query log is a key asset in a commercial search engine. Everyday millions of users rely on search engines to find information on the Web by entering a few...", "weights": [[8, 13, 1.0], [49, 54, 1.0], [90, 96, 1.0], [97, 103, 1.0], [140, 146, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A1.3": {"snippet": "Query Recommendation for Improving Search Engine Results", "weights": [[0, 5, 1.0], [35, 41, 1.0], [42, 48, 1.0]]}, "2013.wsdm_conference-2013.13": {"snippet": "... web search engines use significant hardware and energy resources to process hundreds of millions of queries each day, and a lot of research has focused on how to improve query processing efficiency. ...", "weights": [[8, 14, 1.0], [174, 179, 1.0]]}, "2011.wsdm_conference-2011.35": {"snippet": "...s in search engine query logs", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [28, 32, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A3.6": {"snippet": "... the search queries is critical for search engines. Hence, there has been an increasing attention on studying how to effectively mine the intents of search queries by analyzing search engine query log...", "weights": [[8, 14, 1.0], [39, 45, 1.0], [152, 158, 1.0], [180, 186, 1.0], [187, 193, 1.0], [194, 199, 1.0]]}, "2011.cikm_conference-2011.246": {"snippet": "...that query logs from Web search are a gold mine for search business, because they reflect users' preference over Web pages presented by search engines, so a lot of studies based on query logs have bee...", "weights": [[8, 13, 1.0], [14, 18, 1.0], [28, 34, 1.0], [55, 61, 1.0], [139, 145, 1.0], [184, 189, 1.0], [190, 194, 1.0]]}, "C10-1148": {"snippet": "...with Search Engine Query Logs\n\n\n This paper proposes a method that extracts paraphrases from search engine query logs. The method first extracts paraphrase query-title pairs based on an assumption tha...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 27, 1.0], [28, 32, 1.0], [96, 102, 1.0], [103, 109, 1.0], [110, 115, 1.0], [116, 120, 1.0], [159, 164, 1.0]]}, "2016.tois_journal-ir0volumeA35A2.0": {"snippet": "... web search services for millions of users who compose search queries by different languages. Hence, the search engine query log, which serves as the backbone of many search engine applications, recor...", "weights": [[8, 14, 1.0], [58, 64, 1.0], [108, 114, 1.0], [115, 121, 1.0], [122, 127, 1.0], [170, 176, 1.0], [177, 183, 1.0]]}, "2012.cikm_conference-2012.196": {"snippet": "...uery logs record the actual usage of search systems and their analysis has proven critical to improving search engine functionality. Yet, despite the deluge of information, query log analysis often su...", "weights": [[8, 12, 1.0], [40, 46, 1.0], [107, 113, 1.0], [114, 120, 1.0], [176, 181, 1.0]]}, "2014.tois_journal-ir0volumeA32A4.4": {"snippet": "...more search sessions ]. 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Questioning the ter...", "weights": []}, "W17-1214": {"snippet": "Improving the Character Ngram Model for the {DSL} Task with {BM}25 Weighting and Less Frequently Used Feature Sets\n\n\n This paper describes the system developed by the Centre for English Corpus Linguis...", "weights": []}, "2013.iir_workshop-2013.1": {"snippet": "Are There New BM25 Expectations?", "weights": []}, "2004.trec_conference-2004.8": {"snippet": "Can We Get A Better Retrieval Function From Machine?\n\n\n The quality of an information retrieval system heavily depends on its retrieval function, which returns a similarity measurement between the que...", "weights": []}, "2004.cikm_conference-2004.6": {"snippet": "Simple BM25 extension to multiple weighted fields\n\n\n ABSTRACTThis paper describes a simple way of adapting the BM25 ranking formula to deal with structured documents. 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This...", "weights": []}, "2012.trec_conference-2012.15": {"snippet": "DCU@TRECMed 2012: Using adhoc Baselines for Domain-Specific Retrieval\n\n\n This paper describes the first participation of DCU in the TREC Medical Records Track (TRECMed) 2012. We performed initial expe...", "weights": []}, "2011.cikm_conference-2011.250": {"snippet": "Adaptive term frequency normalization for BM25\n\n\n ABSTRACTA key component of BM25 contributing to its success is its sub-linear term frequency (TF) normalization formula. The scale and shape of this T...", "weights": []}, "2011.sigirconf_conference-2011.117": {"snippet": "Ad hoc IR: not much room for improvement\n\n\n ABSTRACTRanking function performance reached a plateau in 1994. The reason for this is investigated. 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The next ten search results...", "weights": []}, "2016.ntcir_conference-2016.12": {"snippet": "DCU at the NTCIR-12 SpokenQuery&Doc-2 Task\n\n\n We describe DCU's participation in the NTCIR-12 Spoken-Query&Doc (SQD-2) task. In the context of the slide-group retrieval sub-task, we experiment with a ...", "weights": []}, "2012.trec_conference-2012.42": {"snippet": "Microblog Search and Filtering with Time Sensitive Feedback and Thresholding bsed on BM25", "weights": []}, "2019.fire_conference-2019w.7": {"snippet": "FIRE2019@AILA: Legal Information Retrieval Using Improved BM25\n\n\n This paper details the approaches of implementing the tasks of identifying relevant precedents and identifying relevant statues in the...", "weights": []}, "2007.sigirconf_conference-2007.214": {"snippet": "Efficient integration of proximity for text, semi-structured and graph retrieval\n\n\n ABSTRACTSearch engines rely on scoring functions to rank query results. 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The main feature of this model is to vi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [94, 104, 1.0], [130, 140, 1.0]]}, "2021.hcinlp-1.8": {"snippet": "... the Natural Language Processing Loop: A Survey\n\n\n How can we design Natural Language Processing (NLP) systems that learn from human feedback? There is a growing research body of Human-in-the-loop (HI...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [72, 79, 1.0], [80, 88, 1.0], [89, 99, 1.0]]}, "W18-1106": {"snippet": "...Make Natural Language Processing about People again\n\n\n Over the years, natural language processing has increasingly focused on tasks that can be solved by statistical models, but ignored the social as...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [74, 81, 1.0], [82, 90, 1.0], [91, 101, 1.0]]}, "W13-0206": {"snippet": "...s in natural language semantics have been characterized in regular languages, with strings representing sequences of temporal observations. 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The most frequentl...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [48, 58, 1.0], [93, 100, 1.0], [101, 109, 1.0], [110, 120, 1.0], [140, 150, 1.0]]}, "P92-1029": {"snippet": "...ased Natural Language Processing with Neural Networks\n\n\n This paper describes a natural language processing system reinforced by the use of association of words and concepts, implemented as a neural n...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [83, 90, 1.0], [91, 99, 1.0], [100, 110, 1.0]]}, "H90-1078": {"snippet": "...tive Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "W17-8106": {"snippet": "Natural Language Processing in Political Campaigns", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2018.ecir_conference-2018.70": {"snippet": "...many natural language processing tasks, which can lead to enhanced performance and execution time. Many existing methods either rely on a predefined list of stopwords or compute word significance base...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "P80-1030": {"snippet": "...ased Natural Language Understander\n\n\n We have developed an approach to natural language processing in which the natural language processor is viewed as a knowledge-based system whose knowledge is abou...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [74, 81, 1.0], [82, 90, 1.0], [91, 101, 1.0], [115, 122, 1.0], [123, 131, 1.0]]}, "2005.clef_workshop-2005w.8": {"snippet": "...ight Natural Language Processing to Ad-Hoc Cross Language Information Retrieval\n\n\n In the CLEF 2005 Ad-Hoc Track we experimented with language-specific morphosyntactic processing and light Natural Lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [52, 60, 1.0], [137, 145, 1.0], [171, 181, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "C88-2112": {"snippet": "...ting Natural Language Systems: A Sourcebook Approach\n\n\n This paper reports progress in development of evaluation methodologies for natural language systems. Without a common classification of the prob...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [134, 141, 1.0], [142, 150, 1.0]]}, "2014.sigirconf_conference-2014.40": {"snippet": "...nize natural language questions or assertions into an analytic hierarchy, which can be evaluated as part of an ad hoc decision process or as a documented, repeatable analytic process. We then introduc...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2013.wwwconf_conference-2013c.133": {"snippet": "...into natural language. In addition to generating verbalizations, our approach can also explain the output of queries by providing a natural-language description of the reasons that led to each element...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [135, 142, 1.0], [143, 151, 1.0]]}, "2016.sigirconf_conference-2016.1": {"snippet": "...uman Language: Can NLP and Deep Learning Help?\n\n\n ABSTRACTThere is a lot of overlap between the core problems of information retrieval (IR) and natural language processing (NLP). An IR system gains fr...", "weights": [[8, 16, 1.0], [147, 154, 1.0], [155, 163, 1.0], [164, 174, 1.0]]}, "H93-1101": {"snippet": "...h in Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "A88-1034": {"snippet": "...l in natural language processing systems which makes use of canonical structures as a way of collapsing multiple analyses in individual components. We give an overview here of how the Lucy natural lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [50, 55, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "T87-1024": {"snippet": "...s in Natural Language Processing\n\n\n With all due respect, the rate of progress in natural laffguage processing has been disappointing to many, including myself. It is not just that the popular press h...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [85, 92, 1.0], [103, 113, 1.0]]}, "P92-1014": {"snippet": "...bust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effectiveness of traditional key-wor...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [113, 120, 1.0], [121, 129, 1.0], [130, 140, 1.0]]}, "2007.wwwconf_conference-2007.6": {"snippet": "...of a natural-language-processing system and an intelligent form system that observes an assistant processing requests. The intelligent form system then generates a labeled training corpus by interpret...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [101, 111, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.17": {"snippet": "...with natural language is a fundamental challenge for computer vision and natural language processing. Recently, progress in this problem has been achieved through two steps: 1) employing 2-D and/or 3-...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [76, 83, 1.0], [84, 92, 1.0], [93, 103, 1.0]]}, "W18-3719": {"snippet": "...ncy: Natural Language Processing for Translator Training\n\n\n This study explores the use of natural language processing techniques to enhance bilingual lexical access beyond simple equivalents, to enab...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [94, 101, 1.0], [102, 110, 1.0], [111, 121, 1.0]]}, "2022.lnls-1.5": {"snippet": "...ning makes learning from explanations a promising way to establish a fruitful human-machine interaction. Several methods have been proposed for improving natural language processing (NLP) models with ...", "weights": [[8, 13, 1.0], [157, 164, 1.0], [165, 173, 1.0], [174, 184, 1.0]]}, "C14-1088": {"snippet": "...uage processing\n\n\n This paper proposes an architecture for an open-domain conversational system and evaluates an implemented system. The proposed architecture is fully composed of modules based on nat...", "weights": [[8, 18, 1.0], [200, 207, 1.0]]}, "R09-1050": {"snippet": "...s of natural language technology in areas as diverse as natural language generation, natural language understanding, machine translation, dialog system etc. Here a mathematical exploration of the issu...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [59, 66, 1.0], [67, 75, 1.0], [88, 95, 1.0], [96, 104, 1.0]]}, "1983.sigirconf_conference-83.4": {"snippet": "...sing natural language processing techniques to conceptually analyze the documents being stored in a system greatly expands the effectiveness of queries about given pieces of text. However, only robust...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2010.cikm_conference-2010.240": {"snippet": "...g of natural language processing\n\n\n ABSTRACTWe present a new framework for feature engineering of natural language processing that is based on a relational data model of text. It includes fast and fle...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [101, 108, 1.0], [109, 117, 1.0], [118, 128, 1.0]]}, "N12-4008": {"snippet": "...s in natural language processing problems often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate what assignments ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2010.clef_workshop-2010w.61": {"snippet": "... the natural language used in the queries. Language identification is in fact a key task within Cross Language Information Retrieval systems and the challenge is particularly difficult in the case of ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [46, 54, 1.0], [105, 113, 1.0]]}, "P16-4010": {"snippet": "...n of language and other modalities, most notably vision, is becoming increasingly important in natural language processing. We introduce a toolkit that can be used to obtain feature representations fo...", "weights": [[8, 16, 1.0], [98, 105, 1.0], [106, 114, 1.0], [115, 125, 1.0]]}, "W05-1613": {"snippet": "...s in natural language. Natural language directed inference represents an interesting challenge to research in automated reasoning and natural language processing.", "weights": [[8, 15, 1.0], [16, 24, 1.0], [26, 33, 1.0], [34, 42, 1.0], [137, 144, 1.0], [145, 153, 1.0], [154, 164, 1.0]]}, "W18-6902": {"snippet": "... for Natural Language Generation and Human-Robot Interaction\n\n\n One of the most natural ways for human robot communication is through spoken language. Training human-robot interaction systems require ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [83, 90, 1.0], [144, 152, 1.0]]}, "2020.lincr-1.3": {"snippet": "...uman Language Processing Signals for Natural Language Processing\n\n\n NLP models are imperfect and lack intricate capabilities that humans access automatically when processing speech or reading a text. ...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [40, 47, 1.0], [48, 56, 1.0], [57, 67, 1.0], [166, 176, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.0": {"snippet": "...y in Natural Language Processing - Colocated with WSDM 2020, Houston, USA, Feb 7, 2020", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2014.ipm_journal-ir0volumeA50A5.12": {"snippet": "...t in Natural Language Processing. In order to overcome this limitation we propose an inference method based on Integer Programming formulation of the problem, where long distance dependencies are incl...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "P96-1004": {"snippet": "...Most natural language processing tasks require lexical semantic information. Automated acquisition of this information would thus increase the robustness and portability of NLP systems. This paper des...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "C88-1008": {"snippet": "... for Natural Language Processing\n\n\n This paper presents the most important characteristic of the new formalism used in Eurotra, the E-Framework. It is a formalism for natural language processing withi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [170, 177, 1.0], [178, 186, 1.0], [187, 197, 1.0]]}, "W07-2432": {"snippet": "...hich makes natural language processing based on the English language not always applicable for Swedish material. One area where there is a difference is compounding. The word-forming process of compou...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 30, 1.0], [31, 41, 1.0], [63, 71, 1.0]]}, "2008.wwwconf_conference-2008.233": {"snippet": "...ols. Natural language processing (NLP) is one of the key technologies for solving the above Web usability problems. Almost all the Web page provide with the essential information in the form of natura...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [197, 204, 1.0]]}, "2004.ipm_journal-ir0volumeA40A6.4": {"snippet": "... the natural language processing (NLP) system for improving the system. Therefore, the necessity of co-occurrence word information in the natural language processing system increases further and vario...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [141, 148, 1.0], [149, 157, 1.0], [158, 168, 1.0]]}, "P16-2043": {"snippet": "Natural Language Generation enhances human decision-making with uncertain information\n\n\n Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilit...", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2020.wsdm_conference-2020.120": {"snippet": "...p on Natural Language Processing for Recommendations\n\n\n Natural language processing is becoming more and more important in recommender systems. This half day workshop explores challenges and potential...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [59, 66, 1.0], [67, 75, 1.0], [76, 86, 1.0]]}, "C02-2006": {"snippet": "...y of Natural Language Processing\n\n\n Natural language processing NLP programs are confronted with various di culties in processing HTML and XML documents, and have the potential to produce better resul...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [39, 46, 1.0], [47, 55, 1.0], [56, 66, 1.0], [122, 132, 1.0]]}, "P97-1028": {"snippet": "...g-up Natural Language Generation\n\n\n This paper presents a method for the automatic extraction of subgrammars to control and speeding-up natural language generation NLG. The method is based on explanat...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [139, 146, 1.0], [147, 155, 1.0]]}, "W00-1703": {"snippet": "...ving Natural Language Processing by Linguistic Document Annotation", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "W17-3704": {"snippet": "...that Natural Language is a suitable tool for describing human perceptions, building selfexplanatory computational systems ready to communicate with humans in Natural Language becomes a hot challenge. ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [161, 168, 1.0], [169, 177, 1.0]]}, "2016.wsdm_conference-2016.92": {"snippet": "...sing natural language, in a way similar to how they document code or communicate with peers. However, using automatic natural language processing techniques to address programming questions is very di...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [121, 128, 1.0], [129, 137, 1.0], [138, 148, 1.0]]}, "J86-2001": {"snippet": "...y, a natural language interface should be constructed in accord with principles of integrated processing: processing syntax and semantics at the same time, processing syntax and semantics using the sa...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [97, 107, 1.0], [109, 119, 1.0], [159, 169, 1.0]]}, "C69-4101": {"snippet": "...ated Language Processing\n\n\n This paper is concerned with natural language, computers, and two groups of people interested in natural language: ]inguists, and persons engaged in computer processing of ...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [60, 67, 1.0], [68, 76, 1.0], [128, 135, 1.0], [136, 144, 1.0], [189, 199, 1.0]]}, "1985.tmi-1.17": {"snippet": "... and Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2022.acl-long.504": {"snippet": "...cial Language: Studying Transferable Knowledge in Language Models\n\n\n We investigate what kind of structural knowledge learned in neural network encoders is transferable to processing natural language....", "weights": [[8, 16, 1.0], [53, 61, 1.0], [175, 185, 1.0], [186, 193, 1.0], [194, 202, 1.0]]}, "1985.tois_journal-ir0volumeA3A2.1": {"snippet": "...LSP) natural language processing system has been developed as a domain-independent natural language processing system. Initially utilized for processing sets of medical messages and other texts in the...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [86, 93, 1.0], [94, 102, 1.0], [103, 113, 1.0], [145, 155, 1.0]]}, "2011.freeopmt-1.2": {"snippet": "...gual language processing library providing a wide range of language analyzers for several languages. It offers text processing and language annotation facilities to natural language processing applica...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [62, 70, 1.0], [119, 129, 1.0], [134, 142, 1.0], [168, 175, 1.0], [176, 184, 1.0], [185, 195, 1.0]]}, "W11-2205": {"snippet": "... for natural language processing tasks\n\n\n The development of unsupervised learning methods for natural language processing tasks has become an important and popular area of research. The primary advan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [98, 105, 1.0], [106, 114, 1.0], [115, 125, 1.0]]}, "J95-3006": {"snippet": "...ews: Natural Language Processing: A Paninian Perspective\n\n\n This book is an elementary introduction to natural language processing, but it is a very unusual one for two reasons. First, its subject lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [106, 113, 1.0], [114, 122, 1.0], [123, 133, 1.0]]}, "W04-0609": {"snippet": "...ased Natural Language Processing\n\n\n Conceptualising a domain has long been recognised as a prerequisite for understanding that domain and processing information about it. Ontologies are explicit speci...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [141, 151, 1.0]]}, "2021.teachingnlp-1.3": {"snippet": "Natural Language Processing 4 All ({NLP}4{A}ll): A New Online Platform for Teaching and Learning {NLP} Concepts\n\n\n Natural Language Processing offers new insights into language data across almost all ...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [115, 122, 1.0], [123, 131, 1.0], [132, 142, 1.0], [168, 176, 1.0]]}, "2006.claw-1.5": {"snippet": "...lled Natural Language\n\n\n In this paper, we present PENG Online, a web-based authoring tool which allows authors to write RSS feeds in a machine-processable controlled natural language. The authoring t...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [170, 177, 1.0], [178, 186, 1.0]]}, "2011.cikm_conference-2011.257": {"snippet": "...ars, natural language processing techniques have been used more and more in IR. Among other syntactic and semantic parsing are effective methods for the design of complex applications like for example...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "J01-2007": {"snippet": "...ews: Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language\n\n\n The strong connection between knowledge representation and reasoning and natural lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [66, 74, 1.0], [107, 115, 1.0], [192, 199, 1.0], [200, 208, 1.0]]}, "2020.rocling-1.36": {"snippet": "Natural Language Processing for Digital Humanities", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2001.ipm_journal-ir0volumeA37A6.0": {"snippet": "Natural language processing in support of decision-making: phrases and part-of-speech tagging", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "W14-4919": {"snippet": "...tudy makes preliminary steps towards detection of decision style, based on an annotated dataset of image-based clinical reasoning in which speech data were collected from physicians as they inspected ...", "weights": [[8, 13, 1.0]]}, "2020.emnlp-demos.6": {"snippet": "...-Art Natural Language Processing\n\n\n Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilita...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [58, 65, 1.0], [66, 74, 1.0], [75, 85, 1.0]]}, "2007.sigirconf_conference-2007.3": {"snippet": "...NLP -natural language processing -or NLIP -processing information conveyed in natural language. At that time, most researchers thought primarily of the efficiency gains that could be Copyright is held...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [46, 56, 1.0], [81, 88, 1.0], [89, 97, 1.0]]}, "2022.acl-demo.12": {"snippet": "...a\n\n\n Natural Language Processing (NLP)'s applied nature makes it necessary to select the most effective and robust models. However, just producing slightly higher performance is insufficient; we want ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [59, 64, 1.0]]}, "1984.jasis_journal-ir0volumeA35A5.5": {"snippet": "Natural language processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2016.wwwconf_conference-2016.82": {"snippet": "...s in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text un...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2021.trustnlp-1.6": {"snippet": "...s in Natural Language Processing Across Human Languages\n\n\n Natural Language Processing (NLP) systems are at the heart of many critical automated decision-making systems making crucial recommendations ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [62, 69, 1.0], [70, 78, 1.0], [79, 89, 1.0]]}, "2011.ipm_journal-ir0volumeA47A4.4": {"snippet": "... for natural language applications on mobile devices is increasing. This paper proposes an automatic word spacing system, the first step module of natural language processing (NLP) for many languages ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [150, 157, 1.0], [158, 166, 1.0], [167, 177, 1.0]]}, "M95-1021": {"snippet": "...UNO} Natural Language Processing System as Used for {MUC}-6\n\n\n The UNO natural language processing (NLP) system implements a Boolean algebra computational model o f natural language [Iwadska, 1992 ] [...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [74, 81, 1.0], [82, 90, 1.0], [91, 101, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "J89-3012": {"snippet": "...ews: Natural Language Processing", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2005.wwwconf_conference-2005.49": {"snippet": "... for natural language applications\n\n\n ABSTRACTMany modern natural language-processing applications utilize search engines to locate large numbers of Web documents or to compute statistics over the Web...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [61, 68, 1.0], [69, 77, 1.0], [78, 88, 1.0]]}, "J81-4005": {"snippet": "Natural Language Processing", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "N12-4004": {"snippet": "...y of natural language content and the integration and advancement of Natural Language Processing, Information Retrieval, Machine Learning, Knowledge Representation and Reasoning, and massively paralle...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [72, 79, 1.0], [80, 88, 1.0], [89, 99, 1.0]]}, "W11-4601": {"snippet": "...lled Natural Language\n\n\n There are two approaches to the natural language processing -one is going in width to cover at shallow level (parsing, syntax) the rich linguistic variety found in the natural...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [60, 67, 1.0], [68, 76, 1.0], [77, 87, 1.0], [196, 203, 1.0]]}, "2017.clef_conference-2017.9": {"snippet": "...o CM language pairs: Hinglish (Matrix language: Hindi, Embedded language: English) and Tenglish (Matrix language: Telugu, Embedded language: English). Lack of language resources such as annotated corp...", "weights": [[8, 16, 1.0], [41, 49, 1.0], [67, 75, 1.0], [107, 115, 1.0], [134, 142, 1.0], [162, 170, 1.0]]}, "1973.sigirconf_conference-73.4": {"snippet": "...in a natural language vocabulary is first characterized in terms of their frequency distribution over the documents of a collection. The construction of \"good\" natural language vocabularies is then de...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [163, 170, 1.0], [171, 179, 1.0]]}, "L18-1025": {"snippet": "...s in natural language processing papers, can account for a variety of types of research reports. These dimensions are reproducibility of a conclusion, of a finding, and of a value. Three biomedical na...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [201, 208, 1.0]]}, "T87-1044": {"snippet": "...}\n\n\n Natural language understanding and natural language generation could employ the same knowledge of language. 1 They could even represent their knowledge in the same way, provided that it was a non...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [43, 50, 1.0], [51, 59, 1.0], [106, 114, 1.0]]}, "2017.wsdm_conference-2017.32": {"snippet": "...s in Natural Language Processing\n\n\n ABSTRACTIncreasingly central to online search and information discovery are methods from Natural Language Processing (NLP). Research in the field, however, is progr...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [128, 135, 1.0], [136, 144, 1.0], [145, 155, 1.0]]}, "A88-1026": {"snippet": "... the natural language interface. Luke also makes natural language available in its own user interface. This makes it possible for a knowledge base builder to exploit natural language both as a way of ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [46, 51, 1.0], [52, 59, 1.0], [60, 68, 1.0], [111, 116, 1.0], [169, 176, 1.0], [177, 185, 1.0]]}, "2020.cikm_conference-2020.67": {"snippet": "...d in natural language and convert it into forms that can be more easily and precisely processed by computers. Knowledge Graphs (KGs) offer offer a good solution for such processing. Natural Language P...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [173, 183, 1.0], [185, 192, 1.0], [193, 201, 1.0], [202, 212, 1.0]]}, "P15-1120": {"snippet": "Can Natural Language Processing Become Natural Language Coaching?\n\n\n How we teach and learn is undergoing a revolution, due to changes in technology and connectivity. Education may be one of the best ...", "weights": [[4, 11, 1.0], [12, 20, 1.0], [21, 31, 1.0], [39, 46, 1.0], [47, 55, 1.0]]}, "2004.wwwconf_workshop-2004sw.12": {"snippet": "...ence makes it appropriate for usage in scenarios involving distributed and collaborative design and manufacturing process. The use of OWL and Process Grammar makes SOM very suitable for coordination o...", "weights": [[8, 13, 1.0], [161, 166, 1.0]]}, "U05-1027": {"snippet": "...cted Natural Language\n\n\n We argue it is better to program in a natural language such as English, instead of a programming language like Java. A natural language interface for programming should result...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [66, 73, 1.0], [74, 82, 1.0], [125, 133, 1.0], [147, 154, 1.0], [155, 163, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...t of natural language processing techniques. Different groups focused on different aspects of the overall problem, ranging from the development of language-independent tools such as stemmers to much w...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [150, 158, 1.0]]}, "W13-3815": {"snippet": "...d in natural language. Even if ad-hoc natural language examples are often provided in argumentation theory works, no automated processing of such natural language arguments is carried out, making it i...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [41, 48, 1.0], [49, 57, 1.0], [130, 140, 1.0], [149, 156, 1.0], [157, 165, 1.0]]}, "1995.sigirjournals_journal-ir0volumeA29A2.2": {"snippet": "...ion: Natural Language Processing and Information Retrieval Group Information Access and User Interfaces Division National Institute of Standards and Technology\n\n\n OverviewThe Natural Language Processi...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [178, 185, 1.0], [186, 194, 1.0], [195, 205, 1.0]]}, "Y12-1005": {"snippet": "...e of Natural Language Processing?\n\n\n In this talk, I will outline some of the myriad of challenges and opportunities that social media offer for natural language processing. I will present analysis of...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [148, 155, 1.0], [156, 164, 1.0], [165, 175, 1.0]]}, "L06-1315": {"snippet": "Natural Language Processing: A Terminological and Statistical Approach", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2016.fire_conference-2016w.53": {"snippet": "...ce \u2192 Natural language processing \u2192 Language resources Computing methodologies \u2192 Artificial intelligence \u2192 Natural language processing \u2192 Lexical semantics", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [38, 46, 1.0], [109, 116, 1.0], [117, 125, 1.0], [126, 136, 1.0]]}, "Y09-1039": {"snippet": "...nal) Natural Language Processing: A Cognitive Perspective\n\n\n In this paper I aim at sketching out in bare outline a new model/framework of language processing with its implications for natural languag...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [142, 150, 1.0], [151, 161, 1.0], [188, 195, 1.0], [196, 204, 1.0]]}, "J80-2001": {"snippet": "...ward Natural Language Computation {I}\n\n\n A computer programming system called the \"Natural Language Computer\" (NLC) is described which allows a user to type English commands while watching them execut...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [86, 93, 1.0], [94, 102, 1.0]]}, "2019.ipm_journal-ir0volumeA56A4.14": {"snippet": "Exploring the group holiday decision-making process with the support of technology\n\n\n A B S T R A C TNumerous studies have examined the decision-making process and the associated information search be...", "weights": []}, "2000.cikm_conference-2000.57": {"snippet": "...sing Natural Language Processing\n\n\n ABSTRACTA t rst sight, it might appear that natural language processing should improve the accuracy of information retrieval systems, by making available a more det...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [83, 90, 1.0], [91, 99, 1.0], [100, 110, 1.0]]}, "W15-5955": {"snippet": "Natural Language Processing for Solving Simple Word Problems\n\n\n This paper describes our system which solves simple arithmetic word problems. 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Such a framework can be...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "2011.cikm_workshop-2011pikm.2": {"snippet": "... the principle of polyrepresentation\n\n\n ABSTRACTRecently, the cognitively motivated principle of polyrepresentation has been shown to correlate with quantum mechanics-inspired IR models. The principle...", "weights": [[8, 17, 1.0], [87, 96, 1.0], [194, 203, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.7": {"snippet": "...rent indexing languages\n\n\n AbstractAn integrated information retrieval system generally contains multiple databases that are inconsistent in terms of their content and indexing. This paper proposes a ...", "weights": [[8, 16, 1.0], [52, 63, 1.0], [64, 73, 1.0], [171, 179, 1.0]]}, "1998.sigirconf_conference-98.34": {"snippet": "...h to Information Retrieval\n\n\n AbstractModels of document indexing and document retrieval have been extensively studied. The integration of these two classes of models has been the goal of several rese...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [60, 68, 1.0], [82, 91, 1.0]]}, "2010.cikm_conference-2010.109": {"snippet": "...rted indexing for feedback and expansion\n\n\n ABSTRACTTraditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a list...", "weights": [[8, 16, 1.0], [79, 90, 1.0], [91, 100, 1.0]]}, "2005.trec_conference-2005.27": {"snippet": "...cale Information Retrieval\n\n\n We describe indexing and retrieval techniques that are suited to perform terabyte-scale information retrieval tasks on a standard desktop PC. Starting from an Okapi-BM25-...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [45, 53, 1.0], [58, 67, 1.0], [121, 132, 1.0], [133, 142, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.3": {"snippet": "...lop' information retrieval beyond the mere keyword approach. In the closing pages of my earlier book on Information Retrieval (Van Rijsbergen, 1979) I said the following: \"It has never been assuned th...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [107, 118, 1.0], [119, 128, 1.0]]}, "2009.ictir_conference-2009.21": {"snippet": "...king Principle for Information Retrieval", "weights": [[8, 17, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2006.jasis_journal-ir0volumeA57A10.13": {"snippet": "Information retrieval design: Principles and options for information description, organization, display, and access in information retrieval databases, digital libraries, catalogs, and indexes", "weights": [[0, 11, 1.0], [12, 21, 1.0], [57, 68, 1.0], [119, 130, 1.0], [131, 140, 1.0]]}, "2011.cikm_conference-2011.406": {"snippet": "...ical information retrieval modelling: from the probability ranking principle to recent advances in diversity, portfolio theory, and beyond\n\n\n ABSTRACTStatistical modelling of Information Retrieval (IR...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [70, 79, 1.0], [178, 189, 1.0], [190, 199, 1.0]]}, "1993.sigirconf_conference-93.14": {"snippet": "...Text Retrieval System\n\n\n AbstractThere has been a great deal of interest within the Information Retrieval community in evahrating the use of linguistic knowledge to improve the indexing and searching ...", "weights": [[8, 17, 1.0], [87, 98, 1.0], [99, 108, 1.0], [180, 188, 1.0]]}, "2015.ictir_conference-2015.38": {"snippet": "...rted indexing. In this paper, we explore a radical proposition: What if we abandon document inversion and instead adopt an architecture based on brute force scans of document representations? In such ...", "weights": [[8, 16, 1.0]]}, "2020.sigirconf_conference-2020.279": {"snippet": "...ment indexing is a key component for efficient information retrieval (IR). After preprocessing steps such as stemming and stopword removal, document indexes usually store term-frequencies (tf). Along ...", "weights": [[8, 16, 1.0], [50, 61, 1.0], [62, 71, 1.0]]}, "W89-0207": {"snippet": "...ting principle-based syntactic theories directly in a parsing algorithm in order to determ ine whether or not a principle-based parsing algorithm can be com patible with psycholinguistic evidence. Pri...", "weights": [[8, 17, 1.0], [115, 124, 1.0], [200, 209, 1.0]]}, "2013.cikm_conference-2013.240": {"snippet": "...tion indexing: fast approximate retrieval from large corpora\n\n\n ABSTRACTInverted indexing is a ubiquitous technique used in retrieval systems including web search. Despite its popularity, it has a dra...", "weights": [[8, 16, 1.0], [35, 44, 1.0], [84, 92, 1.0], [127, 136, 1.0]]}, "2004.sigirconf_conference-2004.14": {"snippet": "... and indexing is a key problem for document analysis and processing, such as clustering, classification and retrieval. Conventionally, Latent Semantic Indexing (LSI) is considered effective in derivin...", "weights": [[8, 16, 1.0], [111, 120, 1.0], [154, 162, 1.0]]}, "2009.jasis_journal-ir0volumeA60A5.0": {"snippet": "...tive indexing of emotions in images. A study in emotional information retrieval", "weights": [[8, 16, 1.0], [61, 72, 1.0], [73, 82, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.11": {"snippet": "... web retrieval effectiveness\n\n\n AbstractThis paper talks about several schemes for improving retrieval effectiveness that can be used in the named page finding tasks of web information retrieval (Over...", "weights": [[8, 17, 1.0], [96, 105, 1.0], [176, 187, 1.0], [188, 197, 1.0]]}, "2008.ir_journal-ir0volumeA11A3.3": {"snippet": "...king principle for interactive information retrieval", "weights": [[8, 17, 1.0], [34, 45, 1.0], [46, 55, 1.0]]}, "2006.cikm_conference-2006.111": {"snippet": "...rted indexing, TFIDF and Latent Semantic Indexing(LSI)) to content-based brain image retrieval. Our results show that efficient and accurate retrieval of brain images is possible, and that representat...", "weights": [[8, 16, 1.0], [44, 52, 1.0], [88, 97, 1.0], [144, 153, 1.0]]}, "1991.sigirconf_conference-91.23": {"snippet": "...uted Indexing: A Scalable Mechanism for Distributed Information Retrieval", "weights": [[8, 16, 1.0], [55, 66, 1.0], [67, 76, 1.0]]}, "2012.trec_conference-2012.60": {"snippet": "...Page Indexing\n\n\n Indexing is a crucial technique for dealing with the massive amount of data present on the web. In our third participation in the web track at TREC 2012, we explore the idea of buildi...", "weights": [[8, 16, 1.0], [20, 28, 1.0]]}, "2006.sigirconf_conference-2006.57": {"snippet": "...lly, information retrieval systems aim to maximize the number of relevant documents returned to a user within some window of the top. For that goal, the probability ranking principle, which ranks docu...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [176, 185, 1.0]]}, "1986.sigirconf_conference-86.31": {"snippet": "...s of Retrieval with Probabilistic Indexing\n\n\n AbstractWe describe two retrieval modeb for probabilistic indexing. The binary independence indexing (BII)  model is a generalised version of the Maron & ...", "weights": [[8, 17, 1.0], [37, 45, 1.0], [73, 82, 1.0], [107, 115, 1.0], [141, 149, 1.0]]}, "1999.cikm_conference-99.17": {"snippet": "... (or principle) for model selection in learning. Our method is unique in that decision lists are constructed on the basis of the principle of minimizing a quantity called Extended Stochastic Complexit...", "weights": [[8, 17, 1.0], [132, 141, 1.0]]}, "W00-1105": {"snippet": "... and Retrieval Effectiveness of Phrasal Indexing Terms\n\n\n In spite of long controversy, effectiveness of phrasal indexing is not yet clear. Recently, correlation between query length and effect of phr...", "weights": [[8, 17, 1.0], [43, 51, 1.0], [116, 124, 1.0]]}, "1998.sigirconf_conference-98.42": {"snippet": "...ment Indexing: A Linguistic Contribution to Knowledge-Based IR\n\n\n AbstractThis poster presents Condorcet, a domain-specific prototype indexing system for tens of thousands of documents covering two sc...", "weights": [[8, 16, 1.0], [137, 145, 1.0]]}, "2005.ntcir_workshop-2005.71": {"snippet": "...nese Information Retrieval\n\n\n In our formal runs, we have experimented with hybrid-term indexing and bigram indexing because hybrid-term indexing is a more distinct type of indexing strategy for bette...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [91, 99, 1.0], [111, 119, 1.0], [140, 148, 1.0], [176, 184, 1.0]]}, "2016.sigirconf_conference-2016.124": {"snippet": "...h in-principle analysis and in practice in retrieval, with small but consistent gains.", "weights": [[8, 17, 1.0], [46, 55, 1.0]]}, "W99-1009": {"snippet": "...\n An indexing tool was built to provide for one of several information seeking tasks. In ac cordance with the basic principles of work held by the HUMLE laboratory at SICS, a so lution regarding index...", "weights": [[8, 16, 1.0], [62, 73, 1.0], [198, 206, 1.0]]}, "2010.wwwconf_conference-2010.11": {"snippet": "...dard information retrieval (IR). Recently, there has been a great deal of research into developing advanced match ranking algorithms. However, no previous research has addressed the ad indexing proble...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [188, 196, 1.0]]}, "1983.jasis_journal-ir0volumeA34A4.14": {"snippet": "...s in information retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2013.sigirconf_conference-2013.18": {"snippet": "An information-theoretic account of static index pruning\n\n\n ABSTRACTIn this paper, we recast static index pruning as a model induction problem under the framework of Kullback's principle of minimum cr...", "weights": [[3, 14, 1.0], [177, 186, 1.0]]}, "1996.cikm_conference-96.18": {"snippet": "...ntic Indexing into a Neural Network Model for Information Retrieval", "weights": [[8, 16, 1.0], [49, 60, 1.0], [61, 70, 1.0]]}, "2011.sigirconf_conference-2011.58": {"snippet": "...s in information retrieval. For each unique word occurring in a document collection, the inverted index stores a list of the documents in which this word occurs. Compression techniques are often appli...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.8": {"snippet": "...sfer principle, the j-index satisfies the opposite transfer principle while the h-and R-indices do not satisfy any of these principles. We study general inequalities between these measures and also de...", "weights": [[8, 17, 1.0], [63, 72, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.25": {"snippet": "...line information retrieval concepts, principles, and techniques", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2000.ir_journal-ir0volumeA2A4.2": {"snippet": "... for Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2009.sigirconf_conference-2009.16": {"snippet": "...y of information retrieval\n\n\n ABSTRACTThis paper studies document ranking under uncertainty. It is tackled in a general situation where the relevance predictions of individual documents have uncertain...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.jasis_journal-ir0volumeA62A1.5": {"snippet": "...dge: Indexing and information retrieval as an example", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0]]}, "2004.spire_conference-2004.15": {"snippet": "Indexing Text Documents Based on Topic Identification\n\n\n Abstract. This work provides algorithms and heuristics to index text documents by determining important topics in the documents. To index text ...", "weights": [[0, 8, 1.0]]}, "1998.jasis_journal-ir0volumeA49A6.5": {"snippet": "...ropy Principle for Information Retrieval", "weights": [[8, 17, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "N09-1020": {"snippet": "... for Information Retrieval\n\n\n We propose a principled probabilisitc framework which uses trees over the vocabulary to capture similarities among terms in an information retrieval setting. This allows ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [160, 171, 1.0], [172, 181, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.3": {"snippet": "...ment indexing: a concept-based approach to term weight estimation\n\n\n AbstractTraditional index weighting approaches for information retrieval from texts depend on the term frequency based analysis of ...", "weights": [[8, 16, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2007.sigirconf_conference-2007.68": {"snippet": "A time machine for text search\n\n\n ABSTRACTText search over temporally versioned document collections such as web archives has received little attention as a research problem. As a consequence, there i...", "weights": []}, "1985.ipm_journal-ir0volumeA21A6.0": {"snippet": "... for information retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "P13-4024": {"snippet": "...file indexing, PoS information from BNC, and semantic indexing based on Latent Dirichlet Allocation with Google Web 1T. The method involves parsing the query to transforming it into several keyword re...", "weights": [[8, 16, 1.0], [22, 33, 1.0], [57, 65, 1.0], [201, 210, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.22": {"snippet": "...ased retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic i...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [202, 213, 1.0]]}, "2008.cikm_conference-2008.45": {"snippet": "...onal information retrieval (IR) approaches assume that the indexing terms are independent, which is not true in reality. Although some previous studies have tried to consider term relationships, stron...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [62, 70, 1.0]]}, "2009.cikm_conference-2009.47": {"snippet": "... for Information Retrieval systems. The new model is based on partitioning of the inverted index across the terms in it. We exploit the query log to partition the on-disk inverted index into two types...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1991.jasis_journal-ir0volumeA42A10.2": {"snippet": "...king principle in information retrieval", "weights": [[8, 17, 1.0], [21, 32, 1.0], [33, 42, 1.0]]}, "1986.sigirconf_conference-86.34": {"snippet": "...ropy Principle in Information Retrieval\n\n\n ABSTRACTApplications, assumptions and properties of the maximum entropy principle ate discussed. The maximum", "weights": [[8, 17, 1.0], [21, 32, 1.0], [33, 42, 1.0], [118, 127, 1.0]]}, "2008.ipm_journal-ir0volumeA44A5.1": {"snippet": "...tThe principle of polyrepresentation offers a theoretical framework for handling multiple contexts in information retrieval (IR). This paper presents an empirical laboratory study of polyrepresentatio...", "weights": [[8, 17, 1.0], [105, 116, 1.0], [117, 126, 1.0]]}, "2003.spire_conference-2003.21": {"snippet": "...rase Indexing and Mining for an Information Retrieval System\n\n\n Abstract. In this paper, we present a noun phrase indexing and mining methodology for French Information Retrieval. Our assumption is th...", "weights": [[8, 16, 1.0], [35, 46, 1.0], [47, 56, 1.0], [117, 125, 1.0], [160, 171, 1.0], [172, 181, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "1993.ipm_journal-ir0volumeA29A3.7": {"snippet": "...ased Retrieval of Hypermedia Information: From Term Indexing to Semantic Hyperindexing", "weights": [[8, 17, 1.0], [32, 43, 1.0], [55, 63, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ment retrieval. The shot retrieval and the video segment model re-use the probability of relevance and a language modeling ranking function respectively, basing themselves on decades of text retrieval...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [194, 203, 1.0]]}, "Y17-1002": {"snippet": "...emic Information Retrieval based on Human-Machine Dialogue\n\n\n With the rapid progress of computer technology and world-wide development of information networks, a vast amount of information is now bei...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 153, 1.0], [181, 192, 1.0]]}, "2015.tois_journal-ir0volumeA33A4.3": {"snippet": "...back Retrieval\n\n\n The inverted index is the dominant indexing method in information retrieval systems. It enables fast return of the list of all documents containing a given query term. However, for r...", "weights": [[8, 17, 1.0], [56, 64, 1.0], [75, 86, 1.0], [87, 96, 1.0], [202, 211, 1.0]]}, "2004.sigirconf_conference-2004.13": {"snippet": "...able indexing and ranking for XML information retrieval\n\n\n ABSTRACTIndexing and ranking are two key factors for efficient and effective XML information retrieval. Inappropriate indexing may result in ...", "weights": [[8, 16, 1.0], [37, 48, 1.0], [49, 58, 1.0], [143, 154, 1.0], [155, 164, 1.0], [180, 188, 1.0]]}, "1997.ecir_conference-1997.2": {"snippet": "... for Information Retrieval Indexing", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 38, 1.0]]}, "W06-0804": {"snippet": "... how information from the textual context of citations in scientific papers could improve indexing of the cited papers. We first present examples which show that the context should in principle provid...", "weights": [[8, 19, 1.0], [93, 101, 1.0], [187, 196, 1.0]]}, "2010.tois_journal-ir0volumeA28A1.0": {"snippet": "...s\n\n\n Information retrieval (IR) systems typically compress their indexes in order to increase their efficiency. Static pruning is a form of lossy data compression: it removes from the index, data that...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1977.jasis_journal-ir0volumeA28A1.5": {"snippet": "On indexing, retrieval and the meaning of about", "weights": [[3, 11, 1.0], [13, 22, 1.0]]}, "2007.sigirconf_conference-2007.86": {"snippet": "...s in information retrieval and databases. Efficient indexing for this operation is known to be a hard problem for arbitrary data distributions. However, text corpora used in Information Retrieval appl...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [55, 63, 1.0], [177, 188, 1.0], [189, 198, 1.0]]}, "2010.sigirconf_conference-2010.123": {"snippet": "... for Information Retrieval. However, under non-trivial conditions PT violates the original Probability Ranking Principle (PRP). In this poster, we shall explore whether PT upholds a different ranking ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [114, 123, 1.0]]}, "2016.jasis_journal-ir0volumeA67A1.0": {"snippet": "...atic indexing or classification in the context of retrieval", "weights": [[8, 16, 1.0], [53, 62, 1.0]]}, "W03-2001": {"snippet": "...ment Retrieval System Addressing Both Semantic and Syntactic Properties\n\n\n Combining the principle of Differential Latent Semantic Index (DLSI) (Chen et  al., 2001)  and the Template Matching Techniqu...", "weights": [[8, 17, 1.0], [92, 101, 1.0]]}, "1993.cikm_conference-93.22": {"snippet": "...Join indexing is needed for the efficient maintenance of such rule-derived daa but existing join indexing methods are not suitable for this purpose. We develop a family of join indexing techniques --j...", "weights": [[8, 16, 1.0], [100, 108, 1.0], [180, 188, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.31": {"snippet": "...stic indexing and information retrieval\n\n\n AbstractForty-eight years ago Maron and Kuhns published their paper, ''On Relevance, Probabilistic Indexing and Information  Retrieval\" (1960). This was the ...", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0], [145, 153, 1.0], [158, 169, 1.0], [171, 180, 1.0]]}, "P90-1014": {"snippet": "... The principle known as 'free indexation' plays an important role in the determination of the referential properties of noun phrases in the principleand-parameters language framework. First, by invest...", "weights": [[8, 17, 1.0]]}, "2000.cikm_conference-2000.29": {"snippet": "... New Indexing Scheme for High-Dimensional Data\n\n\n ABSTRACTThis paper presents a new indexing scheme, the Subspace Coding Method (SCM), that offers high performance similarity retrieval. It is based on...", "weights": [[8, 16, 1.0], [87, 95, 1.0], [178, 187, 1.0]]}}}
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The engine analyzes the HTML files crawled from web forums, deduces t...", "weights": [[8, 14, 1.0], [19, 22, 1.0], [105, 111, 1.0], [116, 119, 1.0], [138, 144, 1.0], [182, 185, 1.0]]}, "2001.wwwconf_conference-2001p.86": {"snippet": "Web Query Characteristics and their Implications on Search Engines", "weights": [[0, 3, 1.0], [52, 58, 1.0]]}, "2011.wsdm_conference-2011.48": {"snippet": "...able web sites recommendation. Given a query, our system will recommend a list of searchable web sites ranked by relevance, which can be used to complement the web page results and ads from a search e...", "weights": [[8, 11, 1.0], [96, 99, 1.0], [163, 166, 1.0], [195, 201, 1.0], [202, 208, 1.0]]}, "2010.sigirconf_conference-2010.74": {"snippet": "... the search engine's architecture for rank-time query reduction. Experiments on a large collection of long queries issued to a commercial search engine show that the proposed techniques significantly ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [24, 36, 1.0], [141, 147, 1.0], [148, 154, 1.0]]}, "2007.wwwconf_conference-2007.205": {"snippet": "...able search and query engine for the web", "weights": [[8, 14, 1.0], [25, 31, 1.0], [40, 43, 1.0]]}, "2017.sigirconf_workshop-2017ecom.10": {"snippet": "The Architecture of eBay Search\n\n\n The architecture of a large-scale search engine is dependent on the application. In the case of a web search engine, the document collection can be considered to be ...", "weights": [[4, 16, 1.0], [25, 31, 1.0], [39, 51, 1.0], [69, 75, 1.0], [76, 82, 1.0], [133, 136, 1.0], [137, 143, 1.0], [144, 150, 1.0]]}, "2005.wwwconf_conference-2005si.88": {"snippet": "...meta-search engine\n\n\n ABSTRACTIn this short paper we introduce Helios, a flexible and efficient open source meta-search engine. Helios currently runs on the top of 18 search engines (in Web, Books, Ne...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [116, 122, 1.0], [123, 129, 1.0], [170, 176, 1.0], [189, 192, 1.0]]}, "2000.riao_conference-2000.19": {"snippet": "...y of Web Search Engines", "weights": [[8, 11, 1.0], [12, 18, 1.0]]}, "2011.wwwconf_conference-2011c.11": {"snippet": "Web-scale entity-relation search architecture\n\n\n ABSTRACTEnabling entity search and ranking at Web-scale is fraught with many challenges: annotating the corpus with entities and types, query language ...", "weights": [[0, 3, 1.0], [26, 32, 1.0], [33, 45, 1.0], [73, 79, 1.0], [95, 98, 1.0]]}, "2002.sigirjournals_journal-ir0volumeA36A2.1": {"snippet": "...s in web search engines", "weights": [[8, 11, 1.0], [12, 18, 1.0]]}, "2010.wwwconf_conference-2010.44": {"snippet": "...cial search engine like Aardvark lies in finding the right person to satisfy a user's information need. Further, while trust in a traditional search engine is based on authority, in a social search en...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [145, 151, 1.0], [152, 158, 1.0], [194, 200, 1.0], [201, 207, 1.0]]}, "2013.wwwconf_conference-2013c.263": {"snippet": "...text search to explore this historical information. This survey provides an overview of web archive search architectures designed for time-travel search, i.e. full-text search on the web within a user...", "weights": [[8, 14, 1.0], [91, 94, 1.0], [103, 109, 1.0], [149, 155, 1.0], [172, 178, 1.0], [186, 189, 1.0]]}, "2006.wwwconf_conference-2006.116": {"snippet": "...: an architecture and web service design for an academic document search engine\n\n\n ABSTRACTCiteSeer is a scientific literature digital library and search engine which automatically crawls and indexes ...", "weights": [[8, 20, 1.0], [25, 28, 1.0], [69, 75, 1.0], [76, 82, 1.0], [150, 156, 1.0], [157, 163, 1.0]]}, "2008.sigirconf_conference-2008.19": {"snippet": "...ance web search engines\n\n\n ABSTRACTResults caching is an efficient technique for reducing the query processing load, hence it is commonly used in real search engines. This technique, however, bounds t...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [154, 160, 1.0]]}, "2012.wsdm_conference-2012.73": {"snippet": "...s of web users, and recommendation services (including search engines, advertisement engines, and collaborative filtering engines) provide a motivation to adapt recommendations to the interests of the...", "weights": [[8, 11, 1.0], [58, 64, 1.0]]}, "2010.ipm_journal-ir0volumeA46A3.4": {"snippet": "...uage search engines provide multimedia searching, and what multimedia search functionalities are available in Chinese language Web search engines. Specifically, we examine each Web search engine's (1)...", "weights": [[8, 14, 1.0], [73, 79, 1.0], [130, 133, 1.0], [134, 140, 1.0], [180, 183, 1.0], [184, 190, 1.0], [191, 197, 1.0]]}, "2006.jasis_journal-ir0volumeA57A14.2": {"snippet": "Web searching on the Vivisimo search engine", "weights": [[0, 3, 1.0], [30, 36, 1.0], [37, 43, 1.0]]}, "2008.wwwconf_conference-2008.201": {"snippet": "...cale web search. Our contribution here is to design a basic system architecture for GPU-based highperformance IR, and to describe how to perform highly efficient query processing within such an archit...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [70, 82, 1.0], [197, 209, 1.0]]}, "2004.sigirconf_conference-2004.129": {"snippet": "...ased search engine ACES(A Contextual Engine for Search), which puts the search in context . Although there have been several attempts at building a personalized or contextual search engine  or session...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [40, 46, 1.0], [51, 57, 1.0], [75, 81, 1.0], [178, 184, 1.0], [185, 191, 1.0]]}, "2010.tois_journal-ir0volumeA28A2.0": {"snippet": "...y of search engines: Load-driven routing and incremental caching to reduce and balance the load\n\n\n This article introduces an architecture for a document-partitioned search engine, based on a novel ap...", "weights": [[8, 14, 1.0], [129, 141, 1.0], [169, 175, 1.0], [176, 182, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.15": {"snippet": "...peer search. Furthermore, we introduce our own architecture for peer-to-peer search systems, inspired by BitTorrent.Distributing the task of providing search results for queries introduces the problem...", "weights": [[8, 14, 1.0], [50, 62, 1.0], [80, 86, 1.0], [154, 160, 1.0]]}, "2003.jasis_journal-ir0volumeA54A13.1": {"snippet": "...n of Web search engines: I. Theory and background", "weights": [[8, 11, 1.0], [12, 18, 1.0]]}, "2017.wsdm_conference-2017.1": {"snippet": "...e on web search and data mining. We start from its inception during 2006 to the first conference held at Stanford in 2008, driven by some key people in the main web search engines of that time. We wer...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [164, 167, 1.0], [168, 174, 1.0]]}, "1999.cikm_conference-99.28": {"snippet": "Architecture of a Metasearch Engine That Supports User Information Needs\n\n\n AbstractWhen a query is submitted to a metasearch engine, decisions are made with respect to the underlying search engines t...", "weights": [[0, 12, 1.0], [29, 35, 1.0], [126, 132, 1.0], [184, 190, 1.0]]}, "2003.sigirconf_conference-2003.102": {"snippet": "... the search engine.(3) Search Engine Discovery: If a user submits a URL that does not have a search engine on the Webpage, this component will crawl Webpages \"nearby\" to find search engine interfaces....", "weights": [[8, 14, 1.0], [15, 21, 1.0], [26, 32, 1.0], [33, 39, 1.0], [96, 102, 1.0], [103, 109, 1.0], [178, 184, 1.0], [185, 191, 1.0]]}, "2014.tois_journal-ir0volumeA32A4.5": {"snippet": "...ased Search Engine Architectures: An Experimental Study\n\n\n Caching is an important optimization in search engine architectures. Existing caching techniques for search engine optimization are mostly bi...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [102, 108, 1.0], [109, 115, 1.0], [163, 169, 1.0], [170, 176, 1.0]]}, "2011.wwwconf_conference-2011c.115": {"snippet": "...uted web retrieval\n\n\n ABSTRACTIn the ocean of Web data, Web search engines are the primary way to access content. As the data is on the order of petabytes, current search engines are very large centra...", "weights": [[8, 11, 1.0], [49, 52, 1.0], [59, 62, 1.0], [63, 69, 1.0], [167, 173, 1.0]]}, "2009.cikm_conference-2009.1": {"snippet": "...lt a web search engine that is capable of managing 100 million web pages per node in a non-parallel configuration using Odysseus. This engine has been successfully tested in many commercial environmen...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [19, 25, 1.0], [66, 69, 1.0], [138, 144, 1.0]]}, "2012.tweb_journal-ir0volumeA6A4.0": {"snippet": "... for Search Engines\n\n\n In practice, a search engine may fail to serve a query due to various reasons such as hardware/network failures, excessive query load, lack of matching documents, or service con...", "weights": [[8, 14, 1.0], [41, 47, 1.0], [48, 54, 1.0]]}, "W03-1115": {"snippet": "...onal search engines based on a centralized architecture to retrieve fresh information, because they take a long time to collect documents via Web robots. In contrast to a centralized architecture, a s...", "weights": [[8, 14, 1.0], [46, 58, 1.0], [145, 148, 1.0], [186, 198, 1.0], [202, 208, 1.0]]}, "2012.cikm_workshop-2012shb.6": {"snippet": "...ent\" web services that pre-process and alter information on the user's behalf. Specifically, this paper describes the design of a personalized search engine that utilizes patient data (either stored i...", "weights": [[8, 11, 1.0], [146, 152, 1.0], [153, 159, 1.0]]}, "2015.wsdm_conference-2015.58": {"snippet": "...many search engines provide specialized vertical search results for local search 1 and for real-time search 2 . Furthermore, vertical search results are often slotted into general Web search results ....", "weights": [[8, 14, 1.0], [52, 58, 1.0], [77, 83, 1.0], [104, 110, 1.0], [137, 143, 1.0], [183, 186, 1.0], [187, 193, 1.0]]}, "2018.wsdm_conference-2018.97": {"snippet": "...ntic Search: Looking Beyond Web Search, Q&A and Dialog Systems\n\n\n ABSTRACTUser expectations of web search are changing. They are expecting search engines to answer questions, to be more conversational...", "weights": [[8, 14, 1.0], [31, 34, 1.0], [35, 41, 1.0], [98, 101, 1.0], [102, 108, 1.0], [142, 148, 1.0]]}, "2012.ipm_journal-ir0volumeA48A5.2": {"snippet": "...ache architecture for web search engines\n\n\n a b s t r a c tCaching is a crucial performance component of large-scale web search engines, as it greatly helps reducing average query response times and q...", "weights": [[8, 20, 1.0], [25, 28, 1.0], [29, 35, 1.0], [120, 123, 1.0], [124, 130, 1.0]]}, "2009.wwwconf_conference-2009.100": {"snippet": "...ible web , the set of documents that have no incoming links and can only be retrieved through web applications and web forms. The Sitemaps protocol is a fast-growing web protocol supported jointly by ...", "weights": [[8, 11, 1.0], [97, 100, 1.0], [118, 121, 1.0], [169, 172, 1.0]]}, "2015.wsdm_conference-2015.50": {"snippet": "...cale web search engines. In particular, the tutorial provides an in-depth architectural overview of a web search engine, mainly focusing on the web crawling, indexing, and query processing components....", "weights": [[8, 11, 1.0], [12, 18, 1.0], [105, 108, 1.0], [109, 115, 1.0], [116, 122, 1.0], [147, 150, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.16": {"snippet": "...four Web search engines was 84.9%, shared by two of the three Web search engines was 11.4%, shared by three of the Web search engines was 2.6%, and shared by all four Web search engines was 1.1%. This...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [65, 68, 1.0], [69, 75, 1.0], [118, 121, 1.0], [122, 128, 1.0], [170, 173, 1.0], [174, 180, 1.0]]}, "H05-1071": {"snippet": "... the Web\n\n\n Numerous NLP applications rely on search-engine queries, both to extract information from and to compute statistics over the Web corpus. But search engines often limit the number of availa...", "weights": [[8, 11, 1.0], [49, 55, 1.0], [56, 62, 1.0], [140, 143, 1.0], [156, 162, 1.0]]}, "1998.jasis_journal-ir0volumeA49A11.1": {"snippet": "Web Search Engines", "weights": [[0, 3, 1.0], [4, 10, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...d to web search, some of these assumptions became less tenable (the web is not curated; pages are more variable in length and quality; and users of web search engines are less experienced and tend to ...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [71, 74, 1.0], [151, 154, 1.0], [155, 161, 1.0]]}, "2012.wwwconf_workshop-2012dec3d.8": {"snippet": "... the Web Architecture\n\n\n In this paper we present a guide for writing use cases for the Declarative 3D for the Web Architecture, use cases where embedding 3D data in HTML using declarative approach pr...", "weights": [[8, 11, 1.0], [12, 24, 1.0], [114, 117, 1.0], [118, 130, 1.0]]}, "2015.ictir_conference-2015.35": {"snippet": "...ined Search Engine in the Browser\n\n\n ABSTRACTJavaScript engines inside modern web browsers are capable of running sophisticated multi-player games, rendering impressive 3D scenes, and supporting compl...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [81, 84, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A3.0": {"snippet": "...e, a search engine. Excellence of the tightlycoupled IR features 2 has been demonstrated through the parallel web search engine implemented using Odysseus, which is capable of managing 100 million web...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [113, 116, 1.0], [117, 123, 1.0], [124, 130, 1.0], [200, 203, 1.0]]}, "2019.wwwconf_conference-2019.126": {"snippet": "...aset Search: Building a search engine for datasets in an open Web ecosystem", "weights": [[8, 14, 1.0], [27, 33, 1.0], [34, 40, 1.0], [65, 68, 1.0]]}, "2015.wsdm_conference-2015.26": {"snippet": "...ting Search with Deep Understanding of Contents and Users\n\n\n ABSTRACTRecent years have witnessed dramatic changes in how people interact with search engines. Search engines are expected to be more int...", "weights": [[8, 14, 1.0], [145, 151, 1.0], [161, 167, 1.0]]}, "2009.cikm_conference-2009.46": {"snippet": "...site web search engines\n\n\n ABSTRACTWeb search engines are often implemented as centralized systems. Designing and implementing a Web search engine in a distributed environment is a challenging enginee...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [42, 48, 1.0], [132, 135, 1.0], [136, 142, 1.0], [143, 149, 1.0]]}, "2016.sigirconf_conference-2016.224": {"snippet": "...cale web search engines. In particular, the tutorial provides an in-depth architectural overview of a web search engine, mainly focusing on the web crawling, indexing, and query processing components....", "weights": [[8, 11, 1.0], [12, 18, 1.0], [105, 108, 1.0], [109, 115, 1.0], [116, 122, 1.0], [147, 150, 1.0]]}, "2014.sigirconf_conference-2014.216": {"snippet": "...cale web search engines\n\n\n ABSTRACTLarge-scale web search engines rely on massive compute infrastructures to be able to cope with the continuous growth of the Web and their user bases. In such search ...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [50, 53, 1.0], [54, 60, 1.0], [162, 165, 1.0], [196, 202, 1.0]]}, "2001.wwwconf_conference-2001p.48": {"snippet": "...n of Search Engines with User Interfaces\n\n\n In this poster we analyze the dependency problem of the user interface with the information retrieval software. We propose an architecture that allows the s...", "weights": [[8, 14, 1.0], [173, 185, 1.0]]}, "2007.wwwconf_conference-HeMLYW07.0": {"snippet": "... the Search Interfaces of the Deep Web\n\n\n Abstract Many databases have become Web-accessible through form-based search interfaces (i.e., HTML forms) that allow users to specify complex and precise que...", "weights": [[8, 14, 1.0], [38, 41, 1.0], [81, 84, 1.0], [115, 121, 1.0]]}, "2005.wwwconf_conference-2005si.16": {"snippet": "...ious web search engine: improving web searching through linguistic analysis\n\n\n ABSTRACTIn this paper we present the Infocious Web search engine [23]. Our goal in creating Infocious is to improve the w...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [19, 25, 1.0], [37, 40, 1.0], [129, 132, 1.0], [133, 139, 1.0], [140, 146, 1.0]]}, "2007.ipm_journal-ir0volumeA43A3.4": {"snippet": "Architecture of a grid-enabled Web search engine\n\n\n AbstractSearch Engine for South-East Europe (SE4SEE) is a socio-cultural search engine running on the grid infrastructure. It offers a personalized,...", "weights": [[0, 12, 1.0], [31, 34, 1.0], [35, 41, 1.0], [42, 48, 1.0], [67, 73, 1.0], [125, 131, 1.0], [132, 138, 1.0]]}, "2007.cikm_conference-2007.90": {"snippet": "... the Web, ii) an information extraction engine that extracts useful fields from these Web pages, and iii) a query engine that allows both typical keyword based queries on the pages and advanced querie...", "weights": [[8, 11, 1.0], [43, 49, 1.0], [89, 92, 1.0], [117, 123, 1.0]]}, "2012.wwwconf_conference-2012.42": {"snippet": "...e in web search queries\n\n\n ABSTRACTWeb search queries are an encoding of the user's search intent and extracting structured information from them can facilitate central search engine operations like i...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [42, 48, 1.0], [87, 93, 1.0], [172, 178, 1.0], [179, 185, 1.0]]}, "2012.wwwconf_workshop-2012dec3d.6": {"snippet": "...D in Web Architecture\n\n\n The recent WebGL integration in major web browser has open the way to many 3D applications as well as high-level libraries targeting 3D content developers. While most of these...", "weights": [[8, 11, 1.0], [12, 24, 1.0], [66, 69, 1.0]]}, "2012.wsdm_conference-2012.45": {"snippet": "...zing web search\n\n\n ABSTRACTWe present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorit...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [74, 77, 1.0], [78, 84, 1.0], [135, 138, 1.0], [139, 145, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.20": {"snippet": "...edia Web search functionality is generally limited. Our findings show that despite the increasing level of interest in multimedia Web search, those few Web search engines offering multimedia Web searc...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [133, 136, 1.0], [137, 143, 1.0], [155, 158, 1.0], [159, 165, 1.0], [194, 197, 1.0], [198, 204, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.3": {"snippet": "...uted web search engines\n\n\n a b s t r a c tLarge-scale web search engines are composed of multiple data centers that are geographically distant to each other. Typically, a user query is processed in a ...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [57, 60, 1.0], [61, 67, 1.0]]}, "2014.wwwconf_conference-2014c.40": {"snippet": "...cale web search engines. In particular, the tutorial provides an in-depth architectural overview of a web search engine, mainly focusing on the web crawling, indexing, and query processing components....", "weights": [[8, 11, 1.0], [12, 18, 1.0], [105, 108, 1.0], [109, 115, 1.0], [116, 122, 1.0], [147, 150, 1.0]]}, "P05-3009": {"snippet": "...st's Search Engine (LSE) was designed to provide an intuitive, easy-touse interface that enables language researchers to seek linguistically interesting examples on the Web, based on syntactic and lex...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [172, 175, 1.0]]}, "2015.wwwconf_conference-2015.2": {"snippet": "... the architecture of an interactive thematic map search engine, Frankenplace, designed to facilitate document exploration at the intersection of theme and place. The map interface enables a user to zo...", "weights": [[8, 20, 1.0], [52, 58, 1.0], [59, 65, 1.0]]}, "2012.wwwconf_workshop-2012dec3d.9": {"snippet": "... the Web Architecture\". In this demo description, we present a selection of features of XML3D, that demonstrate the advantages of a web-based declarative approach to 3D graphics.", "weights": [[8, 11, 1.0], [12, 24, 1.0], [135, 138, 1.0]]}, "2019.wsdm_conference-2019.110": {"snippet": "... for Web Image Search\n\n\n ABSTRACTWeb-based image search engines di er from Web search engines greatly. The intents or goals behind human interactions with image search engines are di erent. In image s...", "weights": [[8, 11, 1.0], [18, 24, 1.0], [52, 58, 1.0], [78, 81, 1.0], [82, 88, 1.0], [164, 170, 1.0], [202, 208, 1.0]]}, "2008.cikm_workshop-2008lsdsir.1": {"snippet": "Search engine architectures from conventional to Brian Ulicny and Ken Baclawski. New metrics for newsblog\n\n\n ABSTRACTA significant amount of research has focused on the problem of implementing large-s...", "weights": [[0, 6, 1.0], [7, 13, 1.0]]}, "2006.wwwconf_conference-2006.135": {"snippet": "... the web to search for the required information: search terms, relevancy feedback and so on. Implicit information is the information in which the web users interact with the web and the users are usua...", "weights": [[8, 11, 1.0], [15, 21, 1.0], [52, 58, 1.0], [149, 152, 1.0], [177, 180, 1.0]]}, "2005.wwwconf_conference-2005.8": {"snippet": "... for search engines\n\n\n ABSTRACTWhen a query is submitted to a search engine, the search engine returns a dynamically generated result page containing the result records, each of which usually consists...", "weights": [[8, 14, 1.0], [65, 71, 1.0], [72, 78, 1.0], [84, 90, 1.0], [91, 97, 1.0]]}, "2002.wwwconf_conference-2002.40": {"snippet": "... the web\n\n\n ABSTRACTWeb-based search engines such as Google and NorthernLight return documents that are relevant to a user query, not answers to user questions. We have developed an architecture that ...", "weights": [[8, 11, 1.0], [33, 39, 1.0], [185, 197, 1.0]]}, "N06-1053": {"snippet": "...cale Web-Search Architectures\n\n\n Large-scale web-search engines are generally designed for linear text. The linear text representation is suboptimal for audio search, where accuracy can be significant...", "weights": [[8, 11, 1.0], [12, 18, 1.0], [48, 51, 1.0], [52, 58, 1.0], [162, 168, 1.0]]}, "2005.wwwconf_conference-2005si.46": {"snippet": "... the search engine that works for you\n\n\n ABSTRACTA search engine evaluation model that considers over seventy performance and feature parameters is presented. The design of a web-based system that all...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [54, 60, 1.0], [61, 67, 1.0], [178, 181, 1.0]]}, "2015.wsdm_conference-2015.25": {"snippet": "... APP search because of less text within term matching search engine.In this talk, we describe a semantic matching architecture in APP search\uff0c which mining topics and tags in big data. It enriches quer...", "weights": [[8, 14, 1.0], [57, 63, 1.0], [64, 70, 1.0], [117, 129, 1.0], [137, 143, 1.0]]}, "2013.sigirconf_conference-2013.184": {"snippet": "...cale web search engines . 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Throughout the article, connections to information r...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [140, 149, 1.0], [190, 201, 1.0], [202, 211, 1.0]]}, "2015.sigirconf_conference-2015.56": {"snippet": "... for Information Retrieval\n\n\n AbstractThe task of information retrieval (IR) has traditionally been defined as to rank a collection of documents in response to a query. While this definition has enabl...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 64, 1.0], [65, 74, 1.0]]}, "2012.sigirconf_conference-2012.220": {"snippet": "...sual information retrieval using Java and LIRE\n\n\n ABSTRACTVisual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotat...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [68, 79, 1.0], [80, 89, 1.0]]}, "2000.jasis_journal-ir0volumeA51A11.5": {"snippet": "...ling information retrieval system", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2004.sigirconf_conference-2004.132": {"snippet": "...Most retrieval and database, even those tailored to geographic information, provide only nascent approaches to the technologically and conceptually difficult challenges of Geographic Information Retri...", "weights": [[8, 17, 1.0], [66, 77, 1.0], [186, 197, 1.0], [198, 207, 1.0]]}, "1986.sigirjournals_journal-ir0volumeA20A14.4": {"snippet": "... the retrieval processes of document retrieval systems\u00b0 Two basic categories of models that have been employed in information retrieval are the vector processing models and the Boolean retrieval model", "weights": [[8, 17, 1.0], [40, 49, 1.0], [117, 128, 1.0], [129, 138, 1.0], [188, 197, 1.0]]}, "2001.cikm_conference-2001.24": {"snippet": "...uted Information Retrieval\n\n\n ABSTRACTQuery expansion has been shown effective for both single database retrieval and for distributed information retrieval where complete collection information is ava...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [107, 116, 1.0], [137, 148, 1.0], [149, 158, 1.0], [185, 196, 1.0]]}, "2018.chiir_conference-2018.66": {"snippet": "...s of information visualization, explain how information visualization can support interactive information retrieval, and survey search interfaces from my own research that leverage information visuali...", "weights": [[8, 19, 1.0], [47, 58, 1.0], [97, 108, 1.0], [109, 118, 1.0], [184, 195, 1.0]]}, "2012.wsdm_conference-2012.2": {"snippet": "...ject retrieval\n\n\n ABSTRACTThe success of image object retrieval systems relies on the visual bag-of-words paradigm, which allows image retrieval systems to adopt a retrieval strategy analogous to text...", "weights": [[8, 17, 1.0], [57, 66, 1.0], [138, 147, 1.0], [167, 176, 1.0]]}, "2015.ictir_conference-2015.2": {"snippet": "...y of Retrieval: The Retrievability of Information\n\n\n ABSTRACTRetrievability is an important and interesting indicator that can be used in a number of ways to analyse Information Retrieval systems and ...", "weights": [[8, 17, 1.0], [41, 52, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2014.clef_conference-2014.5": {"snippet": "An Information Retrieval Ontology for Information Retrieval Nanopublications", "weights": [[3, 14, 1.0], [15, 24, 1.0], [38, 49, 1.0], [50, 59, 1.0]]}, "2017.cikm_conference-2017.301": {"snippet": "...thin Information Retrieval. 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We propose several contextsensitive retrieval ...", "weights": [[8, 19, 1.0], [65, 76, 1.0], [89, 98, 1.0], [126, 137, 1.0], [138, 147, 1.0], [193, 202, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.1": {"snippet": "...ased retrieval in language modeling approach\n\n\n AbstractThe term mismatch problem in information retrieval is a critical problem, and several techniques have been developed, such as query expansion, c...", "weights": [[8, 17, 1.0], [88, 99, 1.0], [100, 109, 1.0]]}, "2005.clef_workshop-2005w.54": {"snippet": "...ical information retrieval techniques cause to information retrieval. The baseline approach is based on the development of named entity recognition and geospatial information retrieval tools and on it...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0], [166, 177, 1.0], [178, 187, 1.0]]}, "2015.ictir_conference-2015.28": {"snippet": "...text Retrieval for Web Tables\n\n\n ABSTRACTMany modern knowledge bases are built by extracting information from millions of web pages. Though existing extraction methods primarily focus on web pages' ma...", "weights": [[8, 17, 1.0], [96, 107, 1.0]]}, "2004.sigirconf_conference-2004.25": {"snippet": "...ased retrieval using language models\n\n\n ABSTRACTPrevious research on cluster-based retrieval has been inconclusive as to whether it does bring improved retrieval effectiveness over document-based retr...", "weights": [[8, 17, 1.0], [86, 95, 1.0], [155, 164, 1.0], [199, 208, 1.0]]}, "2004.clef_workshop-2004.30": {"snippet": "...gual information. While monolingual information retrieval was improved by using the analyzer, bi-lingual information retrieval still has room for improvement.", "weights": [[8, 19, 1.0], [39, 50, 1.0], [51, 60, 1.0], [108, 119, 1.0], [120, 129, 1.0]]}, "1991.sigirconf_conference-91.24": {"snippet": "...ssor Information Retrieval Systems\n\n\n Abstract.Information retrieval is the selection of documents that are potentially relevant to a user's information need. Given the vast volume of data stored in m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0], [144, 155, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.8": {"snippet": "...cale information retrieval systems. Distributing different aspects of search (e.g., crawling, indexing, and query processing) is essential to achieve scalability in large-scale information retrieval s...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords Information Retrieval (IR) solutions to retrieve passages matching a set of terms extracted from the question.There is a fundamental disconnect between the capabilities of the bag-of-words retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [197, 206, 1.0]]}, "2014.cikm_conference-2014.9": {"snippet": "...ween Retrieval Bias and Retrieval Performance\n\n\n ABSTRACTRetrievability provides an alternative way to assess an Information Retrieval (IR) system by measuring how easily documents can be retrieved. R...", "weights": [[8, 17, 1.0], [27, 36, 1.0], [116, 127, 1.0], [128, 137, 1.0]]}, "2016.ictir_conference-2016.7": {"snippet": "... for Retrieval and Ranking\n\n\n ABSTRACTData fusion has been shown to be a simple and effective way to improve retrieval results. Most existing data fusion methods combine ranked lists from different re...", "weights": [[8, 17, 1.0], [112, 121, 1.0], [201, 210, 1.0]]}, "2016.ictir_conference-2016.32": {"snippet": "...tity Retrieval\n\n\n ABSTRACTThe premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing ...", "weights": [[8, 17, 1.0], [51, 60, 1.0]]}, "2010.cikm_conference-2010.10": {"snippet": "...g to information retrieval\n\n\n ABSTRACTThe probabilistic formalism of quantum physics is said to provide a sound basis for building a principled information retrieval framework. Such a framework can be...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "2015.jasis_journal-ir0volumeA66A11.9": {"snippet": "...tive information retrieval behavior to inform design", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...n of retrieval models particularly for recall oriented retrieval domains, where the focus is on retrieving all relevant information but not just retrieving a subset of relevant information. Available ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [123, 134, 1.0], [180, 191, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "2005.sigirconf_conference-2005.9": {"snippet": "... hoc information retrieval\n\n\n ABSTRACTInformation retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information nee...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 62, 1.0], [164, 173, 1.0], [188, 199, 1.0]]}, "2003.sigirconf_conference-2003.73": {"snippet": "XML retrieval: what to retrieve?\n\n\n ABSTRACTThe fundamental difference between standard information retrieval and XML retrieval is the unit of retrieval. In traditional IR, the unit of retrieval is fi...", "weights": [[4, 13, 1.0], [88, 99, 1.0], [100, 109, 1.0], [118, 127, 1.0], [143, 152, 1.0], [185, 194, 1.0]]}, "W98-0704": {"snippet": "...l in information retrieval applications. Experiments using several standard information retrieval test collections show that our method results in a significant improvement of information retrieval pe", "weights": [[8, 19, 1.0], [20, 29, 1.0], [79, 90, 1.0], [91, 100, 1.0], [179, 190, 1.0], [191, 200, 1.0]]}, "2006.airs_conference-2006.36": {"snippet": "...mage Retrieval Using the Insight Toolkit (ITK)\n\n\n Abstract. Visual information retrieval is an emerging domain in the medical field as it has been in computer vision for more than ten years. It has th...", "weights": [[8, 17, 1.0], [70, 81, 1.0], [82, 91, 1.0]]}, "2006.jasis_journal-ir0volumeA57A10.13": {"snippet": "Information retrieval design: Principles and options for information description, organization, display, and access in information retrieval databases, digital libraries, catalogs, and indexes", "weights": [[0, 11, 1.0], [12, 21, 1.0], [57, 68, 1.0], [119, 130, 1.0], [131, 140, 1.0]]}, "A00-1003": {"snippet": "...edia Information Retrieval\n\n\n Simple measures can achieve high-accuracy cross-language retrieval in carefully chosen applications. Image retrieval is one of those applications, with results ranging fr...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [90, 99, 1.0], [140, 149, 1.0]]}, "2018.ictir_conference-2018.13": {"snippet": "...sage retrieval and textual entailment share a common information space, while POS and NER information is used only at a compositional level in the lower layers of an information retrieval model. Lastl...", "weights": [[8, 17, 1.0], [56, 67, 1.0], [93, 104, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2004.clef_workshop-2004w.54": {"snippet": "...gual information retrieval. The performance of cross-lingual information retrieval was poor and that of monolingual information retrieval was not good. We need to check our modules and procedures.", "weights": [[8, 19, 1.0], [20, 29, 1.0], [64, 75, 1.0], [76, 85, 1.0], [119, 130, 1.0], [131, 140, 1.0]]}, "2006.clef_workshop-2006.108": {"snippet": "...Page Retrieval", "weights": [[8, 17, 1.0]]}, "2020.sigirconf_conference-2020.360": {"snippet": "...tive Information Retrieval: Models, Algorithms, and Evaluation\n\n\n Since Information Retrieval (IR) is an interactive process in general, it is important to study Interactive Information Retrieval (IIR...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [75, 86, 1.0], [87, 96, 1.0], [177, 188, 1.0], [189, 198, 1.0]]}, "1990.jasis_journal-ir0volumeA41A2.10": {"snippet": "...s of information retrieval and automatic text processing: The transformation analysis, and retrieval of information by computer", "weights": [[8, 19, 1.0], [20, 29, 1.0], [94, 103, 1.0], [107, 118, 1.0]]}, "O01-2005": {"snippet": "...s of Information Retrieval with Clustering and Fusion\n\n\n Fusion and clustering are two approaches to improving the effectiveness of information retrieval. In fusion, ranked lists are combined together...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [135, 146, 1.0], [147, 156, 1.0]]}, "2015.ecir_conference-2015.22": {"snippet": "... and Retrieval Bias: A Comparison of Inequality Measures", "weights": [[8, 17, 1.0]]}, "2020.sigirconf_conference-2020.172": {"snippet": "...cipe Retrieval with Visual Query of Ingredients\n\n\n Recipe retrieval is a representative and useful application of crossmodal information retrieval. Recent studies have proposed frameworks for retrievi...", "weights": [[8, 17, 1.0], [61, 70, 1.0], [128, 139, 1.0], [140, 149, 1.0]]}, "2020.sigirconf_conference-2020.367": {"snippet": "... for Information Retrieval: Fundamentals and Advances\n\n\n Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users' information needs by suggesting...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [60, 71, 1.0], [72, 81, 1.0], [172, 183, 1.0]]}, "2018.sigirconf_conference-2018.196": {"snippet": "...stic Information Retrieval\n\n\n ABSTRACTMedical information retrieval suffers from a dual problem: users struggle in describing what they are experiencing from a medical perspective and the search engin...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [49, 60, 1.0], [61, 70, 1.0]]}, "W09-4632": {"snippet": "...e\n\n\n Information retrieval is a field of research reaching from computer and information science to lingusitics. As a linguist in the information retrieval field, I leave the quest for effective searc...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [137, 148, 1.0], [149, 158, 1.0]]}, "2017.fire_conference-2017w.19": {"snippet": "... the Information Retrieval task. The distributed representation of text attained 3rd position in subtask 1 and attained satisfactory score in subtask 2. CCS CONCEPTS \u2022 Information systems \u2192 Informatio...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [171, 182, 1.0], [193, 204, 1.0]]}, "2004.jasis_journal-ir0volumeA55A10.2": {"snippet": "...tive retrieval strategies in the same information retrieval system", "weights": [[8, 17, 1.0], [41, 52, 1.0], [53, 62, 1.0]]}, "2004.ntcir_workshop-2004.57": {"snippet": "...onal Retrieval Task 2 that was conducted from 2003 to 2004 as a subtask of the WEB Task at the Fourth NTCIR Workshop ('NTCIR-4 WEB'). In the Informational Retrieval Task, we attempted to assess the re...", "weights": [[8, 17, 1.0], [158, 167, 1.0], [201, 210, 1.0]]}, "2017.trec_conference-2017.21": {"snippet": "...ular information retrieval application is web search engine such as Google [2] , Baidu [3] , Bing [4] and Sogou [5]. These application will return top-N best retrieval result to users. Information Ret...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [161, 170, 1.0], [188, 199, 1.0], [200, 209, 1.0]]}, "T78-1026": {"snippet": "... for retrieval only, and not for retrieval and updating simultaneously. A related assumption (or constraint) that in questions descriptions are used referentially only (i.e., a question cannot be used...", "weights": [[8, 17, 1.0], [36, 45, 1.0]]}, "1999.ntcir_workshop-1999.17": {"snippet": "Information Retrieval Based on Stochastic Models\n\n\n Our method for information retrieval uses Robertson's 2-poisson model which is one type of probablistic approach. This method achieved a comparative...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [67, 78, 1.0], [79, 88, 1.0]]}, "2017.airs_conference-2017.16": {"snippet": "... for Information Retrieval Models Over Industry Data Sets\n\n\n Abstract. The development of Information Retrieval (IR) techniques heavily depends on empirical studies over real world data collections. U...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [93, 104, 1.0], [105, 114, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2012.ipm_journal-ir0volumeA48A1.9": {"snippet": "... and information science, the same cannot be said about the subfield of information retrieval. We address that in this work by sketching the information retrieval intellectual landscape through visual...", "weights": [[8, 19, 1.0], [75, 86, 1.0], [87, 96, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "2016.sigirconf_conference-2016.89": {"snippet": "...This information retrieval test collection is the first to combine multilingual search, passage retrieval, and informal online genre text.", "weights": [[8, 19, 1.0], [20, 29, 1.0], [99, 108, 1.0]]}, "2020.sigirconf_conference-2020.5": {"snippet": "... for Information Retrieval\n\n\n Information retrieval (IR) is the science of search, the search of user query relevant pieces of information from a collection of unstructured resources. Information in t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [33, 44, 1.0], [45, 54, 1.0], [130, 141, 1.0], [187, 198, 1.0]]}, "2018.chiir_conference-2018.33": {"snippet": "... the information to write the paper. Information use is another component in information behavior models such as Wilson's [10]  model. In the Information Retrieval (IR) research community, tasks invol...", "weights": [[8, 19, 1.0], [40, 51, 1.0], [80, 91, 1.0], [145, 156, 1.0], [157, 166, 1.0]]}, "2004.airs_conference-2004.26": {"snippet": "...bile information services focus on providing location information related to the user's current location. In contrast, we propose an information retrieval method that searches for 'local information' ...", "weights": [[8, 19, 1.0], [57, 68, 1.0], [136, 147, 1.0], [148, 157, 1.0], [190, 201, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.10": {"snippet": "... XML retrieval. XML retrieval deals with retrieving those document parts that specifically answer a query. It is concerned with using the document structure to improve the retrieval of information fro...", "weights": [[8, 17, 1.0], [23, 32, 1.0], [175, 184, 1.0], [188, 199, 1.0]]}, "1992.sigirconf_conference-92.3": {"snippet": "...of a Retrieval Process", "weights": [[8, 17, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2016.ictir_conference-2016.20": {"snippet": "...t of retrieval systems that performance and efficiency measures do not. Retrievability is often used to calculate the retrievability bias, an indication of how accessible a system makes all the docume...", "weights": [[8, 17, 1.0]]}, "2013.ictir_conference-2013.19": {"snippet": "... for Information Retrieval\n\n\n ABSTRACTOver many years a number of theoretical constructs and models have evolved for the study of information retrieval in its various manifestations. Gradually these h...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2004.tois_journal-ir0volumeA22A3.4": {"snippet": "...uted information retrieval, which aims to reduce the network traffic of query passing in the network. We carry out experiments to show the distributed image retrieval system and the Firework informati...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [160, 169, 1.0], [194, 205, 1.0]]}, "2017.ictir_conference-2017.57": {"snippet": "...s of information retrieval, such as online learning to rank, interactive information retrieval or dynamic information retrieval. ese Permission to make digital or hard copies of part or all of this wo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [76, 87, 1.0], [88, 97, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "2001.ntcir_workshop-2001.29": {"snippet": "On a Retrieval Support System by Suggesting Terms to a User\n\n\n We propose a support method for information retrieval. This method automatically suggests terms relevant to a query, to a user, and when ...", "weights": [[5, 14, 1.0], [95, 106, 1.0], [107, 116, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.2": {"snippet": "... for information retrieval\n\n\n AbstractThis paper presents a probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [77, 88, 1.0], [89, 98, 1.0], [122, 131, 1.0]]}, "2016.sigirconf_conference-2016.36": {"snippet": "...ding Information Need: An fMRI Study\n\n\n ABSTRACTThe raison d'etre of IR is to satisfy human information need. But, do we really understand information need? Despite advances in the past few decades in...", "weights": [[8, 19, 1.0], [95, 106, 1.0], [142, 153, 1.0]]}, "1995.jasis_journal-ir0volumeA46A9.12": {"snippet": "Information Retrieval: New Systems and Current Research. Proceedings of the 15th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, edited by Ruben Leon", "weights": [[0, 11, 1.0], [12, 21, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2005.spire_conference-2005.25": {"snippet": "Retrieval Status Values in Information Retrieval Evaluation", "weights": [[0, 9, 1.0], [27, 38, 1.0], [39, 48, 1.0]]}, "2017.sigirconf_workshop-2017birndl.8": {"snippet": "... of \"Information Retrieval\" - A Tale of Three Databases\n\n\n Coverage is an important criterion when evaluating information systems. This exploratory study investigates this issue by submitting the same...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [113, 124, 1.0]]}, "2012.cikm_conference-2012.18": {"snippet": "...vant information\n\n\n ABSTRACTThe goal of a typical information retrieval system is to satisfy a user's information need-e.g., by providing an answer or information \"nugget\"-while the actual search spac...", "weights": [[8, 19, 1.0], [53, 64, 1.0], [65, 74, 1.0], [105, 116, 1.0], [154, 165, 1.0]]}, "2020.clef_conference-2020w.170": {"snippet": "...k\n\n\n Information retrieval (IR) processes deal with the retrieval of ranked documents based on the similarity among documents and the key words specified in user's query. CLEF 2020 eHealth task on con...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [59, 68, 1.0]]}, "1996.sigirjournals_journal-ir0volumeA30A1.2": {"snippet": "Information Retrieval and Situation Theory\n\n\n AbstractIn 1986, Van Rijsbergen suggested a model of an information retrieval system based on logic. We have advocated in ~ earlier work that a logical ap...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [102, 113, 1.0], [114, 123, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "2005.ntcir_workshop-2005.47": {"snippet": "...tent Retrieval Experiments at Hitachi\n\n\n In NTCIR-5, we used five retrieval methods proposed in NTCIR-4: (1) query term weighting using only document frequency, (2) stopword deletion, (3) two-stage pa...", "weights": [[8, 17, 1.0], [69, 78, 1.0]]}, "2002.ntcir_workshop-2002.1": {"snippet": "...run\" retrieval topics of the task. In this report we will give a brief description about our basic method for web information retrieval, our web information retrieval system and some retrieval experim", "weights": [[8, 17, 1.0], [117, 128, 1.0], [129, 138, 1.0], [148, 159, 1.0], [160, 169, 1.0], [186, 195, 1.0]]}, "2009.cikm_conference-2009.134": {"snippet": "...opic retrieval\n\n\n ABSTRACTTraditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [63, 72, 1.0], [167, 178, 1.0], [194, 203, 1.0]]}, "2005.clef_workshop-2005w.119": {"snippet": "... All retrieval results rely heavily on two retrieval systems: for visual retrieval we use the GNU Image Finding Tool (GIFT), and for textual retrieval the EasyIR retrieval system. For the ad-hoc retri...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [76, 85, 1.0], [144, 153, 1.0], [165, 174, 1.0], [198, 207, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.31": {"snippet": "... and Information  Retrieval\" (1960). This was the first paper to present a probabilistic approach to information retrieval, and perhaps the first paper on ranked retrieval. Although it is one of the m...", "weights": [[8, 19, 1.0], [21, 30, 1.0], [104, 115, 1.0], [116, 125, 1.0], [165, 174, 1.0]]}, "1998.tois_journal-ir0volumeA16A3.1": {"snippet": "...s in Information Retrieval\n\n\n We analyze the kinematics of probabilistic term weights at retrieval time for different Information Retrieval models. We present four models based on different notions of...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [92, 101, 1.0], [121, 132, 1.0], [133, 142, 1.0]]}, "W03-1115": {"snippet": "...resh information. However, fast indexing alone is not enough to retrieve fresh information, as support for temporal information based retrieval is also required. In this paper, we describe temporal in...", "weights": [[8, 19, 1.0], [82, 93, 1.0], [119, 130, 1.0], [137, 146, 1.0], [201, 212, 1.0]]}, "1987.sigirjournals_journal-ir0volumeA21A34.1": {"snippet": "... for Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "I05-2024": {"snippet": "Information Retrieval Capable of Visualization and High Precision\n\n\n We present a neural-network based selforganizing approach that enables visualization of the information retrieval while at the same...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [161, 172, 1.0], [173, 182, 1.0]]}, "2020.sigirconf_workshop-2020birds.5": {"snippet": "...ween Information Science, Information Retrieval, and Data Science) at SIGIR 2020. This talk reflects well my research area, which uniquely connects Information Retrieval (IR), Information Science (IS)...", "weights": [[8, 19, 1.0], [29, 40, 1.0], [41, 50, 1.0], [151, 162, 1.0], [163, 172, 1.0], [179, 190, 1.0]]}, "2017.clef_conference-2017w.74": {"snippet": "...ered Information Retrieval Task-1: Improving Medical Document Retrieval with Query Expansion\n\n\n In this report, we describe our retrieval framework for participating in CLEF eHealth 2017 Patient-Cente...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [65, 74, 1.0], [131, 140, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.13": {"snippet": "...evel information seeking (MLIS) in information retrieval situations\n\n\n AbstractElicitation is a communicative act to request information during a dialogue, which reflects the questioner\u00d5s problems at ...", "weights": [[8, 19, 1.0], [38, 49, 1.0], [50, 59, 1.0], [128, 139, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...n of Information Retrieval Models\n\n\n Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is nece...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [61, 70, 1.0], [118, 129, 1.0], [130, 139, 1.0]]}, "2019.ictir_conference-2019.39": {"snippet": "...sage Retrieval for Entity Retrieval", "weights": [[8, 17, 1.0], [29, 38, 1.0]]}, "2005.sigirconf_conference-2005.64": {"snippet": "...s to information retrieval\n\n\n ABSTRACTExisting retrieval models generally do not offer any guarantee for optimal retrieval performance. Indeed, it is even difficult, if not impossible, to predict a mo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 59, 1.0], [116, 125, 1.0]]}, "2012.ecir_conference-2012.15": {"snippet": "...eech Retrieval", "weights": [[8, 17, 1.0]]}, "1983.sigirconf_conference-83.28": {"snippet": "...s in Information Retrieval\n\n\n What are the crucial issues and open problems in our field? I propose to answer this question indirectly by offering you a view of the terrain that we call information re...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [189, 200, 1.0], [201, 210, 1.0]]}, "2010.jasis_journal-ir0volumeA61A11.15": {"snippet": "Information Retrieval: Searching in the 21st Century; Human Information Retrieval", "weights": [[0, 11, 1.0], [12, 21, 1.0], [60, 71, 1.0], [72, 81, 1.0]]}, "2018.sigirconf_conference-2018.223": {"snippet": "....com.Information retrieval has a natural role to play in e-commerce, especially in connecting people to goods and services. Information discovery in e-commerce concerns different types of search (expl...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [127, 138, 1.0]]}, "2014.ecir_workshop-2014gamifir.3": {"snippet": "... the Information Retrieval processes. This work contributes to the emerging research field of Gamification in Information Retrieval by providing an overview on: 1) the fundamental elements of a game; ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [113, 124, 1.0], [125, 134, 1.0]]}, "2007.cikm_workshop-2007pikm.3": {"snippet": "...e of Information Retrieval techniques for vague queries and relevance detection.Our work aims for the development of a search engine for XMLdocuments, where Information Retrieval methods are enhanced ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [160, 171, 1.0], [172, 181, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "1981.sigirconf_conference-81.7": {"snippet": "...stic Retrieval\n\n\n ABSTRACTThe objective is to relate the effectiveness of retrieval, the fuzzy set concept and the processing of Boolean query. The use of a probabilistic retrieval scheme is motivated...", "weights": [[8, 17, 1.0], [77, 86, 1.0], [174, 183, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.5": {"snippet": "...tive Information Retrieval in an information-intensive domain\n\n\n AbstractIn this article we investigate the expressions of collaborative activities within information seeking and retrieval processes (...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [36, 47, 1.0], [158, 169, 1.0], [182, 191, 1.0]]}, "2020.ictir_conference-2020.28": {"snippet": "...s on Retrieval Bias and Effectiveness\n\n\n Prior work on using retrievability measures in the evaluation of information retrieval (IR) systems has laid out the foundations for investigating the relation...", "weights": [[8, 17, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "1987.jasis_journal-ir0volumeA38A2.2": {"snippet": "...ment retrieval: II. The retrieval process", "weights": [[8, 17, 1.0], [27, 36, 1.0]]}, "2008.sigirconf_conference-2008.170": {"snippet": "...ment retrieval has shown to be a competitive approach in XML element retrieval, which is counter-intuitive since the element retrieval task requests all and only relevant document parts to be retrieve...", "weights": [[8, 17, 1.0], [72, 81, 1.0], [128, 137, 1.0]]}, "2016.ictir_conference-2016.14": {"snippet": "...y of Information Retrieval Models\n\n\n ABSTRACTDeveloping effective information retrieval models has been a long standing challenge in Information Retrieval (IR), and significant progresses have been ma...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [69, 80, 1.0], [81, 90, 1.0], [136, 147, 1.0], [148, 157, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ment retrieval. The shot retrieval and the video segment model re-use the probability of relevance and a language modeling ranking function respectively, basing themselves on decades of text retrieval...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [194, 203, 1.0]]}, "2014.sigirconf_conference-2014.222": {"snippet": "... for Information Retrieval, where his research interests include: (i) Models for the retrieval of documents, sentences, experts and other information objects ; (ii) Probabilistic models of user intera...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [88, 97, 1.0], [141, 152, 1.0]]}, "1976.sigirjournals_journal-ir0volumeA11A2.4": {"snippet": "... of \"Information Retrieval and Processing by Lauren B. Doyle\"; Los Angeles, Melville Publishing Company, 1975\n\n\n While the titles of these two recent books on information retrieval suggest that they m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [162, 173, 1.0], [174, 183, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.13": {"snippet": "...y of retrieval modalities for video shot retrieval", "weights": [[8, 17, 1.0], [44, 53, 1.0]]}, "2018.ictir_conference-2018.10": {"snippet": "...ment Retrieval\n\n\n ABSTRACTIn this paper, we study jointly query reformulation and document relevance estimation, the two essential aspects of information retrieval (IR). Their interactions are modelle...", "weights": [[8, 17, 1.0], [145, 156, 1.0], [157, 166, 1.0]]}, "2004.sigirconf_conference-2004.8": {"snippet": "...y of information retrieval heuristics\n\n\n ABSTRACTEmpirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [73, 84, 1.0], [85, 94, 1.0], [118, 127, 1.0], [181, 190, 1.0]]}, "2016.ictir_conference-2016.16": {"snippet": "Retrievability in API-Based \"Evaluation as a Service\"\n\n\n ABSTRACT \"Evaluation as a service\" (EaaS) refers to a family of related evaluation methodologies that enables community-wide evaluations and th...", "weights": []}, "2009.sigirconf_conference-2009.203": {"snippet": "...tive retrieval: study of emotion in adaptive retrieval", "weights": [[8, 17, 1.0], [48, 57, 1.0]]}, "2008.ipm_journal-ir0volumeA44A3.9": {"snippet": "...sing information gain to improve multi-modal information retrieval systems\n\n\n AbstractNowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval ...", "weights": [[8, 19, 1.0], [48, 59, 1.0], [60, 69, 1.0], [109, 120, 1.0], [193, 202, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ical information retrieval\n\n\n AbstractIn this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 153, 1.0], [154, 163, 1.0]]}, "1999.sigirconf_conference-99.73": {"snippet": "Information Retrieval Library (IRLIB) (demonstration abstract)\n\n\n ABSTRACTThe IRLIB is a full-text web searchable digital library, whose contents are publications and documents relative to the field o...", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "1987.sigirconf_conference-87.21": {"snippet": "...near Information Retrieval Models\n\n\n AbstractLinear decision (retrieval)functions have been widely adopted in information retrieval systems such as in Boolean, vector, and probabilistic", "weights": [[8, 19, 1.0], [20, 29, 1.0], [65, 74, 1.0], [113, 124, 1.0], [125, 134, 1.0]]}, "2004.ntcir_workshop-2004.87": {"snippet": "...tent Retrieval Method Considering Claim Structure\n\n\n This paper proposes a patent retrieval method that consists of two processing stages. In Stage 1, analysis and retrieval methods to improve recall ...", "weights": [[8, 17, 1.0], [85, 94, 1.0], [167, 176, 1.0]]}, "2011.iir_workshop-2011.22": {"snippet": "Web Information Retrieval for Complex Not-Informational Intents: An Introduction to User Intent Analysis", "weights": [[4, 15, 1.0], [16, 25, 1.0]]}, "W01-1012": {"snippet": "...iven Retrieval and Executive Summarization\n\n\n Decanter illustrates a heuristic approach to extraction for information retrieval and question answering. Generic information about argumentative text is ...", "weights": [[8, 17, 1.0], [109, 120, 1.0], [121, 130, 1.0], [163, 174, 1.0]]}, "2002.cikm_conference-2002.1": {"snippet": "... the Information Retrieval Laboratory at the Illinois Institute of Technology. His research area is scalable information systems spanning communication networks, parallel processing, and information r...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [112, 123, 1.0], [190, 201, 1.0], [202, 211, 1.0]]}, "2005.ecir_conference-2005.37": {"snippet": "...rier Information Retrieval Platform\n\n\n Abstract. Terrier is a modular platform for the rapid development of large-scale Information Retrieval (IR) applications. It can index various document collectio...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2021.emnlp-main.297": {"snippet": "...rase Retrieval Learns Passage Retrieval, Too\n\n\n Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems. Among them, dense phrase retrieval-the most f...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [57, 66, 1.0], [112, 121, 1.0], [183, 192, 1.0]]}, "L08-1002": {"snippet": "...eech Information Retrieval\n\n\n In this article we present a method for combining different information retrieval models in order to increase the retrieval performance in a Speech Information Retrieval ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [93, 104, 1.0], [105, 114, 1.0], [147, 156, 1.0], [181, 192, 1.0], [193, 202, 1.0]]}, "1997.sigirjournals_journal-ir0volumeA31A1.8": {"snippet": "...ing. Information Processing and  Management, 24, 3 (1988), 269-280.    Historical note: the past thirty years in information retrieval. Journal of the American Society for Information Science (JASIS) ...", "weights": [[8, 19, 1.0], [116, 127, 1.0], [128, 137, 1.0], [175, 186, 1.0]]}, "2013.ictir_conference-2013.10": {"snippet": "...d of Information Retrieval (IR) is under steady development and change. Information collections have become larger and more diversified; elaborated IT platforms penetrate most life situations; compute...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [75, 86, 1.0]]}, "1988.riao_conference-1988.41": {"snippet": "...Text Retrieval for Legal Information Retrieval Systems", "weights": [[8, 17, 1.0], [28, 39, 1.0], [40, 49, 1.0]]}, "2001.clef_workshop-2001w.21": {"snippet": "...gual Information Retrieval\n\n\n This paper reports the work of NTU on bilingual-retrieval task at CLEF 2001. We proposed five models. Model 1 used co-occurrence information to disambiguate translation e...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [81, 90, 1.0], [162, 173, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.8": {"snippet": "...tive Information Retrieval: Using a Connectionist Representation to Retrieve and Leasrn about Documents", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2007.clef_workshop-2007w.78": {"snippet": "...n of retrieval from fully and lightly annotated images. Some of the findings for multilingual visual information retrieval from generic collections of lightly annotated photographs include: bilingual ...", "weights": [[8, 17, 1.0], [104, 115, 1.0], [116, 125, 1.0]]}, "1988.sigirconf_conference-88.34": {"snippet": "... for Information Retrieval Systems\n\n\n This paper is a contribution to the construction of a general model for information retrieval.As in the paper of Van Rijsbergen ([RIJSG]), the implicit base in al...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [113, 124, 1.0], [125, 134, 1.0]]}, "2015.ictir_conference-2015.8": {"snippet": "...rial Retrieval Settings\n\n\n ABSTRACTThe probability ranking principle (PRP) -ranking documents in response to a query by their relevance probabilities -is the theoretical foundation of most ad hoc docu...", "weights": [[8, 17, 1.0]]}, "2015.ictir_conference-2015.9": {"snippet": "...tive Information Retrieval variant have guided the development of ranking and retrieval algorithms for decades, yet they are not capable of helping us model problems in Dynamic Information Retrieval w...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [81, 90, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "2014.cikm_conference-2014.187": {"snippet": "...uage Information Retrieval (CLIR) addresses the problem of finding documents in some language that are relevant to a question posed in a different language. Retrieving answers to questions written usi...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2013.ictir_conference-2013.14": {"snippet": "Information Retrieval with Time Series Query\n\n\n ABSTRACTWe study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find rele...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [73, 84, 1.0], [85, 94, 1.0], [170, 179, 1.0]]}, "2020.sigirconf_conference-2020.301": {"snippet": "...Fast Retrieval-based Chat-bots\n\n\n Response retrieval is a subset of neural ranking in which a model selects a suitable response from a set of candidates given a conversation history. Retrieval-based c...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [186, 195, 1.0]]}}}
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An alte...", "weights": [[0, 9, 1.0]]}, "C94-2169": {"snippet": "...ased Efficient Example Retrieval by Generating Retrieval Queries from Similarities\n\n\n In example-based NLP, the problem of eoml)utational cost of example retrieval is severe, since the retrieval time ...", "weights": [[8, 17, 1.0], [26, 35, 1.0], [50, 59, 1.0], [157, 166, 1.0], [188, 197, 1.0]]}, "2009.sigirconf_conference-2009.21": {"snippet": "...duce algorithms are introduced: one based on brute force, a second where the problem is treated as large-scale ad hoc retrieval, and a third based on the Cartesian product of postings lists. Each algo...", "weights": [[8, 18, 1.0], [121, 130, 1.0]]}, "2011.sigirconf_conference-2011.13": {"snippet": "... for efficient ranked retrieval\n\n\n ABSTRACTThere is a fundamental tradeoff between effectiveness and efficiency when designing retrieval models for large-scale document collections. Effectiveness tend...", "weights": [[8, 17, 1.0], [25, 34, 1.0], [130, 139, 1.0]]}, "2012.sigirconf_conference-2012.25": {"snippet": "Efficient in-memory top-k document retrieval", "weights": [[0, 9, 1.0], [35, 44, 1.0]]}, "2011.wsdm_conference-2011.32": {"snippet": "...n of algorithms for this interactive retrieval setting. Furthermore, we propose two dynamic ranking algorithms, both of which are computationally efficient. We prove that these algorithms provide retr...", "weights": [[8, 18, 1.0], [40, 49, 1.0], [103, 113, 1.0], [149, 158, 1.0], [180, 190, 1.0], [199, 208, 1.0]]}, "2017.sigirconf_conference-2017.58": {"snippet": "Retrieval Algorithms Optimized for Human Learning\n\n\n ABSTRACTWhile search technology is widely used for learning-oriented information needs, the results provided by popular services such as Web search...", "weights": [[0, 9, 1.0], [10, 20, 1.0]]}, "2018.sigirconf_workshop-2018birndl.5": {"snippet": "...ning efficient algorithms for expert finding in large databases in various application domains. The evaluation of such recommender systems lies most of the time on humanannotated sets of experts assoc...", "weights": [[8, 17, 1.0], [18, 28, 1.0]]}, "2018.tist_journal-ir0volumeA9A6.1": {"snippet": "...tion retrieval systems such as Web search engines, which have to return highly relevant documents in response to user query within fractions of seconds. The most effective LtR algorithms adopt a gradi...", "weights": [[8, 17, 1.0], [179, 189, 1.0]]}, "P06-2052": {"snippet": "Efficient Sentence Retrieval Based on Syntactic Structure\n\n\n This paper proposes an efficient method of sentence retrieval based on syntactic structure. Collins proposed Tree Kernel to calculate struc...", "weights": [[0, 9, 1.0], [19, 28, 1.0], [84, 93, 1.0], [113, 122, 1.0]]}, "2020.wwwconf_conference-2020.181": {"snippet": "Efficient Algorithms towards Network Intervention", "weights": [[0, 9, 1.0], [10, 20, 1.0]]}, "2018.wsdm_conference-2018.6": {"snippet": "...ing, efficient algorithms are now in higher demand more than ever before. Big Data fundamentally challenges the classical notion of efficient algorithms: Algorithms that used to be considered efficien...", "weights": [[8, 17, 1.0], [18, 28, 1.0], [135, 144, 1.0], [145, 155, 1.0], [157, 167, 1.0], [195, 204, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A2.9": {"snippet": "...ideo retrieval. The past studies of kNN query processing did not consider the case that the server may receive multiple kNN queries at one time. Their algorithms process queries independently. Thus, t...", "weights": [[8, 17, 1.0], [154, 164, 1.0]]}, "2012.mir_conference-2012.110": {"snippet": "Efficient Medical Information Retrieval in Encrypted Electronic Health Records", "weights": [[0, 9, 1.0], [30, 39, 1.0]]}, "2012.ijirr_journal-ir0volumeA2A2.3": {"snippet": "Efficient Retrieval Technique for Microarray Gene Expression", "weights": [[0, 9, 1.0], [10, 19, 1.0]]}, "1991.riao_conference-1991.33": {"snippet": "... for efficient full-text retrieval", "weights": [[8, 17, 1.0], [28, 37, 1.0]]}, "2001.jasis_journal-ir0volumeA52A5.6": {"snippet": "... for efficient retrieval", "weights": [[8, 17, 1.0], [18, 27, 1.0]]}, "2011.cikm_conference-2011.313": {"snippet": "...ting efficient information extraction pipelines\n\n\n ABSTRACTInformation Extraction (IE) pipelines analyze text through several stages. The pipeline's algorithms determine both its effectiveness and its...", "weights": [[8, 17, 1.0], [152, 162, 1.0]]}, "2004.trec_conference-2004.8": {"snippet": "...tter Retrieval Function From Machine?\n\n\n The quality of an information retrieval system heavily depends on its retrieval function, which returns a similarity measurement between the query and each doc...", "weights": [[8, 17, 1.0], [74, 83, 1.0], [114, 123, 1.0]]}, "2016.wsdm_conference-2016.10": {"snippet": "... how efficient are social media users at selecting their information sources.In this work, we propose a computational framework to quantify users' efficiency at selecting information sources. Our fram...", "weights": [[8, 17, 1.0]]}, "C69-5101": {"snippet": "...r an efficient information system. It can be implemented for text processing and document retrieval; numerical data retrieval; and for handling of large files such as dictionaries, catalogs, and perso...", "weights": [[8, 17, 1.0], [93, 102, 1.0], [119, 128, 1.0]]}, "2018.wsdm_conference-2018.51": {"snippet": "...tage Retrieval\n\n\n ABSTRACTLarge scale retrieval systems often employ cascaded ranking architectures, in which an initial set of candidate documents is iteratively refined and re-ranked by increasingly...", "weights": [[8, 17, 1.0], [41, 50, 1.0]]}, "2014.trec_conference-2014.70": {"snippet": "...pass retrieval framework, with the first pass consisting of a simple and efficient retrieval model that focuses on recall, and the second pass a wave of feature extraction algorithms run on the set of...", "weights": [[8, 17, 1.0], [76, 85, 1.0], [86, 95, 1.0], [175, 185, 1.0]]}, "2012.cikm_conference-2012.269": {"snippet": "...op-k retrieval using conditional preference networks\n\n\n ABSTRACTThis paper considers top-k retrieval using Conditional Preference Network (CP-Net). As a model for expressing user preferences on multip...", "weights": [[8, 17, 1.0], [94, 103, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.25": {"snippet": "...tion retrieval in complex networks\n\n\n Abstract Spanking information retrieval in large-scale Web and network has attracted increasing interest in the research community, many typical approaches have b...", "weights": [[8, 17, 1.0], [71, 80, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A3.1": {"snippet": "...ment algorithms the cache is able to keep the objects that are best suited for users preferences and dump the other objects. We compare our proposed algorithms to the Least-Recently-Used algorithm, an...", "weights": [[8, 18, 1.0], [152, 162, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A2.1": {"snippet": "...h an efficient strategy to read the precomputed kernel values, reusing precomputed kernel values with an efficient retrieval is much faster than computing them on-the-fly. This also alleviates the res...", "weights": [[8, 17, 1.0], [108, 117, 1.0], [118, 127, 1.0]]}, "1999.tois_journal-ir0volumeA17A2.2": {"snippet": "...ased Retrieval by Spatial Similarity in Image Databases\n\n\n A framework for retrieving images by spatial similarity (FRISS) in image databases is presented. In this framework, a robust retrieval by spa...", "weights": [[8, 17, 1.0], [187, 196, 1.0]]}, "2006.sigirconf_conference-2006.24": {"snippet": "...-hoc retrieval\n\n\n ABSTRACTSearch algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and...", "weights": [[8, 17, 1.0], [36, 46, 1.0], [121, 130, 1.0], [159, 168, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A2.6": {"snippet": "...onal algorithms based on CPU, we aim at designing efficient GPU based algorithms. We firstly design inverted index in GPU, then choose ScanCount, a straightforward but efficient T-Overlap algorithm, a...", "weights": [[8, 18, 1.0], [53, 62, 1.0], [73, 83, 1.0], [171, 180, 1.0]]}, "2003.sigirconf_conference-2003.65": {"snippet": "...ems, retrieval of relevant documents, and generation of metasearch lists.\nCategories and Subject Descriptors:H.3.3 [Information Search and Retrieval]: Retrieval Models.General Terms: Algorithms, Theor...", "weights": [[8, 17, 1.0], [142, 151, 1.0], [154, 163, 1.0], [186, 196, 1.0]]}, "2003.spire_conference-2003.18": {"snippet": "...L-M\" algorithms. Of them, the FINDSTEM and A-F algorithms employ inflectional and derivational stemmers, whereas the L-M one handles only inflectional rules. Comparison of stemming algorithms was done...", "weights": [[8, 18, 1.0], [50, 60, 1.0], [184, 194, 1.0]]}, "2006.spire_conference-2006.23": {"snippet": "...t an efficient algorithm which recognizes tight matrices over a certain ring, M, implies an efficient algorithm that solves SBT on an important class of permutations, called simple permutations. Such ...", "weights": [[8, 17, 1.0], [95, 104, 1.0]]}, "2018.sigirconf_conference-2018.11": {"snippet": "...tion algorithms such as Rank-S or Taily to rank index shards and determine how many to search for a given query. Most prior research evaluated solutions by their ability to improve efficiency without ...", "weights": [[8, 18, 1.0]]}, "1991.sigirconf_conference-91.30": {"snippet": "An Efficient Directory System for Document Retrieval", "weights": [[3, 12, 1.0], [43, 52, 1.0]]}, "2013.wsdm_conference-2013.93": {"snippet": "...tion retrieval (LSDS-IR 2013)\n\n\n ABSTRACTThe LSDS-IR'13 workshop aims to bring together both information retrieval practitioners from industry, as well as academic researchers concerned with efficient...", "weights": [[8, 17, 1.0], [108, 117, 1.0], [194, 203, 1.0]]}, "2016.tweb_journal-ir0volumeA10A3.0": {"snippet": "... two algorithms that achieve linear complexity without compromising the retrieval effectiveness. Instead of an exhaustive comparison of documents, these algorithms first perform a clustering phase and...", "weights": [[8, 18, 1.0], [75, 84, 1.0], [156, 166, 1.0]]}, "1995.ipm_journal-ir0volumeA31A3.6": {"snippet": "Efficient Retrieval of Partial Documents", "weights": [[0, 9, 1.0], [10, 19, 1.0]]}, "2016.ijirr_journal-ir0volumeA6A4.1": {"snippet": "Efficient Textual Web Retrieval using Wavelet Tree", "weights": [[0, 9, 1.0], [22, 31, 1.0]]}, "2008.spire_conference-2008.16": {"snippet": "An Efficient Linear Space Algorithm for Consecutive Suffix Alignment under Edit Distance (Short Preliminary Paper)", "weights": [[3, 12, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.19": {"snippet": "...base retrieval. To overcome the problem of over-fitting when the number of training samples in dataset is limited, this paper proposes an image database retrieval algorithm based on the framework of t...", "weights": [[8, 17, 1.0], [156, 165, 1.0]]}, "2012.spire_conference-2012.1": {"snippet": "... and efficient algorithms. We will also show some interesting connections between error correction codes and periodicity.We will try to pinpoint the issues involved, the context in the literature, and...", "weights": [[8, 17, 1.0], [18, 28, 1.0]]}, "2008.mir_conference-2008.62": {"snippet": "An efficient algorithm for keyframe-based motion retrieval in the presence of temporal deformations", "weights": [[3, 12, 1.0], [49, 58, 1.0]]}, "2002.cikm_conference-2002.69": {"snippet": "...ting algorithms.\nCATEGORIES AND SUBJECT DESCRIPTORSInformation systems: information storage and retrieval: information search and retrieval: search process.\nGENERAL TERMSAlgorithms and performance.\nIN...", "weights": [[8, 18, 1.0], [99, 108, 1.0], [133, 142, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2003.cikm_conference-2003.60": {"snippet": "Efficient query evaluation using a two-level retrieval process\n\n\n ABSTRACTWe present an efficient query evaluation method based on a two level approach: at the first level, our method iterates in para...", "weights": [[0, 9, 1.0], [45, 54, 1.0], [88, 97, 1.0]]}, "2008.ecir_conference-2008.49": {"snippet": "Efficient Multimedia Time Series Data Retrieval Under Uniform Scaling and Normalisation", "weights": [[0, 9, 1.0], [38, 47, 1.0]]}, "2010.airs_conference-2010.21": {"snippet": "An Integrated Deterministic and Nondeterministic Inference Algorithm for Sequential Labeling\n\n\n Abstract. In this paper, we present a new search algorithm for sequential labeling tasks based on the co...", "weights": []}, "2012.ecir_conference-2012.49": {"snippet": "...s an efficient dynamic processing of data points as they arrive in groups. Our experiments on the TDT5 benchmark collection show the superiority of both versions of this algorithm compared to other st...", "weights": [[8, 17, 1.0]]}, "2017.ictir_conference-2017.57": {"snippet": "...ndit algorithms as well as new applications for these algorithms. However, not all of these algorithms are equality suitable to all information retrieval seings or all types of data.Bandit algorithms ...", "weights": [[8, 18, 1.0], [57, 67, 1.0], [95, 105, 1.0], [147, 156, 1.0], [192, 202, 1.0]]}, "1991.riao_conference-1991.36": {"snippet": "Efficient probabilistic Inference for text retrieval", "weights": [[0, 9, 1.0], [43, 52, 1.0]]}, "2012.spire_conference-2012.30": {"snippet": "Efficient Data Structures for the Factor Periodicity Problem\n\n\n Abstract. We present several efficient data structures for answering queries related to periods in words. For a given word w of length n...", "weights": [[0, 9, 1.0], [93, 102, 1.0]]}, "2012.ipm_journal-ir0volumeA48A5.11": {"snippet": "...tion retrieval\n\n\n a b s t r a c tIn this paper we present a new algorithm for relevance feedback (RF) in information retrieval. Unlike conventional RF algorithms which use the top ranked documents for...", "weights": [[8, 17, 1.0], [120, 129, 1.0], [154, 164, 1.0]]}, "2017.ecir_conference-2017.4": {"snippet": "Entity Linking in Queries: Efficiency vs. Effectiveness\n\n\n Abstract. Identifying and disambiguating entity references in queries is one of the core enabling components for semantic search. While there...", "weights": []}, "C10-1096": {"snippet": "... and Efficient Algorithm for Approximate Dictionary Matching\n\n\n This paper presents a simple and efficient algorithm for approximate dictionary matching designed for similarity measures such as cosine...", "weights": [[8, 17, 1.0], [100, 109, 1.0]]}, "D11-1047": {"snippet": "Efficient retrieval of tree translation examples for Syntax-Based Machine Translation\n\n\n We propose an algorithm allowing to efficiently retrieve example treelets in a parsed tree database in order to...", "weights": [[0, 9, 1.0], [10, 19, 1.0]]}, "1969.ipm_journal-ir0volumeA5A3.1": {"snippet": "...tion retrieval system", "weights": [[8, 17, 1.0]]}, "2009.clef_workshop-2009.11": {"snippet": "... and efficient stemming algorithm, Perstem, on Persian information retrieval. Our experiments on the Hamshahri corpus at CLEF2009 show that the Perstem algorithm greatly improved both precision (+91% ...", "weights": [[8, 17, 1.0], [70, 79, 1.0]]}, "2017.spire_conference-2017.18": {"snippet": "...ngth Algorithms\n\n\n Abstract. Some of the most efficient algorithms for computing the length of a longest common subsequence (LLCS) between two strings are based on so-called \"bit-parallelism\". They ac...", "weights": [[8, 18, 1.0], [49, 58, 1.0], [59, 69, 1.0]]}, "1983.sigirconf_conference-83.19": {"snippet": "...ment Retrieval\n\n\n AbstractA network organization for implementing a document retrieval system is proposed.This organization has significant advantages in terms of the range of searches that can be use...", "weights": [[8, 17, 1.0], [80, 89, 1.0]]}, "2005.airs_conference-2005.63": {"snippet": "An Efficient Incremental Nearest Neighbor Algorithm for Processing k-Nearest Neighbor Queries with Visal and Semantic Predicates in Multimedia Information Retrieval System", "weights": [[3, 12, 1.0], [155, 164, 1.0]]}, "2012.cikm_conference-2012.57": {"snippet": "...m of efficient retrieval of recommendations in a MF framework. We reduce the retrieval in a MF model to an apparently simple task of finding the maximum dot-product for the user vector over the set of...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [80, 89, 1.0]]}, "2016.ipm_journal-ir0volumeA52A6.9": {"snippet": "... LtR algorithms has tuning parameters that can influence both effectiveness and efficiency, where higher ranking quality is generally obtained with more complex and expensive models. Moreover, LtR alg...", "weights": [[8, 18, 1.0], [200, 210, 1.0]]}, "2022.tacl-1.29": {"snippet": "... and efficient crossmodal retrieval, we propose a novel fine-tuning framework that turns any pretrained text-image multi-modal model into an efficient retrieval model. The framework is based on a coop...", "weights": [[8, 17, 1.0], [29, 38, 1.0], [144, 153, 1.0], [154, 163, 1.0]]}, "2011.cikm_conference-2011.348": {"snippet": "...more efficient solutions. We propose two branch and bound algorithms, the Estimation Expanding Pruning (EEP) algorithm and the Bounding Influence Pruning (BIP) algorithm. These algorithms exploit vari...", "weights": [[8, 17, 1.0], [61, 71, 1.0], [180, 190, 1.0]]}, "1995.sigirconf_conference-95.6": {"snippet": "Efficient Recompression Techniques for Dynamic Full-Text Retrieval Systems", "weights": [[0, 9, 1.0], [57, 66, 1.0]]}, "1981.sigirconf_conference-81.6": {"snippet": "...ment Retrieval System\n\n\n AbstractMany effective search strategies derived from different models are available for document retrieval systems.However, it does not appear that there is a single most eff...", "weights": [[8, 17, 1.0], [126, 135, 1.0]]}, "2014.wsdm_conference-2014.79": {"snippet": "...tion retrieval (LSDS-IR 2014)\n\n\n ABSTRACTThe LSDS-IR'14 workshop aims to bring together information retrieval practitioners from industry and academic researchers concerned with efficient and distribu...", "weights": [[8, 17, 1.0], [103, 112, 1.0], [181, 190, 1.0]]}, "2004.trec_conference-2004.41": {"snippet": "...deed efficient in improving retrieval, although further work is needed in order to improve the performance of the prediction algorithms and their uses.", "weights": [[8, 17, 1.0], [31, 40, 1.0], [128, 138, 1.0]]}, "2012.sigirconf_conference-2012.64": {"snippet": "...rmit efficient retrieval by not fully scoring all postings of the documents matching a query -without degrading the retrieval effectiveness of the topranked results. However, the amount of pruning ach...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [119, 128, 1.0]]}, "2012.sigirconf_conference-2012.221": {"snippet": "...tion retrieval\n\n\n ABSTRACTFor many information retrieval applications, we need to deal with the ranking problem on very large scale graphs. However, it is non-trivial to perform efficient and effectiv...", "weights": [[8, 17, 1.0], [50, 59, 1.0], [181, 190, 1.0]]}, "1996.tois_journal-ir0volumeA14A2.1": {"snippet": "... for Efficient Information Retrieval with Redundant Information Filters\n\n\n We consider information retrieval when the data-for instance, multimedia-is computationally expensive to fetch. Our approach ...", "weights": [[8, 17, 1.0], [30, 39, 1.0], [102, 111, 1.0]]}, "2005.ismir_conference-2005.114": {"snippet": "... and Efficient Match Algorithm for Content-Based Music Retrieval on Acoustic Data", "weights": [[8, 17, 1.0], [58, 67, 1.0]]}, "2014.sigirconf_conference-2014.73": {"snippet": "... and retrieval methods. This paper proposes an innovative mathematics retrieval system along with the novel algorithms, which enables efficient formula index and retrieval from both webpages and PDF d...", "weights": [[8, 17, 1.0], [73, 82, 1.0], [111, 121, 1.0], [137, 146, 1.0], [165, 174, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...ment retrieval, long enough spans of speech can be readily handled.However, information retrieval is not just about document retrieval. There are other problems in and around IR where ASR is still lik...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [128, 137, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.6": {"snippet": "...data retrieval, distinguished from current retrieval methods. In this model, an answer to a keyword query is a connected subgraph that contains all the query keywords. In addition, the answer is minim...", "weights": [[8, 17, 1.0], [46, 55, 1.0]]}, "1991.ipm_journal-ir0volumeA27A23.1": {"snippet": "...s of retrieval algorithms", "weights": [[8, 17, 1.0], [18, 28, 1.0]]}, "2005.cikm_conference-2005.126": {"snippet": "...kNN: efficient reverse k-nearest neighbors retrieval with local kNN-distance estimation\n\n\n ABSTRACTThe Reverse k-Nearest Neighbors (RkNN) queries are important in profile-based marketing, information ...", "weights": [[8, 17, 1.0], [46, 55, 1.0]]}, "2007.cikm_conference-2007.133": {"snippet": "An efficient algorithm for approximate biased quantile computation in data streams\n\n\n ABSTRACTWe propose an efficient algorithm for approximate biased quantile computation in large data streams. Our a...", "weights": [[3, 12, 1.0], [108, 117, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A4.1": {"snippet": "...tion Retrieval Algorithms for Efficient Ranking of Webpages", "weights": [[8, 17, 1.0], [18, 28, 1.0], [33, 42, 1.0]]}, "2014.sigirconf_conference-2014.50": {"snippet": "...stic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy.We first point out that greedy algorithms are essentially finding a self-cons...", "weights": [[8, 18, 1.0], [54, 63, 1.0], [157, 167, 1.0]]}, "2012.spire_conference-2012.33": {"snippet": "...eral algorithms have been proposed to count runs in a string efficiently, and \u03c1(n) can be obtained for small n by these algorithms. In this paper, we focus on computing \u03c1(n) for given length parameter...", "weights": [[8, 18, 1.0], [123, 133, 1.0]]}, "2004.airs_conference-2004.13": {"snippet": "Efficient PageRank with Same Out-Link Groups\n\n\n Abstract. Traditional PageRank algorithm suffers from heavy computation cost due to the huge number of web pages. In this paper, we propose a more effic...", "weights": [[0, 9, 1.0], [195, 204, 1.0]]}, "2013.riao_conference-2013.25": {"snippet": "...nder algorithms for enhanced information retrieval", "weights": [[8, 18, 1.0], [44, 53, 1.0]]}, "1999.cikm_conference-99.29": {"snippet": "... the retrieval effectiveness of our algorithm is the same as that of evaluating the user query against all database representatives. We also show that our algorithm is efficient. In addition, we propo...", "weights": [[8, 17, 1.0], [171, 180, 1.0]]}, "2011.tois_journal-ir0volumeA29A4.2": {"snippet": "... and efficient stemming algorithm for information retrieval\n\n\n A novel graph-based language-independent stemming algorithm suitable for information retrieval is proposed in this article. The main feat...", "weights": [[8, 17, 1.0], [53, 62, 1.0], [151, 160, 1.0]]}, "C96-2145": {"snippet": "...s an efficient algorithm for retrieving from a database of trees, all trees that match a given query tree appro,imately, that is, within a certain error tolerance. It has natural language processing a...", "weights": [[8, 17, 1.0]]}, "2012.wsdm_conference-2012.50": {"snippet": "... the retrieval has to be fast (few milliseconds) to load the page quickly. Fast retrieval is well studied in the information retrieval (IR) literature, especially in the context of document retrieval ...", "weights": [[8, 17, 1.0], [83, 92, 1.0], [128, 137, 1.0], [193, 202, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2012.sigirconf_conference-2012.38": {"snippet": "... new algorithms from scratch, we study challenges when adopting existing top-k algorithms in the literature that heavily rely on two basic list-access methods: random access and sorted access. We pres...", "weights": [[8, 18, 1.0], [82, 92, 1.0]]}, "1978.sigirconf_conference-78.8": {"snippet": "...tive retrieval, in which searches are performed using more than one key.A nearest neighbors associative retrieval algorithm, suitable for document retrieval using similarity matching, is described. Th...", "weights": [[8, 17, 1.0], [107, 116, 1.0], [150, 159, 1.0]]}, "2011.sigirconf_conference-2011.55": {"snippet": "Efficient manifold ranking for image retrieval\n\n\n ABSTRACTManifold Ranking (MR), a graph-based ranking algorithm, has been widely applied in information retrieval and shown to have excellent performan...", "weights": [[0, 9, 1.0], [37, 46, 1.0], [153, 162, 1.0]]}, "J08-4003": {"snippet": "...zing algorithms for deterministic incremental dependency parsing, formalized as transition systems. We then describe and analyze two families of such algorithms: stack-based and list-based algorithms....", "weights": [[8, 18, 1.0], [153, 163, 1.0], [192, 202, 1.0]]}, "2011.sigirconf_conference-2011.227": {"snippet": "Efficient and effective solutions for search engines\n\n\n AbstractEffectiveness and efficiency are two of the main issues in Information Retrieval (IR). IR efficiency is normally addressed in terms of a...", "weights": [[0, 9, 1.0], [135, 144, 1.0]]}, "2016.sigirconf_conference-2016.137": {"snippet": "...tion algorithms are used; and the effect of two policies for assigning index shards to machines. Results obtained for two large datasets and four large query logs confirm that selective search is sign...", "weights": [[8, 18, 1.0]]}, "Y18-1019": {"snippet": "... FCA Retrieval a fast and efficient algorithm for information retrieval from a binary concept lattice. This algorithm can respond to queries containing several types of logical operators (conjunction,...", "weights": [[8, 17, 1.0], [29, 38, 1.0], [65, 74, 1.0]]}}}
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In this paper, we focus on one prevalent type of spam -redirection spam -where one can identify spam pages by the third-party domains that these pages redirect...", "weights": [[8, 12, 1.0], [35, 42, 1.0], [93, 97, 1.0], [111, 115, 1.0], [140, 144, 1.0]]}, "2006.wwwconf_conference-2006.8": {"snippet": "... web spam\n\n\n ABSTRACTWeb spam is behavior that attempts to deceive search engine ranking algorithms. TrustRank is a recent algorithm that can combat web spam. However, TrustRank is vulnerable in the s...", "weights": [[8, 12, 1.0], [28, 32, 1.0], [156, 160, 1.0]]}, "2013.ijirr_journal-ir0volumeA3A1.2": {"snippet": "...g of Spam", "weights": [[8, 12, 1.0]]}, "2008.wwwconf_conference-2008.114": {"snippet": "... web spam detection\n\n\n ABSTRACTCombating Web spam has become one of the top challenges for Web search engines. State-of-the-art spam detection techniques are usually designed for specific known types ...", "weights": [[8, 12, 1.0], [48, 52, 1.0], [131, 135, 1.0]]}, "2005.trec_conference-2005.73": {"snippet": "...Anti-SPAM Solution at the TREC 2005 Spam Track: Improving the Performance of Naive Bayes for Spam Detection\n\n\n Recently, the spam already constituted a serious problem for both e-mail users and Intern...", "weights": [[8, 12, 1.0], [39, 43, 1.0], [96, 100, 1.0], [128, 132, 1.0]]}, "2015.cikm_conference-2015.162": {"snippet": "... and spam message detection as two separate tasks. However, social spammers and spam messages have strong connections, since social spammers tend to post more spam messages and spam messages have high...", "weights": [[8, 12, 1.0], [83, 87, 1.0], [162, 166, 1.0], [180, 184, 1.0]]}, "N10-1126": {"snippet": "...w to avoid misunderstandings, we investigated how wizards dealt with uncertainty in voice search results. Wizards were quite successful at selecting the correct title from query results that included ...", "weights": [[8, 13, 1.0], [100, 107, 1.0], [181, 188, 1.0]]}, "2012.sigirconf_conference-2012.42": {"snippet": "... web spam: a novel propagation method based on click-through data\n\n\n ABSTRACTCombating Web spam is one of the greatest challenges for Web search engines. State-of-the-art anti-spam techniques focus ma...", "weights": [[8, 12, 1.0], [94, 98, 1.0], [179, 183, 1.0]]}, "2016.wwwconf_conference-2016.35": {"snippet": "...nion spam has been widespread and has attracted a lot of research attention. While the problem has been approached on a variety of dimensions, the temporal dynamics in which opinion spamming operates ...", "weights": [[8, 12, 1.0]]}, "2018.tweb_journal-ir0volumeA12A4.5": {"snippet": "... web spam and infinite autogenerated web content, large-scale web crawlers require low-complexity ranking methods to effectively budget their limited resources and allocate bandwidth to reputable site...", "weights": [[8, 12, 1.0]]}, "2017.ipm_journal-ir0volumeA53A4.5": {"snippet": "...uted results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache ca...", "weights": [[8, 15, 1.0], [161, 168, 1.0]]}, "2007.airweb_workshop-2007.5": {"snippet": "...Link Spam using Biased Random Walks from Spam Seed Sets", "weights": [[8, 12, 1.0], [44, 48, 1.0]]}, "2008.wwwconf_conference-2008.127": {"snippet": "...king results have a strong bias towards seeds. In this paper, we analyze the relationship between the result bias and the number of seeds. Furthermore, we experimentally show that an automatically sel...", "weights": [[8, 15, 1.0]]}, "2007.tois_journal-ir0volumeA25A3.1": {"snippet": "...tive results indicate that content-based filters can eliminate 98% of spam while incurring 0.1% legitimate email loss. Qualitative results indicate that the risk of loss depends on the nature of the m...", "weights": [[8, 15, 1.0], [73, 77, 1.0], [134, 141, 1.0]]}, "2011.wwwconf_conference-2011c.21": {"snippet": "...n to avoid detection and to increase the reach of their generated spam, such as forming 'spam-farms' and creating large number of links with targeted legitimate users. The observations are verified th...", "weights": [[8, 13, 1.0], [69, 73, 1.0], [92, 96, 1.0]]}, "2008.wsdm_conference-2008.23": {"snippet": "...nion spam and analysis", "weights": [[8, 12, 1.0]]}, "2008.cikm_conference-2008.36": {"snippet": "... web spam with HTTP session information\n\n\n ABSTRACTWeb spam is a widely-recognized threat to the quality and security of the Web. Web spam pages pollute search engine indexes, burden Web crawlers and ...", "weights": [[8, 12, 1.0], [58, 62, 1.0], [137, 141, 1.0]]}, "2020.ecir_conference-20202.58": {"snippet": "...king results. We present the content-focused Neural-IR-Explorer, which empowers users to browse through retrieval results and inspect the inner workings and fine-grained results of neural re-ranking m...", "weights": [[8, 15, 1.0], [117, 124, 1.0], [173, 180, 1.0]]}, "2012.jasis_journal-ir0volumeA63A10.12": {"snippet": "...w to avoid them", "weights": [[8, 13, 1.0]]}, "2013.cikm_conference-2013.52": {"snippet": "...work spam detection\n\n\n ABSTRACTSocial network spam increases explosively with the rapid development and wide usage of various social networks on the Internet. To timely detect spam in large social net...", "weights": [[8, 12, 1.0], [49, 53, 1.0], [179, 183, 1.0]]}, "2007.airweb_workshop-2007.12": {"snippet": "...sing Spam Farm to Boost PageRank", "weights": [[8, 12, 1.0]]}, "2020.ecir_conference-20202.60": {"snippet": ".... To avoid re-entering formulas, chips can be reused, edited, or used in creating other formulas. As users enter formulas, our novel autocompletion facility returns entity cards searchable by formula ...", "weights": [[8, 13, 1.0]]}, "W17-1908": {"snippet": "...r to avoid erroneous (spam) crowdsourced results, we used a novel taskspecific two-phase filtering process where users were asked to identify synonyms in the target language, and remove erroneous sens", "weights": [[8, 13, 1.0], [25, 29, 1.0], [44, 51, 1.0]]}, "W10-1734": {"snippet": "...w to Avoid Burning Ducks: Combining Linguistic Analysis and Corpus Statistics for {G}erman Compound Processing\n\n\n Compound splitting is an important problem in many NLP applications which must be solv...", "weights": [[8, 13, 1.0]]}, "2011.cikm_conference-2011.282": {"snippet": "...n of spam and irrelevant documents. Additionally we find that when users are given two documents of equal utility, the one with the lower spam score will be preferred; a result page without any spam d...", "weights": [[8, 12, 1.0], [141, 145, 1.0], [197, 201, 1.0]]}, "2009.cikm_conference-2009.254": {"snippet": "...ntal results show that with such two metrics, our method can filter out spam sites and identify reputable ones more effectively than previous algorithms such as TrustRank.", "weights": [[8, 15, 1.0], [75, 79, 1.0]]}, "2011.ipm_journal-ir0volumeA47A2.8": {"snippet": "...ting spam blogs from blog search results\n\n\n a b s t r a c tBlogging has been an emerging media for people to express themselves. However, the presence of spam blogs (also known as splogs) may reduce t...", "weights": [[8, 12, 1.0], [36, 43, 1.0], [157, 161, 1.0]]}, "P09-2081": {"snippet": "...bile spam filters. Experimental results show that the use of stylistic information is potentially effective for enhancing the performance of the mobile spam filters.", "weights": [[8, 12, 1.0], [35, 42, 1.0], [155, 159, 1.0]]}, "2007.sigirconf_conference-2007.181": {"snippet": "...SMS) spam filtering\n\n\n ABSTRACTMobile spam in an increasing threat that may be addressed using filtering systems like those employed against email spam. We believe that email filtering techniques requ...", "weights": [[8, 12, 1.0], [41, 45, 1.0], [150, 154, 1.0]]}, "2010.cikm_workshop-2010smuc.7": {"snippet": "Spam detection with a content-based random-walk algorithm\n\n\n ABSTRACTIn this work we tackle the problem of the spam detection on the Web. Spam web pages have become a problem for Web search engines, d...", "weights": [[0, 4, 1.0], [111, 115, 1.0], [138, 142, 1.0]]}, "2020.cikm_conference-2020.98": {"snippet": "...rior Results: A General and Feature-Free Model for Spam Detection", "weights": [[8, 15, 1.0], [54, 58, 1.0]]}, "2006.trec_conference-2006.91": {"snippet": "...2006 Spam Track Overview", "weights": [[8, 12, 1.0]]}, "2016.wwwconf_conference-2016.31": {"snippet": "... SEO spam is implemented on cloud hosting platforms. After identifying 3,186 cloud directories and 318,470 doorway pages on the leading cloud platforms for long-tail SEO spam, we characterize their ab...", "weights": [[8, 12, 1.0], [173, 177, 1.0]]}, "2017.tweb_journal-ir0volumeA11A1.3": {"snippet": "...with spam apps. These spam apps reduce the users' quality of experience and increase the workload of app market operators to identify these apps and remove them. Spam apps can come in many forms such ...", "weights": [[8, 12, 1.0], [25, 29, 1.0], [165, 169, 1.0]]}, "2010.ntcir_workshop-2010.3": {"snippet": "... Web spam task. The track uses the ClueWeb09 dataset as its document collection. This collection consists of roughly 1 billion web pages in multiple languages, comprising approximately 25TB of uncompr...", "weights": [[8, 12, 1.0]]}, "2016.wwwconf_conference-2016c.47": {"snippet": "...eing spam and ham (i.e., non-spam). In our experiments, we observe that it is much easier to recommend \"correct\" hashtags for spam tweets than ham tweets, because of the near duplicates in spam tweets...", "weights": [[8, 12, 1.0], [32, 36, 1.0], [129, 133, 1.0], [192, 196, 1.0]]}, "2008.wwwconf_conference-2008.154": {"snippet": "... web spam detection with re-extracted features\n\n\n ABSTRACTWeb spam detection has become one of the top challenges for the Internet search industry. Instead of using some heuristic rules, we propose a ...", "weights": [[8, 12, 1.0], [65, 69, 1.0]]}, "2013.wwwconf_conference-2013c.276": {"snippet": "...arch results and increases the motivation of spammers to invent new spam techniques. At the same time, ranking algorithms become more complicated, as well as web spam detection methods. So, web spam c...", "weights": [[8, 15, 1.0], [71, 75, 1.0], [165, 169, 1.0], [197, 201, 1.0]]}, "2007.sigirconf_conference-2007.56": {"snippet": "... web spam detection using the web topology\n\n\n ABSTRACTWeb spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to dete...", "weights": [[8, 12, 1.0], [61, 65, 1.0], [125, 132, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A6.30": {"snippet": "...heir spam behavior in a very low density to try to conceal themselves in normal users. These camouflaged spammers, driven by profits, are hired by some stores to write fake reviews in groups so as to ...", "weights": [[8, 12, 1.0]]}, "2009.airweb_workshop-2009.7": {"snippet": "...cial spam detection", "weights": [[8, 12, 1.0]]}, "2012.tweb_journal-ir0volumeA6A1.1": {"snippet": "... Web spam and are incapable of dealing with newly appearing spam types efficiently. With user-behavior analyses from Web access logs, a spam page-detection algorithm is proposed based on a learning sc...", "weights": [[8, 12, 1.0], [63, 67, 1.0], [139, 143, 1.0]]}, "2011.wsdm_conference-2011.60": {"snippet": "... the spam websites by identifying specific link patterns of them. However, spam websites have appeared to be more and more similar to normal or even good websites in their link structures, by reformin...", "weights": [[8, 12, 1.0], [78, 82, 1.0]]}, "2013.ipm_journal-ir0volumeA49A1.5": {"snippet": "...h as spam filtering, blog classification, and web resource categorization. We extend the ideas of online clustering and batch-mode centroid-based classification to online learning with negative feedba...", "weights": [[8, 12, 1.0]]}, "2007.sigirconf_conference-2007.180": {"snippet": "...ting spam from non-spam pages.", "weights": [[8, 12, 1.0], [22, 26, 1.0]]}, "2013.jasis_journal-ir0volumeA64A3.15": {"snippet": "...w to avoid them", "weights": [[8, 13, 1.0]]}, "2021.ecir_conference-20212.25": {"snippet": "...ntal results demonstrate that SQE-GAN can be 37.3% faster than state-of-theart SQE solutions while outperforming some recently proposed neural models in the retrieval quality.", "weights": [[8, 15, 1.0]]}, "2008.cikm_conference-2008.35": {"snippet": "...ting spam in P2P query result sets. Experimental results show that the proposed techniques successfully decrease the amount of spam by 9% in the top-200 results and by 92% in the top-20 results.", "weights": [[8, 12, 1.0], [52, 59, 1.0], [130, 134, 1.0], [156, 163, 1.0], [189, 196, 1.0]]}, "2021.ecir_conference-20212.14": {"snippet": "...y to avoid zero hits. We investigate what kinds of query revision strategies turn a zero-hit query into a successful query, by analyzing data from a major Japanese e-commerce site. Our analysis shows ...", "weights": [[8, 13, 1.0]]}, "2007.wwwconf_conference-2007.154": {"snippet": "Spam and popularity ratings for combating link spam\n\n\n ABSTRACTWe present a new approach for propagating spam scores in web graphs, in order to combat link spam. The resulting spam rating is then used...", "weights": [[0, 4, 1.0], [47, 51, 1.0], [105, 109, 1.0], [156, 160, 1.0], [176, 180, 1.0]]}, "2008.tweb_journal-ir0volumeA2A4.4": {"snippet": "...ting spam in tagging systems: An evaluation\n\n\n Tagging systems allow users to interactively annotate a pool of shared resources using descriptive strings called tags. Tags are used to guide users to i...", "weights": [[8, 12, 1.0]]}, "2008.wwwconf_conference-2008.116": {"snippet": "...r of spam blogs in order to assess their current state in the blogosphere. To extract spam blogs, I developed a traversal method among co-citation clusters of blogs from a spam seed. Spam seeds were c...", "weights": [[8, 12, 1.0], [89, 93, 1.0], [175, 179, 1.0], [186, 190, 1.0]]}, "2011.wsdm_conference-2011.86": {"snippet": "... the spam filtering problem are not handled appropriately in existing work. Principal among these are adversarial spammer efforts -spammers routinely tune their spam emails to bypass spam-filters, and...", "weights": [[8, 12, 1.0], [164, 168, 1.0], [186, 190, 1.0]]}, "2013.wwwconf_conference-2013c.208": {"snippet": "...uals avoid regrettable online disclosures, we employed lessons from behavioral decision research and research on soft paternalism to design mechanisms that \"nudge\" users to consider the content and co...", "weights": [[8, 13, 1.0]]}, "2013.airs_conference-2013.21": {"snippet": "Web Spam Detection: New Approach with Hidden Markov Models\n\n\n Abstract. Web Spam is the result of a number of methods to deceive search engine algorithms so as to obtain higher ranks in the search res...", "weights": [[4, 8, 1.0], [76, 80, 1.0], [197, 204, 1.0]]}, "2009.wwwconf_conference-2009.174": {"snippet": "...mage spam using local invariant features and pyramid match kernel\n\n\n ABSTRACTImage spam is a new obfuscating method which spammers invented to more effectively bypass conventional text based spam filt...", "weights": [[8, 12, 1.0], [86, 90, 1.0], [194, 198, 1.0]]}, "2005.trec_conference-2005.22": {"snippet": "...2005 Spam Track: Spam Filtering Using N-gram-based Techniques\n\n\n We briefly describe DalTREC 2005 Spam submission. DalTREC is the TREC research project at Dalhousie University. Four packages were subm...", "weights": [[8, 12, 1.0], [20, 24, 1.0], [101, 105, 1.0]]}, "2006.sigirconf_conference-2006.17": {"snippet": "...oped spam filters may be combined in simple ways to provide substantially better filtering than any of the individual filters. The results of fifty-three spam filters evaluated at the TREC 2005 Spam T...", "weights": [[8, 12, 1.0], [134, 141, 1.0], [157, 161, 1.0], [197, 201, 1.0]]}, "2005.wwwconf_conference-2005si.14": {"snippet": "...ntal results show that we can identify most link farm spam pages and the final ranking results are improved for almost all tested queries.", "weights": [[8, 15, 1.0], [57, 61, 1.0], [90, 97, 1.0]]}, "2015.wwwconf_conference-2015.17": {"snippet": "...ting spam opinions in threads. The results of spam detection on first posts show: (1) spam first posts put more focus on certain topics such as the user experiences' on the promoted items, (2) spam fi...", "weights": [[8, 12, 1.0], [38, 45, 1.0], [49, 53, 1.0], [89, 93, 1.0], [196, 200, 1.0]]}, "2005.trec_conference-2005.110": {"snippet": "...2005 Spam Track Overview", "weights": [[8, 12, 1.0]]}, "2015.wwwconf_conference-2015.88": {"snippet": "...n of Spam Mobile Apps\n\n\n ABSTRACTIncreased popularity of smartphones has attracted a large number of developers to various smartphone platforms. As a result, app markets are also populated with spam a...", "weights": [[8, 12, 1.0], [197, 201, 1.0]]}, "2006.wwwconf_conference-2006.10": {"snippet": "...ting spam web pages through content analysis\n\n\n ABSTRACTIn this paper, we continue our investigations of \"web spam\": the injection of artificially-created pages into the web in order to influence the ...", "weights": [[8, 12, 1.0], [113, 117, 1.0]]}, "W00-0719": {"snippet": "...tive Spam Filtering\n\n\n Spam filtering is a text categorization task that shows especial features that make it interesting and difficult. First, the task has been performed traditionally using heuristi...", "weights": [[8, 12, 1.0], [26, 30, 1.0]]}, "2009.airweb_workshop-2009.1": {"snippet": "... web spam", "weights": [[8, 12, 1.0]]}, "2020.ecir_conference-20201.29": {"snippet": "...s to avoid retrieving duplicate results and thus needs to use some form of clustering. In this paper we propose a principled probabilistic ranking framework for premises based on the idea of tf-idf th...", "weights": [[8, 13, 1.0], [35, 42, 1.0]]}}}
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Direct optimization of the listwise objective functions is computationally prohibitive for most learning algorithms,...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [192, 202, 1.0]]}, "1998.ipm_journal-ir0volumeA34A4.2": {"snippet": "...m in Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.wsdm_conference-2011.32": {"snippet": "...n of algorithms for this interactive retrieval setting. Furthermore, we propose two dynamic ranking algorithms, both of which are computationally efficient. We prove that these algorithms provide retr...", "weights": [[8, 18, 1.0], [40, 49, 1.0], [103, 113, 1.0], [180, 190, 1.0], [199, 208, 1.0]]}, "2017.sigirconf_conference-2017.58": {"snippet": "Retrieval Algorithms Optimized for Human Learning\n\n\n ABSTRACTWhile search technology is widely used for learning-oriented information needs, the results provided by popular services such as Web search...", "weights": [[0, 9, 1.0], [10, 20, 1.0], [122, 133, 1.0]]}, "2004.ntcir_workshop-2004.24": {"snippet": "...text retrieval algorithm that is specialized for QA and a new algorithm for sorting the answer candidates. Using our new algorithm for text retrieval, articles containing the answer for the question c...", "weights": [[8, 17, 1.0], [143, 152, 1.0]]}, "2019.ecir_workshop-2019amir.1": {"snippet": "... The information retrieval (IR) community, however, has paid relatively little attention to the algorithm selection problem. The 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2009.spire_conference-2009.17": {"snippet": "... all algorithms achieve a mean word accuracy across all lexicons of over 90%. However, Syllabification by Analogy performs better than the other algorithms tested with a mean word accuracy of 96.84% (...", "weights": [[8, 18, 1.0], [148, 158, 1.0]]}, "2004.sigirconf_conference-2004.132": {"snippet": "...phic information from texts can be exploited to provide effective and accurate ranked retrieval of geospatial information and relevant text documents using retrieval algorithms based on logistic regre...", "weights": [[8, 19, 1.0], [89, 98, 1.0], [113, 124, 1.0], [159, 168, 1.0], [169, 179, 1.0]]}, "2004.airs_conference-2004.4": {"snippet": "...sian Information Criterion. Experimental results on two different corpora are given to show the effectiveness of our algorithm.", "weights": [[8, 19, 1.0]]}, "1971.sigirconf_conference-71.15": {"snippet": "... and Retrieval Algorithm", "weights": [[8, 17, 1.0]]}, "1996.sigirconf_conference-96.35": {"snippet": "...ning Algorithms for Linear Text Classifiers\n\n\n AbstractSystems for text retrieval, routing, categorization and other IR tasks rely heavily on linear classifiers. We propose that two machine learning a...", "weights": [[8, 18, 1.0], [75, 84, 1.0], [202, 212, 1.0]]}, "1995.jasis_journal-ir0volumeA46A3.2": {"snippet": "... for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms", "weights": [[8, 19, 1.0], [20, 29, 1.0], [79, 89, 1.0]]}, "2007.cikm_conference-2007.76": {"snippet": "...tive retrieval\n\n\n ABSTRACTIt has been shown that using phrases properly in the document retrieval leads to higher retrieval effectiveness. In this paper, we define four types of noun phrases and prese...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [117, 126, 1.0]]}, "2014.ipm_journal-ir0volumeA50A1.8": {"snippet": "...that information, the accuracy of information retrieval can be improved.In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our tec...", "weights": [[8, 19, 1.0], [37, 48, 1.0], [49, 58, 1.0]]}, "P10-2060": {"snippet": "Optimizing Informativeness and Readability for Sentiment Summarization\n\n\n We propose a novel algorithm for sentiment summarization that takes account of informativeness and readability, simultaneously...", "weights": []}, "2019.ecir_workshop-2019amir.0": {"snippet": "...g in Information Retrieval co-located with the 41st European Conference on Information Retrieval (ECIR 2019), Cologne, Germany, April 14, 2019", "weights": [[8, 19, 1.0], [20, 29, 1.0], [78, 89, 1.0], [90, 99, 1.0]]}, "2007.cikm_conference-2007.88": {"snippet": "...nion retrieval from blogs\n\n\n ABSTRACTOpinion retrieval is a document retrieval process, which requires documents to be retrieved and ranked according to their opinions about a query topic. A relevant ...", "weights": [[8, 17, 1.0], [48, 57, 1.0], [72, 81, 1.0]]}, "1993.sigirjournals_journal-ir0volumeA27A3.6": {"snippet": "Information Retrieval Data Structures & Algorithms, by William B. Frakes and Ricardo Baeza-Yates (Book Review)", "weights": [[0, 11, 1.0], [12, 21, 1.0], [40, 50, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.25": {"snippet": "...ards information retrieval in complex networks\n\n\n Abstract Spanking information retrieval in large-scale Web and network has attracted increasing interest in the research community, many typical appro...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [71, 82, 1.0], [83, 92, 1.0]]}, "2015.jasis_journal-ir0volumeA66A11.9": {"snippet": "...tive information retrieval behavior to inform design", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2000.cikm_conference-2000.40": {"snippet": "...s of algorithms for knowledge discovery in databases (KDD) relies heavily on similarity queries, i.e. range queries or nearest neighbor queries, in multidimensional feature spaces. Many KDD algorithms...", "weights": [[8, 18, 1.0], [193, 203, 1.0]]}, "2000.ipm_journal-ir0volumeA36A5.2": {"snippet": "...etic algorithms to query optimization in document retrieval", "weights": [[8, 18, 1.0], [53, 62, 1.0]]}, "W12-3614": {"snippet": "... the information retrieval community during recent years, and efficient algorithms have been devised. We finally show how the approach has been implemented in a lexicographic project, and describe the...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [75, 85, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...n of retrieval models particularly for recall oriented retrieval domains, where the focus is on retrieving all relevant information but not just retrieving a subset of relevant information. Available ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [123, 134, 1.0], [180, 191, 1.0]]}, "1999.tois_journal-ir0volumeA17A2.2": {"snippet": "...ased Retrieval by Spatial Similarity in Image Databases\n\n\n A framework for retrieving images by spatial similarity (FRISS) in image databases is presented. In this framework, a robust retrieval by spa...", "weights": [[8, 17, 1.0], [187, 196, 1.0]]}, "2005.sigirconf_conference-2005.9": {"snippet": "... hoc information retrieval\n\n\n ABSTRACTInformation retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information nee...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 62, 1.0], [164, 173, 1.0], [188, 199, 1.0]]}, "2008.cikm_workshop-2008lsdsir.10": {"snippet": "...uted information retrieval environments\n\n\n ABSTRACTIn this paper, a new source selection algorithm for uncooperative distributed information retrieval environments is presented. The algorithm function...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [132, 143, 1.0], [144, 153, 1.0]]}, "1988.jasis_journal-ir0volumeA39A3.0": {"snippet": "...king algorithms to improve the effectiveness of full-text retrieval. II. On the effectiveness of ranking algorithms on full-text retrieval", "weights": [[8, 18, 1.0], [61, 70, 1.0], [108, 118, 1.0], [132, 141, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.12": {"snippet": "...ent. Information retrieval systems offering automated assistance could greatly improve search effectiveness by suggesting or implementing assistance automatically. A critical issue in designing such s...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2007.ntcir_workshop-2007.42": {"snippet": "...gual retrieval subtask, at the Yahoo group, focusing on the parameter optimization in information retrieval (IR). Unlike regression approaches, we optimized parameters completely independent from retr...", "weights": [[8, 17, 1.0], [89, 100, 1.0], [101, 110, 1.0], [199, 208, 1.0]]}, "2008.sigirconf_conference-2008.42": {"snippet": "...h to information retrieval: algorithmic mediation for intentional, synchronous collaborative exploratory search. Using our system, two or more users with a common information need search together, sim...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [166, 177, 1.0]]}, "I11-1117": {"snippet": "...sage Retrieval for Information Extraction using Distant Supervision\n\n\n In this paper, we propose a keyword-based passage retrieval algorithm for information extraction, trained by distant supervision....", "weights": [[8, 17, 1.0], [22, 33, 1.0], [124, 133, 1.0], [148, 159, 1.0]]}, "2003.sigirconf_conference-2003.8": {"snippet": "...sage retrieval algorithms vary with the choice of document retriever, which suggests significant interactions between document retrieval and passage retrieval. The best algorithms in our evaluation em...", "weights": [[8, 17, 1.0], [18, 28, 1.0], [130, 139, 1.0], [152, 161, 1.0], [172, 182, 1.0]]}, "2009.spire_conference-2009.9": {"snippet": "...time algorithms.", "weights": [[8, 18, 1.0]]}, "P09-3008": {"snippet": "...odel Information Retrieval System with Expectation Maximization Algorithm\n\n\n Statistical language modeling (SLM) has been used in many different domains for decades and has also been applied to inform...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [197, 208, 1.0]]}, "2020.sigirconf_conference-2020.360": {"snippet": "...tive Information Retrieval: Models, Algorithms, and Evaluation\n\n\n Since Information Retrieval (IR) is an interactive process in general, it is important to study Interactive Information Retrieval (IIR...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [39, 49, 1.0], [75, 86, 1.0], [87, 96, 1.0], [177, 188, 1.0], [189, 198, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.6": {"snippet": "... for information retrieval applications. Sorting is a fundamental and universal problem in computer science. Even if sort has been extensively addressed by many research works, it still remains an int...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1981.sigirconf_conference-81.9": {"snippet": "...king Algorithms in Information Retrieval", "weights": [[8, 18, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2017.cikm_conference-2017.274": {"snippet": "...e\ue03cer retrieval performance than several stateof-the-art stemming algorithms, for the same queries.", "weights": [[8, 17, 1.0], [68, 78, 1.0]]}, "2013.wsdm_conference-2013.27": {"snippet": "...y of information retrieval functions by combining their result lists and tracking clicks. A sequence of such algorithms have been proposed , each being shown to address problems in earlier algorithms....", "weights": [[8, 19, 1.0], [20, 29, 1.0], [112, 122, 1.0], [192, 202, 1.0]]}, "2002.ir_journal-ir0volumeA5A23.8": {"snippet": "Information Retrieval: Algorithms and Heuristics", "weights": [[0, 11, 1.0], [12, 21, 1.0], [23, 33, 1.0]]}, "2016.sigirconf_conference-2016.201": {"snippet": "...tual information into RS algorithms? This question is broken down into four specific questions: RQ1. Which information can be considered as contextual information for a specific recommendation task? I...", "weights": [[8, 19, 1.0], [28, 38, 1.0], [110, 121, 1.0], [154, 165, 1.0]]}, "2014.sigirconf_conference-2014.221": {"snippet": "...amic information retrieval modeling\n\n\n ABSTRACTDynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [69, 80, 1.0], [81, 90, 1.0]]}, "1988.jasis_journal-ir0volumeA39A2.0": {"snippet": "...king algorithms to improve the effectiveness of full-text retrieval. I. On the effectiveness of full-text retrieval", "weights": [[8, 18, 1.0], [61, 70, 1.0], [109, 118, 1.0]]}, "2017.tist_journal-ir0volumeA8A5.1": {"snippet": "... for information relevance, by which information produced by users who are closer to the user issuing a request is considered more relevant. This query model raises new challenges for effectiveness an...", "weights": [[8, 19, 1.0], [40, 51, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.19": {"snippet": "... and retrieval to access information quickly on social media. However, even with humongous information available, there are certain categories of images which are important for certain applications bu...", "weights": [[8, 17, 1.0], [28, 39, 1.0], [94, 105, 1.0]]}, "2004.ntcir_workshop-2004.57": {"snippet": "...onal Retrieval Task 2 that was conducted from 2003 to 2004 as a subtask of the WEB Task at the Fourth NTCIR Workshop ('NTCIR-4 WEB'). In the Informational Retrieval Task, we attempted to assess the re...", "weights": [[8, 17, 1.0], [158, 167, 1.0], [201, 210, 1.0]]}, "2017.trec_conference-2017.21": {"snippet": "...ular information retrieval application is web search engine such as Google [2] , Baidu [3] , Bing [4] and Sogou [5]. These application will return top-N best retrieval result to users. Information Ret...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [161, 170, 1.0], [188, 199, 1.0], [200, 209, 1.0]]}, "2006.clef_workshop-2006w.1": {"snippet": "...sion Information Retrieval Method for WiQA\n\n\n This paper presents Wolverhampton University's participation in the WiQA competition. The method chosen for this task combines a high precision, but low r...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2018.wwwconf_conference-2018c.14": {"snippet": "...rent algorithms. Given a network G, the ASCD problem is \nA Community Detection ToolboxFigure 1: An illustration of the ASCD problem. A community detection toolbox, such as CoDeT , includes CNM, Spectr...", "weights": [[8, 18, 1.0]]}, "2017.ipm_journal-ir0volumeA53A2.5": {"snippet": "...arch algorithms is a key research avenue. In particular, thematic search algorithms should lead to and benefit from the emergence of semantic communities that are the result of the interaction among p...", "weights": [[8, 18, 1.0], [76, 86, 1.0]]}, "1990.sigirconf_conference-90.27": {"snippet": "... for Information Retrieval, Based on the Theory of Relative Concentration\n\n\n ABSTRACTThis paper introduces a new method for information retrieval of documents that are represented by a vector. The nov...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [127, 138, 1.0], [139, 148, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2018.airs_conference-2018.4": {"snippet": "...ster information between a searcher and authors of contents. This paper also describes the evaluation experiments to confirm effectiveness of the proposed algorithm by implementing the proposed algori...", "weights": [[8, 19, 1.0]]}, "2016.sigirconf_conference-2016.89": {"snippet": "...This information retrieval test collection is the first to combine multilingual search, passage retrieval, and informal online genre text.", "weights": [[8, 19, 1.0], [20, 29, 1.0], [99, 108, 1.0]]}, "1994.riao_conference-1994.13": {"snippet": "...o an Information Retrieval System", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2005.ismir_conference-2005.91": {"snippet": "...udio Retrieval", "weights": [[8, 17, 1.0]]}, "2007.sigirconf_conference-2007.18": {"snippet": "...mage retrieval\n\n\n ABSTRACTRelevance feedback is a powerful technique to enhance ContentBased Image Retrieval (CBIR) performance. It solicits the user's relevance judgments on the retrieved images retu...", "weights": [[8, 17, 1.0], [102, 111, 1.0]]}, "2005.sigirconf_conference-2005.77": {"snippet": "...s of retrieval performance into metasearch algorithms. Our focus is both on the analysis of retrieval measures themselves and on the development of new metasearch algorithms. Given the conversion meth...", "weights": [[8, 17, 1.0], [46, 56, 1.0], [95, 104, 1.0], [166, 176, 1.0]]}, "1992.sigirconf_conference-92.3": {"snippet": "...of a Retrieval Process", "weights": [[8, 17, 1.0]]}, "2004.tois_journal-ir0volumeA22A3.4": {"snippet": "...uted information retrieval, which aims to reduce the network traffic of query passing in the network. We carry out experiments to show the distributed image retrieval system and the Firework informati...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [160, 169, 1.0], [194, 205, 1.0]]}, "2017.ictir_conference-2017.57": {"snippet": "...ndit algorithms; 2) application of bandits in information retrieval; 3) Optimization of information retrieval systems based on bandit algorithms. In the rst part will provide an overview of bandit alg...", "weights": [[8, 18, 1.0], [49, 60, 1.0], [61, 70, 1.0], [91, 102, 1.0], [103, 112, 1.0], [137, 147, 1.0], [200, 210, 1.0]]}, "2012.ipm_journal-ir0volumeA48A5.11": {"snippet": "...k in information retrieval\n\n\n a b s t r a c tIn this paper we present a new algorithm for relevance feedback (RF) in information retrieval. Unlike conventional RF algorithms which use the top ranked d...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [120, 131, 1.0], [132, 141, 1.0], [166, 176, 1.0]]}, "2015.spire_conference-2015.9": {"snippet": "...hese algorithms achieve remarkable performance in terms of I/O complexity and speed, their designs are quite complex and their disk requirements remain rather heavy. Currently, the core algorithmic pa...", "weights": [[8, 18, 1.0]]}, "2007.sigirconf_conference-2007.36": {"snippet": "...tive algorithms for constructing refined query language models from user term feedback. Our algorithms are shown to bring significant improvement in retrieval accuracy over a non-feedback baseline, an...", "weights": [[8, 18, 1.0], [95, 105, 1.0], [152, 161, 1.0]]}, "2019.ecir_workshop-2019amir.3": {"snippet": "Algorithm Selection with Librec-auto", "weights": []}, "N06-5003": {"snippet": "...ased Algorithms for Natural Language Processing and Information Retrieval\n\n\n Graph theory is a well studied discipline, and so are the fields of natural language processing and information retrieval. ...", "weights": [[8, 18, 1.0], [55, 66, 1.0], [67, 76, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "2019.ecir_workshop-2019amir.6": {"snippet": "... the algorithms that generated the recommendations. The logs enable researchers to conduct various analyses about a real-world recommender system. This includes the evaluation of meta-learning approac...", "weights": [[8, 18, 1.0]]}, "S15-2084": {"snippet": "...: An Information Retrieval-based System For Multi-Domain Sentiment Analysis\n\n\n This paper describes the SHELLFBK system that participated in SemEval 2015 Tasks 9, 10, and 11. Our system takes a superv...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2002.cikm_conference-2002.43": {"snippet": "... the retrieval accuracy of information retrieval systems of Latin-based languages. We present two stemming algorithms for Arabic information retrieval systems. We empirically investigate the effective...", "weights": [[8, 17, 1.0], [30, 41, 1.0], [42, 51, 1.0], [110, 120, 1.0], [132, 143, 1.0], [144, 153, 1.0]]}, "2004.sigirconf_conference-2004.41": {"snippet": "...ring algorithms for information retrieval models with named attributes and proximity operators\n\n\n ABSTRACTIn the selective dissemination of information (or publish/ subscribe) paradigm, clients subscr...", "weights": [[8, 18, 1.0], [23, 34, 1.0], [35, 44, 1.0], [143, 154, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...mage Retrieval by Hypertext Links\n\n\n This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, th...", "weights": [[8, 17, 1.0], [72, 81, 1.0], [189, 199, 1.0]]}, "2007.ipm_journal-ir0volumeA43A5.3": {"snippet": "...ning algorithms for cross-language patent retrieval and classification\n\n\n AbstractWe study several machine learning algorithms for cross-lan7guage patent retrieval and classification. In comparison wi...", "weights": [[8, 18, 1.0], [45, 54, 1.0], [119, 129, 1.0], [157, 166, 1.0]]}, "2010.ecir_conference-2010.30": {"snippet": "...king Algorithms for Information Retrieval", "weights": [[8, 18, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "1996.ipm_journal-ir0volumeA32A2.4": {"snippet": "...etic Algorithms for Information Retrieval and Filtering: An Empirical Basis for Grammatical Rules", "weights": [[8, 18, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "2013.wwwconf_conference-2013c.31": {"snippet": "... and algorithms for composite retrieval", "weights": [[8, 18, 1.0], [33, 42, 1.0]]}, "2002.jasis_journal-ir0volumeA53A11.6": {"snippet": "...etic algorithms for multimodal relevance optimization in information retrieval", "weights": [[8, 18, 1.0], [60, 71, 1.0], [72, 81, 1.0]]}, "1987.sigirjournals_journal-ir0volumeA21A34.1": {"snippet": "... for Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2012.sigirconf_conference-2012.84": {"snippet": "...ized retrieval algorithms. One simple approach is to learn a set of combination weights for multiple centralized retrieval algorithms (e.g., logistic regression) to compute comparable document scores....", "weights": [[8, 17, 1.0], [18, 28, 1.0], [116, 125, 1.0], [126, 136, 1.0]]}, "2005.cikm_conference-2005.154": {"snippet": "... set retrieval\n\n\n ABSTRACTThis paper presents a novel formulation and approach to the minimal document set retrieval problem. Minimal Document Set Retrieval (MDSR) is a promising information retrieval...", "weights": [[8, 17, 1.0], [110, 119, 1.0], [150, 159, 1.0], [182, 193, 1.0], [194, 203, 1.0]]}, "2012.ecir_conference-2012.15": {"snippet": "...eech Retrieval", "weights": [[8, 17, 1.0]]}, "1996.tois_journal-ir0volumeA14A2.1": {"snippet": "...ient Information Retrieval with Redundant Information Filters\n\n\n We consider information retrieval when the data-for instance, multimedia-is computationally expensive to fetch. Our approach uses \"info...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [45, 56, 1.0], [80, 91, 1.0], [92, 101, 1.0], [199, 210, 1.0]]}, "2000.jasis_journal-ir0volumeA51A11.9": {"snippet": "...iew: Information retrieval: Algorithms and heuristics, by David A. Grossman and Ophir Frieder", "weights": [[8, 19, 1.0], [20, 29, 1.0], [31, 41, 1.0]]}, "2019.ecir_workshop-2019amir.2": {"snippet": "...rent algorithms on many problem instances, developers learn an intuitive mapping from some characteristics of a given problem instance (e.g., the number of features of a dataset) to a well-performing ...", "weights": [[8, 18, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.16": {"snippet": "... for information access\n\n\n AbstractSince the middle of last century, information retrieval has gained an increasing interest. Since its inception, much research has been devoted to finding optimal way...", "weights": [[8, 19, 1.0], [72, 83, 1.0], [84, 93, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2020.ecir_conference-20202.84": {"snippet": "... the information retrieval community. In this workshop, we aim to collect novel contributions in this emerging field and to provide a common ground for interested researchers and practitioners.", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1999.jasis_journal-ir0volumeA50A9.2": {"snippet": "...tive Information Retrieval Agent", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2022.naacl-srw.8": {"snippet": "...hing algorithms based on term matching have been widely used. Recently, neural-based algorithms (termed as neural retrievers) have gained more attention which can mitigate the limitations of tradition...", "weights": [[8, 18, 1.0], [88, 98, 1.0]]}, "2013.ictir_conference-2013.12": {"snippet": "...nced Information Retrieval by Exploiting Recommender Techniques in Cluster-Based Link Analysis\n\n\n ABSTRACTInspired by the use of PageRank algorithms in document ranking, we develop and evaluate a clus...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [141, 151, 1.0]]}, "2013.sigirconf_conference-2013.37": {"snippet": "...text retrieval, where keywords carry enough semantics to distinguish text documents and rank them, math symbols do not contain much semantic information on their own. In fact, mathematical expressions...", "weights": [[8, 17, 1.0], [144, 155, 1.0]]}, "2004.wwwconf_conference-2004.67": {"snippet": "...ring algorithms to demonstrate its superiority. Even though our algorithm is slightly more computationally intensive than one of the algorithms, it generates better hierarchies. Our user studies also ...", "weights": [[8, 18, 1.0], [136, 146, 1.0]]}, "2002.sigirconf_conference-2002.21": {"snippet": "...line algorithms for category ranking\n\n\n ABSTRACTWe describe a new family of topic-ranking algorithms for multi-labeled documents. The motivation for the algorithms stems from recent advances in online...", "weights": [[8, 18, 1.0], [93, 103, 1.0], [156, 166, 1.0]]}, "2021.ecir_conference-20212.83": {"snippet": "...tion algorithms has been traditionally the main objective for the industrial and academic research communities. However, recent studies have shown that optimizing models through these algorithms may r...", "weights": [[8, 18, 1.0], [187, 197, 1.0]]}, "1991.ipm_journal-ir0volumeA27A23.1": {"snippet": "...s of retrieval algorithms", "weights": [[8, 17, 1.0], [18, 28, 1.0]]}, "1985.sigirconf_conference-85.28": {"snippet": "... for Information Retrieval Research: The Utah Retrieval System Architecture\n\n\n AbstractThe Utah Retrieval System Architecture provides an excellent testbed for the development and testing of new algor...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [49, 58, 1.0], [99, 108, 1.0], [198, 208, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A4.1": {"snippet": "...ased Information Retrieval Algorithms for Efficient Ranking of Webpages", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 40, 1.0]]}, "2019.ecir_conference-20192.53": {"snippet": "...d in information retrieval, and also how techniques from IR can be used for automated algorithm selection and metalearning.\nMotivationThere is a plethora of algorithms for information retrieval applic...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [160, 170, 1.0], [175, 186, 1.0], [187, 196, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ical information retrieval\n\n\n AbstractIn this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 153, 1.0], [154, 163, 1.0]]}, "2006.ecir_conference-2006.14": {"snippet": "... new algorithms that support topic detection, search, and navigation of news documents. This work presents an algorithm for topic detection that considers the temporal evolution of news and the struct...", "weights": [[8, 18, 1.0]]}, "2010.sigirconf_conference-2010.138": {"snippet": "...uted information retrieval system . However, most post-retrieval query performance predictors are expensive to compute, and not well-suited for the web.The key idea behind RAPP is to use retrieval sco...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [58, 67, 1.0], [190, 199, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "... for information retrieval\n\n\n ABSTRACTIn recent years, we have witnessed successful application of machine learning techniques to a wide range of information retrieval problems, including Web search e...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [149, 160, 1.0], [161, 170, 1.0]]}, "2014.sigirconf_conference-2014.219": {"snippet": "...3.3 [Information Search and Retrieval]: Retrieval modelsGeneral Terms: Algorithms Keywords: axiomatic analysis; information retrieval models; retrieval constraints; optimization\nOVERVIEWOne of the mos...", "weights": [[8, 19, 1.0], [31, 40, 1.0], [43, 52, 1.0], [74, 84, 1.0], [115, 126, 1.0], [127, 136, 1.0], [145, 154, 1.0]]}, "2013.riao_conference-2013.25": {"snippet": "...nder algorithms for enhanced information retrieval", "weights": [[8, 18, 1.0], [32, 43, 1.0], [44, 53, 1.0]]}, "1981.sigirconf_conference-81.13": {"snippet": "...m in Information Retrieval: An Algorithm Using Upperbounds", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1991.sigirconf_conference-91.12": {"snippet": "Retrieval Algorithm Effectiveness in a Wide Area Network Information Filter\n\n\n AbstractWe present an application of the usefulness performance measure in a WAN-based SDI system.", "weights": [[0, 9, 1.0], [57, 68, 1.0]]}, "2011.tois_journal-ir0volumeA29A4.2": {"snippet": "... for information retrieval\n\n\n A novel graph-based language-independent stemming algorithm suitable for information retrieval is proposed in this article. The main features of the algorithm are retriev...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [106, 117, 1.0], [118, 127, 1.0], [196, 205, 1.0]]}, "2010.sigirconf_conference-2010.173": {"snippet": "...ning retrieval scores (or the rankings) assigned by different retrieval models or ranking algorithms. In this work, we focus on the problem of choosing between retrieval models using performance estim...", "weights": [[8, 17, 1.0], [65, 74, 1.0], [93, 103, 1.0], [163, 172, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "1978.sigirconf_conference-78.8": {"snippet": "...tive retrieval, in which searches are performed using more than one key.A nearest neighbors associative retrieval algorithm, suitable for document retrieval using similarity matching, is described. Th...", "weights": [[8, 17, 1.0], [107, 116, 1.0], [150, 159, 1.0]]}, "C14-1057": {"snippet": "...ely, information retrieval research has focused on the retrieval of text documents and images, with almost no attention specifically directed toward the retrieval of information graphics. Our work is ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [58, 67, 1.0], [156, 165, 1.0], [169, 180, 1.0]]}, "2015.ictir_conference-2015.9": {"snippet": "...tive Information Retrieval variant have guided the development of ranking and retrieval algorithms for decades, yet they are not capable of helping us model problems in Dynamic Information Retrieval w...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [81, 90, 1.0], [91, 101, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "2005.trec_conference-2005.133": {"snippet": "...ical information retrieval. The three problems are: (1) how to deal with synonyms? (2) how to deal with the frequent use of acronyms? (3) how to deal with homonyms? In particular, we propose two query...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2013.ictir_conference-2013.14": {"snippet": "Information Retrieval with Time Series Query\n\n\n ABSTRACTWe study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find rele...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [73, 84, 1.0], [85, 94, 1.0], [170, 179, 1.0]]}, "1992.trec_conference-1992.3": {"snippet": "...t in Information Retrieval Using Genetic Algorithms - A Report on the Experiments of the TREC Project", "weights": [[8, 19, 1.0], [20, 29, 1.0], [44, 54, 1.0]]}, "Y18-1019": {"snippet": "... FCA Retrieval a fast and efficient algorithm for information retrieval from a binary concept lattice. This algorithm can respond to queries containing several types of logical operators (conjunction,...", "weights": [[8, 17, 1.0], [53, 64, 1.0], [65, 74, 1.0]]}}}
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{"snippet": "...rse\" queries were more effective than the \"short\" queries. This unlikely result may be because of how queries were constructed. In the example listed, the terse query is a phrase that is probably used...", "weights": [[8, 15, 1.0], [53, 60, 1.0], [105, 112, 1.0]]}, "Y11-1032": {"snippet": "...s\n\n\n Misspellings, abbreviations and acronyms are very popular in clinical notes and can be an obstacle to high quality information extraction and classification. In addition, another important part o...", "weights": [[8, 20, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.9": {"snippet": "...lled queries in Cross-Language Information Retrieval", "weights": [[8, 15, 1.0]]}, "W19-4429": {"snippet": "Anglicized Words and Misspelled Cognates in Native Language Identification\n\n\n In this paper, we present experiments that estimate the impact of specific lexical choices of people writing in a second l...", "weights": []}, "O14-1015": {"snippet": "\u4f7f\u7528\u4e2d\u6587\u5b57\u7b46\u756b\u69cb\u5f62\u8cc7\u6599\u5eab\u6821\u6b63\u5b57\u5f62\u76f8\u4f3c\u4e4b\u5225\u5b57 (Using {C}hinese Orthography Database to Correct {C}hinese Misspelling Words With Graphemic Similarity) [In {C}hinese]", "weights": []}, "W18-1208": {"snippet": "Fast Query Expansion on an Accounting Corpus using Sub-Word Embeddings\n\n\n We present early results from a system under development which uses sub-word embeddings for query expansion in the presence of...", "weights": []}, "W12-2012": {"snippet": "...word misspellings in student essays\n\n\n In this paper we present a new spell-checking system that utilizes contextual information for automatic correction of non-word misspellings. The system is evalua...", "weights": [[8, 20, 1.0], [169, 181, 1.0]]}, "2012.wwwconf_conference-2012c.141": {"snippet": "CloudSpeller: query spelling correction by using a unified hidden markov model with web-scale resources\n\n\n ABSTRACTQuery spelling correction is an important component of modern search engines that can...", "weights": []}, "2005.ecir_conference-2005.8": {"snippet": "... and queries in vector spaces and build indexes for processing text-centric semi-structured queries that support a proximity measure between XML documents. The idea of using vector spaces for XML retr...", "weights": [[8, 15, 1.0], [95, 102, 1.0]]}, "2020.lrec-1.835": {"snippet": "...t of Misspellings and Grammatical Errors\n\n\n The lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction (GE...", "weights": [[8, 20, 1.0]]}, "P11-1006": {"snippet": "A Fast and Accurate Method for Approximate String Search\n\n\n This paper proposes a new method for approximate string search, specifically candidate generation in spelling error correction, which is a t...", "weights": []}, "2016.clef_conference-2016.7": {"snippet": "...tive queries combining the users' native language and English language with lay and medico-scientific terminology. To assess how the language and terminology impact the use of suggestions, we conducte...", "weights": [[8, 15, 1.0]]}, "2017.chiir_conference-2017.68": {"snippet": "....e., queries that have been issued in the past by users of a retrieval system. Some calculate explicit transition probabilities between queries in a log -as in a Markov model -to recommend likely subs...", "weights": [[8, 15, 1.0], [139, 146, 1.0]]}, "2016.tois_journal-ir0volumeA35A2.0": {"snippet": "...arch queries by different languages. Hence, the search engine query log, which serves as the backbone of many search engine applications, records millions of users' search history in a wide spectrum o...", "weights": [[8, 15, 1.0]]}, "L18-1134": {"snippet": "... all queries for lexical entries not currently in the database are indistinguishable from a larger list of misspellings, meaning that potential new or trending entries can get lost easily. In this pro...", "weights": [[8, 15, 1.0], [110, 122, 1.0]]}, "2020.lrec-1.857": {"snippet": "...ling misspellings generated by children in both essay writing and online search tasks. As a byproduct of our study, we create two new datasets comprised of spelling errors generated by children from h...", "weights": [[8, 20, 1.0]]}, "2001.tois_journal-ir0volumeA19A1.2": {"snippet": "...ural queries, which retrieve configurations matching a particular spatio-temporal structure, and (iv) special cases of motion queries. Considering the current large availability of multidimensional da...", "weights": [[8, 15, 1.0], [129, 136, 1.0]]}, "2020.findings-emnlp.37": {"snippet": "...from misspellings. On the contrary, humans can easily infer the corresponding correct words from their misspellings and surrounding context. Inspired by this, we address the standalone spelling correc...", "weights": [[8, 20, 1.0], [106, 118, 1.0]]}, "2020.emnlp-demos.21": {"snippet": "...ring misspellings from multiple sources. We find that many systems do not adequately leverage the context around the misspelt token. To remedy this, (i) we train neural models using spelling errors in...", "weights": [[8, 20, 1.0]]}, "2012.sigirconf_conference-2012.63": {"snippet": "A generalized hidden Markov model with discriminative training for query spelling correction\n\n\n ABSTRACTQuery spelling correction is a crucial component of modern search engines. Existing methods in t...", "weights": []}, "2006.tois_journal-ir0volumeA24A1.1": {"snippet": "...ssed queries. SDC extracts from historical usage data the results of the most frequently submitted queries and stores them in a static, read-only portion of the cache. The remaining entries of the cac...", "weights": [[8, 15, 1.0], [102, 109, 1.0]]}, "D14-1171": {"snippet": "...ding misspellings fast. However, the classical approach is based on a quadratic time algorithm with 80% coverage. We present a novel algorithm for misspelling detection, which runs in constant time an...", "weights": [[8, 20, 1.0]]}, "2007.cikm_conference-2007.4": {"snippet": "...ting queries for xml\n\n\n ABSTRACTIt has been observed that queries over XML data sources are often unsatisfiable. Unsatisfiability may stem from several different sources, e.g., the user may be insuffi...", "weights": [[8, 15, 1.0], [61, 68, 1.0]]}, "2009.sigirconf_conference-2009.73": {"snippet": "...long queries using query quality predictors", "weights": [[8, 15, 1.0]]}, "2011.ipm_journal-ir0volumeA47A2.9": {"snippet": "...lled queries in IR applications", "weights": [[8, 15, 1.0]]}, "2004.cikm_conference-2004.7": {"snippet": "Scoring missing terms in information retrieval tasks\n\n\n ABSTRACTAn usual approach to address mismatching vocabulary problem is to augment the original query using dictionaries and other lexical resour...", "weights": []}, "A97-1025": {"snippet": "Contextual Spelling Correction Using Latent Semantic Analysis\n\n\n Contextual spelling errors are defined as the use of an incorrect, though valid, word in a particular sentence or context. Traditional ...", "weights": []}, "2003.sigirconf_conference-2003.86": {"snippet": "...s of queries constituted 40% to 52% of queries. Other research using web search query logs has sought to characterize the individual queries , or to use the clickthroughs to optimize retrieval [1] per...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [136, 143, 1.0]]}, "2001.tois_journal-ir0volumeA19A4.1": {"snippet": "...ical queries over XML data\n\n\n The rapid evolution of XML from a mere data exchange format to a universal syntax for encoding domain-specific information raises the need for new query languages specifi...", "weights": [[8, 15, 1.0]]}, "2021.hcinlp-1.2": {"snippet": "Spellchecking for Children in Web Search: a Natural Language Interface Case-study\n\n\n Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are...", "weights": []}, "2012.cikm_conference-2012.94": {"snippet": "...s of queries over SQL databases\n\n\n ABSTRACTData completeness is an important aspect of data quality. We consider a setting, where databases can be incomplete in two ways: records may be missing and re...", "weights": [[8, 15, 1.0]]}, "O12-1012": {"snippet": "\u5b57\u5f62\u76f8\u4f3c\u5225\u5b57\u4e4b\u81ea\u52d5\u6821\u6b63\u65b9\u6cd5 (Automatic Correction for Graphemic {C}hinese Misspelled Words) [In {C}hinese]\n\n\n No matter that learning Chinese as a first or second language, a quite important issue, misspelled words...", "weights": []}, "N19-1326": {"snippet": "...t to misspellings. Existing word embeddings have limited applicability to malformed texts, which contain a non-negligible amount of outof-vocabulary words. We propose a method combining FastText with ...", "weights": [[8, 20, 1.0]]}, "P19-1561": {"snippet": "...rial Misspellings with Robust Word Recognition\n\n\n To combat adversarial spelling mistakes, we propose placing a word recognition model in front of the downstream classifier. Our word recognition model...", "weights": [[8, 20, 1.0]]}, "N10-1064": {"snippet": "...ling misspellings. The enhanced system decodes a word-based confusion network representing spelling variations of the input text. We present extensive experimental results on two translation tasks of ...", "weights": [[8, 20, 1.0]]}, "2012.tois_journal-ir0volumeA30A1.1": {"snippet": "Static index pruning in web search engines: Combining term and document popularities with query views\n\n\n Static index pruning techniques permanently remove a presumably redundant part of an inverted f...", "weights": []}, "2012.wwwconf_conference-2012c.168": {"snippet": "Query spelling correction using multi-task learning\n\n\n ABSTRACTThis paper explores the use of online multi-task learning for search query spelling correction, by effectively transferring information f...", "weights": []}, "2010.spire_conference-2010.0": {"snippet": "...that queries are issued by trained library scientists. Consequently, early ranking algorithms leveraged textual features, based on the words or \"terms\" contained in documents and queries. Examples of ...", "weights": [[8, 15, 1.0], [182, 189, 1.0]]}, "2015.fire_conference-2015.10": {"snippet": "MESS: A Multilingual Error based String Similarity measure for transliterated name variants\n\n\n ABSTRACTCross-lingual name matching is an important problem in the fields of machine translation and data...", "weights": []}, "2002.tois_journal-ir0volumeA20A3.1": {"snippet": "...uage queries\n\n\n Missing information, imprecision, inconsistency, vagueness, uncertainty, and ignorance abound in information systems. Such imperfection is a fact of life in database systems. Although ...", "weights": [[8, 15, 1.0]]}, "2010.tweb_journal-ir0volumeA4A4.2": {"snippet": "...gine Queries\n\n\n Web search engines can perform poorly for long queries (i.e., those containing four or more terms), in part because of their high level of query specificity. The automatic assignment o...", "weights": [[8, 15, 1.0], [66, 73, 1.0]]}, "W15-1901": {"snippet": "Misspellings in Responses to Listening Comprehension Questions: Prospects for Scoring based on Phonetic Normalization\n\n\n Automated scoring systems which evaluate content require robust ways of dealing...", "weights": [[0, 12, 1.0]]}, "2022.findings-acl.98": {"snippet": "{MDCS}pell: A Multi-task Detector-Corrector Framework for {C}hinese Spelling Correction\n\n\n Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts. CSC...", "weights": []}, "2007.tois_journal-ir0volumeA25A2.3": {"snippet": "... Web queries using very large unlabeled query logs\n\n\n Accurate topical classification of user queries allows for increased effectiveness and efficiency in general-purpose Web search systems. Such clas...", "weights": [[8, 15, 1.0], [97, 104, 1.0]]}, "2019.wsdm_conference-2019.9": {"snippet": "...sive misspellings and user-created abbreviations for these mentions. In addition, these mentions are usually context dependent. In this paper, we propose a neural approach with hierarchical tweet repr...", "weights": [[8, 20, 1.0]]}, "2014.sigirconf_conference-2014.143": {"snippet": "...arch queries, in particular by avoiding common mistakes such as misspellings and using particularly ambiguous queries.\nCategories and Subject DescriptorsHowever, the details of user's interactions wit...", "weights": [[8, 15, 1.0], [67, 79, 1.0], [113, 120, 1.0]]}, "2016.sigirconf_conference-2016.43": {"snippet": "...ting queries to explore diverse aspects and deciding when to stop searching. With the goal of helping searchers discover unexplored aspects and find the appropriate timing for search stopping in intri...", "weights": [[8, 15, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.3": {"snippet": "...uous queries in web search\n\n\n a b s t r a c tIt is widely believed that many queries submitted to search engines are inherently ambiguous (e.g., java and apple). However, few studies have tried to cla...", "weights": [[8, 15, 1.0], [80, 87, 1.0]]}, "H05-1120": {"snippet": "Learning a Spelling Error Model from Search Query Logs\n\n\n Applying the noisy channel model to search query spelling correction requires an error model and a language model. Typically, the error model ...", "weights": []}, "2011.wwwconf_conference-2011.15": {"snippet": "...ines queries. In order to help users effectively express their information needs, mechanisms for automatically correcting misspelled queries are required. Online spelling correction aims to provide sp...", "weights": [[8, 15, 1.0], [136, 143, 1.0]]}, "2003.cikm_conference-2003.2": {"snippet": "...ated queries", "weights": [[8, 15, 1.0]]}, "2008.cikm_workshop-2008wicow.8": {"snippet": "...gine queries on a pre-determined set of commonly misspelled words.", "weights": [[8, 15, 1.0]]}, "2007.clef_workshop-2007.98": {"snippet": "... Web Queries\n\n\n Abstract. In this paper we present preliminary results for a patternbased approach to parse web-based queries. The approach is designed to identify and categorize queries that include ...", "weights": [[8, 15, 1.0], [121, 128, 1.0], [182, 189, 1.0]]}, "2021.eacl-main.250": {"snippet": "...t to misspellings and perturbations of the input text. 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Our approach uses simple...", "weights": [[8, 16, 1.0], [58, 66, 1.0], [162, 170, 1.0]]}, "C82-2065": {"snippet": "...}: A Language Synthetizing Natural Language, Computer Language and Logic", "weights": [[8, 16, 1.0], [38, 46, 1.0], [57, 65, 1.0]]}, "Y05-1027": {"snippet": "Language Identification for Person Names Based on Statistical Information\n\n\n Language identification has been an interesting and fascinating issue in natural language processing for decades, and there...", "weights": [[0, 8, 1.0], [62, 73, 1.0], [77, 85, 1.0], [158, 166, 1.0]]}, "2001.clef_workshop-2000w.5": {"snippet": "...tudy different types of compound languages, and to study the effects query structuring in different languages. We designed and implemented a method for automated query construction in FIN \uf8ecSWE \uf8ecGER-> ...", "weights": [[8, 17, 1.0], [93, 102, 1.0]]}, "W07-1302": {"snippet": "...even different phonetic similarity metrics are implemented and evaluated on their effectiveness within such multilateral comparison systems when deployed to detect genetic relations among the Indo-Eur...", "weights": [[8, 17, 1.0]]}, "W16-3716": {"snippet": "...ross-Language Information Retrieval using Multilingual Word Clusters\n\n\n In Cross-Language Information Retrieval, finding the appropriate translation of the source language query has always been a diff...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [84, 92, 1.0], [93, 104, 1.0], [166, 174, 1.0]]}, "W13-0206": {"snippet": "...free language and a regular language results in a context-free language. Previous work on subsumption and entailment is inapplicable to context-free languages, due to the undecidability of the subset ...", "weights": [[8, 16, 1.0], [31, 39, 1.0], [66, 74, 1.0]]}, "P19-1395": {"snippet": "... one language. However, current representations in machine learning are language dependent. In this work, we present a method to decouple the language from the problem by learning language agnostic re...", "weights": [[8, 16, 1.0], [75, 83, 1.0], [145, 153, 1.0], [183, 191, 1.0]]}, "2019.ipm_journal-ir0volumeA56A4.12": {"snippet": "...line information seeker group had geographically wider movement patterns than the online information seekers. In other words, the offline information seeker group showed movements in various direction...", "weights": [[8, 19, 1.0], [92, 103, 1.0], [141, 152, 1.0]]}, "C14-1074": {"snippet": "Language for Communication: Language as Rational Inference", "weights": [[0, 8, 1.0], [28, 36, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.7": {"snippet": "...acy. Information retrieval tests on KTSET and NTCIR-1 test collection show that our transliteration model can improve the performance of an information retrieval system about 10-34%.", "weights": [[8, 19, 1.0], [143, 154, 1.0]]}, "2022.naacl-main.114": {"snippet": "...rive language-universal abstractions about grammar. In particular, we investigate whether morphosyntactic information is encoded in the same subset of neurons in different languages. We conduct the fi...", "weights": [[8, 16, 1.0], [109, 120, 1.0], [165, 174, 1.0]]}, "2007.tc-1.14": {"snippet": "...ness information management in the language industry\n\n\n LTC has almost a decade of experience in helping corporate clients and language companies implement business information and workflow control to...", "weights": [[8, 19, 1.0], [38, 46, 1.0], [130, 138, 1.0], [168, 179, 1.0]]}, "W16-4807": {"snippet": "...atic language widely spoken in North Africa. It is a minority language compared to Arabic. 2 Among the freely available language identification tools, we tried Google Language Identifier, Open Xerox l...", "weights": [[8, 16, 1.0], [65, 73, 1.0], [123, 131, 1.0], [170, 178, 1.0], [202, 210, 1.0]]}, "N19-1006": {"snippet": "...ared language in these tasks is the target language text, not the source language audio. Applying this insight, we show that pre-training on ASR helps ST even when the ASR language differs from both s...", "weights": [[8, 16, 1.0], [46, 54, 1.0], [76, 84, 1.0], [175, 183, 1.0]]}, "2022.bea-1.19": {"snippet": "...rman language learners with broad linguistic modeling or readability formulas: When do linguistic insights make a difference?\n\n\n The paper presents a new state-of-the-art sentence-wise readability ass...", "weights": [[8, 16, 1.0]]}, "2005.clef_workshop-2005w.8": {"snippet": "...ural Language Processing to Ad-Hoc Cross Language Information Retrieval\n\n\n In the CLEF 2005 Ad-Hoc Track we experimented with language-specific morphosyntactic processing and light Natural Language Pr...", "weights": [[8, 16, 1.0], [44, 52, 1.0], [53, 64, 1.0], [129, 137, 1.0], [192, 200, 1.0]]}, "C10-1044": {"snippet": "...etic language groups, which are motivated largely by genetic relationships, with language groupings formed by clustering methods using typological features only. Using data from the World Atlas of Lan...", "weights": [[8, 16, 1.0], [84, 92, 1.0], [200, 208, 1.0]]}, "2009.clef_workshop-2009.7": {"snippet": "...ross-Language Information Retrieval Using Meta-language Index Construction and Structural Queries", "weights": [[8, 16, 1.0], [17, 28, 1.0], [50, 58, 1.0]]}, "L06-1274": {"snippet": "...ring Language Identification for Written Language Resources", "weights": [[8, 16, 1.0], [44, 52, 1.0]]}, "N10-1027": {"snippet": "Language Identification: The Long and the Short of the Matter\n\n\n Language identification is the task of identifying the language a given document is written in. This paper describes a detailed examina...", "weights": [[0, 8, 1.0], [65, 73, 1.0], [120, 128, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.11": {"snippet": "...urce language differ from the grammatical structures of the target language because the acquired rules have the information to cope with the different word orders of source language and target languag...", "weights": [[8, 16, 1.0], [70, 78, 1.0], [115, 126, 1.0], [144, 153, 1.0], [176, 184, 1.0], [196, 204, 1.0]]}, "P06-1099": {"snippet": "...eral-language collocations. The implicit assumption of this research was that newly designed term measures involving more sophisticated statistical criteria would outperform simple counts of cooccurre...", "weights": [[8, 16, 1.0]]}, "2020.ldl-1.4": {"snippet": "...ning different types of knowledge about a given natural language. As a case study, we develop and present a language profile of an example language. At this stage, a language profile is an independent...", "weights": [[8, 17, 1.0], [59, 67, 1.0], [111, 119, 1.0], [142, 150, 1.0], [169, 177, 1.0]]}, "2002.sigirconf_conference-2002.7": {"snippet": "...itle language model for information retrieval\n\n\n ABSTRACTIn this paper, we propose a new language model, namely, a title language model, for information retrieval. Different from the traditional langu...", "weights": [[8, 16, 1.0], [27, 38, 1.0], [92, 100, 1.0], [124, 132, 1.0], [144, 155, 1.0], [167, 176, 1.0], [198, 206, 1.0]]}, "2021.bea-1.7": {"snippet": "...tive language transfer in learner errors using {POS} information\n\n\n A common mistake made by language learners is the misguided usage of first language rules when communicating in another language. In...", "weights": [[8, 16, 1.0], [56, 67, 1.0], [96, 104, 1.0], [146, 154, 1.0], [191, 199, 1.0]]}, "L10-1432": {"snippet": "Language Technology Challenges of a {`}Small{'} Language ({C}atalan)", "weights": [[0, 8, 1.0], [48, 56, 1.0]]}, "N07-5002": {"snippet": "...ical Language Models for Information Retrieval\n\n\n Statistical language models have recently been successfully applied to many information retrieval problems. A great deal of recent work has shown that...", "weights": [[8, 16, 1.0], [28, 39, 1.0], [65, 73, 1.0], [129, 140, 1.0]]}, "2013.cikm_conference-2013.185": {"snippet": "...ross language similarity and activity\n\n\n ABSTRACTWikipedia is an online free and open access encyclopedia available in many languages. Wikipedia articles across over 280 languages are written by milli...", "weights": [[8, 16, 1.0]]}, "I08-1058": {"snippet": "...me a different language is used. This is cumbersome, especially when language changes are frequent. To solve this problem, we propose TypeAny, a novel front-end interface that detects the language of ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [72, 80, 1.0], [191, 199, 1.0]]}, "N16-1149": {"snippet": "...side information over the baselines for two different datasets and genres in two languages. Interestingly, we found that side information in a foreign language can be highly beneficial in modelling te...", "weights": [[8, 19, 1.0], [47, 56, 1.0], [129, 140, 1.0], [154, 162, 1.0]]}, "2019.icon-1.7": {"snippet": "...ross-Language Information Retrieval\n\n\n This paper describes our first experiment on Neural Machine Translation (NMT) based query translation for Amharic-Arabic Cross-Language Information Retrieval (CL...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [169, 177, 1.0], [178, 189, 1.0]]}, "2012.wwwconf_conference-2012c.153": {"snippet": "...lish language, Yoruba language, and other commonly used \"pidgin\" Yoruba language words. Since Yoruba is still a resource constrained language, existing sentiment or subjectivity analysis algorithms ha...", "weights": [[8, 16, 1.0], [25, 33, 1.0], [75, 83, 1.0], [136, 144, 1.0]]}, "P19-1017": {"snippet": "...d on language pairs whose best translation path is available and is applied to the unseen language pairs for path selection. Experiments on 20 languages and 294 distant language pairs demonstrate the ...", "weights": [[8, 16, 1.0], [93, 101, 1.0], [172, 180, 1.0]]}, "W11-3312": {"snippet": "... the Language Library, a new initiative that has the purpose of building a huge archive of structured colletion of linguistic information. The Language Library is conceived as a community built reposi...", "weights": [[8, 16, 1.0], [129, 140, 1.0], [146, 154, 1.0]]}, "2004.clef_workshop-2004.36": {"snippet": "...ross-language information access. The task was to answer factual questions from a large collection of documents written in a language in which the user has little proficiency. An interactive cross-lan...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [128, 136, 1.0], [200, 208, 1.0]]}, "2005.cikm_conference-2005.111": {"snippet": "...ent, information retrieval and information integration. We present a general language modeling method for quantifying the difficulty of information extraction tasks. We demonstrate the viability of ou...", "weights": [[8, 19, 1.0], [34, 45, 1.0], [80, 88, 1.0], [139, 150, 1.0]]}, "2021.americasnlp-1.3": {"snippet": "...ased language models\n\n\n We describe experiments with character-based language modeling for written variants of Nahuatl. Using a standard LSTM model and publicly available Bible translations, we explor...", "weights": [[8, 16, 1.0], [72, 80, 1.0]]}, "2011.sigirconf_conference-2011.76": {"snippet": "...n in different languages about a hot news event. The news articles in different languages are written in different ways to reflect different standpoints. For example, the Chinese news agencies and the...", "weights": [[8, 17, 1.0], [73, 82, 1.0], [108, 117, 1.0], [134, 143, 1.0]]}, "H89-2077": {"snippet": "...same language. The ultimate goal of work on spoken language systems is to overcome this language barrier by building systems that provide the necessary interpretive function between various languages,...", "weights": [[8, 16, 1.0], [54, 62, 1.0], [91, 99, 1.0]]}, "C12-1057": {"snippet": "... use information from documents in the source language. We overcome this limitation by using information from retrieved documents in the source language to improve the retrieval quality of the target ...", "weights": [[8, 19, 1.0], [49, 57, 1.0], [96, 107, 1.0], [147, 155, 1.0]]}, "P10-1095": {"snippet": "...each language and the same model can be used across at least two different languages.", "weights": [[8, 16, 1.0], [68, 77, 1.0]]}, "2021.iwpt-1.9": {"snippet": "...ific language combinations involving contemporary contact languages or genetically related languages. The results of the experiments show that multilingual models that involve high-resource languages ...", "weights": [[8, 16, 1.0]]}, "2022.lchange-1.8": {"snippet": "...ther language proximity models has been an active research area for many years. In this paper we explore a part-of-speech model for defining proximity between languages using a multilingual language m...", "weights": [[8, 16, 1.0], [193, 201, 1.0]]}, "2014.sigirconf_conference-2014.163": {"snippet": "...h as Information Difference, which measures the magnitude of the difference between search engines in their ranking of documents. for which we have relevance information. Using Information Difference ...", "weights": [[8, 19, 1.0], [161, 172, 1.0], [180, 191, 1.0]]}, "2004.clef_workshop-2004.47": {"snippet": "...urce Language in Bilingual Question Answering\n\n\n Abstract. This paper presents a bilingual question answering system that has Finnish as its source language and English as its target language. The sys...", "weights": [[8, 16, 1.0], [151, 159, 1.0], [186, 194, 1.0]]}, "2016.sigirconf_workshop-2016sal.6": {"snippet": "...cond-Language Searching\n\n\n In this paper we describe learning that happens when searchers look for information in a foreign language. A strong disparity exists between the language distribution of Web...", "weights": [[8, 16, 1.0], [102, 113, 1.0], [127, 135, 1.0], [175, 183, 1.0]]}, "2010.clef_workshop-2010w.61": {"snippet": "...ural language used in the queries. Language identification is in fact a key task within Cross Language Information Retrieval systems and the challenge is particularly difficult in the case of search q...", "weights": [[8, 16, 1.0], [38, 46, 1.0], [97, 105, 1.0], [106, 117, 1.0]]}, "2008.clef_workshop-2008w.152": {"snippet": "...s in different languages. Especially in morphologically complex languages, such as Finnish, Turkish and Arabic, morpheme analysis is important for lexical modeling of words in speech recognition, info...", "weights": [[8, 17, 1.0], [199, 210, 1.0]]}, "2021.acl-long.66": {"snippet": "...hree different language families and show that our technique significantly improves translation into low-resource language compared to other translation baselines.", "weights": [[8, 17, 1.0], [18, 26, 1.0], [117, 125, 1.0]]}, "2020.sltu-1.38": {"snippet": "...ased language models for Uyghur, a language that combines both challenges, i.e. it is a low-resource and agglutinative language. Fortunately, there exists a closely-related rich-resource language, nam...", "weights": [[8, 16, 1.0], [38, 46, 1.0], [122, 130, 1.0], [190, 198, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.13": {"snippet": "... The language modeling approach to information retrieval has recently attracted much attention. In the language modeling approach, we assume that a query is a sample drawn from a language model: given...", "weights": [[8, 16, 1.0], [38, 49, 1.0], [106, 114, 1.0], [182, 190, 1.0]]}, "W19-6808": {"snippet": "...dent language and return initial results within 24 hours. Given this surprise language evaluation scenario, Representative Language packs are designed to support research into cross-language projectio...", "weights": [[8, 16, 1.0], [81, 89, 1.0], [126, 134, 1.0], [185, 193, 1.0]]}, "2016.fire_conference-2016.3": {"snippet": "...gual Information Retrieval for Bantu Languages\n\n\n ABSTRACTDespite the availability of massive open information and efforts to promote multilingualism on the Web, content in Bantu languages remains neg...", "weights": [[8, 19, 1.0], [102, 113, 1.0]]}, "2020.sltu-1.39": {"snippet": "...ding language-independent situational awareness in emerging scenarios in a low resource language context. Twenty-four Representative Language Packs cover a broad range of language families and typolog...", "weights": [[8, 16, 1.0], [91, 99, 1.0], [136, 144, 1.0], [174, 182, 1.0]]}, "2018.fire_conference-2018w.14": {"snippet": "...tive Language Identification\n\n\n Native Language Identification (NLI) is the task which involves identification of native language (L1) of an individual based on his/her language production in a learne...", "weights": [[8, 16, 1.0], [42, 50, 1.0], [124, 132, 1.0], [172, 180, 1.0]]}, "2013.wwwconf_conference-2013c.27": {"snippet": "...tive language. This makes it non-trivial for a machine to distinguish the language of one comment from another. This situation presents an opportunity for offering following language based services fo...", "weights": [[8, 16, 1.0], [77, 85, 1.0], [177, 185, 1.0]]}, "N16-2017": {"snippet": "...late language models trained on the target language and on the related language. In our second experiment, we select the sentences most similar to the target language and add them to our training corp...", "weights": [[8, 16, 1.0], [46, 54, 1.0], [74, 82, 1.0], [161, 169, 1.0]]}, "2006.wwwconf_conference-2006.201": {"snippet": "... the different data formats available on the Web. In contrast, Xcerpt is a versatile semi-structured query language, i.e., a query language able to access all kinds of Web data such as XML and RDF in ...", "weights": [[8, 17, 1.0], [110, 118, 1.0], [134, 142, 1.0]]}, "2018.fire_conference-2018.5": {"snippet": "... and Information Extraction (IE) tasks. This overview paper will be detailing more on the existing Indian language corpora development and the steps taken for building our own corpus along with its st", "weights": [[8, 19, 1.0], [109, 117, 1.0]]}, "2020.repl4nlp-1.16": {"snippet": "...thin-language performance. We consider three tasks: Named Entity Recognition (99 languages), Part-of-speech Tagging, and Dependency Parsing (54 languages each). mBERT does better than or comparable to...", "weights": [[8, 16, 1.0]]}, "P91-1017": {"snippet": "... one language using statistical data on lexical relations in another language. This approach exploits the differences between mappings of words to senses in different languages. We concentrate on the ...", "weights": [[8, 16, 1.0], [72, 80, 1.0], [160, 169, 1.0]]}, "2018.chiir_conference-2018.23": {"snippet": "...ment information, and academic resources, go online shopping, seek personal health information, and need social networking. Findings highlight when the participants need prompt information, they searc...", "weights": [[8, 19, 1.0], [86, 97, 1.0], [180, 191, 1.0]]}, "1999.trec_conference-1999.18": {"snippet": "...many different languages. Compared to the usual definition of cross-language information retrieval, where systems work with a single language pair, retrieving documents in a language L1 using queries ...", "weights": [[8, 17, 1.0], [71, 79, 1.0], [80, 91, 1.0], [136, 144, 1.0], [177, 185, 1.0]]}, "W18-5120": {"snippet": "...led \"language ideologies\"-the set of beliefs and ways of speaking about language-in the practices of abusive language classification in modern machine learning-based NLP. This argument is made at both...", "weights": [[8, 16, 1.0], [75, 83, 1.0], [112, 120, 1.0]]}, "1963.ipm_journal-ir0volumeA1A23.3": {"snippet": "...n an information language for geometry and a translation algorithm from Russian into this information language", "weights": [[8, 19, 1.0], [20, 28, 1.0], [93, 104, 1.0], [105, 113, 1.0]]}, "D08-1063": {"snippet": "...ting information from a language with richer resources such as English into a foreign language alone (no resources or models in the foreign language). Furthermore, while examining the performance usin...", "weights": [[8, 19, 1.0], [27, 35, 1.0], [89, 97, 1.0], [143, 151, 1.0]]}, "W99-0508": {"snippet": "...from different language famdles, extracted from an on-hne parallel corpus of George Orwell's Nmeteen Eighty-Four The goal of the study is to determine the degree to which translatmn equivalents for di...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [201, 210, 1.0]]}, "2020.lrec-1.458": {"snippet": "...ghly language-specific: adding more languages to a training set is often better, but too many harms performance-the best number depends on the source language. Furthermore, training on related languag...", "weights": [[8, 16, 1.0], [153, 161, 1.0]]}, "P84-1075": {"snippet": "...uter Language for Linguistic Information\n\n\n A considerable body of accumulated knowledge about the design of languages for communicating information to computers has been derived from the subfields of...", "weights": [[8, 16, 1.0], [32, 43, 1.0], [140, 151, 1.0]]}, "2020.findings-emnlp.11": {"snippet": "...tial information is fused with or disentangled from the contextual information to facilitate final knowledge selection. Automatic, human observational, and interactive evaluation shows that our method...", "weights": [[8, 19, 1.0], [70, 81, 1.0]]}, "1997.mtsummit-workshop.6": {"snippet": "..., on language-specific information in an interlingual approach to machine translation. At that workshop I argued that language-specific information must be present in the IL representation, since any ...", "weights": [[8, 16, 1.0], [26, 37, 1.0], [121, 129, 1.0], [139, 150, 1.0]]}, "S13-2023": {"snippet": "...n in different languages. Our approach is language independent, and was used to participate in the CLTE task (Task#8) organized within Semeval 2013 (Negri et al., 2013) . The four runs submitted, one ...", "weights": [[8, 17, 1.0], [45, 53, 1.0]]}, "W96-0514": {"snippet": "...d by different means in different languages. My approach constitutes a cognitively oriented and highly contextdependent model for computing the informational status embedded in a concept-tospeech mode...", "weights": [[8, 17, 1.0], [27, 36, 1.0]]}, "2020.lt4gov-1.5": {"snippet": "...rian Language Resource Portal stresses the importance of language resources specific to a language variety, thus paving the way for the re-use of variety-specific language data for human language tech...", "weights": [[8, 16, 1.0], [60, 68, 1.0], [93, 101, 1.0], [166, 174, 1.0], [190, 198, 1.0]]}, "E12-3010": {"snippet": "...rop\" language. We use the Europarl corpus to evaluate two MT systems on their performance regarding null subject translation: Its-2, a rule-based system developed at LATL, and a statistical system bui...", "weights": [[8, 16, 1.0]]}, "2009.clef_workshop-2009w.93": {"snippet": "...s in different languages were evaluated in various practical applications. Morpheme analysis is particularly useful in speech recognition, information retrieval and machine translation for morphologic...", "weights": [[8, 17, 1.0], [142, 153, 1.0]]}, "P11-1155": {"snippet": "... the information from the original documents in the source language or the information from the translated documents in the target language. In this study, we propose to use the bilingual information ...", "weights": [[8, 19, 1.0], [62, 70, 1.0], [78, 89, 1.0], [134, 142, 1.0], [191, 202, 1.0]]}, "2003.tc-1.9": {"snippet": "... The language industry is currently facing a knowledge management crisis. The rapidly growing gap between the demand for high quality multilingual content and the lag in the supply of language profess...", "weights": [[8, 16, 1.0], [187, 195, 1.0]]}, "2022.acl-long.504": {"snippet": "...cial Language: Studying Transferable Knowledge in Language Models\n\n\n We investigate what kind of structural knowledge learned in neural network encoders is transferable to processing natural language....", "weights": [[8, 16, 1.0], [53, 61, 1.0], [194, 202, 1.0]]}, "O00-3003": {"snippet": "...gual information can be divided into two major categories: (1) alphabetic language information (English-like alphabetic languages) and ( 2 ) ideographic language information (Chinese-like ideographic ...", "weights": [[8, 19, 1.0], [77, 85, 1.0], [86, 97, 1.0], [156, 164, 1.0], [165, 176, 1.0]]}, "2016.iwslt-1.8": {"snippet": "...urce language affect the efficiency of the MLAN schemes. We experiment with three different scenarios using, i) French, as a source language uncorrelated to the target language, ii) Ukrainian, as a so...", "weights": [[8, 16, 1.0], [85, 94, 1.0], [135, 143, 1.0], [171, 179, 1.0]]}, "2004.airs_conference-2004.26": {"snippet": "...ocal information through the Internet for use in daily activities. Conventional mobile information services focus on providing location information related to the user's current location. In contrast,...", "weights": [[8, 19, 1.0], [90, 101, 1.0], [139, 150, 1.0]]}, "2003.clef_workshop-2003w.24": {"snippet": "...gual information retrieval tasks of the Cross-Language Evaluation Forum (CLEF) 2003: for natural language queries in 9 European languages (German, French, Italian, Spanish, Dutch, Finnish, Swedish, Ru...", "weights": [[8, 19, 1.0], [49, 57, 1.0], [100, 108, 1.0]]}, "2011.clef_workshop-2011w.44": {"snippet": "...face Language, User Language and Success Rates in the European Library\n\n\n In this paper, TEL 2010 action logs are analyzed with a particular focus on the impact of language (user native language and i...", "weights": [[8, 16, 1.0], [23, 31, 1.0], [167, 175, 1.0], [189, 197, 1.0]]}, "2004.clef_workshop-2004.33": {"snippet": "...ross-Language Question Answering: Searching Passages Versus Searching Documents\n\n\n Abstract. iCLEF 2004 is the first comparative evaluation of interactive Cross-Language Question Answering systems. Th...", "weights": [[8, 16, 1.0], [164, 172, 1.0]]}, "2000.amta-papers.8": {"snippet": "Information structure transfer: bridging the information gap in structurally different languages", "weights": [[0, 11, 1.0], [45, 56, 1.0], [77, 86, 1.0]]}, "W03-1120": {"snippet": "...ross-Language Information Retrieval Based on Category Matching Between Language Versions of a Web Directory\n\n\n Since the Web consists of documents in various domains or genres, the method for Cross-La...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [74, 82, 1.0], [201, 209, 1.0]]}, "W05-0705": {"snippet": "...ural Language Processing System for {E}uropean Languages to Treat {A}rabic in Information Processing and Information Retrieval Applications\n\n\n The goal of many natural language processing platforms is...", "weights": [[8, 16, 1.0], [81, 92, 1.0], [108, 119, 1.0], [171, 179, 1.0]]}, "2021.vardial-1.8": {"snippet": "...atic language identification is a challenging problem. Discriminating between closely related languages is especially difficult. This paper presents a machine learning approach for automatic language ...", "weights": [[8, 16, 1.0], [194, 202, 1.0]]}, "W17-7505": {"snippet": "...from different language families.", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "N06-5003": {"snippet": "...ural language processing and information retrieval. However, most of the times, they are perceived as different disciplines, with different algorithms, different applications, and different potential ...", "weights": [[8, 16, 1.0], [32, 43, 1.0], [105, 114, 1.0], [133, 142, 1.0], [155, 164, 1.0], [183, 192, 1.0]]}, "D13-1187": {"snippet": "...phic Language Variations to Improve Multilingual Sentiment Analysis in Social Media\n\n\n Different demographics, e.g., gender or age, can demonstrate substantial variation in their language use, particu...", "weights": [[8, 16, 1.0], [90, 99, 1.0], [182, 190, 1.0]]}, "W09-1602": {"snippet": "...uasi-language-independent subword recognizer trained on multiple languages, to obtain an abstracted representation of speech data in an unknown language. Languageindependent query expansion is achieve...", "weights": [[8, 16, 1.0], [147, 155, 1.0]]}, "W16-1208": {"snippet": "...ding Language Divergence ({WELD}) as a Quantitative Measure of Language Distance\n\n\n We introduce a new measure of distance between languages based on word embedding, called word embedding language div...", "weights": [[8, 16, 1.0], [66, 74, 1.0], [191, 199, 1.0]]}, "D18-1334": {"snippet": "...nder information into NMT systems. Our contribution is twofold: (1) the compilation of large datasets with speaker information for 20 language pairs, and (2) a simple set of experiments that incorpora...", "weights": [[8, 19, 1.0], [118, 129, 1.0], [137, 145, 1.0]]}, "2020.udw-1.2": {"snippet": "...sian language which shares little typological similarity with any existing high-resource languages. We show that a monolingual model developed on minimal target language data consistently outperforms ...", "weights": [[8, 16, 1.0], [164, 172, 1.0]]}, "L18-1352": {"snippet": "...ific language combinations enable improved dependency parsing when compared to previous work, allowing for wider reuse of pre-existing resources when parsing low-resource languages. The study also exp...", "weights": [[8, 16, 1.0]]}, "2005.clef_workshop-2005w.118": {"snippet": "...r an information retrieval task. We used them under different forms in different information retrieval models, particularly in a language model. For the bilingual part we tried two simple tests on Spa...", "weights": [[8, 19, 1.0], [55, 64, 1.0], [74, 83, 1.0], [84, 95, 1.0], [132, 140, 1.0]]}, "C12-2095": {"snippet": "...s of Language Structures (WALS) has spurred investigations regarding their utility for language classification, the stability of typological features in genetic linguistics and typological universals ...", "weights": [[8, 16, 1.0], [90, 98, 1.0]]}, "C69-3401": {"snippet": "...ible Language System (The {REL} Language Processor)\n\n\n REL, Rapidly Extenslble Language System, permits a variety of languages to coexist within a single computer system. Here the term \"language\" is u...", "weights": [[8, 16, 1.0], [35, 43, 1.0], [82, 90, 1.0], [189, 197, 1.0]]}, "2003.clef_workshop-2003w.52": {"snippet": "The University of Amsterdam at CLEF 2003\n\n\n This paper describes our official runs for CLEF 2003. We took part in the monolingual task (for Dutch, Finnish, French, German, Italian, Russian, Spanish, a...", "weights": []}, "2004.clef_workshop-2004.79": {"snippet": "...ross-Language Spoken Document Retrieval Track", "weights": [[8, 16, 1.0]]}, "1990.tois_journal-ir0volumeA8A4.0": {"snippet": "...bout Information Systems\n\n\n We describe Telos, a language intended to support the development of information systems. The design principles for the language are based on the premise that information s...", "weights": [[8, 19, 1.0], [52, 60, 1.0], [100, 111, 1.0], [151, 159, 1.0], [190, 201, 1.0]]}, "2015.wwwconf_conference-2015c.204": {"snippet": "...eign Language\n\n\n ABSTRACTOnline searching is a central element of internet users' information behaviors. Searching is usually executed in a user's native language, but searching in English as a foreig...", "weights": [[8, 16, 1.0], [85, 96, 1.0], [157, 165, 1.0]]}, "N18-2085": {"snippet": "...same information. We then conduct a study on 21 languages, demonstrating that in some languages, the textual expression of the information is harder to predict with both n-gram and LSTM language model...", "weights": [[8, 19, 1.0], [130, 141, 1.0], [189, 197, 1.0]]}, "2013.cikm_workshop-2013unlp.3": {"snippet": "...from different sources but also provides the opportunity to generate new information out of the complementary information in different sources and levels of description. This paper investigates the po...", "weights": [[8, 17, 1.0], [76, 87, 1.0], [113, 124, 1.0], [128, 137, 1.0]]}, "R15-1065": {"snippet": "...ural Language Query into Different {SQL} Clauses\n\n\n Attribute information in a natural language query is one of the key features for converting a natural language query into a Structured Query Languag...", "weights": [[8, 16, 1.0], [28, 37, 1.0], [65, 76, 1.0], [90, 98, 1.0], [157, 165, 1.0], [196, 204, 1.0]]}, "I08-6001": {"snippet": "...s of Language Relatedness on Multilingual Information Retrieval: A Case Study With {I}ndo-{E}uropean and {S}emitic Languages\n\n\n We explore the effects of language relatedness within a multilingual inf...", "weights": [[8, 16, 1.0], [45, 56, 1.0], [157, 165, 1.0], [200, 211, 1.0]]}, "2020.acl-main.737": {"snippet": "Phonetic and Visual Priors for Decipherment of Informal {R}omanization\n\n\n Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script ...", "weights": []}, "2020.lrec-1.289": {"snippet": "...ver, different language editions of Wikipedia differ significantly in terms of their information coverage. We present a systematic comparison of information coverage in English Wikipedia (most exhaust...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [88, 99, 1.0], [148, 159, 1.0]]}, "2001.clef_workshop-2001w.12": {"snippet": "...gual Information Retrieval tasks (German, French, Italian, Spanish and Dutch) of the Cross-Language Evaluation Forum (CLEF) 2001. For each language, at least one SearchServer run had more average prec...", "weights": [[8, 19, 1.0], [94, 102, 1.0], [142, 150, 1.0]]}, "2004.ipm_journal-ir0volumeA40A6.3": {"snippet": "... the information in languages other than English has grown exponentially. Cross-lingual information retrieval (CLIR) across different European languages, such as English, Spanish, and French, has been...", "weights": [[8, 19, 1.0], [91, 102, 1.0], [127, 136, 1.0]]}, "E03-2009": {"snippet": "...able information extraction tool\n\n\n In this demo we will present GATE, an architecture and framework for language engineering, and ANNIE, an information extraction system developed within it. We will ...", "weights": [[8, 19, 1.0], [108, 116, 1.0], [144, 155, 1.0]]}, "2016.chiir_conference-2016.62": {"snippet": "...cond language or foreign languages when they search for information online. The difference between a second language and a foreign language is that the second language is spoken in the immediate envir...", "weights": [[8, 16, 1.0], [59, 70, 1.0], [111, 119, 1.0], [134, 142, 1.0], [162, 170, 1.0]]}, "1999.sigirconf_conference-99.43": {"snippet": "...ding language models of these documents. One obstacle in applying statistical language modeling to information retrieval is the sparse data problem. It is serious in information retrieval for two reas...", "weights": [[8, 16, 1.0], [81, 89, 1.0], [102, 113, 1.0], [169, 180, 1.0]]}, "I08-3008": {"snippet": "...ross-Language Parser Adaptation between Related Languages\n\n\n The present paper describes an approach to adapting a parser to a new language. Presumably the target language is much poorer in linguistic...", "weights": [[8, 16, 1.0], [134, 142, 1.0], [166, 174, 1.0]]}, "2004.clef_workshop-2004.38": {"snippet": "...ross-language combinations between nine source languages and seven target languages were exploited to set up more than fifty different tasks, both monolingual and bilingual. New types of questions (Ho...", "weights": [[8, 16, 1.0], [128, 137, 1.0]]}, "2019.ipm_journal-ir0volumeA56A1.5": {"snippet": "The language of information need: Differentiating conscious and formalized information needs\n\n\n information need classification. We analyse the linguistic differences between conscious and formalized ...", "weights": [[4, 12, 1.0], [16, 27, 1.0], [75, 86, 1.0], [96, 107, 1.0]]}, "W08-1909": {"snippet": "...ther language, but lexical functions represented by these two constructions are different. The paper lists different types of correspondences and gives the reasons for their existence. It also shows h...", "weights": [[8, 16, 1.0], [83, 92, 1.0], [110, 119, 1.0]]}, "L16-1521": {"snippet": "...each language. Each representative language corpus, therefore, both stands on its own as a resource for the specific language and forms part of a large multilingual resource for broader cross-language...", "weights": [[8, 16, 1.0], [38, 46, 1.0], [120, 128, 1.0], [195, 203, 1.0]]}, "2013.ecir_conference-2013.66": {"snippet": "...ross-Language Plagiarism Detection Using a Multilingual Semantic Network\n\n\n Abstract. Cross-language plagiarism refers to the type of plagiarism where the source and suspicious documents are in differ...", "weights": [[8, 16, 1.0], [95, 103, 1.0], [197, 206, 1.0]]}, "L08-1007": {"snippet": "Language-Sites: Accessing and Presenting Language Resources via Geographic Information Systems", "weights": [[0, 8, 1.0], [41, 49, 1.0], [75, 86, 1.0]]}, "D17-1053": {"snippet": "...ific language, and different classification models are used in different languages. In this paper we aim to build a universal sentiment classifier with a single classification model in multiple differ...", "weights": [[8, 16, 1.0], [22, 31, 1.0], [66, 75, 1.0], [197, 206, 1.0]]}, "W07-1703": {"snippet": "A Language Independent Approach for Name Categorization and Discrimination\n\n\n We present a language independent approach for fine-grained categorization and discrimination of names on the basis of tex...", "weights": [[2, 10, 1.0], [91, 99, 1.0]]}, "W15-4623": {"snippet": "...oken information access in English. Our new demo shows a robot speaking different languages, getting content from different language Wikipedias, and switching languages to meet the linguistic capabili...", "weights": [[8, 19, 1.0], [75, 84, 1.0], [117, 126, 1.0], [127, 135, 1.0]]}, "2021.ccl-1.80": {"snippet": "...lack language resources and tools. Moreover, metaphorical expressions have different meanings in different language environments. We therefore established a deep neural network (DNN) framework for Uyg...", "weights": [[8, 16, 1.0], [78, 87, 1.0], [100, 109, 1.0], [110, 118, 1.0]]}, "2003.clef_workshop-2003w.41": {"snippet": "...gual Information Retrieval tasks of the Cross-Language Evaluation Forum (CLEF) 2003. We used our system with same kind of stemmer, same options and different parameters for 5 European languages to com...", "weights": [[8, 19, 1.0], [49, 57, 1.0], [151, 160, 1.0]]}, "2002.clef_workshop-2002w.9": {"snippet": "...e in information retrieval, the performance can be affected negatively if the lexicon of the pilot and the test collection differ.", "weights": [[8, 19, 1.0]]}, "W18-0311": {"snippet": "...ch a language can be significantly bolstered by connecting words in the lowresource language with cognates in a related, higher-resource language. We present three methods for weighting edit distance ...", "weights": [[8, 16, 1.0], [87, 95, 1.0], [140, 148, 1.0]]}, "2020.coling-main.141": {"snippet": "...atic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines. Lan...", "weights": [[8, 16, 1.0], [169, 177, 1.0], [200, 208, 1.0]]}, "L16-1710": {"snippet": "... and Information for Language and Linguistic Analysis ({GORILLA})\n\n\n The infrastructure Global Open Resources and Information for Language and Linguistic Analysis (GORILLA) was created as a resource t...", "weights": [[8, 19, 1.0], [24, 32, 1.0], [117, 128, 1.0], [133, 141, 1.0]]}, "W12-3902": {"snippet": "...lish language. This paper details a novel clustering and co-occurrence based approach to map English NEs with their equivalent representations from different languages recognized in a language-indepen...", "weights": [[8, 16, 1.0], [151, 160, 1.0], [187, 195, 1.0]]}, "E17-2102": {"snippet": "...with language vectors\n\n\n Most existing models for multilingual natural language processing (NLP) treat language as a discrete category, and make predictions for either one language or the other. In co...", "weights": [[8, 16, 1.0], [74, 82, 1.0], [106, 114, 1.0], [175, 183, 1.0]]}, "2003.clef_workshop-2003w.5": {"snippet": "...lian language pair. As we did not have any German-to-Italian translation resources, we used the Babel Fish translation service provided by Altavista.com for translating German topics into Italian, wit...", "weights": [[8, 16, 1.0]]}, "2016.sigirconf_conference-2016.27": {"snippet": "...rget language, and the classification performance is far from satisfactory because of the language gap between the source language and the translated target language.In this paper, to address the abov...", "weights": [[8, 16, 1.0], [93, 101, 1.0], [125, 133, 1.0], [160, 168, 1.0]]}, "W18-5012": {"snippet": "...bot. Different styles were found to produce different rates of miscommunication, and correlations were found between style differences and individual user variation, trust, and interaction experience ...", "weights": [[8, 17, 1.0], [47, 56, 1.0]]}, "2016.ictir_conference-2016.45": {"snippet": "...ross-language information retrieval approaches. In this paper, we address the task of retrieving relevant tweets given another tweet in a different language. We build parallel corpora for tweets in di...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [141, 150, 1.0], [151, 159, 1.0], [201, 210, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.5": {"snippet": "...sian language, and then unifies the extracted terms and their equivalences in the native language as one index unit. For Cross-Language Information Retrieval (CLIR), one of the major hindrances to ach...", "weights": [[8, 16, 1.0], [92, 100, 1.0], [130, 138, 1.0], [139, 150, 1.0]]}, "2004.clef_workshop-2004.13": {"snippet": "...ross-Language Information Retrieval: An Experiment at CLEF 2004", "weights": [[8, 16, 1.0], [17, 28, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [42, 53, 1.0], [92, 103, 1.0], [122, 130, 1.0], [147, 158, 1.0]]}, "2004.clef_workshop-2004.22": {"snippet": "... our information retrieval system to document languages other than English, and completing the multilingual task comprising four languages: English, French, Russian and Finnish. Our retrieval system i...", "weights": [[8, 19, 1.0]]}, "P16-1176": {"snippet": "...tive language and translations are closer to each other than each of them is to native language; and (3) some of these characteristics depend on the source or native language, while others do not, ref...", "weights": [[8, 16, 1.0], [90, 98, 1.0], [169, 177, 1.0]]}, "D17-1302": {"snippet": "...with language-adversarial training and bidirectional language modeling as auxiliary objectives to better represent language-general information while not losing the information about a specific target...", "weights": [[8, 16, 1.0], [56, 64, 1.0], [118, 126, 1.0], [135, 146, 1.0], [168, 179, 1.0]]}, "W14-4207": {"snippet": "...ring Language Closeness by Modeling Regularity\n\n\n This paper addresses the problems of measuring similarity between languageswhere the term language covers any of the senses denoted by language, diale...", "weights": [[8, 16, 1.0], [143, 151, 1.0], [188, 196, 1.0]]}, "L12-1490": {"snippet": "New language resources for the {P}ashto language", "weights": [[4, 12, 1.0], [40, 48, 1.0]]}, "2013.fire_conference-2013.10": {"snippet": "...ross Language News Story Search\n\n\n ABSTRACTCross Language News story search (CLNSS) is concerned with finding documents describing the same events in documents in different languages. As well as suppo...", "weights": [[8, 16, 1.0], [52, 60, 1.0], [166, 175, 1.0]]}, "2022.computel-1.4": {"snippet": "...2$}} Language Documentation Meets Natural Language Processing for Revitalising Endangered Languages\n\n\n Language revitalisation should not be understood as a direct outcome of language documentation, w...", "weights": [[8, 16, 1.0], [45, 53, 1.0], [106, 114, 1.0], [178, 186, 1.0]]}, "W19-6602": {"snippet": "...gual Information Retrieval in Low-Resource Settings\n\n\n The goal of cross-lingual information retrieval (CLIR) is to find relevant documents written in languages different from that of the query. Robus...", "weights": [[8, 19, 1.0], [84, 95, 1.0], [164, 173, 1.0]]}, "2002.clef_workshop-2002w.11": {"snippet": "...gual Information Retrieval tasks of the Cross-Language Evaluation Forum (CLEF) 2002. Enabling stemming in SearchServer increased average precision by 16 points in Finnish, 9 points in German, 4 points...", "weights": [[8, 19, 1.0], [49, 57, 1.0]]}, "I05-1075": {"snippet": "...ngle-language cases, but in fact, mixed-case performance always exceeds the maximum of the single-language cases. We observed this effect in all six experiments we ran, involving three different sourc...", "weights": [[8, 16, 1.0], [101, 109, 1.0], [188, 197, 1.0]]}, "2020.acl-srw.22": {"snippet": "...many language pairs, especially in large training data scenarios. Since the condition of large parallel corpora is not met for Indian-English language pairs, we present our efforts towards building ef...", "weights": [[8, 16, 1.0], [145, 153, 1.0]]}, "E85-1015": {"snippet": "Language-Based Environment for Natural Language Parsing", "weights": [[0, 8, 1.0], [39, 47, 1.0]]}, "1998.sigirconf_conference-98.8": {"snippet": "...urce language word is translated to a target language by replacing it with all of its target language equivalents. 'The INQUERY information retrieval system is provided by the Information", "weights": [[8, 16, 1.0], [48, 56, 1.0], [96, 104, 1.0], [131, 142, 1.0], [179, 190, 1.0]]}, "2007.ntcir_workshop-2007.77": {"snippet": "...gram Language Models for Monolingual and Cross-Language IR\n\n\n Due to the lack of explicit word boundaries in Chinese, and Japanese, and to some extent in Korean, an additional problem in IR in these l...", "weights": [[8, 16, 1.0], [50, 58, 1.0]]}, "D19-1089": {"snippet": "... for language clustering: (1) using prior knowledge, where we cluster languages according to language family, and (2) using language embedding, in which we represent each language by an embedding vect...", "weights": [[8, 16, 1.0], [96, 104, 1.0], [127, 135, 1.0], [174, 182, 1.0]]}, "2004.clef_workshop-2004.59": {"snippet": "...ross-Language Image Retrieval Track\n\n\n Abstract. The purpose of this paper is to outline efforts from the 2004 CLEF cross-language image retrieval campaign (ImageCLEF). The aim of this CLEF track is t...", "weights": [[8, 16, 1.0], [125, 133, 1.0]]}, "2021.mrl-1.11": {"snippet": "...gual Language Models for Low-resourced Languages\n\n\n Pretrained multilingual language models have been shown to work well on many languages for a variety of downstream NLP tasks. However, these models ...", "weights": [[8, 16, 1.0], [79, 87, 1.0]]}, "2013.fire_conference-2013.11": {"snippet": "...ther language and even more challenging if translation language uses a different script and syntactical structure. This year, in CLINSS, focus was on journalistic text reuse between texts written in t...", "weights": [[8, 16, 1.0], [58, 66, 1.0], [74, 83, 1.0]]}, "O05-3002": {"snippet": "...main information databases included in the Formosan Language Archive, and the language processing programs that have been built upon it. We first discuss problems related to the transcription of diffe...", "weights": [[8, 19, 1.0], [55, 63, 1.0], [81, 89, 1.0], [198, 207, 1.0]]}, "2003.clef_workshop-2003w.36": {"snippet": "...ross-language Information Retrieval\n\n\n The aim of this project is the automatic conversion of query terms in one language into their equivalents in a second, historically related, language, so that do...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [116, 124, 1.0], [183, 191, 1.0]]}, "L10-1576": {"snippet": "Language Service Management with the Language Grid", "weights": [[0, 8, 1.0], [37, 45, 1.0]]}, "2004.clef_workshop-2004.66": {"snippet": "...ross-Language and Cross-Media Image Retrieval", "weights": [[8, 16, 1.0]]}, "W11-4101": {"snippet": "... and Language Technologies", "weights": [[8, 16, 1.0]]}, "L12-1530": {"snippet": "The Language Archive {---} a new hub for language resources", "weights": [[4, 12, 1.0], [41, 49, 1.0]]}, "2015.wwwconf_conference-2015c.201": {"snippet": "...iple language pairs and for two very different CLIR tasks. Our experiments show that the MT systems perform differently on average for different tasks and language pairs, but more significantly for di...", "weights": [[8, 16, 1.0], [40, 49, 1.0], [138, 147, 1.0], [158, 166, 1.0], [201, 210, 1.0]]}, "2020.coling-main.559": {"snippet": "...sive language detection. Finally, we show that domain-and language-adaptation, via intermediate masked language modeling on abusive corpora in the target language, can lead to substantially improved a...", "weights": [[8, 16, 1.0], [61, 69, 1.0], [106, 114, 1.0], [157, 165, 1.0]]}, "2022.acl-long.539": {"snippet": "... can language technology address the diverse situations of the world's languages? In one view, languages exist on a resource continuum and the challenge is to scale existing solutions, bringing under-...", "weights": [[8, 16, 1.0]]}, "C18-1293": {"snippet": "...tive language identification. Punctuation is specific to each language, and is part of the indicators that overtly represent the manner in which each language organizes and conveys information. Our ex...", "weights": [[8, 16, 1.0], [65, 73, 1.0], [153, 161, 1.0], [184, 195, 1.0]]}, "U05-1012": {"snippet": "...ajor language (the base language) to 'fake' TTS for a minor language (the target language). This paper describes the design for an experiment which involves finding a suitable base language for the Au...", "weights": [[8, 16, 1.0], [27, 35, 1.0], [63, 71, 1.0], [84, 92, 1.0], [184, 192, 1.0]]}, "2003.mtsummit-systems.10": {"snippet": "...tive language. PRIME translates a query in the user language into the target language, retrieves patents relevant to the query, and translates retrieved patents into the user language. To update a tra...", "weights": [[8, 16, 1.0], [55, 63, 1.0], [80, 88, 1.0], [178, 186, 1.0]]}, "C10-2128": {"snippet": "...rget language and then be used for automated reasoning. We present and compare the most mature of these novel languages, show how they can balance the disadvantages of natural languages and formal lan...", "weights": [[8, 16, 1.0]]}, "E17-1087": {"snippet": "...gual Language Identification on Character Window\n\n\n In language identification, a common first step in natural language processing, we want to automatically determine the language of some input text. ...", "weights": [[8, 16, 1.0], [58, 66, 1.0], [114, 122, 1.0], [174, 182, 1.0]]}, "2004.clef_workshop-2004.45": {"snippet": "...n of Language Resources\n\n\n Abstract. This paper describes the Bulgarian part of a BulgarianEnglish question answering system. The Bulgarian modules are implemented as a question analysis procedure wit...", "weights": [[8, 16, 1.0]]}, "P15-2044": {"snippet": "If all you have is a bit of the {B}ible: Learning {POS} taggers for truly low-resource languages\n\n\n We present a simple method for learning part-of-speech taggers for languages like Akawaio, Aukan, or...", "weights": []}, "2020.computerm-1.3": {"snippet": "...lyse information in more than one language. Many organisations tend to develop their language resources first in one language (often English). When it comes to analysing data sources in other language...", "weights": [[8, 19, 1.0], [37, 45, 1.0], [88, 96, 1.0], [120, 128, 1.0]]}, "S10-1001": {"snippet": "... six different languages (Catalan, Dutch, English, German, Italian, and Spanish) in four evaluation settings and using four different metrics. 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To ease the understandin...", "weights": [[8, 21, 1.0], [110, 123, 1.0]]}, "W15-3810": {"snippet": "...many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used. Besides the fact that there is no universal dictionary of clinical abb...", "weights": [[8, 21, 1.0], [98, 111, 1.0], [200, 213, 1.0]]}, "2010.sigirconf_conference-2010.39": {"snippet": "...word queries but are not, in general, effective for verbose queries. 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The second strategy forms an inverted index over the textual information (i.e., query terms and result sni...", "weights": [[8, 15, 1.0], [88, 95, 1.0]]}, "I13-1073": {"snippet": "Generalized Abbreviation Prediction with Negative Full Forms and Its Application on Improving {C}hinese Web Search\n\n\n In Chinese abbreviation prediction, prior studies are limited on positive full for...", "weights": []}, "2012.tois_journal-ir0volumeA30A1.1": {"snippet": "Static index pruning in web search engines: Combining term and document popularities with query views\n\n\n Static index pruning techniques permanently remove a presumably redundant part of an inverted f...", "weights": []}, "W09-2909": {"snippet": "Abbreviation Generation for {J}apanese Multi-Word Expressions", "weights": []}, "U08-1001": {"snippet": "...arch Queries\n\n\n Traditionally, information retrieval examines the search query in isolation: a query is used to retrieve documents, and the relevance of the documents returned is evaluated in relation...", "weights": [[8, 15, 1.0]]}, "2002.tois_journal-ir0volumeA20A1.3": {"snippet": "...s of queries, approaches based on keywords are not suitable for query clustering. This paper describes a new query clustering method that makes use of user logs which allow us to identify the document...", "weights": [[8, 15, 1.0]]}, "P02-1021": {"snippet": "...rous abbreviations are used routinely throughout such texts and knowing their meaning is critical to data retrieval from the document. In this paper I will demonstrate a method of automatically genera...", "weights": [[8, 21, 1.0]]}, "O08-3005": {"snippet": "...n of Queries with Infrequent Unknown Abbreviations and Proper Names\n\n\n Unknown term translation is important to CLIR and MT systems, but it is still an unsolved problem. Recently, a few researchers ha...", "weights": [[8, 15, 1.0], [40, 53, 1.0]]}, "2013.clef_conference-2013w.109": {"snippet": "...n of Abbreviations/Acronyms: THCIB at CLEF eHealth 2013 Task 2\n\n\n This paper describes the THCIB systems that used in the ShARe/CLEF eHealth Lab 2013 task 2. We built a baseline system using open sour...", "weights": [[8, 21, 1.0]]}, "2013.clef_conference-2013w.159": {"snippet": "...nyms/Abbreviations Challenge\n\n\n Objective: Abbreviations and acronyms are widely used in the clinical documents. This paper describes using of a machine learner to automatically extract spans of abbre...", "weights": [[8, 21, 1.0], [46, 59, 1.0], [198, 211, 1.0]]}, "2013.clef_conference-2013w.167": {"snippet": "... and abbreviations normalized to concepts from a standardized, medical vocabulary for the ShARe/CLEF eHealth 2013 challenge. In this paper, we review prior text normalization shared tasks, reference s...", "weights": [[8, 21, 1.0]]}, "2006.trec_conference-2006.1": {"snippet": "...ured queries comparing to Okapi? 2. Will the query expansion based on domain knowledge increase retrieval effectiveness? 3. Will our abbreviation database from MEDLINE help improve query expansion and...", "weights": [[8, 15, 1.0]]}, "2008.sigirconf_conference-2008.202": {"snippet": "... two queries, it is quite obvious that usage of structural information in query can increase the precision of a retrieval system theoretically. But users need to explicitly add structural information,...", "weights": [[8, 15, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...nced queries. We submitted both the original and modified queries to three major Web search engines; a total of 600 queries were submitted and 5,748 documents evaluated. We compared the results from t...", "weights": [[8, 15, 1.0], [61, 68, 1.0], [119, 126, 1.0]]}, "2009.trec_conference-2009.75": {"snippet": "...long queries in Prior Art (PA) task? In particular, we propose a query expansion algorithm for TS task and a keyword-selection algorithm for PA task. The Mean Average Precision (MAP) for our TS task r...", "weights": [[8, 15, 1.0]]}, "2008.ipm_journal-ir0volumeA44A6.3": {"snippet": "...n of queries with new terms, study the effect of different mutation rates on the exploration of query-space, and discuss the use of a especially developed fitness function that favors the construction...", "weights": [[8, 15, 1.0]]}, "2013.clef_conference-2013w.7": {"snippet": "A Supervised Abbreviation Resolution System for Medical Text", "weights": []}, "2000.riao_conference-2000.61": {"snippet": "...n of Abbreviations", "weights": [[8, 21, 1.0]]}, "L10-1510": {"snippet": "Towards a Learning Approach for Abbreviation Detection and Resolution.", "weights": []}, "2013.wwwconf_conference-2013.109": {"snippet": "... are abbreviations formed from the initial components of words or phrases. Acronym usage is becoming more common in web searches, email, text messages, tweets, blogs and posts. Acronyms are typically ...", "weights": [[8, 21, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A2.4": {"snippet": "ELM-based name disambiguation in bibliography\n\n\n AbstractIt is common that different people share the same name. When it occurs in bibliography databases, it worsens the performance of information ret...", "weights": []}, "2008.cikm_workshop-2008ssm.7": {"snippet": "...many queries.In this paper, we propose a Tag Suggestion System that takes advantage of tags associated with query results to expand a searcher's query. Since not every web page is associated with exis...", "weights": [[8, 15, 1.0]]}, "2020.wwwconf_conference-2020.32": {"snippet": "... SQL queries. In this paper, we tackle these challenges by developing a deep learning based TRanslate-Edit Model for Question-to-SQL (TREQS) generation, which adapts the widely used sequence-to-sequen...", "weights": [[8, 15, 1.0]]}, "2022.naacl-main.91": {"snippet": "... the abbreviations into full-phrase options by leveraging conversation context with the power of pretrained large language models (LLMs). Through zero-shot, few-shot, and fine-tuning experiments on fo...", "weights": [[8, 21, 1.0]]}, "2008.trec_conference-2008.5": {"snippet": "CSIR at TREC 2008 Expert Search Task: Modeling Expert Evidence in Expertise Retrieval\n\n\n In this paper, we described our method for the expert search task in TREC 2008. First, we proposed an adaption ...", "weights": []}, "D14-1202": {"snippet": "...ting abbreviations given the full forms is important in many natural language processing systems. In this paper we propose a two-stage method to find the corresponding abbreviation given its full form...", "weights": [[8, 21, 1.0]]}, "2008.sigirconf_conference-2008.122": {"snippet": "...n in queries. Experiments on real Web search queries show promising results.", "weights": [[8, 15, 1.0], [48, 55, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...that queries are issued by trained library scientists. Consequently, early ranking algorithms leveraged textual features, based on the words or \"terms\" contained in documents and queries. Examples of ...", "weights": [[8, 15, 1.0], [182, 189, 1.0]]}, "1988.tois_journal-ir0volumeA6A3.0": {"snippet": "...ague queries directly, forcing their users to retry specific queries repeatedly with minor modifications until they match data that are satisfactory. This article describes a system called VAGUE that ...", "weights": [[8, 15, 1.0], [64, 71, 1.0]]}, "2011.sigirconf_conference-2011.136": {"snippet": "...bset queries, while the latter ignores alternative subset queries. Recently, a novel reformulation framework has been proposed to transform the original query as a distribution of reformulated queries...", "weights": [[8, 15, 1.0], [61, 68, 1.0], [196, 203, 1.0]]}, "2003.cikm_conference-2003.2": {"snippet": "...ated queries", "weights": [[8, 15, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A5.12": {"snippet": "...word queries by suggesting keywords that are unfamiliar to users, offering alternative queries that deviate from the original ones, and even correcting spelling errors. The design goal of QS is to enr...", "weights": [[8, 15, 1.0], [90, 97, 1.0]]}, "2012.cikm_conference-2012.124": {"snippet": "...tify queries that are likely to have low ranking quality to improve the user satisfaction. For instance, the system may suggest to the user alternative queries for such hard queries. In this paper, we...", "weights": [[8, 15, 1.0], [155, 162, 1.0], [177, 184, 1.0]]}, "1976.jasis_journal-ir0volumeA27A2.0": {"snippet": "...e of abbreviations and acronyms in the physics literature", "weights": [[8, 21, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.2": {"snippet": "...when queries are not expanded. On the other hand, when queries are expanded, language modeling gives better results, but only when using a probabilistic dictionary derived from a parallel corpus.We pu...", "weights": [[8, 15, 1.0], [58, 65, 1.0]]}, "C18-2001": {"snippet": "...s\n\n\n Abbreviations and acronyms are a part of textual communication in most domains. However, abbreviations are not necessarily defined in documents that employ them. Understanding all abbreviations u...", "weights": [[8, 21, 1.0], [97, 110, 1.0], [188, 201, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.10": {"snippet": "...ique queries per session. The final data set was composed of 313 search sessions. Three facets of query reformulation (content, format, and resource) as well as nine sub-facets were derived from the d...", "weights": [[8, 15, 1.0]]}, "2011.sigirconf_conference-2011.176": {"snippet": "... the queries from the adhoc task of the TREC 2004 Genomics track. Our preliminary analysis shows the potential of both acronym and long-form expansions for biomedical IR.", "weights": [[8, 15, 1.0]]}, "C12-1187": {"snippet": "...heir abbreviations will be helpful in many applications. For example, it can improve the recall of information retrieval systems. An intuition to solve this is to build an abbreviation dictionary in a...", "weights": [[8, 21, 1.0]]}, "2014.fire_conference-2014.24": {"snippet": "...ular abbreviations through text messaging. The system receives a text input of acronym (possibly wrongly typed) in Roman script. The application returns a very brief information from the first few lin...", "weights": [[8, 21, 1.0]]}, "L18-1325": {"snippet": "...heir abbreviations. However, due to the deficiency in the abbreviation corpora, such a task is limited in current studies, especially considering general abbreviation prediction should also include th...", "weights": [[8, 21, 1.0]]}, "N09-2069": {"snippet": "...ting abbreviations for Chinese organization names. Abbreviations are commonly used in spoken Chinese, especially for organization names. The generation of Chinese abbreviation is much more complex tha...", "weights": [[8, 21, 1.0], [54, 67, 1.0]]}, "I08-2127": {"snippet": "...nese abbreviations through the use of previous approaches, examining actual usages of parenthetical expressions in newspaper articles. In order to bridge the gap between Japanese abbreviations and the...", "weights": [[8, 21, 1.0], [182, 195, 1.0]]}, "2007.wwwconf_conference-2007.33": {"snippet": "...tion queries in dynamic data dissemination networks\n\n\n ABSTRACTContinuous queries are used to monitor changes to time varying data and to provide results useful for online decision making. Typically a...", "weights": [[8, 15, 1.0], [77, 84, 1.0]]}, "2002.tois_journal-ir0volumeA20A3.0": {"snippet": "...ngle queries, consistent with the fact that the ordered sets of expansion terms suggested for each query by the different functions are largely uncorrelated. Motivated by these findings, we argue that...", "weights": [[8, 15, 1.0]]}, "2001.tois_journal-ir0volumeA19A2.2": {"snippet": "...tree queries-a variant and extension of Kilpel\u00e4i-nen's Tree Matching formalism-we introduce the notion of a \"complete answer aggregate\" for a given query. This new data structure offers a compact view...", "weights": [[8, 15, 1.0]]}, "2012.wwwconf_conference-2012c.5": {"snippet": "...math queries. Among the queries that should be answered, math queries are special because of the infinite combinations of numbers and symbols, and rather few keywords that form them. Answering math qu...", "weights": [[8, 15, 1.0], [27, 34, 1.0], [65, 72, 1.0], [201, 208, 1.0]]}, "2010.tois_journal-ir0volumeA28A2.1": {"snippet": "...vant queries for a given query, which helps users better specify their information needs. Previous work on query suggestion has been limited to the same language. In this article, we extend it to cros...", "weights": [[8, 15, 1.0]]}, "W19-5010": {"snippet": "...uous abbreviations are essential for biomedical natural language processing applications, such as information retrieval and question answering systems. In this paper, we present DEep Contextualized Bi...", "weights": [[8, 21, 1.0]]}, "2012.wwwconf_conference-2012.42": {"snippet": "...arch queries\n\n\n ABSTRACTWeb search queries are an encoding of the user's search intent and extracting structured information from them can facilitate central search engine operations like improving th...", "weights": [[8, 15, 1.0], [38, 45, 1.0]]}, "D19-6207": {"snippet": "...uous abbreviations in medical notes on the basis of morphologically annotated texts, without using additional domain resources. We work on Polish data but the described approaches can be used for othe...", "weights": [[8, 21, 1.0]]}, "2014.wwwconf_conference-2014c.177": {"snippet": "...mory queries using transactive search\n\n\n ABSTRACTMemory queries denote queries where the user is trying to recall from his/her past personal experiences. Neither Web search nor structured queries can ...", "weights": [[8, 15, 1.0], [59, 66, 1.0], [74, 81, 1.0], [191, 198, 1.0]]}, "1977.jasis_journal-ir0volumeA28A2.16": {"snippet": "...sics abbreviations and acronyms", "weights": [[8, 21, 1.0]]}, "W06-0103": {"snippet": "...cter abbreviations more easily. With only a few training iterations, the acquisition accuracy of the proposed SCR model achieves 62% and 50 % precision for training set and test set, respectively, fro...", "weights": [[8, 21, 1.0]]}, "2011.wwwconf_conference-2011c.8": {"snippet": "...seen queries. In other words we are targeting queries in the long tail. The model is based on a graph having two sets of nodes: Term nodes, and Query nodes. The graph induces a Markov chain on which a...", "weights": [[8, 15, 1.0], [49, 56, 1.0]]}, "1986.tois_journal-ir0volumeA4A4.1": {"snippet": "...with queries. These queries reflect the presuppositions of their authors about the system and the information it contains. With most query processors, queries that are based on erroneous presuppositio...", "weights": [[8, 15, 1.0], [23, 30, 1.0], [154, 161, 1.0]]}, "U07-1015": {"snippet": "...rack queries and collection. Our results show that for highly ambiguous abbreviations the query collocation effect isn't strong enough to deter the retrieval of erroneous passages. We conclude that fu...", "weights": [[8, 15, 1.0], [75, 88, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...rse\" queries were more effective than the \"short\" queries. This unlikely result may be because of how queries were constructed. In the example listed, the terse query is a phrase that is probably used...", "weights": [[8, 15, 1.0], [53, 60, 1.0], [105, 112, 1.0]]}, "2010.jasis_journal-ir0volumeA61A9.13": {"snippet": "HAADS: A Hebrew Aramaic abbreviation disambiguation system", "weights": []}, "2018.sigirjournals_journal-ir0volumeA52A2.25": {"snippet": "...fied queries. When a classifier that predicts the most appropriate mapping for each query is used, an additional 5.2% improvement over non-clarified queries is achieved.Furthermore, I introduce a set ...", "weights": [[8, 15, 1.0], [152, 159, 1.0]]}, "2004.ipm_journal-ir0volumeA40A1.2": {"snippet": "...n of abbreviations by using context and character information", "weights": [[8, 21, 1.0]]}, "1990.tois_journal-ir0volumeA8A4.2": {"snippet": "Queries and Query Processing in Object-Oriented Database Systems\n\n\n Object-oriented database management systems (OODBMS) combine the data abstraction and computational models of object-oriented progra...", "weights": [[0, 7, 1.0]]}, "2015.clef_conference-2015.5": {"snippet": "...tive abbreviations. As mentioned earlier, these context sensitive abbreviations hinder accuracy of the classification and retrieval methods. We try to address this issue by adding more Tweet Expansion...", "weights": [[8, 21, 1.0], [69, 82, 1.0]]}, "I11-1046": {"snippet": "...arch queries. A label propagation algorithm was applied on a clickthrough graph, and the candidates were reranked using a query language model. Our method first enumerates query candidates with common...", "weights": [[8, 15, 1.0]]}, "2008.trec_conference-2008.46": {"snippet": "... the abbreviations of query concepts in documents are recognized. This helps in identifying whether an opinion is in the vicinity of a query concept (which can be an abbreviation) in a document. The t...", "weights": [[8, 21, 1.0]]}, "N19-4001": {"snippet": "...n of abbreviations that are challenging for Unsupervised Abbreviation Disambiguation (UAD). Abbreviation Explorer helps to identify long-forms that are easily confused, and to pinpoint likely causes s...", "weights": [[8, 21, 1.0]]}, "L04-1229": {"snippet": "...ased Queries: Combining and Reusing Linguistic Corpus Formats and Query Languages", "weights": [[8, 15, 1.0]]}, "2016.wwwconf_conference-2016.126": {"snippet": "...nize queries that seek the value of an entity on a specific attribute (referred to as e+a queries) and provide direct answers for them using a combination of knowledge bases, web tables and documents....", "weights": [[8, 15, 1.0], [93, 100, 1.0]]}, "2012.cikm_conference-2012.393": {"snippet": "...arch queries\n\n\n ABSTRACTEven though queries received by traditional information retrieval systems are quite short, there are many application scenarios where long natural language queries are more eff...", "weights": [[8, 15, 1.0], [39, 46, 1.0], [183, 190, 1.0]]}, "2010.tweb_journal-ir0volumeA4A4.2": {"snippet": "...gine Queries\n\n\n Web search engines can perform poorly for long queries (i.e., those containing four or more terms), in part because of their high level of query specificity. The automatic assignment o...", "weights": [[8, 15, 1.0], [66, 73, 1.0]]}, "2014.cikm_conference-2014.14": {"snippet": "...onal Queries\n\n\n ABSTRACTThe term weighting and document ranking functions used with informational queries are typically optimized for cases in which queries are short and documents are long. It is rea...", "weights": [[8, 15, 1.0], [101, 108, 1.0], [152, 159, 1.0]]}, "W12-2406": {"snippet": "...n of Abbreviations from Biomedical Text\n\n\n We present an algorithm for extracting abbreviation definitions from biomedical text. Our approach is based on an alignment HMM, matching abbreviations and t...", "weights": [[8, 21, 1.0], [184, 197, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.3": {"snippet": "...uous queries in web search\n\n\n a b s t r a c tIt is widely believed that many queries submitted to search engines are inherently ambiguous (e.g., java and apple). However, few studies have tried to cla...", "weights": [[8, 15, 1.0], [80, 87, 1.0]]}, "D09-1154": {"snippet": "...arch Queries\n\n\n In this paper we address the problem of identifying a broad range of term variations in Japanese web search queries, where these variations pose a particularly thorny problem due to th...", "weights": [[8, 15, 1.0], [127, 134, 1.0]]}, "2004.spire_conference-2004.21": {"snippet": "...s in queries, and if not properly translated or transliterated, query performance may deteriorate significantly. As proper names often need no translation, a trivial solution is to include the untrans...", "weights": [[8, 15, 1.0]]}, "2004.tois_journal-ir0volumeA22A4.2": {"snippet": "...uate queries extremely fast, a challenging task given the quantities of data being indexed. A significant proportion of the queries posed to search engines involve phrases. In this article we consider...", "weights": [[8, 15, 1.0], [127, 134, 1.0]]}, "C08-1083": {"snippet": "...ting abbreviations and their full forms appearing in actual text. The task of abbreviation recognition is formalized as a sequential alignment problem, which finds the optimal alignment (origins of ab...", "weights": [[8, 21, 1.0]]}, "2008.tois_journal-ir0volumeA27A1.3": {"snippet": "Extended probabilistic HAL with close temporal association for psychiatric query document retrieval\n\n\n Psychiatric query document retrieval can assist individuals to locate query documents relevant to...", "weights": []}, "W09-1309": {"snippet": "...ical Abbreviations\n\n\n Abbreviations are common in biomedical documents and many are ambiguous in the sense that they have several potential expansions. Identifying the correct expansion is necessary f...", "weights": [[8, 21, 1.0], [25, 38, 1.0]]}}}
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The proposed...", "weights": [[8, 21, 1.0]]}, "W19-5117": {"snippet": "...ural Lemmatization of Multiword Expressions\n\n\n This article focuses on the lemmatization of multiword expressions (MWEs). We propose a deep encoder-decoder architecture generating for every MWE word i...", "weights": [[8, 21, 1.0], [78, 91, 1.0]]}, "N19-1155": {"snippet": "...ural Lemmatization\n\n\n English verbs have multiple forms. For instance, talk may also appear as talks, talked or talking, depending on the context. The NLP task of lemmatization seeks to map these dive...", "weights": [[8, 21, 1.0], [166, 179, 1.0]]}, "W18-3602": {"snippet": "{B}in{L}in: A Simple Method of Dependency Tree Linearization\n\n\n Surface Realization Shared Task 2018 is a workshop on generating sentences from lemmatized sets of dependency triples. This paper descri...", "weights": []}, "W19-4009": {"snippet": "...iple lemmatization strategies that different projects adopt. In this paper we discuss how we tackle the challenges raised by harmonizing different lemmatization criteria in a project that aims to conn...", "weights": [[8, 21, 1.0], [150, 163, 1.0]]}, "2007.clef_workshop-2007w.15": {"snippet": "...age, lemmatization significantly improves retrieval results and manually created queries are only slightly better than queries automatically generated from topic specifications.", "weights": [[8, 21, 1.0]]}, "2014.airs_conference-2014.28": {"snippet": "...ming algorithms (i.e., morphologyand syntax-based lemmatization; and morphology-based Information Science Research Institute (ISRI) stemming on clustering Arabic text dataset. We aim to identify the b...", "weights": [[8, 18, 1.0], [53, 66, 1.0]]}, "W09-2403": {"snippet": "... the lemmatization and part of speech tags used, through the accuracy of the most frequent sense baseline, to highly targeted individual systems. Our supervised systems include a ranking algorithm and...", "weights": [[8, 21, 1.0]]}, "L14-1142": {"snippet": "...ting Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage", "weights": [[8, 21, 1.0]]}, "2021.naacl-main.322": {"snippet": "...s\n\n\n Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Most prior work on ML based lemmatization has focused on high resource langu...", "weights": [[8, 21, 1.0], [155, 168, 1.0]]}, "R17-1006": {"snippet": "An Extensible Multilingual Open Source Lemmatizer", "weights": []}, "W09-4006": {"snippet": "...ines lemmatization, hand-crafted transformation rules, and weighted Levenshtein distance. The experimental results show an 11-pt interpolated average precision of 90.58%, which represents a sizeable i...", "weights": [[8, 21, 1.0]]}, "W07-1704": {"snippet": "Lemmatization of {P}olish Person Names\n\n\n The paper presents two techniques for lemmatization of Polish person names. First, we apply a rule-based approach which relies on linguistic information and h...", "weights": [[0, 13, 1.0], [80, 93, 1.0]]}, "W19-4206": {"snippet": "... and lemmatization in context. Our model is a multi-task sequence to sequence neural network, which jointly learns morphological tagging and lemmatization. On the encoding side, we exploit character-l...", "weights": [[8, 21, 1.0], [144, 157, 1.0]]}, "R13-1012": {"snippet": "...h as lemmatization, syllabification, lexical stress prediction and phonetic transcription. These steps usually require knowledge of the word's lexical composition (derivative morphology, inflectional ...", "weights": [[8, 21, 1.0]]}, "A92-1019": {"snippet": "{DILEMMA}-2: A Lemmatizer-Tagger for Medical Abstracts", "weights": []}, "2017.ipm_journal-ir0volumeA53A1.16": {"snippet": "...tion algorithms, and they also fail to capture the correlation between link formulation and community evolution. In this paper we introduce a balanced Modularity-Maximization Link Prediction (MMLP) mo...", "weights": [[8, 18, 1.0]]}, "2013.clef_conference-2013w.11": {"snippet": "An Approach to the Main Task of QA4MRE-2013\n\n\n This article describes the participation of a group from the University of\u00c9vora in the CLEF2013 QA4MRE main task. Our system has a superficial text analy...", "weights": []}, "2016.spire_conference-2016.12": {"snippet": "Parallel Computation for the All-Pairs Suffix-Prefix Problem\n\n\n Abstract.We show how to parallelize the optimal algorithm proposed by Tustumi et al. [19]  to solve the all-pairs suffix-prefix matching...", "weights": []}, "C98-2118": {"snippet": "...tive lemmatization. Its large lexicon of more than 320,000 word forms plus its ability to process German compound nouns guarantee a wide morphological coverage. Syntactic ambiguities can be resolved w...", "weights": [[8, 21, 1.0]]}, "W79-0111": {"snippet": "... and lemmatization) [In {D}anish]", "weights": [[8, 21, 1.0]]}, "2019.wsdm_conference-2019.47": {"snippet": "A General View for Network Embedding as Matrix Factorization\n\n\n ABSTRACTWe propose a general view that demonstrates the relationship between network embedding approaches and matrix factorization. Unli...", "weights": []}, "P16-1108": {"snippet": "... and Lemmatization.\n\n\n We present several methods for stemming and lemmatization based on discriminative string transduction. We exploit the paradigmatic regularity of semi-structured inflection table...", "weights": [[8, 21, 1.0], [70, 83, 1.0]]}, "2015.cikm_conference-2015.82": {"snippet": "...ased algorithms can reduce the expensive computation cost of SVD for nuclear norm minimization. However, most matrix factorization based algorithms fail to provide the theoretical guarantee for conver...", "weights": [[8, 18, 1.0], [140, 150, 1.0]]}, "W19-6133": {"snippet": "...c\n\n\n Lemmatization, finding the basic morphological form of a word in a corpus, is an important step in many natural language processing tasks when working with morphologically rich languages. We desc...", "weights": [[8, 21, 1.0]]}, "W79-0115": {"snippet": "...dsk (Lemmatization of {I}celandic) [In {D}anish]", "weights": [[8, 21, 1.0]]}, "N18-1126": {"snippet": "...ural Lemmatization with {L}ematus\n\n\n The main motivation for developing contextsensitive lemmatizers is to improve performance on unseen and ambiguous words. Yet previous systems have not carefully ev...", "weights": [[8, 21, 1.0]]}, "2019.ecir_workshop-2019amir.3": {"snippet": "Algorithm Selection with Librec-auto", "weights": []}, "L16-1081": {"snippet": "... and Lemmatization\n\n\n In this paper we present a rule-based method for multi-word term extraction that relies on extensive lexical resources in the form of electronic dictionaries and finite-state tra...", "weights": [[8, 21, 1.0]]}, "2019.ecir_workshop-2019amir.6": {"snippet": "... the algorithms that generated the recommendations. The logs enable researchers to conduct various analyses about a real-world recommender system. This includes the evaluation of meta-learning approac...", "weights": [[8, 18, 1.0]]}, "R17-1064": {"snippet": "Lemmatization of Multi-word Common Noun Phrases and Named Entities in {P}olish", "weights": [[0, 13, 1.0]]}, "W19-4204": {"snippet": "... and lemmatization in context, using a transformer-based sequence to sequence model and a biaffine attention based BiLSTM model. First, a lemma is produced for a given word, and then both the lemma an...", "weights": [[8, 21, 1.0]]}, "2006.clef_workshop-2006w.16": {"snippet": "...ased Lemmatization for Information Retrieval\n\n\n This paper reports on our system used in the CLEF 2006 ad hoc mono-lingual Hungarian retrieval task. Our experiments focus on the benefits that deeper N...", "weights": [[8, 21, 1.0]]}, "2017.spire_conference-2017.18": {"snippet": "...ngth Algorithms\n\n\n Abstract. Some of the most efficient algorithms for computing the length of a longest common subsequence (LLCS) between two strings are based on so-called \"bit-parallelism\". They ac...", "weights": [[8, 18, 1.0], [59, 69, 1.0]]}, "P09-1055": {"snippet": "...oint lemmatization and part-of-speech prediction\n\n\n We present a global joint model for lemmatization and part-of-speech prediction. Using only morphological lexicons and unlabeled data, we learn a pa...", "weights": [[8, 21, 1.0], [91, 104, 1.0]]}, "H05-1080": {"snippet": "...rate lemmatization of German nouns mandates the use of a lexicon. Comprehensive lexicons, however, are expensive to build and maintain. We present a selflearning lemmatizer capable of automatically cr...", "weights": [[8, 21, 1.0]]}, "2020.lt4hala-1.4": {"snippet": "Lemmatization and {POS}-tagging process by using joint learning approach. Experimental results on {C}lassical {A}rmenian, {O}ld {G}eorgian, and {S}yriac\n\n\n Classical Armenian, Old Georgian and Syriac ...", "weights": [[0, 13, 1.0]]}, "P98-2123": {"snippet": "...tive lemmatization. Its large lexicon of more than 320,000 word forms plus its ability to process German compound nouns guarantee a wide morphological coverage. Syntactic ambiguities can be resolved w...", "weights": [[8, 21, 1.0]]}, "2003.clef_workshop-2003w.3": {"snippet": "... the lemmatization of the text in order to avoid inflectional variation. Our second approach consists of the employment of syntactic dependencies as complex index terms, in an attempt to solve the pro...", "weights": [[8, 21, 1.0]]}, "2013.wwwconf_conference-2013.131": {"snippet": "Localized matrix factorization for recommendation based on matrix block diagonal forms\n\n\n ABSTRACTMatrix factorization on user-item rating matrices has achieved significant success in collaborative fi...", "weights": []}, "2011.cikm_conference-2011.194": {"snippet": "The list Viterbi training algorithm and its application to keyword search over databases\n\n\n ABSTRACTHidden Markov Models (HMMs) are today employed in a variety of applications, ranging from speech rec...", "weights": []}, "2009.ipm_journal-ir0volumeA45A4.3": {"snippet": "...se a lemmatization method for Mongolian. The advantage of our lemmatization method is that it does not rely on noun dictionaries, enabling us to lemmatize out-of-dictionary words. We also apply our me...", "weights": [[8, 21, 1.0], [65, 78, 1.0]]}, "W15-3716": {"snippet": "... and lemmatization in {L}atin: A comparison of six taggers and two lemmatization methods\n\n\n We present a survey of tagging accuracies -concerning part-of-speech and full morphological tagging -for sev...", "weights": [[8, 21, 1.0], [70, 83, 1.0]]}, "2016.spire_conference-2016.20": {"snippet": "...mory algorithms that construct the suffix array and the LCP array simultaneously in the optimal I/O complexity of O(sort(n)), for several reasons it would be desirable to construct the suffix array fi...", "weights": [[8, 18, 1.0]]}, "L16-1585": {"snippet": "...-art algorithms for extractive summarization and the effect of stopword removal, lemmatization, and stemming is analyzed. Results showed that these pre-processing settings improve the results.", "weights": [[8, 18, 1.0], [84, 97, 1.0]]}, "2020.lt4hala-1.19": {"snippet": "... and Lemmatization\n\n\n The paper presents the system used in the EvaLatin shared task to POS tag and lemmatize Latin. It consists of two components. A gradient boosting machine (LightGBM) is used for P...", "weights": [[8, 21, 1.0]]}, "2013.clef_conference-2013w.75": {"snippet": "... and lemmatization. Then each tweet content is analyzed and 18 features are obtained, related to presence of polarized term, negation before polarized expression and entity reference. For the first ru...", "weights": [[8, 21, 1.0]]}, "2004.cikm_conference-2004.91": {"snippet": "... and lemmatization in the clustering of finnish text documents\n\n\n ABSTRACTStemming and lemmatization were compared in the clustering of Finnish text documents. Since Finnish is a highly inflectional a...", "weights": [[8, 21, 1.0], [90, 103, 1.0]]}, "2021.eacl-main.272": {"snippet": "...ural Lemmatization with External Resources\n\n\n We propose a novel hybrid approach to lemmatization that enhances the seq2seq neural model with additional lemmas extracted from an external lexicon or a ...", "weights": [[8, 21, 1.0], [87, 100, 1.0]]}, "2006.tois_journal-ir0volumeA24A4.3": {"snippet": "... and algorithms are among the first steps in developing document-type-independent indexing methods for the full text in heterogeneous XML collections.", "weights": [[8, 18, 1.0]]}, "W14-0130": {"snippet": "Facilitating Multi-Lingual Sense Annotation: Human Mediated Lemmatizer\n\n\n Sense marked corpora is essential for supervised word sense disambiguation (WSD). The marked sense ids come from wordnets. How...", "weights": []}, "2018.wwwconf_conference-2018.57": {"snippet": "...ring algorithms are a fundamental tool in any machine learning library. Most of the eorts in developing dynamic machine learning and data mining algorithms have been focusing on the sliding window mod...", "weights": [[8, 18, 1.0], [148, 158, 1.0]]}, "2009.ir_journal-ir0volumeA12A3.3": {"snippet": "... and lemmatization for highly inflectional languages", "weights": [[8, 21, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A5.15": {"snippet": "Extreme learning machine with errors in variables\n\n\n Abstract Extreme learning machine (ELM) is widely used in training single-hidden layer feedforward neural networks (SLFNs) because of its good gene...", "weights": []}, "W79-0116": {"snippet": "...atic lemmatization) [In {D}anish]", "weights": [[8, 21, 1.0]]}, "P15-2068": {"snippet": "... The lemmatization task is evaluated on three German corpora on which we compare our results with existing software for lemmatization. With Wiktionary as a resource, we obtain a high accuracy for the ...", "weights": [[8, 21, 1.0], [123, 136, 1.0]]}, "2021.nodalida-main.25": {"snippet": "...e\n\n\n Lemmatization is often used with morphologically rich languages to address issues caused by morphological complexity, performed by grammar-based lemmatizers. We propose an alternative for this, i...", "weights": [[8, 21, 1.0]]}, "W12-5213": {"snippet": "...cing Lemmatization for {M}ongolian and its Application to Statistical Machine Translation\n\n\n Lemmatization is crucial in natural language processing and information retrieval especially for highly inf...", "weights": [[8, 21, 1.0], [96, 109, 1.0]]}, "2005.spire_conference-2005.9": {"snippet": "...nown algorithms). To our knowledge, our algorithm is the first that spends in the worst case O(log n) per every single input symbol.We also provide a simple algorithm that constructs online an indexin...", "weights": [[8, 18, 1.0]]}, "2008.sigirconf_workshop-2008and.16": {"snippet": "...val, lemmatization and stemming. Both stemming and lemmatization share a common goal of reducing a word to its base. However, lemmatization is more robust than stemming as it often involves usage of v...", "weights": [[8, 21, 1.0], [54, 67, 1.0], [129, 142, 1.0]]}, "I08-1001": {"snippet": "A Lemmatization Method for {M}odern {M}ongolian and its Application to Information Retrieval\n\n\n In Modern Mongolian, a content word can be inflected when concatenated with suffixes. Identifying the or...", "weights": [[2, 15, 1.0]]}, "2013.ecir_conference-2013.58": {"snippet": "... for lemmatization and phrase (multi word units) retrieval. We show by applying simple rule-based modifications and only targeting specific language instances (noun phrases) the usage of general NLP t...", "weights": [[8, 21, 1.0]]}, "W19-6203": {"snippet": "...hile lemmatization is indeed not necessary for English, the situation is different for Russian. It seems that for rich-morphology languages, using lemmatized training and testing data yields small but...", "weights": [[8, 21, 1.0]]}, "D18-1532": {"snippet": "... and lemmatization in Czech, German, and Arabic.", "weights": [[8, 21, 1.0]]}, "U06-1005": {"snippet": "...tion algorithms with various popular feature selection methods. The results highlight similarities between sentence and text classification, such as the superiority of Support Vector Machines, as well...", "weights": [[8, 18, 1.0]]}, "2016.spire_conference-2016.7": {"snippet": "...free algorithms, i.e., alternative algorithms that avoid the computationally expensive alignment, has become one of the recent hot topics in algorithmic bioinformatics.Analysis of protein structures i...", "weights": [[8, 18, 1.0], [38, 48, 1.0]]}}}
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However, recent trends expanding the set of all...", "weights": [[8, 12, 1.0]]}, "2018.airs_conference-2018.15": {"snippet": "Ad-hoc Video Search Improved by the Word Sense Filtering of Query Terms", "weights": [[0, 2, 1.0]]}, "2005.wwwconf_conference-2005si.44": {"snippet": "...n to filter unwanted images (such as advertisements or flashy graphics) based on minimal user feedback. To do so, we apply the weighted majority algorithm using pieces of the Uniform Resource Locators...", "weights": [[8, 14, 1.0]]}, "2010.ecir_conference-2010.60": {"snippet": "...ring Documents with Subspaces", "weights": [[8, 17, 1.0]]}, "2016.cikm_conference-2016.81": {"snippet": "Document Filtering for Long-tail Entities", "weights": []}, "2003.trec_conference-2003.1": {"snippet": "...o an ad hoc search but the target of the search is a particular document (or a small set of documents) that the searcher knows to exist in the collection and wants to find again. 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The texts are manually translated into English and then annotated for the same ERE tasks on the English translation, resulting in a rich ...", "weights": [[8, 17, 1.0], [198, 202, 1.0]]}, "1999.trec_conference-1999.56": {"snippet": "...EC-8 Ad-Hoc, Query and Filtering Track Experiments using PIRCS\n\n\n In TREC-8, we participated in automatic ad-hoc retrieval as well as the query and filtering tracks. The theme of our participation is ...", "weights": [[8, 10, 1.0], [109, 111, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A2.11": {"snippet": "... the documents that match information needs specified by user profiles. The most challenging aspect in adaptive filtering is to develop a system to learn user profiles efficiently and effectively from...", "weights": [[8, 17, 1.0]]}, "R11-1104": {"snippet": "...from documents according only to the context of the question. In this work, we complemented this approach by adding a filtering process on top of the document retrieval. This way, the system reevaluat...", "weights": [[8, 17, 1.0]]}, "2015.wsdm_conference-2015.10": {"snippet": "...plex ad treatments involving various ad characteristics. The diversity and generality of ad treatments call for accurate and causal measurement of ad effectiveness, i.e., how the ad treatment causes t...", "weights": [[8, 10, 1.0], [40, 42, 1.0], [92, 94, 1.0], [150, 152, 1.0], [182, 184, 1.0]]}, "W11-2128": {"snippet": "...ally rich language, is not straightforward.", "weights": [[8, 12, 1.0]]}, "2010.wwwconf_conference-2010.11": {"snippet": "... the ad indexing problem. Unlike most traditional search problems, the ad corpus is defined hierarchically in terms of advertiser accounts, campaigns, and ad groups, which further consist of creatives...", "weights": [[8, 10, 1.0], [74, 76, 1.0], [158, 160, 1.0]]}, "H93-1072": {"snippet": "An Overview of {DR-LINK} and Its Approach to Document Filtering", "weights": []}, "2017.cikm_conference-2017.113": {"snippet": "...tive ad impression volume ranking method for each ad campaign, followed by a sequential selection strategy considering the remaining ad volume and budget, to smoothly deliver the volume filtering whil...", "weights": [[8, 10, 1.0], [53, 55, 1.0], [136, 138, 1.0]]}, "2017.ecir_conference-2017.33": {"snippet": "...thms filter large volumes of streaming documents and emit sentences that constitute salient event updates. Systems developed typically combine in an ad-hoc fashion traditional retrieval and document s...", "weights": [[8, 14, 1.0], [42, 51, 1.0], [152, 154, 1.0]]}, "2002.trec_conference-2002.35": {"snippet": "Kernel Methods for Document Filtering", "weights": []}, "2013.trec_conference-2013.28": {"snippet": "...e of documents. These methods are based on linear combination with scores of document fields. The distinction is different weight factors. Score of base line method is the combination with score of ba...", "weights": [[8, 17, 1.0]]}, "2003.wwwconf_conference-2003.22": {"snippet": "...HTML documents\n\n\n AbstractWeb pages often contain clutter (such as pop-up ads, unnecessary images and extraneous links) around the body of an article that distracts a user from actual content. Extract...", "weights": [[8, 17, 1.0]]}, "1996.jasis_journal-ir0volumeA47A10.1": {"snippet": "Filtered Document Retrieval with Frequency-Sorted Indexes", "weights": []}, "2018.desires_conference-2018.13": {"snippet": "...vant documents) varies widely in our tasks of interest. We used 658 categories from the RCV1-v2 collection to study the impact of richness on QBD variants supported by Elasticsearch. BM25 weighting on...", "weights": [[8, 17, 1.0]]}, "2016.cikm_conference-2016.63": {"snippet": "...with Rich Ad Formats", "weights": [[8, 12, 1.0], [13, 15, 1.0]]}, "2016.ecir_conference-2016.43": {"snippet": "Implicit Look-Alike Modelling in Display Ads - Transfer Collaborative Filtering to CTR Estimation", "weights": []}, "2000.sigirconf_conference-2000.23": {"snippet": "...rack documents, the proposed method obtained a significant improvement in LF1, LF2, F1 and F3 measures compared to the best results submitted by other TREC entries.", "weights": [[8, 17, 1.0]]}, "2010.cikm_conference-2010.100": {"snippet": "...ting rich user information\n\n\n ABSTRACTOne-Class Collaborative Filtering (OCCF) is an emerging setup in collaborative filtering in which only positive examples or implicit feedback can be observed. Com...", "weights": [[8, 12, 1.0]]}, "2015.ipm_journal-ir0volumeA51A6.3": {"snippet": "Evaluating document filtering systems over time", "weights": []}, "2000.sigirconf_conference-2000.57": {"snippet": "...ding documents to users based on how similar their tastes are to other users. If two users tend to agree on what they like, the system will recommend the same documents to them. The generalized vector...", "weights": [[8, 17, 1.0], [162, 171, 1.0]]}, "2008.cikm_conference-2008.104": {"snippet": "... any documents lexically related to the query, in Web advertising it is acceptable, and occasionally even desirable, not to show any results. When no ads are relevant to the user's interests, then sho...", "weights": [[8, 17, 1.0]]}, "2000.sigirconf_conference-2000.24": {"snippet": "...ieve documents relevant to a user's profile from a flow of documents. Generally, filtering systems calculate the similarity between the profile and each incoming document, and retrieve documents with ...", "weights": [[8, 17, 1.0], [62, 71, 1.0], [188, 197, 1.0]]}, "2009.sigirconf_conference-2009.148": {"snippet": "...ased filter for improving ad quality", "weights": [[8, 14, 1.0], [29, 31, 1.0]]}, "2004.cikm_conference-2004.22": {"snippet": "Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model\n\n\n ABSTRACTCollaborative filtering and content-based filtering are two types ...", "weights": []}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...t of documents (e.g., Web pages) that are likely to be relevant to a user's query. All of the techniques that are needed to find documents, to extract their key concepts, to recognize and avoid \"spam\"...", "weights": [[8, 17, 1.0], [132, 141, 1.0]]}, "2008.cikm_conference-2008.105": {"snippet": "...vant documents. Classifying these documents with respect to an external taxonomy and identifying salient named entities give rise to two new feature types. Empirical evaluation based on over 9,000 que...", "weights": [[8, 17, 1.0], [37, 46, 1.0]]}, "2001.trec_conference-2001.49": {"snippet": "... the ad-hoc task and the entry page finding task, we used an information retrieval system based on a simple unigram language model. In the Ad hoc task we experimented with alternatieve approaches to s...", "weights": [[8, 10, 1.0], [142, 144, 1.0]]}, "2017.wwwconf_conference-2017c.165": {"snippet": "...Text-Rich Setting. In the context-rich environment, there exists not only different kinds of relationships between users, items and objects of various types, but also a rich amount of text information...", "weights": [[8, 12, 1.0], [37, 41, 1.0], [172, 176, 1.0]]}, "2015.ecir_conference-2015.33": {"snippet": "...able Documents", "weights": [[8, 17, 1.0]]}, "2015.clef_conference-2015.5": {"snippet": "...s to filter new tweets using cosine similarity.\nOverviewThe short length of tweets is the main reason that we believe content expansion can be helpful. The users on Twitter website must enter their me...", "weights": [[8, 14, 1.0]]}, "2020.sigirconf_workshop-2020birds.12": {"snippet": "Content-based Image Search System Design for Capturing User Preferences during Query Formulation", "weights": []}, "1998.trec_conference-1998.19": {"snippet": "...atic Ad Hoc, Filtering, VLC and Interactive", "weights": [[8, 10, 1.0]]}, "2007.wwwconf_conference-2007.61": {"snippet": "...ally filter the content to prevent injected script code from running in browsers that view the site. The diversity of browser rendering algorithms and the desire to allow rich content make filtering q...", "weights": [[8, 14, 1.0], [174, 178, 1.0]]}, "2012.wwwconf_conference-2012c.101": {"snippet": "...eous ad groups. Our approach is not only theoretically motivated by a topical mixture model; but empirically justified by the characteristics of the ad domain, that is, short and semantically focused ...", "weights": [[8, 10, 1.0], [152, 154, 1.0]]}, "1994.sigirconf_conference-94.35": {"snippet": "Document Filtering for Fast Ranking", "weights": []}, "2018.wwwconf_conference-2018c.280": {"snippet": "...amic Ad Allocation\n\n\n ABSTRACTModern search engines present result pages composed of two most prominent types of information: sponsored and organic search results. The whole-page results must satisfy ...", "weights": [[8, 10, 1.0]]}, "W19-5345": {"snippet": "...both rich modeling of limited contextual information and integration of large-scale document-level context with a less rich representation. We train this model by fine-tuning a big Transformer baselin...", "weights": [[8, 12, 1.0], [122, 126, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.8": {"snippet": "...eled documents as training examples. Manually labeling documents is costly and time-consuming. More importantly, it becomes unrealistic to know all the categories covered by the documents beforehand. ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [181, 190, 1.0]]}, "2010.cikm_conference-2010.171": {"snippet": "...vant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents accor...", "weights": [[8, 17, 1.0], [118, 122, 1.0], [188, 197, 1.0]]}, "1995.trec_conference-1995.28": {"snippet": "...EC-4 Ad-Hoc, Routing Retrieval and Filtering Experiments using PIRCS", "weights": [[8, 10, 1.0]]}, "2008.cikm_conference-2008.153": {"snippet": "...ture-rich sequence classifier that removes irrelevant parts from pages, while keeping the content intact. Most of the features the system uses are purely form-related: HTML tags and their positions, s...", "weights": [[8, 12, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.1": {"snippet": "...able documents into suitable categories. A popular way to achieve integrated DF and DC is to associate each category with a threshold. A document d may be classified into a category c only if its degr...", "weights": [[8, 17, 1.0]]}, "1998.trec_conference-1998.28": {"snippet": "...EC-7 Ad-Hoc, High Precision and Filtering Experiments using PIRCS", "weights": [[8, 10, 1.0]]}, "2001.trec_conference-2001.40": {"snippet": "...nked documents per category, and the training process and similarity score calculation algorithm are the same for batch filtering and routing, we will focus our discussion on batch filtering and adapt", "weights": [[8, 17, 1.0]]}, "2009.cikm_conference-2009.276": {"snippet": "...h to ad recommendation using the query-ad click graph\n\n\n ABSTRACTSearch engine logs contain a large amount of click-through data that can be leveraged as soft indicators of relevance. In this paper we...", "weights": [[8, 10, 1.0], [42, 44, 1.0]]}, "2019.wwwconf_conference-2019.134": {"snippet": "...alid Ad Traffic", "weights": [[8, 10, 1.0]]}, "2013.trec_conference-2013.24": {"snippet": "...tric Documents using Numerics and Temporals features within RF Classifier", "weights": [[8, 17, 1.0]]}}}
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The dataset we present in this paper is unique for the richness of annotated information, including detailed descriptions of drug reactions with full...", "weights": [[8, 14, 1.0]]}, "2019.ecir_conference-20192.29": {"snippet": "... the online doctor appointment and medical consultation sites to route the patient or question to the right medical specialty. The system returns the top three relevant specialties when the estimated ...", "weights": [[8, 14, 1.0]]}, "W14-3202": {"snippet": "..., to online therapy for depression and anxiety. 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Interactive symptom elicitation builds a model from the user's initial description of the symptoms and interactively elicitates new infor...", "weights": [[8, 15, 1.0], [78, 85, 1.0]]}, "2017.chiir_conference-2017.31": {"snippet": "User Click Detection in Ideal Sessions\n\n\n ABSTRACTUsers interact with search engine result pages in various ways, including their clicks, cursor movements, and page scrolls. Researchers model such use...", "weights": []}, "W13-5102": {"snippet": "...each symptom. Precisions in the range of 0.8 to 0.99 have been obtained.", "weights": [[8, 15, 1.0]]}, "2007.wwwconf_conference-2007.142": {"snippet": "MedSearch: a specialized search engine for medical information\n\n\n ABSTRACTPeople are thirsty for medical information. Existing Web search engines cannot handle medical search well because they do not ...", "weights": []}, "2020.coling-main.63": {"snippet": "...rent online medical platforms provide millions of public available conversations between real patients and doctors, where the patients propose their medical problems and the registered doctors offer d...", "weights": [[8, 14, 1.0], [23, 32, 1.0]]}, "2015.clef_conference-2015w.41": {"snippet": "...ign, symptom or condition and that seek information through a search engine, with the aim to understand which condition they may have. As part of this evaluation exercise, 66 query topics were created...", "weights": [[8, 15, 1.0]]}, "W17-2332": {"snippet": "Toward Automated Early Sepsis Alerting: Identifying Infection Patients from Nursing Notes\n\n\n Severe sepsis and septic shock are conditions that affect millions of patients and have close to 50% mortal...", "weights": []}, "2020.wwwconf_conference-2020c.94": {"snippet": "...s in Online Service Platform", "weights": [[8, 14, 1.0]]}, "W17-2346": {"snippet": "...s in online support groups using minimal supervision. In recent years, several online support groups have been established which has led to a huge increase in the amount of patient-authored text avail...", "weights": [[8, 14, 1.0], [82, 88, 1.0]]}, "R19-1096": {"snippet": "...ion, symptom-disease relations, as well as drug-symptom relations (e.g., searching for the drug that might be most useful for treating a given set of symptoms).", "weights": [[8, 15, 1.0], [51, 58, 1.0], [76, 85, 1.0]]}, "2008.cikm_conference-2008.16": {"snippet": "MedSearch: a specialized search engine for medical information retrieval\n\n\n ABSTRACTPeople are thirsty for medical information. Existing Web search engines often cannot handle medical search well beca...", "weights": []}, "2013.sigirconf_conference-2013.198": {"snippet": "...oked online for health information in the past year, with 35% of respondents attempting to diagnose a medical condition online . Health information seeking is conducted using both search systems such ...", "weights": [[8, 14, 1.0], [123, 129, 1.0]]}, "L16-1306": {"snippet": "...tion online. Developing search techniques that aid these information needs is challenging. In this paper we present the datasets created by CLEF eHealth Lab from 2013-2015 for evaluation of search sol...", "weights": [[8, 14, 1.0]]}, "2020.ecir_conference-20202.54": {"snippet": "...ease-Symptom Relations\n\n\n The effective extraction of ranked disease-symptom relationships is a critical component in various medical tasks, including computer-assisted medical diagnosis or the discov...", "weights": [[8, 15, 1.0], [72, 79, 1.0]]}, "2018.cikm_workshop-2018w.40": {"snippet": "...k in online social platforms such as Facebook and LinkedIn. However, it is often that one has only the impression about the desired targets, depicted by labels of social contexts (e.g. gender, interes...", "weights": [[8, 14, 1.0], [22, 31, 1.0]]}, "2016.wwwconf_conference-2016.27": {"snippet": "...ning Online Social Data for Detecting Social Network Mental Disorders\n\n\n ABSTRACTAn increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Over...", "weights": [[8, 14, 1.0]]}, "W12-5801": {"snippet": "... for Online Collaboration Platform\n\n\n Accelerated growth of the World Wide Web has resulted in the evolution of many online collaboration platforms in various domains, including education domain. Thes...", "weights": [[8, 14, 1.0], [120, 126, 1.0], [141, 150, 1.0]]}, "2019.clef_conference-2019w.165": {"snippet": "...s\n\n\n Online social media platforms allow open sharing of thoughts and dialogue. These platforms generate large amounts of data about their users' online behaviour, which can be repurposed for the deve...", "weights": [[8, 14, 1.0], [28, 37, 1.0], [89, 98, 1.0], [149, 155, 1.0]]}, "2022.bionlp-1.29": {"snippet": "...cine symptom self-reports show that our approach outperforms strong baselines, and that additional examples improve performance on the long-tail entities.", "weights": [[8, 15, 1.0]]}, "2020.wsdm_conference-2020.121": {"snippet": "...t of Online Job Search and Job Reviews on Job Decision\n\n\n Online platforms such as LinkedIn or specialized platforms such as Glassdoor are widely used by job seekers before applying for the job. These...", "weights": [[8, 14, 1.0], [61, 67, 1.0], [68, 77, 1.0], [110, 119, 1.0]]}, "2013.tweb_journal-ir0volumeA7A4.5": {"snippet": "...e to symptom searches on search-result click-through behavior. We show that users are significantly more likely to examine and click on captions containing potentially-alarming medical terminology suc...", "weights": [[8, 15, 1.0]]}, "2013.wwwconf_conference-2013.134": {"snippet": "...s of online information seeking behavior usually focus on the use of search engines or question answering (Q&A) websites. Recently, the fast growth of online social platforms such as Twitter and Faceb...", "weights": [[8, 14, 1.0], [154, 160, 1.0], [168, 177, 1.0]]}, "2021.wwwconf_conference-2021.19": {"snippet": "... vs. Symptom Checkers: A Comparison of their Effectiveness for Online Health Advice", "weights": [[8, 15, 1.0], [66, 72, 1.0]]}, "2021.ecir_conference-20211.24": {"snippet": "...h as symptom checkers. Existing methods have not fully exploited medical knowledge: this likely hinders their effectiveness. In this work, we propose a knowledgeaware diagnosis ranking framework based...", "weights": [[8, 15, 1.0]]}, "2008.wwwconf_conference-2008.181": {"snippet": "...l of accuracy, particularly when personalization or user grouping is employed. These findings have implications for the design of applications to support more effective online searching.", "weights": [[8, 16, 1.0], [172, 178, 1.0], [179, 188, 1.0]]}, "P18-2033": {"snippet": "...m an online medical forum by extracting symptoms from both patients' self-reports and conversational data between patients and doctors. Then we propose a taskoriented dialogue system framework to make...", "weights": [[8, 14, 1.0]]}, "2012.sigirconf_conference-2012.29": {"snippet": "...n in searching and browsing for health information. 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These ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [99, 106, 1.0], [107, 115, 1.0]]}, "2009.trec_conference-2009.35": {"snippet": "Machine Learning for Information Retrieval: TREC 2009 Web, Relevance Feedback and Legal Tracks", "weights": [[0, 7, 1.0], [8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0]]}, "2011.tist_journal-ir0volumeA2A2.1": {"snippet": "...tive learning in multimedia annotation and retrieval: A survey\n\n\n Active learning is a machine learning technique that selects the most informative samples for labeling and uses them as training data....", "weights": [[8, 16, 1.0], [46, 55, 1.0], [76, 84, 1.0], [90, 97, 1.0], [98, 106, 1.0]]}, "2004.trec_conference-2004.8": {"snippet": "...good retrieval function can be discovered by a pure machine learning approach, without using probabilistic theories and knowledge-based techniques. Two machine learning algorithms, Support Vector Mach...", "weights": [[8, 17, 1.0], [55, 62, 1.0], [63, 71, 1.0], [139, 149, 1.0], [155, 162, 1.0], [163, 171, 1.0], [199, 206, 1.0]]}, "2011.sigirconf_conference-2011.43": {"snippet": "...n: a machine learning approach\n\n\n ABSTRACTAutomatic query expansion technologies have been proven to be effective in many information retrieval tasks. Most existing approaches are based on the assumpt...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [125, 136, 1.0], [137, 146, 1.0]]}, "H93-1077": {"snippet": "Machine Learning Techniques for Document Filtering\n\n\n BoozoAllen & Hamilton's Advanced Decision Systems Division is conducting a program of research to investigate machine learning techniques that can...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0], [164, 171, 1.0], [172, 180, 1.0], [181, 191, 1.0]]}, "2019.clef_conference-2019.19": {"snippet": "...tive Learning-Based Retrieval Technique for Visual Lifelogging", "weights": [[8, 16, 1.0], [23, 32, 1.0]]}, "2012.freeopmt-1.1": {"snippet": "...ated machine-learning techniques have a vital role to play in machine translation. But, as I will show in this talk, they are not enough to finish the job because much of the information required to b...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0], [65, 72, 1.0], [178, 189, 1.0]]}, "2006.coria_conference-2006.2": {"snippet": "...e of Machine Learning in Information Retrieval Research", "weights": [[8, 15, 1.0], [16, 24, 1.0], [28, 39, 1.0], [40, 49, 1.0]]}, "2005.sigirconf_conference-2005.9": {"snippet": "... hoc information retrieval\n\n\n ABSTRACTInformation retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information nee...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 62, 1.0], [164, 173, 1.0], [188, 199, 1.0]]}, "2011.cikm_conference-2011.267": {"snippet": "...h on learning to rank concerns the problem of appropriate choice of the objective function to maximize by means of machine learning algorithms. We describe a novel technique of smoothing an arbitrary ...", "weights": [[8, 16, 1.0], [118, 125, 1.0], [126, 134, 1.0]]}, "2016.sigirconf_workshop-2016sal.11": {"snippet": "Retrieval Techniques for Contextual Learning\n\n\n Following constructivist models of contextual learning, knowledge acquisition goes beyond mere absorption of isolated facts, and, instead is enabled, st...", "weights": [[0, 9, 1.0], [10, 20, 1.0], [36, 44, 1.0], [94, 102, 1.0]]}, "2011.cikm_conference-2011.406": {"snippet": "...ical information retrieval modelling: from the probability ranking principle to recent advances in diversity, portfolio theory, and beyond\n\n\n ABSTRACTStatistical modelling of Information Retrieval (IR...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [178, 189, 1.0], [190, 199, 1.0]]}, "2008.sigirconf_conference-2008.198": {"snippet": "...tive information retrieval (IR). The value of relevance assessment lies in the disambiguation of the user's information need, which is achieved by applying various feedback techniques. Such techniques...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [111, 122, 1.0], [176, 186, 1.0], [193, 203, 1.0]]}, "N06-1051": {"snippet": "A Machine Learning based Approach to Evaluating Retrieval Systems\n\n\n Test collections are essential to evaluate Information Retrieval (IR) systems. The relevance assessment set has been recognized as ...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [48, 57, 1.0], [112, 123, 1.0], [124, 133, 1.0]]}, "2018.sigirconf_workshop-2018profs.3": {"snippet": "...call information retrieval is crucial to tasks such as electronic discovery and systematic review. Continuous Active Learning (CAL) is a technique where a human reviewer works in loop with a machine l...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [120, 128, 1.0], [194, 201, 1.0], [202, 210, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.14": {"snippet": "...atic techniques and hand-crafted rules. Automatic extraction techniques focus on various machine learning methods, but implementing these techniques increases time complexity of the extraction process...", "weights": [[8, 18, 1.0], [64, 74, 1.0], [92, 99, 1.0], [100, 108, 1.0], [141, 151, 1.0]]}, "W11-4523": {"snippet": "...ised machine learning techniques.", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2007.ipm_journal-ir0volumeA43A3.1": {"snippet": "Information retrieval and machine learning for probabilistic schema matching", "weights": [[0, 11, 1.0], [12, 21, 1.0], [26, 33, 1.0], [34, 42, 1.0]]}, "2007.ntcir_workshop-2007.53": {"snippet": "...king Techniques: A Comparison of Label Propagation, KNN, and Relevance Feedback\n\n\n This paper describes our work at the sixth NTCIR workshop on the subtask of CC single language information retrieval....", "weights": [[8, 18, 1.0], [181, 192, 1.0], [193, 202, 1.0]]}, "2018.chiir_conference-2018.62": {"snippet": "Diversity-Enhanced Recommendation Interface and Evaluation\n\n\n ABSTRACTe beyond accuracy user experience of using recommender system is drawing more and more a ention. For example, the system interface...", "weights": []}, "2020.sigirconf_conference-2020.367": {"snippet": "...ment Learning for Information Retrieval: Fundamentals and Advances\n\n\n Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users' information needs ...", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0], [73, 84, 1.0], [85, 94, 1.0], [100, 110, 1.0], [185, 196, 1.0]]}, "2003.sigirjournals_journal-ir0volumeA37A2.1": {"snippet": "...icit techniques can be limited.In this paper we consider the use of implicit feedback techniques for query expansion and user profiling in information retrieval tasks. These techniques unobtrusively o...", "weights": [[8, 18, 1.0], [89, 99, 1.0], [142, 153, 1.0], [154, 163, 1.0], [177, 187, 1.0]]}, "2014.sigirconf_conference-2014.221": {"snippet": "...amic information retrieval modeling\n\n\n ABSTRACTDynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [69, 80, 1.0], [81, 90, 1.0]]}, "2008.cikm_conference-2008.48": {"snippet": "...ving retrieval accuracy. The existing relevance feedback algorithms based on language models and vector space models are not effective in learning from negative feedback documents, which are abundant ...", "weights": [[8, 17, 1.0], [141, 149, 1.0]]}, "2016.tist_journal-ir0volumeA7A3.0": {"snippet": "...tary information (i.e., tags), which helps to cover multiple aspects or topics related to the target object. Previous work has addressed the tag recommendation problem by exploiting at most two of the...", "weights": [[8, 19, 1.0]]}, "L16-1312": {"snippet": "A Machine Learning based Music Retrieval and Recommendation System\n\n\n In this paper, we present a music retrieval and recommendation system using machine learning techniques. We propose a query by hum...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [31, 40, 1.0], [104, 113, 1.0], [146, 153, 1.0], [154, 162, 1.0], [163, 173, 1.0]]}, "2017.ictir_workshop-2017learner.8": {"snippet": "...s on Learning to Rank\n\n\n Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of documents an...", "weights": [[8, 16, 1.0], [28, 36, 1.0], [51, 61, 1.0], [71, 78, 1.0], [79, 87, 1.0]]}, "2015.ecir_conference-2015.95": {"snippet": "...ying information needs by techniques including preference elicitation, pattern recognition, and prediction, recommender systems connect the research areas information retrieval and machine learning.", "weights": [[8, 19, 1.0], [29, 39, 1.0], [158, 169, 1.0], [170, 179, 1.0], [184, 191, 1.0], [192, 200, 1.0]]}, "P13-1050": {"snippet": "... Hoc Information Retrieval\n\n\n Techniques that compare short text segments using dependency paths (or simply, paths) appear in a wide range of automated language processing applications including quest...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [33, 43, 1.0]]}, "2014.sigirconf_workshop-2014medir.7": {"snippet": "... for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use. A novel ...", "weights": [[8, 19, 1.0], [84, 95, 1.0], [96, 105, 1.0], [147, 157, 1.0]]}, "2007.sigirconf_conference-2007.18": {"snippet": "...mage retrieval\n\n\n ABSTRACTRelevance feedback is a powerful technique to enhance ContentBased Image Retrieval (CBIR) performance. It solicits the user's relevance judgments on the retrieved images retu...", "weights": [[8, 17, 1.0], [102, 111, 1.0]]}, "2008.sigirconf_conference-2008.31": {"snippet": "...back information. The new approach addresses the overfitting problem by projecting the original feature space onto a more compact set which retains the necessary information. The new set of features c...", "weights": [[8, 19, 1.0], [165, 176, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "....3.3 Information Search and Retrieval -relevance feedback\nGeneral TermsAlgorithms, Performance, Experimentation\nKeywordsRelevance Feedback, Web Search, Evaluation\n1.INTRODUCTIONRelevance feedback is a...", "weights": [[8, 19, 1.0], [31, 40, 1.0]]}, "2017.ictir_conference-2017.57": {"snippet": "...s of information retrieval, such as online learning to rank, interactive information retrieval or dynamic information retrieval. ese Permission to make digital or hard copies of part or all of this wo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 54, 1.0], [76, 87, 1.0], [88, 97, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "2022.suki-1.4": {"snippet": "...ting information retrieval and machine reading resources to focus on relevant regions of a corpus. The approach formulates the learning problem as a Markov decision process with a state representation...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 41, 1.0], [130, 138, 1.0]]}, "W99-0625": {"snippet": "... via machine learning. We discuss a more restrictive definition of similarity than traditional, document-level and information retrieval-oriented, notions of similarity, and motivate it by showing its...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [118, 129, 1.0], [130, 139, 1.0]]}, "2007.clef_workshop-2007w.70": {"snippet": "...e in information retrieval systems. In the MorphoChallenge 2007 evaluation we applied a simple zero-knowledge technique that is based on frequency counts rather than machine learning. Our method is ba...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [169, 176, 1.0], [177, 185, 1.0]]}, "2013.sigirconf_conference-2013.200": {"snippet": "...ovel information for the given object and user. Moreover, as we argued in [1], objects on the Web 2.0 may be multifaceted, being related to various topics, thus requiring more diversified tags to cove...", "weights": [[8, 19, 1.0]]}, "2020.sigirconf_conference-2020.353": {"snippet": "...s in machine translation, cross-lingual information retrieval, and document classification, as well as learning cross-lingual representations. Here, we describe the end-to-end process of searching the...", "weights": [[8, 15, 1.0], [43, 54, 1.0], [55, 64, 1.0], [106, 114, 1.0]]}, "2008.trec_conference-2008.1": {"snippet": "... and machine learning approach for estimating and ranking the opinionated documents. For the Polarized Opinion Retrieval subtask, we employ machine learning for predicting the polarity and linear comb...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [114, 123, 1.0], [143, 150, 1.0], [151, 159, 1.0]]}, "2016.sigirconf_conference-2016.219": {"snippet": "Deep Learning for Information Retrieval\n\n\n ABSTRACTRecent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being succ...", "weights": [[5, 13, 1.0], [18, 29, 1.0], [30, 39, 1.0], [104, 115, 1.0], [116, 125, 1.0], [168, 176, 1.0]]}, "2011.sigirconf_conference-2011.220": {"snippet": "...lick information.Yisong Yue is a postdoctoral researcher in the Machine Learning Department and the Heinz College at Carnegie Mellon University, where he works on machine learning approaches to inform...", "weights": [[8, 19, 1.0], [67, 74, 1.0], [75, 83, 1.0], [166, 173, 1.0], [174, 182, 1.0], [197, 208, 1.0]]}, "2007.ipm_journal-ir0volumeA43A5.3": {"snippet": "...eral machine learning algorithms for cross-lan7guage patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [63, 72, 1.0], [144, 151, 1.0], [152, 160, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "2009.cikm_conference-2009.247": {"snippet": "...lick information no better than a linear function of the field attributes. We also find query click information to be the single most important field for retrieval. In response, we develop a machine l...", "weights": [[8, 19, 1.0], [103, 114, 1.0], [157, 166, 1.0], [194, 201, 1.0], [202, 210, 1.0]]}, "2005.clef_workshop-2005w.119": {"snippet": "... All retrieval results rely heavily on two retrieval systems: for visual retrieval we use the GNU Image Finding Tool (GIFT), and for textual retrieval the EasyIR retrieval system. For the ad-hoc retri...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [76, 85, 1.0], [144, 153, 1.0], [165, 174, 1.0], [198, 207, 1.0]]}, "2020.cikm_conference-2020.341": {"snippet": "...n of Machine Learning Techniques for Sleep Apnea Screening", "weights": [[8, 15, 1.0], [16, 24, 1.0], [25, 35, 1.0]]}, "2018.sigirconf_conference-2018.219": {"snippet": "...sly, machine learning methods have been exploited to address the problem, which learns a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning h...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [150, 158, 1.0], [193, 201, 1.0]]}, "2006.ecir_conference-2006.30": {"snippet": "Machine Learning Ranking for Structured Information Retrieval\n\n\n Abstract. We consider the Structured Information Retrieval task which consists in ranking nested textual units according to their relev...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [40, 51, 1.0], [52, 61, 1.0], [102, 113, 1.0], [114, 123, 1.0]]}, "2006.airs_conference-2006.59": {"snippet": "...ised learning algorithm to integrate pseudo relevant documents with documents to be re-ranked. Given an initial ranked list of retrieved documents, the document re-ranking approach picks a set of docu...", "weights": [[8, 16, 1.0]]}, "2004.cikm_conference-2004.48": {"snippet": "...hm's learning function gradually improves document representations, over time and across users. Results show that the modified document representations yield lower dimensionality while improving retri...", "weights": [[8, 16, 1.0], [198, 207, 1.0]]}, "2011.sigirconf_conference-2011.12": {"snippet": "...se a machine-learning framework for simultaneously optimizing freshness and relevance, in which the trade-off is automatically adaptive to query temporal characteristics. We start by illustrating diff...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "1992.trec_conference-1992.31": {"snippet": "Machine Learning and Relevance Feedback", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.23": {"snippet": "... for information retrieval\n\n\n AbstractMany machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieva...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 53, 1.0], [54, 62, 1.0], [183, 194, 1.0], [195, 204, 1.0]]}, "2012.sigirconf_conference-2012.171": {"snippet": "Learning to select a time-aware retrieval model\n\n\n ABSTRACTTime-aware retrieval models exploit one of two time dimensions, namely, (a) publication time or (b) content time (temporal expressions mentio...", "weights": [[0, 8, 1.0], [32, 41, 1.0], [70, 79, 1.0]]}, "2008.trec_conference-2008.42": {"snippet": "...s of techniques, query expansion and search result re-ranking based on document relevance model, are adopted to improve the retrieval performance. The TMiner search engine, from IR group of Tsinghua U...", "weights": [[8, 18, 1.0], [127, 136, 1.0]]}, "2009.trec_conference-2009.53": {"snippet": "...ment retrieval. These features include co-citation measures, URL similarities, as well as features often used in machine learning systems for document ranking such as the difference in scores assigned...", "weights": [[8, 17, 1.0], [116, 123, 1.0], [124, 132, 1.0]]}, "2014.sigirconf_conference-2014.138": {"snippet": "...into learning-to-rank models\n\n\n ABSTRACTRelevance feedback has been shown to improve retrieval for a broad range of retrieval models. It is the most common way of adapting a retrieval model for a spec...", "weights": [[8, 16, 1.0], [88, 97, 1.0], [119, 128, 1.0], [177, 186, 1.0]]}, "1995.sigirconf_conference-95.28": {"snippet": "...pare learning techniques based on statistical classification to traditional methods of relevance feedback for the document routing problem. We consider three classification techniques which have decis...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [176, 186, 1.0]]}, "2008.ecir_conference-2008.44": {"snippet": "...sage Retrieval\n\n\n Abstract. Passage retrieval and pseudo relevance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, ...", "weights": [[8, 17, 1.0], [39, 48, 1.0], [160, 169, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [22, 32, 1.0], [94, 105, 1.0], [106, 115, 1.0], [116, 126, 1.0]]}, "P98-1009": {"snippet": "... and Machine Learning\n\n\n We describe a trainable and scalable summarization system which utilizes features derived from information retrieval, information extraction, and NLP techniques and on-line re...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [123, 134, 1.0], [135, 144, 1.0], [146, 157, 1.0], [178, 188, 1.0]]}, "2018.ir_journal-ir0volumeA21A1.1": {"snippet": "Machine learning techniques for XML (co-)clustering by structure-constrained phrases", "weights": [[0, 7, 1.0], [8, 16, 1.0], [17, 27, 1.0]]}, "2007.sigirconf_conference-2007.51": {"snippet": "...y in information retrieval (IR). Many machine learning techniques have been proposed for ranking problem, such as RankSVM, RankBoost, and RankNet. Among them, RankNet, which is based on a probabilisti...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [41, 48, 1.0], [49, 57, 1.0], [58, 68, 1.0]]}, "2010.cikm_conference-2010.51": {"snippet": "...ased retrieval comprising multiple ranked lists. Our ground truth is defined in terms of \"information nuggets\", which are obviously not known to the retrieval system when processing a new user query. ...", "weights": [[8, 17, 1.0], [93, 104, 1.0], [152, 161, 1.0]]}, "2014.sigirconf_conference-2014.212": {"snippet": "... and information retrieval\n\n\n ABSTRACTThe development and evaluation of Information Retrieval and Recommender Systems has traditionally focused on the relevance and accuracy of retrieved documents and...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [75, 86, 1.0], [87, 96, 1.0]]}, "2013.ictir_conference-2013.12": {"snippet": "...nced Information Retrieval by Exploiting Recommender Techniques in Cluster-Based Link Analysis\n\n\n ABSTRACTInspired by the use of PageRank algorithms in document ranking, we develop and evaluate a clus...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [56, 66, 1.0]]}, "2009.ipm_journal-ir0volumeA45A3.2": {"snippet": "... new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSRANK, aims to use the advantages of both the traditional Information Retrieval...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [76, 85, 1.0], [182, 193, 1.0], [194, 203, 1.0]]}, "1997.sigirconf_conference-97.4": {"snippet": "...d of information retrieval.[12] When a user has marked several articles as relevant to his/her information need, a system should be able to automatically learn the user's \"profile\" and should be able ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [98, 109, 1.0]]}, "2018.sigirconf_conference-2018.224": {"snippet": "...n in Information Retrieval: SIGIR 2018 Half-Day Tutorial\n\n\n ABSTRACTFusion is an important and central concept in Information Retrieval. The goal of fusion methods is to merge different sources of inf...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [117, 128, 1.0], [129, 138, 1.0], [200, 211, 1.0]]}, "C98-1009": {"snippet": "... and Machine Learning\n\n\n We describe a trainable and scalable summarization system which utilizes features derived from information retrieval, inibrmation extraction, and NLP techniques and on-line re...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [123, 134, 1.0], [135, 144, 1.0], [178, 188, 1.0]]}, "2018.wwwconf_conference-2018c.367": {"snippet": "Learning Large Scale Ordinal Ranking Model via Divide-and-Conquer Technique\n\n\n ABSTRACTStructured prediction, where outcomes have a precedence order, lies at the heart of machine learning for informat...", "weights": [[0, 8, 1.0], [171, 178, 1.0], [179, 187, 1.0], [192, 203, 1.0]]}, "I08-1068": {"snippet": "...that learning these user profiles using snippets surrounding the results for a query gives better performance than learning from entire document collection.", "weights": [[8, 16, 1.0], [118, 126, 1.0]]}, "2021.wsdm_conference-2021.156": {"snippet": "...onal Machine Reasoning Conference (MRC 2021)\n\n\n Recent years have witnessed the success of machine learning and especially deep learning in many research areas such as Vision and Language Processing, ...", "weights": [[8, 15, 1.0], [94, 101, 1.0], [102, 110, 1.0], [131, 139, 1.0]]}, "2019.wsdm_conference-2019.113": {"snippet": "...sly, machine learning methods have been exploited to address the problem, which learn a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning ha...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [149, 157, 1.0], [192, 200, 1.0]]}, "2017.tist_journal-ir0volumeA8A3.12": {"snippet": "... for Information Retrieval\n\n\n In many research and application areas, such as information retrieval and machine learning, we often encounter dealing with a probability distribution that is mixed by on...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [81, 92, 1.0], [93, 102, 1.0], [107, 114, 1.0], [115, 123, 1.0]]}, "2007.sigirconf_conference-2007.184": {"snippet": "... and machine meet: a study of combining cognitive and algorithmic relevance feedback\n\n\n ABSTRACTUsing Saracevic's relevance types, we explore approaches to combining algorithm and cognitive relevance ...", "weights": [[8, 15, 1.0]]}, "2010.cikm_conference-2010.189": {"snippet": "...line learning for recency search ranking using real-time user feedback\n\n\n ABSTRACTTraditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance...", "weights": [[8, 16, 1.0], [97, 104, 1.0]]}, "W97-1002": {"snippet": "...onal Learning of Pattern-Match Rules for Information Extraction\n\n\n Information extraction systems process natural language documents and locate a specific set of relevant items. Given the recent succe...", "weights": [[8, 16, 1.0], [44, 55, 1.0], [70, 81, 1.0]]}, "1995.cikm_conference-95.28": {"snippet": "...sion information filtering software has been developed to disseminate on-line electronic information.While the robustness and scalability of statistical approaches to information retrieval were a majo...", "weights": [[8, 19, 1.0], [92, 103, 1.0], [170, 181, 1.0], [182, 191, 1.0]]}, "2007.clef_workshop-2007w.104": {"snippet": "...p on machine learning, image processing and information retrieval (TIA) in the photographic retrieval task at ImageCLEF2007. Many experiments were performed comprising most of the query and target lan...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [47, 58, 1.0], [59, 68, 1.0], [95, 104, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.8": {"snippet": "...this information overload, because rich information about various kinds of events, user opinions, and daily life activities are generated in an unprecedented speed. 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The large ov...", "weights": [[8, 14, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.7": {"snippet": "...onal Boolean retrieval systems", "weights": [[8, 15, 1.0]]}, "2002.sigirconf_conference-2002.89": {"snippet": "... for boolean queries", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1982.jasis_journal-ir0volumeA33A6.1": {"snippet": "...with boolean search request formulations", "weights": [[8, 15, 1.0]]}, "1993.jasis_journal-ir0volumeA44A6.2": {"snippet": "...n of Boolean Queries: A Prototype Implementation and Evaluation", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1999.tois_journal-ir0volumeA17A4.1": {"snippet": "...lar, Boolean query operators are conveniently modeled as link matrices of the Bayesian Network. Prior work has shown, however, that these operators do not perform as well as the the pnorm operators us...", "weights": [[8, 15, 1.0]]}, "2019.clef_conference-2019w.27": {"snippet": "...e, a boolean query constructed by Cochrane experts, and a set of PubMed Document Identifiers (PID's) returned by running the boolean query in MEDLINE. We handle the problem under the Continuous Active...", "weights": [[8, 15, 1.0], [128, 135, 1.0]]}, "2010.airs_conference-2010.55": {"snippet": "... and Boolean IR Models for Question Answering", "weights": [[8, 15, 1.0]]}, "2007.spire_conference-2007.13": {"snippet": "...tive Boolean queries are a fundamental operation in web search engines. These queries can be reduced to the problem of intersecting ordered sets of integers, where each set represents the documents co...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...sing Boolean queries. The model is applied to the task of ranking documents, or other structural elements, in the order of their expected relevance. Features such as phrase matching, truncation, and s...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...nced queries. We submitted both the original and modified queries to three major Web search engines; a total of 600 queries were submitted and 5,748 documents evaluated. We compared the results from t...", "weights": [[8, 15, 1.0], [61, 68, 1.0], [119, 126, 1.0]]}, "I08-6013": {"snippet": "...lded queries and two different boolean formulations of query translations. We find that a hybrid boolean formulation using a combination of boolean AND and boolean OR operators improves ranking of doc...", "weights": [[8, 15, 1.0], [34, 41, 1.0], [100, 107, 1.0], [143, 150, 1.0], [159, 166, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0], [133, 140, 1.0]]}, "2010.cikm_conference-2010.75": {"snippet": "... for queries over text databases. Research on selectivity estimation for such queries has focused on Boolean retrieval, i.e., a document may be relevant for the query or not. But with the coalescence ...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [104, 111, 1.0]]}, "2007.clef_workshop-2007w.50": {"snippet": "...lded queries and two different boolean formulations of query translations. We find that a hybrid boolean formulation using a combination of boolean AND and boolean OR operators improves ranking of doc...", "weights": [[8, 15, 1.0], [34, 41, 1.0], [100, 107, 1.0], [143, 150, 1.0], [159, 166, 1.0]]}, "2012.ipm_journal-ir0volumeA48A6.9": {"snippet": "...s as Boolean filters. The best working queries were structured into 2-3 main facets out of 3-5 possible search facets, and expressed with terms reflecting the focal view of the search task. The findin...", "weights": [[8, 15, 1.0], [42, 49, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "... the Boolean operators and search terms that comprise the submitted queries. Our analysis shows that both Boolean training and the use of an assisted interface improved the participants' ability to co...", "weights": [[8, 15, 1.0], [71, 78, 1.0], [109, 116, 1.0]]}, "2009.jasis_journal-ir0volumeA60A6.9": {"snippet": "...earn Boolean queries: A comparative study", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...nded Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulate...", "weights": [[8, 15, 1.0], [73, 80, 1.0], [81, 88, 1.0]]}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0], [8, 15, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.1": {"snippet": "...g of Boolean queries by means of a multiobjective IQBE evolutionary algorithm", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1994.sigirconf_conference-94.22": {"snippet": "... vs. Boolean Query Evaluation: A Comparison of Retrieval Performance", "weights": [[8, 15, 1.0]]}, "2021.paclic-1.11": {"snippet": "...ion, contra Kishimoto's (2001) classification of Japanese ditransitive verbs, in which send-type verbs carry only a caused motion meaning whereas sell-type verbs carry both caused motion and caused po...", "weights": [[8, 14, 1.0]]}, "2018.sigirconf_conference-2018.50": {"snippet": "...hese Boolean queries are carefully crafted by researchers and information specialists, and often reviewed by a panel of experts. However, little is known about the effectiveness of the Boolean queries...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [188, 195, 1.0], [196, 203, 1.0]]}, "1979.jasis_journal-ir0volumeA30A6.10": {"snippet": "...sing boolean queries with a clustered file organization", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2004.trec_conference-2004.90": {"snippet": "...y of boolean approaches to IR and the positive remarks made about their utility for QA by other TREC participants [Harabagiu et al., 2000], for TREC 2003 we implemented a boolean search engine called ...", "weights": [[8, 15, 1.0], [174, 181, 1.0]]}, "1997.jasis_journal-ir0volumeA48A2.3": {"snippet": "...ring Boolean and Probabilistic Information Retrieval Systems across Queries and Disciplines", "weights": [[8, 15, 1.0], [71, 78, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...that queries are issued by trained library scientists. Consequently, early ranking algorithms leveraged textual features, based on the words or \"terms\" contained in documents and queries. Examples of ...", "weights": [[8, 15, 1.0], [182, 189, 1.0]]}, "1999.tois_journal-ir0volumeA17A1.0": {"snippet": "...s in Boolean queries into native subsuming forms, which is a basis of translating complex queries. In addition, we present experimental results for evaluating the cost of postfiltering. We also discus...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [93, 100, 1.0]]}, "2012.sigirconf_workshop-2012osir.0": {"snippet": "... CNF queries. These CNF queries are highly effective, and can outperform bag of word queries by a large margin. However, not many effective tools exist that can facilitate the efficient manual creatio...", "weights": [[8, 15, 1.0], [27, 34, 1.0], [88, 95, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.6": {"snippet": "...atic Boolean Query Processing", "weights": [[8, 15, 1.0]]}, "2006.wwwconf_conference-2006.42": {"snippet": "...ough boolean or TFIDF retrieval, which reduces the specificity of the query. We define a scale for evaluating query substitution, and show that our method performs well at generating new queries relat...", "weights": [[8, 15, 1.0], [190, 197, 1.0]]}, "2006.trec_conference-2006.32": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator because it can not be generalized to nodes. The fuzzy te...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [99, 106, 1.0], [176, 183, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.10": {"snippet": "...n in Boolean IR queries\n\n\n a b s t r a c tThe negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include negated terms in Boole...", "weights": [[8, 15, 1.0], [19, 26, 1.0], [130, 137, 1.0], [198, 205, 1.0]]}, "R15-1095": {"snippet": "...than pro or contra. Second, they cannot be learned as there is no labeled data available. We address the challenge using graph clustering methods to group the members, including the similarity of thei...", "weights": [[8, 11, 1.0], [15, 21, 1.0]]}, "2015.ictir_conference-2015.40": {"snippet": "...tive queries in a boolean model. We show that this represents an upper bound on retrievability for all other best match algorithms. We follow this with an observation of imbalance in the distribution ...", "weights": [[8, 15, 1.0], [21, 28, 1.0]]}, "1984.ipm_journal-ir0volumeA20A56.6": {"snippet": "... for boolean query relevancy feedback", "weights": [[8, 15, 1.0]]}, "2003.trec_conference-2003.94": {"snippet": "...that Boolean structured queries expanded with automatically collected high frequency words and names of organisms performed considerably better than queries containing gene names only as keys. In the ...", "weights": [[8, 15, 1.0], [27, 34, 1.0], [152, 159, 1.0]]}, "2000.sigirconf_conference-2000.5": {"snippet": "... for Boolean IR systems is proposed. It is based on a controlled formulation of inclusive query plans, on an automatic conversion of query plans into elementary queries, and on combining elementary qu...", "weights": [[8, 15, 1.0], [164, 171, 1.0], [201, 208, 1.0]]}, "2006.trec_conference-2006.28": {"snippet": "...ated Boolean Queries of the TREC 2006 Legal Discovery Track\n\n\n We analyze the results of several experimental runs submitted to the Legal Discovery and Terabyte Tracks of TREC 2006. In the Legal Track...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1989.ipm_journal-ir0volumeA25A1.3": {"snippet": "...fied boolean queries: A comparison of inverted and signature file organizations", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2020.wwwconf_conference-2020.97": {"snippet": "...atic Boolean Query Formulation for Systematic Review Literature Search", "weights": [[8, 15, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...d on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in answer to search queries but they are not easy to use by uninitiated patrons. An extended Boo...", "weights": [[8, 15, 1.0], [127, 134, 1.0], [200, 207, 1.0]]}, "2006.clef_workshop-2006w.37": {"snippet": "...dles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes it possible to...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2001.ir_journal-ir0volumeA4A34.4": {"snippet": "...s of Boolean Queries in Large Full-Text Databases", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2004.ntcir_workshop-2004.80": {"snippet": "...ying queries that combines the probabilistic IR model and the Boolean IR model. Since it is not easy to construct a Boolean query that covers all relevant documents, a mechanism for clarifying the Boo...", "weights": [[8, 15, 1.0], [65, 72, 1.0], [119, 126, 1.0], [200, 207, 1.0]]}, "2010.wwwconf_conference-2010.52": {"snippet": "...word queries and support Lowest Common Ancestor (LCA) semantics for defining query results, where the relevance of each document to a query is determined by properties of the LCA of those nodes in the...", "weights": [[8, 15, 1.0]]}, "2011.sigirconf_conference-2011.85": {"snippet": "... for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [151, 158, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.4": {"snippet": "Boolean interpretation of conjunctions for document retrieval", "weights": [[0, 7, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.9": {"snippet": "...ting Boolean queries in conjunctive normal form with probabilistic retrieval models", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [124, 131, 1.0], [196, 203, 1.0]]}, "2010.sigirconf_conference-2010.119": {"snippet": "...into pro or con. Our approach extracts topic related terms, pro/con related terms, and pairs of topic related and pro/con related terms and uses them as the basis for constructing a pro query and a co...", "weights": [[8, 11, 1.0], [63, 66, 1.0], [117, 120, 1.0], [185, 188, 1.0]]}, "2014.sigirconf_conference-2014.152": {"snippet": "...lity Boolean queries that can be applied deterministically during filtering. We call these Boolean statements sufficient queries. We argue that using deterministic queries for entity-centric filtering...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [94, 101, 1.0], [124, 131, 1.0], [167, 174, 1.0]]}, "1997.sigirconf_conference-97.15": {"snippet": "...g of Boolean Operators\n\n\n The inference network model of information retrieval allows for a probabilistic interpretation of Boolean query operators. Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [127, 134, 1.0]]}, "2007.trec_conference-2007.22": {"snippet": "...ated Boolean Queries of the TREC 2007 Legal Discovery Track\n\n\n We analyze the results of several experimental runs submitted for the TREC 2007 Legal Track (also sometimes known as the Legal Discovery ...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2005.trec_conference-2005.42": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator. The fuzzy term proximity is controlled with an influenc...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2005.clef_workshop-2005w.11": {"snippet": "Boolean Operators in Interactive Search\n\n\n This paper presents the participation of the MIRACLE team at the ImageCLEF interactive search task. Basically, queries consisting on several terms can be pro...", "weights": [[0, 7, 1.0], [154, 161, 1.0]]}, "Y00-1041": {"snippet": "...ment contra Belletti and Rizzi 1988, Grimshaw 1990  and that the structure proposed by Belletti and Rizzi 1988 is not correct. Bouchard 1995, Iwata 1995, and Chung 1998 have already pointed out that t...", "weights": [[8, 14, 1.0]]}, "2007.trec_conference-2007.51": {"snippet": "...rent Boolean queries were provided for each topic (defendant, plaintiff and final). In 2006, the plaintiff and final queries had (usually) been the same. 1 Although often referred to as \"Boolean,\" the...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [120, 127, 1.0], [190, 197, 1.0]]}, "1985.sigirconf_conference-85.3": {"snippet": "...s of Boolean Structures for Probabilistic Retrieval", "weights": [[8, 15, 1.0]]}, "1985.riao_conference-1985.20": {"snippet": "...ting boolean structure within a probabilistic framework", "weights": [[8, 15, 1.0]]}, "1981.sigirconf_conference-81.8": {"snippet": "...(non-Boolean) queries and indexing functions.The meanings of recall and precision in such a generalized system will be discussed.Finally, we shall explore the meaning and difficulty of using such meas...", "weights": [[8, 15, 1.0], [17, 24, 1.0]]}, "2003.clef_workshop-2003.32": {"snippet": "...dish Queries for Boolean Combinations of Truncated Terms\n\n\n Abstract. In languages that use compound words such as Swedish, it is often neccessary to split compound words when indexing documents or qu...", "weights": [[8, 15, 1.0], [20, 27, 1.0], [201, 208, 1.0]]}, "2008.wwwconf_conference-2008.201": {"snippet": "...s of queries per second over billions of documents. To deal with this heavy workload, current engines use massively parallel architectures of thousands of machines that require large hardware investme...", "weights": [[8, 15, 1.0]]}, "2016.ecir_workshop-2016newsir.3": {"snippet": "Boolean Queries for News Monitoring: Suggesting New Query Terms to Expert Users\n\n\n In this paper, we evaluate query suggestion for Boolean queries in a news monitoring system. Users of this system rec...", "weights": [[0, 7, 1.0], [8, 15, 1.0], [131, 138, 1.0], [139, 146, 1.0]]}, "2009.cikm_conference-2009.188": {"snippet": "...ured queries, although containing more information than keyword queries, are often less effective than unstructured queries. This paper studies retrieval of sentences with annotations for a question a...", "weights": [[8, 15, 1.0], [67, 74, 1.0], [119, 126, 1.0]]}, "2012.sigirconf_conference-2012.106": {"snippet": "...late Boolean queries to represent their information need. We want to work on three basic problems:1. Boolean query formulation -Our primary goal is to study Boolean query formulation in the light of t...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [104, 111, 1.0], [160, 167, 1.0]]}, "2003.tois_journal-ir0volumeA21A3.0": {"snippet": "...user queries with a variable number of keywords in two manners. Positive expansion adds terms to a user's keywords with a Boolean \"and,\" negative expansion adds terms to the user's keywords with a Boo...", "weights": [[8, 15, 1.0], [125, 132, 1.0], [200, 207, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "... for boolean query formulation\n\n\n AbstractA new model named Boolean Latent Semantic Indexing model based on the Singular Value Decomposition and Boolean query formulation is introduced. While the Sing...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [148, 155, 1.0]]}, "2021.ir_journal-ir0volumeA24A1.1": {"snippet": "...atic Boolean query formulation for systematic reviews", "weights": [[8, 15, 1.0]]}}}
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Extended Boolean model has been widely used in business system for its easiness to be complemented and not bad results. In this paper, a stat...", "weights": [[8, 17, 1.0], [71, 78, 1.0]]}, "2015.ictir_conference-2015.40": {"snippet": "...in a boolean model. We show that this represents an upper bound on retrievability for all other best match algorithms. We follow this with an observation of imbalance in the distribution of retrievabi...", "weights": [[8, 15, 1.0]]}, "1996.tois_journal-ir0volumeA14A4.0": {"snippet": "...Text Retrieval\n\n\n and JUSTIN ZOBEL RMITQuery-processing costs on large text databases are dominated by the need to retrieve and scan the inverted list of each query term. Retrieval time for inverted l...", "weights": [[8, 17, 1.0], [174, 183, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...ment retrieval, long enough spans of speech can be readily handled.However, information retrieval is not just about document retrieval. There are other problems in and around IR where ASR is still lik...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [128, 137, 1.0]]}, "1997.sigirconf_conference-97.15": {"snippet": "...g of Boolean Operators\n\n\n The inference network model of information retrieval allows for a probabilistic interpretation of Boolean query operators. Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [72, 81, 1.0], [127, 134, 1.0]]}, "1982.sigirconf_conference-82.12": {"snippet": "...into Boolean Retrieval System", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1981.ipm_journal-ir0volumeA17A3.0": {"snippet": "... and Boolean retrieval systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1980.sigirconf_conference-80.3": {"snippet": "... for Boolean Retrieval", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.1": {"snippet": "...tion retrieval \u2013 The potential for improvements in conventional Boolean retrieval systems", "weights": [[8, 17, 1.0], [67, 74, 1.0], [75, 84, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0]]}, "2010.cikm_conference-2010.75": {"snippet": "...d on Boolean retrieval, i.e., a document may be relevant for the query or not. But with the coalescence of database and information retrieval (IR) technology, selectivity estimation for other, more so...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [135, 144, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.11": {"snippet": "...tion retrieval systems. The results reveal that these professions share many fundamental needs and face similar challenges. In particular a continuing preference to formulate queries as Boolean expres...", "weights": [[8, 17, 1.0], [189, 196, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.22": {"snippet": "...ying retrieval biases in probabilistic IR models and then develop new document priors to improve retrieval performance . Next, we discuss the accessibility bias of retrieval models, and for Boolean re...", "weights": [[8, 17, 1.0], [100, 109, 1.0], [167, 176, 1.0], [193, 200, 1.0], [201, 210, 1.0]]}, "1981.jasis_journal-ir0volumeA32A4.5": {"snippet": "...hted boolean retrieval", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1998.sigirconf_conference-98.36": {"snippet": "...tive Boolean queries and ranked queries. We also describe a pruning method to reduce the number of candidate documents considered during the evaluation of ranked queries. Experimental results with a d...", "weights": [[8, 15, 1.0]]}, "1986.sigirconf_conference-86.21": {"snippet": "... for Boolean Query Processing\n\n\n Abstract. An infamation retrieval model, named the Generaliied Vectm Spice Model (GVSM). is extended m handle situations where queries are specitied as (extended) Bool...", "weights": [[8, 15, 1.0], [60, 69, 1.0], [199, 206, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.12": {"snippet": "...fied boolean document retrieval systems", "weights": [[8, 15, 1.0], [25, 34, 1.0]]}, "1985.sigirconf_conference-85.3": {"snippet": "...s of Boolean Structures for Probabilistic Retrieval", "weights": [[8, 15, 1.0], [45, 54, 1.0]]}, "2018.sigirconf_conference-2018.82": {"snippet": "...item retrieval systems assume the user can formulate a good Boolean or SQL-style query to retrieve items, as one would do with a database, but this is often challenging, particularly given multiple nu...", "weights": [[8, 17, 1.0], [63, 70, 1.0]]}, "1990.sigirconf_conference-90.28": {"snippet": "...nded Boolean Retrieval: A Heuristic Approach?", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1988.sigirconf_conference-88.39": {"snippet": "...tion retrieval systems accessing collections of documents. Advantages offered by the theory are: the implicit inclusion of boolean logic; term weighting; and the ability to rank retrieved documents. I...", "weights": [[8, 17, 1.0], [62, 72, 1.0], [126, 133, 1.0]]}, "1994.ipm_journal-ir0volumeA30A3.4": {"snippet": "...s in Boolean Retrieval Systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2022.naacl-main.226": {"snippet": "...eral advantages compared to standard dense retrieval models. It produces representations which are 4x more compact, while delivering comparable retrieval results. It also performs surprisingly well un...", "weights": [[8, 18, 1.0], [46, 55, 1.0], [147, 156, 1.0]]}, "1981.sigirconf_conference-81.8": {"snippet": "...uzzy Retrieval Environment\n\n\n ABSTRACTWe shall consider retrieval performance measures for generalized (non-Boolean) queries and indexing functions.The meanings of recall and precision in such a gener...", "weights": [[8, 17, 1.0], [59, 68, 1.0], [111, 118, 1.0]]}, "1988.tois_journal-ir0volumeA6A1.1": {"snippet": "... but retrieval is usually limited to finding documents that contain a specified Boolean pattern of words. Effective retrieval requires that documents with similar meanings be found through a process o...", "weights": [[8, 17, 1.0], [83, 90, 1.0], [119, 128, 1.0]]}, "1979.ipm_journal-ir0volumeA15A5.2": {"snippet": "...hted boolean retrieval system", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2017.cikm_conference-2017.285": {"snippet": "...tive Boolean query is one fundamental operation for document retrieval in many information systems and databases. Various algorithms have been put up in terms of maximizing the query efficiency. In re...", "weights": [[8, 15, 1.0], [64, 73, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...Soft Boolean Operators\n\n\n AbstractThe conventional bibliographic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [68, 77, 1.0], [99, 106, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "...tion retrieval performance: training in Boolean logic and the type of search interface. As reported previously, we found that both Boolean training and the use of an assisted interface improved the pa...", "weights": [[8, 17, 1.0], [43, 50, 1.0], [134, 141, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "... and retrieval performance. In extended Boolean models, the relevance feedback implies not only that new query terms must be identified and re-weighted, but also that the terms must be connected with ...", "weights": [[8, 17, 1.0], [43, 50, 1.0]]}, "2013.ictir_conference-2013.28": {"snippet": "... and Retrieval]: Retrieval models\nGeneral Terms\nTheory\nKeywordsClassical Probabilistic Models, Information Visualization\nBACKGROUNDIn the Binary Independence Model (BIM) , documents and queries are de...", "weights": [[8, 17, 1.0], [20, 29, 1.0]]}, "1998.ipm_journal-ir0volumeA34A23.3": {"snippet": "... vs. Boolean: A Comparison of Partial and Exact Match Retrieval Systems", "weights": [[8, 15, 1.0], [57, 66, 1.0]]}, "2017.chiir_conference-2017.73": {"snippet": "...tion Retrieval. Recently, there has been a move to developing formal models of the interaction between the user and the system, whether it be to: (i) run a simulation, (ii) conduct an economic analysi...", "weights": [[8, 17, 1.0]]}, "1994.sigirconf_conference-94.22": {"snippet": "... vs. Boolean Query Evaluation: A Comparison of Retrieval Performance", "weights": [[8, 15, 1.0], [50, 59, 1.0]]}, "2004.ntcir_workshop-2004.80": {"snippet": "... the Boolean IR model. Since it is not easy to construct a Boolean query that covers all relevant documents, a mechanism for clarifying the Boolean query is required. In this paper, we propose \"approp...", "weights": [[8, 15, 1.0], [62, 69, 1.0], [143, 150, 1.0]]}, "1998.sigirconf_conference-98.26": {"snippet": "... the retrieval effectiveness of such queries. We investigate ways to improve this query expansion process by refining the set of documents used in feedback. We start by using manually formulated Boole...", "weights": [[8, 17, 1.0], [198, 205, 1.0]]}, "2011.sigirconf_conference-2011.85": {"snippet": "... for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we...", "weights": [[8, 15, 1.0], [151, 158, 1.0]]}, "1994.sigirconf_conference-94.19": {"snippet": "...nded Boolean Models in Information Retrieval", "weights": [[8, 15, 1.0], [38, 47, 1.0]]}, "1996.trec_conference-1996.38": {"snippet": "...nce, Boolean conjunct training, negative terms, and foreign language retrieval: probabilistic algorithms at TREC-5", "weights": [[8, 15, 1.0], [72, 81, 1.0]]}, "1997.jasis_journal-ir0volumeA48A2.3": {"snippet": "...ring Boolean and Probabilistic Information Retrieval Systems across Queries and Disciplines", "weights": [[8, 15, 1.0], [46, 55, 1.0]]}, "2004.trec_conference-2004.90": {"snippet": "...ed a boolean search engine called MadCow which does word indexing at the sentence level. This year we decided to explore systematically strategies for boolean retrieval for the purpose of QA measuring...", "weights": [[8, 15, 1.0], [154, 161, 1.0], [162, 171, 1.0]]}, "1993.jasis_journal-ir0volumeA44A2.1": {"snippet": "...zing Boolean Information Retrieval: A Model and Its Evaluation", "weights": [[8, 15, 1.0], [28, 37, 1.0]]}, "1982.jasis_journal-ir0volumeA33A6.1": {"snippet": "...tion retrieval in systems with boolean search request formulations", "weights": [[8, 17, 1.0], [34, 41, 1.0]]}, "1994.ipm_journal-ir0volumeA30A2.2": {"snippet": "... for Retrieval Performance and Their Use Measuring Performance and Generating Optimal Boolean Queries: Can It Get Any Better Than This?", "weights": [[8, 17, 1.0], [89, 96, 1.0]]}, "2015.jasis_journal-ir0volumeA66A8.2": {"snippet": "... for boolean retrieval and human decision-making during searches", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.4": {"snippet": "Boolean interpretation of conjunctions for document retrieval", "weights": [[0, 7, 1.0], [52, 61, 1.0]]}, "2012.sigirconf_workshop-2012osir.0": {"snippet": "...tion Retrieval, OSIR@SIGIR 2012, Portland, Oregon, USA, 16th August 2012\n\n\n Boolean Conjunctive Normal Form (CNF) expansion can effectively address the vocabulary mismatch problem, a problem that curr...", "weights": [[8, 17, 1.0], [79, 86, 1.0]]}, "2018.clef_conference-2018w.15": {"snippet": "...ng a boolean query and submitting it to a database, and then screening the retrieved documents for relevant ones. Task 2 of CLEF 2018 eHealth lab focuses on automating this process on two fronts: Sub-...", "weights": [[8, 15, 1.0]]}, "1985.sigirconf_conference-85.25": {"snippet": "...nded Boolean language / 10/. Besides the operators 'and', 'or' and' not' the GI~PS retrieval language contains thesaurus --operators to extend the query and truncation --and context-operators for free...", "weights": [[8, 15, 1.0], [86, 95, 1.0]]}, "1987.sigirconf_conference-87.38": {"snippet": "...text retrieval systems, which accommodate metrical as well as Boolean operators, the traditional approach to query processing uses a \"concordance\", from which large sets of coordinates are retrieved a...", "weights": [[8, 17, 1.0], [65, 72, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...tion retrieval skills by (a) comparing student use of printed and electronic forms of an encyclopedia, (b) examining the effects of menu or command driven interfaces and task difficulty on students' s...", "weights": [[8, 17, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.9": {"snippet": "...ting Boolean queries in conjunctive normal form with probabilistic retrieval models", "weights": [[8, 15, 1.0], [70, 79, 1.0]]}, "2001.ipm_journal-ir0volumeA37A3.2": {"snippet": "...tion retrieval with real users: Boolean searching and batch retrieval evaluations", "weights": [[8, 17, 1.0], [35, 42, 1.0], [63, 72, 1.0]]}, "1989.sigirconf_conference-89.5": {"snippet": "...tion Retrieval\n\n\n AbstractIn conventional Boolean retrieval systems,", "weights": [[8, 17, 1.0], [45, 52, 1.0], [53, 62, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2012.sigirconf_conference-2012.106": {"snippet": "...late Boolean queries to represent their information need. We want to work on three basic problems:1. Boolean query formulation -Our primary goal is to study Boolean query formulation in the light of t...", "weights": [[8, 15, 1.0], [104, 111, 1.0], [160, 167, 1.0]]}, "2006.clef_workshop-2006w.121": {"snippet": "... Our retrieval system consists of four modules: the geographic knowledge base; the indexing module; the document retrieval module and the ranking module. The geographic knowledge base provides informa...", "weights": [[8, 17, 1.0], [116, 125, 1.0]]}, "2002.ipm_journal-ir0volumeA38A6.0": {"snippet": "...nded Boolean retrieval model", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [50, 59, 1.0], [85, 94, 1.0], [124, 131, 1.0], [132, 141, 1.0], [196, 203, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "... for boolean query formulation\n\n\n AbstractA new model named Boolean Latent Semantic Indexing model based on the Singular Value Decomposition and Boolean query formulation is introduced. While the Sing...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [148, 155, 1.0]]}, "1978.jasis_journal-ir0volumeA29A3.7": {"snippet": "...ging boolean and weighted retrieval systems", "weights": [[8, 15, 1.0], [29, 38, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.5": {"snippet": "...nced Boolean retrieval: Experiences with the smart and sire systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2016.ictir_conference-2016.34": {"snippet": "...nked retrieval have been studied for decades. In this paper we propose a new strategy, which we call rank-at-a-time query processing, that evaluates documents in descending order of quantized scores a...", "weights": [[8, 17, 1.0]]}, "1998.sigirconf_conference-98.70": {"snippet": "... and Boolean Retrieval", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1994.ipm_journal-ir0volumeA30A1.5": {"snippet": "...ased Boolean Retrieval Systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...s of Boolean algebra are obeyed, and the exact-match semantics of Boolean retrieval are preserved. Simple inverted-list file structures provide an efficient implementation. Retrieval effectiveness is ...", "weights": [[8, 15, 1.0], [69, 76, 1.0], [77, 86, 1.0], [176, 185, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [45, 54, 1.0], [99, 106, 1.0], [107, 116, 1.0], [176, 183, 1.0], [199, 208, 1.0]]}}}
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We partly defuse the Boolean challenge by showing that the data actually argue against a ...", "weights": [[8, 15, 1.0], [135, 142, 1.0]]}, "P03-1018": {"snippet": "...dard Boolean methods. By altering the query vector itself, vector negation removes not only unwanted strings but unwanted meanings.", "weights": [[8, 15, 1.0]]}, "2005.clef_workshop-2005w.11": {"snippet": "Boolean Operators in Interactive Search\n\n\n This paper presents the participation of the MIRACLE team at the ImageCLEF interactive search task. Basically, queries consisting on several terms can be pro...", "weights": [[0, 7, 1.0], [8, 17, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0]]}, "2005.trec_conference-2005.42": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator. The fuzzy term proximity is controlled with an influenc...", "weights": [[8, 15, 1.0], [81, 88, 1.0]]}, "1982.sigirconf_conference-82.5": {"snippet": "...d on Boolean combinations. These novel methods have not been adopted by the large operational systems. In this paper, a software implementation of an ' i n t e l l i g e n t terminal' is described. I ...", "weights": [[8, 15, 1.0]]}, "2007.clef_workshop-2007w.50": {"snippet": "...rent boolean formulations of query translations. We find that a hybrid boolean formulation using a combination of boolean AND and boolean OR operators improves ranking of documents. We also find that ...", "weights": [[8, 15, 1.0], [74, 81, 1.0], [117, 124, 1.0], [133, 140, 1.0], [144, 153, 1.0]]}, "2015.wwwconf_conference-2015c.38": {"snippet": "...-way Boolean tensor. Standard sparql queries then can be expressed using elementary Boolean algebra operations. We show how this representation helps to estimate the size of joins. Such estimates are ...", "weights": [[8, 15, 1.0], [87, 94, 1.0]]}, "2007.trec_conference-2007.51": {"snippet": "... as \"Boolean,\" these queries contain additional operators (e.g., proximity and truncation operators) that are commonly found in the query languages of commercial search systems that employ Boolean log", "weights": [[8, 15, 1.0], [51, 60, 1.0], [93, 102, 1.0], [192, 199, 1.0]]}, "2007.trec_conference-2007.65": {"snippet": "...lds, boolean constraints and structured operators. Evaluation results show that structured queries outperform bag-of-words ones. Boolean constraints improve both precision and recall. 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The same answers are returned fo...", "weights": []}, "1985.sigirconf_conference-85.8": {"snippet": "...Soft Boolean Operators\n\n\n AbstractThe conventional bibliographic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [99, 106, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "... the Boolean operators and search terms that comprise the submitted queries. Our analysis shows that both Boolean training and the use of an assisted interface improved the participants' ability to co...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [109, 116, 1.0]]}, "2003.clef_workshop-2003w.44": {"snippet": "...ed a Boolean quorum-level type of query combination so that documents were ranked according to both the tf-idf factor but also to the number of matching Boolean combinations. 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Typical approaches add s...", "weights": [[0, 7, 1.0]]}, "2011.sigirconf_conference-2011.85": {"snippet": "... for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we...", "weights": [[8, 15, 1.0], [151, 158, 1.0]]}, "J90-1008": {"snippet": "Book Reviews: Prosody and Speech Recognition\n\n\n Book Reviews Prosody and Speech Recognition chart parsing is reduced into a set of matrix operations dealing with sparse matrices. Appendices list sampl...", "weights": []}, "2007.wwwconf_conference-2007.106": {"snippet": "...d OR boolean operators as well. We first analyze properties of the LCA computation and propose improved algorithms to solve the traditional keyword search problem (with only AND semantics). We then ex...", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1999.tois_journal-ir0volumeA17A4.1": {"snippet": "...uery operators. 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In the first plac...", "weights": [[8, 17, 1.0]]}, "2014.tois_journal-ir0volumeA32A2.0": {"snippet": "Efficient Index-Based Snippet Generation\n\n\n Ranked result lists with query-dependent snippets have become state of the art in text search. They are typically implemented by searching, at query time, f...", "weights": []}, "2021.findings-acl.146": {"snippet": "Documents Representation via Generalized Coupled Tensor Chain with the Rotation Group constraint\n\n\n Continuous representations of linguistic structures are an important part of modern natural language...", "weights": []}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...uery operators, with the other 90% using extremely simple queries. It is often assumed that the use of query operators, such as Boolean operators and phrase searching, improves the effectiveness of We...", "weights": [[8, 17, 1.0], [112, 121, 1.0], [131, 138, 1.0], [139, 148, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [99, 106, 1.0], [176, 183, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.10": {"snippet": "...n in Boolean IR queries\n\n\n a b s t r a c tThe negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include negated terms in Boole...", "weights": [[8, 15, 1.0], [198, 205, 1.0]]}}}
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Digital libraries, with their full text, multimedia content, along with searching and browsing capabilities, represent ideal environments in which to investigate the mutually beneficial relations...", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2000.jasis_journal-ir0volumeA51A4.2": {"snippet": "...of a digital library", "weights": [[8, 15, 1.0]]}, "2002.ismir_conference-2002.55": {"snippet": "...ical Digital Libraries: a Multimodal Analysis", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1980.jasis_journal-ir0volumeA31A6.5": {"snippet": "The technology of library and information networks", "weights": []}, "2006.jasis_journal-ir0volumeA57A10.13": {"snippet": "...ses, digital libraries, catalogs, and indexes", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2004.jasis_journal-ir0volumeA55A5.3": {"snippet": "...king digital libraries effective: Automatic generation of links for similarity search across hyper-textbooks", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2001.sigirjournals_journal-ir0volumeA35A1.4": {"snippet": "...e on digital libraries", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2006.sigirconf_conference-2006.147": {"snippet": "...ased digital libraries\n\n\n EXTENDED ABSTRACTRecent research in designing and developing digital library services has been focused on approaches to indexing and searching in a steadily increasing range ...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [90, 97, 1.0]]}, "2007.wwwconf_conference-2007.169": {"snippet": "... for digital libraries\n\n\n ABSTRACTFigures in digital documents contain important information. Current digital libraries do not summarize and index information available within figures for document ret...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [48, 55, 1.0], [105, 112, 1.0], [113, 122, 1.0]]}, "2000.cikm_conference-2000.2": {"snippet": "Digital Libraries: Extending and Applying Library and Information Science and Technology", "weights": [[0, 7, 1.0], [8, 17, 1.0]]}, "2005.ipm_journal-ir0volumeA41A1.0": {"snippet": "...sian digital libraries perspective\n\n\n AbstractThis introduces a special issue of IP&M that covers some of the best works from the International Conference on Asian Digital Libraries, 2002. Eight paper...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [167, 174, 1.0], [175, 184, 1.0]]}, "1999.tois_journal-ir0volumeA17A3.3": {"snippet": "...blic Libraries and Structured Databases\n\n\n The main purpose of a digital library is to facilitate users easy access to enormous amount of globally networked information. Typically, this information in...", "weights": [[8, 17, 1.0], [68, 75, 1.0]]}, "2004.cikm_conference-2004.85": {"snippet": "...OSU) Libraries. SERF encourages users to enter longer and more informative queries, and collects ratings from users as to whether search results meet their information need or not. These ratings are u...", "weights": [[8, 17, 1.0]]}, "2000.jasis_journal-ir0volumeA51A4.7": {"snippet": "Digital libraries: Situating use in changing information infrastructure", "weights": [[0, 7, 1.0], [8, 17, 1.0]]}, "2009.cikm_conference-2009.314": {"snippet": "...s in digital libraries\n\n\n ABSTRACTQueries on digital libraries generally involve the retrieval of specific documents, but most techniques lack the ability to efficiently explore these collections. The...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [48, 55, 1.0], [56, 65, 1.0]]}, "2004.mir_workshop-2004.13": {"snippet": "... for digital libraries", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "R13-1063": {"snippet": "...y of digital content from various types of providers and institutions. Therefore the accessibility to these resources requires the development of environments enabling to manage multilingual complexit...", "weights": [[8, 15, 1.0]]}, "W07-0905": {"snippet": "...tage Digital Library\n\n\n This paper describes the mutually beneficial relationship between a cultural heritage digital library and a historical treebank: an established digital library can provide the ...", "weights": [[8, 15, 1.0], [113, 120, 1.0], [171, 178, 1.0]]}, "2010.coria_conference-2010.34": {"snippet": "Digital Annotations and Digital Libraries", "weights": [[0, 7, 1.0], [24, 31, 1.0], [32, 41, 1.0]]}, "2020.ipm_journal-ir0volumeA57A3.7": {"snippet": "...ging digital divides in the Southern and Central Appalachian rural libraries", "weights": [[8, 15, 1.0], [70, 79, 1.0]]}, "2001.sigirconf_conference-2001.80": {"snippet": "...able Digital Library Services: MARIAN, Open Archives and NDLTD\n\n\n ABSTRACTIn this demonstration, we present interoperable and personalized search services for the Networked Digital Library of Theses a...", "weights": [[8, 15, 1.0], [176, 183, 1.0]]}, "2001.trec_conference-2001.80": {"snippet": "...edia Digital Video Library System\n\n\n Background: The Informedia Digital Video Library System.", "weights": [[8, 15, 1.0], [67, 74, 1.0]]}, "2002.jasis_journal-ir0volumeA53A14.12": {"snippet": "... for digital libraries: global knowledge discovery tools exclusively for librarians and libraries on the web", "weights": [[8, 15, 1.0], [16, 25, 1.0], [91, 100, 1.0]]}, "1995.sigirconf_conference-95.1": {"snippet": "Digital Libraries: Bridging the Two Cultures", "weights": [[0, 7, 1.0], [8, 17, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...blic libraries searched the 22 test questions in hardcopy bibliographic indexes and their equivalent online versions for a two-year period. 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If we understand digital cities as exploration of cyber space and ...", "weights": [[0, 7, 1.0], [75, 82, 1.0], [151, 158, 1.0]]}, "2017.jasis_journal-ir0volumeA68A10.9": {"snippet": "... the digital technology era: An empirical study", "weights": [[8, 15, 1.0]]}, "2002.sigirjournals_journal-ir0volumeA36A2.12": {"snippet": "...y of digital libraries @ JCDL'02", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.6": {"snippet": "...e on digital libraries in the context of users' broader activities", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2000.jasis_journal-ir0volumeA51A3.7": {"snippet": "...eous digital library", "weights": [[8, 15, 1.0]]}, "2008.ipm_journal-ir0volumeA44A3.21": {"snippet": "...s of digital libraries. 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A user being informed which documents are...", "weights": [[8, 15, 1.0]]}, "2010.sigirconf_conference-2010.210": {"snippet": "...s in digital libraries", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1998.sigirconf_conference-98.45": {"snippet": "...to a Digital Library", "weights": [[8, 15, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.16": {"snippet": "...rge, Digital Libraries: Principles and Practice in a Global Environment, K.G. Saur, Munich, 2005, 280 pp., ISBN: 3598116276", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2004.cikm_conference-2004.83": {"snippet": "... for digital libraries\n\n\n ABSTRACTWe introduce CiteSeer-API, a public API to CiteSeer-like services. 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The focus of this yea...", "weights": [[8, 17, 1.0], [96, 103, 1.0], [104, 113, 1.0]]}, "2000.riao_conference-2000.17": {"snippet": "A Digital Library System for Easy Creation/Manipulation of New Documents from Existing Resources", "weights": [[2, 9, 1.0]]}, "2003.jasis_journal-ir0volumeA54A6.7": {"snippet": "...tive digital libraries: The implications of constructionism for the development of digital libraries", "weights": [[8, 15, 1.0], [16, 25, 1.0], [86, 93, 1.0], [94, 103, 1.0]]}, "2016.eamt-2.23": {"snippet": "Digital curation technologies ({DKT})\n\n\n Digital Curation Technologies (\"Digitale Kuratierungstechnologien\", DKT) is a project that involves four Berlin-based SMEs (ART+COM AG, Condat AG, 3pc GmbH and...", "weights": [[0, 7, 1.0], [17, 29, 1.0], [41, 48, 1.0], [58, 70, 1.0]]}, "1998.jasis_journal-ir0volumeA49A13.4": {"snippet": "... for Digital Libraries and the Internet: Human, Database, and Domain Factors", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1997.tois_journal-ir0volumeA15A2.0": {"snippet": "...ng a Digital Library: The Contents of the CORE Project", "weights": [[8, 15, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.14": {"snippet": "Search in audiovisual broadcast archives\n\n\n Documentary makers, journalists, news editors, and other media professionals routinely require previously recorded audiovisual material for new productions....", "weights": []}, "1999.jasis_journal-ir0volumeA50A13.0": {"snippet": "... for Digital Library Applications", "weights": [[8, 15, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.2": {"snippet": "...ween libraries and companies such as Amazon, Google, and Microsoft. 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The law is investigated for two langua...", "weights": [[8, 11, 1.0], [36, 40, 1.0], [43, 46, 1.0], [169, 172, 1.0]]}, "1998.sigirconf_conference-98.3": {"snippet": "...o an information need. In particular, the relationship between the document frequency and the weight of evidence is investigated.A correlation between document frequency normalized by collection size ...", "weights": [[8, 19, 1.0]]}, "2018.wsdm_conference-2018.101": {"snippet": "...ata. Information Retrieval (IR) is not an exception. Many IR algorithms and applications require the use of users' personal information, contextual information and other sensitive and private informat...", "weights": [[8, 19, 1.0], [127, 138, 1.0], [151, 162, 1.0], [195, 206, 1.0]]}, "2020.acl-main.44": {"snippet": "...ower law when \u03b2 = 0 and the Zipf-Mandelbrot law when \u03b1 = 0. 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The frequency falls below the frequency predicted by Zipf's law for English words for rank greater tha...", "weights": [[8, 12, 1.0], [16, 19, 1.0], [154, 158, 1.0], [161, 164, 1.0]]}, "2010.spire_conference-2010.32": {"snippet": "... and Zipf-Like Scoring Function for Web Document Similarity Retrieval", "weights": [[8, 12, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.10": {"snippet": "... and information science research\n\n\n a b s t r a c tThis study identified the influence of the main concepts contained in Zipf's classic 1949 book entitled Human Behavior and the Principle of Least Ef...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [125, 129, 1.0]]}, "1999.jasis_journal-ir0volumeA50A3.4": {"snippet": "... the Law of Zipf-Mandelbrot for Multi-Wort Phrases", "weights": [[8, 11, 1.0], [15, 19, 1.0]]}, "I08-3003": {"snippet": "...ower law or the Zipf's law. This makes NLP hard. 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The law named for him is ubiquitous, but Zipf did not actually discover the law so much as provide a plausible explanation. Others have proposed modifications to Zipf's Law, and closer examination un...", "weights": [[8, 11, 1.0], [45, 49, 1.0], [80, 83, 1.0], [166, 170, 1.0], [173, 176, 1.0]]}, "2009.ipm_journal-ir0volumeA45A4.6": {"snippet": "...ower-law regularities have been discovered behind many complex natural and social phenomenons. 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precision and recall perfo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [82, 93, 1.0], [94, 103, 1.0]]}, "2017.cikm_conference-2017.268": {"snippet": "Boolean Matrix Decomposition by Formal Concept Sampling\n\n\n ABSTRACTFinding interesting patterns is a classical problem in data mining. Boolean matrix decomposition is nowadays a standard tool that can...", "weights": [[0, 7, 1.0], [135, 142, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "1981.sigirconf_conference-81.7": {"snippet": "...stic Retrieval\n\n\n ABSTRACTThe objective is to relate the effectiveness of retrieval, the fuzzy set concept and the processing of Boolean query. The use of a probabilistic retrieval scheme is motivated...", "weights": [[8, 17, 1.0], [77, 86, 1.0], [132, 139, 1.0], [174, 183, 1.0]]}, "1997.sigirconf_conference-97.15": {"snippet": "...g of Boolean Operators\n\n\n The inference network model of information retrieval allows for a probabilistic interpretation of Boolean query operators. Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [60, 71, 1.0], [72, 81, 1.0], [127, 134, 1.0]]}, "1982.sigirconf_conference-82.12": {"snippet": "...into Boolean Retrieval System", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1981.ipm_journal-ir0volumeA17A3.0": {"snippet": "... and Boolean retrieval systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1980.sigirconf_conference-80.3": {"snippet": "... for Boolean Retrieval", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.1": {"snippet": "...h on information retrieval \u2013 The potential for improvements in conventional Boolean retrieval systems", "weights": [[8, 19, 1.0], [20, 29, 1.0], [79, 86, 1.0], [87, 96, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0]]}, "2010.cikm_conference-2010.75": {"snippet": "...d on Boolean retrieval, i.e., a document may be relevant for the query or not. But with the coalescence of database and information retrieval (IR) technology, selectivity estimation for other, more so...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.22": {"snippet": "... the information available to us has been notably increasing. In this overload of information, Information Retrieval (IR) has been playing a prominent role by developing systems capable of separating ...", "weights": [[8, 19, 1.0], [85, 96, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "1981.jasis_journal-ir0volumeA32A4.5": {"snippet": "...hted boolean retrieval", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1986.sigirconf_conference-86.21": {"snippet": "... for Boolean Query Processing\n\n\n Abstract. An infamation retrieval model, named the Generaliied Vectm Spice Model (GVSM). is extended m handle situations where queries are specitied as (extended) Bool...", "weights": [[8, 15, 1.0], [60, 69, 1.0], [199, 206, 1.0]]}, "1985.sigirconf_conference-85.3": {"snippet": "...s of Boolean Structures for Probabilistic Retrieval", "weights": [[8, 15, 1.0], [45, 54, 1.0]]}, "1994.ipm_journal-ir0volumeA30A3.4": {"snippet": "...s in Boolean Retrieval Systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1988.sigirconf_conference-88.39": {"snippet": "...s of information retrieval Finally we present a small scale evaluation of rough sets which indicates its potential in information retrieval.", "weights": [[8, 19, 1.0], [20, 29, 1.0], [121, 132, 1.0], [133, 142, 1.0]]}, "1980.sigirconf_conference-80.22": {"snippet": "...ased Information Retrieval System with a Boolean Search Request Formulation", "weights": [[8, 19, 1.0], [20, 29, 1.0], [44, 51, 1.0]]}, "1982.sigirconf_conference-82.18": {"snippet": "...uage Information System\n\n\n AbstractLarge operational information retrieval systems typically employ inverted file structures and Boolean logic operators for efficient text retrieval. These systems req...", "weights": [[8, 19, 1.0], [56, 67, 1.0], [68, 77, 1.0], [132, 139, 1.0], [175, 184, 1.0]]}, "1990.sigirconf_conference-90.28": {"snippet": "...nded Boolean Retrieval: A Heuristic Approach?", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1988.tois_journal-ir0volumeA6A1.1": {"snippet": "... but retrieval is usually limited to finding documents that contain a specified Boolean pattern of words. Effective retrieval requires that documents with similar meanings be found through a process o...", "weights": [[8, 17, 1.0], [83, 90, 1.0], [119, 128, 1.0]]}, "1981.sigirconf_conference-81.8": {"snippet": "...uzzy Retrieval Environment\n\n\n ABSTRACTWe shall consider retrieval performance measures for generalized (non-Boolean) queries and indexing functions.The meanings of recall and precision in such a gener...", "weights": [[8, 17, 1.0], [59, 68, 1.0], [111, 118, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...Soft Boolean Operators\n\n\n AbstractThe conventional bibliographic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [68, 77, 1.0], [99, 106, 1.0]]}, "1979.ipm_journal-ir0volumeA15A5.2": {"snippet": "...hted boolean retrieval system", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1986.sigirjournals_journal-ir0volumeA20A14.4": {"snippet": "... the retrieval processes of document retrieval systems\u00b0 Two basic categories of models that have been employed in information retrieval are the vector processing models and the Boolean retrieval model", "weights": [[8, 17, 1.0], [40, 49, 1.0], [117, 128, 1.0], [129, 138, 1.0], [180, 187, 1.0], [188, 197, 1.0]]}, "1993.sigirconf_conference-93.36": {"snippet": "...s on Information Retrieval System Performance\n\n\n AbstractFive independently generated Boolean query formulations for ten different TREC topics were produced by ten different expert online searchers. T...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [89, 96, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "...user information retrieval performance: training in Boolean logic and the type of search interface. As reported previously, we found that both Boolean training and the use of an assisted interface imp...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [55, 62, 1.0], [146, 153, 1.0]]}, "2006.clef_workshop-2006w.37": {"snippet": "...dles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes it possible to...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [89, 100, 1.0], [101, 110, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...uses information obtained from a user about a set of initially retrieved documents to improve subsequent search formulations and retrieval performance. In extended Boolean models, the relevance feedba...", "weights": [[8, 19, 1.0], [132, 141, 1.0], [167, 174, 1.0]]}, "2013.ictir_conference-2013.28": {"snippet": "...3.3 [Information Search and Retrieval]: Retrieval models\nGeneral Terms\nTheory\nKeywordsClassical Probabilistic Models, Information Visualization\nBACKGROUNDIn the Binary Independence Model (BIM) , docum...", "weights": [[8, 19, 1.0], [31, 40, 1.0], [43, 52, 1.0], [121, 132, 1.0]]}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0], [55, 64, 1.0]]}, "1990.sigirconf_conference-90.1": {"snippet": "...ment Retrieval\n\n\n AbstractThe use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolea", "weights": [[8, 17, 1.0], [79, 88, 1.0], [121, 130, 1.0], [197, 204, 1.0]]}, "2004.ntcir_workshop-2004.80": {"snippet": "... and Boolean IR Models for WWW Documents Retrieval\n\n\n In this paper, we describe our information retrieval (IR) system that is used for the NTCIR-4 Web Task A. First, we introduce our IR system, which...", "weights": [[8, 15, 1.0], [44, 53, 1.0], [88, 99, 1.0], [100, 109, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.7": {"snippet": "...onal Boolean retrieval systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ical information retrieval\n\n\n AbstractIn this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 153, 1.0], [154, 163, 1.0]]}, "1987.sigirconf_conference-87.25": {"snippet": "...Text Retrieval and Analysis System\n\n\n AbstractMICROARRAS is an advanced full-text retrieval and analysis system. It supports fast, efficient browsing of a document's vocabulary as well as its text, re...", "weights": [[8, 17, 1.0], [85, 94, 1.0]]}, "1993.jasis_journal-ir0volumeA44A2.1": {"snippet": "...zing Boolean Information Retrieval: A Model and Its Evaluation", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0]]}, "1994.sigirconf_conference-94.19": {"snippet": "...nded Boolean Models in Information Retrieval", "weights": [[8, 15, 1.0], [26, 37, 1.0], [38, 47, 1.0]]}, "2004.trec_conference-2004.90": {"snippet": "...ed a boolean search engine called MadCow which does word indexing at the sentence level. This year we decided to explore systematically strategies for boolean retrieval for the purpose of QA measuring...", "weights": [[8, 15, 1.0], [154, 161, 1.0], [162, 171, 1.0]]}, "1997.jasis_journal-ir0volumeA48A2.3": {"snippet": "...ring Boolean and Probabilistic Information Retrieval Systems across Queries and Disciplines", "weights": [[8, 15, 1.0], [34, 45, 1.0], [46, 55, 1.0]]}, "1987.sigirconf_conference-87.21": {"snippet": "...near Information Retrieval Models\n\n\n AbstractLinear decision (retrieval)functions have been widely adopted in information retrieval systems such as in Boolean, vector, and probabilistic", "weights": [[8, 19, 1.0], [20, 29, 1.0], [65, 74, 1.0], [113, 124, 1.0], [125, 134, 1.0], [154, 161, 1.0]]}, "2012.sigirconf_conference-2012.111": {"snippet": "...p in information systems is to afford seamless movement between boolean and ranked retrieval of such different retrieval types enabling users to easily negotiate this complex information space. We int...", "weights": [[8, 19, 1.0], [67, 74, 1.0], [86, 95, 1.0], [114, 123, 1.0], [178, 189, 1.0]]}, "1982.jasis_journal-ir0volumeA33A6.1": {"snippet": "...h to information retrieval in systems with boolean search request formulations", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 53, 1.0]]}, "2015.jasis_journal-ir0volumeA66A8.2": {"snippet": "... for boolean retrieval and human decision-making during searches", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.4": {"snippet": "Boolean interpretation of conjunctions for document retrieval", "weights": [[0, 7, 1.0], [52, 61, 1.0]]}, "1985.sigirconf_conference-85.25": {"snippet": "...heir retrieval experlrnent. FIZ4 IS using the Information retrieval system GRIPS \u2022 (General Relation based Information l~oceeeing System) which was developed by DIMDI (Deutsches Institut flLer Medlzin...", "weights": [[8, 17, 1.0], [49, 60, 1.0], [61, 70, 1.0], [110, 121, 1.0]]}, "2001.ipm_journal-ir0volumeA37A3.2": {"snippet": "...ated information retrieval with real users: Boolean searching and batch retrieval evaluations", "weights": [[8, 19, 1.0], [20, 29, 1.0], [47, 54, 1.0], [75, 84, 1.0]]}, "2020.sigirconf_conference-2020.379": {"snippet": "...sing Boolean techniques. Given this situation, an e cient system for legal case retrieval can be of great bene t and thus has drawn increasing attention in academic and industrial IR research. General...", "weights": [[8, 15, 1.0], [83, 92, 1.0]]}, "1989.sigirconf_conference-89.5": {"snippet": "... for Information Retrieval\n\n\n AbstractIn conventional Boolean retrieval systems,", "weights": [[8, 19, 1.0], [20, 29, 1.0], [57, 64, 1.0], [65, 74, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords Information Retrieval (IR) solutions to retrieve passages matching a set of terms extracted from the question.There is a fundamental disconnect between the capabilities of the bag-of-words retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [197, 206, 1.0]]}, "2012.sigirconf_conference-2012.106": {"snippet": "...late Boolean queries to represent their information need. We want to work on three basic problems:1. Boolean query formulation -Our primary goal is to study Boolean query formulation in the light of t...", "weights": [[8, 15, 1.0], [43, 54, 1.0], [104, 111, 1.0], [160, 167, 1.0]]}, "1997.sigirjournals_journal-ir0volumeA31A1.8": {"snippet": "...atic Boolean query processing. Information Processing and  Management, 24, 3 (1988), 269-280.    Historical note: the past thirty years in information retrieval. Journal of the American Society for In...", "weights": [[8, 15, 1.0], [34, 45, 1.0], [142, 153, 1.0], [154, 163, 1.0], [201, 212, 1.0]]}, "2007.spire_conference-2007.13": {"snippet": "... for Information Retrieval\n\n\n Abstract. Conjunctive Boolean queries are a fundamental operation in web search engines. These queries can be reduced to the problem of intersecting ordered sets of integ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [55, 62, 1.0]]}, "2006.clef_workshop-2006w.121": {"snippet": "... Our retrieval system consists of four modules: the geographic knowledge base; the indexing module; the document retrieval module and the ranking module. The geographic knowledge base provides informa...", "weights": [[8, 17, 1.0], [116, 125, 1.0], [196, 207, 1.0]]}, "2002.ipm_journal-ir0volumeA38A6.0": {"snippet": "...nded Boolean retrieval model", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.10": {"snippet": "...ment Retrieval\n\n\n AbstractThe use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolea", "weights": [[8, 17, 1.0], [79, 88, 1.0], [121, 130, 1.0], [197, 204, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [50, 59, 1.0], [85, 94, 1.0], [124, 131, 1.0], [132, 141, 1.0], [196, 203, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "...onal information retrieval model, Boolean query formulation can help users to make precise representation of their information search needs. Retrieval experiments on a number of test collections seem ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [37, 44, 1.0], [118, 129, 1.0], [144, 153, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...n of retrieval models particularly for recall oriented retrieval domains, where the focus is on retrieving all relevant information but not just retrieving a subset of relevant information. Available ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [123, 134, 1.0], [180, 191, 1.0]]}, "1990.sigirconf_conference-90.8": {"snippet": "... for Boolean Information Retrieval via Natural Language Query", "weights": [[8, 15, 1.0], [16, 27, 1.0], [28, 37, 1.0]]}, "1994.ipm_journal-ir0volumeA30A1.5": {"snippet": "...ased Boolean Retrieval Systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...s of Boolean algebra are obeyed, and the exact-match semantics of Boolean retrieval are preserved. Simple inverted-list file structures provide an efficient implementation. Retrieval effectiveness is ...", "weights": [[8, 15, 1.0], [69, 76, 1.0], [77, 86, 1.0], [176, 185, 1.0]]}, "1998.sigirconf_conference-98.70": {"snippet": "... and Boolean Retrieval", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2008.trec_conference-2008.30": {"snippet": "...veR) Information Retrieval platform, DFRee model, Bo1 (Bose-Einstein statistics) term weighting models, Boolean re-rank, query lexicon, query performance prediction, Z-Score. \u2022 Open Text Corporation: ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [107, 114, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [45, 54, 1.0], [99, 106, 1.0], [107, 116, 1.0], [176, 183, 1.0], [199, 208, 1.0]]}}}
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These ratings are u...", "weights": [[8, 17, 1.0]]}, "W07-0905": {"snippet": "...such libraries.", "weights": [[8, 17, 1.0]]}, "2020.emnlp-demos.3": {"snippet": "{N}eural{QA}: A Usable Library for Question Answering (Contextual Query Expansion + {BERT}) on Large Datasets\n\n\n Existing tools for Question Answering (QA) have challenges that limit their use in prac...", "weights": []}, "1999.jasis_journal-ir0volumeA50A14.8": {"snippet": "... the Limits: The CONFU Digital-Images and Multimedia Guidelines and Their Consequences for Libraries and Educators", "weights": [[8, 14, 1.0], [94, 103, 1.0]]}, "1977.jasis_journal-ir0volumeA28A3.0": {"snippet": "The industrial special library universe - A \"base line\" study of its extent and characteristics", "weights": []}, "2005.ecir_conference-2005.5": {"snippet": "...ital Libraries in Hierarchical Peer-to-Peer Networks\n\n\n Abstract. 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I compare and evaluate 12 Java libraries de...", "weights": [[8, 17, 1.0], [191, 200, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.7": {"snippet": "Writing in the library: Exploring tighter integration of digital library use with the writing process", "weights": []}, "2008.jasis_journal-ir0volumeA59A13.15": {"snippet": "...w of libraries and archives", "weights": [[8, 17, 1.0]]}, "2007.wwwconf_conference-2007.220": {"snippet": "...ital libraries have multiple granularities of semantic units: book, chapter, page, paragraph and word. 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These items are often accompanied by text that ...", "weights": []}, "1976.jasis_journal-ir0volumeA27A2.2": {"snippet": "A library network model", "weights": []}, "2008.jasis_journal-ir0volumeA59A5.18": {"snippet": "Library 2.0: A guide to participatory library service", "weights": []}, "1990.jasis_journal-ir0volumeA41A4.10": {"snippet": "... law libraries", "weights": [[8, 17, 1.0]]}, "2008.jasis_journal-ir0volumeA59A2.16": {"snippet": "...dium libraries", "weights": [[8, 17, 1.0]]}, "1990.jasis_journal-ir0volumeA41A2.8": {"snippet": "India's national library: Systematization and modernization", "weights": []}, "W11-4105": {"snippet": "Digital Library of {P}oland-related Old Ephemeral Prints: Preserving Multilingual Cultural Heritage\n\n\n The article presents the results from the project of the thematic Digital Library of Polish and P...", "weights": []}, "2003.jasis_journal-ir0volumeA54A4.9": {"snippet": "Book reviews: Current theory in library and information science", "weights": []}, "2018.iwslt-1.6": {"snippet": "...hus, limits their accessibility to scholars specialized in the time period in which a certain document was written. 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Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [127, 134, 1.0], [141, 150, 1.0], [194, 203, 1.0]]}, "2019.wwwconf_conference-2019.151": {"snippet": "...atic Boolean Query Refinement for Systematic Review Literature Search", "weights": [[8, 15, 1.0]]}, "1977.ipm_journal-ir0volumeA13A2.0": {"snippet": "...s of library systems", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1982.sigirconf_conference-82.12": {"snippet": "...into Boolean Retrieval System", "weights": [[8, 15, 1.0]]}, "2005.trec_conference-2005.42": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator. The fuzzy term proximity is controlled with an influenc...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [122, 125, 1.0]]}, "W14-1411": {"snippet": "...with Boolean operations. The second relates to adjectival modification of question and propositional entities. We partly defuse the Boolean challenge by showing that the data actually argue against a ...", "weights": [[8, 15, 1.0], [135, 142, 1.0]]}, "2000.sigirconf_conference-2000.5": {"snippet": "...n of Boolean query effectiveness across a wide operational range\n\n\n ABSTRACTTraditional methods for the system-oriented evaluation of Boolean IR systems suffer from validity and reliability problems. ...", "weights": [[8, 15, 1.0], [137, 144, 1.0], [148, 155, 1.0]]}, "1981.ipm_journal-ir0volumeA17A3.0": {"snippet": "... and Boolean retrieval systems", "weights": [[8, 15, 1.0], [26, 33, 1.0]]}, "2005.clef_workshop-2005w.11": {"snippet": "Boolean Operators in Interactive Search\n\n\n This paper presents the participation of the MIRACLE team at the ImageCLEF interactive search task. Basically, queries consisting on several terms can be pro...", "weights": [[0, 7, 1.0], [8, 17, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0]]}, "1982.sigirconf_conference-82.5": {"snippet": "...eval Systems\n\n\n ABSTRACTResearch has shown that document r e t r i e v a l based on weighting functions and incorporating relevance feedback may be more effective than r e t r i e v a l based on Boole...", "weights": [[8, 15, 1.0], [198, 205, 1.0]]}, "2007.clef_workshop-2007w.50": {"snippet": "...rent boolean formulations of query translations. We find that a hybrid boolean formulation using a combination of boolean AND and boolean OR operators improves ranking of documents. We also find that ...", "weights": [[8, 15, 1.0], [74, 81, 1.0], [117, 124, 1.0], [133, 140, 1.0], [144, 153, 1.0]]}, "1985.sigirconf_conference-85.3": {"snippet": "...s of Boolean Structures for Probabilistic Retrieval", "weights": [[8, 15, 1.0]]}, "2007.trec_conference-2007.51": {"snippet": "... as \"Boolean,\" these queries contain additional operators (e.g., proximity and truncation operators) that are commonly found in the query languages of commercial search systems that employ Boolean log", "weights": [[8, 15, 1.0], [51, 60, 1.0], [93, 102, 1.0], [172, 179, 1.0], [192, 199, 1.0]]}, "2007.trec_conference-2007.65": {"snippet": "...lds, boolean constraints and structured operators. Evaluation results show that structured queries outperform bag-of-words ones. Boolean constraints improve both precision and recall. For the routing ...", "weights": [[8, 15, 1.0], [43, 52, 1.0], [132, 139, 1.0]]}, "1994.ipm_journal-ir0volumeA30A3.4": {"snippet": "...s in Boolean Retrieval Systems", "weights": [[8, 15, 1.0], [26, 33, 1.0]]}, "1985.riao_conference-1985.20": {"snippet": "...ting boolean structure within a probabilistic framework", "weights": [[8, 15, 1.0]]}, "1982.sigirconf_conference-82.18": {"snippet": "...eval systems typically employ inverted file structures and Boolean logic operators for efficient text retrieval. These systems require considerable user training for effective use. As a consequence, s...", "weights": [[8, 15, 1.0], [62, 69, 1.0], [76, 85, 1.0], [122, 129, 1.0], [179, 182, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.12": {"snippet": "...fied boolean document retrieval systems", "weights": [[8, 15, 1.0], [35, 42, 1.0]]}, "2017.cikm_conference-2017.285": {"snippet": "...tive Boolean query is one fundamental operation for document retrieval in many information systems and databases. Various algorithms have been put up in terms of maximizing the query efficiency. In re...", "weights": [[8, 15, 1.0], [94, 101, 1.0]]}, "1982.ipm_journal-ir0volumeA18A5.4": {"snippet": "... the Boolean logic of a program", "weights": [[8, 15, 1.0]]}, "1981.sigirconf_conference-81.8": {"snippet": "...pare Boolean and non-Boolean retrieval systems.", "weights": [[8, 15, 1.0], [24, 31, 1.0], [42, 49, 1.0]]}, "1979.ipm_journal-ir0volumeA15A5.2": {"snippet": "...hted boolean retrieval system", "weights": [[8, 15, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...Soft Boolean Operators\n\n\n AbstractThe conventional bibliographic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [78, 85, 1.0], [99, 106, 1.0], [166, 173, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "... the Boolean operators and search terms that comprise the submitted queries. Our analysis shows that both Boolean training and the use of an assisted interface improved the participants' ability to co...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [109, 116, 1.0], [134, 137, 1.0]]}, "2006.clef_workshop-2006w.37": {"snippet": "...dles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes it possible to...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [127, 130, 1.0]]}, "1972.jasis_journal-ir0volumeA23A1.7": {"snippet": "... the use of boolean not logic to improve the precision of selective dissemination of information", "weights": [[8, 11, 1.0], [15, 22, 1.0]]}, "2009.jasis_journal-ir0volumeA60A6.9": {"snippet": "... the use of multi-objective evolutionary algorithms to learn Boolean queries: A comparative study", "weights": [[8, 11, 1.0], [64, 71, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...nded Boolean models, the relevance feedback implies not only that new query terms must be identified and re-weighted, but also that the terms must be connected with Boolean And/Or operators properly. ...", "weights": [[8, 15, 1.0], [168, 175, 1.0], [183, 192, 1.0]]}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0]]}, "2004.ntcir_workshop-2004.80": {"snippet": "... the Boolean IR model. Since it is not easy to construct a Boolean query that covers all relevant documents, a mechanism for clarifying the Boolean query is required. In this paper, we propose \"approp...", "weights": [[8, 15, 1.0], [62, 69, 1.0], [143, 150, 1.0]]}, "1999.sigirconf_conference-99.10": {"snippet": "...nded Boolean Models", "weights": [[8, 15, 1.0]]}, "2007.sigirconf_conference-2007.184": {"snippet": "...mple Boolean operators. Results show minimal differences in performance with respect to the different techniques.", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1979.jasis_journal-ir0volumeA30A6.10": {"snippet": "...sing boolean queries with a clustered file organization", "weights": [[8, 15, 1.0]]}, "1973.jasis_journal-ir0volumeA24A2.0": {"snippet": "...ical library. Part III: Comparison of on-line and batch operations and cost analysis", "weights": [[8, 15, 1.0]]}, "1994.sigirconf_conference-94.19": {"snippet": "...nded Boolean Models in Information Retrieval", "weights": [[8, 15, 1.0]]}, "1997.jasis_journal-ir0volumeA48A2.3": {"snippet": "...ring Boolean and Probabilistic Information Retrieval Systems across Queries and Disciplines", "weights": [[8, 15, 1.0], [56, 63, 1.0]]}, "1988.jasis_journal-ir0volumeA39A1.10": {"snippet": "Electronics, the cost disease, and the operation of libraries", "weights": []}, "2015.jasis_journal-ir0volumeA66A8.2": {"snippet": "... for boolean retrieval and human decision-making during searches", "weights": [[8, 15, 1.0]]}, "1999.tois_journal-ir0volumeA17A1.0": {"snippet": "...eous Boolean systems.", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1999.tois_journal-ir0volumeA17A4.1": {"snippet": "...uery operators. In particular, Boolean query operators are conveniently modeled as link matrices of the Bayesian Network. Prior work has shown, however, that these operators do not perform as well as ...", "weights": [[8, 17, 1.0], [34, 41, 1.0], [48, 57, 1.0], [167, 176, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.4": {"snippet": "Boolean interpretation of conjunctions for document retrieval", "weights": [[0, 7, 1.0]]}, "2008.trec_conference-2008.43": {"snippet": "...ment Library Boolean search system. Our experiment achieved reasonable precision but suffered significantly from low recall. These results, together with the appealing and adjudication results, sugges...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1997.sigirjournals_journal-ir0volumeA31A1.7": {"snippet": "...pert systems research is the new frontier and the next area of development in library information science. Expert systems will enable users to make more effective use of the automated systems and on-l...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [117, 124, 1.0], [166, 169, 1.0], [187, 194, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.12": {"snippet": "... and Boolean operators used as well as their relationships with search failure. Among other findings, results indicate that a majority of the queries were simple, with short query lengths and a low us...", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1985.sigirconf_conference-85.25": {"snippet": "...nded Boolean language / 10/. Besides the operators 'and', 'or' and' not' the GI~PS retrieval language contains thesaurus --operators to extend the query and truncation --and context-operators for free...", "weights": [[8, 15, 1.0], [44, 53, 1.0], [126, 135, 1.0], [185, 194, 1.0]]}, "1987.sigirconf_conference-87.38": {"snippet": "...Text Systems\n\n\n ABSTRACTIn static full-text retrieval systems, which accommodate metrical as well as Boolean operators, the traditional approach to query processing uses a \"concordance\", from which la...", "weights": [[8, 15, 1.0], [57, 64, 1.0], [104, 111, 1.0], [112, 121, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...mpus library use. The model developed for those scholars who used primary source materials \"seldom\" or \"never\" contained two variables and explained 44% of the variance of campus library use. The mode...", "weights": [[8, 15, 1.0], [16, 19, 1.0], [182, 189, 1.0], [190, 193, 1.0]]}, "1983.jasis_journal-ir0volumeA34A3.1": {"snippet": "Electronics, the cost disease, and the operation of libraries", "weights": []}, "2012.sigirconf_conference-2012.106": {"snippet": "...tudy Boolean query formulation in the light of the E-discovery task. This will include automatic Boolean query generation, expansion and learning the effect of proximity operators in Boolean searches....", "weights": [[8, 15, 1.0], [100, 107, 1.0], [173, 182, 1.0], [186, 193, 1.0]]}, "2007.spire_conference-2007.13": {"snippet": "...tive Boolean queries are a fundamental operation in web search engines. These queries can be reduced to the problem of intersecting ordered sets of integers, where each set represents the documents co...", "weights": [[8, 15, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [42, 49, 1.0], [124, 131, 1.0], [196, 203, 1.0]]}, "2001.cikm_conference-2001.28": {"snippet": "...ting systems, database systems (DBSs), as well as applications are used to cover the whole life cycle of a product. Initial approaches primarily focusing on problems of data heterogeneity were federat...", "weights": [[8, 15, 1.0], [26, 33, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...s of Boolean algebra are obeyed, and the exact-match semantics of Boolean retrieval are preserved. Simple inverted-list file structures provide an efficient implementation. Retrieval effectiveness is ...", "weights": [[8, 15, 1.0], [69, 76, 1.0]]}, "1990.sigirconf_conference-90.8": {"snippet": "... for Boolean Information Retrieval via Natural Language Query", "weights": [[8, 15, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...uery operators, with the other 90% using extremely simple queries. It is often assumed that the use of query operators, such as Boolean operators and phrase searching, improves the effectiveness of We...", "weights": [[8, 17, 1.0], [99, 102, 1.0], [112, 121, 1.0], [131, 138, 1.0], [139, 148, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [99, 106, 1.0], [176, 183, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.10": {"snippet": "... the use of negation in Boolean IR queries\n\n\n a b s t r a c tThe negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include neg...", "weights": [[8, 11, 1.0], [27, 34, 1.0]]}}}
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A measure of the similarity between incomplete rankings should handle nonconjointness, weight high ranks more heavily than low, and be monotonic with increasing depth of evaluatio...", "weights": [[8, 14, 1.0], [194, 204, 1.0]]}, "2004.ntcir_workshop-2004.52": {"snippet": "...meta search engine as an alternative to listing of ranked retrieved results, which most search engines adopt to present the retrieval results. The proposed method clusters the retrieved results dynami...", "weights": [[8, 14, 1.0], [71, 78, 1.0], [91, 97, 1.0], [137, 144, 1.0], [189, 196, 1.0]]}, "2006.sigirconf_conference-2006.4": {"snippet": "... web search ranking by incorporating user behavior information\n\n\n ABSTRACTWe show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [165, 172, 1.0], [185, 191, 1.0]]}, "2015.trec_conference-2015.24": {"snippet": "...r re-ranking a set of retrieved results. Evaluation results in the 2015 TREC Clinical Decision Support Track illustrate that query expansion can increase recall by retrieving more relevant hits, while...", "weights": [[8, 15, 1.0], [35, 42, 1.0], [44, 54, 1.0], [55, 62, 1.0]]}, "2010.tois_journal-ir0volumeA29A1.2": {"snippet": "...king search results\n\n\n We present a novel language-model-based approach to reranking search results; that is, reordering the documents in an initially retrieved list so as to improve precision at top ...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [88, 94, 1.0], [95, 102, 1.0]]}, "2012.sigirconf_conference-2012.24": {"snippet": "...ated search engine results pages (SERPs) for each query, which were manipulated to exhibit different levels of performance. Following this, subjects reevaluated the queries, were interviewed about the...", "weights": [[8, 14, 1.0], [22, 29, 1.0]]}, "2008.sigirconf_conference-2008.173": {"snippet": "Re-ranking search results using document-passage graphs\n\n\n ABSTRACTWe present a novel passage-based approach to re-ranking documents in an initially retrieved list so as to improve precision at top ra...", "weights": [[3, 10, 1.0], [11, 17, 1.0], [18, 25, 1.0], [115, 122, 1.0]]}, "2013.clef_conference-2013.7": {"snippet": "...rent evaluation criteria prior to conducting an evaluation. We analyse results to show which criteria users most value, together with the inter-relationships between them. Our results highlight the ne...", "weights": [[8, 18, 1.0], [51, 61, 1.0], [74, 81, 1.0], [179, 186, 1.0]]}, "2008.cikm_conference-2008.190": {"snippet": "Ranking in folksonomy systems: can context help?\n\n\n ABSTRACTFolksonomy systems have shown to contribute to the quality of Web search ranking strategies. In this paper, we analyze and compare different...", "weights": [[0, 7, 1.0], [126, 132, 1.0], [133, 140, 1.0]]}, "2017.tist_journal-ir0volumeA8A3.7": {"snippet": "...sity Evaluation Measures: A New Approach to Search Result Diversification\n\n\n The queries issued to search engines are often ambiguous or multifaceted, which requires search engines to return diverse r...", "weights": [[8, 18, 1.0], [47, 53, 1.0], [102, 108, 1.0], [169, 175, 1.0], [202, 209, 1.0]]}, "2011.cikm_conference-2011.230": {"snippet": "...main search answers to queries spanning multiple entities, like \"Find an affordable house in a city with low criminality index, good schools and medical services\", by producing ranked sets of entity c...", "weights": [[8, 14, 1.0]]}, "2009.sigirconf_conference-2009.76": {"snippet": "...zing search engine revenue in sponsored search\n\n\n ABSTRACTDisplaying sponsored ads alongside the search results is a key monetization strategy for search engine companies. Since users are more likely ...", "weights": [[8, 14, 1.0], [43, 49, 1.0], [100, 106, 1.0], [107, 114, 1.0], [150, 156, 1.0]]}, "2012.ipm_journal-ir0volumeA48A1.10": {"snippet": "...tion search process\n\n\n a b s t r a c tRelevance judgments occur within an information search process, where time, context and situation can impact the judgments. The determination of relevance is depe...", "weights": [[8, 14, 1.0], [89, 95, 1.0]]}, "2005.sigirconf_conference-2005.67": {"snippet": "... web search results using affinity graph\n\n\n ABSTRACTIn this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics:(1) diversity --whi...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [89, 96, 1.0], [119, 126, 1.0], [143, 149, 1.0], [150, 157, 1.0]]}, "2011.jasis_journal-ir0volumeA62A6.15": {"snippet": "Re-ranking question search results by clustering questions", "weights": [[3, 10, 1.0], [20, 26, 1.0], [27, 34, 1.0]]}, "2009.eamt-1.11": {"snippet": "Relevance of Different Segmentation Options on {S}panish-{B}asque {SMT}\n\n\n Segmentation is widely used in adapting Statistical Machine Translation to highly inflected languages as Basque. The way this...", "weights": []}, "2018.cikm_conference-2018.159": {"snippet": "...line evaluation only for rankers that achieved high performance in offline evaluation. The analysis of evaluation results showed that offline and online evaluation results did not fully agree, and a l...", "weights": [[8, 18, 1.0], [78, 88, 1.0], [106, 116, 1.0], [117, 124, 1.0], [156, 166, 1.0], [167, 174, 1.0]]}, "2011.cikm_conference-2011.237": {"snippet": "... the search results for these queries on both search engines. The results allow us to determine the search engines correlations, crawling coverage, information overlap, and information entropy.", "weights": [[8, 14, 1.0], [15, 22, 1.0], [49, 55, 1.0], [69, 76, 1.0], [103, 109, 1.0]]}, "2005.sigirconf_conference-2005.136": {"snippet": "...th a search engine; (2) identify query coherence and search session boundaries; (3) perform implicit user modeling based on the past queries and the user-viewed results in the same search session; (4)...", "weights": [[8, 14, 1.0], [56, 62, 1.0], [164, 171, 1.0], [184, 190, 1.0]]}, "2012.sigirconf_conference-2012.155": {"snippet": "...rson search in email\n\n\n ABSTRACTThis paper describes an entity ranking model for examplebased person search in email. Evaluation by comparison to manually resolved named references in Enron email yiel...", "weights": [[8, 14, 1.0], [66, 73, 1.0], [104, 110, 1.0], [121, 131, 1.0]]}, "2021.emnlp-main.575": {"snippet": "...NLG} Evaluation Metrics\n\n\n Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequ...", "weights": [[8, 18, 1.0], [64, 74, 1.0]]}, "2001.wwwconf_conference-2001p.91": {"snippet": "...hich Search Engine is Best at Finding Online Services?\n\n\n We report results for an independent, blind evaluation of the performance of 11 commercial search engines on 106 online service queries and on...", "weights": [[8, 14, 1.0], [71, 78, 1.0], [105, 115, 1.0], [152, 158, 1.0]]}, "2001.ntcir_workshop-2001.50": {"snippet": "... the search results in each of the sub-tasks and evaluated the search results using the relevance judgments in the pools. Almost the same rankings were produced by all the relevance judgments. Therefo...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [66, 72, 1.0], [73, 80, 1.0]]}, "1995.trec_conference-1995.36": {"snippet": "... and Ranking in Interactive Searching", "weights": [[8, 15, 1.0]]}, "2017.sigirconf_conference-2017.225": {"snippet": "...ntic ranking refers to ranking search results using semantic information. In a standard search engine, a rank is computed by using signals or features coming from the search query, from the documents ...", "weights": [[8, 15, 1.0], [26, 33, 1.0], [34, 40, 1.0], [41, 48, 1.0], [91, 97, 1.0], [170, 176, 1.0]]}, "2007.wwwconf_conference-2007.121": {"snippet": "...atic search engine performance evaluation with click-through data analysis\n\n\n ABSTRACTPerformance evaluation is an important issue in Web search engine researches. Traditional evaluation methods rely ...", "weights": [[8, 14, 1.0], [34, 44, 1.0], [101, 111, 1.0], [141, 147, 1.0], [179, 189, 1.0]]}, "2012.sigirconf_conference-2012.192": {"snippet": "...sWeb search snippet examination; web search behavior; search evaluation.\nMOTIVATION AND OVERVIEWAs terabytes of search log data are being generated daily, correct interpretation of patterns in these d...", "weights": [[8, 14, 1.0], [40, 46, 1.0], [57, 63, 1.0], [64, 74, 1.0], [115, 121, 1.0]]}, "2018.tweb_journal-ir0volumeA12A3.4": {"snippet": "... Web Search\n\n\n Modern search engines aggregate results from different verticals: webpages, news, images, video, shopping, knowledge cards, local maps, and so on. Unlike \"ten blue links,\" these search ...", "weights": [[8, 14, 1.0], [25, 31, 1.0], [50, 57, 1.0], [196, 202, 1.0]]}, "2015.wwwconf_conference-2015.106": {"snippet": "...tive ranking measure. All our algorithms are evaluated on a large-scale query log provided by a major commercial search engine. The results of the experiments prove that the current state-of-the-art p...", "weights": [[8, 15, 1.0], [116, 122, 1.0], [135, 142, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "... new ranking algorithm, the importance of experimental studies cannot be overemphasized.\nUsing Hyperlinks to Rank Web Search ResultsMarchiori was the first to propose leveraging hyperlinks for ranking...", "weights": [[8, 15, 1.0], [121, 127, 1.0], [196, 203, 1.0]]}, "2011.sigirconf_conference-2011.81": {"snippet": "...ocal search ranking through external logs\n\n\n ABSTRACTThe signals used for ranking in local search are very different from web search: in addition to (textual) relevance, measures of (geographic) dista...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [77, 84, 1.0], [94, 100, 1.0], [129, 135, 1.0]]}, "2011.sigirconf_conference-2011.190": {"snippet": "...s of search quality, such as freshness and diversity. Prior work on multi-objective ranking trained rankers focused on using hybrid labels that combine overall quality of documents, and implicitly inc...", "weights": [[8, 14, 1.0], [87, 94, 1.0]]}, "2007.sigirconf_conference-2007.39": {"snippet": "...ning ranking functions using relative relevance judgments\n\n\n ABSTRACTEffective ranking functions are an essential part of commercial search engines. We focus on developing a regression framework for l...", "weights": [[8, 15, 1.0], [82, 89, 1.0], [136, 142, 1.0]]}, "2011.cikm_conference-2011.282": {"snippet": "...with search engines\n\n\n ABSTRACTMeaningful evaluation of web search must take account of spam. Here we conduct a user experiment to investigate whether satisfaction with search engine result pages as a...", "weights": [[8, 14, 1.0], [45, 55, 1.0], [63, 69, 1.0], [172, 178, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.13": {"snippet": "...ment ranking.To validate our proposal and to gain more insights about approaches for document ranking, we (1) analyse PRP, qPRP and other ranking approaches, exposing the assumptions underlying their ...", "weights": [[8, 15, 1.0], [97, 104, 1.0], [141, 148, 1.0]]}, "2020.ecir_conference-20202.83": {"snippet": "...e of search results for users of Dutch legal IR systems, the employment of usage and citation variables to improve the ranking of search results (bibliometricenhanced information retrieval), and user-...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [122, 129, 1.0], [133, 139, 1.0], [140, 147, 1.0]]}, "2010.wwwconf_conference-2010.185": {"snippet": "...k on search result lists. We tried to incorporate this measure of reliability into relevance feedback for improving ranking performances. Experimental results indicate that the proposed approach is bo...", "weights": [[8, 14, 1.0], [119, 126, 1.0], [154, 161, 1.0]]}, "2013.sigirconf_conference-2013.119": {"snippet": "... two search result lists. Unlike the traditional method of collecting relevance labels per single result, this method allows to consider the interaction between search results as part of the judging c...", "weights": [[8, 14, 1.0], [164, 170, 1.0], [171, 178, 1.0]]}, "2013.cikm_conference-2013.72": {"snippet": "...line evaluation method to compare two alternative ranking functions based on the users' implicit feedback. In an interleaving experiment, the results from two ranking functions are merged in a single ...", "weights": [[8, 18, 1.0], [53, 60, 1.0], [145, 152, 1.0], [162, 169, 1.0]]}, "2018.cikm_conference-2018.66": {"snippet": "... 000 search results. Experimental results show that the proposed JRE model achieves better performance than state-of-the-art ranking solutions as well as the original ranking of commercial search engi", "weights": [[8, 14, 1.0], [15, 22, 1.0], [37, 44, 1.0], [128, 135, 1.0], [170, 177, 1.0], [192, 198, 1.0]]}, "2004.ntcir_workshop-2004.3": {"snippet": "...e by ranking each document using the density of keywords and by a threshold. For evaluation of our system, we participated in NTCIR4-WEB Topical classification task. Only a part of \"evaluation based o...", "weights": [[8, 15, 1.0], [84, 94, 1.0], [185, 195, 1.0]]}, "2012.sigirconf_conference-2012.77": {"snippet": "...ing, ranking and evaluation\n\n\n ABSTRACTIn this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes ...", "weights": [[8, 15, 1.0], [20, 30, 1.0], [144, 151, 1.0], [162, 172, 1.0]]}, "2006.ecir_conference-2006.55": {"snippet": "...e Re-ranking of Search Results", "weights": [[8, 15, 1.0], [19, 25, 1.0], [26, 33, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "... the evaluation of retrieval results. Very high correlations were found among the rankings of systems produced using different relevance judgment sets. The high correlations indicate that the comparat...", "weights": [[8, 18, 1.0], [32, 39, 1.0]]}, "2011.cikm_conference-2011.202": {"snippet": "...plex results in databases. While there is a large body of work that provides different mechanisms for computing keyword search results efficiently, a recent study has shown that the problem of ranking...", "weights": [[8, 15, 1.0], [123, 129, 1.0], [130, 137, 1.0], [196, 203, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "...em's evaluation of document relevance back to the system, which then uses this information to provide a revised list of search results. Even with state-of-the-art search engines users are often dissat...", "weights": [[8, 18, 1.0], [123, 129, 1.0], [130, 137, 1.0], [166, 172, 1.0]]}, "2008.cikm_conference-2008.195": {"snippet": "Search-based query suggestion\n\n\n ABSTRACTIn this paper, we proposed a unified strategy to combine query log and search results for query suggestion. In this way, we leverage both the users' search int...", "weights": [[0, 6, 1.0], [112, 118, 1.0], [119, 126, 1.0], [190, 196, 1.0]]}, "2006.cikm_conference-2006.127": {"snippet": "... the ranking of search results for any given query by constructing the query context from search query logs. Evaluation results show that Q-Rank gains a considerable advantage over the current ranking...", "weights": [[8, 15, 1.0], [19, 25, 1.0], [26, 33, 1.0], [93, 99, 1.0], [112, 122, 1.0], [123, 130, 1.0], [196, 203, 1.0]]}, "2017.cikm_conference-2017.79": {"snippet": "...Many search e ectiveness evaluation measures penalize the importance of results at lower ranks.is is usually explained as an a empt to model users' persistence when sequentially examining results-lowe...", "weights": [[8, 14, 1.0], [28, 38, 1.0], [75, 82, 1.0], [191, 198, 1.0]]}, "2018.cikm_conference-2018.226": {"snippet": "... Web search results that are useful in discovering the veracity of claims from the ones that are not. We also propose a re-ranking method to improve ranking of search results for fact-checking. To eva...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [126, 133, 1.0], [152, 159, 1.0], [163, 169, 1.0], [170, 177, 1.0]]}, "2002.sigirconf_conference-2002.9": {"snippet": "... top ranking sentences: an evaluation of two alternative schemes\n\n\n ABSTRACTIn this paper we present an evaluation of techniques that are designed to encourage web searchers to interact more with the ...", "weights": [[8, 15, 1.0], [30, 40, 1.0], [107, 117, 1.0]]}, "2017.trec_conference-2017.29": {"snippet": "... the results of assessing the higher-ranked documents, relative to the results of other participants' runs (cf. [6]). Overall, the qrels for each topic represent a non-statistical sample of the docume...", "weights": [[8, 15, 1.0], [74, 81, 1.0]]}, "2013.sigirconf_conference-2013.209": {"snippet": "...irst search engine results page (SERP) can be viewed as the information scent of all returned results; searchers may predict the potential value of the set of search results based on the characteristi...", "weights": [[8, 14, 1.0], [22, 29, 1.0], [97, 104, 1.0], [162, 168, 1.0], [169, 176, 1.0]]}, "2013.dir_workshop-2013.19": {"snippet": "...n of Search Facets\n\n\n We present a novel method for summarization and expansion of search facets. To dynamically extract key facets, the ranked list of search results generated from a keyword search i...", "weights": [[8, 14, 1.0], [86, 92, 1.0], [155, 161, 1.0], [162, 169, 1.0], [195, 201, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A5.13": {"snippet": "...ch a search result supports the fact statement based on semantic constituent analysis, and we make use of credibility ranking to rank the search results. At last, we combine the support score and cred...", "weights": [[8, 14, 1.0], [121, 128, 1.0], [141, 147, 1.0], [148, 155, 1.0]]}, "2015.cikm_conference-2015.141": {"snippet": "...ting search engines often rely on crowdsourced assessments of document relevance with respect to a user query. To use such assessments for either evaluation or learning, we propose a new framework for...", "weights": [[8, 14, 1.0], [149, 159, 1.0]]}, "2009.cikm_conference-2009.265": {"snippet": "...quer search ranking. Experimental results on RankSVM and RankNet with a largescale evaluation dataset show that the proposed method can achieve significant improvement in the task of web search rankin", "weights": [[8, 14, 1.0], [15, 22, 1.0], [37, 44, 1.0], [86, 96, 1.0], [190, 196, 1.0], [197, 204, 1.0]]}, "2009.cikm_conference-2009.296": {"snippet": "... the ranking score distribution across search results. We then propose two alternatives to the NDCG metric that both incorporate ranking robustness into ranking function evaluation and selection. A ma...", "weights": [[8, 15, 1.0], [42, 48, 1.0], [49, 56, 1.0], [132, 139, 1.0], [156, 163, 1.0], [173, 183, 1.0]]}, "2007.wwwconf_conference-2007.124": {"snippet": "... the evaluation of search results\n\n\n ABSTRACTWe investigate the effect of search engine brand (i.e., the identifying name or logo that distinguishes a product from its competitors) on evaluation of sy...", "weights": [[8, 18, 1.0], [22, 28, 1.0], [29, 36, 1.0], [77, 83, 1.0], [187, 197, 1.0]]}, "2002.sigirconf_conference-2002.83": {"snippet": "...item search results. Because the engines perform their search on overlapping (but different) subsets of the web collected at different points in time, evaluation of search engines poses significant ch...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [58, 64, 1.0], [154, 164, 1.0], [168, 174, 1.0]]}, "2015.wwwconf_conference-2015c.218": {"snippet": "...ased Ranking Evaluation on a Budget\n\n\n ABSTRACTWe address the problem of assessing the quality of a ranking system (e.g., search engine, recommender system, review ranker) given a fixed budget for col...", "weights": [[8, 15, 1.0], [16, 26, 1.0], [103, 110, 1.0], [125, 131, 1.0]]}, "2005.wwwconf_workshop-2004webdyn.1": {"snippet": "...-ten results, since users usually inspect only the first page returned by the search engine, which normally contains ten results. In particular, we compare rankings of the top ten results of the searc...", "weights": [[8, 15, 1.0], [81, 87, 1.0], [124, 131, 1.0], [183, 190, 1.0], [198, 204, 1.0]]}, "2013.ecir_conference-2013.52": {"snippet": "...d Re-ranking of Text-Based Image Search Results\n\n\n Abstract. This article presents a method for re-ranking images retrieved by classical search engine using key words for entering queries. This method...", "weights": [[8, 15, 1.0], [36, 42, 1.0], [43, 50, 1.0], [102, 109, 1.0], [140, 146, 1.0]]}, "2011.wsdm_conference-2011.53": {"snippet": "... for ranking, which enables principled and natural integration with features derived from both quality-based and relevance-based algorithms. Through experimental verification, the combined methods can...", "weights": [[8, 15, 1.0]]}, "2014.ipm_journal-ir0volumeA50A2.9": {"snippet": "...w of ranking approaches for semantic search on Web\n\n\n a b s t r a c tWith ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining...", "weights": [[8, 15, 1.0], [40, 46, 1.0], [139, 145, 1.0]]}, "2014.tweb_journal-ir0volumeA8A2.1": {"snippet": "...e of ranking features in a search engine's ranking function, provide guidelines for SEOs and Web masters to optimize their Web pages, validate or disprove new ranking features, and evaluate search eng...", "weights": [[8, 15, 1.0], [30, 36, 1.0], [46, 53, 1.0], [162, 169, 1.0], [193, 199, 1.0]]}, "2010.wsdm_conference-2010.19": {"snippet": "...good results. This is particularly notable given that the baseline we use is the heavily optimized ranking function of a leading commercial search engine. A deeper analysis shows that the new feature ...", "weights": [[8, 15, 1.0], [102, 109, 1.0], [143, 149, 1.0]]}, "2012.cikm_conference-2012.220": {"snippet": "...zing search results for non-native speakers\n\n\n ABSTRACTBlog posts, news articles and other webpages are present on the web in multiple languages. Standard search engines evaluate the relevance of the ...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [158, 164, 1.0]]}, "2016.trec_conference-2016.9": {"snippet": "...1) a ranking method using information crawled from an authoritative search engine; (2) search ranking based on clickthrough data taken from user feedback; and (3) a unified modeling method that combin...", "weights": [[8, 15, 1.0], [71, 77, 1.0], [90, 96, 1.0], [97, 104, 1.0]]}, "2001.wwwconf_conference-2001p.73": {"snippet": "...ance Evaluation of Search Engines' Query Results", "weights": [[8, 18, 1.0], [22, 28, 1.0], [44, 51, 1.0]]}, "2013.cikm_conference-2013.71": {"snippet": "...ated search using interleaving\n\n\n ABSTRACTA result page of a modern web search engine is often much more complicated than a simple list of \"ten blue links.\" In particular, a search engine may combine ...", "weights": [[8, 14, 1.0], [75, 81, 1.0], [177, 183, 1.0]]}, "2013.ecir_conference-2013.6": {"snippet": "...l Re-ranking of Product Image Search Results", "weights": [[8, 15, 1.0], [33, 39, 1.0], [40, 47, 1.0]]}, "L16-1583": {"snippet": "...ring Evaluation Measure\n\n\n Ranking is used for a wide array of problems, most notably information retrieval (search). Kendall's \u03c4 , Average Precision, and nDCG are a few popular approaches to the eval...", "weights": [[8, 18, 1.0], [30, 37, 1.0], [112, 118, 1.0], [199, 209, 1.0]]}, "2020.sigirconf_workshop-2020birds.2": {"snippet": "...y in Ranking\n\n\n Ranking in Information Retrieval (IR) has been traditionally evaluated from the perspective of the relevance of search engine results to people searching for information, i.e., the ext...", "weights": [[8, 15, 1.0], [19, 26, 1.0], [131, 137, 1.0], [145, 152, 1.0]]}, "2016.ntcir_conference-2016.2": {"snippet": "...tual search. Additionally, it uses common patterns to improve the search output. To combine various scoring algorithms, it uses hybrid ranking mechanism to re-rank the documents retrieved from Elastic...", "weights": [[8, 14, 1.0], [69, 75, 1.0], [138, 145, 1.0]]}, "2016.clef_conference-2016.20": {"snippet": "... web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concen...", "weights": [[8, 14, 1.0], [67, 74, 1.0], [82, 88, 1.0]]}, "2013.sigirconf_conference-2013.68": {"snippet": "...n of results from multiple search engines. We introduce copula-based versions of standard relevance estimators and fusion methods and show that these lead to significant performance improvements on se...", "weights": [[8, 15, 1.0], [30, 36, 1.0]]}, "2013.wwwconf_conference-2013c.278": {"snippet": "...Thus search engines face the challenge of ranking results that are equally relevant from the perspective of the traditional relevance grading approach. To solve this problem we propose to consider add...", "weights": [[8, 14, 1.0], [45, 52, 1.0], [53, 60, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.1": {"snippet": "... Web Search Ranking by Incorporating User Behavior Information\n\n\n ABSTRACTWe show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [165, 172, 1.0], [185, 191, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.5": {"snippet": "...y in Ranking\n\n\n AbstractRanking in Information Retrieval (IR) has been traditionally evaluated from the perspective of the relevance of search engine results to people searching for information, i.e.,...", "weights": [[8, 15, 1.0], [139, 145, 1.0], [153, 160, 1.0]]}, "2004.wwwconf_conference-2004at.87": {"snippet": "... web search\n\n\n ABSTRACTMaximizing only the relevance between queries and documents will not satisfy users if they want the top search results to present a wide coverage of topics by a few representati...", "weights": [[8, 14, 1.0], [130, 136, 1.0], [137, 144, 1.0]]}, "2013.wwwconf_conference-2013.57": {"snippet": "...tory search for multi page search results\n\n\n ABSTRACTModern information retrieval interfaces typically involve multiple pages of search results, and users who are recall minded or engaging in explorat...", "weights": [[8, 14, 1.0], [30, 36, 1.0], [37, 44, 1.0], [132, 138, 1.0], [139, 146, 1.0]]}, "2005.ipm_journal-ir0volumeA41A6.13": {"snippet": "...s of search results on the Web", "weights": [[8, 14, 1.0], [15, 22, 1.0]]}, "2017.sigirconf_conference-2017.236": {"snippet": "...ized Ranking of Facets for Exploratory Search\n\n\n ABSTRACTFaceted Search Systems (FSS) have gained prominence in research as one of the exploratory search approaches that support complex search tasks. ...", "weights": [[8, 15, 1.0], [42, 48, 1.0], [68, 74, 1.0], [150, 156, 1.0], [189, 195, 1.0]]}, "2014.wwwconf_conference-2014.14": {"snippet": "...nly\" ranking, federated search ranking and aggregated search), our evaluation results demonstrate significant performance improvement for a highly heterogeneous web collection.", "weights": [[8, 15, 1.0], [27, 33, 1.0], [34, 41, 1.0], [57, 63, 1.0], [70, 80, 1.0], [81, 88, 1.0]]}, "2013.tweb_journal-ir0volumeA7A2.6": {"snippet": "... For ranking results, we analyze the second-order agreement between the results. Further extending SourceRank to multidomain search, we propose a source ranking sensitive to the query domains. 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Such a measure usually combines a behavior-based rank d...", "weights": [[8, 18, 1.0]]}, "2005.wwwconf_conference-2005si.46": {"snippet": "...ng a search engine's efficiency and effectiveness, each user has his/her own preference and is highly subjective in judging the relevance of the search results.We have proposed a formal evaluation mod...", "weights": [[8, 14, 1.0], [148, 154, 1.0], [155, 162, 1.0], [189, 199, 1.0]]}, "2011.cikm_conference-2011.60": {"snippet": "...line search results\n\n\n ABSTRACTWe present a new image search and ranking algorithm for retrieving unannotated images by collaboratively mining online search results which consist of online image and t...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [57, 63, 1.0], [68, 75, 1.0], [153, 159, 1.0], [160, 167, 1.0]]}, "2007.jasis_journal-ir0volumeA58A9.3": {"snippet": "...s of search engine results", "weights": [[8, 14, 1.0], [22, 29, 1.0]]}, "2013.sigirconf_conference-2013.116": {"snippet": "... for search results\n\n\n ABSTRACTInternet search engines typically compute a relevance score for webpages given the query terms, and then rank the pages by decreasing relevance scores. The popular searc...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [43, 49, 1.0], [198, 204, 1.0]]}, "2011.cikm_conference-2011.248": {"snippet": "... the results will contain items relevant to the user's query . Hence, diverse Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee...", "weights": [[8, 15, 1.0]]}, "2020.ipm_journal-ir0volumeA57A1.15": {"snippet": "...irer ranking in search engine results", "weights": [[8, 15, 1.0], [19, 25, 1.0], [33, 40, 1.0]]}}}
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Each document is represented by a set of fields (e.g. Content, Titl...", "weights": [[8, 17, 1.0]]}, "2018.sigirconf_conference-2018.167": {"snippet": "Online Job Search: Study of Users' Search Behavior using Search Engine Query Logs\n\n\n ABSTRACTOver the last few years, an increasing number of user's and enterprises on the internet has generated a glo...", "weights": []}, "2007.tc-1.14": {"snippet": "...ness information management in the language industry\n\n\n LTC has almost a decade of experience in helping corporate clients and language companies implement business information and workflow control to...", "weights": [[8, 19, 1.0], [168, 179, 1.0]]}, "2011.wwwconf_workshop-2011ldow.1": {"snippet": "...oral Information Retrieval: Challenges and Opportunities\n\n\n Time is an important dimension of any information space. It can be very useful for a wide range of information retrieval tasks such as docum...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [101, 112, 1.0], [162, 173, 1.0], [174, 183, 1.0]]}, "2015.ictir_conference-2015.2": {"snippet": "...y of Retrieval: The Retrievability of Information\n\n\n ABSTRACTRetrievability is an important and interesting indicator that can be used in a number of ways to analyse Information Retrieval systems and ...", "weights": [[8, 17, 1.0], [41, 52, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2020.computerm-1.5": {"snippet": "...ired information is condensed into the job title. Several organised efforts have been made to map job titles onto a hand-made knowledge base as to provide this information, but these only cover around...", "weights": [[8, 19, 1.0], [163, 174, 1.0]]}, "2008.sigirjournals_journal-ir0volumeA42A2.18": {"snippet": "...t of information retrieval in the workplace\n\n\n Increasingly, workplace information seeking takes place in digital information environments and is reliant upon search systems. Existing systems are desi...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [74, 85, 1.0], [117, 128, 1.0]]}, "P06-2068": {"snippet": "...e of Information Retrieval in Answering Complex Questions\n\n\n This paper explores the role of information retrieval in answering \"relationship\" questions, a new class complex information needs formally...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [96, 107, 1.0], [108, 117, 1.0], [177, 188, 1.0]]}, "2020.sigirconf_conference-2020.10": {"snippet": "...ired retrieval models, and some well-known neural retrieval models in the ad-hoc retrieval task. CCS CONCEPTS \u2022 Information systems \u2192 Retrieval models and ranking.", "weights": [[8, 17, 1.0], [53, 62, 1.0], [84, 93, 1.0], [115, 126, 1.0], [137, 146, 1.0]]}, "2021.ecir_conference-20212.18": {"snippet": "...s in Information Retrieval Relevance Judgement Datasets\n\n\n Recent studies in information retrieval have shown that gender biases have found their way into representational and algorithmic aspects of c...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [92, 101, 1.0]]}, "1991.sigirconf_conference-91.24": {"snippet": "...ssor Information Retrieval Systems\n\n\n Abstract.Information retrieval is the selection of documents that are potentially relevant to a user's information need. Given the vast volume of data stored in m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0], [144, 155, 1.0]]}, "2021.icnlsp-1.15": {"snippet": "An interpretable person-job fitting approach based on classification and ranking\n\n\n While the development of online recruitment platforms has allowed companies to post their job offers and for job see...", "weights": []}, "2020.tois_journal-ir0volumeA38A3.6": {"snippet": "...call Information Retrieval: Locating the Last Few Relevant Documents for Technology-assisted Reviews", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2013.sigirconf_conference-2013.88": {"snippet": "...vant information graphics\n\n\n ABSTRACTInformation retrieval research has made significant progress in the retrieval of text documents and images. However, relatively little attention has been given to ...", "weights": [[8, 19, 1.0], [52, 61, 1.0], [108, 117, 1.0]]}, "1986.tc-1.6": {"snippet": "... the information to manageable proportions. Information overload and underload are facts of life, and any methods which can help to optimise information retrieval are of value. In this presentation, I...", "weights": [[8, 19, 1.0], [47, 58, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "2020.lt4gov-1.3": {"snippet": "...duce information asymmetries between job seekers and employers. The project uses webscraping techniques to retrieve job vacancies posted on private job portals on a daily basis and with the purpose of...", "weights": [[8, 19, 1.0]]}, "2001.wwwconf_conference-2001p.74": {"snippet": "Segmentation of On-line Job Advertisements", "weights": []}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...n of retrieval models particularly for recall oriented retrieval domains, where the focus is on retrieving all relevant information but not just retrieving a subset of relevant information. Available ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [123, 134, 1.0], [180, 191, 1.0]]}, "2005.sigirconf_conference-2005.9": {"snippet": "... hoc information retrieval\n\n\n ABSTRACTInformation retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information nee...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 62, 1.0], [164, 173, 1.0], [188, 199, 1.0]]}, "2010.sigirconf_conference-2010.10": {"snippet": "...y of information retrieval approaches applied to match-making systems. Specifically, we focus on retrieval for a dating service. This domain offers several unique problems not found in traditional inf...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [100, 109, 1.0], [200, 211, 1.0]]}, "2018.cikm_conference-2018.275": {"snippet": "...s of information networks from the historical job data: (i) job transition network, (ii) job-skill network, and (iii) skill co-occurrence network. We provide a representation learning model which can ...", "weights": [[8, 19, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.18": {"snippet": "Careers in other fields for librarians: Successful strategies for finding the job", "weights": []}, "2019.cikm_conference-2019.190": {"snippet": "Towards Effective and Interpretable Person-Job Fitting", "weights": []}, "N07-1012": {"snippet": "Information Retrieval On Empty Fields\n\n\n We explore the problem of retrieving semi-structured documents from a realworld collection using a structured query. We formally develop Structured Relevance M...", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "1978.jasis_journal-ir0volumeA29A4.4": {"snippet": "...rary jobs", "weights": [[8, 12, 1.0]]}, "2011.wsdm_conference-2011.6": {"snippet": "Web retrieval: the role of users\n\n\n ABSTRACTWeb retrieval methods have evolved through three major steps in the last decade or so. They started from standard documentcentric IR in the early days of th...", "weights": [[4, 13, 1.0], [48, 57, 1.0]]}, "2010.sigirconf_conference-2010.136": {"snippet": "...TXML retrieval provides a focused access to the relevant content of documents. However, in evaluation, full document retrieval has appeared competitive to focused XML retrieval. We analyze the density...", "weights": [[8, 17, 1.0], [120, 129, 1.0], [170, 179, 1.0]]}, "2014.ipm_journal-ir0volumeA50A5.9": {"snippet": "...tive information retrieval\n\n\n a b s t r a c tCollaborative information retrieval involves retrieval settings in which a group of users collaborates to satisfy the same underlying need. One core issue ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [62, 73, 1.0], [74, 83, 1.0], [93, 102, 1.0]]}, "2020.cikm_conference-2020.308": {"snippet": "... for Jobs Marketplace at LinkedIn", "weights": [[8, 12, 1.0]]}, "2021.triton-1.22": {"snippet": "The Post-Editing Workflow: Training Challenges for {LSP}s, Post-Editors and Academia\n\n\n Language technology is already largely adopted by most Language Service Providers (LSPs) and integrated into the...", "weights": []}, "C90-1014": {"snippet": "Semantic Matching Between Job Offers and Job Search Requests\n\n\n The primary objective of this system which was developed for the LE MONDE daily newspaper, is to offer an efficient tool for a rapid and...", "weights": []}, "2020.sigirconf_conference-2020.179": {"snippet": "...able Retrieval Models for Precision Medicine Literature Search\n\n\n In professional search tasks such as precision medicine literature search, queries often involve multiple aspects. To assess the relev...", "weights": [[8, 17, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A1.12": {"snippet": "...s on jobs is crucial to job search engines as the prediction can be used to provide personalized job recommendations for job seekers. This paper presents a realworld job recommender system in which jo...", "weights": [[8, 12, 1.0]]}, "2009.wwwconf_conference-2009.107": {"snippet": "Predicting click through rate for job listings", "weights": []}, "2011.eamt-1.9": {"snippet": "...ting information would have to be manually added by localization professionals. A few attempts to bridge the localization industry and Moses were already done in the past, however, the method describe...", "weights": [[8, 19, 1.0]]}, "2014.sigirconf_conference-2014.18": {"snippet": "...call retrieval with query pooling and interactive classification\n\n\n ABSTRACTWe consider a scenario where a searcher requires both high precision and high recall from an interactive retrieval process. ...", "weights": [[8, 17, 1.0], [184, 193, 1.0]]}, "2017.fire_conference-2017w.9": {"snippet": "...017: Information Retrieval from Microblog during Disasters\n\n\n Microblogging sites like Twitter are increasingly being used for aiding relief operations during disaster events. In such situations, iden...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "O01-2005": {"snippet": "...s of Information Retrieval with Clustering and Fusion\n\n\n Fusion and clustering are two approaches to improving the effectiveness of information retrieval. In fusion, ranked lists are combined together...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [135, 146, 1.0], [147, 156, 1.0]]}, "2020.ictir_conference-2020.7": {"snippet": "...ment retrieval; that is, using information induced from clusters of similar documents to rank documents. Specifically, we propose a conceptual framework of retrieval templates that can adapt cluster-b...", "weights": [[8, 17, 1.0], [34, 45, 1.0], [159, 168, 1.0]]}, "2018.sigirconf_workshop-2018ecom.15": {"snippet": "... new information retrieval task for eCommerce that we call the high accuracy recall task. That task is to identify as many relevant documents, and as few non-relevant documents as possible, such that ...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2018.ecir_workshop-2018text2story.6": {"snippet": "...duce information overload by generating personalized job suggestions. Although in the literature exists a variety of techniques and strategies used as part of job recommender systems, most of them fai...", "weights": [[8, 19, 1.0]]}, "2007.ecir_conference-2007.59": {"snippet": "... XML Retrieval\n\n\n Abstract. The objective of XML retrieval is to return relevant XML document fragments that answer a given user information need, by exploiting the document structure. The focus in th...", "weights": [[8, 17, 1.0], [52, 61, 1.0], [132, 143, 1.0]]}, "2004.ntcir_workshop-2004.57": {"snippet": "...onal Retrieval Task 2 that was conducted from 2003 to 2004 as a subtask of the WEB Task at the Fourth NTCIR Workshop ('NTCIR-4 WEB'). In the Informational Retrieval Task, we attempted to assess the re...", "weights": [[8, 17, 1.0], [158, 167, 1.0], [201, 210, 1.0]]}, "2017.trec_conference-2017.21": {"snippet": "...ular information retrieval application is web search engine such as Google [2] , Baidu [3] , Bing [4] and Sogou [5]. These application will return top-N best retrieval result to users. Information Ret...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [161, 170, 1.0], [188, 199, 1.0], [200, 209, 1.0]]}, "2016.ecir_conference-2016.51": {"snippet": "...tise Retrieval: The Information Seeking Behaviour of Recruitment Professionals", "weights": [[8, 17, 1.0], [23, 34, 1.0]]}, "2015.sigirconf_conference-2015.185": {"snippet": "Bringing Order to the Job Market: Efficient Job Offer Categorization in E-Recruitment", "weights": []}, "2018.sigirconf_conference-2018.191": {"snippet": "An Information Retrieval Experiment Framework for Domain Specific Applications\n\n\n ABSTRACTWe present a framework for constructing and executing information retrieval experiment pipelines. The framewor...", "weights": [[3, 14, 1.0], [15, 24, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "2019.sigirconf_conference-2019.258": {"snippet": "Measuring Job Search Effectiveness", "weights": []}, "2010.sigirconf_conference-2010.201": {"snippet": "Web retrieval: the role of users\n\n\n Abstract:Web retrieval methods have evolved through three major steps in the last decade or so. They started from standard document-centric IR in the early days of ...", "weights": [[4, 13, 1.0], [49, 58, 1.0]]}, "2012.ecir_conference-2012.9": {"snippet": "...ment Retrieval", "weights": [[8, 17, 1.0]]}, "2001.ntcir_workshop-2001.29": {"snippet": "On a Retrieval Support System by Suggesting Terms to a User\n\n\n We propose a support method for information retrieval. This method automatically suggests terms relevant to a query, to a user, and when ...", "weights": [[5, 14, 1.0], [95, 106, 1.0], [107, 116, 1.0]]}, "2017.fire_conference-2017w.18": {"snippet": "...ack: Information Retrieval from Legal Documents\n\n\n The FIRE 2017 IRLeD Track focused on creating a framework for evaluating different methods of Information Retrieval from legal documents. There were ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [148, 159, 1.0], [160, 169, 1.0]]}, "2007.trec_conference-2007.88": {"snippet": "...nion retrieval task. An information retrieval step retrieves the query-relevant documents. A following opinion identification step identifies the opinionative texts in these documents. A ranking step ...", "weights": [[8, 17, 1.0], [27, 38, 1.0], [39, 48, 1.0]]}, "2011.cikm_conference-2011.325": {"snippet": "...ital information on daily basis. Previously efforts have been made to support their work by employing different indexing and search tools that make information more accessible. However, recently it ha...", "weights": [[8, 19, 1.0], [151, 162, 1.0]]}, "2016.sigirconf_workshop-2016sal.16": {"snippet": "...rger information seeking, workspace, and learning environment, giving users the tools and understanding to become experts over time. We present some findings of user interaction with engineering domai...", "weights": [[8, 19, 1.0]]}, "2009.cikm_conference-2009.134": {"snippet": "...opic retrieval\n\n\n ABSTRACTTraditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [63, 72, 1.0], [167, 178, 1.0], [194, 203, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.31": {"snippet": "... and Information  Retrieval\" (1960). This was the first paper to present a probabilistic approach to information retrieval, and perhaps the first paper on ranked retrieval. Although it is one of the m...", "weights": [[8, 19, 1.0], [21, 30, 1.0], [104, 115, 1.0], [116, 125, 1.0], [165, 174, 1.0]]}, "2016.wsdm_conference-2016.31": {"snippet": "... for Information Retrieval\n\n\n ABSTRACT Document relevance has been the primary focus in the design, optimization and evaluation of retrieval systems. Traditional test collections are constructed by as...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [134, 143, 1.0]]}, "2018.sigirconf_conference-2018.5": {"snippet": "...d to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person-Job Fit, which is the bridge for adapting the right job seekers to the right posi...", "weights": [[8, 19, 1.0]]}, "2007.sigirconf_conference-2007.76": {"snippet": "...n of information retrieval systems is one of the core tasks in information retrieval. Problems include the inability to exhaustively label all documents for a topic, nongeneralizability from a small n...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [66, 77, 1.0], [78, 87, 1.0]]}, "2020.sigirconf_conference-2020.317": {"snippet": "...hing jobs with processionals. Improving job targeting accuracy and hire efficiency align with LinkedIn's Member First Motto. To achieve those goals, we need to understand unstructured job postings wit...", "weights": [[8, 12, 1.0]]}, "2005.wwwconf_conference-2005si.154": {"snippet": "... for information retrieval using key resource page selection\n\n\n ABSTRACTWith the page explosion of WWW, how to cover more useful information with limited storage and computation resources becomes more...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [132, 143, 1.0]]}, "2016.ipm_journal-ir0volumeA52A6.2": {"snippet": "...e in jobs ads was found. On the contrary, English used in the company information seemed to have a deterring effect. Support was found for a genre-specific viewing pattern for job ads, which, however,...", "weights": [[8, 12, 1.0], [73, 84, 1.0]]}, "2007.sigirconf_conference-2007.150": {"snippet": "... and jobs based on relevance models\n\n\n ABSTRACTWe investigate the difficult problem of matching semi-structured resumes and jobs in a large scale real-world collection. We compare standard approaches ...", "weights": [[8, 12, 1.0], [127, 131, 1.0]]}, "2010.cikm_conference-2010.70": {"snippet": "...cted information is presented in the form of facets to aid recruiters in the task of screening. We also employ Information Retrieval techniques to rank all applicants for a given job opening. In our e...", "weights": [[8, 19, 1.0], [114, 125, 1.0], [126, 135, 1.0]]}, "2020.wsdm_conference-2020.121": {"snippet": "... and jobs. Hence a job seeker do online search for the employer, before applying for the job. They try to find if the employer and job is good for them or not, what are the pros and cons of working th...", "weights": [[8, 12, 1.0]]}, "2020.acl-main.281": {"snippet": "...pany information and job description. To this end, we propose a novel task of Job Posting Generation (JPG) that is cast as a conditional text generation problem to generate job requirements according ...", "weights": [[8, 19, 1.0]]}, "2012.wwwconf_conference-2012c.163": {"snippet": "...onal Information Retrieval (IR) models, which assume that scores (relevance) for documents given an information need are static. In this paper, we formulate a general Dynamical Information Retrieval p...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [103, 114, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "2008.ntcir_workshop-2008.81": {"snippet": "The Information Retrieval Facility and Professional Search", "weights": [[4, 15, 1.0], [16, 25, 1.0]]}, "2007.sigirconf_conference-2007.213": {"snippet": "...ment retrieval. Some of this new attention has fallen on entity retrieval. This emerging area of entity retrieval differs from traditional document retrieval in a number of ways. Entities are not repr...", "weights": [[8, 17, 1.0], [67, 76, 1.0], [107, 116, 1.0], [151, 160, 1.0]]}, "2021.rocling-1.22": {"snippet": "...l of information retrieval is to search the most relevant results for the user's query from a large set of documents. Although BERTbased retrieval models have shown excellent results in many studies, ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [140, 149, 1.0]]}, "N18-3027": {"snippet": "...ilar jobs that enables an offline evaluation. (2) In addition, we conduct an online A/B test and evaluate the best performing method on our platform reaching more than 1 million users. We achieve the ...", "weights": [[8, 12, 1.0]]}, "2019.ecir_conference-20192.55": {"snippet": "...nced Information Retrieval: 8th International BIR Workshop\n\n\n Abstract. The Bibliometric-enhanced Information Retrieval workshop series (BIR) at ECIR tackles issues related to academic search, at the ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [101, 112, 1.0], [113, 122, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "W14-1118": {"snippet": "... for information retrieval, focusing on care episode retrieval, based on textual similarity, where similarity is measured through domain-specific modelling of the distributional semantics of words. Mo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [56, 65, 1.0]]}, "2013.wwwconf_conference-2013c.93": {"snippet": "Unsupervised approach to generate informative structured snippets for job search engines\n\n\n ABSTRACTAiming to improve user experience for a job search engine, in this paper we propose an idea to switc...", "weights": []}, "P19-3008": {"snippet": "...emic jobs. We set out to make the market for advanced research skills more visible to job seekers. In this paper, we present PostAc \u00ae , an online platform of authentic job postings that helps PhD grad...", "weights": [[8, 12, 1.0]]}, "2022.naacl-srw.8": {"snippet": "...l\n\n\n Information Retriever (IR) aims to find the relevant documents (e.g. snippets, passages, and articles) to a given query at large scale. IR plays an important role in many tasks such as open domai...", "weights": [[8, 19, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.11": {"snippet": "Information retrieval in the workplace: A comparison of professional search practices\n\n\n Legal researchers, recruitment professionals, healthcare information professionals, and patent analysts all und...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [146, 157, 1.0]]}, "1999.tc-1.4": {"snippet": "Resource and Workflow Management Support in Teletranslation\n\n\n Value-added service providers in the technical and business translation market are increasingly organizing themselves into teams whose me...", "weights": []}, "2008.ntcir_workshop-2008.28": {"snippet": "Information Retrieval Using PU Learning Based Re-ranking\n\n\n In this paper, we describe our approach for information retrieval for question answering (IR4QA) on simple Chinese language of NTCIR-7 tasks...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [104, 115, 1.0], [116, 125, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.10": {"snippet": "...ring retrieval time, the proposed system smoothes FAQs using the query log clusters. In the experiment, the proposed system outperformed the conventional information retrieval systems in FAQ retrieval...", "weights": [[8, 17, 1.0], [157, 168, 1.0], [169, 178, 1.0], [194, 203, 1.0]]}, "2018.ictir_conference-2018.10": {"snippet": "...ment Retrieval\n\n\n ABSTRACTIn this paper, we study jointly query reformulation and document relevance estimation, the two essential aspects of information retrieval (IR). Their interactions are modelle...", "weights": [[8, 17, 1.0], [145, 156, 1.0], [157, 166, 1.0]]}, "2017.cikm_conference-2017.143": {"snippet": "... the jobs in the corpus for all members is not possible given the latency constraints. On the other hand, reducing the candidate set could potentially involve loss of relevant jobs. We present a way t...", "weights": [[8, 12, 1.0], [179, 183, 1.0]]}, "2021.wwwconf_conference-2021.331": {"snippet": "Auditing for Discrimination in Algorithms Delivering Job Ads", "weights": []}, "2008.airs_conference-2008.16": {"snippet": "Job Information Retrieval Based on Document Similarity", "weights": [[4, 15, 1.0], [16, 25, 1.0]]}, "2014.tc-1.14": {"snippet": "Top-down or bottom-up: what do industry approaches to translation quality mean for effective integration of standards and tools?\n\n\n The diverse approaches to translation quality in the industry can be...", "weights": []}, "1999.sigirconf_conference-99.73": {"snippet": "Information Retrieval Library (IRLIB) (demonstration abstract)\n\n\n ABSTRACTThe IRLIB is a full-text web searchable digital library, whose contents are publications and documents relative to the field o...", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "P80-1039": {"snippet": "... for Information Retrieval\n\n\n The representation of whole texts is a major concern of the field known as information retrieval (IR), an importaunt aspect of which might more precisely be called 'docum...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [108, 119, 1.0], [120, 129, 1.0]]}, "2019.ecir_conference-20192.52": {"snippet": "...rds: Information need \u00b7 Backstory \u00b7 Intent ambiguity\nMotivationAmbiguity in information retrieval processes is a common problem which retrieval systems need to handle to provide suitable results for s...", "weights": [[8, 19, 1.0], [79, 90, 1.0], [91, 100, 1.0], [137, 146, 1.0]]}, "2013.wwwconf_conference-2013c.246": {"snippet": "... and jobs. The data collected from the website is modeled using a directed, weighted, and multirelational graph, and the 3A ranking algorithm is exploited to rank items according to their relevance to...", "weights": [[8, 12, 1.0]]}, "2020.alta-1.8": {"snippet": "A machine-learning based model to identify {P}h{D}-level skills in job ads\n\n\n Around 60% of doctoral graduates worldwide ended up working in industry rather than academia. There have been calls to mor...", "weights": []}, "2008.cikm_conference-2008.59": {"snippet": "... the retrieval system. In this paper, we outline some emerging information access related scenarios that require knowledge about how the retrieval system affects the users' ability to access informati...", "weights": [[8, 17, 1.0], [66, 77, 1.0], [140, 149, 1.0], [194, 205, 1.0]]}, "2017.wwwconf_conference-2017c.113": {"snippet": "...ural Information Retrieval\n\n\n ABSTRACTTo deploy deep learning models for ad-hoc information retrieval, suitable representations of query-document pairs are needed. Such representations ought to captur...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [83, 94, 1.0], [95, 104, 1.0]]}, "2018.sigirconf_workshop-2018profs.5": {"snippet": "...f an information retrieval (IR) system is to identify and return to the user content relevant or useful in addressing the information need which required them to use the system. The development and ev...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [125, 136, 1.0]]}, "2007.tc-1.13": {"snippet": "...ated information such as manuals, clerical, technical and legal texts are translated into a current total of 10 languages. The majority of the workload of approx. 150 translation jobs per month, i.e. ...", "weights": [[8, 19, 1.0], [182, 186, 1.0]]}, "2016.sigirconf_workshop-2016sal.10": {"snippet": "...h in information retrieval (IR) focuses on optimization of the rank of relevant retrieval results for single shot ad hoc IR tasks with straightforward information needs. Relatively little research has...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [83, 92, 1.0], [154, 165, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.14": {"snippet": "... for retrieval with automatically generated content metadata from video, Specifically we focus on their application for improving audiovisual fragment search in the audiovisual broadcast archive. We u...", "weights": [[8, 17, 1.0]]}, "2012.sigirconf_conference-2012.132": {"snippet": "...n of information retrieval\n\n\n ABSTRACTAmazon CloudSearch is a new hosted search service, built on top of many cloud-based AWS services, and based on the same technology that powers search on Amazon's ...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2013.sigirconf_conference-2013.47": {"snippet": "... the Information Retrieval (IR) task that performs document retrieval for a search session. During a session, a user constantly modifies queries in order to find relevant documents that fulfill the in...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [63, 72, 1.0], [201, 212, 1.0]]}, "2020.coling-main.513": {"snippet": "Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework\n\n\n We introduce a deep learning model to learn the set of enumerated job skills associated ...", "weights": []}, "2013.ictir_conference-2013.14": {"snippet": "Information Retrieval with Time Series Query\n\n\n ABSTRACTWe study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find rele...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [73, 84, 1.0], [85, 94, 1.0], [170, 179, 1.0]]}, "2017.sigirconf_conference-2017.248": {"snippet": "... for Information Retrieval: And Related Tasks\n\n\n MOTIVATIONe main task of an Information Retrieval (IR) system is to return relevant documents to users in response to a query. However, this task is in...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [92, 101, 1.0]]}}}
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In the Relevance Feedback task, we found that the i...", "weights": [[8, 15, 1.0], [135, 142, 1.0]]}, "2007.cikm_conference-2007.24": {"snippet": "Boolean representation based data-adaptive correlation analysis over time series streams\n\n\n ABSTRACTCorrelation analysis is a basic problem in the field of data stream mining. Typical approaches add s...", "weights": [[0, 7, 1.0]]}, "1994.sigirconf_conference-94.19": {"snippet": "...nded Boolean Models in Information Retrieval", "weights": [[8, 15, 1.0]]}, "1999.tois_journal-ir0volumeA17A4.1": {"snippet": "...lar, Boolean query operators are conveniently modeled as link matrices of the Bayesian Network. Prior work has shown, however, that these operators do not perform as well as the the pnorm operators us...", "weights": [[8, 15, 1.0]]}, "2008.tweb_journal-ir0volumeA2A1.5": {"snippet": "...uery algebra and optimization\n\n\n We present a query algebra that supports optimized access of Web services through service-oriented queries. The service query algebra is defined based on a formal serv...", "weights": [[8, 15, 1.0], [55, 62, 1.0], [162, 169, 1.0]]}, "2012.wsdm_conference-2012.80": {"snippet": "The early bird gets the buzz: detecting anomalies and emerging trends in information networks\n\n\n ABSTRACTIn this work we propose a novel approach to anomaly detection in streaming communication data. ...", "weights": []}, "1996.cikm_conference-96.17": {"snippet": "...ts.O-Algebra is an algebra whose operands are collections of internal objects. Due to uniform operands and simple operators defined in O-Algebra, a small yet powerful set of O-Algebra laws can be obta...", "weights": [[8, 15, 1.0], [22, 29, 1.0], [140, 147, 1.0], [179, 186, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...s of Boolean algebra are obeyed, and the exact-match semantics of Boolean retrieval are preserved. Simple inverted-list file structures provide an efficient implementation. Retrieval effectiveness is ...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [69, 76, 1.0]]}, "P95-1003": {"snippet": "The Replace Operator\n\n\n This paper introduces to the calculus of regular expressions a replace operator and defines a set of replacement expressions that concisely encode alternate variations of the o...", "weights": []}, "W89-0203": {"snippet": "...to a boolean combination of propositional variables. We call P 4> a contexted constraint, where P is the context and 4> is called the base constraint.", "weights": [[8, 15, 1.0]]}, "W19-3106": {"snippet": "...eene algebra (by Kozen and others) are considered within Monadic Second Order logic over strings, where they are likened to statives in natural language. Reducts are formed over tests and non-tests al...", "weights": [[8, 15, 1.0]]}, "2019.wwwconf_conference-2019.151": {"snippet": "...atic Boolean Query Refinement for Systematic Review Literature Search", "weights": [[8, 15, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0]]}, "2011.iir_workshop-2011.3": {"snippet": "...uery Algebra for Quantum Information Retrieval", "weights": [[8, 15, 1.0]]}, "2021.findings-acl.97": {"snippet": "...ctic algebra and a semantic algebra, thus encouraging algebraic recombination. Specifically, we learn two modules jointly: a Composer for producing latent syntax, and an Interpreter for assigning sema...", "weights": [[8, 15, 1.0], [31, 38, 1.0]]}, "2017.cikm_conference-2017.285": {"snippet": "...tive Boolean query is one fundamental operation for document retrieval in many information systems and databases. Various algorithms have been put up in terms of maximizing the query efficiency. In re...", "weights": [[8, 15, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "...g in Boolean logic and the type of search interface. As reported previously, we found that both Boolean training and the use of an assisted interface improved the participants' ability to find correct...", "weights": [[8, 15, 1.0], [99, 106, 1.0]]}, "1972.jasis_journal-ir0volumeA23A1.7": {"snippet": "...e of boolean not logic to improve the precision of selective dissemination of information", "weights": [[8, 15, 1.0]]}, "1997.tois_journal-ir0volumeA15A1.1": {"snippet": "...onal Algebra for the Integration of Information Retrieval and Database Systems\n\n\n We present a probabilistic relational algebra (PRA) which is a generalization of standard relational algebra. In PRA, ...", "weights": [[8, 15, 1.0], [123, 130, 1.0], [186, 193, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...nded Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulate...", "weights": [[8, 15, 1.0], [73, 80, 1.0]]}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0]]}, "W11-0136": {"snippet": "Algebraic Approaches to Compositional Distributional Semantics\n\n\n The question of how to compose meaning in distributional representations of meaning has recently been recognised as a central issue in...", "weights": []}, "1979.jasis_journal-ir0volumeA30A6.10": {"snippet": "...sing boolean queries with a clustered file organization", "weights": [[8, 15, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...near algebra notation, as:   p, t and the rows of W are typically constrained to add up to 1, such that they can be viewed as probability distributions. That raises the question of how to deal with a ...", "weights": [[8, 15, 1.0]]}, "P83-1021": {"snippet": "Parsing as Deduction\n\n\n By exploring the relationship between parsing and deduction, a new and more general view of chart parsing is obtained, which encompasses parsing for grammar formalisms based on...", "weights": []}, "J76-4004": {"snippet": "Algebraic Parsing of Context-Free Languages", "weights": []}, "C10-1029": {"snippet": "...983) algebra, Bruce (1972) algebra, and the algebra derived from the TempEval-07 campaign. These algebras encode different granularities of relations and have different inferential properties. They in...", "weights": [[8, 15, 1.0], [30, 37, 1.0], [47, 54, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...plex boolean expression involving both ANDs and ORs. In other words, the qualification list may have an arbitrary number of disjuncts. The query graph of each disjunct may also have any number of comp...", "weights": [[8, 15, 1.0]]}, "2006.trec_conference-2006.32": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator because it can not be generalized to nodes. The fuzzy te...", "weights": [[8, 15, 1.0], [81, 88, 1.0]]}, "1990.sigirconf_conference-90.8": {"snippet": "... for Boolean Information Retrieval via Natural Language Query", "weights": [[8, 15, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [99, 106, 1.0], [176, 183, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.10": {"snippet": "...n in Boolean IR queries\n\n\n a b s t r a c tThe negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include negated terms in Boole...", "weights": [[8, 15, 1.0], [198, 205, 1.0]]}, "2017.cikm_conference-2017.268": {"snippet": "Boolean Matrix Decomposition by Formal Concept Sampling\n\n\n ABSTRACTFinding interesting patterns is a classical problem in data mining. Boolean matrix decomposition is nowadays a standard tool that can...", "weights": [[0, 7, 1.0], [135, 142, 1.0]]}, "2009.tois_journal-ir0volumeA27A2.3": {"snippet": "...udes Boolean and word proximity operators.", "weights": [[8, 15, 1.0]]}, "1995.trec_conference-1995.4": {"snippet": "Boolean System Revisited: Its Performance and its Behavior", "weights": [[0, 7, 1.0]]}, "2000.sigirconf_conference-2000.5": {"snippet": "...n of Boolean query effectiveness across a wide operational range\n\n\n ABSTRACTTraditional methods for the system-oriented evaluation of Boolean IR systems suffer from validity and reliability problems. ...", "weights": [[8, 15, 1.0], [137, 144, 1.0]]}, "2009.clef_workshop-2009w.113": {"snippet": "...onal algebra (PRA) [3] and SQL, and then executed on a relational high-performance database system [2]. The four search strategies compare boolean search, ranked retrieval, and categorybased re-rankin...", "weights": [[8, 15, 1.0], [142, 149, 1.0]]}, "2020.emnlp-main.661": {"snippet": "...tive boolean logic. Existing NLI stress tests do not consider non-boolean usages of conjunctions and use templates for testing such model knowledge. Hence, we introduce CONJNLI, a challenge stress-tes...", "weights": [[8, 15, 1.0], [69, 76, 1.0]]}, "2020.wwwconf_conference-2020.97": {"snippet": "...atic Boolean Query Formulation for Systematic Review Literature Search", "weights": [[8, 15, 1.0]]}, "S18-2013": {"snippet": "...asic Boolean operations like negation or disjunction, let alone intensional phenomena. Moving from graphs to named graphs leads to representations that stand some chance of having sufficient expressiv...", "weights": [[8, 15, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...Soft Boolean Operators\n\n\n AbstractThe conventional bibliographic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [99, 106, 1.0]]}, "W10-2806": {"snippet": "...ient algebra of the tensor algebra can allow such comparisons to be made, offering the possibility of data-driven models of semantic composition.", "weights": [[8, 15, 1.0], [30, 37, 1.0]]}, "M95-1021": {"snippet": "...ts a Boolean algebra computational model o f natural language [Iwadska, 1992 ] [Iwanska, 1993 ] [Iwanska, 1994 ] [Iwanska, 1996b] and reflects our researc h hypothesis that natural language is a very ...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2004.ntcir_workshop-2004.80": {"snippet": "... the Boolean IR model. Since it is not easy to construct a Boolean query that covers all relevant documents, a mechanism for clarifying the Boolean query is required. In this paper, we propose \"approp...", "weights": [[8, 15, 1.0], [62, 69, 1.0], [143, 150, 1.0]]}, "1987.sigirconf_conference-87.26": {"snippet": ".... An algebra for hendling documents of one type can be extended to an algebra for handling documents of several types. Further, an algebra for handling documents can be extended by the relational alge...", "weights": [[8, 15, 1.0], [73, 80, 1.0], [134, 141, 1.0], [199, 206, 1.0]]}, "P86-1038": {"snippet": "A Logical Semantics for Feature Structures\n\n\n Unification-based grammar formalisms use structures containing sets of features to describe linguistic objects. Although computational algorithms for unif...", "weights": []}, "2015.jasis_journal-ir0volumeA66A8.2": {"snippet": "... for boolean retrieval and human decision-making during searches", "weights": [[8, 15, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.4": {"snippet": "Boolean interpretation of conjunctions for document retrieval", "weights": [[0, 7, 1.0]]}, "W11-0118": {"snippet": "Formalising and specifying underquantification\n\n\n This paper argues that all subject noun phrases can be given a quantified formalisation in terms of the intersection between their denotation set and ...", "weights": []}, "2020.findings-emnlp.56": {"snippet": "...ixed Boolean-Arithmetic (MBA) expressions involve both arithmetic calculation (e.g., plus, minus, multiply) and bitwise computation (e.g., and, or, negate, xor). MBA expressions have been widely appli...", "weights": [[8, 15, 1.0]]}, "2020.sigirconf_conference-2020.379": {"snippet": "...sing Boolean techniques. Given this situation, an e cient system for legal case retrieval can be of great bene t and thus has drawn increasing attention in academic and industrial IR research. General...", "weights": [[8, 15, 1.0]]}, "1995.iwpt-1.9": {"snippet": "An Abstract Machine for Attribute-Value Logics\n\n\n A direct abstract machine implementation of the core attribute-value logic operations is shown to decrease the number of operations and conserve the a...", "weights": []}, "1988.ipm_journal-ir0volumeA24A3.9": {"snippet": "...ting Boolean queries in conjunctive normal form with probabilistic retrieval models", "weights": [[8, 15, 1.0]]}, "2016.cikm_conference-2016.92": {"snippet": "Empowering Truth Discovery with Multi-Truth Prediction\n\n\n ABSTRACTTruth discovery is the problem of detecting true values from the conflicting data provided by multiple sources on the same data items....", "weights": []}, "2002.ipm_journal-ir0volumeA38A6.0": {"snippet": "...nded Boolean retrieval model", "weights": [[8, 15, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [124, 131, 1.0], [196, 203, 1.0]]}, "2000.iwpt-1.24": {"snippet": "...tree algebra. A parsing schema is derived from the tree algebra by means of algebraic operations such as homomorphic images, direct products, subalgebras and quotient algebras. The latter yields a tab...", "weights": [[8, 15, 1.0], [59, 66, 1.0]]}, "2005.cikm_conference-2005.5": {"snippet": "...ical algebra, named score region algebra, that enables transparent specification of information retrieval (IR) models for XML databases. The transparency is achieved by a possibility to instantiate va...", "weights": [[8, 15, 1.0], [36, 43, 1.0]]}, "1995.iwpt-1.20": {"snippet": "...ling algebra. The theorem proving strategy is particularly well suited to the treatment of categorial gram mar, because it allows us to distribute the computational cost between the algorithm which de...", "weights": [[8, 15, 1.0]]}, "1997.sigirconf_conference-97.15": {"snippet": "...g of Boolean Operators\n\n\n The inference network model of information retrieval allows for a probabilistic interpretation of Boolean query operators. Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [127, 134, 1.0]]}, "1981.ipm_journal-ir0volumeA17A3.0": {"snippet": "... and Boolean retrieval systems", "weights": [[8, 15, 1.0]]}, "1992.trec_conference-1992.22": {"snippet": "A Boolean Approximation Method for Query Construction and Topic Assignment in TREC", "weights": [[2, 9, 1.0]]}, "2005.trec_conference-2005.42": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator. The fuzzy term proximity is controlled with an influenc...", "weights": [[8, 15, 1.0], [81, 88, 1.0]]}, "2005.clef_workshop-2005w.11": {"snippet": "Boolean Operators in Interactive Search\n\n\n This paper presents the participation of the MIRACLE team at the ImageCLEF interactive search task. Basically, queries consisting on several terms can be pro...", "weights": [[0, 7, 1.0]]}, "2006.sigirconf_conference-2006.142": {"snippet": "...ical algebra, named score region algebra (SRA), that enables transparent specification of IR models for XML databases (see  for more details).The transparency is achieved by a possibility to instantia...", "weights": [[8, 15, 1.0], [36, 43, 1.0]]}, "2007.trec_conference-2007.51": {"snippet": "...rent Boolean queries were provided for each topic (defendant, plaintiff and final). In 2006, the plaintiff and final queries had (usually) been the same. 1 Although often referred to as \"Boolean,\" the...", "weights": [[8, 15, 1.0], [190, 197, 1.0]]}, "1999.jasis_journal-ir0volumeA50A1.7": {"snippet": "Boolean Search: Current State and Perspectives", "weights": [[0, 7, 1.0]]}, "1985.sigirconf_conference-85.3": {"snippet": "...s of Boolean Structures for Probabilistic Retrieval", "weights": [[8, 15, 1.0]]}, "1985.riao_conference-1985.20": {"snippet": "...ting boolean structure within a probabilistic framework", "weights": [[8, 15, 1.0]]}, "1982.ipm_journal-ir0volumeA18A5.4": {"snippet": "... the Boolean logic of a program", "weights": [[8, 15, 1.0]]}, "2012.sigirconf_conference-2012.106": {"snippet": "...late Boolean queries to represent their information need. We want to work on three basic problems:1. Boolean query formulation -Our primary goal is to study Boolean query formulation in the light of t...", "weights": [[8, 15, 1.0], [104, 111, 1.0], [160, 167, 1.0]]}, "Q15-1027": {"snippet": "...to a Boolean structure of the kind in which natural language terms are assumed to denote. We evaluate this Boolean Distributional Semantic Model (BDSM) on recognizing entailment between words and sent...", "weights": [[8, 15, 1.0], [110, 117, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "... for boolean query formulation\n\n\n AbstractA new model named Boolean Latent Semantic Indexing model based on the Singular Value Decomposition and Boolean query formulation is introduced. While the Sing...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [148, 155, 1.0]]}, "1985.ipm_journal-ir0volumeA21A6.2": {"snippet": "... for boolean search request formulations, including some experimental results", "weights": [[8, 15, 1.0]]}, "2001.cikm_conference-2001.28": {"snippet": "... any boolean combination of comparison predicates of columns with constants or list of constants. Such comparison predicates contain an operator 0 E {=, f, <, 5, >, 2).Core and base functionality coul...", "weights": [[8, 15, 1.0]]}, "P16-1003": {"snippet": "Inferring Logical Forms From Denotations\n\n\n A core problem in learning semantic parsers from denotations is picking out consistent logical forms-those that yield the correct denotation-from a combinat...", "weights": []}, "1988.ipm_journal-ir0volumeA24A3.5": {"snippet": "...nced Boolean retrieval: Experiences with the smart and sire systems", "weights": [[8, 15, 1.0]]}, "Y15-2021": {"snippet": "Distinguishing between True and False Stories using various Linguistic Features\n\n\n This paper analyzes what linguistic features differentiate true and false stories written in Hebrew. To do so, we hav...", "weights": []}, "1983.jasis_journal-ir0volumeA34A4.3": {"snippet": "...dern algebra and information science", "weights": [[8, 15, 1.0]]}}}
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Our labeling results indicate that the new ass...", "weights": [[8, 14, 1.0], [147, 155, 1.0], [170, 177, 1.0]]}, "2012.sigirconf_conference-2012.192": {"snippet": "...anic results such as ads, embedded image results, news or local results, etc. -thereby recreating a realistic search experience for the subjects. Paid annotators rated every search result for relevanc...", "weights": [[8, 15, 1.0], [44, 51, 1.0], [67, 74, 1.0], [113, 119, 1.0], [177, 183, 1.0], [195, 204, 1.0]]}, "2006.jasis_journal-ir0volumeA57A10.8": {"snippet": "Relevance criteria identified by health information users during Web searches", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2012.cikm_workshop-2012dtmbio.7": {"snippet": "...lity criteria. Given a small set of sample documents D , |D | |D|, a user has initially identified as relevant e.g., via a user query interface, our scoring method automatically suggests eligibility c...", "weights": [[8, 16, 1.0], [105, 113, 1.0], [202, 210, 1.0]]}, "2010.airs_conference-2010.20": {"snippet": "...eudo-Relevance Feedback Based on mRMR Criteria", "weights": [[8, 17, 1.0], [41, 49, 1.0]]}, "2012.iir_workshop-2012.9": {"snippet": "...phic Relevance Criteria", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2004.trec_conference-2004.53": {"snippet": "...t of criteria aiming at capturing complementary aspects of relevance. Moreover, we provide aggregation procedures that are based on decision rules, to get the ranking of relevant documents.", "weights": [[8, 16, 1.0], [62, 71, 1.0], [173, 181, 1.0]]}, "2002.sigirconf_conference-2002.52": {"snippet": "... and relevance criteria", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2011.sigirconf_conference-2011.189": {"snippet": "...ring relevance, diversity and prestige in information networks\n\n\n ABSTRACTMany network-based ranking approaches have been proposed to rank objects according to different criteria, including relevance,...", "weights": [[8, 17, 1.0], [173, 181, 1.0], [193, 202, 1.0]]}, "2021.birws_workshop-2021.7": {"snippet": "... and Relevance Criteria - An Online Experiment", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2012.ipm_journal-ir0volumeA48A2.10": {"snippet": "...ulti-criteria relevance. The following criteria are considered to estimate document relevance: aboutness, coverage, appropriateness, and reliability.The originality of this approach lies in the aggreg...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [42, 50, 1.0], [87, 96, 1.0]]}, "D19-1351": {"snippet": "...ping Criteria for Search Results using {P}oisson Processes\n\n\n Text retrieval systems often return large sets of documents, particularly when applied to large collections. Stopping criteria can reduce ...", "weights": [[8, 16, 1.0], [21, 27, 1.0], [28, 35, 1.0], [183, 191, 1.0]]}, "2012.ipm_journal-ir0volumeA48A1.10": {"snippet": "...n of relevance is dependent on a number of factors and variables which include the criteria used to determine relevance. The relevance judgment process and the criteria used to make those judgments ar...", "weights": [[8, 17, 1.0], [86, 94, 1.0], [113, 122, 1.0], [128, 137, 1.0], [163, 171, 1.0]]}, "2011.wsdm_conference-2011.53": {"snippet": "... two criteria, quality and relevance, as a preliminary work in multi-criteria recommendation. We first give qualitative and quantitative analysis of competitive quality-based and relevance-based algor...", "weights": [[8, 16, 1.0], [30, 39, 1.0], [72, 80, 1.0], [182, 191, 1.0]]}, "1999.jasis_journal-ir0volumeA50A14.0": {"snippet": "...en's Relevance Criteria and Information Seeking on Electronic Resources", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2002.sigirconf_conference-2002.44": {"snippet": "...aded relevance corpus.Relevance criteria were defined so that a distinction was made between documents rich in topical information (relevant and highly relevant documents) and poor in topical informat...", "weights": [[8, 17, 1.0], [25, 34, 1.0], [35, 43, 1.0], [135, 143, 1.0], [155, 163, 1.0]]}, "2014.ipm_journal-ir0volumeA50A2.9": {"snippet": "...wned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance ...", "weights": [[8, 14, 1.0], [101, 107, 1.0], [148, 154, 1.0], [193, 202, 1.0]]}, "2003.sigirconf_conference-2003.61": {"snippet": "...ers' criteria For assessing web pages\n\n\n ABSTRACTWe investigate the criteria used by online searchers when assessing the relevance of web pages to information-seeking tasks. Twenty four searchers were...", "weights": [[8, 16, 1.0], [71, 79, 1.0], [124, 133, 1.0]]}, "2021.ecir_conference-20212.80": {"snippet": "...onal Relevance in Task-Specific Retrieval\n\n\n Several criteria of relevance have been proposed in the literature. However, relevance criteria are strongly related to the search task. Thus, it is import...", "weights": [[8, 17, 1.0], [56, 64, 1.0], [68, 77, 1.0], [125, 134, 1.0], [135, 143, 1.0], [172, 178, 1.0]]}, "2013.sigirconf_conference-2013.119": {"snippet": "... two search result lists. Unlike the traditional method of collecting relevance labels per single result, this method allows to consider the interaction between search results as part of the judging c...", "weights": [[8, 14, 1.0], [73, 82, 1.0], [164, 170, 1.0], [171, 178, 1.0], [202, 210, 1.0]]}, "2009.jasis_journal-ir0volumeA60A4.6": {"snippet": "...ers' relevance criteria for evaluating answers in a social Q&A site", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A2.14": {"snippet": "... the search results by promoting in the ranking pages belonging to relevant topic categories, or categories similar to the relevant categories. By following external links and searching for the retrie...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [70, 78, 1.0], [126, 134, 1.0]]}, "2014.cikm_conference-2014.192": {"snippet": "...plex Relevance Spaces with Copulas\n\n\n ABSTRACTModern relevance models consider a wide range of criteria in order to identify those documents that are expected to satisfy the user's information need. W...", "weights": [[8, 17, 1.0], [56, 65, 1.0], [98, 106, 1.0]]}, "2001.jasis_journal-ir0volumeA52A8.7": {"snippet": "...e of relevance criteria across stages of document evaluation: On the complementarity of experimental and naturalistic studies", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "1997.sigirconf_conference-97.36": {"snippet": "...e of relevant documents may be very small, terms maybe extracted from non-relevant sections of otherwise relevant documents, and some relevant terms may also attract non-relevant topics that are alrea...", "weights": [[8, 16, 1.0], [77, 85, 1.0], [108, 116, 1.0], [137, 145, 1.0], [173, 181, 1.0]]}, "2016.sigirconf_conference-2016.103": {"snippet": "...hese criteria. Because of the focus on single metrics, offline optimization of IR systems does not account for different preferences in balancing relevance criteria.We propose to mitigate this by view...", "weights": [[8, 16, 1.0], [149, 158, 1.0], [159, 167, 1.0]]}, "2014.sigirconf_conference-2014.103": {"snippet": "...ince search engines can only observe server-side actions (i.e., activity on the search engine result page), server-side dwell times are estimated by measuring the time between a search result click an...", "weights": [[8, 14, 1.0], [83, 89, 1.0], [181, 187, 1.0]]}, "2007.ipm_journal-ir0volumeA43A4.15": {"snippet": "...tify relevance criteria beyond topical relevance. 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The law is investigated for two langua...", "weights": [[8, 11, 1.0], [36, 40, 1.0], [43, 46, 1.0], [169, 172, 1.0]]}, "2020.acl-main.44": {"snippet": "...ower law when \u03b2 = 0 and the Zipf-Mandelbrot law when \u03b1 = 0. We illustrate that \u03b1 is related to the analytic features of syntax and \u03b2 + \u03b3 to those of morphology in natural languages from an investigati...", "weights": [[8, 11, 1.0], [31, 35, 1.0], [47, 50, 1.0]]}, "2001.jasis_journal-ir0volumeA52A12.3": {"snippet": "... and Zipf models", "weights": [[8, 12, 1.0]]}, "2010.jasis_journal-ir0volumeA61A12.15": {"snippet": "...ka's law and Zipf's law", "weights": [[8, 11, 1.0], [16, 20, 1.0], [23, 26, 1.0]]}, "N18-1102": {"snippet": "...e of Zipf's law, which states that most content words occur very rarely. In this paper, we propose novel methods with a neural model of P (path|w 1 , w 2 ) to solve this problem. Our proposed model of...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "O03-4004": {"snippet": "...pus, Zipf 's law for both words in English and characters in Chinese does not hold for all ranks. The frequency falls below the frequency predicted by Zipf's law for English words for rank greater tha...", "weights": [[8, 12, 1.0], [16, 19, 1.0], [154, 158, 1.0], [161, 164, 1.0]]}, "1987.jasis_journal-ir0volumeA38A6.3": {"snippet": "...n of Zipf parameters", "weights": [[8, 12, 1.0]]}, "2021.naacl-main.350": {"snippet": "...ngs (Zipf's law of abbreviation), the need for a productive and open-ended vocabulary, local constraints on sequences of symbols, and various other factors all shape the lexicons of the world's langua...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.10": {"snippet": "... of \"Zipf's law\" was cited most (64.8%), followed by \"the principle of least effort\" (24.5%). Although the concept of \"the principle of least effort,\" the focus of HBPLE, was not most frequently obser...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "1999.jasis_journal-ir0volumeA50A3.4": {"snippet": "... the Law of Zipf-Mandelbrot for Multi-Wort Phrases", "weights": [[8, 11, 1.0], [15, 19, 1.0]]}, "I08-3003": {"snippet": "...ower law or the Zipf's law. This makes NLP hard. Interestingly, the distribution of speakers over the world, content over the web and linguistic resources available across languages also follow power ...", "weights": [[8, 11, 1.0], [19, 23, 1.0], [26, 29, 1.0]]}, "2019.gwc-1.44": {"snippet": "...ency law with wordnets as sense inventories\n\n\n According to George K. Zipf, more frequent words have more senses. We have tested this law using corpora and wordnets of English, Spanish, Portuguese, Fr...", "weights": [[8, 11, 1.0], [73, 77, 1.0], [137, 140, 1.0]]}, "2012.wwwconf_conference-2012.31": {"snippet": "...e if Zipf's law is a good description of the frequency with which passwords are chosen. We use a number of standard statistics, which measure the security of password distributions, to see if modellin...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "1987.ipm_journal-ir0volumeA23A3.0": {"snippet": "...of a zipf size variable: Sampling properties and relationship to other parameters", "weights": [[8, 12, 1.0]]}, "1982.jasis_journal-ir0volumeA33A5.5": {"snippet": "...n of Zipf's Law and Its Application to the Bibliographic Database Environment", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "C14-1035": {"snippet": "... The Zipf's law of meaning is used as a way of pre-setting the sense number for the parametric model. We further extend the parametric model to non-parametric model which not only simplifies the probl...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2021.acl-srw.6": {"snippet": "...{'}s Law of Abbreviation and Interfering Noise in Emergent Languages\n\n\n This paper studies whether emergent languages in a signaling game follow Zipf's law of abbreviation (ZLA), especially when the c...", "weights": [[8, 11, 1.0], [148, 152, 1.0], [155, 158, 1.0]]}, "W98-1218": {"snippet": "... The law named for him is ubiquitous, but Zipf did not actually discover the law so much as provide a plausible explanation. Others have proposed modifications to Zipf's Law, and closer examination un...", "weights": [[8, 11, 1.0], [45, 49, 1.0], [80, 83, 1.0], [166, 170, 1.0], [173, 176, 1.0]]}, "2007.sigirconf_conference-2007.86": {"snippet": "...ower-law constraints (also known as Zipf's Law and long tails) that allow us to materialize carefully chosen combinations of multi-keyword indexes, which significantly improve worst-case performance w...", "weights": [[8, 11, 1.0], [39, 43, 1.0], [46, 49, 1.0]]}, "2009.ipm_journal-ir0volumeA45A4.6": {"snippet": "...ower-law regularities have been discovered behind many complex natural and social phenomenons. We discover that the power-law regularities, especially the Zipf's and Heaps' laws, also exist in large-s...", "weights": [[8, 11, 1.0], [125, 128, 1.0], [158, 162, 1.0]]}, "1972.jasis_journal-ir0volumeA23A3.8": {"snippet": "...eral Zipf-type distributions in their goodness of fit to language data", "weights": [[8, 12, 1.0]]}, "1987.ipm_journal-ir0volumeA23A3.1": {"snippet": "...s of Zipf's law: An index approach", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2005.ipm_journal-ir0volumeA41A6.4": {"snippet": "...g of Zipf-type. A generalization of Zipf distribution (a size-frequency form of the Zipf\u00d5s law), named the generalized Zipf distribution, is introduced. Two main characterizations of the generalized Z...", "weights": [[8, 12, 1.0], [39, 43, 1.0], [94, 97, 1.0], [122, 126, 1.0], [202, 206, 1.0]]}, "E17-4009": {"snippet": "...t to Zipf's law, an empirical law referring to the phenomenon that many types of data in social sciences can be approximated with a Zipfian distribution. Additionally, we also analyse Benford's law, i...", "weights": [[8, 12, 1.0], [15, 18, 1.0], [33, 36, 1.0], [197, 200, 1.0]]}, "2021.ccl-1.93": {"snippet": "...{'}s law: evidence from danmu comments\n\n\n Zipf's law is a succinct yet powerful mathematical law in linguistics. However, the meaningfulness and units of the law have remained controversial. The curre...", "weights": [[8, 11, 1.0], [45, 49, 1.0], [52, 55, 1.0], [96, 99, 1.0], [161, 164, 1.0]]}, "2018.jasis_journal-ir0volumeA69A11.6": {"snippet": "...s of Zipf's meaning-frequency law", "weights": [[8, 12, 1.0], [33, 36, 1.0]]}, "2017.sigirconf_conference-2017.41": {"snippet": "...ago, Zipf postulated that the word frequency distribution of languages is a power law, i.e., it is a straight line on a log-log plot. Over the years, this phenomenon has been documented and studied ex...", "weights": [[8, 12, 1.0], [85, 88, 1.0]]}, "W96-0106": {"snippet": "...{'}s Law\n\n\n An asymptote is derived from Turing's local reestimation formula for population frequencies, and a local reestimation formula is derived from Zipf's law for the asymptotic behavior of popu...", "weights": [[8, 11, 1.0], [157, 161, 1.0], [164, 167, 1.0]]}, "1986.jasis_journal-ir0volumeA37A5.4": {"snippet": "...ka's Law, Bradford's Law, and Zipf's Law", "weights": [[8, 11, 1.0], [24, 27, 1.0], [33, 37, 1.0], [40, 43, 1.0]]}}}
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Prior work has shown, however, that these operators...", "weights": [[8, 15, 1.0], [51, 56, 1.0], [72, 81, 1.0], [127, 134, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.1": {"snippet": "...tion retrieval \u2013 The potential for improvements in conventional Boolean retrieval systems", "weights": [[8, 17, 1.0], [67, 74, 1.0], [75, 84, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0]]}, "2010.cikm_conference-2010.75": {"snippet": "...d on Boolean retrieval, i.e., a document may be relevant for the query or not. But with the coalescence of database and information retrieval (IR) technology, selectivity estimation for other, more so...", "weights": [[8, 15, 1.0], [16, 25, 1.0], [135, 144, 1.0]]}, "1982.sigirconf_conference-82.5": {"snippet": "...onal Retrieval Systems\n\n\n ABSTRACTResearch has shown that document r e t r i e v a l based on weighting functions and incorporating relevance feedback may be more effective than r e t r i e v a l base...", "weights": [[8, 17, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.22": {"snippet": "...ying retrieval biases in probabilistic IR models and then develop new document priors to improve retrieval performance . Next, we discuss the accessibility bias of retrieval models, and for Boolean re...", "weights": [[8, 17, 1.0], [100, 109, 1.0], [167, 176, 1.0], [193, 200, 1.0], [201, 210, 1.0]]}, "1981.jasis_journal-ir0volumeA32A4.5": {"snippet": "...hted boolean retrieval", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1986.sigirconf_conference-86.21": {"snippet": "...pace Model for Boolean Query Processing\n\n\n Abstract. An infamation retrieval model, named the Generaliied Vectm Spice Model (GVSM). is extended m handle situations where queries are specitied as (exte...", "weights": [[8, 13, 1.0], [18, 25, 1.0], [70, 79, 1.0], [80, 85, 1.0], [121, 126, 1.0]]}, "1985.sigirconf_conference-85.3": {"snippet": "...s of Boolean Structures for Probabilistic Retrieval", "weights": [[8, 15, 1.0], [45, 54, 1.0]]}, "2007.trec_conference-2007.51": {"snippet": "...rent Boolean queries were provided for each topic (defendant, plaintiff and final). In 2006, the plaintiff and final queries had (usually) been the same. 1 Although often referred to as \"Boolean,\" the...", "weights": [[8, 15, 1.0], [190, 197, 1.0]]}, "2007.trec_conference-2007.65": {"snippet": "...lds, boolean constraints and structured operators. Evaluation results show that structured queries outperform bag-of-words ones. Boolean constraints improve both precision and recall. For the routing ...", "weights": [[8, 15, 1.0], [132, 139, 1.0]]}, "1994.ipm_journal-ir0volumeA30A3.4": {"snippet": "...s in Boolean Retrieval Systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1980.sigirconf_conference-80.22": {"snippet": "A Model of a Document-Clustering-Based Information Retrieval System with a Boolean Search Request Formulation", "weights": [[2, 7, 1.0], [51, 60, 1.0], [75, 82, 1.0]]}, "1982.sigirconf_conference-82.18": {"snippet": "...tion retrieval systems typically employ inverted file structures and Boolean logic operators for efficient text retrieval. These systems require considerable user training for effective use. As a cons...", "weights": [[8, 17, 1.0], [72, 79, 1.0], [115, 124, 1.0]]}, "1990.sigirconf_conference-90.28": {"snippet": "...nded Boolean Retrieval: A Heuristic Approach?", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2018.sigirconf_conference-2018.82": {"snippet": "...item retrieval systems assume the user can formulate a good Boolean or SQL-style query to retrieve items, as one would do with a database, but this is often challenging, particularly given multiple nu...", "weights": [[8, 17, 1.0], [63, 70, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...mage Retrieval by Hypertext Links\n\n\n This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, th...", "weights": [[8, 17, 1.0], [62, 67, 1.0], [72, 81, 1.0]]}, "1981.sigirconf_conference-81.8": {"snippet": "...uzzy Retrieval Environment\n\n\n ABSTRACTWe shall consider retrieval performance measures for generalized (non-Boolean) queries and indexing functions.The meanings of recall and precision in such a gener...", "weights": [[8, 17, 1.0], [59, 68, 1.0], [111, 118, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...Soft Boolean Operators\n\n\n AbstractThe conventional bibliographic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in an...", "weights": [[8, 15, 1.0], [68, 77, 1.0], [99, 106, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "...tion retrieval performance: training in Boolean logic and the type of search interface. As reported previously, we found that both Boolean training and the use of an assisted interface improved the pa...", "weights": [[8, 17, 1.0], [43, 50, 1.0], [134, 141, 1.0]]}, "1979.ipm_journal-ir0volumeA15A5.2": {"snippet": "...ical model of a weighted boolean retrieval system", "weights": [[8, 13, 1.0], [28, 35, 1.0], [36, 45, 1.0]]}, "1986.sigirjournals_journal-ir0volumeA20A14.4": {"snippet": "... the retrieval processes of document retrieval systems\u00b0 Two basic categories of models that have been employed in information retrieval are the vector processing models and the Boolean retrieval model", "weights": [[8, 17, 1.0], [40, 49, 1.0], [129, 138, 1.0], [180, 187, 1.0], [188, 197, 1.0]]}, "2008.wwwconf_conference-2008.201": {"snippet": "...ming model offered by the GPU, which is a data-parallel model reminiscent of the massively parallel SIMD models studied in the 1980s.Recently, GPU vendors have started to offer better support for gene...", "weights": [[8, 13, 1.0], [59, 64, 1.0]]}, "2006.clef_workshop-2006w.37": {"snippet": "... Our model handles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes ...", "weights": [[8, 13, 1.0], [22, 29, 1.0], [95, 102, 1.0], [115, 124, 1.0], [125, 130, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...nded Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulate...", "weights": [[8, 15, 1.0], [16, 21, 1.0], [73, 80, 1.0]]}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0], [55, 64, 1.0]]}, "2004.ntcir_workshop-2004.80": {"snippet": "...c IR model and the Boolean IR model. Since it is not easy to construct a Boolean query that covers all relevant documents, a mechanism for clarifying the Boolean query is required. In this paper, we p...", "weights": [[8, 13, 1.0], [22, 29, 1.0], [33, 38, 1.0], [76, 83, 1.0], [157, 164, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.7": {"snippet": "...onal Boolean retrieval systems", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "1990.sigirconf_conference-90.1": {"snippet": "...ment Retrieval\n\n\n AbstractThe use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolea", "weights": [[8, 17, 1.0], [79, 88, 1.0], [121, 130, 1.0], [131, 136, 1.0], [197, 204, 1.0]]}, "1999.sigirconf_conference-99.10": {"snippet": "...nded Boolean Models", "weights": [[8, 15, 1.0]]}, "1993.jasis_journal-ir0volumeA44A2.1": {"snippet": "...zing Boolean Information Retrieval: A Model and Its Evaluation", "weights": [[8, 15, 1.0], [28, 37, 1.0], [41, 46, 1.0]]}, "2011.sigirconf_conference-2011.85": {"snippet": "... for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we...", "weights": [[8, 15, 1.0], [151, 158, 1.0]]}, "1994.sigirconf_conference-94.19": {"snippet": "...nded Boolean Models in Information Retrieval", "weights": [[8, 15, 1.0], [38, 47, 1.0]]}, "2004.trec_conference-2004.90": {"snippet": "...ed a boolean search engine called MadCow which does word indexing at the sentence level. This year we decided to explore systematically strategies for boolean retrieval for the purpose of QA measuring...", "weights": [[8, 15, 1.0], [154, 161, 1.0], [162, 171, 1.0]]}, "1997.jasis_journal-ir0volumeA48A2.3": {"snippet": "...ring Boolean and Probabilistic Information Retrieval Systems across Queries and Disciplines", "weights": [[8, 15, 1.0], [46, 55, 1.0]]}, "1987.sigirconf_conference-87.21": {"snippet": "...tion Retrieval Models\n\n\n AbstractLinear decision (retrieval)functions have been widely adopted in information retrieval systems such as in Boolean, vector, and probabilistic", "weights": [[8, 17, 1.0], [53, 62, 1.0], [113, 122, 1.0], [142, 149, 1.0]]}, "1982.jasis_journal-ir0volumeA33A6.1": {"snippet": "...tion retrieval in systems with boolean search request formulations", "weights": [[8, 17, 1.0], [34, 41, 1.0]]}, "1999.tois_journal-ir0volumeA17A4.1": {"snippet": "...work model of information retrieval allows for a probabilistic interpretation of query operators. In particular, Boolean query operators are conveniently modeled as link matrices of the Bayesian Netwo...", "weights": [[8, 13, 1.0], [29, 38, 1.0], [116, 123, 1.0]]}, "2010.sigirconf_conference-2010.48": {"snippet": "... two retrieval models that incorporate user feedback on document facets. Evaluated based on user feedback collected through Amazon Mechanical Turk, our experimental results show that the Boolean filte...", "weights": [[8, 17, 1.0], [190, 197, 1.0]]}, "1987.jasis_journal-ir0volumeA38A4.4": {"snippet": "Boolean interpretation of conjunctions for document retrieval", "weights": [[0, 7, 1.0], [52, 61, 1.0]]}, "1985.sigirconf_conference-85.25": {"snippet": "...nded Boolean language / 10/. Besides the operators 'and', 'or' and' not' the GI~PS retrieval language contains thesaurus --operators to extend the query and truncation --and context-operators for free...", "weights": [[8, 15, 1.0], [86, 95, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.9": {"snippet": "...ting Boolean queries in conjunctive normal form with probabilistic retrieval models", "weights": [[8, 15, 1.0], [70, 79, 1.0]]}, "2012.sigirconf_conference-2012.106": {"snippet": "...late Boolean queries to represent their information need. We want to work on three basic problems:1. Boolean query formulation -Our primary goal is to study Boolean query formulation in the light of t...", "weights": [[8, 15, 1.0], [104, 111, 1.0], [160, 167, 1.0]]}, "2007.spire_conference-2007.13": {"snippet": "...tion Retrieval\n\n\n Abstract. Conjunctive Boolean queries are a fundamental operation in web search engines. These queries can be reduced to the problem of intersecting ordered sets of integers, where e...", "weights": [[8, 17, 1.0], [43, 50, 1.0]]}, "2006.clef_workshop-2006w.121": {"snippet": "... The Boolean model is used in the document retrieval module to retrieve documents that meet both textual and geographic criteria. The ranking module applied ranking functions that are learned using Ge...", "weights": [[8, 15, 1.0], [16, 21, 1.0], [46, 55, 1.0]]}, "2002.ipm_journal-ir0volumeA38A6.0": {"snippet": "...nded Boolean retrieval model", "weights": [[8, 15, 1.0], [16, 25, 1.0], [26, 31, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.10": {"snippet": "...ment Retrieval\n\n\n AbstractThe use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolea", "weights": [[8, 17, 1.0], [79, 88, 1.0], [121, 130, 1.0], [131, 136, 1.0], [197, 204, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [50, 59, 1.0], [85, 94, 1.0], [124, 131, 1.0], [132, 141, 1.0], [173, 178, 1.0], [196, 203, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "...xing model for boolean query formulation\n\n\n AbstractA new model named Boolean Latent Semantic Indexing model based on the Singular Value Decomposition and Boolean query formulation is introduced. Whil...", "weights": [[8, 13, 1.0], [18, 25, 1.0], [61, 66, 1.0], [73, 80, 1.0], [106, 111, 1.0], [158, 165, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [69, 78, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...ring retrieval and ranking\n\n\n We present a model for arbitrary passage retrieval using Boolean queries. The model is applied to the task of ranking documents, or other structural elements, in the orde...", "weights": [[8, 17, 1.0], [46, 51, 1.0], [74, 83, 1.0], [90, 97, 1.0], [111, 116, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...h as Boolean operators and phrase searching, improves the effectiveness of Web searching. We test this assumption by examining the effects of query operators on the performance of three major Web sear...", "weights": [[8, 15, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [45, 54, 1.0], [99, 106, 1.0], [107, 116, 1.0], [176, 183, 1.0], [199, 208, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.10": {"snippet": "...n in Boolean IR queries\n\n\n a b s t r a c tThe negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include negated terms in Boole...", "weights": [[8, 15, 1.0], [120, 129, 1.0], [198, 205, 1.0]]}}}
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The prominent feature of the proposed method is that documents, terms, and ...", "weights": [[8, 16, 1.0], [97, 108, 1.0], [109, 118, 1.0], [119, 126, 1.0]]}, "1988.tois_journal-ir0volumeA6A1.1": {"snippet": "... but retrieval is usually limited to finding documents that contain a specified Boolean pattern of words. Effective retrieval requires that documents with similar meanings be found through a process o...", "weights": [[8, 17, 1.0], [119, 128, 1.0]]}, "2007.sigirconf_conference-2007.123": {"snippet": "...ntic indexing\n\n\n ABSTRACTThis poster introduces a novel approach to information retrieval that uses statistical model averaging to improve latent semantic indexing (LSI). Instead of choosing a single ...", "weights": [[8, 16, 1.0], [71, 82, 1.0], [83, 92, 1.0], [158, 166, 1.0]]}, "W97-0502": {"snippet": "...AAC) systems. In this paper, the differences between traditional information retrieval and the requirements for text retrieval in a communication aid are highlighted. We then present the overall desig...", "weights": [[8, 15, 1.0], [68, 79, 1.0], [80, 89, 1.0], [120, 129, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.17": {"snippet": "...arge information spaces such as the Web challenge information retrieval in these environments. Collection of information in advance and centralization of IR operations are hardly possible because syst...", "weights": [[8, 19, 1.0], [53, 64, 1.0], [65, 74, 1.0], [112, 123, 1.0], [199, 206, 1.0]]}, "1982.ipm_journal-ir0volumeA18A4.3": {"snippet": "...tive indexing in a document retrieval system model", "weights": [[8, 16, 1.0], [31, 40, 1.0]]}, "1997.jasis_journal-ir0volumeA48A12.0": {"snippet": "...atic Indexing and a Relational Thesaurus in a Chinese Information Retrieval System", "weights": [[8, 16, 1.0], [57, 68, 1.0], [69, 78, 1.0]]}, "2002.ntcir_workshop-2002.68": {"snippet": "...form Indexing and Retrieval Scheme for Chinese, Japanese, and Korean\n\n\n This paper reports on our work at the third NTCIR workshop on the subtasks of Chinese, Japanese, and Korean monolingual informat...", "weights": [[8, 16, 1.0], [21, 30, 1.0], [195, 206, 1.0]]}, "2006.ecir_conference-2006.21": {"snippet": "...Text Retrieval Systems\n\n\n Abstract. In-place and merge-based index maintenance are the two main competing strategies for on-line index construction in dynamic information retrieval systems based on in...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [162, 173, 1.0], [174, 183, 1.0], [184, 191, 1.0]]}, "1985.ipm_journal-ir0volumeA21A5.2": {"snippet": "...tive indexing in fuzzy retrieval systems", "weights": [[8, 16, 1.0], [26, 35, 1.0], [36, 43, 1.0]]}, "2012.clef_workshop-2012w.187": {"snippet": "Word Indexing Versus Conceptual Indexing in Medical Image Retrieval", "weights": [[5, 13, 1.0], [32, 40, 1.0], [58, 67, 1.0]]}, "2002.cikm_conference-2002.14": {"snippet": "...ased retrieval\n\n\n ABSTRACTPhotograph retrieval systems face the difficulty to deal with the different ways to apprehend the content of images. We consider and demonstrate here the use of multiple inde...", "weights": [[8, 17, 1.0], [40, 49, 1.0], [50, 57, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.3": {"snippet": "...uted Indexing: To MapReduce or Not?\n\n\n Information Retrieval (IR) systems require input corpora to be indexed. The advent of terabyte-scale Web corpora has reinvigorated the need for efficient indexin...", "weights": [[8, 16, 1.0], [42, 53, 1.0], [54, 63, 1.0], [69, 76, 1.0], [196, 204, 1.0]]}, "1998.sigirconf_conference-98.68": {"snippet": "...d on indexing and interrogation functionalities.\nl Indexing facilitiesImage indexing is performed with a computer-assisted pmcess, according to a particular image indexing model [Mec95]. A basic part ...", "weights": [[8, 16, 1.0], [54, 62, 1.0], [79, 87, 1.0], [166, 174, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.7": {"snippet": "...rent indexing languages\n\n\n AbstractAn integrated information retrieval system generally contains multiple databases that are inconsistent in terms of their content and indexing. This paper proposes a ...", "weights": [[8, 16, 1.0], [52, 63, 1.0], [64, 73, 1.0], [171, 179, 1.0]]}, "2010.cikm_conference-2010.109": {"snippet": "...rted indexing for feedback and expansion\n\n\n ABSTRACTTraditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a list...", "weights": [[8, 16, 1.0], [79, 90, 1.0], [91, 100, 1.0], [101, 108, 1.0]]}, "1986.sigirconf_conference-86.20": {"snippet": "...ment retrieval [GRIFM. GRIF851. Recent research by the author corroborates these findings and also shows that the partial ranking produced by a topdown search of the complete link hierarchy can be mor...", "weights": [[8, 17, 1.0]]}, "1993.sigirconf_conference-93.14": {"snippet": "... the Information Retrieval community in evahrating the use of linguistic knowledge to improve the indexing and searching of textual databases.Such systems must often employ a lexicon to store informat...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [101, 109, 1.0], [150, 157, 1.0], [195, 206, 1.0]]}, "1981.sigirconf_conference-81.21": {"snippet": "...or a retrieval data-base, that interfaces between the user and a retrieval system. The system's objective is to perform the information consultant's job in assisting a user to select the right vocabul...", "weights": [[8, 17, 1.0], [68, 77, 1.0], [127, 138, 1.0]]}, "1989.sigirconf_conference-89.11": {"snippet": "... for Information Retrieval\n\n\n ABSTRACTIn this paper we present a parallel document ranking algorithm suitable for use on databases of l-1000 GB, resident on primary or secondary storage. The algorithm...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2020.sigirconf_conference-2020.279": {"snippet": "...ment indexing is a key component for efficient information retrieval (IR). After preprocessing steps such as stemming and stopword removal, document indexes usually store term-frequencies (tf). Along ...", "weights": [[8, 16, 1.0], [50, 61, 1.0], [62, 71, 1.0]]}, "2013.cikm_conference-2013.240": {"snippet": "...tion indexing: fast approximate retrieval from large corpora\n\n\n ABSTRACTInverted indexing is a ubiquitous technique used in retrieval systems including web search. Despite its popularity, it has a dra...", "weights": [[8, 16, 1.0], [35, 44, 1.0], [84, 92, 1.0], [127, 136, 1.0], [137, 144, 1.0]]}, "2010.cikm_conference-2010.128": {"snippet": "...rent Information Retrieval systems use inverted index structures for efficient query processing. Due to the extremely large size of many data sets, these index structures are usually kept in compresse...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0]]}, "H94-1073": {"snippet": "...eech retrieval system affects the retrieval effectiveness. A speech retrieval system facilitates content-based retrieval of speech documents, i.e. audio recordings containing spoken text. The speech r...", "weights": [[8, 17, 1.0], [37, 46, 1.0], [71, 80, 1.0], [114, 123, 1.0], [202, 211, 1.0]]}, "2000.ir_journal-3.6": {"snippet": "...gent Indexing and Semantic Retrieval of Multimodal Documents", "weights": [[8, 16, 1.0], [30, 39, 1.0]]}, "1999.cikm_conference-99.51": {"snippet": "...ntic indexing and retrieval in Information Retrieval. The prototype is motivated by a desire to provide a more efficient and effective information retrieval system compared to the current state of the...", "weights": [[8, 16, 1.0], [21, 30, 1.0], [34, 45, 1.0], [46, 55, 1.0], [138, 149, 1.0], [150, 159, 1.0]]}, "2009.cikm_conference-2009.172": {"snippet": "...port Systems (DSSs) to improve query performance. In this paper, we evaluate the use of compressed inverted indexes with adapted query processing strategies from Information Retrieval as an alternativ...", "weights": [[8, 15, 1.0], [165, 176, 1.0], [177, 186, 1.0]]}, "W01-1202": {"snippet": "...s on retrieval time. If the QA system is combined with a traditional Information Retrieval system, it can improve the document retrieval precision for closed-class questions after minimum loss of retr", "weights": [[8, 17, 1.0], [72, 83, 1.0], [84, 93, 1.0], [130, 139, 1.0], [199, 208, 1.0]]}, "2013.wsdm_conference-2013.93": {"snippet": "...uted information retrieval (IR) systems. Scalability and efficiency of these systems also have an impact on their effectiveness. Consequently, scalability and efficiency in the context of retrieval sy...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [35, 42, 1.0], [80, 87, 1.0], [191, 200, 1.0], [201, 208, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.7": {"snippet": "...uted information retrieval systems to efficiently search and locate the desired information. This work is a case study of different architectures for a distributed information retrieval system, in ord...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0], [83, 94, 1.0], [167, 178, 1.0], [179, 188, 1.0]]}, "2005.cikm_conference-2005.157": {"snippet": "...text retrieval systems. They can be constructed quickly using off-line mergebased methods, and provide efficient support for a variety of querying modes. In this paper we examine the task of on-line i...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2012.cikm_conference-2012.227": {"snippet": "...ased indexing of n-words\n\n\n ABSTRACTFormulating and processing phrases and other term dependencies to improve query effectiveness is an important problem in information retrieval. However, accessing t...", "weights": [[8, 16, 1.0], [160, 171, 1.0], [172, 181, 1.0]]}, "2014.tois_journal-ir0volumeA32A1.2": {"snippet": "Indexing Word Sequences for Ranked Retrieval\n\n\n Formulating and processing phrases and other term dependencies to improve query effectiveness is an important problem in information retrieval. However,...", "weights": [[0, 8, 1.0], [35, 44, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2008.tois_journal-ir0volumeA26A3.3": {"snippet": "...ased retrieval using compressed cluster-skipping inverted files\n\n\n We propose a unique cluster-based retrieval (CBR) strategy using a new cluster-skipping inverted file for improving query processing ...", "weights": [[8, 17, 1.0], [104, 113, 1.0]]}, "2010.cikm_conference-2010.127": {"snippet": "... and information retrieval. Such techniques return the top-k documents without scanning and evaluating the full inverted lists of the query terms. Thus, they can greatly improve query processing effic...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1994.jasis_journal-ir0volumeA45A8.1": {"snippet": "...abic Information Retrieval System", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2019.wsdm_conference-2019.119": {"snippet": "... two information objects is the core of many different information retrieval (IR) applications including Web search, question answering, and recommendation. Recently, deep learning methods have yielde...", "weights": [[8, 19, 1.0], [58, 69, 1.0], [70, 79, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A3.0": {"snippet": "...ng \u00b7 Information retrieval \u00b7 DB-IR integration \u00b7 Odysseus\nDB-IR integrationDatabases (DB) and information retrieval (IR) have been parallel universes. However, many recent applications such as custome...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "2000.tois_journal-ir0volumeA18A2.2": {"snippet": "...ased indexing of large image databases\n\n\n Large image databases have emerged in various applications in recent years. A prime requisite of these databases is the means by which their contents can be i...", "weights": [[8, 16, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...ased information systems have indicated that the content of the knowledge base and the reasoning behavior of systems analysts are two important factors in the development of computer based information...", "weights": [[8, 19, 1.0], [20, 27, 1.0], [112, 119, 1.0], [192, 203, 1.0]]}, "2005.cikm_conference-2005.127": {"snippet": "...amic indexing, and identifies features worth demonstrating. The concept of dynamic index implies that the aim is to reflect the creation of, deletion of, and updates to relevant files in the search in...", "weights": [[8, 16, 1.0]]}, "W00-1105": {"snippet": "... and Retrieval Effectiveness of Phrasal Indexing Terms\n\n\n In spite of long controversy, effectiveness of phrasal indexing is not yet clear. Recently, correlation between query length and effect of phr...", "weights": [[8, 17, 1.0], [43, 51, 1.0], [116, 124, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.7": {"snippet": "... for Information Retrieval Systems: A Posting-Based Approach\n\n\n Static index pruning methods have been proposed to reduce size of the inverted index of information retrieval systems. The goal is to in...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0], [155, 166, 1.0], [167, 176, 1.0], [177, 184, 1.0]]}, "2004.sigirconf_conference-2004.40": {"snippet": "...ment retrieval service by processing huge Inverted File indexes. Inverted File indexes allow fast query resolution and good memory utilization since their d-gaps representation can be effectively and ...", "weights": [[8, 17, 1.0]]}, "2011.sigirconf_conference-2011.153": {"snippet": "...s an indexing method to speed-up music retrieval\n\n\n ABSTRACTComputations in most music retrieval systems strongly depend on the size of data compared. We propose to enhance performances of a music ret...", "weights": [[8, 16, 1.0], [42, 51, 1.0], [90, 99, 1.0], [100, 107, 1.0], [200, 209, 1.0]]}, "2005.ntcir_workshop-2005.71": {"snippet": "...nese Information Retrieval\n\n\n In our formal runs, we have experimented with hybrid-term indexing and bigram indexing because hybrid-term indexing is a more distinct type of indexing strategy for bette...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [91, 99, 1.0], [111, 119, 1.0], [140, 148, 1.0], [176, 184, 1.0]]}, "2006.cikm_conference-2006.23": {"snippet": "...text retrieval systems\n\n\n ABSTRACTWe present a static index pruning method, to be used in ad-hoc document retrieval tasks, that follows a documentcentric approach to decide whether a posting for a giv...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [109, 118, 1.0]]}, "2009.trec_conference-2009.65": {"snippet": "...onal Indexing Model for Chemical Patent Search\n\n\n For the TREC-2009 Chemical IR Track, we explore development of a distributed information retrieval system based on a dimensional data model. The index...", "weights": [[8, 16, 1.0], [130, 141, 1.0], [142, 151, 1.0], [198, 206, 1.0]]}, "2007.wwwconf_conference-2007.147": {"snippet": "...iven indexing for peer-to-peer text retrieval\n\n\n ABSTRACTWe describe a query-driven indexing framework for scalable text retrieval over structured P2P networks. To cope with the bandwidth consumption ...", "weights": [[8, 16, 1.0], [39, 48, 1.0], [87, 95, 1.0], [124, 133, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2005.ipm_journal-ir0volumeA41A2.0": {"snippet": "...stic information retrieval model for a dependency structured indexing system\n\n\n AbstractMost previous information retrieval (IR) models assume that terms of queries and documents are statistically ind...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [64, 72, 1.0], [105, 116, 1.0], [117, 126, 1.0]]}, "2011.sigirconf_conference-2011.58": {"snippet": "...s in information retrieval. For each unique word occurring in a document collection, the inverted index stores a list of the documents in which this word occurs. Compression techniques are often appli...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2001.sigirconf_conference-2001.6": {"snippet": "... for Information Retrieval Systems\n\n\n ABSTRACTWe introduce static index pruning methods that significantly reduce the index size in information retrieval systems. We investigate uniform and term-based...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0], [135, 146, 1.0], [147, 156, 1.0], [157, 164, 1.0]]}, "2006.tois_journal-ir0volumeA24A4.2": {"snippet": "...amic retrieval of XML elements, which requires only a single indexing of the documents at the level of the basic indexing node, is presented. This method, which we refer to as flexible retrieval, prod...", "weights": [[8, 17, 1.0], [64, 72, 1.0], [116, 124, 1.0], [188, 197, 1.0]]}, "2013.tweb_journal-ir0volumeA7A3.3": {"snippet": "...g on retrieval systems that adopt a term-based index partitioning strategy, the high communication overhead due to the transfer of large amounts of data from the index servers forms a major performanc...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [44, 52, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "2005.ipm_journal-ir0volumeA41A2.6": {"snippet": "...Many information retrieval systems use the inverted file as indexing structure. The inverted file, however, requires inefficient reorganization when new documents are to be added to an existing collec...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0], [63, 71, 1.0]]}, "1971.ipm_journal-ir0volumeA7A5.3": {"snippet": "Retrieval efficiency from titles and the cost of indexing", "weights": [[0, 9, 1.0], [49, 57, 1.0]]}, "2004.sigirconf_conference-2004.15": {"snippet": "...ntic indexing for information retrieval\n\n\n ABSTRACTLatent Semantic Indexing (LSI) is a well established and effective framework for conceptual information retrieval. In traditional implementations of ...", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0], [70, 78, 1.0], [146, 157, 1.0], [158, 167, 1.0]]}, "2001.tois_journal-ir0volumeA19A3.0": {"snippet": "... Web indexing system that we have implemented.", "weights": [[8, 16, 1.0]]}, "P96-1003": {"snippet": "... for Information Retrieval\n\n\n Information retrieval is an important application area of natural-language processing where one encounters the genuine challenge of processing large quantities of unrestr...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [33, 44, 1.0], [45, 54, 1.0]]}, "1967.ipm_journal-ir0volumeA3A4.9": {"snippet": "Indexing and classification for interactive retrieval systems", "weights": [[0, 8, 1.0], [44, 53, 1.0], [54, 61, 1.0]]}, "2003.clef_workshop-2003.37": {"snippet": "...s in information retrieval had been on monolingual (mainly English) collections; and there had been few experiments in multilingual information retrieval systems 2 . \nThe GIRT Data in the Evaluation o...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [135, 146, 1.0], [147, 156, 1.0], [157, 164, 1.0]]}, "2005.sigirconf_conference-2005.124": {"snippet": "Indexing emails and email threads for retrieval\n\n\n ABSTRACTElectronic mail poses a number of unusual challenges for the design of information retrieval systems and test collections, including informal...", "weights": [[0, 8, 1.0], [38, 47, 1.0], [130, 141, 1.0], [142, 151, 1.0], [152, 159, 1.0]]}, "A97-1049": {"snippet": "...gual Information Browsing and Retrieval System Using Information Extraction\n\n\n In this paper, we describe our multilingual (or cross-linguistic) information browsing and retrieval system, which is aim...", "weights": [[8, 19, 1.0], [33, 42, 1.0], [56, 67, 1.0], [148, 159, 1.0], [173, 182, 1.0]]}, "2016.sigirconf_conference-2016.224": {"snippet": "...Web, indexing discovered pages, and processing user queries. The scalability and efficiency of these infrastructures require careful performance optimizations in every major component of the search en...", "weights": [[8, 16, 1.0]]}, "2002.ir_journal-ir0volumeA5A4.2": {"snippet": "...nual Indexing to Improve Collection Selection and Retrieval Effectiveness", "weights": [[8, 16, 1.0], [53, 62, 1.0]]}, "2014.wsdm_conference-2014.79": {"snippet": "...uted information retrieval (IR) systems. Scalability and efficiency of these systems also have an impact on their effectiveness. Consequently, scalability and efficiency in the context of retrieval sy...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [35, 42, 1.0], [80, 87, 1.0], [191, 200, 1.0], [201, 208, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.22": {"snippet": "...ased retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic i...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [202, 213, 1.0]]}, "2011.cikm_conference-2011.43": {"snippet": "...ased indexing and retrieval. Performance is comparable to the state of the art inverted file based systems, including language models and BM25. These findings suggest that file signatures offer a viab...", "weights": [[8, 16, 1.0], [21, 30, 1.0], [102, 109, 1.0]]}, "2004.ntcir_workshop-2004.49": {"snippet": "...tion Retrieval with Site Anchor Text\n\n\n In this paper we present an information retrieval system that indexes only site anchor text to verify the efficiency of reference information in a navigation re...", "weights": [[8, 17, 1.0], [71, 82, 1.0], [83, 92, 1.0], [173, 184, 1.0], [201, 210, 1.0]]}, "2009.cikm_conference-2009.47": {"snippet": "... for Information Retrieval systems. The new model is based on partitioning of the inverted index across the terms in it. We exploit the query log to partition the on-disk inverted index into two types...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 37, 1.0]]}, "1988.ipm_journal-ir0volumeA24A4.2": {"snippet": "... for indexing and retrieval", "weights": [[8, 16, 1.0], [21, 30, 1.0]]}, "2010.wsdm_conference-2010.38": {"snippet": "...ment retrieval\n\n\n ABSTRACTThere has been a large amount of research on efficient document retrieval in both IR and web search areas. One important technique to improve retrieval efficiency is early te...", "weights": [[8, 17, 1.0], [93, 102, 1.0], [171, 180, 1.0]]}, "W00-1104": {"snippet": "...lean Information l~.etrieval system that adds word semantics to the classic word based indexing. Two of the main tasks of our system, namely the indexing and retrieval components, are using a combined...", "weights": [[8, 19, 1.0], [90, 98, 1.0], [148, 156, 1.0], [161, 170, 1.0]]}, "2010.wwwconf_conference-2010.32": {"snippet": "Scalable techniques for document identifier assignment ininverted indexes\n\n\n ABSTRACTWeb search engines use the full-text inverted index data structure. Because query processing performance is impacte...", "weights": []}, "2007.coria_conference-2007.30": {"snippet": "Fusion d'informations pour l'indexation de photos", "weights": []}, "2003.spire_conference-2003.21": {"snippet": "...rase Indexing and Mining for an Information Retrieval System\n\n\n Abstract. In this paper, we present a noun phrase indexing and mining methodology for French Information Retrieval. Our assumption is th...", "weights": [[8, 16, 1.0], [35, 46, 1.0], [47, 56, 1.0], [117, 125, 1.0], [160, 171, 1.0], [172, 181, 1.0]]}, "1990.sigirconf_conference-90.12": {"snippet": "...Text Retrieval System\n\n\n ABSTRACTThis paper presents a new data structure and an associated strategy to be utilized by indexing facilities for text retrieval systemsThe paperstarts by reviewing some o...", "weights": [[8, 17, 1.0], [122, 130, 1.0], [151, 160, 1.0]]}, "1983.sigirconf_conference-83.1": {"snippet": "...ware Systems for Text Information Retrieval\n\n\n As databases become very large, conventional digital computers cannot provide satisfactory response time. This is particularly true for text databases, w...", "weights": [[8, 15, 1.0], [25, 36, 1.0], [37, 46, 1.0]]}, "2017.sigirconf_conference-2017.63": {"snippet": "...text indexing. In recent years the Bing search engine has developed and deployed an index based on bit-sliced signatures. This index, known as BitFunnel, replaced an existing production system based o...", "weights": [[8, 16, 1.0]]}, "2014.sigirconf_conference-2014.73": {"snippet": "... and retrieval methods. This paper proposes an innovative mathematics retrieval system along with the novel algorithms, which enables efficient formula index and retrieval from both webpages and PDF d...", "weights": [[8, 17, 1.0], [73, 82, 1.0], [165, 174, 1.0]]}, "1991.sigirjournals_journal-ir0volumeA25A2.6": {"snippet": "... and Retrieval of Documents Using Index Expressions", "weights": [[8, 17, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...odic information to isolate sarcasm. -Language models. ASR systems generally contain a language model that provides word sequence probabilities. 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There has been a remarkable amount of work towards the improvement of the effectiveness of the opinion retr...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [199, 208, 1.0]]}, "2011.cikm_conference-2011.245": {"snippet": "...rove retrieval quality over the initial query. Typical relevance feedback methods usually select top-ranked documents for relevance judgments, then query expansion or model updating are carried out ba...", "weights": [[8, 17, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.22": {"snippet": "...t of retrieval systems empirically designed to be biased towards relevant information. However, other biases have been observed, which counteract retrieval performance. In this thesis, the reduction o...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [149, 158, 1.0]]}, "2012.wsdm_conference-2012.41": {"snippet": "...tion retrieval systems such as search engines relies on good test collections, including assessments of retrieved content. The widely employed \"Cranfield paradigm\" dictates that the information releva...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2003.sigirconf_conference-2003.49": {"snippet": "... and Retrieval]: Retrieval Models.General Terms: Theory, Experimentation. Keywords: Ranking Retrieval Systems.\nINTRODUCTIONSoboroff, Nicholas and Cahan [2] recently proposed a method for evaluating re...", "weights": [[8, 17, 1.0], [20, 29, 1.0], [95, 104, 1.0], [105, 112, 1.0], [201, 210, 1.0]]}, "2012.cikm_conference-2012.135": {"snippet": "...mage retrieval systems have to cope with two different regimes: understanding broadly the categories of interest to the user, and refining the search in this or these categories to converge to specifi...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2004.sigirconf_conference-2004.132": {"snippet": "...tion retrieval (GIR): searching where and what\n\n\n EXTENDED ABSTRACTInformation retrieval systems in operation today for applications ranging from Digital Libraries to Web Search make very little use o...", "weights": [[8, 17, 1.0], [82, 91, 1.0], [92, 99, 1.0]]}, "2001.cikm_conference-2001.5": {"snippet": "...tion Retrieval\n\n\n ABSTRACTWe consider the problem of organizing and browsing the top ranked portion of the documents returned by an information retrieval system. We study the effectiveness of a docume...", "weights": [[8, 17, 1.0], [147, 156, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.17": {"snippet": "...arch systems continue to struggle with scalability problems of today, the future of search likely requires a decentralized architecture where many information systems can participate. As individual sy...", "weights": [[8, 15, 1.0], [54, 62, 1.0], [162, 169, 1.0], [201, 208, 1.0]]}, "1981.sigirconf_conference-81.2": {"snippet": "...ized Retrieval Problem", "weights": [[8, 17, 1.0]]}, "2015.ictir_conference-2015.2": {"snippet": "...y of Retrieval: The Retrievability of Information\n\n\n ABSTRACTRetrievability is an important and interesting indicator that can be used in a number of ways to analyse Information Retrieval systems and ...", "weights": [[8, 17, 1.0], [181, 190, 1.0], [191, 198, 1.0]]}, "1991.tois_journal-ir0volumeA9A3.3": {"snippet": "...line retrieval systems experience many difficulties, particularly with search tactics, User studies have indicated that searchers use vocabulary incorrectly and do not take full advantage of iteration...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2008.sigirjournals_journal-ir0volumeA42A2.18": {"snippet": "...tion retrieval in the workplace\n\n\n Increasingly, workplace information seeking takes place in digital information environments and is reliant upon search systems. Existing systems are designed to retr...", "weights": [[8, 17, 1.0], [157, 164, 1.0], [175, 182, 1.0]]}, "2017.cikm_conference-2017.301": {"snippet": "... the systems that perform be er, tend to have a higher chance of retrieving a relevant document rather than a non-relevant document for a topic prior to retrieval, indicating that retrieval systems wh...", "weights": [[8, 15, 1.0], [156, 165, 1.0], [183, 192, 1.0], [193, 200, 1.0]]}, "2005.sigirconf_conference-2005.7": {"snippet": "...tion retrieval using implicit feedback\n\n\n ABSTRACTA major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collect...", "weights": [[8, 17, 1.0], [89, 98, 1.0], [110, 117, 1.0], [130, 139, 1.0]]}, "2007.sigirconf_conference-2007.25": {"snippet": "...ment retrieval in main memory\n\n\n ABSTRACTDisk access performance is a major bottleneck in traditional information retrieval systems. Compared to system memory, disk bandwidth is poor, and seek times a...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [127, 134, 1.0]]}, "2013.tois_journal-ir0volumeA31A2.2": {"snippet": "... and systems in these environments. Searching for information in these information spaces has brought about problems beyond traditional boundaries of information retrieval (IR) research. This article ...", "weights": [[8, 15, 1.0], [111, 119, 1.0], [165, 174, 1.0]]}, "1992.tois_journal-ir0volumeA10A2.0": {"snippet": "...tion retrieval systems, We report on an analysis of lexlcal ambiguity in information retrieval test collections and on experiments to determine the utility of word meanings for separating relevant fro...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [88, 97, 1.0]]}, "1979.sigirconf_conference-79.13": {"snippet": "...phic Retrieval System", "weights": [[8, 17, 1.0]]}, "2007.tois_journal-ir0volumeA25A1.2": {"snippet": "...mage retrieval faces many challenges. Traditional text retrieval techniques on image descriptions have not been very successful. This is mainly due to the inconsistent textual descriptions and the dis...", "weights": [[8, 17, 1.0], [58, 67, 1.0]]}, "1983.ipm_journal-ir0volumeA19A4.4": {"snippet": "...s in retrieval systems", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "1991.sigirconf_conference-91.24": {"snippet": "...tion Retrieval Systems\n\n\n Abstract.Information retrieval is the selection of documents that are potentially relevant to a user's information need. Given the vast volume of data stored in modern inform...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [50, 59, 1.0]]}, "1980.sigirconf_conference-80.15": {"snippet": "Problems in the Simulation of Bibliographic Retrieval Systems", "weights": [[0, 8, 1.0], [44, 53, 1.0], [54, 61, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.8": {"snippet": "...tion retrieval systems. Distributing different aspects of search (e.g., crawling, indexing, and query processing) is essential to achieve scalability in large-scale information retrieval systems. The ...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [180, 189, 1.0], [190, 197, 1.0]]}, "L04-1241": {"snippet": "...tion Retrieval System Using Latent Contextual Relevance", "weights": [[8, 17, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...tion Retrieval (IR) solutions to retrieve passages matching a set of terms extracted from the question.There is a fundamental disconnect between the capabilities of the bag-of-words retrieval model an...", "weights": [[8, 17, 1.0], [185, 194, 1.0]]}, "1986.tc-1.6": {"snippet": "...tion retrieval are of value. In this presentation, I shall attempt to outline the main techniques for personal information retrieval systems, both manual and computer-based. Whilst I shall be talking ...", "weights": [[8, 17, 1.0], [126, 135, 1.0], [136, 143, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...s of retrieval models on the basis of their retrieval bias. This also offers a basis for optimizing retrieval systems for specific collections without the need to provide manually annotated ground tru...", "weights": [[8, 17, 1.0], [47, 56, 1.0], [103, 112, 1.0], [113, 120, 1.0]]}, "2005.sigirconf_conference-2005.9": {"snippet": "...tion retrieval\n\n\n ABSTRACTInformation retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information need. The retri...", "weights": [[8, 17, 1.0], [41, 50, 1.0], [152, 161, 1.0], [198, 207, 1.0]]}, "1984.sigirconf_conference-84.5": {"snippet": "...phic Retrieval Systems", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.13": {"snippet": "...ence retrieval and novelty detection. We analyze the strengths and weaknesses of current state of the art methods and, subsequently, new mechanisms to address sentence retrieval and novelty detection ...", "weights": [[8, 17, 1.0], [171, 180, 1.0]]}, "2008.cikm_conference-2008.183": {"snippet": "...nion retrieval system with the proposed opinion retrieval techniques and the polarity classification model outperformed the best reported systems respectively.", "weights": [[8, 17, 1.0], [51, 60, 1.0], [141, 148, 1.0]]}, "2010.ntcir_workshop-2010evia.5": {"snippet": "...king Retrieval Systems without Relevance Assessments: Revisited\n\n\n We reexamine the problem of ranking retrieval systems without relevance assessments in the context of collaborative evaluation forums...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [106, 115, 1.0], [116, 123, 1.0]]}, "1981.sigirconf_conference-81.21": {"snippet": "...or a Retrieval Data-Base with Semantic Network of Concepts\n\n\n AbstractThis paper describes a development and implementation of an expert/consultation system for a retrieval data-base, that interfaces ...", "weights": [[8, 17, 1.0], [166, 175, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.2": {"snippet": "...tive systems.CCS Concepts: \u2022 Information systems \u2192 Retrieval effectiveness; Learning to rank; Information retrieval query processing;Additional Key Words and Phrases: Information systems, information ...", "weights": [[8, 15, 1.0], [44, 51, 1.0], [54, 63, 1.0], [109, 118, 1.0], [182, 189, 1.0]]}, "2011.cikm_workshop-2011pikm.12": {"snippet": "...tion retrieval testcollections\n\n\n ABSTRACTWe consider the problem of optimally allocating limited resources to construct relevance judgements for a test collection that facilities reliable evaluation ...", "weights": [[8, 17, 1.0]]}, "2008.sigirconf_conference-2008.198": {"snippet": "...ore, systems that can detect and respond accordingly to user emotions could potentially improve the naturalness of human-computer interaction and progressively optimize their retrieval strategy. The c...", "weights": [[8, 15, 1.0], [178, 187, 1.0]]}, "2003.sigirconf_conference-2003.65": {"snippet": "... and Retrieval]: Retrieval Models.General Terms: Algorithms, Theory. Keywords: Metasearch, Pooling, Retrieval Systems.\nINTRODUCTIONIn the annual TREC competition, participating systems return ranked l...", "weights": [[8, 17, 1.0], [20, 29, 1.0], [103, 112, 1.0], [113, 120, 1.0], [180, 187, 1.0]]}, "2010.sigirconf_conference-2010.136": {"snippet": "...TXML retrieval provides a focused access to the relevant content of documents. However, in evaluation, full document retrieval has appeared competitive to focused XML retrieval. We analyze the density...", "weights": [[8, 17, 1.0], [120, 129, 1.0], [170, 179, 1.0]]}, "2003.sigirconf_conference-2003.58": {"snippet": "...g of retrieval systems in imperfect environments\n\n\n ABSTRACTThe empirical investigation of the effectiveness of information retrieval (IR) systems requires a test collection, a set of query topics, an...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [127, 136, 1.0], [142, 149, 1.0]]}, "H94-1073": {"snippet": "...eech retrieval system affects the retrieval effectiveness. A speech retrieval system facilitates content-based retrieval of speech documents, i.e. audio recordings containing spoken text. The speech r...", "weights": [[8, 17, 1.0], [37, 46, 1.0], [71, 80, 1.0], [114, 123, 1.0], [202, 211, 1.0]]}, "2006.cikm_conference-2006.124": {"snippet": "...ased retrieval model with limited relevance judgments for document re-ranking. Probabilities of the document-context based retrieval model are estimated from the top N (=20) documents in the initial r...", "weights": [[8, 17, 1.0], [126, 135, 1.0], [202, 211, 1.0]]}, "1996.ipm_journal-ir0volumeA32A5.2": {"snippet": "...e on Retrieval Performance in a Hypertext-Based Bibliographic Retrieval System", "weights": [[8, 17, 1.0], [65, 74, 1.0]]}, "2011.cikm_conference-2011.78": {"snippet": "...ting systems, and (ii) generalize to new, previously unseen systems. We propose a two stage approach. For a given set of queries, we adopt the traditional pooling method and use a portion of the budge...", "weights": [[8, 15, 1.0], [63, 70, 1.0]]}, "2000.cikm_conference-2000.17": {"snippet": "...tion retrieval system. We describe an interactive relevance feedback agent that analyzes the inter-document similarities and can help the user to locate the interesting information quickly. We show ho...", "weights": [[8, 17, 1.0]]}, "2013.wsdm_conference-2013.93": {"snippet": "...tion retrieval (IR) systems. Scalability and efficiency of these systems also have an impact on their effectiveness. Consequently, scalability and efficiency in the context of retrieval systems contin...", "weights": [[8, 17, 1.0], [23, 30, 1.0], [68, 75, 1.0], [179, 188, 1.0], [189, 196, 1.0]]}, "2003.sigirconf_conference-2003.4": {"snippet": "...opic retrieval\n\n\n ABSTRACTWe present a non-traditional retrieval problem we call subtopic retrieval. The subtopic retrieval problem is concerned with finding documents that cover many different subtop...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [93, 102, 1.0], [117, 126, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.3": {"snippet": "...ased retrieval technique, the proposed system could partially bridge lexical chasms between queries and FAQs. In addition, the proposed system outperforms the traditional information retrieval systems", "weights": [[8, 17, 1.0], [186, 195, 1.0], [196, 203, 1.0]]}, "1989.sigirconf_conference-89.8": {"snippet": "Retrieval System Evaluation Using Recall and Precision: Problems and Answers", "weights": [[0, 9, 1.0], [56, 64, 1.0]]}, "2014.sigirconf_conference-2014.221": {"snippet": "...tion retrieval modeling\n\n\n ABSTRACTDynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and information ...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [128, 135, 1.0]]}, "2006.sigirconf_conference-2006.57": {"snippet": "...tion retrieval systems aim to maximize the number of relevant documents returned to a user within some window of the top. For that goal, the probability ranking principle, which ranks documents in dec...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2017.trec_conference-2017.21": {"snippet": "...best retrieval result to users. Information Retrieval systems for the Web, i.e., web search engines, are mainly devoted to finding relevant web documents in response to a user's query [6]. Current ret...", "weights": [[8, 17, 1.0], [47, 56, 1.0], [57, 64, 1.0], [200, 209, 1.0]]}, "2012.sigirconf_conference-2012.83": {"snippet": "...ased retrieval and propose a domain adaptation method based on relative aggregation points (RAPs). Content-based retrieval including image retrieval and spoken document retrieval enables a user to inp...", "weights": [[8, 17, 1.0], [116, 125, 1.0], [142, 151, 1.0], [172, 181, 1.0]]}, "2010.sigirconf_conference-2010.48": {"snippet": "...tive retrieval based on faceted feedback\n\n\n ABSTRACTMotivated by the commonly used faceted search interface in e-commerce, this paper investigates interactive relevance feedback mechanism based on fac...", "weights": [[8, 17, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...tion systems have indicated that the content of the knowledge base and the reasoning behavior of systems analysts are two important factors in the development of computer based information systems. Th...", "weights": [[8, 15, 1.0], [100, 107, 1.0], [192, 199, 1.0]]}, "2015.sigirconf_workshop-2015gsb.6": {"snippet": "...tion Retrieval Boosted by Category for Troubleshooting Search System\n\n\n Troubleshooting search system aims at extracting relevant information to solve the problem at hand. It is often the case that do...", "weights": [[8, 17, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [117, 126, 1.0], [127, 134, 1.0]]}, "T87-1043": {"snippet": "Limits on the human sentence generator\n\n\n The problem of language generation is the problem of translating communicative intent into linguistic form. As such, it implicates the entire richness and com...", "weights": []}, "1989.tois_journal-ir0volumeA7A3.1": {"snippet": "...s of Retrieval System Performance\n\n\n Recall and precision are often used to evaluate the effectiveness of information retrieval systems. They are easy to define if there is a single query and if the r...", "weights": [[8, 17, 1.0], [121, 130, 1.0], [131, 138, 1.0], [202, 211, 1.0]]}, "1982.sigirconf_conference-82.9": {"snippet": "...ment Retrieval Problem", "weights": [[8, 17, 1.0]]}, "1981.sigirconf_conference-81.16": {"snippet": "...tion Retrieval System Design\n\n\n AbstractThe representation problem confronting information retrieval system designers is outlined in terms of three issues:what to represent, forms of representation, a...", "weights": [[8, 17, 1.0], [94, 103, 1.0]]}, "2013.sigirconf_conference-2013.118": {"snippet": "...tion retrieval systems. Traditional methods of collecting relevance judgments are based on collecting binary or graded nominal judgments, but such judgments are limited by factors such as inter-assess...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2007.ipm_journal-ir0volumeA43A4.14": {"snippet": "...rank retrieval systems without relevance judgments\n\n\n AbstractThis paper addresses the problem of how to rank retrieval systems without the need for human relevance judgments, which are very resource ...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [113, 122, 1.0], [123, 130, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "... and Retrieval -relevance feedback\nGeneral TermsAlgorithms, Performance, Experimentation\nKeywordsRelevance Feedback, Web Search, Evaluation\n1.INTRODUCTIONRelevance feedback is a classical information ...", "weights": [[8, 17, 1.0]]}, "1985.ipm_journal-ir0volumeA21A2.4": {"snippet": "...ment retrieval systems and DBMS for retrieval of bibliographic data", "weights": [[8, 17, 1.0], [18, 25, 1.0], [39, 48, 1.0]]}, "2001.ntcir_workshop-2001.29": {"snippet": "On a Retrieval Support System by Suggesting Terms to a User\n\n\n We propose a support method for information retrieval. This method automatically suggests terms relevant to a query, to a user, and when ...", "weights": [[5, 14, 1.0], [107, 116, 1.0]]}, "2010.cikm_conference-2010.167": {"snippet": "...text retrieval systems are not very successful in mathematics retrieval. The lack of a definition for similarity between mathematical expressions, and the inadequacy of searching for exact matches onl...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [65, 74, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.2": {"snippet": "...ting retrieval models and accommodate systematic development of new retrieval models. As an example of using the framework to model non-traditional retrieval problems, we derive retrieval models for s...", "weights": [[8, 17, 1.0], [71, 80, 1.0], [151, 160, 1.0], [161, 169, 1.0], [181, 190, 1.0]]}, "2020.sigirconf_conference-2020.380": {"snippet": "...tion retrieval systems. Recently, deep learning-based biomedical information retrieval systems have been adopted to address this problem, which has the potential improvements in the performance of BMI...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [80, 89, 1.0], [90, 97, 1.0]]}, "1981.sigirconf_conference-81.11": {"snippet": "...hese problems cannot be solved without experimentation with information retrieval concepts and systems.Most information retrieval systems that currently exist have had to make operational assumptions ...", "weights": [[8, 16, 1.0], [75, 84, 1.0], [98, 105, 1.0], [123, 132, 1.0], [133, 140, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.16": {"snippet": "...text retrieval\n\n\n Even though modern retrieval systems typically use a multitude of features to rank documents, the backbone for search ranking is usually the standard tf.idf retrieval models.This the...", "weights": [[8, 17, 1.0], [40, 49, 1.0], [50, 57, 1.0], [178, 187, 1.0]]}, "2006.sigirconf_conference-2006.80": {"snippet": "...ting retrieval systems using a limited number of relevance judgments. Recent work has demonstrated that one can accurately estimate average precision via a judged pool corresponding to a relatively sm...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2012.cikm_conference-2012.18": {"snippet": "...tion retrieval system is to satisfy a user's information need-e.g., by providing an answer or information \"nugget\"-while the actual search space of a typical information retrieval system consists of d...", "weights": [[8, 17, 1.0], [173, 182, 1.0]]}, "2018.cikm_conference-2018.22": {"snippet": "...call Retrieval: Less is More\n\n\n ABSTRACTHigh-recall retrieval -finding all or nearly all relevant documents -is critical to applications such as electronic discovery, systematic review, and the constr...", "weights": [[8, 17, 1.0], [55, 64, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...mage Retrieval by Hypertext Links\n\n\n This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, th...", "weights": [[8, 17, 1.0], [72, 81, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...phic retrieval systems are based on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in answer to search queries but they are not easy to use by unini...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [106, 113, 1.0]]}, "2014.sigirconf_workshop-2014medir.6": {"snippet": "...ical retrieval task is cognitively demanding. Previous research has highlighted the challenges and issues information retrieval systems are faced with when determining the relevance of documents in th...", "weights": [[8, 17, 1.0], [121, 130, 1.0], [131, 138, 1.0]]}, "2007.sigirconf_conference-2007.76": {"snippet": "...tion retrieval systems is one of the core tasks in information retrieval. Problems include the inability to exhaustively label all documents for a topic, nongeneralizability from a small number of top...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [66, 75, 1.0], [77, 85, 1.0]]}, "2004.ntcir_workshop-2004.18": {"snippet": "...tent Retrieval\n\n\n This paper describes our retrieval system participated in the Patent Retrieval Task at the Fourth NTCIR Workshop. The main task was an associative patent retrieval task, in which a p...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [90, 99, 1.0], [175, 184, 1.0]]}, "A83-1019": {"snippet": "... the problems of database o rganlzat ion, retrieval problems and query languages, one of the major economic problems of many potential databases remains the entry of the original information into the ...", "weights": [[8, 16, 1.0], [45, 54, 1.0], [55, 63, 1.0], [111, 119, 1.0]]}, "2016.cikm_conference-2016.74": {"snippet": "...tion Retrieval\n\n\n ABSTRACTA common limitation of many information retrieval (IR) models is that relevance scores are solely based on exact (i.e., syntactic) matching of words in queries and documents ...", "weights": [[8, 17, 1.0], [69, 78, 1.0]]}, "C12-3040": {"snippet": "...tion Retrieval System\n\n\n We introduce a translation retrieval system THUTR, which casts translation as a retrieval problem. Translation retrieval aims at retrieving a list of target-language translati...", "weights": [[8, 17, 1.0], [55, 64, 1.0], [108, 117, 1.0], [139, 148, 1.0]]}, "1983.sigirconf_conference-83.26": {"snippet": "...tion retrieval components are currently incorporated in several types of information systems, including bibliographic retrieval systems, data base management systems and question-answering systems.Som...", "weights": [[8, 17, 1.0], [88, 95, 1.0], [121, 130, 1.0], [131, 138, 1.0], [161, 168, 1.0], [192, 199, 1.0]]}, "2001.wwwconf_conference-2001p.48": {"snippet": "...tion retrieval systems or search engines. We present a model for visualizing large collections of documents in World Wide Web retrieval, independently of the retrieval system.", "weights": [[8, 17, 1.0], [18, 25, 1.0], [129, 138, 1.0], [161, 170, 1.0]]}, "2020.clef_conference-2020w.189": {"snippet": "...tion Retrieval Systems\n\n\n We report on the systems that the Math Information Retrieval group at Masaryk University (MIRMU) prepared for tasks 1 (find answers) and 2 (formula search) of the ARQMath lab...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [46, 53, 1.0], [80, 89, 1.0]]}, "1983.sigirconf_conference-83.28": {"snippet": "Open Problems in Information Retrieval\n\n\n What are the crucial issues and open problems in our field? I propose to answer this question indirectly by offering you a view of the terrain that we call in...", "weights": [[5, 13, 1.0], [29, 38, 1.0], [79, 87, 1.0]]}, "2000.sigirconf_conference-2000.12": {"snippet": "...ment retrieval\n\n\n ABSTRACTCombined word-based indexes and phonetic indexes have been used to improve the performance of spoken document retrieval systems primarily by addressing the out-of-vocabulary ...", "weights": [[8, 17, 1.0], [139, 148, 1.0], [149, 156, 1.0]]}, "2021.naacl-main.428": {"snippet": "...tion systems 050 (Jiang et al., 2019). 051 In many scenarios, we need to distinguish dif-052 ferent keyword-document (query-document) rele-053 vance levels. For instance, in our recommendation 054 sys...", "weights": [[8, 15, 1.0]]}, "1973.sigirconf_conference-73.5": {"snippet": "...tion Retrieval and the Query Language\n\n\n ABSTRACTThis paper addresses itself to the information retrieval problem of a manager within an organization.We feel that the approach taken in specifying such...", "weights": [[8, 17, 1.0], [99, 108, 1.0]]}, "1995.sigirconf_conference-95.28": {"snippet": "...oved retrieval performance for the routing problem. We use the Tipster collection and the TREC-2 and TREC-3 routing tasks to test classifiers and representations .There are some risks associated with ...", "weights": [[8, 17, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...ment retrieval, long enough spans of speech can be readily handled.However, information retrieval is not just about document retrieval. There are other problems in and around IR where ASR is still lik...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [128, 137, 1.0], [155, 163, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.4": {"snippet": "...s of Problems in Online Judge Systems\n\n\n Online Judge (OJ) systems have been widely used in many areas, including programming, mathematical problems solving, and job interviews. Unlike other online le...", "weights": [[8, 16, 1.0], [33, 40, 1.0], [62, 69, 1.0], [143, 151, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...tion retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC -required s...", "weights": [[8, 17, 1.0], [42, 51, 1.0], [92, 101, 1.0], [147, 156, 1.0]]}, "1997.jasis_journal-ir0volumeA48A1.2": {"snippet": "...tion Retrieval: An Experiment on the Worm Community System", "weights": [[8, 17, 1.0]]}, "2014.sigirconf_conference-2014.222": {"snippet": "...tion Retrieval systems and document collections. Rather than focusing totally on relevance, retrievability examines what is retrieved, how often it is retrieved, and whether a user is likely to retrie...", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "W15-5955": {"snippet": "...Word Problems\n\n\n This paper describes our system which solves simple arithmetic word problems. The system takes a word problem described in natural language, extracts information required for represen...", "weights": [[8, 16, 1.0], [88, 96, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.10": {"snippet": "...tion retrieval systems in FAQ retrieval. Based on various experiments, we found that the proposed system could alleviate critical lexical disagreement problems in short document retrieval. In addition...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [33, 42, 1.0], [154, 162, 1.0], [181, 190, 1.0]]}, "2019.ecir_conference-20192.53": {"snippet": "...tion retrieval (IR) community, however, has paid little attention to the algorithm selection problem, although the problem is highly relevant in information retrieval. This workshop will bring togethe...", "weights": [[8, 17, 1.0], [160, 169, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ased retrieval.Directions for future work are using these concepts for communication between user and retrieval system, extending upon the translation models and extending CLIR-enhanced concept-based ...", "weights": [[8, 17, 1.0], [105, 114, 1.0]]}, "2005.ntcir_workshop-2005.25": {"snippet": "...tent Retrieval Task\n\n\n This paper describes our system participated in the Document and Passage Retrieval Subtasks at the NTCIR-5 Patent Retrieval Task. The purpose of these subtasks was the invalidit...", "weights": [[8, 17, 1.0], [99, 108, 1.0], [140, 149, 1.0]]}, "1999.jasis_journal-ir0volumeA50A6.7": {"snippet": "...tion Retrieval System: Modeling Information Flow", "weights": [[8, 17, 1.0]]}, "1991.sigirconf_conference-91.3": {"snippet": "...tion Retrieval Systems\n\n\n The purpose of a formal system model is to describe the common features of a set of systems which have been developed for similar problems.The model will explain the structur...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [113, 120, 1.0], [159, 167, 1.0]]}, "1981.ipm_journal-ir0volumeA17A5.2": {"snippet": "...phic retrieval system", "weights": [[8, 17, 1.0]]}, "W99-1017": {"snippet": "...ment Retrieval\n\n\n It is only during the last few years that attention has started to shift from pure textbased retrieval towards other media. Information retrieval from spoken documents is analogous t...", "weights": [[8, 17, 1.0], [114, 123, 1.0], [157, 166, 1.0]]}, "2012.airs_conference-2012.0": {"snippet": "...tion Retrieval Technology, 8th Asia Information Retrieval Societies Conference, AIRS 2012, Tianjin, China, December 17-19, 2012. Proceedings\n\n\n The annual AIRS conference has established its reputatio...", "weights": [[8, 17, 1.0], [51, 60, 1.0]]}, "2017.jasis_journal-ir0volumeA68A2.14": {"snippet": "...tion retrieval: System design, implementation, and evaluation", "weights": [[8, 17, 1.0]]}, "2005.sigirconf_conference-2005.85": {"snippet": "Characterization of a simple case of the reassignment of document identifiers as a pattern sequencing problem\n\n\n ABSTRACTIn this poster, we analyze recent work in the document identifiers reassignment...", "weights": []}, "2005.sigirconf_conference-2005.116": {"snippet": "...tion retrieval effectiveness of Web search engines can be expensive if human relevance judgments are required to evaluate search results. Using implicit user feedback for search engine evaluation prov...", "weights": [[8, 17, 1.0]]}, "1985.riao_conference-1985.38": {"snippet": "...tion retrieval system based on user problem statements", "weights": [[8, 17, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.14": {"snippet": "... for retrieval evaluation. These are used as the basis for experiments aimed at solving specific problems that are faced when searching with automatically generated descriptions of video content. Fina...", "weights": [[8, 17, 1.0], [100, 108, 1.0]]}, "1988.sigirconf_conference-88.27": {"snippet": "...tion Retrieval\n\n\n AbstractThis paper addresses the problem of building expert interfaces to information retrieval systems.", "weights": [[8, 17, 1.0], [107, 116, 1.0], [117, 124, 1.0]]}, "1968.ipm_journal-ir0volumeA4A4.2": {"snippet": "... and retrieval system evaluation", "weights": [[8, 17, 1.0]]}, "1987.sigirconf_conference-87.34": {"snippet": "...phic Retrieval: A Prolog Prototype\n\n\n ABSTRACTA prototype Prolog system has been developed for online bibliographic retrieval. Most online bibliographic retrieval systems may be characterized by queri...", "weights": [[8, 17, 1.0], [119, 128, 1.0], [156, 165, 1.0], [166, 173, 1.0]]}, "2013.ictir_conference-2013.14": {"snippet": "...tion Retrieval with Time Series Query\n\n\n ABSTRACTWe study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find relevant do...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [166, 175, 1.0]]}, "1971.sigirconf_conference-71.6": {"snippet": "...ment Retrieval: Hebrew Legal Texts\n\n\n ABSTRACTA full text retrieval system was designed for the responsa literature, which is a large corpus of Hebrew legal cases. The unique problems of the data base...", "weights": [[8, 17, 1.0], [61, 70, 1.0], [178, 186, 1.0]]}, "2011.cikm_conference-2011.248": {"snippet": "...opic retrieval that have been used to motivate diverse retrieval. \nCategories and Subject Descriptors\nGeneral TermsAlgorithms Keywords diversity, set-level relevance, maximal marginal relevance\nDIVERS...", "weights": [[8, 17, 1.0], [58, 67, 1.0]]}}}
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Analyzing content in these forums can uncover useful information for improving student retention and help in initiating instructor intervention. In this work, we explore t...", "weights": [[8, 14, 1.0]]}, "C14-1117": {"snippet": "Online Gaming for Crowd-sourcing Phrase-equivalents\n\n\n We propose the use of a game with a purpose (GWAP) to facilitate crowd-sourcing of phraseequivalents, as an alternative to expert or paid crowd-s...", "weights": [[0, 6, 1.0]]}, "2010.cikm_conference-2010.7": {"snippet": "Online annotation of text streams with structured entities", "weights": [[0, 6, 1.0]]}, "2013.tist_journal-ir0volumeA4A1.17": {"snippet": "...sing online system to deal with these problems. The novelty of this system is that laser and vision are integrated with tracking and online learning to complement each other in one framework: when the...", "weights": [[8, 14, 1.0], [136, 142, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.4": {"snippet": "...s in Online Judge Systems\n\n\n Online Judge (OJ) systems have been widely used in many areas, including programming, mathematical problems solving, and job interviews. Unlike other online learning syste...", "weights": [[8, 14, 1.0], [32, 38, 1.0], [182, 188, 1.0]]}, "2005.tc-1.11": {"snippet": "...sful online experience. This paper presents the key stages and challenges involved in implementing this approach to the multilingual dissemination of online content, whereby free online machine transl...", "weights": [[8, 14, 1.0], [153, 159, 1.0], [182, 188, 1.0]]}, "2021.ranlp-srw.28": {"snippet": "... for Quizzes and Answer-Aware Question Generators\n\n\n In education, quiz questions have become an important tool for assessing the knowledge of students. Yet, manually preparing such questions is a ted...", "weights": [[8, 15, 1.0]]}, "R19-1053": {"snippet": "...from online quizzes in history. While the quiz authors gave no relevant context, we incorporate knowledge from Wikipedia, retrieving documents matching the combination of question + each answer option...", "weights": [[8, 14, 1.0], [15, 22, 1.0]]}, "2009.cikm_workshop-2009swsm.9": {"snippet": "...s in online social networks\n\n\n ABSTRACTToday, online social networks in the World Wide Web become increasingly interactive and networked. Web 2.0 technologies provide a multitude of platforms, such as...", "weights": [[8, 14, 1.0], [49, 55, 1.0]]}, "2013.tois_journal-ir0volumeA31A1.4": {"snippet": "...d an online version, referred to as batch RLSI and online RLSI, respectively-to scale up topic modeling. Batch RLSI and online RLSI are as effective as existing topic modeling techniques and can scale...", "weights": [[8, 14, 1.0], [54, 60, 1.0], [123, 129, 1.0]]}, "2021.ranlp-srw.15": {"snippet": "...ment quizzes and games. Although the research on the automatic or semi-automatic generation of multiple-choice test items has been conducted since the beginning of this millennium, most approaches foc...", "weights": [[8, 15, 1.0]]}, "2021.wsdm_conference-2021.55": {"snippet": "...into online approaches-that learn by directly interacting with users-and counterfactual approaches-that learn from historical interactions. Existing online methods are hindered without online interven...", "weights": [[8, 14, 1.0], [152, 158, 1.0], [188, 194, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A4.11": {"snippet": "...cale online news\n\n\n Abstract Many of today's online news websites and aggregator apps have enabled users to publish their opinions without respect to time and place. Existing works on topic-based sent...", "weights": [[8, 14, 1.0], [48, 54, 1.0]]}, "E12-1037": {"snippet": "... for online forums. There are a lot of differences between online forums and formal media. For example, content generated by users in online forums contains more noise compared to formal documents. Co...", "weights": [[8, 14, 1.0], [62, 68, 1.0], [137, 143, 1.0]]}, "2016.wwwconf_conference-2016.16": {"snippet": "...n in Online User Content\n\n\n ABSTRACTDetection of abusive language in user generated online content has become an issue of increasing importance in recent years. Most current commercial methods make us...", "weights": [[8, 14, 1.0], [87, 93, 1.0]]}, "W14-1305": {"snippet": "...s in Online Debates\n\n\n This work suggests a fine-grained mining of contentious documents, specifically online debates, towards a summarization of contention issues. We propose a Joint Topic Viewpoint ...", "weights": [[8, 14, 1.0], [106, 112, 1.0]]}, "2012.wwwconf_workshop-2012lile.10": {"snippet": "...n of Quizzes and Learning Artifacts from Linked Data\n\n\n In this position paper, we illustrate how Linked Data can be effectively used in a Technology-enhanced Learning scenario. Specifically, we aim a...", "weights": [[8, 15, 1.0]]}, "1980.tc-1.4": {"snippet": "... and online\n\n\n Information can be retrieved by direct interrogation of a remote computer by means of a keyboard terminal and a telephone. The advantages of such an online system are fast access to lar...", "weights": [[8, 14, 1.0], [167, 173, 1.0]]}, "P07-2030": {"snippet": "...rate Quizzes for Interactive Information Presentation\n\n\n This paper proposes the idea of ranking definitions of a person (a set of biographical facts) to automatically generate \"Who is this?\" quizzes....", "weights": [[8, 15, 1.0], [195, 202, 1.0]]}, "2011.ir_journal-ir0volumeA14A6.0": {"snippet": "Online community search using conversational structures", "weights": [[0, 6, 1.0]]}, "2012.wsdm_conference-2012.28": {"snippet": "Online selection of diverse results\n\n\n ABSTRACTThe phenomenal growth in the volume of easily accessible information via various web-based services has made it essential for service providers to provid...", "weights": [[0, 6, 1.0]]}, "2018.cikm_conference-2018.160": {"snippet": "... and quizzes (sets of questions with concise answers) are a popular tool for this purpose. This paper focuses on the generation of balanced quizzes, i.e., quizzes that relate to a given set of documen...", "weights": [[8, 15, 1.0], [143, 150, 1.0], [158, 165, 1.0]]}, "2014.wwwconf_conference-2014.46": {"snippet": "...open online programming courses\n\n\n ABSTRACTMassive open online courses (MOOCs), one of the latest internet revolutions have engendered hope that constant iterative improvement and economies of scale m...", "weights": [[8, 14, 1.0], [59, 65, 1.0]]}, "2001.wwwconf_conference-2001p.63": {"snippet": "Online Learning in Hong Kong: A Preliminary Comparison of the Lecture and Online Versions of a Computing Fundamentals Course\n\n\n This case study compares students' learning experience and outcomes in t...", "weights": [[0, 6, 1.0], [74, 80, 1.0]]}, "C10-1083": {"snippet": "...ered Online Opinions\n\n\n We study the problem of integrating scattered online opinions. For this purpose, we propose to exploit structured ontology to obtain well-formed relevant aspects to a topic and...", "weights": [[8, 14, 1.0], [73, 79, 1.0]]}, "2021.emnlp-main.582": {"snippet": "...d in quizzes and interviews to bring out and evaluate the creative reasoning abilities of humans. To do the same for AI systems, we present two datasets: 1) A collection of 1k real-world FPs sourced f...", "weights": [[8, 15, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A2.9": {"snippet": "...p of online short texts, are becoming increasingly prevalent with the development of online communications. These short texts are characterized by their co-occurrences with usually lengthier normal do...", "weights": [[8, 14, 1.0], [88, 94, 1.0]]}, "2021.woah-1.20": {"snippet": "... the online context, contributing to mental or emotional stress on users. Automatic tools for detecting abuse can alleviate the issue. In practice, developing automated methods to detect abusive langu...", "weights": [[8, 14, 1.0]]}, "1992.jasis_journal-ir0volumeA43A5.1": {"snippet": "Online-Expert: An Expert System for Online Database Selection", "weights": [[0, 6, 1.0], [36, 42, 1.0]]}, "2010.cikm_conference-2010.189": {"snippet": "Online learning for recency search ranking using real-time user feedback\n\n\n ABSTRACTTraditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevan...", "weights": [[0, 6, 1.0]]}, "C10-2100": {"snippet": "...s in online debates, i.e., support or oppose the main topic of an online debate, by exploiting local information in their remarks within the debate. An online debate is a forum where each user post an...", "weights": [[8, 14, 1.0], [69, 75, 1.0], [155, 161, 1.0]]}, "2016.wsdm_conference-2016.11": {"snippet": "...Open Online Courses (MOOCs), which collect complete records of all student interactions in an online learning environment, offer us an unprecedented opportunity to analyze students' learning behavior ...", "weights": [[8, 14, 1.0], [97, 103, 1.0]]}, "2015.ipm_journal-ir0volumeA51A4.5": {"snippet": "...ase, online news sources) and experimental conditions (i.e., the type of media used to present online content).", "weights": [[8, 14, 1.0], [98, 104, 1.0]]}, "D08-1043": {"snippet": "...arge Online {Q}{\\&}{A} Collections with Compact Translation Models\n\n\n Lexical gaps between queries and questions (documents) have been a major issue in question retrieval on large online question and ...", "weights": [[8, 14, 1.0], [183, 189, 1.0]]}, "2017.ictir_workshop-2017learner.4": {"snippet": "... for Online Evaluation of Rankers\n\n\n In online learning to rank we are faced with a tradeoff between exploring new, potentially superior rankers, and exploiting our preexisting knowledge of what ranke...", "weights": [[8, 14, 1.0], [43, 49, 1.0]]}, "2013.jasis_journal-ir0volumeA64A2.2": {"snippet": "...s in online credibility evaluation", "weights": [[8, 14, 1.0]]}, "1990.jasis_journal-ir0volumeA41A8.1": {"snippet": "...e of online searching: An analysis of Online and Online Review", "weights": [[8, 14, 1.0], [41, 47, 1.0], [52, 58, 1.0]]}, "2007.ipm_journal-ir0volumeA43A3.20": {"snippet": "...lled online supplements, which includes the instructor and student companion sites, test bank, on-line quizzes and PowerPoint presentations. The second feature that I particularly liked is the set of ...", "weights": [[8, 14, 1.0], [106, 113, 1.0]]}, "2018.wwwconf_conference-2018c.219": {"snippet": "...n of Quizzes from DBpedia According to Educational Standards", "weights": [[8, 15, 1.0]]}, "2016.sigirconf_conference-2016.218": {"snippet": "...mize online metrics without subjecting users to an online learning algorithm? This tutorial summarizes and unifies the emerging body of methods on counterfactual evaluation and learning. These counter...", "weights": [[8, 14, 1.0], [54, 60, 1.0]]}, "2013.ecir_conference-2013.107": {"snippet": "...rast online evaluation with standard offline evaluation, and provide an overview of online approaches.Keywords: Interleaving, Clicks, Search Engine, Online Evaluation.\nMotivationGiven the growing brea...", "weights": [[8, 14, 1.0], [87, 93, 1.0], [152, 158, 1.0]]}, "2013.wwwconf_conference-2013c.151": {"snippet": "... for online scientific experimentation\n\n\n ABSTRACTThere is currently a huge potential for eLearning in several new online learning initiatives like Massive Open Online Courses (MOOCs) and Open Educati...", "weights": [[8, 14, 1.0], [118, 124, 1.0], [164, 170, 1.0]]}, "2015.wwwconf_conference-2015c.214": {"snippet": "...nts, online evaluation is particularly attractive in settings where reliable assessments are difficult or too expensive to obtain.However, the successful use of online evaluation requires the right me...", "weights": [[8, 14, 1.0], [164, 170, 1.0]]}, "2013.wwwconf_conference-2013c.152": {"snippet": "...from quizzes using intelligent learning companions\n\n\n ABSTRACTIt is widely recognised that engaging games can have a profound impact on learning. Integrating a conversational Artificial Intelligence (...", "weights": [[8, 15, 1.0]]}, "W18-3722": {"snippet": "...lank Quizzes\n\n\n In this paper we formalize the problem automatic fill-in-the-blank question generation using two standard NLP machine learning schemes, proposing concrete deep learning models for each...", "weights": [[8, 15, 1.0]]}, "2017.sigirconf_conference-2017.141": {"snippet": "Online Learning to Rank for Cross-Language Information Retrieval\n\n\n ABSTRACTOnline learning to rank for information retrieval has shown great promise in optimization of Web search results based on use...", "weights": [[0, 6, 1.0]]}, "P15-1008": {"snippet": "... for Online Course Discussion Forums\n\n\n Massive open online courses (MOOCs) are redefining the education system and transcending boundaries posed by traditional courses. With the increase in popularit...", "weights": [[8, 14, 1.0], [56, 62, 1.0]]}, "2014.wwwconf_conference-2014c.17": {"snippet": "Online behavioral genome sequencing from usage logs: decoding the search behaviors\n\n\n ABSTRACTWe present a system to analyze user interests by analyzing their online behaviors from large-scale usage l...", "weights": [[0, 6, 1.0], [159, 165, 1.0]]}, "W14-4712": {"snippet": "...logy quizzes. The experimental results show that our proposed method is effective when using a small corpus.", "weights": [[8, 15, 1.0]]}, "W04-1405": {"snippet": "...als, Quizzes, Resources, Choices, Surveys, Assignments \u2022 Full user logging and tracking -activity reports for each student are available \u2022 Mail integration -copies of forum posts and teacher feedback ...", "weights": [[8, 15, 1.0]]}, "2020.emnlp-main.538": {"snippet": "...e in Online Conversations\n\n\n Quotations are crucial for successful explanations and persuasions in interpersonal communications. However, finding what to quote in a conversation is challenging for bot...", "weights": [[8, 14, 1.0]]}, "2006.jasis_journal-ir0volumeA57A14.1": {"snippet": "...ring online information seeking context, Part 2: Findings and discussion", "weights": [[8, 14, 1.0]]}, "Y00-1007": {"snippet": "...s in quizzes, though not often in daily conversations. 2 I assume that, despite their restriction in the range of registers, WLWQs are also constrained by the knowledge of human language. To my knowle...", "weights": [[8, 15, 1.0]]}, "2012.sigirconf_conference-2012.117": {"snippet": "...data online for easier collaboration. The goal is to create a single online infrastructure that allows colleagues to submit, process, analyze and visualize data, and discuss and prioritize issues thro...", "weights": [[8, 14, 1.0], [72, 78, 1.0]]}, "2014.wwwconf_conference-2014.16": {"snippet": "Quizz: targeted crowdsourcing with a billion (potential) users", "weights": []}, "2014.amta-workshop.4": {"snippet": "...{MT} online learning in computer assisted translation\n\n\n In this paper we propose a cascading framework for optimizing online learning in machine translation for a computer assisted translation scenar...", "weights": [[8, 14, 1.0], [122, 128, 1.0]]}, "1998.sigirconf_conference-98.19": {"snippet": "...Many online documents are similarly structured, though not identically structured. We study the problem of discovering \"typical\" structures of a collection of such documents, where the user specifies ...", "weights": [[8, 14, 1.0]]}, "2018.desires_conference-2018.10": {"snippet": "... for ONline Experiments\n\n\n Although online experiments, often in the form of A/B tests, are increasingly used by academic researchers (with crowd-working platforms offering a large pool of artificial ...", "weights": [[8, 14, 1.0], [39, 45, 1.0]]}, "2020.alw-1.10": {"snippet": "...tect Online Abusive Language\n\n\n In recent years, abusive behavior has become a serious issue in online social networks. In this paper, we present a new corpus for the task of abusive language detectio...", "weights": [[8, 14, 1.0], [99, 105, 1.0]]}, "2014.cikm_conference-2014.35": {"snippet": "...ured Online Knowledge Bases to Answer Natural Language Questions on Community QA Websites\n\n\n ABSTRACTOver the past few years, community QA websites (e.g. Yahoo! Answers) have become a useful platform ...", "weights": [[8, 14, 1.0]]}, "N12-3004": {"snippet": "... for online window-shopping.\n\n\n At present, online shopping is typically a search-oriented activity where a user gains access to products which best match their query. Instead, we propose a surf-orien...", "weights": [[8, 14, 1.0], [47, 53, 1.0]]}, "2009.cikm_conference-2009.169": {"snippet": "...n of online anonymity to enable users to issue personalized queries to an untrusted web service while with their anonymity preserved. The challenge for providing online anonymity is dealing with unkno...", "weights": [[8, 14, 1.0], [165, 171, 1.0]]}, "2013.cikm_workshop-2013ueo.5": {"snippet": "...hms, online controlled experiments are now utilized to make data-driven decisions at many other companies. While the theory of a controlled experiment is simple, running online controlled experiments ...", "weights": [[8, 14, 1.0], [173, 179, 1.0]]}, "P19-3034": {"snippet": "... and quizzes for every target word, and automatically linking named entities to corresponding Wikipedia information. We present a prototype vocabulary learning system, Linggle Booster, that applies th...", "weights": [[8, 15, 1.0]]}, "2011.cikm_conference-2011.60": {"snippet": "...ning online search results\n\n\n ABSTRACTWe present a new image search and ranking algorithm for retrieving unannotated images by collaboratively mining online search results which consist of online imag...", "weights": [[8, 14, 1.0], [153, 159, 1.0], [192, 198, 1.0]]}, "2020.lrec-1.852": {"snippet": "...ling Quizzes Designed for Evaluation of Machine Reading Comprehension\n\n\n This paper describes our developing dataset of Japanese slot filling quizzes designed for evaluation of machine reading compreh...", "weights": [[8, 15, 1.0], [145, 152, 1.0]]}, "2014.wwwconf_conference-2014c.287": {"snippet": "...ent (quizzes, problems, interactive examples). The first part of the talk will review our exploration of a more traditional knowledge-based personalization approach [2; 3] and the second part will foc...", "weights": [[8, 15, 1.0]]}}}
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A limited comparison is made with evaluation in experts systems and Online Public Access Catalogs (O...", "weights": [[8, 18, 1.0], [137, 147, 1.0]]}, "2005.sigirconf_conference-2005.52": {"snippet": "Evaluation of resources for question answering evaluation\n\n\n ABSTRACTControlled and reproducible laboratory experiments, enabled by reusable test collections, represent a well-established methodology ...", "weights": [[0, 10, 1.0], [47, 57, 1.0]]}, "1992.trec_conference-1992.32": {"snippet": "Evaluation Issues", "weights": [[0, 10, 1.0]]}, "2014.sigirconf_workshop-2014medir.10": {"snippet": "... the Evaluation of Consumer Health Search Engines\n\n\n In this paper we propose a method that integrates the notion of understandability, as a factor of document relevance, into the evaluation of inform...", "weights": [[8, 18, 1.0], [183, 193, 1.0]]}, "1999.sigirjournals_journal-ir0volumeA33A1.5": {"snippet": "Evaluation of Web Document Retrieval: A SIGIR'99 Workshop", "weights": [[0, 10, 1.0]]}, "2013.clef_conference-2013.7": {"snippet": "...g an evaluation, key questions include: (i) why conduct the evaluation, (ii) what should be evaluated, and (iii) how the evaluation should be conducted. Over recent years there has been more focus on ...", "weights": [[8, 18, 1.0], [63, 73, 1.0], [124, 134, 1.0]]}, "2011.wwwconf_conference-2011c.152": {"snippet": "...odel evaluation performed between our contextual model, BM25, and language model shows that the model can be effective for contextual opinion retrieval such as faceted opinion retrieval.", "weights": [[8, 18, 1.0]]}, "2021.emnlp-main.589": {"snippet": "...atic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach\n\n\n Reliable automatic evaluation of dialogue systems under an interactive environment has long been overdue. An ideal env...", "weights": [[8, 18, 1.0], [62, 72, 1.0], [104, 114, 1.0]]}, "2005.sigirconf_conference-2005.42": {"snippet": "... the Evaluation of XML Retrieval (INEX) forms the basis for the evaluation.", "weights": [[8, 18, 1.0], [67, 77, 1.0]]}, "J84-1002": {"snippet": "...ance Evaluation of Natural Language Understanding Systems\n\n\n The task of evaluating the performance of a natural language understanding system, despite its largely recognized relevance, is still poorl...", "weights": [[8, 18, 1.0]]}, "2017.cikm_conference-2017.301": {"snippet": "...e. e evaluation of bias has recently been made possible through the use of retrievability, a quanti able measure of bias. While evaluating bias is relatively novel, the evaluation of performance has b...", "weights": [[8, 18, 1.0], [172, 182, 1.0]]}, "F12-1103": {"snippet": "Perception de la Langue fran{\\c{c}}aise Parl{\\'e}e Compl{\\'e}t{\\'e}e ({LPC}) et effet d{'}expertise chez les normo-entendants ({F}rench Cued Speech perception and expertise effect in hearing people) [...", "weights": []}, "C18-1294": {"snippet": "Investigating Productive and Receptive Knowledge: A Profile for Second Language Learning\n\n\n The literature frequently addresses the differences in receptive and productive vocabulary, but grammar is o...", "weights": []}, "2013.sigirjournals_journal-ir0volumeA47A2.0": {"snippet": "... the evaluation task. We outline advantages as well as disadvantages of this evaluation methodology, and discuss how the approach might be extended to other evaluation scenarios.", "weights": [[8, 18, 1.0], [80, 90, 1.0], [160, 170, 1.0]]}, "2003.cikm_conference-2003.67": {"snippet": "...stem evaluation\n\n\n ABSTRACTWe present a unified model which, given the ranked lists of documents returned by multiple retrieval systems in response to a given query, simultaneously solves the problems...", "weights": [[8, 18, 1.0]]}, "2011.wwwconf_conference-2011c.99": {"snippet": "Visual query system for analyzing social semantic web\n\n\n ABSTRACTThe social web is becoming increasingly popular and important, because it creates the collective intelligence, which can produce more v...", "weights": []}, "2010.clef_conference-2010.12": {"snippet": "Evaluating Information Extraction", "weights": []}, "2018.ecir_conference-2018.43": {"snippet": "...Meta-Evaluation of Evaluation Methods for Diversified Search\n\n\n Abstract. For the evaluation of diversified search results, a number of different methods have been proposed in the literature. Prior to...", "weights": [[8, 18, 1.0], [22, 32, 1.0], [85, 95, 1.0]]}, "C10-2130": {"snippet": "...eval Evaluation without Relevance Judgments\n\n\n Retrieval evaluation without relevance judgments is a hard but also very meaningful work. In this paper, we use clustering technique to improve the perfo...", "weights": [[8, 18, 1.0], [60, 70, 1.0]]}, "W12-2606": {"snippet": "...nual Evaluation of Summaries\n\n\n Today, automatic evaluation metrics such as ROUGE have become the de-facto mode of evaluating an automatic summarization system. However, based on the DUC and the TAC e...", "weights": [[8, 18, 1.0], [52, 62, 1.0], [202, 212, 1.0]]}, "2022.acl-long.240": {"snippet": "...h as evaluation using just 5% instances (selected via ILDAE) achieves as high as 0.93 Kendall correlation with evaluation using complete dataset and computing weighted accuracy using difficulty scores...", "weights": [[8, 18, 1.0], [114, 124, 1.0]]}, "W07-0713": {"snippet": "...uman Evaluation of Machine Translation Through Binary System Comparisons\n\n\n We introduce a novel evaluation scheme for the human evaluation of different machine translation systems. Our method is base...", "weights": [[8, 18, 1.0], [100, 110, 1.0], [132, 142, 1.0]]}, "2002.sigirconf_conference-2002.55": {"snippet": "...ical evaluation of the utility of thumbnail previews in web search results. Results pages were constructed to show text-only summaries, thumbnail previews only, or the combination of text summaries an...", "weights": [[8, 18, 1.0]]}, "2018.cikm_conference-2018.205": {"snippet": "...only evaluation measures ignore these aspects. We used synthetic and real data to compare our proposed framework, named MM, to the understandability-biased information evaluation (UBIRE), an existing ...", "weights": [[8, 18, 1.0], [171, 181, 1.0]]}, "1998.sigirconf_conference-98.40": {"snippet": "...with evaluation of interactive IR systems. The RR measure describes the degree of agreement between the types of relevance applied in evaluation of information retrieval (IR) systems in a non-binary a...", "weights": [[8, 18, 1.0], [137, 147, 1.0]]}, "2013.sigirconf_conference-2013.70": {"snippet": "...meta-evaluation tool whose use we demonstrate on understanding novelty and diversity evaluation; (2) a new evaluation measure, relevance information correlation, which is correlated with traditional e...", "weights": [[8, 18, 1.0], [88, 98, 1.0], [110, 120, 1.0], [202, 212, 1.0]]}, "2015.sigirconf_conference-2015.59": {"snippet": "...eval Evaluation", "weights": [[8, 18, 1.0]]}, "2014.sigirconf_conference-2014.209": {"snippet": "...line evaluation metrics, new online evaluation approaches, and improve the existing online evaluation methods. We also demonstrate how the offline evaluation methods can developed to be in agreement w...", "weights": [[8, 18, 1.0], [39, 49, 1.0], [94, 104, 1.0], [150, 160, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...l IR evaluation paradigm a list of query topics along with their relevance judgments are given. The main limitation of this kind of evaluation paradigm is that it focuses almost exclusively on a small...", "weights": [[8, 18, 1.0], [135, 145, 1.0]]}, "N07-3005": {"snippet": "...tion Evaluation Experiments\n\n\n The goals of my dissertation are: 1) to propose a French terminology for the presentation of evaluation results of automatic summaries, 2) to identify and describe exper...", "weights": [[8, 18, 1.0], [127, 137, 1.0]]}, "2004.ntcir_workshop-2004.19": {"snippet": "...tems evaluation remains based on the use of precision and recall (and related measures), thus hindering IR development and evaluation. We recently questioned these assumptions, and proposed a new meas...", "weights": [[8, 18, 1.0], [126, 136, 1.0]]}, "2005.sigirconf_conference-2005.59": {"snippet": "Modeling task-genre relationships for IR in the workplace\n\n\n ABSTRACTContext influences the search process, but to date research has not definitively identified which aspects of context are the most i...", "weights": []}, "1980.jasis_journal-ir0volumeA31A6.10": {"snippet": "...rnal evaluation", "weights": [[8, 18, 1.0]]}, "2014.sigirconf_conference-2014.5": {"snippet": "Temporal feedback for tweet search with non-parametric density estimation\n\n\n ABSTRACTThis paper investigates the temporal cluster hypothesis: in search tasks where time plays an important role, do rel...", "weights": []}, "2001.sigirconf_conference-2001.10": {"snippet": "Evaluation by Highly Relevant Documents\n\n\n ABSTRACTGiven the size of the w eb,the search engine industry has argued that engines should be evaluated by their ability t o retrieve highly relevant pages...", "weights": [[0, 10, 1.0]]}, "E06-1040": {"snippet": "...uman Evaluation of {NLG} Systems\n\n\n We consider the evaluation problem in Natural Language Generation (NLG) and present results for evaluating several NLG systems with similar functionality, including...", "weights": [[8, 18, 1.0], [55, 65, 1.0]]}, "W97-0601": {"snippet": "...nent Evaluation to Integrated System Evaluation\n\n\n This paper discusses the range of ways in which spoken dialogue system components have been evaluated and discusses approaches to evaluation that att...", "weights": [[8, 18, 1.0], [40, 50, 1.0], [184, 194, 1.0]]}, "2008.clef_workshop-2008w.79": {"snippet": "... the evaluation method and the quality of the assessments is sufficient enough for reliable evaluation. Unfortunately we have to conclude that the evaluation is not suitable. Therefore some alternativ...", "weights": [[8, 18, 1.0], [95, 105, 1.0], [150, 160, 1.0]]}, "2007.cikm_conference-2007.96": {"snippet": "...atic evaluation of retrieval systems using document similarities\n\n\n ABSTRACTTaking advantage of the well-known cluster hypothesis that \"closely associated documents tend to be relevant to the same req...", "weights": [[8, 18, 1.0]]}, "2013.sigirconf_conference-2013.82": {"snippet": "...ical evaluation of the effort users must exert to judge the relevance of document, investigating the effect of relevance level and document size. Results suggest that \"relevant\" documents require more...", "weights": [[8, 18, 1.0]]}, "2003.sigirconf_conference-2003.65": {"snippet": "...ient evaluation of retrieval systems via the hedge algorithm\n\n\n ABSTRACTWe present a unified framework for simultaneously solving both the pooling problem (the construction of efficient document pools...", "weights": [[8, 18, 1.0]]}, "W06-3007": {"snippet": "...ered Evaluation of Interactive Question Answering Systems\n\n\n We describe a large-scale evaluation of four interactive question answering system with real users. The purpose of the evaluation was to de...", "weights": [[8, 18, 1.0], [90, 100, 1.0], [183, 193, 1.0]]}, "C04-1072": {"snippet": "...atic Evaluation Metrics for Machine Translation\n\n\n Comparisons of automatic evaluation metrics for machine translation are usually conducted on corpus level using correlation statistics such as Pearso...", "weights": [[8, 18, 1.0], [79, 89, 1.0]]}, "2010.sigirconf_conference-2010.136": {"snippet": "..., in evaluation, full document retrieval has appeared competitive to focused XML retrieval. We analyze the density of relevance in documents, and show that in sparsely relevant documents focused retri...", "weights": [[8, 18, 1.0]]}, "2007.ntcir_workshop-2007evia.4": {"snippet": "...s to Evaluation of Multilingual Geographic Information Retrieval in GeoCLEF\n\n\n This is the third year of the evaluation of geographic information retrieval (GeoCLEF) within the Cross-Language Evaluati...", "weights": [[8, 18, 1.0], [112, 122, 1.0], [195, 205, 1.0]]}, "2021.cl-1.8": {"snippet": "Python for Linguists\n\n\n Teaching programming skills is a hard task. It is even harder if one targets an audience with no or little mathematical background. Although there are books on programming that...", "weights": []}, "2018.sigirconf_conference-2018.186": {"snippet": "...ased evaluation represents the standard of evaluation for information retrieval systems. Legal IR, more specifically case law retrieval, has no such standard test collection for evaluation. In this pa...", "weights": [[8, 18, 1.0], [46, 56, 1.0], [181, 191, 1.0]]}, "L18-1594": {"snippet": "...ding Evaluation Datasets for Cultural Microblog Retrieval\n\n\n ECLEF Microblog Cultural Contextualization is an evaluation challenge aiming at providing the research community with datasets to gather, o...", "weights": [[8, 18, 1.0], [113, 123, 1.0]]}, "J06-1001": {"snippet": "Squibs and Discussions: {W}ord{N}et Nouns: Classes and Instances\n\n\n WordNet, a lexical database for English that is extensively used by computational linguists, has not previously distinguished hypony...", "weights": []}, "2018.wwwconf_conference-2018c.315": {"snippet": "... the evaluation on query containment allows for a data-independent evaluation that is more informative than the usual syntactic evaluation. In addition, such evaluation modalities may take into accoun...", "weights": [[8, 18, 1.0], [70, 80, 1.0], [131, 141, 1.0], [161, 171, 1.0]]}, "2014.clef_conference-2014w.82": {"snippet": "...tion evaluation lab (NEWSREEL 2014). NEWSREEL targeted the evaluation of news recommendation algorithms in the form of a campaignstyle evaluation lab. Participants had the chance to apply two types of...", "weights": [[8, 18, 1.0], [62, 72, 1.0], [138, 148, 1.0]]}, "2008.ecir_conference-2008.43": {"snippet": "Evaluating Text Representations for Retrieval of the Best Group of Documents", "weights": []}, "2011.clef_workshop-2011w.81": {"snippet": "... the evaluation tailored to their unique characteristics? Moreover, can we distinguish different types of usage (e.g., the professionals versus the public) and how does this impact the evaluation?", "weights": [[8, 18, 1.0], [188, 198, 1.0]]}, "P06-2078": {"snippet": "...mary Evaluation Using Multiple Evaluation Results by a Manual Method\n\n\n To solve a problem of how to evaluate computer-produced summaries, a number of automatic and manual methods have been proposed. ...", "weights": [[8, 18, 1.0], [34, 44, 1.0]]}, "Y04-1032": {"snippet": "Three {E}nglish Learner Assistance Systems Using Automatic Paraphrasing Techniques\n\n\n We developed three systems based on automatic paraphrasing techniques to help English learners and English-languag...", "weights": []}, "2020.lrec-1.815": {"snippet": "{FAB}: The {F}rench Absolute Beginner Corpus for Pronunciation Training\n\n\n We introduce the French Absolute Beginner (FAB) speech corpus. The corpus is intended for the development and study of Comput...", "weights": []}, "W04-1014": {"snippet": "Evaluation Measures Considering Sentence Concatenation for Automatic Summarization by Sentence or Word Extraction\n\n\n Automatic summaries of text generated through sentence or word extraction has been ...", "weights": [[0, 10, 1.0]]}, "2003.mtsummit-papers.1": {"snippet": "...{MT} evaluation methods: {RED} vs.{BLEU}\n\n\n This paper experimentally compares two automatic evaluators, RED and BLEU, to determine how close the evaluation results of each automatic evaluator are to ...", "weights": [[8, 18, 1.0], [149, 159, 1.0]]}, "2007.ntcir_workshop-2007evia.9": {"snippet": "...eval Evaluation with Graded Relevance\n\n\n Large-scale information retrieval evaluation efforts such as TREC and NTCIR have tended to adhere to binary-relevance evaluation metrics, even when graded rele...", "weights": [[8, 18, 1.0], [78, 88, 1.0], [162, 172, 1.0]]}, "2015.sigirconf_conference-2015.110": {"snippet": "... two evaluation metrics: Distributional Overlap (DO), which compares the clustering of scores of relevant and non-relevant documents, and Histogram Slope Analysis (HSA), which examines the log of the ...", "weights": [[8, 18, 1.0]]}, "D07-1035": {"snippet": "...ting evaluation standard of product reviews are discovered. To assess the quality of product reviews, a set of specifications for judging the quality of reviews is first defined. A classificationbased...", "weights": [[8, 18, 1.0]]}, "2016.clef_conference-2016w.59": {"snippet": "...eval Evaluation\n\n\n The Living Labs for Information Retrieval Evaluation (LL4IR) initiative have provided a novel framework for evaluating retrieval models that involve real users. In this position pap...", "weights": [[8, 18, 1.0], [64, 74, 1.0]]}, "W09-4408": {"snippet": "...g an evaluation scheme for definition extraction (DE) in terminology. The premise is that not just any information is relevant to defining a given concept in a given expert domain. Therefore, evaluati...", "weights": [[8, 18, 1.0], [195, 205, 1.0]]}, "2007.sigirconf_conference-2007.120": {"snippet": "Evaluating relevant in context: document retrieval with a twist\n\n\n ABSTRACTThe Relevant in Context retrieval task is document or article retrieval with a twist, where not only the relevant articles sh...", "weights": []}, "2010.sigirconf_conference-2010.177": {"snippet": "...stem evaluation: automatic evaluation versus incomplete judgments\n\n\n ABSTRACTIn information retrieval (IR), research aiming to reduce the cost of retrieval system evaluations has been conducted along ...", "weights": [[8, 18, 1.0], [30, 40, 1.0]]}, "2013.sigirconf_conference-2013.43": {"snippet": "...ased evaluation can then take this user profile information into account to better model utility to the space of users.In this work, we propose an evaluation framework that not only can consider impli...", "weights": [[8, 18, 1.0], [150, 160, 1.0]]}, "O17-1008": {"snippet": "\u624b\u6a5f\u5e73\u53f0 {APP} \u4e4b\u56db\u7e23\u5ba2\u8a9e\u8f38\u5165\u6cd5\u7684\u7814\u767c (Research and Implementation of {S}ixian {H}akka {P}inyin Input Method for Mobile Cell {APP}) [In {C}hinese]\n\n\n The proposal scheme called Hakka pinyin input method is based on ...", "weights": []}, "2013.mtsummit-papers.17": {"snippet": "Meta-Evaluation of a Diagnostic Quality Metric for Machine Translation\n\n\n Diagnostic evaluation of machine translation (MT) is an approach to evaluation that provides finer-grained information compare...", "weights": [[5, 15, 1.0], [85, 95, 1.0], [142, 152, 1.0]]}, "2010.tois_journal-ir0volumeA28A2.4": {"snippet": "...cts' evaluation ratings and resulted in subjects using a greater range of evaluation scores.", "weights": [[8, 18, 1.0], [77, 87, 1.0]]}, "2021.humeval-1.10": {"snippet": "...uman evaluation. As a result, there is a lack of best practices for reliable human summarization evaluation grounded by empirical evidence. To investigate human evaluation reliability, we conduct a se...", "weights": [[8, 18, 1.0], [100, 110, 1.0], [164, 174, 1.0]]}, "J06-1006": {"snippet": "Introducing Speech and Language Processing, by John Coleman\n\n\n a group of multidisciplinary researchers convened at the Symposium on Next Generation Automatic Speech Recognition (ASR) to consider new ...", "weights": []}, "W03-2808": {"snippet": "...racy evaluation", "weights": [[8, 18, 1.0]]}, "2020.inlg-1.23": {"snippet": "...uman Evaluation: {NLG} Needs Evaluation Sheets and Standardised Definitions\n\n\n Human assessment remains the most trusted form of evaluation in NLG, but highly diverse approaches and a proliferation of...", "weights": [[8, 18, 1.0], [32, 42, 1.0], [132, 142, 1.0]]}, "W18-3721": {"snippet": "...ing. Evaluation results show that heterogeneous features perform better than each individual type of features.", "weights": [[8, 18, 1.0]]}, "1998.amta-tutorials.1": {"snippet": "{MT} evaluation", "weights": [[5, 15, 1.0]]}, "L16-1306": {"snippet": "...ding Evaluation Datasets for Consumer-Oriented Information Retrieval\n\n\n Common people often experience difficulties in accessing relevant, correct, accurate and understandable health information onlin...", "weights": [[8, 18, 1.0]]}, "2011.cikm_conference-2011.165": {"snippet": "...fair evaluation. Typically, publications glorify the high quality of one approach only justified by an arbitrary evaluation measure. However, such conclusions can only be drawn if the evaluation measu...", "weights": [[8, 18, 1.0], [116, 126, 1.0], [187, 197, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.22": {"snippet": "...ting Evaluation Measure Stability", "weights": [[8, 18, 1.0]]}, "2006.sigirconf_conference-2006.35": {"snippet": "Evaluation in (XML) information retrieval: expected precision-recall with user modelling (EPRUM)\n\n\n ABSTRACTStandard Information Retrieval (IR) metrics assume a simple model where documents are unders...", "weights": [[0, 10, 1.0]]}, "2010.gir_workshop-2010.6": {"snippet": "Evaluation of georeferencing", "weights": [[0, 10, 1.0]]}, "2004.sigirconf_conference-2004.5": {"snippet": "...eval evaluation with incomplete information\n\n\n ABSTRACTThis paper examines whether the Cranfield evaluation methodology is robust to gross violations of the completeness assumption (i.e., the assumpti...", "weights": [[8, 18, 1.0], [100, 110, 1.0]]}, "F13-2030": {"snippet": "An iterative topic segmentation algorithm with intra-content term weighting (Segmentation th{\\'e}matique : processus it{\\'e}ratif de pond{\\'e}ration intra-contenu) [in {F}rench]\n\n\n %&'&()%*&+,$'&(-.\"*...", "weights": []}, "2020.inlg-1.24": {"snippet": "...uman Evaluation Methods: A Classification System to Support Comparability, Meta-Evaluation and Reproducibility Testing\n\n\n Current standards for designing and reporting human evaluations in NLP mean it...", "weights": [[8, 18, 1.0], [83, 93, 1.0]]}, "H94-1017": {"snippet": "...uage Evaluation\n\n\n This session focused on experimental or planned approaches to human language technology evaluation and included an overview and five papers: two papers on experimental evaluation ap...", "weights": [[8, 18, 1.0], [110, 120, 1.0], [190, 200, 1.0]]}, "2021.acl-long.517": {"snippet": "...main evaluation sets. Finally, we provide a qualitative analysis of adversarial (vs standard) data, identifying key differences and offering guidance for future research. 1 Evaluation set \u2192 BERT foole...", "weights": [[8, 18, 1.0], [176, 186, 1.0]]}, "2008.cikm_workshop-2008wicow.17": {"snippet": "...tion evaluation are identified. Finally the paper introduces an Information Evaluation Model (IEM) that uses the identified information characteristics as the assessment criteria to judge the value of", "weights": [[8, 18, 1.0], [79, 89, 1.0]]}, "2020.lrec-1.231": {"snippet": "...s of evaluation methods: intrinsic evaluation and extrinsic evaluation. Word analogy and word relatedness evaluations were performed in terms of intrinsic evaluation, while sentiment analysis and part...", "weights": [[8, 18, 1.0], [38, 48, 1.0], [63, 73, 1.0], [158, 168, 1.0]]}, "2005.mtsummit-invited.7": {"snippet": "...uman evaluation and automatic evaluation are conducted in HTRDP evaluation. In recent years, the evaluation was organized jointly with NICT of Japan. This paper introduces some details of this evaluat", "weights": [[8, 18, 1.0], [33, 43, 1.0], [67, 77, 1.0], [100, 110, 1.0], [196, 206, 1.0]]}, "C10-1065": {"snippet": "...dard evaluation metrics, we adapt various evaluation metrics developed for machine translation and summarization, and also the R-precision evaluation metric from keyphrase evaluation. In evaluation, t...", "weights": [[8, 18, 1.0], [45, 55, 1.0], [142, 152, 1.0], [175, 185, 1.0], [190, 200, 1.0]]}, "2005.mir_workshop-2005.30": {"snippet": "Evaluation strategies for image understanding and retrieval", "weights": [[0, 10, 1.0]]}, "2011.sigirconf_conference-2011.30": {"snippet": "... and evaluation cycle and acquiring reliable evaluation results on tail queries. In this paper, we propose a reordering framework for efficient evaluation problem in the context of clickthrough based ...", "weights": [[8, 18, 1.0], [48, 58, 1.0], [147, 157, 1.0]]}, "2021.gem-1.6": {"snippet": "...uman Evaluation for Style Transfer\n\n\n This paper reviews and summarizes human evaluation practices described in 97 style transfer papers with respect to three main evaluation aspects: style transfer, ...", "weights": [[8, 18, 1.0], [81, 91, 1.0], [167, 177, 1.0]]}, "2014.cikm_conference-2014.87": {"snippet": "Generative Modeling of Entity Comparisons in Text\n\n\n ABSTRACTUsers frequently rely on online reviews for decision making. In addition to allowing users to evaluate the quality of individual products, ...", "weights": []}, "2004.cikm_conference-2004.59": {"snippet": "... the evaluation methodology for flat text retrieval approaches is well established, the evaluation of XML retrieval approaches is a research issue. This is because XML documents are composed of nested...", "weights": [[8, 18, 1.0], [91, 101, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.11": {"snippet": "Exploring topic structure: coherence, diversity and relatedness\n\n\n AbstractThe use of topical information has long been studied in the context of information retrieval. For example, grouping search re...", "weights": []}, "2017.cikm_conference-2017.9": {"snippet": "...eval Evaluation\n\n\n ABSTRACTEvaluation is crucial in Information Retrieval. e development of models, tools and methods has signi cantly bene ted from the availability of reusable test collections forme...", "weights": [[8, 18, 1.0]]}, "2021.acl-long.565": {"snippet": "...uman Evaluation of Generated Text\n\n\n Human evaluations are typically considered the gold standard in natural language generation, but as models' fluency improves, how well can evaluators detect and ju...", "weights": [[8, 18, 1.0]]}, "1986.jasis_journal-ir0volumeA37A5.9": {"snippet": "...n of evaluation", "weights": [[8, 18, 1.0]]}, "2020.acl-main.568": {"snippet": "...onse Evaluation Model\n\n\n Automatic evaluation of open-domain dialogue response generation is very challenging because there are many appropriate responses for a given context. Existing evaluation mode...", "weights": [[8, 18, 1.0], [38, 48, 1.0], [188, 198, 1.0]]}, "W07-1515": {"snippet": "...e of evaluation, developed within the tradition of systemic functional linguistics. The framework describes a taxonomy of the types of language used to convey evaluation and position oneself with resp...", "weights": [[8, 18, 1.0], [162, 172, 1.0]]}, "W06-1419": {"snippet": "...tive evaluation of systems, looking at the systems' performances. It is thus a \"systemoriented\" view of evaluation. We argue here that, if we are to take a system oriented view of evaluation, the comm...", "weights": [[8, 18, 1.0], [107, 117, 1.0], [183, 193, 1.0]]}, "2006.sigirconf_conference-2006.36": {"snippet": "...eval evaluation\n\n\n ABSTRACTAccurate estimation of information retrieval evaluation metrics such as average precision require large sets of relevance judgments. Building sets large enough for evaluatio...", "weights": [[8, 18, 1.0], [75, 85, 1.0], [194, 204, 1.0]]}, "W09-2419": {"snippet": "... the evaluation setting for the SemEval-2010 Word Sense Induction (WSI) task. The setting of the SemEval-2007 WSI task consists of two evaluation schemes, i.e. unsupervised evaluation and supervised e...", "weights": [[8, 18, 1.0], [138, 148, 1.0], [176, 186, 1.0], [202, 212, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...CLIR evaluation and makes a proposal as to how an evaluation of this type could be included in CLEF. Jones also supports the extension of evaluation exercises in order to assess the usefulness of tech...", "weights": [[8, 18, 1.0], [53, 63, 1.0], [141, 151, 1.0]]}, "2015.tois_journal-ir0volumeA33A1.3": {"snippet": "...ased evaluation, we introduce program theory as the evaluation framework. 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In 1992 a new test collection was built at the National Institute of Standards and Technology (NIST) as part of the TIPSTER projec...", "weights": [[8, 18, 1.0], [29, 39, 1.0]]}, "1983.ipm_journal-ir0volumeA19A3.3": {"snippet": "...ment evaluation as a feedback in on-line information retrieval", "weights": [[8, 18, 1.0]]}, "2021.ccl-1.22": {"snippet": "...ding Evaluation Set)\n\n\n Text proofreading evaluation set is the basis of spelling research, including traditional text proofreading evaluation set and standard text proofreading evaluation set. 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It is argue...", "weights": [[3, 13, 1.0], [94, 104, 1.0]]}, "2021.naacl-main.244": {"snippet": "Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification\n\n\n Text classification is a significant branch of natural language processing, and has many ap...", "weights": []}, "2018.chiir_conference-2018.70": {"snippet": "...p on Evaluation of Personalisation in Information Retrieval\n\n\n ABSTRACTThe purpose of the WEPIR 2018 workshop is to bring together researchers from different backgrounds, interested in advancing the e...", "weights": [[8, 18, 1.0], [202, 212, 1.0]]}, "2021.eancs-1.3": {"snippet": "...alog Evaluation Metrics\n\n\n Automatic evaluation metrics are a crucial component of dialog systems research. Standard language evaluation metrics are known to be ineffective for evaluating dialog. As s...", "weights": [[8, 18, 1.0], [40, 50, 1.0], [129, 139, 1.0]]}}}
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In this paper, we propose to explore multiple pairwise relationships between documents in a learning setting to rerank sea...", "weights": [[8, 19, 1.0], [173, 181, 1.0]]}, "2006.coria_conference-2006.2": {"snippet": "...e of Machine Learning in Information Retrieval Research", "weights": [[8, 15, 1.0], [16, 24, 1.0], [28, 39, 1.0], [40, 49, 1.0]]}, "2017.sigirconf_conference-2017.231": {"snippet": "... and retrieval frameworks that are able to e ciently answer most queries. However, the current literature generally focuses on improving the mean or median query processing time in a given system. In ...", "weights": [[8, 17, 1.0]]}, "2005.sigirconf_conference-2005.9": {"snippet": "... hoc information retrieval\n\n\n ABSTRACTInformation retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information nee...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 62, 1.0], [164, 173, 1.0], [188, 199, 1.0]]}, "2016.sigirconf_workshop-2016sal.11": {"snippet": "Retrieval Techniques for Contextual Learning\n\n\n Following constructivist models of contextual learning, knowledge acquisition goes beyond mere absorption of isolated facts, and, instead is enabled, st...", "weights": [[0, 9, 1.0], [36, 44, 1.0], [94, 102, 1.0]]}, "2005.cikm_conference-2005.105": {"snippet": "...se a machine learning process to filter out the undesirable pages. The process is shown experimentally to be very effective. Since obtaining training data for the machine learning process can be time ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [166, 173, 1.0], [174, 182, 1.0]]}, "2017.wsdm_conference-2017.57": {"snippet": "Machine Learning at Amazon\n\n\n ABSTRACTIn this talk I will give an introduction into the field of machine learning and discuss why it is a crucial technology for Amazon.Machine learning is the science ...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [97, 104, 1.0], [105, 113, 1.0], [168, 175, 1.0], [176, 184, 1.0]]}, "2003.ir_journal-ir0volumeA6A34.1": {"snippet": "...ying Machine Learning to Text Segmentation for Information Retrieval", "weights": [[8, 15, 1.0], [16, 24, 1.0], [50, 61, 1.0], [62, 71, 1.0]]}, "1989.sigirconf_conference-89.3": {"snippet": "...tive Information Retrieval: Using a Connectionist Representation to Retrieve and Learn About Documents\n\n\n AbstractAIR represents a connectionist approach to the task of information retrieval. The syst...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [172, 183, 1.0], [184, 193, 1.0]]}, "N06-1051": {"snippet": "A Machine Learning based Approach to Evaluating Retrieval Systems\n\n\n Test collections are essential to evaluate Information Retrieval (IR) systems. The relevance assessment set has been recognized as ...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [48, 57, 1.0], [112, 123, 1.0], [124, 133, 1.0]]}, "2006.sigirconf_conference-2006.145": {"snippet": "...tual information retrieval\n\n\n ABSTRACTWe present an extensible java-based platform for contextual retrieval based on the probabilistic information retrieval model. Modules for dual indexes, relevance ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [101, 110, 1.0], [138, 149, 1.0], [150, 159, 1.0]]}, "1989.ipm_journal-ir0volumeA25A3.3": {"snippet": "...n by machine learning from retrieval sessions", "weights": [[8, 15, 1.0], [16, 24, 1.0], [30, 39, 1.0]]}, "2020.wsdm_conference-2020.84": {"snippet": "...ting learning to rank models for information retrieval are trained based on explicit or implicit query-document relevance information. In this paper, we study the task of learning a retrieval model ba...", "weights": [[8, 16, 1.0], [36, 47, 1.0], [48, 57, 1.0], [125, 136, 1.0], [174, 182, 1.0], [185, 194, 1.0]]}, "W02-2105": {"snippet": "...m: A Machine-learned Generation Module\n\n\n We present an overview of Amalgam, a sentence realization module that combines machine-learned and knowledgeengineered components to produce natural language ...", "weights": [[8, 15, 1.0], [124, 131, 1.0]]}, "2018.sigirconf_workshop-2018profs.3": {"snippet": "...call information retrieval is crucial to tasks such as electronic discovery and systematic review. Continuous Active Learning (CAL) is a technique where a human reviewer works in loop with a machine l...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [120, 128, 1.0], [194, 201, 1.0], [202, 210, 1.0]]}, "2013.ipm_journal-ir0volumeA49A4.14": {"snippet": "...Tree Learning as an appropriate machine learning method and creates rules from the results of this learning method. The second step extracts informative content using rules obtained from the first ste...", "weights": [[8, 16, 1.0], [35, 42, 1.0], [43, 51, 1.0], [102, 110, 1.0]]}, "W11-4523": {"snippet": "...able information for different NLP tasks. In the particular case of social media texts, their special characteristics like the presence of emoticons, slang or colloquial words can be used for obtainin...", "weights": [[8, 19, 1.0]]}, "2007.ipm_journal-ir0volumeA43A3.1": {"snippet": "Information retrieval and machine learning for probabilistic schema matching", "weights": [[0, 11, 1.0], [12, 21, 1.0], [26, 33, 1.0], [34, 42, 1.0]]}, "2020.sigirconf_conference-2020.367": {"snippet": "...ment Learning for Information Retrieval: Fundamentals and Advances\n\n\n Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users' information needs ...", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0], [73, 84, 1.0], [85, 94, 1.0], [185, 196, 1.0]]}, "2018.sigirconf_conference-2018.198": {"snippet": "...ntal Information Retrieval Pipelines\n\n\n ABSTRACTExperimentation using information retrieval (IR) systems has traditionally been a procedural and laborious process. Queries must be run on an index, wit...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [73, 84, 1.0], [85, 94, 1.0]]}, "2015.wsdm_conference-2015.58": {"snippet": "... the information needs of end users continue being refined. One of the emerging trends is using vertical search intent. For example, a user may want to find a restaurant near her current location; ano...", "weights": [[8, 19, 1.0]]}, "2014.cikm_conference-2014.6": {"snippet": "Machine-Assisted Search Preference Evaluation\n\n\n ABSTRACTInformation Retrieval systems are traditionally evaluated using the relevance of web pages to individual queries. Other work on IR evaluation h...", "weights": [[0, 7, 1.0], [69, 78, 1.0]]}, "2005.cikm_conference-2005.70": {"snippet": "Information retrieval and machine learning for probabilistic schema matching", "weights": [[0, 11, 1.0], [12, 21, 1.0], [26, 33, 1.0], [34, 42, 1.0]]}, "2007.sigirconf_conference-2007.212": {"snippet": "...tive information retrieval system?\n\n\n ABSTRACTThis research explores the relationship between Information Retrieval (IR) systems' effectiveness and users' performance (accuracy and speed) and their sa...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "2018.sigirconf_conference-2018.168": {"snippet": "...ms \u2192 Information retrieval \u2192 Query log analysis \u2022 Human computer interaction \u2192 User models; User studies KEYWORDS information seeking behavior; information resumption; crossdevice search; information ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [117, 128, 1.0], [147, 158, 1.0], [191, 202, 1.0]]}, "L16-1312": {"snippet": "A Machine Learning based Music Retrieval and Recommendation System\n\n\n In this paper, we present a music retrieval and recommendation system using machine learning techniques. We propose a query by hum...", "weights": [[2, 9, 1.0], [10, 18, 1.0], [31, 40, 1.0], [104, 113, 1.0], [146, 153, 1.0], [154, 162, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A3.0": {"snippet": "...ng \u00b7 Information retrieval \u00b7 DB-IR integration \u00b7 Odysseus\nDB-IR integrationDatabases (DB) and information retrieval (IR) have been parallel universes. However, many recent applications such as custome...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "2007.sigirconf_conference-2007.39": {"snippet": "... for learning ranking functions for improving relevance of search engines serving diverse streams of user queries. We explore supervised learning methodology from machine learning, and we distinguish ...", "weights": [[8, 16, 1.0], [140, 148, 1.0], [166, 173, 1.0], [174, 182, 1.0]]}, "H05-1020": {"snippet": "...ased Learning for Information Retrieval\n\n\n We approached the problem as learning how to order documents by estimated relevance with respect to a user query. Our support vector machines based classifie...", "weights": [[8, 16, 1.0], [21, 32, 1.0], [33, 42, 1.0], [75, 83, 1.0]]}, "2008.sigirconf_conference-2008.35": {"snippet": "Learning to rank with SoftRank and Gaussian processes\n\n\n ABSTRACTIn this paper we address the issue of learning to rank for document retrieval using Thurstonian models based on sparse Gaussian process...", "weights": [[0, 8, 1.0], [103, 111, 1.0], [133, 142, 1.0]]}, "2017.wsdm_conference-2017.103": {"snippet": "...ting Information Retrieval to User Signals via Stochastic Models\n\n\n ABSTRACTTo address the challenge of adapting Information Retrieval (IR) to the constantly evolving user tasks and needs and to adjus...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [116, 127, 1.0], [128, 137, 1.0]]}, "2017.trec_conference-2017.62": {"snippet": "...call retrieval system for assessors to e ciently nd relevant documents within a limited period of time. e system consists of an AutoTAR Continuous Active Learning (CAL) system based on paragraph/docum...", "weights": [[8, 17, 1.0], [157, 165, 1.0]]}, "P13-1050": {"snippet": "... hoc information retrieval (IR) use paths for document ranking due to the prohibitive cost of parsing a retrieval collection. In this paper, we introduce a flexible notion of paths that describe chain...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [107, 116, 1.0]]}, "2020.sigirconf_conference-2020.5": {"snippet": "...Deep Learning Works for Information Retrieval\n\n\n Information retrieval (IR) is the science of search, the search of user query relevant pieces of information from a collection of unstructured resource...", "weights": [[8, 16, 1.0], [27, 38, 1.0], [39, 48, 1.0], [52, 63, 1.0], [64, 73, 1.0], [149, 160, 1.0]]}, "2010.wsdm_conference-2010.16": {"snippet": "...rch, machine learning, and information retrieval. Recent work on ranking focused on a number of different paradigms, namely, pointwise, pairwise, and list-wise approaches. Each of those paradigms focu...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [30, 41, 1.0], [42, 51, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "....3.3 Information Search and Retrieval -relevance feedback\nGeneral TermsAlgorithms, Performance, Experimentation\nKeywordsRelevance Feedback, Web Search, Evaluation\n1.INTRODUCTIONRelevance feedback is a...", "weights": [[8, 19, 1.0], [31, 40, 1.0]]}, "2017.ictir_conference-2017.57": {"snippet": "...s of information retrieval, such as online learning to rank, interactive information retrieval or dynamic information retrieval. ese Permission to make digital or hard copies of part or all of this wo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 54, 1.0], [76, 87, 1.0], [88, 97, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "2013.ipm_journal-ir0volumeA49A5.5": {"snippet": "...uter information retrieval\n\n\n EditorialIntroduction to special issue on human-computer information retrieval Contemporary search engines are optimized for look-up scenarios where the information targe...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [90, 101, 1.0], [102, 111, 1.0], [186, 197, 1.0]]}, "2008.sigirconf_conference-2008.37": {"snippet": "Learning to rank with ties\n\n\n ABSTRACTDesigning effective ranking functions is a core problem for information retrieval and Web search since the ranking functions directly impact the relevance of the ...", "weights": [[0, 8, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "2008.trec_conference-2008.1": {"snippet": "... and machine learning approach for estimating and ranking the opinionated documents. For the Polarized Opinion Retrieval subtask, we employ machine learning for predicting the polarity and linear comb...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [114, 123, 1.0], [143, 150, 1.0], [151, 159, 1.0]]}, "2018.cikm_conference-2018.22": {"snippet": "... for information retrieval tasks. The effectiveness of current methods for high-recall information retrieval is limited by their reliance on human input, either to generate queries, or to assess the r...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [90, 101, 1.0], [102, 111, 1.0]]}, "2011.sigirconf_conference-2011.220": {"snippet": "...lick information.Yisong Yue is a postdoctoral researcher in the Machine Learning Department and the Heinz College at Carnegie Mellon University, where he works on machine learning approaches to inform...", "weights": [[8, 19, 1.0], [67, 74, 1.0], [75, 83, 1.0], [166, 173, 1.0], [174, 182, 1.0], [197, 208, 1.0]]}, "2007.ipm_journal-ir0volumeA43A5.3": {"snippet": "...eral machine learning algorithms for cross-lan7guage patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, ...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [63, 72, 1.0], [144, 151, 1.0], [152, 160, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "2009.cikm_conference-2009.247": {"snippet": "...lick information no better than a linear function of the field attributes. We also find query click information to be the single most important field for retrieval. In response, we develop a machine l...", "weights": [[8, 19, 1.0], [103, 114, 1.0], [157, 166, 1.0], [194, 201, 1.0], [202, 210, 1.0]]}, "2010.cikm_conference-2010.181": {"snippet": "...ized information retrieval\n\n\n ABSTRACTIndividuals often use search engines to return to web pages they have previously visited. This behaviour, called refinding, accounts for about 38% of all queries....", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2005.clef_workshop-2005w.119": {"snippet": "... All retrieval results rely heavily on two retrieval systems: for visual retrieval we use the GNU Image Finding Tool (GIFT), and for textual retrieval the EasyIR retrieval system. For the ad-hoc retri...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [76, 85, 1.0], [144, 153, 1.0], [165, 174, 1.0], [198, 207, 1.0]]}, "2012.wsdm_conference-2012.84": {"snippet": "Machine learning for query-document matching in search", "weights": [[0, 7, 1.0], [8, 16, 1.0]]}, "2018.sigirconf_conference-2018.150": {"snippet": "...t of machine learning technologies. Recently, within the information retrieval field, a number of neural ranking frameworks have been proposed to address the ad-hoc search. These models usually need a...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [60, 71, 1.0], [72, 81, 1.0]]}, "2018.desires_conference-2018.13": {"snippet": "Retrieval and Richness when Querying by Document\n\n\n A single relevant document can be viewed as a long query for ad hoc retrieval, or a tiny training set for supervised learning. We tested techniques ...", "weights": [[0, 9, 1.0], [120, 129, 1.0], [169, 177, 1.0]]}, "2020.sigirconf_conference-2020.375": {"snippet": "... and Retrieval with Neural Networks\n\n\n Relevance in ad-hoc retrieval is a fundamental problem of text understanding. Developing neural network methods for this foundational task of Information Retriev...", "weights": [[8, 17, 1.0], [62, 71, 1.0], [184, 195, 1.0], [196, 205, 1.0]]}, "2012.sigirconf_conference-2012.5": {"snippet": "...se a machine learning approach to learn the probability that a user may find a follow-up query both useful and relevant, given his initial query. Our approach is based on a machine learning model whic...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [176, 183, 1.0], [184, 192, 1.0]]}, "2018.cikm_conference-2018.68": {"snippet": "...task Learning of Information Retrieval and Reading Comprehension\n\n\n ABSTRACTThis study considers the task of machine reading at scale (MRS) wherein, given a question, a system first performs the infor...", "weights": [[8, 16, 1.0], [20, 31, 1.0], [32, 41, 1.0], [112, 119, 1.0], [198, 209, 1.0]]}, "2018.sigirconf_conference-2018.219": {"snippet": "...sly, machine learning methods have been exploited to address the problem, which learns a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning h...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [150, 158, 1.0], [193, 201, 1.0]]}, "2006.ecir_conference-2006.30": {"snippet": "Machine Learning Ranking for Structured Information Retrieval\n\n\n Abstract. We consider the Structured Information Retrieval task which consists in ranking nested textual units according to their relev...", "weights": [[0, 7, 1.0], [8, 16, 1.0], [40, 51, 1.0], [52, 61, 1.0], [102, 113, 1.0], [114, 123, 1.0]]}, "W19-2603": {"snippet": "...Deep Learning vs. Linguistically-informed Methods\n\n\n An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing w...", "weights": [[8, 16, 1.0], [81, 88, 1.0]]}, "2011.sigirconf_conference-2011.12": {"snippet": "...se a machine-learning framework for simultaneously optimizing freshness and relevance, in which the trade-off is automatically adaptive to query temporal characteristics. We start by illustrating diff...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2017.sigirconf_conference-2017.141": {"snippet": "...line Learning to Rank for Cross-Language Information Retrieval\n\n\n ABSTRACTOnline learning to rank for information retrieval has shown great promise in optimization of Web search results based on user ...", "weights": [[8, 16, 1.0], [44, 55, 1.0], [56, 65, 1.0], [84, 92, 1.0], [105, 116, 1.0], [117, 126, 1.0]]}, "N09-1060": {"snippet": "...h as information retrieval. We propose a new method of computing term specificity, based on modeling the rate of learning of word meaning in Latent Semantic Analysis (LSA). We analyze the performance ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [116, 124, 1.0]]}, "2012.wsdm_conference-2012.45": {"snippet": "...d by machine learning algorithms using either direct human relevance judgments or indirect judgments obtained from click-through data from millions of users. The rankings are thus optimized to this ge...", "weights": [[8, 15, 1.0], [16, 24, 1.0]]}, "2021.ecir_conference-20212.34": {"snippet": "... for Learning to Rank\n\n\n LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items. In the past few years...", "weights": [[8, 16, 1.0], [28, 36, 1.0], [88, 99, 1.0], [100, 109, 1.0], [121, 128, 1.0], [129, 137, 1.0]]}, "1999.mtsummit-1.45": {"snippet": "...gual information retrieval, summarization, and translation system. MuST integrates machine translation and other text processing services to enable users to perform cross-language information retrieva...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [86, 93, 1.0], [183, 194, 1.0], [195, 204, 1.0]]}, "2010.cikm_conference-2010.162": {"snippet": "...evel information to improve web search\n\n\n ABSTRACTRanking Web search results has long evolved beyond simple bag-of-words retrieval models. Modern search engines routinely employ machine learning ranki...", "weights": [[8, 19, 1.0], [124, 133, 1.0], [181, 188, 1.0], [189, 197, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.23": {"snippet": "... for information retrieval\n\n\n AbstractMany machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieva...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [46, 53, 1.0], [54, 62, 1.0], [183, 194, 1.0], [195, 204, 1.0]]}, "2012.sigirconf_conference-2012.171": {"snippet": "Learning to select a time-aware retrieval model\n\n\n ABSTRACTTime-aware retrieval models exploit one of two time dimensions, namely, (a) publication time or (b) content time (temporal expressions mentio...", "weights": [[0, 8, 1.0], [32, 41, 1.0], [70, 79, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2018.wsdm_conference-2018.111": {"snippet": "...ding information retrieval and exploration. With improved speech recognition and information retrieval systems, more Permission to make digital or hard copies of part or all of this work for personal ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [84, 95, 1.0], [96, 105, 1.0]]}, "2009.ipm_journal-ir0volumeA45A3.2": {"snippet": "... new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSRANK, aims to use the advantages of both the traditional Information Retrieval...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [76, 85, 1.0], [182, 193, 1.0], [194, 203, 1.0]]}, "2018.sigirconf_conference-2018.224": {"snippet": "...n in Information Retrieval: SIGIR 2018 Half-Day Tutorial\n\n\n ABSTRACTFusion is an important and central concept in Information Retrieval. The goal of fusion methods is to merge different sources of inf...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [117, 128, 1.0], [129, 138, 1.0], [200, 211, 1.0]]}, "2016.clef_conference-2016w.106": {"snippet": "...ited machine learning techniques to rank query terms and assign an appropriate weight to each one before applying a probabilistic information retrieval model (BM15). Thereafter, only the top-k terms a...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2011.sigirconf_conference-2011.229": {"snippet": "...gual information retrieval\n\n\n AbstractA general characteristic of Information Retrieval (IR) and Multilingual IR (MIR)  systems is that if the same query was submitted by different users, the system w...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [69, 80, 1.0], [81, 90, 1.0]]}, "I08-1068": {"snippet": "...that learning these user profiles using snippets surrounding the results for a query gives better performance than learning from entire document collection.", "weights": [[8, 16, 1.0], [118, 126, 1.0]]}, "2012.tois_journal-ir0volumeA30A4.0": {"snippet": "...line Learning Framework for Refining Recency Search Results with User Click Feedback\n\n\n Traditional machine-learned ranking systems for Web search are often trained to capture stationary relevance of ...", "weights": [[8, 16, 1.0], [103, 110, 1.0]]}, "2019.wsdm_conference-2019.113": {"snippet": "...sly, machine learning methods have been exploited to address the problem, which learn a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning ha...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [149, 157, 1.0], [192, 200, 1.0]]}, "2015.tist_journal-ir0volumeA7A1.0": {"snippet": "Learning to Rank from Noisy Data\n\n\n Learning to rank, which learns the ranking function from training data, has become an emerging research area in information retrieval and machine learning. Most exi...", "weights": [[0, 8, 1.0], [36, 44, 1.0], [148, 159, 1.0], [160, 169, 1.0], [174, 181, 1.0], [182, 190, 1.0]]}, "2018.sigirconf_conference-2018.242": {"snippet": "...s in Machine Learned Models for Information Retrieval", "weights": [[8, 15, 1.0], [35, 46, 1.0], [47, 56, 1.0]]}, "2010.cikm_conference-2010.189": {"snippet": "...line learning for recency search ranking using real-time user feedback\n\n\n ABSTRACTTraditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance...", "weights": [[8, 16, 1.0], [97, 104, 1.0]]}, "2011.ecir_conference-2011.73": {"snippet": "...ying Machine Learning Diversity Metrics to Data Fusion in Information Retrieval", "weights": [[8, 15, 1.0], [16, 24, 1.0], [61, 72, 1.0], [73, 82, 1.0]]}, "2018.sigirconf_conference-2018.222": {"snippet": "...e of information retrieval and machine learning, the target audience of this tutorial is sufficiently broad. And the main objective of this tutorial is to encourage the technical communications betwee...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 41, 1.0], [42, 50, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "...s in machine learning, and will present recent developments in machine learning and its application to information retrieval. Each season is self-contained.\nSession 1: Core Learning Technologies for I...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [66, 73, 1.0], [74, 82, 1.0], [106, 117, 1.0], [118, 127, 1.0], [176, 184, 1.0], [202, 213, 1.0]]}, "2017.wwwconf_conference-2017c.159": {"snippet": "...uted Machine Learning: Foundations, Trends, and Practices\n\n\n ABSTRACTIn recent years, artificial intelligence has achieved great success in many important applications. Both novel machine learning alg...", "weights": [[8, 15, 1.0], [16, 24, 1.0], [183, 190, 1.0], [191, 199, 1.0]]}, "1998.sigirconf_conference-98.56": {"snippet": "... the information corpus available for access symbolizes the wealth and benefits of this medium. 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Current methods mostly treat the EMR as a text sequence and provide explanations based on a...", "weights": [[8, 15, 1.0], [66, 76, 1.0], [77, 89, 1.0], [97, 104, 1.0]]}, "2020.nl4xai-1.10": {"snippet": "...able Artificial Intelligence\n\n\n This paper discusses four major argumentation theoretical frameworks with respect to their use in support of explainable artificial intelligence (XAI). We consider thes...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [156, 166, 1.0], [167, 179, 1.0]]}, "2013.tist_journal-ir0volumeA4A2.13": {"snippet": "...ines artificial (societies), computational (experiments), and parallel (execution) methods in intelligent systems and technology for integrative and predictive medicine, or more generally, precision m...", "weights": [[8, 18, 1.0]]}, "T87-1025": {"snippet": "...}\n\n\n Artificial intelligence is, or should be, at the heart of natural language processing research. 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This, in turn, will encourage development of intuitive, adaptive, and effective empathic AI interfaces that better addr...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2020.acl-main.286": {"snippet": "...onic Medical Record (EMR) documents from hospitals and annotated by professional doctors proves that, the proposed framework outperforms the previous automatic diagnosis methods in accuracy performanc...", "weights": [[8, 15, 1.0]]}, "I08-2125": {"snippet": "... for Medical Patient Records\n\n\n A critical, yet not very well studied problem in medical applications is the issue of accurately labeling patient records according to diagnoses and procedures that pat...", "weights": [[8, 15, 1.0], [84, 91, 1.0]]}, "2008.sigirconf_conference-2008.3": {"snippet": "...xact medical situations and unfamiliar with medical terminology. To address this challenge, we have built an intelligent medical Web search engine called iMed, which uses medical knowledge and an inte...", "weights": [[8, 15, 1.0], [47, 54, 1.0], [124, 131, 1.0], [174, 181, 1.0]]}, "W12-3803": {"snippet": "... the medical domain, misdiagnoses and diagnostic uncertainty put lives at risk and incur substantial financial costs. Clearly, medical reasoning and decision-making need to be better understood. We ex...", "weights": [[8, 15, 1.0], [130, 137, 1.0]]}, "2020.emnlp-main.112": {"snippet": "...pply artificial intelligence to the medical domain. In this paper, we propose to generate radiology reports with memorydriven Transformer, where a relational memory is designed to record key informati...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [39, 46, 1.0]]}, "2020.acl-main.466": {"snippet": "...egal Artificial Intelligence\n\n\n Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the l...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [41, 51, 1.0], [52, 64, 1.0], [113, 123, 1.0], [124, 136, 1.0]]}, "2019.clef_conference-2019w.160": {"snippet": "...rong artificial intelligence problem into multiple weak artificial intelligence problems. First, we use a simple classifier to distinguish the four categories by training questions only. Transfer lear...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [59, 69, 1.0], [70, 82, 1.0]]}, "2020.ipm_journal-ir0volumeA57A5.20": {"snippet": "...with artificial intelligence", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2019.clef_conference-2019w.32": {"snippet": "...pret medical images and summarise their findings in the form of radiology reports. The mapping of visual information present in medical images to the condensed textual description is a tedious, time-c...", "weights": [[8, 15, 1.0], [131, 138, 1.0]]}, "W12-3801": {"snippet": "...eous medical narration. Expert physicians viewed images of dermatological conditions and provided a description while working toward a diagnosis. The narratives were analyzed for differences in filled...", "weights": [[8, 15, 1.0]]}, "2019.jasis_journal-ir0volumeA70A4.4": {"snippet": "Artificial intelligence in healthcare robots: A social informatics study of knowledge embodiment", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2020.cikm_conference-2020.342": {"snippet": "...t of Medical Diagnostic Systems with Large Scale Patient Vignette Simulation", "weights": [[8, 15, 1.0]]}, "W12-6301": {"snippet": "...y of Artificial Intelligence (AI), Turing Test, a question answering imitation game was proposed to determine whether the computer system has intelligence. It becomes the ultimate goal to answer all t...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [145, 157, 1.0]]}, "2017.wwwconf_conference-2017c.190": {"snippet": "... and medical burden coupled with increased morbidity of the aforementioned diseases makes their prevention an active research area. Currently, the traditional method of MetS diagnosis is based on dich...", "weights": [[8, 15, 1.0]]}, "1996.jasis_journal-ir0volumeA47A6.12": {"snippet": "The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents, edited by Luc Steels and Rodney Brooks", "weights": [[4, 14, 1.0], [29, 39, 1.0], [40, 52, 1.0]]}, "2021.dash-1.15": {"snippet": "...ving Artificial Intelligence Hub\n\n\n We present the Everyday Living Artificial Intelligence (AI) Hub, a novel proof-of-concept framework for enhancing human health and wellbeing via a combination of ta...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [70, 80, 1.0], [81, 93, 1.0]]}, "P17-3001": {"snippet": "...h an artificial intelligence system based on natural language processing and machine learning algorithms. This approach has been validated in many works in which the performance of the diagnosis has b...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "W09-2701": {"snippet": "...bio-)medical domain, which has a long tradition of investigating knowledge representation and reasoning and, more generally, artificial intelligence methods (Shortliffe et al., 1975) , and which has s...", "weights": [[8, 15, 1.0], [128, 138, 1.0], [139, 151, 1.0]]}, "W12-6211": {"snippet": "... the medical records generated by the Basque Health Hospital System. As a starting point, a set of manually coded medical records were collected in order to code new medical records on the basis of th...", "weights": [[8, 15, 1.0], [117, 124, 1.0], [169, 176, 1.0]]}, "2015.wwwconf_conference-2015c.329": {"snippet": "...ning artificial neural networks including deep neural networks. We will emphasize how each of these concepts can be used in various Web data analysis. In the second part of the tutorial, we describe t...", "weights": [[8, 18, 1.0]]}, "2013.ntcir_conference-2013.1": {"snippet": "...e of medical records written in electronic format is increasing. As a reuslt, information processing technique in medical elds is in demand. Medical records contain information in natural language suc...", "weights": [[8, 15, 1.0], [117, 124, 1.0], [144, 151, 1.0]]}, "1988.riao_conference-1988.36": {"snippet": "...n of Artificial Intelligence Techniques to Automated Key-Wording", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "1976.ipm_journal-ir0volumeA12A3.4": {"snippet": "Artificial intelligence in information retrieval systems", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2021.ecir_conference-20211.24": {"snippet": "...ited medical knowledge: this likely hinders their effectiveness. In this work, we propose a knowledgeaware diagnosis ranking framework based on medical knowledge graph (KG) and graph convolutional neu...", "weights": [[8, 15, 1.0], [147, 154, 1.0]]}, "2013.sigirconf_conference-2013.205": {"snippet": "...ver, medical records search is challenging, due to the implicit knowledge inherent within the medical records -such knowledge may be known by medical practitioners, but hidden to an information retrie...", "weights": [[8, 15, 1.0], [97, 104, 1.0], [145, 152, 1.0]]}, "1991.jasis_journal-ir0volumeA42A8.3": {"snippet": "...e of artificial intelligence in information retrieval", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2011.ipm_journal-ir0volumeA47A2.2": {"snippet": "...ient medical image database which is paid less attention than that it deserves. In this paper, we provide an efficient approach to develop the archives of large brain CT medical data. Medical images a...", "weights": [[8, 15, 1.0], [173, 180, 1.0], [187, 194, 1.0]]}, "1998.ipm_journal-ir0volumeA34A6.5": {"snippet": "Intelligent Information Retrieval: Diagnosing Information Need. Part II. Uncertainty expansion in a prototype of a diagnostic IR tool", "weights": []}, "1998.jasis_journal-ir0volumeA49A7.0": {"snippet": "...sue: Artificial Intelligence Techniques for Emerging Information Systems Applications", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2014.sigirconf_workshop-2014medir.7": {"snippet": "... for Medical Image Retrieval\n\n\n Medical image retrieval can assist physicians in finding information supporting their diagnosis. Systems that allow searching for medical images need to provide tools f...", "weights": [[8, 15, 1.0], [35, 42, 1.0], [165, 172, 1.0]]}, "2017.cikm_conference-2017.121": {"snippet": "DiagTree: Diagnostic Tree for Differential Diagnosis\n\n\n ABSTRACTDi erential diagnosis is detection of one disease among similar diseases using evidence such as pathologic tests. A Partially Observed M...", "weights": []}, "2021.acl-long.459": {"snippet": "...ying artificial intelligence to medical domain. Previous studies mainly follow the encoder-decoder paradigm and focus on the aspect of text generation, with few studies considering the importance of c...", "weights": [[8, 18, 1.0], [19, 31, 1.0], [35, 42, 1.0]]}, "2022.acl-long.458": {"snippet": "... and artificial intelligence venues, identifying 70 papers discussing the system-level implementation of task-oriented dialogue systems for healthcare applications. We conducted a comprehensive techni...", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2021.wwwconf_conference-2021c.6": {"snippet": "Artificial Neural Network Based Sensor Ontology Matching Technique", "weights": [[0, 10, 1.0]]}, "2017.clef_conference-2017w.39": {"snippet": "...n on Medical Images using Deep Residual Learning Network\n\n\n Medical images are often used in clinical diagnosis. However, interpreting the insights gained from them is often a time-consuming task even...", "weights": [[8, 15, 1.0], [63, 70, 1.0]]}, "1986.sigirconf_conference-86.26": {"snippet": "...d on Artificial Intelligence Techniques", "weights": [[8, 18, 1.0], [19, 31, 1.0]]}, "2020.fire_conference-2020w.11": {"snippet": "Artificial Intelligence as Legal Research Assistant", "weights": [[0, 10, 1.0], [11, 23, 1.0]]}, "2020.findings-emnlp.336": {"snippet": "...s of artificial intelligence is to build computer-based expert systems. 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We exploit the paretian distribution to obtain a term frequency normalization to substitute for the actual term frequency in the probabilistic models of Information...", "weights": [[8, 11, 1.0], [192, 203, 1.0]]}, "O03-4004": {"snippet": "...pus, Zipf 's law for both words in English and characters in Chinese does not hold for all ranks. The frequency falls below the frequency predicted by Zipf's law for English words for rank greater tha...", "weights": [[8, 12, 1.0], [16, 19, 1.0], [154, 158, 1.0], [161, 164, 1.0]]}, "1987.jasis_journal-ir0volumeA38A6.3": {"snippet": "...n of Zipf parameters", "weights": [[8, 12, 1.0]]}, "2010.spire_conference-2010.32": {"snippet": "... and Zipf-Like Scoring Function for Web Document Similarity Retrieval", "weights": [[8, 12, 1.0], [63, 72, 1.0]]}, "2013.ecir_conference-2013.39": {"snippet": "...r of Information Models for IR\n\n\n Abstract. In this paper we explore various methods to estimate the collection parameter of the information based models for ad hoc information retrieval. In previous ...", "weights": [[8, 19, 1.0], [132, 143, 1.0], [168, 179, 1.0], [180, 189, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.10": {"snippet": "...s of information behavior and served to support the citing authors' claims. By contrast, the concept of \"Zipf's law\" received the most attention from bibliometrics research and was used mainly for com...", "weights": [[8, 19, 1.0], [108, 112, 1.0], [115, 118, 1.0]]}, "1999.jasis_journal-ir0volumeA50A3.4": {"snippet": "... the Law of Zipf-Mandelbrot for Multi-Wort Phrases", "weights": [[8, 11, 1.0], [15, 19, 1.0]]}, "W16-6004": {"snippet": "...n to Zipf's law. This causes a lack of representativeness in our NLP tasks, leading to models that can capture the head phenomena in language, but fail when dealing with the long tail. We therefore pr...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2019.gwc-1.44": {"snippet": "...ency law with wordnets as sense inventories\n\n\n According to George K. Zipf, more frequent words have more senses. We have tested this law using corpora and wordnets of English, Spanish, Portuguese, Fr...", "weights": [[8, 11, 1.0], [73, 77, 1.0], [137, 140, 1.0]]}, "1982.jasis_journal-ir0volumeA33A5.5": {"snippet": "...n of Zipf's Law and Its Application to the Bibliographic Database Environment", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "1987.ipm_journal-ir0volumeA23A3.0": {"snippet": "...of a zipf size variable: Sampling properties and relationship to other parameters", "weights": [[8, 12, 1.0]]}, "C12-1191": {"snippet": "...wing Zipf's law (power-law). We argue that these long-tail distributions can also be considered as lognormal. Since we know the conjugate prior distribution for a lognormal likelihood, we propose to g...", "weights": [[8, 12, 1.0], [15, 18, 1.0], [26, 29, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.9": {"snippet": "...e of Zipf and Limits to Parallelization: An Look at the Stragglers Problem in MapReduce\n\n\n This paper explores the problem of \"stragglers\" in Map-Reduce: a common phenomenon where a small number of ma...", "weights": [[8, 12, 1.0]]}, "2009.ipm_journal-ir0volumeA45A4.6": {"snippet": "...ower-law regularities have been discovered behind many complex natural and social phenomenons. We discover that the power-law regularities, especially the Zipf's and Heaps' laws, also exist in large-s...", "weights": [[8, 11, 1.0], [125, 128, 1.0], [158, 162, 1.0]]}, "W98-1218": {"snippet": "... The law named for him is ubiquitous, but Zipf did not actually discover the law so much as provide a plausible explanation. Others have proposed modifications to Zipf's Law, and closer examination un...", "weights": [[8, 11, 1.0], [45, 49, 1.0], [80, 83, 1.0], [166, 170, 1.0], [173, 176, 1.0]]}, "2007.sigirconf_conference-2007.86": {"snippet": "...d in Information Retrieval applications often have convenient power-law constraints (also known as Zipf's Law and long tails) that allow us to materialize carefully chosen combinations of multi-keywor...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [71, 74, 1.0], [102, 106, 1.0], [109, 112, 1.0]]}, "1972.jasis_journal-ir0volumeA23A3.8": {"snippet": "...eral Zipf-type distributions in their goodness of fit to language data", "weights": [[8, 12, 1.0]]}, "P14-1075": {"snippet": "...lows Zipf's law, we derive tradeoff formulae of the perplexity of k-gram models and topic models with respect to the size of the reduced vocabulary. In addition, we show an approximate behavior of eac...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2020.sigirconf_conference-2020.379": {"snippet": "...Case Retrieval\n\n\n Precedents (prior cases decided in courts of law) are primary legal materials in both common and civil law systems. With the rapid growth of digitalized legal documents, it takes gre...", "weights": [[8, 17, 1.0], [66, 69, 1.0], [124, 127, 1.0]]}, "1987.ipm_journal-ir0volumeA23A3.1": {"snippet": "...s of Zipf's law: An index approach", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2005.ipm_journal-ir0volumeA41A6.4": {"snippet": "...e of information sciences and referred to as being of Zipf-type. A generalization of Zipf distribution (a size-frequency form of the Zipf\u00d5s law), named the generalized Zipf distribution, is introduced...", "weights": [[8, 19, 1.0], [57, 61, 1.0], [88, 92, 1.0], [143, 146, 1.0], [171, 175, 1.0]]}, "2005.ipm_journal-ir0volumeA41A6.3": {"snippet": "...ed a Zipf list, is the basic ingredient for all considerations related to power laws and Lotkaian or Zipfian informetrics. Examples where this framework applies are: impact factors, including web impa...", "weights": [[8, 12, 1.0]]}, "E17-4009": {"snippet": "...t to Zipf's law, an empirical law referring to the phenomenon that many types of data in social sciences can be approximated with a Zipfian distribution. Additionally, we also analyse Benford's law, i...", "weights": [[8, 12, 1.0], [15, 18, 1.0], [33, 36, 1.0], [197, 200, 1.0]]}, "2010.sigirconf_conference-2010.142": {"snippet": "... for information retrieval\n\n\n ABSTRACTIn this paper, we propose a new application of Bayesian language model based on Pitman-Yor process for information retrieval. This model is a generalization of th...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "2021.ccl-1.93": {"snippet": "...{'}s law: evidence from danmu comments\n\n\n Zipf's law is a succinct yet powerful mathematical law in linguistics. However, the meaningfulness and units of the law have remained controversial. The curre...", "weights": [[8, 11, 1.0], [45, 49, 1.0], [52, 55, 1.0], [96, 99, 1.0], [161, 164, 1.0]]}, "2018.jasis_journal-ir0volumeA69A11.6": {"snippet": "...s of Zipf's meaning-frequency law", "weights": [[8, 12, 1.0], [33, 36, 1.0]]}, "W96-0106": {"snippet": "...{'}s Law\n\n\n An asymptote is derived from Turing's local reestimation formula for population frequencies, and a local reestimation formula is derived from Zipf's law for the asymptotic behavior of popu...", "weights": [[8, 11, 1.0], [157, 161, 1.0], [164, 167, 1.0]]}, "2017.sigirconf_conference-2017.41": {"snippet": "...ago, Zipf postulated that the word frequency distribution of languages is a power law, i.e., it is a straight line on a log-log plot. Over the years, this phenomenon has been documented and studied ex...", "weights": [[8, 12, 1.0], [85, 88, 1.0]]}, "1988.sigirconf_conference-88.45": {"snippet": "...ated Information Retrieval for Law in a Hypertext Environment", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 37, 1.0]]}, "2012.wwwconf_conference-2012.31": {"snippet": "...e if Zipf's law is a good description of the frequency with which passwords are chosen. We use a number of standard statistics, which measure the security of password distributions, to see if modellin...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}}}
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It is based on a controlled formulation of inclusive query plans, on an automatic conversion of query plans into elementary queries, and on combining elementary qu...", "weights": [[8, 15, 1.0], [164, 171, 1.0], [201, 208, 1.0]]}, "W14-1411": {"snippet": "...with Boolean operations. The second relates to adjectival modification of question and propositional entities. We partly defuse the Boolean challenge by showing that the data actually argue against a ...", "weights": [[8, 15, 1.0], [135, 142, 1.0]]}, "2005.trec_conference-2005.42": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator. The fuzzy term proximity is controlled with an influenc...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2010.tois_journal-ir0volumeA29A1.0": {"snippet": "...tive Boolean queries are a key component of modern information retrieval systems, especially when Webscale repositories are being searched. A conjunctive query q is equivalent to a |q|-way intersectio...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1997.jasis_journal-ir0volumeA48A8.1": {"snippet": "...h to Boolean Query Reformulation", "weights": [[8, 15, 1.0]]}, "2005.clef_workshop-2005w.11": {"snippet": "Boolean Operators in Interactive Search\n\n\n This paper presents the participation of the MIRACLE team at the ImageCLEF interactive search task. Basically, queries consisting on several terms can be pro...", "weights": [[0, 7, 1.0], [154, 161, 1.0]]}, "1989.ipm_journal-ir0volumeA25A1.3": {"snippet": "...fied boolean queries: A comparison of inverted and signature file organizations", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2006.trec_conference-2006.28": {"snippet": "...ated Boolean Queries of the TREC 2006 Legal Discovery Track\n\n\n We analyze the results of several experimental runs submitted to the Legal Discovery and Terabyte Tracks of TREC 2006. In the Legal Track...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2010.cikm_conference-2010.75": {"snippet": "... for queries over text databases. Research on selectivity estimation for such queries has focused on Boolean retrieval, i.e., a document may be relevant for the query or not. But with the coalescence ...", "weights": [[8, 15, 1.0], [81, 88, 1.0], [104, 111, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "....2.1 Boolean Run-otL11BT1 The submitted experimental otL11BT1 run was a Boolean-based run. We created the following short Boolean queries (1 or 2 words or phrases) based on reading the topic statement...", "weights": [[8, 15, 1.0], [75, 82, 1.0], [125, 132, 1.0], [133, 140, 1.0]]}, "1991.jasis_journal-ir0volumeA42A1.2": {"snippet": "...s in Boolean form", "weights": [[8, 15, 1.0]]}, "2015.wwwconf_conference-2015c.38": {"snippet": "...-way Boolean tensor. Standard sparql queries then can be expressed using elementary Boolean algebra operations. We show how this representation helps to estimate the size of joins. Such estimates are ...", "weights": [[8, 15, 1.0], [40, 47, 1.0], [87, 94, 1.0]]}, "1986.sigirconf_conference-86.21": {"snippet": "... for Boolean Query Processing\n\n\n Abstract. An infamation retrieval model, named the Generaliied Vectm Spice Model (GVSM). is extended m handle situations where queries are specitied as (extended) Bool...", "weights": [[8, 15, 1.0], [163, 170, 1.0], [199, 206, 1.0]]}, "1999.jasis_journal-ir0volumeA50A1.7": {"snippet": "Boolean Search: Current State and Perspectives", "weights": [[0, 7, 1.0]]}, "2007.trec_conference-2007.51": {"snippet": "...rent Boolean queries were provided for each topic (defendant, plaintiff and final). In 2006, the plaintiff and final queries had (usually) been the same. 1 Although often referred to as \"Boolean,\" the...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [120, 127, 1.0], [190, 197, 1.0]]}, "2007.trec_conference-2007.65": {"snippet": "...lds, boolean constraints and structured operators. Evaluation results show that structured queries outperform bag-of-words ones. Boolean constraints improve both precision and recall. For the routing ...", "weights": [[8, 15, 1.0], [94, 101, 1.0], [132, 139, 1.0]]}, "1982.sigirjournals_journal-ir0volumeA17A2.2": {"snippet": "...atic Boolean Query Processing", "weights": [[8, 15, 1.0]]}, "2020.wwwconf_conference-2020.97": {"snippet": "...atic Boolean Query Formulation for Systematic Review Literature Search", "weights": [[8, 15, 1.0]]}, "2017.cikm_conference-2017.285": {"snippet": "...tive Boolean query is one fundamental operation for document retrieval in many information systems and databases. Various algorithms have been put up in terms of maximizing the query efficiency. In re...", "weights": [[8, 15, 1.0]]}, "1981.sigirconf_conference-81.8": {"snippet": "...(non-Boolean) queries and indexing functions.The meanings of recall and precision in such a generalized system will be discussed.Finally, we shall explore the meaning and difficulty of using such meas...", "weights": [[8, 15, 1.0], [17, 24, 1.0]]}, "2003.clef_workshop-2003w.44": {"snippet": "...ed a Boolean quorum-level type of query combination so that documents were ranked according to both the tf-idf factor but also to the number of matching Boolean combinations. The Boolean combinations ...", "weights": [[8, 15, 1.0], [156, 163, 1.0], [182, 189, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.5": {"snippet": "... the Boolean operators and search terms that comprise the submitted queries. Our analysis shows that both Boolean training and the use of an assisted interface improved the participants' ability to co...", "weights": [[8, 15, 1.0], [71, 78, 1.0], [109, 116, 1.0]]}, "1985.sigirconf_conference-85.8": {"snippet": "...d on Boolean query formulations and inverted file implementations. Such systems provide rapid responses in answer to search queries but they are not easy to use by uninitiated patrons. An extended Boo...", "weights": [[8, 15, 1.0], [127, 134, 1.0], [200, 207, 1.0]]}, "2003.clef_workshop-2003.32": {"snippet": "...dish Queries for Boolean Combinations of Truncated Terms\n\n\n Abstract. In languages that use compound words such as Swedish, it is often neccessary to split compound words when indexing documents or qu...", "weights": [[8, 15, 1.0], [20, 27, 1.0], [201, 208, 1.0]]}, "2006.clef_workshop-2006w.37": {"snippet": "...dles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes it possible to...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2008.wwwconf_conference-2008.201": {"snippet": "...s of queries per second over billions of documents. To deal with this heavy workload, current engines use massively parallel architectures of thousands of machines that require large hardware investme...", "weights": [[8, 15, 1.0]]}, "2009.jasis_journal-ir0volumeA60A6.9": {"snippet": "...earn Boolean queries: A comparative study", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.0": {"snippet": "...nded Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulate...", "weights": [[8, 15, 1.0], [73, 80, 1.0], [81, 88, 1.0]]}, "1986.jasis_journal-ir0volumeA37A2.2": {"snippet": "Boolean queries and term dependencies in probabilistic retrieval models", "weights": [[0, 7, 1.0], [8, 15, 1.0]]}, "2001.ir_journal-ir0volumeA4A34.4": {"snippet": "...s of Boolean Queries in Large Full-Text Databases", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.1": {"snippet": "...g of Boolean queries by means of a multiobjective IQBE evolutionary algorithm", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2004.ntcir_workshop-2004.80": {"snippet": "...ying queries that combines the probabilistic IR model and the Boolean IR model. Since it is not easy to construct a Boolean query that covers all relevant documents, a mechanism for clarifying the Boo...", "weights": [[8, 15, 1.0], [65, 72, 1.0], [119, 126, 1.0], [200, 207, 1.0]]}, "2008.trec_conference-2008.15": {"snippet": "...ated Boolean Queries of the TREC 2008 Legal Track\n\n\n We analyze the results of several experimental runs submitted for the TREC 2008 Legal Track. In the Ad Hoc task, we found that rank-based merging o...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2018.sigirconf_conference-2018.50": {"snippet": "...hese Boolean queries are carefully crafted by researchers and information specialists, and often reviewed by a panel of experts. However, little is known about the effectiveness of the Boolean queries...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [188, 195, 1.0], [196, 203, 1.0]]}, "2002.sigirconf_conference-2002.89": {"snippet": "... for boolean queries", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1979.jasis_journal-ir0volumeA30A6.10": {"snippet": "...sing boolean queries with a clustered file organization", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2011.sigirconf_conference-2011.85": {"snippet": "... for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [151, 158, 1.0]]}, "1997.jasis_journal-ir0volumeA48A2.3": {"snippet": "...ring Boolean and Probabilistic Information Retrieval Systems across Queries and Disciplines", "weights": [[8, 15, 1.0], [71, 78, 1.0]]}, "2020.ecir_conference-20201.27": {"snippet": "...iven Boolean queries that is now several decades old. Rank fusion is a relatively recent method for combining runs from multiple systems into a single ranking, and has been shown to significantly impr...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...that queries are issued by trained library scientists. Consequently, early ranking algorithms leveraged textual features, based on the words or \"terms\" contained in documents and queries. Examples of ...", "weights": [[8, 15, 1.0], [182, 189, 1.0]]}, "2006.tois_journal-ir0volumeA24A4.4": {"snippet": "...Path queries, as well as other related XPath decision problems such as satisfiability, equivalence, overlap, and coverage. The considered XPath fragment covers most of the language features used in pr...", "weights": [[8, 15, 1.0]]}, "2018.clef_conference-2018w.48": {"snippet": "... the Boolean search as the starting point(sub-task 2). We adopt BM25 with query expansion to acquire basic relationship and utilize a customized Paragraph2Vector to represent queries / documents train...", "weights": [[8, 15, 1.0], [178, 185, 1.0]]}, "1993.jasis_journal-ir0volumeA44A6.2": {"snippet": "...n of Boolean Queries: A Prototype Implementation and Evaluation", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2016.ecir_workshop-2016newsir.3": {"snippet": "Boolean Queries for News Monitoring: Suggesting New Query Terms to Expert Users\n\n\n In this paper, we evaluate query suggestion for Boolean queries in a news monitoring system. Users of this system rec...", "weights": [[0, 7, 1.0], [8, 15, 1.0], [131, 138, 1.0], [139, 146, 1.0]]}, "1999.tois_journal-ir0volumeA17A4.1": {"snippet": "...lar, Boolean query operators are conveniently modeled as link matrices of the Bayesian Network. Prior work has shown, however, that these operators do not perform as well as the the pnorm operators us...", "weights": [[8, 15, 1.0]]}, "1999.tois_journal-ir0volumeA17A1.0": {"snippet": "...s in Boolean queries into native subsuming forms, which is a basis of translating complex queries. In addition, we present experimental results for evaluating the cost of postfiltering. We also discus...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [93, 100, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.12": {"snippet": "... and Boolean operators used as well as their relationships with search failure. Among other findings, results indicate that a majority of the queries were simple, with short query lengths and a low us...", "weights": [[8, 15, 1.0], [145, 152, 1.0]]}, "2012.sigirconf_workshop-2012osir.0": {"snippet": "... CNF queries. These CNF queries are highly effective, and can outperform bag of word queries by a large margin. However, not many effective tools exist that can facilitate the efficient manual creatio...", "weights": [[8, 15, 1.0], [27, 34, 1.0], [88, 95, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.6": {"snippet": "...atic Boolean Query Processing", "weights": [[8, 15, 1.0]]}, "1982.jasis_journal-ir0volumeA33A1.1": {"snippet": "... for boolean search request formulations", "weights": [[8, 15, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.9": {"snippet": "...ting Boolean queries in conjunctive normal form with probabilistic retrieval models", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "2012.sigirconf_conference-2012.106": {"snippet": "...late Boolean queries to represent their information need. We want to work on three basic problems:1. Boolean query formulation -Our primary goal is to study Boolean query formulation in the light of t...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [104, 111, 1.0], [160, 167, 1.0]]}, "2007.spire_conference-2007.13": {"snippet": "...tive Boolean queries are a fundamental operation in web search engines. These queries can be reduced to the problem of intersecting ordered sets of integers, where each set represents the documents co...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "1993.sigirconf_conference-93.31": {"snippet": "...n of Boolean Operators in the Extended Boolean Retrieval Framework\n\n\n ABSTRACTThe retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [124, 131, 1.0], [196, 203, 1.0]]}, "2006.trec_conference-2006.32": {"snippet": "...with Boolean queries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator because it can not be generalized to nodes. The fuzzy te...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [81, 88, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "... for boolean query formulation\n\n\n AbstractA new model named Boolean Latent Semantic Indexing model based on the Singular Value Decomposition and Boolean query formulation is introduced. While the Sing...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [148, 155, 1.0]]}, "2021.ir_journal-ir0volumeA24A1.1": {"snippet": "...atic Boolean query formulation for systematic reviews", "weights": [[8, 15, 1.0]]}, "2001.cikm_conference-2001.28": {"snippet": "...only queries regarding abstract tables, there is no need to implement any functions beyond a subselect (such as union, except, intersect). Separate subselects are usually represented by independent qu...", "weights": [[8, 15, 1.0], [201, 208, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.1": {"snippet": "...sing Boolean queries. The model is applied to the task of ranking documents, or other structural elements, in the order of their expected relevance. Features such as phrase matching, truncation, and s...", "weights": [[8, 15, 1.0], [16, 23, 1.0]]}, "1994.ipm_journal-ir0volumeA30A1.5": {"snippet": "...ased Boolean Retrieval Systems", "weights": [[8, 15, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...nced queries. We submitted both the original and modified queries to three major Web search engines; a total of 600 queries were submitted and 5,748 documents evaluated. We compared the results from t...", "weights": [[8, 15, 1.0], [61, 68, 1.0], [119, 126, 1.0]]}, "2014.sigirconf_conference-2014.152": {"snippet": "...lity Boolean queries that can be applied deterministically during filtering. We call these Boolean statements sufficient queries. We argue that using deterministic queries for entity-centric filtering...", "weights": [[8, 15, 1.0], [16, 23, 1.0], [94, 101, 1.0], [124, 131, 1.0], [167, 174, 1.0]]}, "2007.trec_conference-2007.1": {"snippet": "... the Boolean reference run wit our ranked retrieval run allows us to get the high recall of the Boolean retrieval, whilst precision scores show an improvement over both the Boolean and the ranked retr...", "weights": [[8, 15, 1.0], [99, 106, 1.0], [176, 183, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.10": {"snippet": "...n in Boolean IR queries\n\n\n a b s t r a c tThe negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include negated terms in Boole...", "weights": [[8, 15, 1.0], [19, 26, 1.0], [130, 137, 1.0], [198, 205, 1.0]]}}}
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In this overload of information, Information Retrieval (IR) has been playing a prominent role by developing systems capable of separating ...", "weights": [[8, 19, 1.0], [85, 96, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "2002.ipm_journal-ir0volumeA38A5.5": {"snippet": "...mage retrieval in American history", "weights": [[8, 17, 1.0], [30, 37, 1.0]]}, "2012.wsdm_conference-2012.41": {"snippet": "...TThe development of information retrieval systems such as search engines relies on good test collections, including assessments of retrieved content. The widely employed \"Cranfield paradigm\" dictates ...", "weights": [[8, 19, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "2006.sigirconf_conference-2006.28": {"snippet": "...d of information retrieval, stimulated to a great extent by the importance of link analysis in the development of Web search techniques . This body of work has focused primarily on the network that is...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [102, 113, 1.0]]}, "1995.sigirconf_conference-95.17": {"snippet": "...n in Information Retrieval\n\n\n ABSTRACTEvaluation is a major force in research, development and applications related to information retrieval (IR). This paper is a critical and historical analysis of e...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [82, 93, 1.0], [122, 133, 1.0], [134, 143, 1.0]]}, "2007.ecir_conference-2007.2": {"snippet": "...n in Information Retrieval\n\n\n Abstract. The experimental evaluation of information retrieval systems has a venerable history. Long before the current notion of a search engine, in fact before search b...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [74, 85, 1.0], [86, 95, 1.0], [120, 127, 1.0]]}, "Y08-1046": {"snippet": "Sign language and Computing in a Developing Country: A Research Roadmap for the Next Two Decades in the {P}hilippines\n\n\n This paper presents the current situation of Filipino Sign Language in the Deaf...", "weights": []}, "2001.ipm_journal-ir0volumeA37A5.2": {"snippet": "...mage retrieval tasks in the field of art history", "weights": [[8, 17, 1.0], [44, 51, 1.0]]}, "1977.jasis_journal-ir0volumeA28A2.0": {"snippet": "... - A history of developments and trends from 1966 through 1975", "weights": [[8, 15, 1.0]]}, "1978.jasis_journal-ir0volumeA29A3.12": {"snippet": "...ases-history of developments and trends", "weights": [[8, 15, 1.0]]}, "2013.wsdm_conference-2013.20": {"snippet": "... for information retrieval (IR) holds promise for allowing the development of \"self-learning\" search engines that can automatically adjust to their users. With the large amount of e.g., click data tha...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [66, 77, 1.0]]}, "1991.sigirconf_conference-91.24": {"snippet": "...ssor Information Retrieval Systems\n\n\n Abstract.Information retrieval is the selection of documents that are potentially relevant to a user's information need. Given the vast volume of data stored in m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0], [144, 155, 1.0]]}, "2010.sigirconf_conference-2010.170": {"snippet": "...vant history and P (G|m), a prior on the distribution of relevant history (e.g., more recent history tends to be more relevant than distant history). Let R G,m (R when unambiguous) be the subset of H ...", "weights": [[8, 15, 1.0], [69, 76, 1.0], [96, 103, 1.0], [143, 150, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "2019.chiir_conference-2019.55": {"snippet": "...ised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmic incorporati...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [148, 159, 1.0], [160, 169, 1.0]]}, "1975.jasis_journal-ir0volumeA26A2.0": {"snippet": "...otic development of thesauri and information systems: A case history", "weights": [[8, 19, 1.0], [36, 47, 1.0], [64, 71, 1.0]]}, "2017.ictir_conference-2017.56": {"snippet": "...ving Information Retrieval\nMOTIVATIONThe rapid development of big data, social networks, mobile services and the growing popularity of digital communications have profoundly changed Information Retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [185, 196, 1.0], [197, 206, 1.0]]}, "2011.cikm_conference-2011.406": {"snippet": "...ical information retrieval modelling: from the probability ranking principle to recent advances in diversity, portfolio theory, and beyond\n\n\n ABSTRACTStatistical modelling of Information Retrieval (IR...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [178, 189, 1.0], [190, 199, 1.0]]}, "1993.sigirconf_conference-93.27": {"snippet": "Development of a Modern OPAC: From REVTOLC to MARIAN\n\n\n AbstractSince 1986 we have investigated the problems and possibilities of applying modern information retrieval methods to large online public a...", "weights": [[0, 11, 1.0], [146, 157, 1.0], [158, 167, 1.0]]}, "1996.amta-1.9": {"snippet": "... and development methodology\n\n\n The history of MT shows how the shortcomings of one approach become the catalyst for a new approach. New systems are designed in reaction to old systems' weaknesses. Ye...", "weights": [[8, 19, 1.0], [39, 46, 1.0]]}, "2014.sigirconf_conference-2014.122": {"snippet": "...mily history to improve clinical information retrieval\n\n\n ABSTRACTWe present a study to understand the effect that negated terms (e.g., \"no fever\") and family history (e.g., \"family history of diabete...", "weights": [[8, 15, 1.0], [36, 47, 1.0], [48, 57, 1.0], [162, 169, 1.0], [185, 192, 1.0]]}, "2017.sigirconf_conference-2017.1": {"snippet": "... Pre-history of Web Search\n\n\n ABSTRACTTo me, an awareness of history is a fundamental requirement for progress; and I believe that we in the eld of information retrieval are currently ill-served in th...", "weights": [[8, 15, 1.0], [64, 71, 1.0], [151, 162, 1.0], [163, 172, 1.0]]}, "2014.tc-1.2": {"snippet": "... its development and will reflect as to what the future has in store. To start with, the early developments after the Second World War II will be outlined. Next, the presentation will elaborate that t...", "weights": [[8, 19, 1.0]]}, "1999.sigirjournals_journal-ir0volumeA33A2.3": {"snippet": "...Text REtrieval Conference (TREC): History and Plans for TREC-9", "weights": [[8, 17, 1.0], [37, 44, 1.0]]}, "1982.sigirconf_conference-82.20": {"snippet": "...r an Information Retrieval System. Some early experience with a micro-based Information Retrieval system developed at Belfast, called Micro-BIRD, shows that it is not difficult to build a simple voice...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [79, 90, 1.0], [91, 100, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A2.6": {"snippet": "... the development of recommendation, retrieval, and personalisation techniques. The evaluation of these systems is still based on traditional information retrieval and statistics metrics, e.g., precisi...", "weights": [[8, 19, 1.0], [39, 48, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "W11-1506": {"snippet": "...s of History research project. The project aims at the creation of resources for a historical information retrieval system that can handle the time-based dynamics and varying perspectives of Dutch his...", "weights": [[8, 15, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "2001.sigirconf_conference-2001.9": {"snippet": "...king Retrieval Systems without Relevance Judgments\n\n\n ABSTRACTThe most prevalent experimental methodology for comparing the effectiveness of information retrieval systems requires a test collection, c...", "weights": [[8, 17, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2017.cikm_conference-2017.131": {"snippet": "...tent history.is is motivated by our nding that most users do not share their visitation history, but have ample content history from tweet posts.We formulate ne-grained geolocation as a ranking proble...", "weights": [[8, 15, 1.0], [91, 98, 1.0], [123, 130, 1.0]]}, "2003.sigirconf_conference-2003.57": {"snippet": "...uery history for document ranking in interactive information retrieval\n\n\n ABSTRACTIn this poster,we incorporate user query history, as context information, to improve the retrieval performance in inte...", "weights": [[8, 15, 1.0], [52, 63, 1.0], [64, 73, 1.0], [126, 133, 1.0], [146, 157, 1.0], [174, 183, 1.0]]}, "2017.ipm_journal-ir0volumeA53A1.9": {"snippet": "... the development of multilingual information access systems. However, a systematic and quantitative assessment of the impact of evaluation initiatives on multilingual information access and retrieval ...", "weights": [[8, 19, 1.0], [36, 47, 1.0], [170, 181, 1.0], [193, 202, 1.0]]}, "2010.sigirconf_conference-2010.143": {"snippet": "...tity retrieval for news applications and the importance of the news trail history (i.e. past related articles) to determine the relevant entities in current articles. We construct a novel entitylabele...", "weights": [[8, 17, 1.0], [77, 84, 1.0]]}, "W02-1302": {"snippet": "Towards a Road Map on Human Language Technology: Natural Language Processing\n\n\n This document summarizes contributions and discussions from two workshops that took place in November 2000 and July 2001...", "weights": []}, "W12-3202": {"snippet": "...onal History of the {ACL}: 1980-2008\n\n\n We develop a people-centered computational history of science that tracks authors over topics and apply it to the history of computational linguistics. We prese...", "weights": [[8, 15, 1.0], [86, 93, 1.0], [157, 164, 1.0]]}, "O98-4002": {"snippet": "Information Extraction: Beyond Document Retrieval\n\n\n In this paper we give a synoptic view of the growth text processing technology of information extraction (IE) whose function is to extract informat...", "weights": [[0, 11, 1.0], [40, 49, 1.0], [135, 146, 1.0], [192, 203, 1.0]]}, "2002.tmi-tmiw.8": {"snippet": "Towards a road map on human language technology: Natural language processing\n\n\n This document summarizes contributions and discussions from two workshops that took place in November 2000 and July 2001...", "weights": []}, "2006.jasis_journal-ir0volumeA57A9.6": {"snippet": "...ific information retrieval systems: An analysis of the query logs from PsycINFO and ABC-Clio's Historical Abstracts/America: History and Life", "weights": [[8, 19, 1.0], [20, 29, 1.0], [128, 135, 1.0]]}, "2016.sigirconf_conference-2016.219": {"snippet": "... for Information Retrieval\n\n\n ABSTRACTRecent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being successfully appl...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "2021.latechclfl-1.1": {"snippet": "...n of information in the European early modern period is a wellresearched topic, the question how people were engaging with the information explosion that occurred in early modern Europe, is still unde...", "weights": [[8, 19, 1.0], [130, 141, 1.0]]}, "2022.naacl-main.426": {"snippet": "...ogue history on a large scale, based on which we can handle more dialogue history and obtain more abundant and accurate persona information. Specifically, we design an MSP model which consists of thre...", "weights": [[8, 15, 1.0], [77, 84, 1.0], [131, 142, 1.0]]}, "2003.ecir_conference-2003.1": {"snippet": "...ment Retrieval: Shallow Data, Deep Theories; Historical Reflections, Potential Directions\n\n\n Abstract. This paper reviews the development of statistically-based retrieval. Since the 1950s statistical ...", "weights": [[8, 17, 1.0], [129, 140, 1.0], [164, 173, 1.0]]}, "W17-5912": {"snippet": "...from History Textbooks\n\n\n Event timeline serves as the basic structure of history, and it is used as a disposition of key phenomena in studying history as a subject in secondary school. In order to en...", "weights": [[8, 15, 1.0], [77, 84, 1.0], [147, 154, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.11": {"snippet": "...t of Information Retrieval. We contributed to the field of Information Retrieval evaluation by organizing an evaluation initiative for Entity Retrieval.Opinions and other relevant information about en...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [62, 73, 1.0], [74, 83, 1.0], [145, 154, 1.0], [183, 194, 1.0]]}, "2003.jasis_journal-ir0volumeA54A6.2": {"snippet": "...mage retrieval in American history", "weights": [[8, 17, 1.0], [30, 37, 1.0]]}, "Y03-1025": {"snippet": "The development of Tagged {U}yghur Corpus\n\n\n The history and development of Uyghur language is introduced. After a brief introduction to the development of Uyghur words, morphology and syntax, we expl...", "weights": [[4, 15, 1.0], [49, 56, 1.0], [61, 72, 1.0], [141, 152, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.2": {"snippet": "...h in Information Retrieval?\n\n\n AbstractModern Information Retrieval (IR) systems, such as search engines, recommender systems, and conversational agents, are best thought of as interactive systems. An...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [49, 60, 1.0], [61, 70, 1.0]]}, "2020.sigirconf_conference-2020.301": {"snippet": "...Fast Retrieval-based Chat-bots\n\n\n Response retrieval is a subset of neural ranking in which a model selects a suitable response from a set of candidates given a conversation history. Retrieval-based c...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [177, 184, 1.0], [186, 195, 1.0]]}, "1999.ntcir_workshop-1999.9": {"snippet": "Development of a Related Document Retrieval System and Evaluation of the System Using NTCIR-1", "weights": [[0, 11, 1.0], [34, 43, 1.0]]}, "H92-1040": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a fully automated Information Retrieval System which uses advanced natural language processing techniques to enhance the ...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "2004.cikm_conference-2004.59": {"snippet": "...n of retrieval approaches. Whereas the evaluation methodology for flat text retrieval approaches is well established, the evaluation of XML retrieval approaches is a research issue. This is because XM...", "weights": [[8, 17, 1.0], [79, 88, 1.0], [143, 152, 1.0]]}, "2015.sigirconf_conference-2015.191": {"snippet": "...ics, Information Science and other sources of key ideas that reappear in the current development of Information Retrieval as it applies to Search Engines, Social Media, and Collaborative Systems. They...", "weights": [[8, 19, 1.0], [88, 99, 1.0], [103, 114, 1.0], [115, 124, 1.0]]}, "P13-2145": {"snippet": "...sion retrieval step.", "weights": [[8, 17, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...hop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in New Orleans, ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [150, 161, 1.0], [165, 176, 1.0], [177, 186, 1.0]]}, "2018.wsdm_conference-2018.101": {"snippet": "...ata. Information Retrieval (IR) is not an exception. Many IR algorithms and applications require the use of users' personal information, contextual information and other sensitive and private informat...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [127, 138, 1.0], [151, 162, 1.0], [195, 206, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [42, 53, 1.0], [54, 63, 1.0], [92, 103, 1.0], [104, 113, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "2010.airs_conference-2010.12": {"snippet": "... for Retrieval in Historic Documents", "weights": [[8, 17, 1.0]]}, "W18-5613": {"snippet": "...tive development of family history annotation guidelines using a synthetic corpus of clinical text\n\n\n In this article, we describe the development of annotation guidelines for family history informati...", "weights": [[8, 19, 1.0], [30, 37, 1.0], [138, 149, 1.0], [186, 193, 1.0], [194, 205, 1.0]]}, "2008.sigirconf_conference-2008.170": {"snippet": "...ment retrieval has shown to be a competitive approach in XML element retrieval, which is counter-intuitive since the element retrieval task requests all and only relevant document parts to be retrieve...", "weights": [[8, 17, 1.0], [72, 81, 1.0], [128, 137, 1.0]]}, "2021.eacl-main.273": {"snippet": "Summarising Historical Text in Modern Languages\n\n\n We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern ...", "weights": []}, "J01-2009": {"snippet": "...s in Information Retrieval: Recent Research from the Center for Intelligent Information Retrieval\n\n\n The recent advances in information retrieval (IR) in this collection of ten original papers reflect...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [79, 90, 1.0], [91, 100, 1.0], [127, 138, 1.0], [139, 148, 1.0]]}, "2007.jasis_journal-ir0volumeA58A9.1": {"snippet": "...ased information retrieval: Structuring undergraduate history essays for better course evaluation using essay-type visualizations", "weights": [[8, 19, 1.0], [20, 29, 1.0], [57, 64, 1.0]]}, "2007.sigirconf_conference-2007.216": {"snippet": "... for information retrieval have been conceived, which add an additional layer of abstraction to the information retrieval task: probabilistic models for the retrieval of documents are expressed in tho...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [103, 114, 1.0], [115, 124, 1.0], [160, 169, 1.0]]}, "1976.sigirjournals_journal-ir0volumeA11A2.4": {"snippet": "... of \"Information Retrieval and Processing by Lauren B. Doyle\"; Los Angeles, Melville Publishing Company, 1975\n\n\n While the titles of these two recent books on information retrieval suggest that they m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [162, 173, 1.0], [174, 183, 1.0]]}, "2012.airs_conference-2012.14": {"snippet": "...User History in Twitter", "weights": [[8, 15, 1.0]]}, "Y17-1002": {"snippet": "...emic Information Retrieval based on Human-Machine Dialogue\n\n\n With the rapid progress of computer technology and world-wide development of information networks, a vast amount of information is now bei...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [127, 138, 1.0], [142, 153, 1.0], [181, 192, 1.0]]}, "2018.sigirconf_conference-2018.234": {"snippet": "...many information retrieval (IR) tasks. Entity retrieval from KGs has been a long-studied IR research topic for many years . The utilization of entities and KG information for text-centric retrieval ta...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [49, 58, 1.0], [162, 173, 1.0], [191, 200, 1.0]]}, "1985.tc-1.3": {"snippet": "...rmal history of the translation course at the {U}niversity of {A}msterdam", "weights": [[8, 15, 1.0]]}, "2007.sigirconf_conference-2007.187": {"snippet": "... the development of interactive information retrieval (IR) systems. This paper investigated a simple method to facilitate such a task by looking at aggregated relevance judgements of retrieved documen...", "weights": [[8, 19, 1.0], [35, 46, 1.0], [47, 56, 1.0]]}, "2006.trec_conference-2006.103": {"snippet": "...ment retrieval, passage retrieval, etc.) and statistical classifiers were developed that we use for various classification tasks.", "weights": [[8, 17, 1.0], [27, 36, 1.0]]}, "J11-2009": {"snippet": "...uage Information Retrieval by Jian-Yun Nie\n\n\n Cross-Language Information Retrieval is a compact book introducing a branch of information retrieval that has gained considerable research interest since ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [64, 75, 1.0], [76, 85, 1.0], [128, 139, 1.0], [140, 149, 1.0]]}, "2010.trec_conference-2010.65": {"snippet": "...ment Retrieval: A Way of Thinking that Leads to Method Choices\n\n\n This paper discusses a theory and proposed design for a retrieval artifact using a bag of words (BOW) approach for terms and a standar...", "weights": [[8, 17, 1.0], [125, 134, 1.0]]}, "2012.tois_journal-ir0volumeA30A2.2": {"snippet": "...Peer Information Retrieval: An Overview\n\n\n Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been develo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [157, 168, 1.0], [169, 178, 1.0]]}, "2021.acl-long.57": {"snippet": "...ogue history, the model is forced to better capture dialogue history and generate more informative responses. Experimental results on two benchmark datasets show that our approach can better model dia...", "weights": [[8, 15, 1.0], [64, 71, 1.0]]}, "2003.sigirconf_conference-2003.2": {"snippet": "...e of information retrieval (IR) research, however, needs to be placed in the context of its history and relationship to other fields. Although IR has had a very strong relationship with library and in...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [95, 102, 1.0], [201, 212, 1.0]]}, "2003.clef_workshop-2003w.18": {"snippet": "...h in information access technologies, such as information retrieval, text summarization, question answering, text mining, etc., by providing infrastructure of large-scale evaluation. A brief history, ...", "weights": [[8, 19, 1.0], [49, 60, 1.0], [61, 70, 1.0], [194, 201, 1.0]]}, "2007.ipm_journal-ir0volumeA43A1.2": {"snippet": "...s in information seeking and use. This study investigated the use of search history information in legal information seeking. Qualitative methods were used to explore how attorneys and law librarians ...", "weights": [[8, 19, 1.0], [79, 86, 1.0], [87, 98, 1.0], [108, 119, 1.0]]}, "2015.clef_conference-2015w.86": {"snippet": "...urce Retrieval: Notebook for PAN at CLEF 2015\n\n\n Source retrieval is a part of a plagiarism discovery process, where only a selected set of candidate documents is retrieved from a large corpus of pote...", "weights": [[8, 17, 1.0], [59, 68, 1.0]]}, "W12-6301": {"snippet": "...play information, where knowledge is important for information accumulation, understanding and serving. This presentation will present the history and development of the Question Answering, its relate...", "weights": [[8, 19, 1.0], [54, 65, 1.0], [142, 149, 1.0], [154, 165, 1.0]]}, "2014.ntcir_conference-2014.46": {"snippet": "...ood: History and Development of a Winning Strategy\n\n\n This paper describes and summarizes experience of Masaryk University Math Information Retrieval team (MIRMU) with the mathematical search develope...", "weights": [[8, 15, 1.0], [20, 31, 1.0], [131, 142, 1.0], [143, 152, 1.0]]}, "1997.sigirjournals_journal-ir0volumeA31A1.7": {"snippet": "... and information retrieval consists in introducing the expert system approach:  \"I believe that expert systems research is the new frontier and the next area of development in library information scie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [164, 175, 1.0], [187, 198, 1.0]]}, "2002.cikm_conference-2002.1": {"snippet": "...able information retrieval systems\n\n\n AbstractImplementing scalable information retrieval systems requires the design and development of efficient methods to ingest data from multiple sources, search ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [71, 82, 1.0], [83, 92, 1.0], [125, 136, 1.0]]}, "2016.trec_conference-2016.54": {"snippet": "...ised Information Retrieval System for Clinical Decision Support\n\n\n This article summarizes the approach developed for TREC 2016 Clinical Decision Support Track. In order to address the daunting challe...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.sigirconf_conference-2011.218": {"snippet": "...ics, Information Science and other sources of key ideas that reappear in the current development of Information Retrieval as it applies to Search Engines, Social Media, and Collaborative Systems. They...", "weights": [[8, 19, 1.0], [88, 99, 1.0], [103, 114, 1.0], [115, 124, 1.0]]}, "2020.lincr-1.5": {"snippet": "...ther information present in a history of previous sentences can be used to predict a valence value for the following sentence. We explored both linear and non-linear models applied with a range of dif...", "weights": [[8, 19, 1.0], [33, 40, 1.0]]}, "1991.sigirconf_conference-91.25": {"snippet": "... the History of Optimal Retrievals\n\n\n AbstractLearning techniques are used in IR to exploit userfeedback in order that the system can improve its peflormance with respect to particular queries. This p...", "weights": [[8, 15, 1.0]]}, "2015.ictir_conference-2015.6": {"snippet": "... for Information Retrieval Evaluation\n\n\n ABSTRACTA key component of experimentation in IR is statistical hypothesis testing, which researchers and developers use to make inferences about the effective...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.23": {"snippet": "...rove Information Retrieval Systems and Evaluation\n\n\n AbstractProgress and innovation are driven by experiments, but experimentation is useless without an objective evaluation measure that allow resear...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2018.sigirconf_workshop-2018profs.5": {"snippet": "... the Development of Effective Systems for Professional Legal Search\n\n\n The key objective of an information retrieval (IR) system is to identify and return to the user content relevant or useful in add...", "weights": [[8, 19, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "W17-0509": {"snippet": "Data-driven Morphology and Sociolinguistics for Early {M}odern {D}utch\n\n\n The advent of Early Modern Dutch (starting \u223c1550) marked significant developments in language use in the Netherlands. Examples...", "weights": []}, "2013.sigirconf_conference-2013.205": {"snippet": "...o an information retrieval (IR) system . For instance, the mention of a treatment such as a drug may indicate to a practitioner that a particular diagnosis has been made even if this was not explicitl...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2018.sigirconf_workshop-2018birndl.2": {"snippet": "... and information retrieval communities can lead the way in addressing this problem through the development of automation technologies that facilitate search and synthesis of evidence; but developing t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [98, 109, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2004.sigirconf_conference-2004.7": {"snippet": "...g an information retrieval test collection for spontaneous conversational speech\n\n\n ABSTRACTTest collections model use cases in ways that facilitate evaluation of information retrieval systems. This p...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [166, 177, 1.0], [178, 187, 1.0]]}, "2020.sltu-1.40": {"snippet": "On the Exploration of {E}nglish to {U}rdu Machine Translation\n\n\n Machine Translation is the inevitable technology to reduce communication barriers in today's world. It has made substantial progress in...", "weights": []}, "2013.ictir_conference-2013.14": {"snippet": "Information Retrieval with Time Series Query\n\n\n ABSTRACTWe study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find rele...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [73, 84, 1.0], [85, 94, 1.0], [170, 179, 1.0]]}, "W07-0909": {"snippet": "...ntic Retrieval for Cultural Heritage Appreciation\n\n\n We describe a system which enhances the experience of museum visits by providing users with language-technology-based information retrieval capabil...", "weights": [[8, 17, 1.0], [174, 185, 1.0], [186, 195, 1.0]]}, "2017.sigirconf_conference-2017.248": {"snippet": "... for Information Retrieval: And Related Tasks\n\n\n MOTIVATIONe main task of an Information Retrieval (IR) system is to return relevant documents to users in response to a query. However, this task is in...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [92, 101, 1.0]]}}}
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Not only ...", "weights": []}, "2009.wwwconf_conference-2009.41": {"snippet": "Inverted index compression and query processing with optimized document ordering\n\n\n ABSTRACTWeb search engines use highly optimized compression schemes to decrease inverted index size and improve quer...", "weights": []}, "2000.tois_journal-ir0volumeA18A2.0": {"snippet": "Fast and flexible word searching on compressed text\n\n\n We present a fast compression and decompression technique for natural language texts. The novelties are that (1) decompression of arbitrary porti...", "weights": []}, "O03-4004": {"snippet": "...pus, Zipf 's law for both words in English and characters in Chinese does not hold for all ranks. 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It requires some extra space over the compressed tex...", "weights": []}, "2021.naacl-main.350": {"snippet": "...ngs (Zipf's law of abbreviation), the need for a productive and open-ended vocabulary, local constraints on sequences of symbols, and various other factors all shape the lexicons of the world's langua...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.10": {"snippet": "... of \"Zipf's law\" was cited most (64.8%), followed by \"the principle of least effort\" (24.5%). Although the concept of \"the principle of least effort,\" the focus of HBPLE, was not most frequently obser...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "1999.jasis_journal-ir0volumeA50A3.4": {"snippet": "... the Law of Zipf-Mandelbrot for Multi-Wort Phrases", "weights": [[8, 11, 1.0], [15, 19, 1.0]]}, "2014.ecir_conference-2014.30": {"snippet": "On Inverted Index Compression for Search Engine Efficiency\n\n\n Abstract. Efficient access to the inverted index data structure is a key aspect for a search engine to achieve fast response times to user...", "weights": []}, "N06-5004": {"snippet": "Automatic Spoken Document Processing for Retrieval and Browsing\n\n\n Ever increasing computing power and connectivity bandwidth together with falling storage costs is resulting in overwhelming amounts o...", "weights": []}, "2011.wwwconf_conference-2011.52": {"snippet": "Inverted index compression via online document routing\n\n\n ABSTRACTModern search engines are expected to make documents searchable shortly after they appear on the ever changing Web. To satisfy this re...", "weights": []}, "2019.gwc-1.44": {"snippet": "...ency law with wordnets as sense inventories\n\n\n According to George K. Zipf, more frequent words have more senses. 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The automatic learning method shows about the same performance compared with the manual linguistic a...", "weights": [[8, 16, 1.0], [87, 95, 1.0], [107, 116, 1.0]]}, "2010.ipm_journal-ir0volumeA46A3.3": {"snippet": "Automatic index construction for multimedia digital libraries\n\n\n a b s t r a c tIndexing remains one of the most popular tools provided by digital libraries to help users identify and understand the c...", "weights": [[0, 9, 1.0]]}, "1977.ipm_journal-ir0volumeA13A1.1": {"snippet": "Automatic versus manual indexing", "weights": [[0, 9, 1.0], [24, 32, 1.0]]}, "L02-1157": {"snippet": "... for Automatic Indexing of Multimedia Material", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2003.cikm_conference-2003.55": {"snippet": "...ndly indexing lots of popular documents) are ineffective. We model the competition between search engines as a stochastic game, and propose a reinforcement learning approach to managing search index c...", "weights": [[8, 16, 1.0]]}, "1982.sigirjournals_journal-ir0volumeA16A3.3": {"snippet": "Automatic Indexing: A Tutorial", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2006.wwwconf_conference-2006.116": {"snippet": "CiteSeerx: an architecture and web service design for an academic document search engine\n\n\n ABSTRACTCiteSeer is a scientific literature digital library and search engine which automatically crawls and...", "weights": []}, "2011.sigirconf_conference-2011.212": {"snippet": "The Meta-Dex Suite: generating and analyzing indexes and meta-indexes\n\n\n ABSTRACTOur Meta-dex software suite extracts content and index text from a corpus of PDF files, and generates a meta-index that...", "weights": []}, "2009.clef_workshop-2009w.5": {"snippet": "...h by indexing a sample of query logs from the European Library, and describe its usefulness for multilingual searching. In our analysis of the search logs, we determine the language of the past querie...", "weights": [[8, 16, 1.0]]}, "1997.jasis_journal-ir0volumeA48A12.0": {"snippet": "...with Automatic Indexing and a Relational Thesaurus in a Chinese Information Retrieval System", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "W07-0810": {"snippet": "{A}rabic to {F}rench Sentence Alignment: Exploration of A Cross-language Information Retrieval Approach\n\n\n Sentence alignment consists in estimating which sentence or sentences in the source language ...", "weights": []}, "1992.ipm_journal-ir0volumeA28A5.0": {"snippet": "Automatic Image Indexation to Support Content-Base Retrieval", "weights": [[0, 9, 1.0]]}, "1974.jasis_journal-ir0volumeA25A4.4": {"snippet": "Indexing and automatic significance analysis", "weights": [[0, 8, 1.0], [13, 22, 1.0]]}, "2015.jasis_journal-ir0volumeA66A9.2": {"snippet": "An automatic approach to weighted subject indexing - an empirical study in the biomedical domain", "weights": [[3, 12, 1.0], [42, 50, 1.0]]}, "1978.ipm_journal-ir0volumeA14A2.5": {"snippet": "Automatic indexing using the SLC-II system", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.6": {"snippet": "Relationship between index term specificity and relevance judgment\n\n\n AbstractConcurrent concepts of specificity are discussed and differentiated from each other to investigate the relationship betwee...", "weights": []}, "1970.ipm_journal-ir0volumeA6A4.1": {"snippet": "...s in automatic extracting and indexing", "weights": [[8, 17, 1.0], [33, 41, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.7": {"snippet": "...s to automatic speech recognition (ASR) for web search have been reported , taking advantage of the large data that started to accumulate on voice search logs (Chelba and Schalkwyk 2013), and applying...", "weights": [[8, 17, 1.0]]}, "2003.ecir_conference-2003.48": {"snippet": "Automatic Construction of Theme Melody Index from Music Database for Fast Content-Based Retrievals", "weights": [[0, 9, 1.0]]}, "2002.ismir_conference-2002.22": {"snippet": "Indexing Music Databases Using Automatic Extraction of Frequent Phrases", "weights": [[0, 8, 1.0], [31, 40, 1.0]]}, "2002.sigirconf_conference-2002.55": {"snippet": "Do thumbnail previews help users make better relevance decisions about web search results?\n\n\n ABSTRACTWe describe an empirical evaluation of the utility of thumbnail previews in web search results. Re...", "weights": []}, "2004.spire_conference-2004.22": {"snippet": "An Efficient Index Data Structure with the Capabilities of Suffix Trees and Suffix Arrays for Alphabets of Non-negligible Size\n\n\n Abstract. The suffix tree and the suffix array are fundamental fulltex...", "weights": []}, "1999.jasis_journal-ir0volumeA50A8.2": {"snippet": "Automatic Indexing of Documents from Journal Descriptors: A Preliminary Investigation", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "1991.riao_conference-1991.50": {"snippet": "Automatic image Indexation and retrieval", "weights": [[0, 9, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.6": {"snippet": "Automatic indexing of online health resources for a French quality controlled gateway", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2020.clef_conference-2020w.133": {"snippet": "...ment automatic indexing systems that assist manual indexing of other types of documents like clinical trials. MESINESP corpus: https://zenodo.org/record/3746596.Xo9WTI zaF A", "weights": [[8, 17, 1.0], [18, 26, 1.0], [54, 62, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.10": {"snippet": "...ning automatic and manual indexing. First, when considering various contexts, this study reveals that the combined indexing strategy always obtains the best retrieval performance. Second, when users w...", "weights": [[8, 17, 1.0], [29, 37, 1.0], [118, 126, 1.0]]}, "2001.sigirconf_conference-2001.81": {"snippet": "...h an automatic indexing and classification tool for documents in different languages.In contrast to other automatic indexing systems AUTINDEX does not only extract thesaurus concepts from a text, but ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [109, 118, 1.0], [119, 127, 1.0]]}, "1997.jasis_journal-ir0volumeA48A1.5": {"snippet": "...h to Automatic Indexing of Controlled Index Terms", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2007.ipm_journal-ir0volumeA43A1.6": {"snippet": "...n of indexing strategies with query terms on retrieval effectiveness were studied. Three of five tested combinations involved indexing strategies that used conflation, in the form of normalization. Fu...", "weights": [[8, 16, 1.0], [129, 137, 1.0]]}, "2013.wsdm_conference-2013.9": {"snippet": "...ered indexing in commerce search\n\n\n ABSTRACTA search engine aims to return a set of relevant documents in response to a query, while minimizing the response time. This has led to the use of a tiered i...", "weights": [[8, 16, 1.0]]}, "1989.jasis_journal-ir0volumeA40A2.5": {"snippet": "...h to automatic phrase indexing for document retrieval", "weights": [[8, 17, 1.0], [25, 33, 1.0]]}, "1987.sigirconf_conference-87.12": {"snippet": "Automatic Phrase Indexing for Document Retrieval: An Examination of Syntactic and Non-Syntactic Methods\n\n\n AbstractAn automatic phrase indexing method based on the term discrimination model is describ...", "weights": [[0, 9, 1.0], [17, 25, 1.0], [118, 127, 1.0], [135, 143, 1.0]]}, "W08-0612": {"snippet": "Automatic inference of indexing rules for {MEDLINE}\n\n\n This paper describes the use and customization of Inductive Logic Programming (ILP) to infer indexing rules from MEDLINE citations. Preliminary r...", "weights": [[0, 9, 1.0], [23, 31, 1.0], [148, 156, 1.0]]}, "2008.ipm_journal-ir0volumeA44A4.8": {"snippet": "...auto-indexing method for Arabic text\n\n\n AbstractThis work addresses the information retrieval problem of auto-indexing Arabic documents. Auto-indexing a text document refers to automatically extractin...", "weights": [[8, 16, 1.0], [113, 121, 1.0], [145, 153, 1.0]]}, "2013.cikm_workshop-2013dare.2": {"snippet": "...cise indexing or quick, automatic filtering to more general concepts. The accuracy of medical indexing systems varies widely, based on the degree of automation and capacity for semantic analysis.", "weights": [[8, 16, 1.0], [27, 36, 1.0], [97, 105, 1.0]]}, "C69-0301": {"snippet": ".... An indexing method using the language in which the document was written corrects this failing, but eliminates the generality of the previous approach. A compromise between indexing generality and sp...", "weights": [[8, 16, 1.0], [177, 185, 1.0]]}, "1988.sigirconf_conference-88.23": {"snippet": "The Automatic Indexing System AIR/PHYS \u2013 From Research to Application", "weights": [[4, 13, 1.0], [14, 22, 1.0]]}, "2004.sigirconf_conference-2004.40": {"snippet": "Assigning identifiers to documents to enhance the clustering property of fulltext indexes\n\n\n ABSTRACTWeb Search Engines provide a large-scale text document retrieval service by processing huge Inverte...", "weights": []}, "1981.sigirjournals_journal-ir0volumeA16A2.5": {"snippet": "... for Automatic Indexing", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2010.sigirconf_conference-2010.13": {"snippet": "Query forwarding in geographically distributed search engines\n\n\n ABSTRACTQuery forwarding is an important technique for preserving the result quality in distributed search engines where the index is g...", "weights": []}, "A83-1023": {"snippet": "... the Automatic Compilation of Printed Subject Indexes and for Online Database Access\n\n\n The nature of the problem and earlier approaches to the automatic compilation of printed subject indexes are rev...", "weights": [[8, 17, 1.0], [147, 156, 1.0]]}, "W99-1009": {"snippet": "... the indexing project is conducted in co-operation with the Swedish Parliament, where a staff of professional indexers currently is investigating the utility of automatic and semi-automatic indexing t...", "weights": [[8, 16, 1.0], [164, 173, 1.0], [183, 192, 1.0], [193, 201, 1.0]]}, "2008.sigirconf_conference-2008.201": {"snippet": "...ment indexing. For example, the documents can be clustered based on task types, where each document can fall into several clusters. Ranking algorithm can use this index in addition to a content-based ...", "weights": [[8, 16, 1.0]]}, "1976.ipm_journal-ir0volumeA12A1.3": {"snippet": "Automatic indexing using term discrimination and term precision measurements", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "1995.cikm_conference-95.8": {"snippet": "The Index Suggestion Problem for Object Database Applications\n\n\n AbstractThe index suggestion problem for an object database management system (ODBMS) application is to automatically identify indexes ...", "weights": []}, "C80-1048": {"snippet": "Automatic Compilation of {M}odern {C}hinese Concordances\n\n\n An automatic indexing experiment in Chinese is described. The first very large volume of modern Chinese concordances (two sets of one millio...", "weights": [[0, 9, 1.0], [63, 72, 1.0], [73, 81, 1.0]]}, "1993.sigirconf_conference-93.3": {"snippet": "Automatic Indexing Based on Bayesian Inference Networks\n\n\n AbstractIn this paper, aBayesian inference network model for automatic indexing with index terms (descriptors) from a prescribed vocabulary i...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [120, 129, 1.0], [130, 138, 1.0]]}, "2005.cikm_conference-2005.78": {"snippet": "...ble.\nINDEXING ALGORITHMOur indexing algorithm is based on a re-merge indexupdate strategy, which Lester et al.  found to have good performance. In the re-merge strategy, new documents are added to a d...", "weights": [[8, 16, 1.0], [30, 38, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.8": {"snippet": "...mage indexing systems. The automatic indexing task is formulated as one of image annotation, or automatic metadata generation for images. The evaluation is composed of two individual methods. First, t...", "weights": [[8, 16, 1.0], [30, 39, 1.0], [40, 48, 1.0], [99, 108, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.4": {"snippet": "Automatic P h r a s e Indexing for Document Retrieval: An Examination of Syntactic and Non-Syntactic Methods\n\n\n AbstractAn automatic phrase indexing method based on the term discrimination model is de...", "weights": [[0, 9, 1.0], [22, 30, 1.0], [123, 132, 1.0], [140, 148, 1.0]]}, "2011.sigirconf_conference-2011.75": {"snippet": "Enhanced results for web search\n\n\n ABSTRACT \"Ten blue links\" have defined web search results for the last fifteen years -snippets of text combined with document titles and URLs. In this paper, we esta...", "weights": []}, "1974.jasis_journal-ir0volumeA25A5.3": {"snippet": "... for automatic indexing", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "W93-0302": {"snippet": "Robust Text Processing in Automated Information Retrieval\n\n\n This paper outlines a prototype text retrieval system which uses relatively advanced natural language processing techniques in order to enh...", "weights": []}, "2017.chiir_conference-2017.64": {"snippet": "... and automatic methods are required. This PhD project explores the possible role and usefulness of named entities in an automatic indexing process. This is done by annotating named entities in textual...", "weights": [[8, 17, 1.0], [123, 132, 1.0], [133, 141, 1.0]]}, "O99-1007": {"snippet": "...lary Indexing for Text Retrieval", "weights": [[8, 16, 1.0]]}, "2008.sigirconf_conference-2008.189": {"snippet": "Ice-tea: an interactive cross-language search engine with translation enhancement\n\n\n EXTENDED ABSTRACTIn cross-language information retrieval (CLIR), relevance feedback (RF) has been demonstrated to b...", "weights": []}, "C90-3085": {"snippet": "Automatic Indexing and Government-Binding Theory\n\n\n This project note describes a systern that receives, parses, indexes, and routes news reports. The core of this ,'mtomatic indexer is a parser based...", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "2011.wwwconf_conference-2011.52": {"snippet": "...ver, indexing by merely appending documents, results in larger index sizes since document reordering for index compactness is no longer performed. This, in turn, degrades search query processing perfo...", "weights": [[8, 16, 1.0]]}, "1990.jasis_journal-ir0volumeA41A2.1": {"snippet": "...r an automatic conceptual phrase-indexing system", "weights": [[8, 17, 1.0], [36, 44, 1.0]]}, "X96-1030": {"snippet": "...text indexing and search backbone provided by the NIST's Prise engine. The original Prise has been modified to allow handling of multi-word phrases, differential term weighting schemes, automatic quer...", "weights": [[8, 16, 1.0], [189, 198, 1.0]]}, "2006.wwwconf_conference-2006.135": {"snippet": "...d an automatic user preference capture and an automatic information clipping function based on a Personalized Information Access technique. In this paper, those techniques will be demonstrated by show...", "weights": [[8, 17, 1.0], [49, 58, 1.0]]}, "2001.cikm_conference-2001.26": {"snippet": "... the indexing vocabulary, it is of little use if it is not used effectively in query formulation. This paper demonstrates that techniques for mapping user queries into the controlled indexing vocabula...", "weights": [[8, 16, 1.0], [186, 194, 1.0]]}, "1991.riao_conference-1991.35": {"snippet": "Automatic indexing using selective NLP and first-order thesauri", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "W07-1026": {"snippet": "Automatic Indexing of Specialized Documents: Using Generic vs. Domain-Specific Document Representations\n\n\n The shift from paper to electronic documents has caused the curation of information sources i...", "weights": [[0, 9, 1.0], [10, 18, 1.0]]}, "C98-1094": {"snippet": "...t in automatic indexing through concept combination. The increase in the volume of indexing is 10.5% for free indexing and 52.3% for controlled indexing. 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We illustrate that \u03b1 is related to the analytic features of syntax and \u03b2 + \u03b3 to those of morphology in natural languages from an investigati...", "weights": [[8, 11, 1.0], [31, 35, 1.0], [47, 50, 1.0]]}, "2001.jasis_journal-ir0volumeA52A12.3": {"snippet": "... and Zipf models", "weights": [[8, 12, 1.0]]}, "2010.jasis_journal-ir0volumeA61A12.15": {"snippet": "...ka's law and Zipf's law", "weights": [[8, 11, 1.0], [16, 20, 1.0], [23, 26, 1.0]]}, "2011.trec_conference-2011.56": {"snippet": "Learning Task Experiments in the TREC 2011 Legal Track\n\n\n The Learning Task of the TREC 2011 Legal Track investigated the effectiveness of e-Discovery search techniques at selecting training examples ...", "weights": []}, "2012.sigirconf_conference-2012.170": {"snippet": "Investigating performance predictors using monte carlo simulation and score distribution models\n\n\n ABSTRACTThe standard deviation of scores in the top k documents of a ranked list has been shown to be...", "weights": []}, "2012.spire_conference-2012.15": {"snippet": "A Zipf-Like Distant Supervision Approach for Multi-document Summarization Using Wikinews Articles", "weights": [[2, 6, 1.0]]}, "1997.sigirconf_conference-97.3": {"snippet": "On Relevance Weights with Little Relevance Information\n\n\n The relationsh~p between the Robertson/Sptxck Jones relevancxi mighting formula and the Croft/Harper version for no relevanee information is d...", "weights": []}, "1986.jasis_journal-ir0volumeA37A5.4": {"snippet": "...ka's Law, Bradford's Law, and Zipf's Law", "weights": [[8, 11, 1.0], [24, 27, 1.0], [33, 37, 1.0], [40, 43, 1.0]]}, "2015.sigirconf_conference-2015.61": {"snippet": "A Probabilistic Model for Information Retrieval Based on Maximum Value Distribution\n\n\n ABSTRACTThe main goal of a retrieval model is to measure the degree of relevance of a document with respect to th...", "weights": []}, "O03-4004": {"snippet": "...pus, Zipf 's law for both words in English and characters in Chinese does not hold for all ranks. The frequency falls below the frequency predicted by Zipf's law for English words for rank greater tha...", "weights": [[8, 12, 1.0], [16, 19, 1.0], [154, 158, 1.0], [161, 164, 1.0]]}, "2002.ecir_conference-2002.13": {"snippet": "...ve a law relating the probability of a given term frequency in a document and its the length. A similar law was derived by Mandelbrot. We exploit the paretian distribution to obtain a term frequency n...", "weights": [[8, 11, 1.0], [107, 110, 1.0]]}, "2010.spire_conference-2010.32": {"snippet": "... and Zipf-Like Scoring Function for Web Document Similarity Retrieval", "weights": [[8, 12, 1.0]]}, "P06-1076": {"snippet": "...ower law distribution with a slope coefficient of 1.6 for documents and 1.7 for sentences and terms. We conclude with an analysis of IDF stability with respect to random, journal, and section partitio...", "weights": [[8, 11, 1.0]]}, "W02-0606": {"snippet": "Unsupervised discovery of morphologically related words based on orthographic and semantic similarity\n\n\n We present an algorithm that takes an unannotated corpus as its input, and returns a ranked lis...", "weights": []}, "2016.ipm_journal-ir0volumeA52A4.10": {"snippet": "... of \"Zipf's law\" was cited most (64.8%), followed by \"the principle of least effort\" (24.5%). Although the concept of \"the principle of least effort,\" the focus of HBPLE, was not most frequently obser...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "1999.jasis_journal-ir0volumeA50A3.4": {"snippet": "... the Law of Zipf-Mandelbrot for Multi-Wort Phrases", "weights": [[8, 11, 1.0], [15, 19, 1.0]]}, "2019.gwc-1.44": {"snippet": "...ency law with wordnets as sense inventories\n\n\n According to George K. Zipf, more frequent words have more senses. We have tested this law using corpora and wordnets of English, Spanish, Portuguese, Fr...", "weights": [[8, 11, 1.0], [73, 77, 1.0], [137, 140, 1.0]]}, "1981.sigirconf_conference-81.5": {"snippet": "A Comparison of Search Term Weighting: Term Relevance vs. Inverse Document Frequency\n\n\n AbstractThe term relevance weighting method has been shown to produce optimal information retrieval queries unde...", "weights": []}, "1982.jasis_journal-ir0volumeA33A5.5": {"snippet": "...n of Zipf's Law and Its Application to the Bibliographic Database Environment", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2020.wosp-1.5": {"snippet": "Term-Recency for {TF}-{IDF}, {BM}25 and {USE} Term Weighting\n\n\n Effectiveness of a recommendation in an Information Retrieval (IR) system is determined by relevancy scores of retrieved results. Term w...", "weights": []}, "W98-1218": {"snippet": "... The law named for him is ubiquitous, but Zipf did not actually discover the law so much as provide a plausible explanation. Others have proposed modifications to Zipf's Law, and closer examination un...", "weights": [[8, 11, 1.0], [45, 49, 1.0], [80, 83, 1.0], [166, 170, 1.0], [173, 176, 1.0]]}, "2009.ipm_journal-ir0volumeA45A4.6": {"snippet": "...ower-law regularities have been discovered behind many complex natural and social phenomenons. We discover that the power-law regularities, especially the Zipf's and Heaps' laws, also exist in large-s...", "weights": [[8, 11, 1.0], [125, 128, 1.0], [158, 162, 1.0]]}, "2012.airs_conference-2012.0": {"snippet": "Information Retrieval Technology, 8th Asia Information Retrieval Societies Conference, AIRS 2012, Tianjin, China, December 17-19, 2012. Proceedings\n\n\n The annual AIRS conference has established its re...", "weights": []}, "1987.ipm_journal-ir0volumeA23A3.1": {"snippet": "...s of Zipf's law: An index approach", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}, "2005.ipm_journal-ir0volumeA41A6.4": {"snippet": "...g of Zipf-type. A generalization of Zipf distribution (a size-frequency form of the Zipf\u00d5s law), named the generalized Zipf distribution, is introduced. Two main characterizations of the generalized Z...", "weights": [[8, 12, 1.0], [39, 43, 1.0], [94, 97, 1.0], [122, 126, 1.0], [202, 206, 1.0]]}, "E17-4009": {"snippet": "...t to Zipf's law, an empirical law referring to the phenomenon that many types of data in social sciences can be approximated with a Zipfian distribution. Additionally, we also analyse Benford's law, i...", "weights": [[8, 12, 1.0], [15, 18, 1.0], [33, 36, 1.0], [197, 200, 1.0]]}, "2021.ccl-1.93": {"snippet": "...{'}s law: evidence from danmu comments\n\n\n Zipf's law is a succinct yet powerful mathematical law in linguistics. However, the meaningfulness and units of the law have remained controversial. The curre...", "weights": [[8, 11, 1.0], [45, 49, 1.0], [52, 55, 1.0], [96, 99, 1.0], [161, 164, 1.0]]}, "2018.jasis_journal-ir0volumeA69A11.6": {"snippet": "...s of Zipf's meaning-frequency law", "weights": [[8, 12, 1.0], [33, 36, 1.0]]}, "2010.wsdm_conference-2010.38": {"snippet": "Revisiting globally sorted indexes for efficient document retrieval\n\n\n ABSTRACTThere has been a large amount of research on efficient document retrieval in both IR and web search areas. One important ...", "weights": []}, "2010.cikm_conference-2010.109": {"snippet": "Reverted indexing for feedback and expansion\n\n\n ABSTRACTTraditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a ...", "weights": []}, "2017.sigirconf_conference-2017.41": {"snippet": "...ago, Zipf postulated that the word frequency distribution of languages is a power law, i.e., it is a straight line on a log-log plot. Over the years, this phenomenon has been documented and studied ex...", "weights": [[8, 12, 1.0], [85, 88, 1.0]]}, "W96-0106": {"snippet": "...{'}s Law\n\n\n An asymptote is derived from Turing's local reestimation formula for population frequencies, and a local reestimation formula is derived from Zipf's law for the asymptotic behavior of popu...", "weights": [[8, 11, 1.0], [157, 161, 1.0], [164, 167, 1.0]]}, "2015.wwwconf_conference-2015c.302": {"snippet": "Terms in Time and Times in Context: A Graph-based Term-Time Ranking Model\n\n\n ABSTRACTApproaches in support of the extraction and exploration of temporal information in documents provide an important i...", "weights": []}, "2012.ipm_journal-ir0volumeA48A5.11": {"snippet": "A split-list approach for relevance feedback in information retrieval\n\n\n a b s t r a c tIn this paper we present a new algorithm for relevance feedback (RF) in information retrieval. Unlike convention...", "weights": []}, "2012.wwwconf_conference-2012.31": {"snippet": "...e if Zipf's law is a good description of the frequency with which passwords are chosen. We use a number of standard statistics, which measure the security of password distributions, to see if modellin...", "weights": [[8, 12, 1.0], [15, 18, 1.0]]}}}
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Term feedback refers to the feedback from a user on specific terms regarding their relevance to a target image. Previous stud...", "weights": [[8, 12, 1.0], [16, 20, 1.0], [26, 34, 1.0], [83, 91, 1.0], [106, 114, 1.0], [122, 126, 1.0], [161, 170, 1.0]]}, "2013.ipm_journal-ir0volumeA49A5.5": {"snippet": "...loys user models to individualize search results to a particular user's interests . Relevance feedback (including implicit feedback from interaction behavior and biometrics from physiological sensors)...", "weights": [[8, 12, 1.0], [37, 43, 1.0], [68, 72, 1.0], [87, 96, 1.0], [97, 105, 1.0], [126, 134, 1.0]]}, "2012.sigirconf_conference-2012.197": {"snippet": "...icit relevance feedback\n\n\n ABSTRACTUsers' search activity has been used as implicit feedback to model search interests and improve the performance of search systems. In search engines, this behavior u...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [45, 51, 1.0], [87, 95, 1.0], [105, 111, 1.0], [153, 159, 1.0], [172, 178, 1.0]]}, "2017.tois_journal-ir0volumeA35A3.0": {"snippet": "...icit relevance feedback for commercial search engines. As existing studies have shown that the search result position in a search engine result page (SERP) has a very strong influence on users' examin...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [42, 48, 1.0], [98, 104, 1.0], [126, 132, 1.0], [133, 139, 1.0]]}, "I11-1116": {"snippet": "...n in user feedback and in each document in the search results using latent Dirichlet allocation, and then we modify the search results so that documents with a similar topic distribution to that of th...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [50, 56, 1.0], [123, 129, 1.0]]}, "2009.trec_conference-2009.73": {"snippet": "...009: Relevance Feedback Track\n\n\n In the Relevance Feedback (RF) task the user is directly involved in the search process: given an initial set of results, he specifies if they are relevant or not to t...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [43, 52, 1.0], [53, 61, 1.0], [76, 80, 1.0], [109, 115, 1.0]]}, "2013.wwwconf_conference-2013.105": {"snippet": "...ting user engagement change with degraded search relevance\n\n\n ABSTRACTUser engagement in search refers to the frequency for users (re-)using the search engine to accomplish their tasks. Among factors ...", "weights": [[8, 12, 1.0], [45, 51, 1.0], [52, 61, 1.0], [92, 98, 1.0], [148, 154, 1.0], [155, 161, 1.0]]}, "I08-1068": {"snippet": "... the relevance feedback obtained from the user. The user profile thus learnt is applied in a re-ranking phase to rescore the search results retrieved using an underlying search engine. We evaluate our...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [45, 49, 1.0], [55, 59, 1.0], [128, 134, 1.0], [173, 179, 1.0], [180, 186, 1.0]]}, "C10-2068": {"snippet": "...ated Relevance Feedback: A Prognostic Search approach\n\n\n Implicit relevance feedback has proved to be a important resource in improving search accuracy and personalization. However, researchers who re...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [41, 47, 1.0], [69, 78, 1.0], [79, 87, 1.0], [139, 145, 1.0]]}, "2005.sigirconf_conference-2005.6": {"snippet": "...icit relevance feedback\n\n\n ABSTRACTImplicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [47, 56, 1.0], [57, 65, 1.0], [98, 104, 1.0]]}, "2012.tois_journal-ir0volumeA30A4.0": {"snippet": "...ency Search Results with User Click Feedback\n\n\n Traditional machine-learned ranking systems for Web search are often trained to capture stationary relevance of documents to queries, which have limited...", "weights": [[8, 14, 1.0], [28, 32, 1.0], [39, 47, 1.0], [103, 109, 1.0], [150, 159, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.1": {"snippet": "...s of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We ...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [51, 57, 1.0], [148, 152, 1.0], [185, 191, 1.0], [192, 198, 1.0]]}, "2006.tois_journal-ir0volumeA24A2.2": {"snippet": "... the relevance feedback process. In detail, in each round of relevance feedback two simple learners are trained from the labeled data, that is, images from user query and user feedback. Each learner t...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [64, 73, 1.0], [74, 82, 1.0], [159, 163, 1.0], [174, 178, 1.0], [179, 187, 1.0]]}, "2003.sigirjournals_journal-ir0volumeA37A2.1": {"snippet": "...icit feedback for inferring user preference: a bibliography\n\n\n Relevance feedback has a history in information retrieval that dates back well over thirty years (c.f [SL96]). Relevance feedback is typi...", "weights": [[8, 16, 1.0], [31, 35, 1.0], [66, 75, 1.0], [76, 84, 1.0], [177, 186, 1.0], [187, 195, 1.0]]}, "2003.sigirconf_conference-2003.102": {"snippet": "... the search engine.(3) Search Engine Discovery: If a user submits a URL that does not have a search engine on the Webpage, this component will crawl Webpages \"nearby\" to find search engine interfaces....", "weights": [[8, 14, 1.0], [15, 21, 1.0], [26, 32, 1.0], [33, 39, 1.0], [56, 60, 1.0], [96, 102, 1.0], [103, 109, 1.0], [178, 184, 1.0], [185, 191, 1.0]]}, "2019.ecir_conference-20192.51": {"snippet": "...s of search intents and relevance over the entire search session. In multi-page search , user clicks of documents and dwell times are implicit signals of relevance. In professional search such as pate...", "weights": [[8, 14, 1.0], [27, 36, 1.0], [53, 59, 1.0], [83, 89, 1.0], [92, 96, 1.0], [157, 166, 1.0], [184, 190, 1.0]]}, "2005.sigirconf_conference-2005.7": {"snippet": "...tual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval acc...", "weights": [[8, 12, 1.0], [17, 23, 1.0], [100, 108, 1.0]]}, "2016.sigirconf_conference-2016.209": {"snippet": "...ling User Feedback in Dynamic Search and Browsing", "weights": [[8, 12, 1.0], [13, 21, 1.0], [33, 39, 1.0]]}, "2009.sigirconf_workshop-2009uiir.5": {"snippet": "...eous user intent. This user intent model is combined with the initial search result rankings in order to present recommended search results to the user as well as to reorder subsequent search engine r...", "weights": [[8, 12, 1.0], [26, 30, 1.0], [73, 79, 1.0], [128, 134, 1.0], [150, 154, 1.0], [188, 194, 1.0], [195, 201, 1.0]]}, "2010.wwwconf_conference-2010.91": {"snippet": "...cial search engine with over 40 million rare queries demonstrate the superiority of our framework, with statistical significance, over the traditional random walk models and pseudo-relevance feedback ", "weights": [[8, 14, 1.0], [15, 21, 1.0], [184, 193, 1.0], [194, 202, 1.0]]}, "2007.sigirconf_conference-2007.212": {"snippet": "... web search engines, in the context of simple search task, are more concerned with precision than completeness of the search. This stemmed from the stronger correlation between users' satisfaction wit...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [121, 127, 1.0]]}, "2008.sigirconf_conference-2008.189": {"snippet": "...uage search engine with translation enhancement\n\n\n EXTENDED ABSTRACTIn cross-language information retrieval (CLIR), relevance feedback (RF) has been demonstrated to be effective in improving retrieval...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [119, 128, 1.0], [129, 137, 1.0]]}, "2015.wwwconf_conference-2015.106": {"snippet": "...icit Relevance Labels for Personalized Web Search\n\n\n ABSTRACTImplicit feedback from users of a web search engine is an essential source providing consistent personal relevance labels from the actual p...", "weights": [[8, 17, 1.0], [46, 52, 1.0], [73, 81, 1.0], [102, 108, 1.0], [109, 115, 1.0], [169, 178, 1.0]]}, "2018.ecir_conference-2018.17": {"snippet": "...s of search results by either the explicit relevance annotations from external assessors or implicit behavior signals from users. Because the mobile search is different from the desktop search in term...", "weights": [[8, 14, 1.0], [46, 55, 1.0], [152, 158, 1.0], [189, 195, 1.0]]}, "2013.wwwconf_conference-2013.57": {"snippet": "... the user's feedback from the first page. We show that document correlations used in result diversification have a significant impact on relevance feedback and its effectiveness over a search session....", "weights": [[8, 12, 1.0], [15, 23, 1.0], [140, 149, 1.0], [150, 158, 1.0], [188, 194, 1.0]]}, "2007.cikm_conference-2007.31": {"snippet": "...icit feedback models personalized for each user and tailored for each search task. We use rich interaction logs (and associated metadata such as relevance judgments), gathered during a longitudinal us...", "weights": [[8, 16, 1.0], [46, 50, 1.0], [73, 79, 1.0], [148, 157, 1.0], [201, 205, 1.0]]}, "2017.sigirconf_conference-2017.69": {"snippet": "...eral user clicking strategies. We highlight in particular what we de ne as a smart strategy, leading to a Nash equilibrium, where every user reaches the maximum possible privacy while preserving the a...", "weights": [[8, 12, 1.0], [139, 143, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.5": {"snippet": "...tive relevance of returned results. However, exploiting such information can help search engines satisfy users by tailoring search results. Interestingly, user interests typically follow the clusterin...", "weights": [[8, 17, 1.0], [85, 91, 1.0], [127, 133, 1.0], [158, 162, 1.0]]}, "2008.sigirconf_conference-2008.52": {"snippet": "...User feedback is considered to be a critical element in the information seeking process, especially in relation to relevance assessment. Current feedback techniques determine content relevance with re...", "weights": [[8, 16, 1.0], [118, 127, 1.0], [148, 156, 1.0], [186, 195, 1.0]]}, "2012.sigirconf_conference-2012.188": {"snippet": "...d on user feedback\n\n\n ABSTRACTUntil recently, the lack of user activity on search results was perceived as a sign of user dissatisfaction from retrieval performance. However, recent studies have repor...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [61, 65, 1.0], [78, 84, 1.0], [120, 124, 1.0]]}, "2007.ipm_journal-ir0volumeA43A3.15": {"snippet": "...ance feedback information to the search engine. We are interested in how that relevance feedback can be used to improve the search results quality for all users, not just the current user. For example...", "weights": [[8, 16, 1.0], [36, 42, 1.0], [43, 49, 1.0], [81, 90, 1.0], [91, 99, 1.0], [127, 133, 1.0], [186, 190, 1.0]]}, "2015.trec_conference-2015.58": {"snippet": "... via User Feedback: Georgetown University at TREC 2015 DD Track\n\n\n There are two principal components involved in a search process, the user and the search engine. In TREC DD, the user is modeled by a...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [119, 125, 1.0], [139, 143, 1.0], [152, 158, 1.0], [159, 165, 1.0], [183, 187, 1.0]]}, "2006.sigirconf_conference-2006.20": {"snippet": "... for relevance feedback in web search\n\n\n ABSTRACTWe study the effect of user supplied relevance feedback in improving web search results. Rather than using query refinement or document similarity meas...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [34, 40, 1.0], [75, 79, 1.0], [89, 98, 1.0], [99, 107, 1.0], [125, 131, 1.0]]}, "2011.sigirconf_conference-2011.12": {"snippet": "... and relevance\n\n\n ABSTRACTFreshness of results is important in modern web search. Failing to recognize the temporal aspect of a query can negatively affect the user experience, and make the search eng...", "weights": [[8, 17, 1.0], [77, 83, 1.0], [163, 167, 1.0], [193, 199, 1.0], [200, 206, 1.0]]}, "2020.mir_workshop-2020lsc.6": {"snippet": "...elog Search Engine with User Feedback", "weights": [[8, 14, 1.0], [15, 21, 1.0], [27, 31, 1.0], [32, 40, 1.0]]}, "2018.sigirconf_conference-2018.46": {"snippet": "...lick user experience. Very commonly, the user rst clicked an item and then found it is not what he wanted after reading its content, which shows there is a gap between user click and user actual prefe...", "weights": [[8, 12, 1.0], [44, 48, 1.0], [171, 175, 1.0], [186, 190, 1.0]]}, "2006.sigirconf_conference-2006.124": {"snippet": "...hoo! Search. We consider these refinements as sub-concepts which help define user intent and use them to improve search relevance. The results obtained via live testing on Yahoo! Search are encouragin", "weights": [[8, 14, 1.0], [80, 84, 1.0], [116, 122, 1.0], [123, 132, 1.0], [182, 188, 1.0]]}, "2010.wwwconf_conference-2010.185": {"snippet": "Are search engine users equally reliable?\n\n\n ABSTRACTIn this paper, we study on the reliability of search engine users using click-through data. We proposed a graph-based approach to evaluate user rel...", "weights": [[4, 10, 1.0], [11, 17, 1.0], [99, 105, 1.0], [106, 112, 1.0], [192, 196, 1.0]]}, "2021.ecir_conference-20211.41": {"snippet": "...onal search and modern Web search engines. Prompting the user for clarification in a search session can be very beneficial to the system as the user's explicit feedback helps the system improve retrie...", "weights": [[8, 14, 1.0], [30, 36, 1.0], [60, 64, 1.0], [88, 94, 1.0], [147, 151, 1.0], [163, 171, 1.0]]}, "2013.sigirconf_conference-2013.157": {"snippet": "...icit feedback in local search tasks\n\n\n ABSTRACTModern search engines make extensive use of people's contextual information to finesse result rankings. Using a searcher's location provides an especiall...", "weights": [[8, 16, 1.0], [26, 32, 1.0], [57, 63, 1.0]]}, "2014.wsdm_conference-2014.21": {"snippet": "...s on search results are the most widely used behavioral signals for predicting search satisfaction. Even though clicks are correlated with satisfaction, they can also be noisy. Previous work has shown...", "weights": [[8, 14, 1.0], [82, 88, 1.0]]}, "2003.tois_journal-ir0volumeA21A3.0": {"snippet": "...on a relevance feedback or genetic algorithm using dynamic query contexts can help casual users search the Internet. These algorithms can draw on implicit user feedback based on the surrounding links ...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [99, 105, 1.0], [158, 162, 1.0], [163, 171, 1.0]]}, "2002.sigirconf_conference-2002.55": {"snippet": "...box. Relevance Prediction: the user inspects the search results page and guesses which items will lead to the desired information. Relevance Evaluation: the user attempts to locate the desired informa...", "weights": [[8, 17, 1.0], [34, 38, 1.0], [52, 58, 1.0], [134, 143, 1.0], [160, 164, 1.0]]}, "2005.sigirconf_conference-2005.116": {"snippet": "...icit user feedback\n\n\n ABSTRACTMeasuring the information retrieval effectiveness of Web search engines can be expensive if human relevance judgments are required to evaluate search results. Using impli...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [90, 96, 1.0], [131, 140, 1.0], [176, 182, 1.0]]}, "2005.tois_journal-ir0volumeA23A2.0": {"snippet": "... the search experience is the collection of implicit user behavior measures (implicit measures) as indications of user interest and user satisfaction. Rather than having to submit explicit user feedba...", "weights": [[8, 14, 1.0], [56, 60, 1.0], [117, 121, 1.0], [135, 139, 1.0], [192, 196, 1.0], [197, 205, 1.0]]}, "2013.cikm_conference-2013.284": {"snippet": "...s of search satisfaction\n\n\n ABSTRACTSearch engines need to model user satisfaction to improve their services. Since it is not practical to request feedback on searchers' perceptions and search outcome...", "weights": [[8, 14, 1.0], [68, 72, 1.0], [150, 158, 1.0], [189, 195, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.9": {"snippet": "...ated user queries from multiple streams of news and user-generated content. Finally, the news-related content integration component aggregates the previously ranked content for the user query into the...", "weights": [[8, 12, 1.0], [55, 59, 1.0], [184, 188, 1.0]]}, "2016.sigirconf_conference-2016.64": {"snippet": "...icit relevance feedback for improving search ranking, beyond using clicks on images displayed in the search results page. In this paper we propose a number of implicit relevance feedback features base...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [41, 47, 1.0], [104, 110, 1.0], [171, 180, 1.0], [181, 189, 1.0]]}, "2019.wsdm_conference-2019.110": {"snippet": "... Web search to image search. To better understand user intent and behavior in image search scenarios, we plan to conduct the labbased user study, eld study and commercial search log analysis. We then ...", "weights": [[8, 14, 1.0], [24, 30, 1.0], [53, 57, 1.0], [87, 93, 1.0], [137, 141, 1.0], [174, 180, 1.0]]}, "2017.sigirconf_conference-2017.238": {"snippet": "...ct a user's rating on venues by exploiting explicit feedback (e.g. users' ratings on venues). However, such explicit feedback may not be available, particularly for inactive users, while implicit feed...", "weights": [[8, 12, 1.0], [55, 63, 1.0], [120, 128, 1.0], [199, 207, 1.0]]}, "2017.wsdm_conference-2017.16": {"snippet": "...ment Relevance Affect the Searcher's Perception of Time?\n\n\n ABSTRACTTime plays an essential role in multiple areas of Information Retrieval (IR) studies such as search evaluation, user behavior analys...", "weights": [[8, 17, 1.0], [95, 99, 1.0], [164, 170, 1.0], [183, 187, 1.0]]}, "2016.sigirconf_conference-2016.118": {"snippet": "...text feedback. We collect in situ feedback from a large commercial search engine, and compare this feedback with the judgments provided by trained assessors. We find that in situ feedback differs sign...", "weights": [[8, 16, 1.0], [37, 45, 1.0], [70, 76, 1.0], [77, 83, 1.0], [102, 110, 1.0], [182, 190, 1.0]]}, "2014.sigirconf_conference-2014.103": {"snippet": "... the search engine) and 40s of \"client-side\" dwell time (observable from the browser). Since search engines can only observe server-side actions (i.e., activity on the search engine result page), serv...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [96, 102, 1.0], [171, 177, 1.0], [178, 184, 1.0]]}, "2003.jasis_journal-ir0volumeA54A6.5": {"snippet": "...ting user search behavior into relevance feedback", "weights": [[8, 12, 1.0], [13, 19, 1.0], [34, 43, 1.0], [44, 52, 1.0]]}, "2005.wwwconf_conference-2005si.46": {"snippet": "...ng a search engine's efficiency and effectiveness, each user has his/her own preference and is highly subjective in judging the relevance of the search results.We have proposed a formal evaluation mod...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [59, 63, 1.0], [131, 140, 1.0], [148, 154, 1.0]]}, "2005.wwwconf_conference-2005si.112": {"snippet": "...s.\n2.RELEVANCE FEEDBACKSearch results are typically presented to the user as a ranked list of documents, in the decreasing order of relevance to the user's query. In a system that involves user releva...", "weights": [[8, 17, 1.0], [72, 76, 1.0], [135, 144, 1.0], [152, 156, 1.0], [192, 196, 1.0], [197, 206, 1.0]]}, "2009.cikm_conference-2009.256": {"snippet": "...ring relevance for clicks\n\n\n ABSTRACTMining feedback information from user click-through data is an important issue for modern Web retrieval systems in terms of architecture analysis, performance eval...", "weights": [[8, 17, 1.0], [47, 55, 1.0], [73, 77, 1.0]]}, "2002.ipm_journal-ir0volumeA38A5.4": {"snippet": "... the role of context of user relevance feedback", "weights": [[8, 12, 1.0], [27, 31, 1.0], [32, 41, 1.0], [42, 50, 1.0]]}, "2018.ictir_conference-2018.1": {"snippet": "...icit user feedback (such as clicks and dwell time) is an important source of data for modern search engines. While heavily biased , it is cheap to collect and particularly useful for user-centric retr...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [96, 102, 1.0], [186, 190, 1.0]]}}}
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multimedia information retrieval applications, contentbased image retrieval is essential for retrieving relevant mnltimedia documents. 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Unfortunately, due to the un-structuredness of Internet envir...", "weights": [[8, 18, 1.0], [19, 28, 1.0], [99, 109, 1.0]]}, "2000.riao_conference-2000.60": {"snippet": "...ased Multimedia Information Retrieval", "weights": [[8, 18, 1.0], [19, 30, 1.0], [31, 40, 1.0]]}, "1994.cikm_conference-94.19": {"snippet": "...with multimedia features and on the other with logic-based models of information retrieval to truly combine structure and content information about the documents in question.", "weights": [[8, 18, 1.0], [72, 83, 1.0], [84, 93, 1.0], [133, 144, 1.0]]}, "2013.clef_conference-2013w.103": {"snippet": "Multimedia Information Modeling and Retrieval (MRIM) /Laboratoire d'Informatique de Grenoble (LIG) at CHiC2013", "weights": [[0, 10, 1.0], [11, 22, 1.0], [36, 45, 1.0]]}, "2002.wwwconf_conference-2002.6": {"snippet": "... Web information processing is the support of multimedia retrieval. However, the most prevailing paradigm for multimedia retrieval, content-based retrieval (CBR), is a rather conservative one whose pe...", "weights": [[8, 19, 1.0], [49, 59, 1.0], [60, 69, 1.0], [113, 123, 1.0], [124, 133, 1.0], [149, 158, 1.0]]}, "2011.tist_journal-ir0volumeA2A2.1": {"snippet": "...d in multimedia research community for its capability of reducing human annotation effort. In this article, we provide a survey on the efforts of leveraging active learning in multimedia annotation an...", "weights": [[8, 18, 1.0], [179, 189, 1.0]]}, "2019.mir_conference-2019.57": {"snippet": "...odal Multimedia Retrieval with vitrivr", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "2011.mir_conference-2011.24": {"snippet": "...ased multimedia retrieval", "weights": [[8, 18, 1.0], [19, 28, 1.0]]}, "1997.iwpt-1.3": {"snippet": "...gent multimedia information access is a multidisciplinary area that lies at the intersec tion of artificial intelligence, information retrieval, human computer interaction, and multimedia computing. I...", "weights": [[8, 18, 1.0], [19, 30, 1.0], [125, 136, 1.0], [137, 146, 1.0], [180, 190, 1.0]]}, "2005.airs_conference-2005.59": {"snippet": "...ased Retrieval of Large Image Databases: A Review\n\n\n Abstract. Content-based image retrieval is a focused problem in current multimedia domain. To obtain better searching results more efficiently in s...", "weights": [[8, 17, 1.0], [86, 95, 1.0], [128, 138, 1.0]]}, "2013.ijmir_journal-ir0volumeA2A2.0": {"snippet": "...s in multimedia information retrieval", "weights": [[8, 18, 1.0], [19, 30, 1.0], [31, 40, 1.0]]}, "2016.ntcir_conference-2016.44": {"snippet": "...2016 Multimedia Benchmarking Initiative\n\n\n MediaEval is a multimedia benchmarking initiative which seeks to evaluate new algorithms for multimedia access and retrieval. MediaEval emphasizes the \"multi...", "weights": [[8, 18, 1.0], [61, 71, 1.0], [139, 149, 1.0], [161, 170, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.16": {"snippet": "...that information gathered using implicit techniques is less accurate than information based on explicit feedback. Abstract A challenging problem in the user profiling domain is to create profiles of u...", "weights": [[8, 19, 1.0], [77, 88, 1.0]]}, "2005.ipm_journal-ir0volumeA41A2.14": {"snippet": "Multimedia Systems and Content-Based Image Retrieval. By Sagarmay Deb, Idea Group Publishing, 2004, $79.95 ISBN 1-59140-156-9", "weights": [[0, 10, 1.0], [43, 52, 1.0]]}, "2014.mir_workshop-2014emr.0": {"snippet": "...ntal Multimedia Retrieval co-located with ACM International Conference on Multimedia Retrieval, EMR@ICMR 2014, Glasgow, UK, April 1, 2014", "weights": [[8, 18, 1.0], [19, 28, 1.0], [77, 87, 1.0], [88, 97, 1.0]]}, "2010.clef_workshop-2010w.26": {"snippet": "...edia Retrieval task. The main focus of our experiments is to evaluate the impact in the image retrieval process of the incorporation of semantic information extracted only from the textual information...", "weights": [[8, 17, 1.0], [97, 106, 1.0], [148, 159, 1.0], [192, 203, 1.0]]}, "1988.riao_conference-1988.4": {"snippet": "Multimedia Information Management and Optical Disk Technologies as a Basis for Advanced Information Retrieval", "weights": [[0, 10, 1.0], [11, 22, 1.0], [88, 99, 1.0], [100, 109, 1.0]]}, "1998.sigirconf_conference-98.32": {"snippet": "...ased Retrieval of Multimedia Objects\n\n\n AbstractWe describe the design and implementation of a system for logic-baaed multimedia retrieval. As highlevel logic for retrieval of hypermedia documents, we...", "weights": [[8, 17, 1.0], [21, 31, 1.0], [121, 131, 1.0], [132, 141, 1.0], [166, 175, 1.0]]}, "2005.wwwconf_conference-2005si.39": {"snippet": "...ther multimedia objects, such as images and video. In this project, we propose a modified Multimedia PageRank algorithm to support the searching of multimedia objects in the Web.", "weights": [[8, 18, 1.0], [93, 103, 1.0], [151, 161, 1.0]]}, "2016.ijmir_journal-ir0volumeA5A4.0": {"snippet": "...s in multimedia information retrieval", "weights": [[8, 18, 1.0], [19, 30, 1.0], [31, 40, 1.0]]}, "2008.sigirjournals_journal-ir0volumeA42A2.9": {"snippet": "... the information retrieval community together. The forum was designed to be conducive to the close interaction and the intense discussion necessary to promote fusion of these fields into a single disc...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.13": {"snippet": "...odal retrieval\n\n\n Abstract Cross-modal retrieval has become a highlighted research topic, to provide flexible retrieval experience across multimedia data such as image, video, text and audio. The core...", "weights": [[8, 17, 1.0], [42, 51, 1.0], [113, 122, 1.0], [141, 151, 1.0]]}, "2011.clef_workshop-2011w.26": {"snippet": "...mage Retrieval\n\n\n This paper present the details of participation of DEMIR (Dokuz Eylul University Multimedia Information Retrieval) research team to the context of our participation to the ImageCLEF ...", "weights": [[8, 17, 1.0], [102, 112, 1.0], [113, 124, 1.0], [125, 134, 1.0]]}, "A00-1003": {"snippet": "...uage Multimedia Information Retrieval\n\n\n Simple measures can achieve high-accuracy cross-language retrieval in carefully chosen applications. Image retrieval is one of those applications, with results...", "weights": [[8, 18, 1.0], [19, 30, 1.0], [31, 40, 1.0], [101, 110, 1.0], [151, 160, 1.0]]}, "2010.wwwconf_conference-2010.84": {"snippet": "...s of multimedia presentations in the web with the multimedia metadata ontology\n\n\n ABSTRACTThe semantics of rich multimedia presentations in the web such as SMIL, SVG, and Flash cannot or only to a ver...", "weights": [[8, 18, 1.0], [53, 63, 1.0], [115, 125, 1.0]]}, "2013.sigirconf_conference-2013.1": {"snippet": "... and retrieval, including IBM Multimedia Analysis and Retrieval System (IMARS), and show recent results for semanticbased classification and retrieval. We conclude with future directions for improving...", "weights": [[8, 17, 1.0], [33, 43, 1.0], [57, 66, 1.0], [144, 153, 1.0]]}, "1996.sigirjournals_journal-ir0volumeA30A2.2": {"snippet": "...onic information in all its forms based upon the notion of the information chain and including the areas of electronic publishing, information dissemination and information retrieval.Examples of some ...", "weights": [[8, 19, 1.0], [66, 77, 1.0], [134, 145, 1.0], [164, 175, 1.0], [176, 185, 1.0]]}, "2000.riao_conference-2000.8": {"snippet": "Multimedia Content Indexing and Retrieval Using Speech and Speaker Recognition", "weights": [[0, 10, 1.0], [32, 41, 1.0]]}, "1999.cikm_conference-99.67": {"snippet": "...n of Multimedia Data\n\n\n AbstractThe management of multimedia information poses special requirements for multimedia information systems. Both representation and retrieval of the complex and multifacete...", "weights": [[8, 18, 1.0], [53, 63, 1.0], [64, 75, 1.0], [107, 117, 1.0], [118, 129, 1.0], [163, 172, 1.0]]}, "2014.ijmir_journal-ir0volumeA3A2.0": {"snippet": "Multimedia information retrieval: best papers and expanding frontiers", "weights": [[0, 10, 1.0], [11, 22, 1.0], [23, 32, 1.0]]}, "L06-1130": {"snippet": "Multimedia Database of Meetings and Informal Interactions for Tracking Participant Involvement and Discourse Flow", "weights": [[0, 10, 1.0]]}, "2009.wwwconf_conference-2009.33": {"snippet": "...ital multimedia data has dramatically increased. Contentbased, cross-media retrieval applications are needed to efficiently access desired information from this variety of data sources. This paper pre...", "weights": [[8, 18, 1.0], [78, 87, 1.0], [142, 153, 1.0]]}, "2009.ecir_conference-2009.16": {"snippet": "XML Multimedia Retrieval: From Relevant Textual Information to Relevant Multimedia Fragments", "weights": [[4, 14, 1.0], [15, 24, 1.0], [48, 59, 1.0], [72, 82, 1.0]]}, "2007.cikm_conference-2007.40": {"snippet": "...n to multimedia retrieval\n\n\n ABSTRACTAs one of the most effective query expansion approaches, local feedback is able to automatically discover new query terms and improve retrieval accuracy for differ...", "weights": [[8, 18, 1.0], [19, 28, 1.0], [174, 183, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.20": {"snippet": "...t in multimedia Web search, those few Web search engines offering multimedia Web search, provide limited multimedia search functionality. Keywords are still the only means of multimedia retrieval, whi...", "weights": [[8, 18, 1.0], [69, 79, 1.0], [108, 118, 1.0], [178, 188, 1.0], [189, 198, 1.0]]}, "2002.sigirconf_conference-2002.91": {"snippet": "...stic multimedia retrieval\n\n\n ABSTRACTWe present a framework in which probabilistic models for textual and visual information retrieval can be integrated seamlessly. The framework facilitates searching...", "weights": [[8, 18, 1.0], [19, 28, 1.0], [116, 127, 1.0], [128, 137, 1.0]]}, "2015.tois_journal-ir0volumeA33A2.4": {"snippet": "...odal information fusion in content-based multimedia information retrieval. Additional Key Words and Phrases: Content-based multimedia information retrieval, information fusion, graph-based methods, cr...", "weights": [[8, 19, 1.0], [44, 54, 1.0], [55, 66, 1.0], [67, 76, 1.0], [126, 136, 1.0], [137, 148, 1.0], [149, 158, 1.0], [160, 171, 1.0]]}, "2012.cikm_conference-2012.386": {"snippet": "...t in multimedia documents. In this paper, we investigate the complementary search strategy, where relevant multimedia documents are retrieved using the photographs they contain. We concentrate our eff...", "weights": [[8, 18, 1.0], [110, 120, 1.0]]}, "2016.mir_conference-2016.60": {"snippet": "Retrieval of Multimedia Objects by Fusing Multiple Modalities", "weights": [[0, 9, 1.0], [13, 23, 1.0]]}, "2009.wwwconf_workshop-2009ldow.10": {"snippet": "...king Multimedia: How to Apply Linked Data Principles to Multimedia Fragments\n\n\n In this paper, we introduce interlinking multimedia (iM), a pragmatic way to apply the linked data principles to fragmen...", "weights": [[8, 18, 1.0], [59, 69, 1.0], [124, 134, 1.0]]}, "2020.acl-demos.11": {"snippet": "...ined Multimedia Knowledge Extraction System\n\n\n We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multime...", "weights": [[8, 18, 1.0], [98, 108, 1.0], [196, 206, 1.0]]}, "2008.mir_conference-2008.33": {"snippet": "Multimedia information retrieval: watershed events", "weights": [[0, 10, 1.0], [11, 22, 1.0], [23, 32, 1.0]]}, "2012.ijmir_journal-ir0volumeA1A3.0": {"snippet": "... for multimedia information retrieval: bridging the utility gap", "weights": [[8, 18, 1.0], [19, 30, 1.0], [31, 40, 1.0]]}, "2006.mir_workshop-2006.3": {"snippet": "...y in multimedia information retrieval research", "weights": [[8, 18, 1.0], [19, 30, 1.0], [31, 40, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A2.7": {"snippet": "Multimedia information retrieval: workshop report", "weights": [[0, 10, 1.0], [11, 22, 1.0], [23, 32, 1.0]]}, "2013.ipm_journal-ir0volumeA49A6.5": {"snippet": "...ural information has been shown to be an effective source of evidence in textual information retrieval, only a few works investigated its interest in multimedia retrieval. More precisely, the task we ...", "weights": [[8, 19, 1.0], [84, 95, 1.0], [96, 105, 1.0], [153, 163, 1.0], [164, 173, 1.0]]}, "P07-2003": {"snippet": "Multimedia Blog Creation System using Dialogue with Intelligent Robot\n\n\n A multimedia blog creation system is described that uses Japanese dialogue with an intelligent robot. Although multimedia blogs...", "weights": [[0, 10, 1.0], [75, 85, 1.0], [184, 194, 1.0]]}, "2014.cikm_conference-2014.148": {"snippet": "... for Information Retrieval\n\n\n ABSTRACTThis paper addresses the problem of joint modeling of multimedia components in different media forms. We consider the information retrieval task across both text ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [95, 105, 1.0], [159, 170, 1.0], [171, 180, 1.0]]}, "2007.sigirconf_conference-2007.221": {"snippet": "...n of retrieval expert coefficients for multimedia retrieval\n\n\n ABSTRACTContent-based Multimedia Information Retrieval can be defined as the task of matching a multi-modal information need against vari...", "weights": [[8, 17, 1.0], [42, 52, 1.0], [53, 62, 1.0], [88, 98, 1.0], [99, 110, 1.0], [111, 120, 1.0], [174, 185, 1.0]]}, "2007.ipm_journal-ir0volumeA43A5.17": {"snippet": "... for multimedia analysis and retrieval.Part two focuses on fundamental multimedia content analysis technologies in five respective chapters. Chapter 5, Structure Identification in an Audiovisual Docum...", "weights": [[8, 18, 1.0], [32, 41, 1.0], [74, 84, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "1987.sigirconf_conference-87.1": {"snippet": "Multimedia Retrieval", "weights": [[0, 10, 1.0], [11, 20, 1.0]]}, "2013.wwwconf_conference-2013c.137": {"snippet": "Multimedia information retrieval on the social web", "weights": [[0, 10, 1.0], [11, 22, 1.0], [23, 32, 1.0]]}, "2016.ijmir_journal-ir0volumeA5A3.0": {"snippet": "Top multimedia information retrieval papers", "weights": [[4, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0]]}, "1995.ipm_journal-ir0volumeA31A1.9": {"snippet": "Multimedia Document Retrieval", "weights": [[0, 10, 1.0], [20, 29, 1.0]]}, "2009.clef_workshop-20092.15": {"snippet": "Multimedia Retrieval by Means of Merge of Results from Textual and Content Based Retrieval Subsystems", "weights": [[0, 10, 1.0], [11, 20, 1.0], [81, 90, 1.0]]}, "2006.mir_workshop-2006.35": {"snippet": "...nted multimedia information retrieval technological evaluation", "weights": [[8, 18, 1.0], [19, 30, 1.0], [31, 40, 1.0]]}, "2007.sigirconf_conference-2007.201": {"snippet": "...udio information. 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The interface was evaluated in a cultural heritage site.", "weights": [[8, 19, 1.0], [25, 35, 1.0], [36, 45, 1.0], [69, 80, 1.0], [121, 132, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [42, 53, 1.0], [54, 63, 1.0], [92, 103, 1.0], [104, 113, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "1997.cikm_workshop-97.16": {"snippet": "... for Retrieval of Documents and Images\n\n\n AbstractThis paper describes a new method of multimedia information retrieval which shows multimedia information on a screen in a multi-directional way, using...", "weights": [[8, 17, 1.0], [90, 100, 1.0], [101, 112, 1.0], [113, 122, 1.0], [135, 145, 1.0], [146, 157, 1.0]]}, "2014.sigirconf_conference-2014.197": {"snippet": "...cale multimedia retrieval\n\n\n ABSTRACTThe tremendous increase of multimedia data in recent years has heightened the need for systems that not only allow to search with keywords, but that also support c...", "weights": [[8, 18, 1.0], [19, 28, 1.0], [67, 77, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ased multimedia retrieval\n\n\n AbstractRepresenting multimedia documents by means of concepts labels attached to parts of these documents has great potential for improving retrieval performance. 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Information retrieval provides a set of information seeki...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [52, 61, 1.0], [66, 77, 1.0], [146, 157, 1.0], [158, 167, 1.0], [186, 197, 1.0]]}, "2013.cikm_workshop-2013unlp.8": {"snippet": "...d on social media sites and blogs. In this work we evaluate different textual representations and retrieval models that aim to make sense of social media data for retail applications. Our task is to l...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [101, 110, 1.0], [144, 150, 1.0], [151, 156, 1.0]]}, "W14-6202": {"snippet": "Information Extraction for Social Media\n\n\n The rapid growth in IT in the last two decades has led to a growth in the amount of information available online. 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Based on this feature of social media, it is difficult for people's posts/comments on social media to be...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [66, 72, 1.0], [73, 78, 1.0], [124, 130, 1.0], [131, 136, 1.0], [185, 191, 1.0], [192, 197, 1.0]]}, "2019.chiir_conference-2019.20": {"snippet": "...mage Retrieval Approach to Searching for Images on Social Media\n\n\n ABSTRACTSearching for images posted within social media services such as Twitter relies on matching textual queries to the contents o...", "weights": [[8, 17, 1.0], [54, 60, 1.0], [61, 66, 1.0], [113, 119, 1.0], [120, 125, 1.0]]}, "W10-0504": {"snippet": "An Information-Retrieval Approach to Language Modeling: Applications to Social Data\n\n\n In this paper we propose the IR-LM (Information Retrieval Language Model) which is an approach to carrying out la...", "weights": [[3, 14, 1.0], [15, 24, 1.0], [72, 78, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A4.11": {"snippet": "...ered social media preservation as necessary. 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Exchange of text is most p...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [61, 67, 1.0], [68, 73, 1.0], [132, 143, 1.0]]}, "2008.wwwconf_conference-2008.48": {"snippet": "... for social media search and retrieval. Our system can be used as a crucial building block for combining results from a variety of social media content with general web search results, and to better i...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [32, 41, 1.0], [134, 140, 1.0], [141, 146, 1.0]]}, "2014.sigirconf_workshop-2014somera.0": {"snippet": "...p on Social Media Retrieval and Analysis, July 11, 2014, Gold Coast, Queensland, Australia", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 30, 1.0]]}, "2020.wnut-1.58": {"snippet": "...a\n\n\n Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informat...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [68, 79, 1.0], [155, 161, 1.0], [162, 167, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.19": {"snippet": "...n of social media, image has become ubiquitous giving rise to the demand and importance of image semantic analysis and retrieval to access information quickly on social media. However, even with humon...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [122, 131, 1.0], [142, 153, 1.0], [165, 171, 1.0], [172, 177, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.17": {"snippet": "...s of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar s...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [60, 66, 1.0], [67, 72, 1.0], [110, 116, 1.0], [117, 122, 1.0], [202, 208, 1.0]]}, "2015.fire_workshop-2015w.25": {"snippet": "... for social media content in English as part of our participation in the shared task on Entity Extraction from Social Media Text in Indian Languages (ESM-IL) organized by Forum for Information Retriev...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [114, 120, 1.0], [121, 126, 1.0], [184, 195, 1.0], [196, 205, 1.0]]}, "D18-1144": {"snippet": "...y in Social Media for Extractive Summarization of Online Conversations\n\n\n We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations. In s...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [113, 119, 1.0], [120, 125, 1.0], [202, 208, 1.0]]}, "2011.mir_conference-2011.33": {"snippet": "Social media driven image retrieval", "weights": [[0, 6, 1.0], [7, 12, 1.0], [26, 35, 1.0]]}, "W19-8666": {"snippet": "... for Social Media - Results of the {TL};{DR} Challenge\n\n\n With most summarization research focused on the news domain and scientific papers, little is known about the capabilities of the state of the ...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2013.wwwconf_conference-2013c.216": {"snippet": "...sion retrieval from twitter\n\n\n ABSTRACTWhile social media receive a lot of attention from the scientific community in general, there is little work on high recall retrieval of messages relevant to a d...", "weights": [[8, 17, 1.0], [48, 54, 1.0], [55, 60, 1.0], [166, 175, 1.0]]}, "2013.cikm_conference-2013.270": {"snippet": "...e of social networks on social media content providers. Additionally, we propose several document expansion methods, which leverage the content generated by the social networks of the authors of socia...", "weights": [[8, 14, 1.0], [27, 33, 1.0], [34, 39, 1.0], [164, 170, 1.0], [198, 204, 1.0]]}, "2011.wsdm_conference-2011.64": {"snippet": "... and social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2012.cikm_conference-2012.349": {"snippet": "...blog retrieval\n\n\n ABSTRACTThe use of social media has profoundly affected social and political dynamics in the Arab world. In this paper, we explore the Arabic microblogs retrieval. We illustrate some...", "weights": [[8, 17, 1.0], [40, 46, 1.0], [47, 52, 1.0], [77, 83, 1.0], [174, 183, 1.0]]}, "2011.sigirconf_conference-2011.122": {"snippet": "...ough media outlets, social media, and by actively seeking information using search engines. In this paper we investigate the temporal relationship between news coverage by media outlets, social media,...", "weights": [[8, 13, 1.0], [23, 29, 1.0], [30, 35, 1.0], [61, 72, 1.0], [175, 180, 1.0], [190, 196, 1.0], [197, 202, 1.0]]}, "2020.wsdm_conference-2020.119": {"snippet": "...y in Social Networks and Media\n\n\n The first Workshop on Integrity in Social Networks and Media is held in conjunction with the 13th ACM Conference on Web Search and Data Mining (WSDM) in Houston, Texa...", "weights": [[8, 14, 1.0], [28, 33, 1.0], [72, 78, 1.0], [92, 97, 1.0]]}, "2019.tois_journal-ir0volumeA37A2.12": {"snippet": "...with social media text, since the social network platforms (e.g., Twitter) generate huge amounts of data in textual form everyday. However, people from different social and knowledge backgrounds have ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [37, 43, 1.0], [165, 171, 1.0]]}, "2012.wsdm_conference-2012.55": {"snippet": "...rent social media sites. Further, we explore ways in which event content identified on one social media site can be used to retrieve additional relevant event content on other social media sites. We a...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [94, 100, 1.0], [101, 106, 1.0], [179, 185, 1.0], [186, 191, 1.0]]}, "2011.cikm_workshop-2011smuc.13": {"snippet": "...from social media\n\n\n ABSTRACTThis contribution proposes a framework to generate auxiliary rich TV content metadata by processing social networks data. Based on simple criteria to identify authoritativ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [132, 138, 1.0]]}, "2014.jasis_journal-ir0volumeA65A1.4": {"snippet": "...in a social media alcoholism community", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2016.wwwjournals_journal-ir0volumeA19A2.7": {"snippet": "...s on social media data\n\n\n Abstract Social media sites contain a considerable amount of data for natural calamities events, such as earthquakes, snowstorms, mud-rock flows. With the increasing amount o...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [38, 44, 1.0], [45, 50, 1.0]]}, "N18-2094": {"snippet": "...mber Retrieval on Social Media Using Textual Information\n\n\n This paper addresses the problem of community membership detection using only text features in a scenario where a small number of positive l...", "weights": [[8, 17, 1.0], [21, 27, 1.0], [28, 33, 1.0], [48, 59, 1.0]]}, "2015.sigirjournals_journal-ir0volumeA49A2.21": {"snippet": "...e in Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2020.sigirjournals_journal-ir0volumeA54A1.16": {"snippet": "Information extraction from digital social trace data with applications to social media and scholarly communication data", "weights": [[0, 11, 1.0], [36, 42, 1.0], [75, 81, 1.0], [82, 87, 1.0]]}, "2020.coling-main.242": {"snippet": "...) on social media such as Twitter is a critical and challenging task due to its noisy, informal, but informative nature. Most existing RC models are developed on formal datasets such as news articles ...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2010.sigirconf_conference-2010.212": {"snippet": "...e of information available in social media, ways of accessing this information intelligently are needed; this is the scope of my research.Why should we care about information in social media? Here are...", "weights": [[8, 19, 1.0], [33, 39, 1.0], [40, 45, 1.0], [70, 81, 1.0], [166, 177, 1.0], [181, 187, 1.0], [188, 193, 1.0]]}, "2014.cikm_conference-2014.151": {"snippet": "...n of Social Media Contents using Focused Topic Hierarchy\n\n\n ABSTRACTWith the popularity of social media platforms such as Facebook and Twitter, the amount of useful data in these sources is rapidly in...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [94, 100, 1.0], [101, 106, 1.0]]}, "2017.ecir_workshop-2017somepeas.0": {"snippet": "...p on Social Media for Personalization and Search co-located with 39th European Conference on Information Retrieval, SoMePeAS@ECIR 2017, Aberdeen, Scotland, UK, April 9, 2017", "weights": [[8, 14, 1.0], [15, 20, 1.0], [96, 107, 1.0], [108, 117, 1.0]]}, "2019.ecir_conference-20192.47": {"snippet": "... for Social Media Retrieval\n\n\n Abstract. The huge amount of information that is available in social media today has resulted in a pressing demand for information retrieval models to support users in d...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [21, 30, 1.0], [63, 74, 1.0], [96, 102, 1.0], [103, 108, 1.0], [153, 164, 1.0], [165, 174, 1.0]]}, "2017.sigirconf_conference-2017.239": {"snippet": "...line Social Media (OSM) in providing actionable situational information. However, in such media, the crucial information is typically obscured by a lot of insignificant information (e.g., personal opi...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [63, 74, 1.0], [93, 98, 1.0], [112, 123, 1.0], [172, 183, 1.0]]}, "2018.wwwconf_conference-2018c.138": {"snippet": "...sing Social Media Messages in Mass Emergency: Survey Summary\n\n\n ABSTRACTMillions of people use social media to share information during disasters and mass emergencies. Information available on social ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [98, 104, 1.0], [105, 110, 1.0], [120, 131, 1.0], [171, 182, 1.0], [196, 202, 1.0]]}, "2018.ecir_conference-2018.76": {"snippet": "... the Retrieval of Related Tweets to News Articles\n\n\n Abstract. Nowadays, social media users react in real-time to local and global events. Therefore, social media can be used to measure the impact of ...", "weights": [[8, 17, 1.0], [76, 82, 1.0], [83, 88, 1.0], [153, 159, 1.0], [160, 165, 1.0]]}, "2013.sigirconf_conference-2013.152": {"snippet": "...n in social media\n\n\n ABSTRACTIt is well recognized that users rely on social media (e.g. Twitter or Digg) to fulfill two common needs (i.e. social need and informational need ) that is to keep in touc...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [73, 79, 1.0], [80, 85, 1.0], [143, 149, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A2.10": {"snippet": "...ment retrieval\n\n\n AbstractThe first international workshop on Social Media Engagement 1 was held on March 29, 2011 in conjunction with the International World Wide Web Conference (WWW 2011 2 ) in Hyde...", "weights": [[8, 17, 1.0], [65, 71, 1.0], [72, 77, 1.0]]}, "2013.sigirconf_conference-2013.210": {"snippet": "...e in social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "L12-1124": {"snippet": "...sing Social Media {E}nglish Text", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2012.cikm_conference-2012.446": {"snippet": "... and social sensor data sources.\nCategories and Subject Descriptors\nH.4: Information Systems applications\nGeneral TermsAlgorithms, Experimentation, Performance, Human Factors.\nKeywordsSocial networks,...", "weights": [[8, 14, 1.0], [76, 87, 1.0]]}, "2015.wwwconf_conference-2015c.336": {"snippet": "... and social media. No prerequisite is expected.\nTOPICS AND DESCRIPTIONThe recent dramatic increase in the usage and prevalence of social media has led to the creation and sharing of a significant amou...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [133, 139, 1.0], [140, 145, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ical information retrieval\n\n\n AbstractIn this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 153, 1.0], [154, 163, 1.0]]}, "W17-1612": {"snippet": "... for Social Media Health Research\n\n\n Social media have transformed datadriven research in political science, the social sciences, health, and medicine. Since health research often touches on sensitive...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [40, 46, 1.0], [47, 52, 1.0], [116, 122, 1.0]]}, "W19-5803": {"snippet": "...nese Social Media Texts with Foreign Words\n\n\n Multilingual speakers often switch between languages and generate enormous quantities of cross-language data. This phenomenon is more frequent observed in...", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2007.sigirjournals_journal-ir0volumeA41A2.7": {"snippet": "... for information retrieval (LR4IR 2007)\n\n\n AbstractWe propose a set of novel quantitative measures for controversy derived from statistical properties of textual social media. We empirically establish...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [165, 171, 1.0], [172, 177, 1.0]]}, "2013.ijirr_journal-ir0volumeA3A1.0": {"snippet": "...k in Social Media via a Content Retrieval and Classification Software", "weights": [[8, 14, 1.0], [15, 20, 1.0], [35, 44, 1.0]]}, "W14-4510": {"snippet": "...e of information being propagated via social media. However, the sheer volume of the data produced on social media is overwhelming and manual inspection of this streaming data for finding, aggregation...", "weights": [[8, 19, 1.0], [41, 47, 1.0], [48, 53, 1.0], [105, 111, 1.0], [112, 117, 1.0]]}, "2017.ecir_workshop-2017smerp.0": {"snippet": "...n of Social Media for Emergency Relief and Preparedness co-located with European Conference on Information Retrieval, SMERP@ECIR 2017, Aberdeen, UK, April 9, 2017", "weights": [[8, 14, 1.0], [15, 20, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "2016.wsdm_conference-2016.88": {"snippet": "...ting Social Media Icebergs by Their Tips: Rumors, Persuasion Campaigns, and Information Needs\n\n\n ABSTRACTOnline activities of more than one billion social media users all over the world form a resourc...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [79, 90, 1.0], [151, 157, 1.0], [158, 163, 1.0]]}, "2014.sigirconf_workshop-2014somera.10": {"snippet": "...n of social media users. In the context of the \"Social Media Retrieval and Analysis\" 2 (SoMeRA) workshop, this paper sheds light on the use of two social media platforms (Last.fm and Twitter) by fans ", "weights": [[8, 14, 1.0], [15, 20, 1.0], [51, 57, 1.0], [58, 63, 1.0], [64, 73, 1.0], [150, 156, 1.0], [157, 162, 1.0]]}, "2013.ictir_conference-2013.10": {"snippet": "...ead; social media and information interaction in many forms are increasingly used in daily life, etc. Simultaneously, the information experiences of users and use of information has become increasingl...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [25, 36, 1.0], [125, 136, 1.0], [169, 180, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.14": {"snippet": "...sing information in social media: (i) the people creating the social media and (ii) their individual utterances (e.g., blog posts, tweets). These entry points act as a doorway to the information in so...", "weights": [[8, 19, 1.0], [23, 29, 1.0], [30, 35, 1.0], [65, 71, 1.0], [72, 77, 1.0], [186, 197, 1.0], [201, 207, 1.0]]}, "2012.ir_journal-ir0volumeA15A34.0": {"snippet": "...e on information retrieval for social media", "weights": [[8, 19, 1.0], [20, 29, 1.0], [34, 40, 1.0], [41, 46, 1.0]]}, "2015.fire_workshop-2015w.21": {"snippet": "... for Social Media Texts in Indian Languages\n\n\n This contemporary work is done as a slice of the shared task on Entity Extraction from Social Media Text Indian Languages in Forum for Information Retrie...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [137, 143, 1.0], [144, 149, 1.0], [185, 196, 1.0], [197, 206, 1.0]]}, "2018.wwwconf_conference-2018c.305": {"snippet": "... for social media applications. In essence it is completely at the discretion of the social network provider how the user's data is used and what content is delivered. However, even though social medi...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [88, 94, 1.0], [192, 198, 1.0], [199, 204, 1.0]]}, "2017.chiir_conference-2017.65": {"snippet": "...tive information seeking (CIS) deals with collaboration in searching for information. In recent years, social media has been steadily increasing as an important information source for tourists who acc...", "weights": [[8, 19, 1.0], [76, 87, 1.0], [106, 112, 1.0], [113, 118, 1.0], [164, 175, 1.0]]}, "2013.mir_conference-2013.21": {"snippet": "...sual information for event-related social media retrieval", "weights": [[8, 19, 1.0], [38, 44, 1.0], [45, 50, 1.0], [51, 60, 1.0]]}, "2014.wwwconf_conference-2014c.106": {"snippet": "...ured social media data can be made searchable. This paper seeks the feasibility of utilizing social media data for developing a Q&A service by examining the topic coverage in Twitter conversations. We...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [96, 102, 1.0], [103, 108, 1.0]]}, "L16-1259": {"snippet": "...eful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose m...", "weights": [[8, 19, 1.0], [172, 178, 1.0], [179, 184, 1.0], [185, 196, 1.0]]}, "2018.mir_conference-2018.39": {"snippet": "...mage Retrieval from Social Media", "weights": [[8, 17, 1.0], [23, 29, 1.0], [30, 35, 1.0]]}, "2017.cikm_conference-2017.349": {"snippet": "...ning social media data for urban informatics. The workshop hopes to foster collaboration between researchers working in information retrieval, social media analytics, linguistics; social scientists, a...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [123, 134, 1.0], [135, 144, 1.0], [146, 152, 1.0], [153, 158, 1.0], [183, 189, 1.0]]}}}
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The model was constructed using convolutional neural networks (CNN) and long s...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [98, 108, 1.0], [151, 156, 1.0], [157, 170, 1.0], [171, 177, 1.0], [178, 186, 1.0]]}, "2018.fire_conference-2018w.37": {"snippet": "A Neural Network based Event Extraction System for Indian Languages\n\n\n In this paper we have described a neural network based approach for Event extraction(EE) task which aims to discover different ty...", "weights": [[2, 8, 1.0], [23, 28, 1.0], [29, 39, 1.0], [105, 111, 1.0], [139, 144, 1.0], [145, 155, 1.0]]}, "W10-1919": {"snippet": "...ards Event Extraction from Full Texts on Infectious Diseases\n\n\n Event extraction approaches based on expressive structured representations of extracted information have been a significant focus of res...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [67, 72, 1.0], [73, 83, 1.0]]}, "W16-2928": {"snippet": "...tion extraction from clinical texts using domain invariant convolutional neural network\n\n\n In recent years extracting relevant information from biomedical and clinical texts such as research articles,...", "weights": [[8, 18, 1.0], [39, 44, 1.0], [62, 75, 1.0], [76, 82, 1.0]]}, "2019.tist_journal-ir0volumeA10A1.5": {"snippet": "...s of Convolutional Neural Networks\n\n\n Deep Convolutional Neural Networks (CNNs) have achieved remarkable success in various fields. However, training an excellent CNN is practically a trial-and-error ...", "weights": [[8, 21, 1.0], [22, 28, 1.0], [29, 37, 1.0], [46, 59, 1.0], [60, 66, 1.0], [67, 75, 1.0]]}, "2018.mir_conference-2018.64": {"snippet": "...ches Using Atrous Convolutional Neural Networks", "weights": [[8, 13, 1.0], [21, 34, 1.0], [35, 41, 1.0], [42, 50, 1.0]]}, "O17-1003": {"snippet": "...udio Event Detection of Broadcast Radio Programs Based on Convolution Neural Networks) [In {C}hinese]", "weights": [[8, 13, 1.0], [73, 79, 1.0], [80, 88, 1.0]]}, "2019.rocling-1.18": {"snippet": "...tion using Convolutional Neural Networks)", "weights": [[8, 13, 1.0], [14, 27, 1.0], [28, 34, 1.0], [35, 43, 1.0]]}, "W18-6121": {"snippet": "...s by using convolutional neural networks. This proposed method is another contribution of this paper. For comparison, character or word input methods and other neural networks are also used. Results s...", "weights": [[8, 13, 1.0], [14, 27, 1.0], [28, 34, 1.0], [35, 43, 1.0], [163, 169, 1.0], [170, 178, 1.0]]}, "N16-1034": {"snippet": "...oint Event Extraction via Recurrent Neural Networks\n\n\n Event extraction is a particularly challenging problem in information extraction. The stateof-the-art models for this problem have either applied...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [39, 45, 1.0], [46, 54, 1.0], [58, 63, 1.0], [64, 74, 1.0], [128, 138, 1.0]]}, "D16-1085": {"snippet": "... for Event Detection with Convolutional Neural Networks\n\n\n Convolutional neural networks (CNN) have achieved the top performance for event detection due to their capacity to induce the underlying stru...", "weights": [[8, 13, 1.0], [29, 42, 1.0], [43, 49, 1.0], [50, 58, 1.0], [62, 75, 1.0], [76, 82, 1.0], [83, 91, 1.0], [136, 141, 1.0]]}, "2020.tist_journal-ir0volumeA11A2.6": {"snippet": "... for Convolutional Neural Networks", "weights": [[8, 21, 1.0], [22, 28, 1.0], [29, 37, 1.0]]}, "W19-3609": {"snippet": "...xism Using Convolutional Neural Network{'}s Filters", "weights": [[8, 13, 1.0], [14, 27, 1.0], [28, 34, 1.0]]}, "K16-1024": {"snippet": "Event Linking with Sentential Features from Convolutional Neural Networks\n\n\n Coreference resolution for event mentions enables extraction systems to process document-level information. Current systems...", "weights": [[0, 5, 1.0], [44, 57, 1.0], [58, 64, 1.0], [65, 73, 1.0], [104, 109, 1.0], [127, 137, 1.0]]}, "W15-1506": {"snippet": "...tion Extraction: Perspective from Convolutional Neural Networks\n\n\n Up to now, relation extraction systems have made extensive use of features generated by linguistic analysis modules. Errors in these ...", "weights": [[8, 18, 1.0], [37, 50, 1.0], [51, 57, 1.0], [58, 66, 1.0], [90, 100, 1.0]]}, "W18-2311": {"snippet": "...ical Event Extraction Using Convolutional Neural Networks and Dependency Parsing\n\n\n Event and relation extraction are central tasks in biomedical text mining. Where relation extraction concerns the de...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [25, 30, 1.0], [31, 44, 1.0], [45, 51, 1.0], [52, 60, 1.0], [87, 92, 1.0], [106, 116, 1.0], [177, 187, 1.0]]}, "2018.clef_conference-2018w.77": {"snippet": "...Data using Convolutional Neural Network\n\n\n Social networks on the Internet are becoming increasingly important in our society. In recent years, this type of media, through communication platforms such...", "weights": [[8, 13, 1.0], [14, 27, 1.0], [28, 34, 1.0], [53, 61, 1.0]]}, "2020.lrec-1.763": {"snippet": "... for Event Detection on {T}witter\n\n\n We present Event2018, a corpus annotated for event detection tasks, consisting of 38 million tweets in French (retweets excluded) including more than 130,000 tweet...", "weights": [[8, 13, 1.0], [85, 90, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.20": {"snippet": "...deep neural networks. First, several deep learning models are utilized to extract useful information from multiple modalities. Among these are pretrained Convolutional Neural Networks (CNNs) for visua...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [157, 170, 1.0], [171, 177, 1.0], [178, 186, 1.0]]}, "2021.ijmir_journal-ir0volumeA10A1.3": {"snippet": "...stem using convolutional neural networks", "weights": [[8, 13, 1.0], [14, 27, 1.0], [28, 34, 1.0], [35, 43, 1.0]]}, "2021.wnut-1.28": {"snippet": "...ers. Event Detection aims at extracting information in the form of an event, but considers each event in isolation, without combining information across sentences or events. Many posts in Crisis NLP c...", "weights": [[8, 13, 1.0], [73, 78, 1.0], [99, 104, 1.0]]}, "W18-2314": {"snippet": "Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings\n\n\n We investigate the incorporation of character-based word representations int...", "weights": [[0, 13, 1.0], [14, 20, 1.0], [21, 29, 1.0], [60, 70, 1.0]]}, "D15-1203": {"snippet": "...tion Extraction via Piecewise Convolutional Neural Networks\n\n\n Two problems arise when using distant supervision for relation extraction. First, in this method, an already existing knowledge base is h...", "weights": [[8, 18, 1.0], [33, 46, 1.0], [47, 53, 1.0], [54, 62, 1.0], [90, 95, 1.0], [129, 139, 1.0]]}, "2020.cikm_conference-2020.258": {"snippet": "...tion Extraction with Self-determined Graph Convolutional Network", "weights": [[8, 18, 1.0], [46, 59, 1.0]]}, "2017.fire_conference-2017w.38": {"snippet": "An Event Extraction System via Neural Networks\n\n\n In this paper we describe the IIT KGP team's participation in the Event Extraction task at FIRE 2017. We have developed an event extraction system whi...", "weights": [[3, 8, 1.0], [9, 19, 1.0], [31, 37, 1.0], [38, 46, 1.0], [116, 121, 1.0], [122, 132, 1.0], [173, 178, 1.0], [179, 189, 1.0]]}, "P19-3006": {"snippet": "...tion extraction tasks that has many applications such as web search and question answering. We present a system for rapidly customizing event extraction capability to find new event types (what happen...", "weights": [[8, 18, 1.0], [139, 144, 1.0], [145, 155, 1.0], [179, 184, 1.0]]}, "P15-1017": {"snippet": "Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks\n\n\n Traditional approaches to the task of ACE event extraction primarily rely on elaborately designed features and complicated n...", "weights": [[0, 5, 1.0], [6, 16, 1.0], [43, 56, 1.0], [57, 63, 1.0], [64, 72, 1.0], [118, 123, 1.0], [124, 134, 1.0]]}, "D19-5533": {"snippet": "...tion Neural Network (SHTCNN), a novel neural relation extraction framework to alleviate this issue. In this method, we apply separate convolution and pooling to the head and tail entity respectively f...", "weights": [[8, 14, 1.0], [41, 47, 1.0], [57, 67, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.6": {"snippet": "...n in Event-based Social Networks\n\n\n Event-based social networks (EBSNs) are becoming popular in recent years. Users can publish a planned event on an EBSN website, calling for other users to participa...", "weights": [[8, 13, 1.0], [27, 35, 1.0], [39, 44, 1.0], [58, 66, 1.0], [141, 146, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.1": {"snippet": "...tion neural network arises and has achieved very promising achievement. Compared with traditional feature extraction techniques (e.g., SIFT, HOG, GIST), the convolutional neural network can extract fe...", "weights": [[8, 14, 1.0], [109, 119, 1.0], [160, 173, 1.0], [174, 180, 1.0]]}, "W18-1507": {"snippet": "Neural Event Extraction from Movies Description\n\n\n We present a novel approach for event extraction and abstraction from movie descriptions. Our event frame consists of 'who\", \"did what\" \"to whom\", \"w...", "weights": [[0, 6, 1.0], [7, 12, 1.0], [13, 23, 1.0], [83, 88, 1.0], [89, 99, 1.0], [145, 150, 1.0]]}, "I17-1083": {"snippet": "... for event argument identification. Recurrent Neural Networks learn to produce meaningful representations of long and short dependency paths. Convolutional Neural Networks learn to decompose the lexic...", "weights": [[8, 13, 1.0], [49, 55, 1.0], [56, 64, 1.0], [145, 158, 1.0], [159, 165, 1.0], [166, 174, 1.0]]}, "2021.econlp-1.10": {"snippet": "...News Event Extraction\n\n\n Event extraction in commodity news is a less researched area as compared to generic event extraction. However, accurate event extraction from commodity news is useful in a bro...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [28, 33, 1.0], [34, 44, 1.0], [112, 117, 1.0], [118, 128, 1.0], [148, 153, 1.0], [154, 164, 1.0]]}, "2019.clef_conference-2019w.87": {"snippet": "...SCI) using Deep Learning Techniques\n\n\n This paper presents our contribution in the identification and detection of Forged files and Steganographic content using Deep Neural Networks like Convolutional...", "weights": [[8, 13, 1.0], [158, 163, 1.0], [169, 175, 1.0], [176, 184, 1.0], [190, 203, 1.0]]}, "Q18-1047": {"snippet": "...NLP, convolutional neural networks (CNNs) have benefited less than recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that this is because the attention in CNNs has been mainly...", "weights": [[8, 21, 1.0], [22, 28, 1.0], [29, 37, 1.0], [80, 86, 1.0], [87, 95, 1.0]]}, "2014.clef_conference-2014w.77": {"snippet": "...tion Using Convolutional Neural Networks for Feature Extraction\n\n\n We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted...", "weights": [[8, 13, 1.0], [14, 27, 1.0], [28, 34, 1.0], [35, 43, 1.0], [56, 66, 1.0]]}, "S16-1197": {"snippet": "...deep neural network based natural language processing system for clinical information (such as time information, event spans, and their attributes) extraction from raw clinical notes and pathology rep...", "weights": [[8, 14, 1.0], [116, 121, 1.0], [151, 161, 1.0]]}, "N16-1065": {"snippet": "... and Convolutional Neural Networks for Relation Classification\n\n\n This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and ...", "weights": [[8, 21, 1.0], [22, 28, 1.0], [29, 37, 1.0], [107, 113, 1.0], [169, 182, 1.0], [183, 189, 1.0], [190, 198, 1.0]]}, "2019.tist_journal-ir0volumeA10A1.6": {"snippet": "...d of using regular Convolutional Neural Networks (CNNs) to learn intensities of the facial images, we convolve the geometric properties on the surface of the 3D model using 3DMCNN. We design a geodesi...", "weights": [[8, 13, 1.0], [22, 35, 1.0], [36, 42, 1.0], [43, 51, 1.0], [170, 175, 1.0]]}, "P15-2060": {"snippet": "Event Detection and Domain Adaptation with Convolutional Neural Networks\n\n\n We study the event detection problem using convolutional neural networks (CNNs) that overcome the two fundamental limitation...", "weights": [[0, 5, 1.0], [43, 56, 1.0], [57, 63, 1.0], [64, 72, 1.0], [89, 94, 1.0], [113, 118, 1.0], [119, 132, 1.0], [133, 139, 1.0], [140, 148, 1.0]]}, "C16-1084": {"snippet": "... new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention...", "weights": [[8, 14, 1.0], [31, 44, 1.0], [55, 61, 1.0], [94, 107, 1.0], [108, 114, 1.0], [115, 123, 1.0], [139, 145, 1.0], [146, 154, 1.0], [160, 173, 1.0], [174, 180, 1.0]]}, "D17-1163": {"snippet": "... and convolutional neural network classifiers. Our model outperforms two off-the-shelf event extractor systems, and it can suggest candidate victim names in some cases faster than one of the major man...", "weights": [[8, 21, 1.0], [22, 28, 1.0], [90, 95, 1.0]]}, "2019.sigirconf_workshop-2019newsir.13": {"snippet": "...ages using features extracted from the images using the convolutional neural network ResNet. These clusters are then ranked by burstiness, identifying the top ranked clusters as detected events. Words...", "weights": [[8, 13, 1.0], [49, 54, 1.0], [59, 72, 1.0], [73, 79, 1.0]]}, "2017.wwwconf_conference-2017.75": {"snippet": "...Drug Event Detection in Tweets with Semi-Supervised Convolutional Neural Networks", "weights": [[8, 13, 1.0], [55, 68, 1.0], [69, 75, 1.0], [76, 84, 1.0]]}, "W16-1618": {"snippet": "...ding Event Detection to New Types via Neural Networks\n\n\n We study the event detection problem in the new type extension setting. In particular, our task involves identifying the event instances of a t...", "weights": [[8, 13, 1.0], [41, 47, 1.0], [48, 56, 1.0], [73, 78, 1.0], [181, 186, 1.0]]}, "W16-3012": {"snippet": "... two event extraction tasks of BioNLP 2016 Shared Task: binary relation extraction of SeeDev task and localization relations extraction of Bacteria Biotope task. Convolutional neural network (CNN) is ...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [75, 85, 1.0], [128, 138, 1.0], [165, 178, 1.0], [179, 185, 1.0]]}, "S17-2171": {"snippet": "...cial neural networks have recently been explored for relation extraction. In this work, we continue this line of work and present a system based on a convolutional neural network to extract relations....", "weights": [[8, 14, 1.0], [15, 23, 1.0], [65, 75, 1.0], [153, 166, 1.0], [167, 173, 1.0]]}, "W16-5311": {"snippet": "...task Convolutional Neural Networks (CNNs) with word embeddings from publicly available word vectors. 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Sub-Event Detection in Social Media\n\n\n ABSTRACTSub-event detection can help faster and deeper understanding of an event by providing human-friendly clusters, and thus has become an important research...", "weights": [[8, 13, 1.0], [27, 33, 1.0], [34, 39, 1.0], [55, 60, 1.0], [118, 123, 1.0]]}, "2018.fire_conference-2018w.37": {"snippet": "... the event arguments form the text documents written in Indian languages like Hindi, Tamil and English as part of our participation in the task on Event Extraction from Newswires and Social Media Text...", "weights": [[8, 13, 1.0], [150, 155, 1.0], [156, 166, 1.0], [186, 192, 1.0], [193, 198, 1.0]]}, "2015.wwwconf_conference-2015c.131": {"snippet": "...from Social Media Streams\n\n\n ABSTRACTMy proposed research addresses fundamental deficiencies in social media-based event detection by discovering highimpact moments and evaluating their credibility ra...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [99, 105, 1.0], [106, 111, 1.0], [118, 123, 1.0]]}, "2011.sigirconf_conference-2011.98": {"snippet": "An event-centric model for multilingual document similarity\n\n\n ABSTRACTDocument similarity measures play an important role in many document retrieval and exploration tasks. Over the past decades, seve...", "weights": [[3, 8, 1.0], [49, 59, 1.0], [80, 90, 1.0]]}, "Q14-1006": {"snippet": "... New similarity metrics for semantic inference over event descriptions\n\n\n We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel deno...", "weights": [[8, 18, 1.0], [19, 26, 1.0], [55, 60, 1.0]]}, "N16-1137": {"snippet": "...ting event extractors for social media suffer from two major problems: lack of context and informal nature. In this paper, instead of conducting event extraction solely on each social media message, w...", "weights": [[8, 13, 1.0], [29, 35, 1.0], [36, 41, 1.0], [148, 153, 1.0], [154, 164, 1.0], [180, 186, 1.0], [187, 192, 1.0]]}, "2017.fire_conference-2017w.41": {"snippet": "... and Social Media text. In this paper we explore the development of an event extraction system for Malayalam Language. This work was done as part of the shared task on Event Extraction from Newswires ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [74, 79, 1.0], [80, 90, 1.0], [171, 176, 1.0], [177, 187, 1.0]]}, "2011.sigirconf_conference-2011.122": {"snippet": "...ough media outlets, social media, and by actively seeking information using search engines. In this paper we investigate the temporal relationship between news coverage by media outlets, social media,...", "weights": [[8, 13, 1.0], [23, 29, 1.0], [30, 35, 1.0], [175, 180, 1.0], [190, 196, 1.0], [197, 202, 1.0]]}, "2012.mir_conference-2012.25": {"snippet": "Event-based classification of social media streams", "weights": [[0, 5, 1.0], [30, 36, 1.0], [37, 42, 1.0]]}, "2017.tist_journal-ir0volumeA8A3.1": {"snippet": "... via Social Tracking\n\n\n Social media websites have become important information sharing platforms. The rapid development of social media platforms has led to increasingly large-scale social media data...", "weights": [[8, 14, 1.0], [27, 33, 1.0], [34, 39, 1.0], [127, 133, 1.0], [134, 139, 1.0], [186, 192, 1.0], [193, 198, 1.0]]}, "D12-1134": {"snippet": "...ying Event-related Bursts via Social Media Activities\n\n\n Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to...", "weights": [[8, 13, 1.0], [33, 39, 1.0], [40, 45, 1.0], [74, 80, 1.0], [81, 86, 1.0], [133, 138, 1.0]]}, "2020.jasis_journal-ir0volumeA71A1.3": {"snippet": "...sing social media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2015.mir_conference-2015.27": {"snippet": "...sual Event Summarization on Social Media using Topic Modelling and Graph-based Ranking Algorithms", "weights": [[8, 13, 1.0], [31, 37, 1.0], [38, 43, 1.0]]}, "P07-1131": {"snippet": "... and event detection modules. We describe the specific features and algorithmic refinements effective for conversational speech. These cumulatively increase the performance of social network extractio...", "weights": [[8, 13, 1.0], [179, 185, 1.0], [194, 204, 1.0]]}, "2014.sigirconf_conference-2014.176": {"snippet": "An event extraction model based on timeline and user analysis in Latent Dirichlet allocation\n\n\n ABSTRACTSocial media such as Twitter has come to reflect the reaction of the general public to major eve...", "weights": [[3, 8, 1.0], [9, 19, 1.0], [111, 116, 1.0]]}, "N15-1066": {"snippet": "...ugh: Event Extraction from Imperfect Microblog Data\n\n\n We introduce a distantly supervised event extraction approach that extracts complex event templates from microblogs. We show that this near real-...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [94, 99, 1.0], [100, 110, 1.0], [142, 147, 1.0]]}, "2020.lrec-1.763": {"snippet": "... for Event Detection on {T}witter\n\n\n We present Event2018, a corpus annotated for event detection tasks, consisting of 38 million tweets in French (retweets excluded) including more than 130,000 tweet...", "weights": [[8, 13, 1.0], [85, 90, 1.0]]}, "C10-1077": {"snippet": "...ting event extraction systems to new event types. This paper investigates two kinds of bootstrapping methods used for event extraction: the document-centric and similarity-centric approaches, and prop...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [40, 45, 1.0], [121, 126, 1.0], [127, 137, 1.0], [164, 174, 1.0]]}, "2017.fire_conference-2017w.39": {"snippet": "Event Extraction from Social Media text using Conditional Random Fields\n\n\n Social Media tools popularized the digital devices among masses making information dissemination easier and faster. Exchange ...", "weights": [[0, 5, 1.0], [6, 16, 1.0], [22, 28, 1.0], [29, 34, 1.0], [75, 81, 1.0], [82, 87, 1.0]]}, "R15-1059": {"snippet": "... the similarity of two sentences in some way. We present new ways of measuring this similarity, based on sentiment analysis and continuous vector space representations, and show that combining these t...", "weights": [[8, 18, 1.0], [87, 97, 1.0]]}, "D10-1105": {"snippet": "...that social bookmarking is a better predictor to the bursty search queries, but news media and social blogging media have a much more compelling coverage. This comparison provides insights on how the ...", "weights": [[8, 14, 1.0], [88, 93, 1.0], [98, 104, 1.0], [114, 119, 1.0]]}, "2012.wsdm_conference-2012.55": {"snippet": "...h an event on different social media sites. Further, we explore ways in which event content identified on one social media site can be used to retrieve additional relevant event content on other socia...", "weights": [[8, 13, 1.0], [27, 33, 1.0], [34, 39, 1.0], [81, 86, 1.0], [113, 119, 1.0], [120, 125, 1.0], [175, 180, 1.0], [198, 204, 1.0]]}, "2010.wsdm_conference-2010.30": {"snippet": "...ment similarity metrics to enable online clustering of media to events. As a key contribution of this paper, we explore a variety of techniques for learning multi-feature similarity metrics for social...", "weights": [[8, 18, 1.0], [19, 26, 1.0], [58, 63, 1.0], [174, 184, 1.0], [185, 192, 1.0], [197, 203, 1.0]]}, "P11-1040": {"snippet": "Event Discovery in Social Media Feeds\n\n\n We present a novel method for record extraction from social streams such as Twitter. Unlike typical extraction setups, these environments are characterized by ...", "weights": [[0, 5, 1.0], [19, 25, 1.0], [26, 31, 1.0], [78, 88, 1.0], [94, 100, 1.0], [141, 151, 1.0]]}, "2012.cikm_conference-2012.446": {"snippet": "... and social sensor data sources.\nCategories and Subject Descriptors\nH.4: Information Systems applications\nGeneral TermsAlgorithms, Experimentation, Performance, Human Factors.\nKeywordsSocial networks,...", "weights": [[8, 14, 1.0]]}, "2017.wwwconf_conference-2017c.8": {"snippet": "...s on Social Network Platforms\n\n\n ABSTRACTMicroblog-based social network platforms like Twitter and Sina Weibo have been important sources for news event extraction. However, existing works on microblo...", "weights": [[8, 14, 1.0], [60, 66, 1.0], [150, 155, 1.0], [156, 166, 1.0]]}, "2013.clef_conference-2013w.138": {"snippet": "...n\n\n\n Social networks have become a large repository of comments which can extract multiple information. Twitter is one of the most widespread social networks and larger and is therefore an important s...", "weights": [[8, 14, 1.0], [145, 151, 1.0]]}, "2019.clef_conference-2019w.3": {"snippet": "...s on social media, our team has developed a data-driven, ensemble model approach. Our system leverages word polarities, token extraction via mutual information, keyword expansion and semantic similari...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [129, 139, 1.0]]}, "C14-1214": {"snippet": "...y of Event Extraction in Newswire and Biomedical Domains\n\n\n Event extraction is a popular research topic in natural language processing. Several event extraction tasks have been defined for both the n...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [63, 68, 1.0], [69, 79, 1.0], [148, 153, 1.0], [154, 164, 1.0]]}, "2018.fire_conference-2018w.36": {"snippet": "...-IL: Event Extraction from Newswires and Social Media Text in Indian Languages @ FIRE 2018 - An Overview\n\n\n Today communication has become very fast and is happening in real time. An event that happen...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [44, 50, 1.0], [51, 56, 1.0], [186, 191, 1.0]]}, "P14-2114": {"snippet": "...h to Event Extraction from {T}witter\n\n\n With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial ...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [69, 75, 1.0], [76, 81, 1.0], [89, 95, 1.0]]}, "2019.sigirconf_conference-2019.136": {"snippet": "...s on Social Media", "weights": [[8, 14, 1.0], [15, 20, 1.0]]}, "2014.wwwconf_conference-2014c.243": {"snippet": "...erse media services, such as blogs, photo/video/link sharing, social networks, and microblogs. These Web media react to and affect realworld events, while the mass media still has big influence on soc...", "weights": [[8, 13, 1.0], [65, 71, 1.0], [108, 113, 1.0], [167, 172, 1.0], [200, 206, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A4.8": {"snippet": "An event detection method for social networks based on hybrid link prediction and quantum swarm intelligent\n\n\n Abstract How can we discover and estimate major events in complex social networks? Even w...", "weights": [[3, 8, 1.0], [30, 36, 1.0], [177, 183, 1.0]]}, "2017.wwwconf_conference-2017c.103": {"snippet": "... new metrics (alt-metrics) that can capture the real world impact of science, including social media buzz created and number of mainstream media mentions gathered by a scientific article. Many popular...", "weights": [[8, 15, 1.0], [21, 28, 1.0], [91, 97, 1.0], [98, 103, 1.0], [142, 147, 1.0]]}, "2017.sigirconf_conference-2017.235": {"snippet": "...sist social scientists to extract topics from social media data, to predict their topical trends, or to classify a social media user (e.g. a Twi er user) into a community. However, while topic modelli...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [56, 61, 1.0], [118, 124, 1.0], [125, 130, 1.0]]}, "W17-1612": {"snippet": "... for Social Media Health Research\n\n\n Social media have transformed datadriven research in political science, the social sciences, health, and medicine. Since health research often touches on sensitive...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [40, 46, 1.0], [47, 52, 1.0], [116, 122, 1.0]]}, "2020.wnut-1.78": {"snippet": "...}-19 Event Extraction on Social Media\n\n\n In this paper, we describe our approach in the shared task: COVID-19 event extraction from Twitter. The objective of this task is to extract answers from COVID...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [28, 34, 1.0], [35, 40, 1.0], [113, 118, 1.0], [119, 129, 1.0]]}, "2017.gir_workshop-2017.9": {"snippet": "...tity similarity computation: The case of social event entities", "weights": [[8, 18, 1.0], [44, 50, 1.0], [51, 56, 1.0]]}, "2017.cikm_conference-2017.278": {"snippet": "...of a social network in a uni ed way, at the same time, bringing together two disparate lines of research: graph-based and content-based event discovery in social media. We model interactions of two ty...", "weights": [[8, 14, 1.0], [139, 144, 1.0], [158, 164, 1.0], [165, 170, 1.0]]}, "2018.ipm_journal-ir0volumeA54A6.17": {"snippet": "...s of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar s...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [60, 66, 1.0], [67, 72, 1.0], [110, 116, 1.0], [117, 122, 1.0], [202, 208, 1.0]]}, "W17-7001": {"snippet": "...from Social Events on Social Media Streaming in {S}panish\n\n\n The problem of event analysis in Spanish social media streaming is that of difficulty on automatically processing the data as well as obtai...", "weights": [[8, 14, 1.0], [25, 31, 1.0], [32, 37, 1.0], [79, 84, 1.0], [105, 111, 1.0], [112, 117, 1.0]]}, "W14-6202": {"snippet": "...tion Extraction for Social Media\n\n\n The rapid growth in IT in the last two decades has led to a growth in the amount of information available online. A new style for sharing information is social medi...", "weights": [[8, 18, 1.0], [23, 29, 1.0], [30, 35, 1.0], [192, 198, 1.0], [199, 204, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.6": {"snippet": "...n in Event-based Social Networks\n\n\n Event-based social networks (EBSNs) are becoming popular in recent years. Users can publish a planned event on an EBSN website, calling for other users to participa...", "weights": [[8, 13, 1.0], [20, 26, 1.0], [39, 44, 1.0], [51, 57, 1.0], [141, 146, 1.0]]}, "2016.wsdm_conference-2016.88": {"snippet": "...ting Social Media Icebergs by Their Tips: Rumors, Persuasion Campaigns, and Information Needs\n\n\n ABSTRACTOnline activities of more than one billion social media users all over the world form a resourc...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [151, 157, 1.0], [158, 163, 1.0]]}, "2010.mir_conference-2010.53": {"snippet": "...ball event through social media", "weights": [[8, 13, 1.0], [22, 28, 1.0], [29, 34, 1.0]]}, "2012.wwwconf_conference-2012c.138": {"snippet": "...mity metrics on the activity prediction task and find that metrics that take into account the attention-limited nature of interactions in social media lead to substantially better predictions.", "weights": [[8, 15, 1.0], [62, 69, 1.0], [141, 147, 1.0], [148, 153, 1.0]]}, "2021.findings-acl.79": {"snippet": "Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech\n\n\n Tackling online hatred using informed textual responses -called counter narratives -has been brought under the spotlight rece...", "weights": []}, "2017.fire_conference-2017w.40": {"snippet": "...ough social media can able to connect the people together, they are vulnerable to crimes like Identity thefts, false information, and identity masking etc. Identifying the event from the social media ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [175, 180, 1.0], [190, 196, 1.0], [197, 202, 1.0]]}, "2021.ecir_conference-20212.31": {"snippet": "...ting event popularity using social media. We focus on predicting the popularity of an event much before its start date. This type of early prediction can be helpful in event recommendation systems, as...", "weights": [[8, 13, 1.0], [31, 37, 1.0], [38, 43, 1.0], [89, 94, 1.0], [171, 176, 1.0]]}, "W13-2010": {"snippet": "...(CG) event extraction tasks. Our event extraction is based on the system we recently proposed for mining relations and events involving genes or proteins in the biomedical literature using a novel, ap...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [36, 41, 1.0], [42, 52, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A3.2": {"snippet": "...o an event, social event summarization aims to generate an informative textual description that can capture all the important moments, and it plays a critical role in mining and analyzing social media...", "weights": [[8, 13, 1.0], [15, 21, 1.0], [22, 27, 1.0], [191, 197, 1.0], [198, 203, 1.0]]}, "2019.sigirconf_workshop-2019newsir.13": {"snippet": "Event Detection using Images of Temporal Word Patterns\n\n\n Detecting events from social media requires to deal with the noisy sequences of user generated text. Previous work typically focuses either on...", "weights": [[0, 5, 1.0], [80, 86, 1.0], [87, 92, 1.0]]}, "2018.wwwconf_conference-2018.10": {"snippet": "...erse event detection task for social media users. Specifically, we propose a generalized Maximum Mean Discrepancy (MMD) criterion to measure the semantic distances between the heterogeneous messages f...", "weights": [[8, 13, 1.0], [33, 39, 1.0], [40, 45, 1.0]]}, "2015.wwwjournals_journal-ir0volumeA18A5.2": {"snippet": "Social event identification and ranking on flickr\n\n\n Abstract Effective event modeling allows accurate event identification and monitoring to enable timely response to emergencies occurring in various...", "weights": [[0, 6, 1.0], [7, 12, 1.0], [72, 77, 1.0], [103, 108, 1.0]]}, "E17-1076": {"snippet": "Event extraction from {T}witter using Non-Parametric {B}ayesian Mixture Model with Word Embeddings\n\n\n To extract structured representations of newsworthy events from Twitter, unsupervised models typic...", "weights": [[0, 5, 1.0], [6, 16, 1.0]]}, "2013.mir_conference-2013.21": {"snippet": "... for event-related social media retrieval", "weights": [[8, 13, 1.0], [22, 28, 1.0], [29, 34, 1.0]]}, "C12-2018": {"snippet": "Does Similarity Matter? The Case of Answer Extraction from Technical Discussion Forums\n\n\n Extracting question-answer pairs from social media discussions has garnered much attention in recent times. Se...", "weights": [[5, 15, 1.0], [43, 53, 1.0], [128, 134, 1.0], [135, 140, 1.0]]}, "2017.sigirconf_conference-2017.132": {"snippet": "Event Early Embedding: Predicting Event Volume Dynamics at Early Stage\n\n\n ABSTRACTSocial media has become one of the most credible sources for delivering messages, breaking news, as well as events. Pr...", "weights": [[0, 5, 1.0], [34, 39, 1.0], [89, 94, 1.0]]}, "2017.ipm_journal-ir0volumeA53A3.5": {"snippet": "...Text Similarity (STS) is concerned with the degree of that similarity. 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6, "score": 19.124980306816326, "doc_id": "2016.wwwconf_conference-2016.8"}, {"rank": 7, "score": 17.766786567541, "doc_id": "E89-1022"}, {"rank": 8, "score": 16.906079696062815, "doc_id": "2020.sigirjournals_journal-ir0volumeA54A1.16"}, {"rank": 9, "score": 16.72217311109355, "doc_id": "C65-1002"}, {"rank": 10, "score": 15.912170513079094, "doc_id": "P06-1023"}]}], "docs": {"C08-1096": {"snippet": "...r is event frame extraction, rather than event instance extraction, the issue of low recall could be solved by applying the methods to a larger-scale corpus. Introduction This paper describes the auto...", "weights": [[8, 13, 1.0], [20, 30, 1.0], [44, 49, 1.0], [59, 69, 1.0]]}, "2011.wwwconf_conference-2011c.79": {"snippet": "...ract event snippets from contexts. \"Events\" are atomic text snippets and a news article is constituted by more than one event snippet. Event Snippet Extraction (ESE) aims to mine these snippets out. T...", "weights": [[8, 13, 1.0], [123, 128, 1.0], [138, 143, 1.0], [152, 162, 1.0]]}, "2018.fire_conference-2018w.37": {"snippet": "...ased Event Extraction System for Indian Languages\n\n\n In this paper we have described a neural network based approach for Event extraction(EE) task which aims to discover different types of events alon...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [124, 129, 1.0], [130, 140, 1.0]]}, "L10-1389": {"snippet": "...s on Event Extraction Systems", "weights": [[8, 13, 1.0], [14, 24, 1.0]]}, "P11-1040": {"snippet": "Event Discovery in Social Media Feeds\n\n\n We present a novel method for record extraction from social streams such as Twitter. Unlike typical extraction setups, these environments are characterized by ...", "weights": [[0, 5, 1.0], [78, 88, 1.0], [141, 151, 1.0]]}, "2017.wwwconf_conference-2017c.8": {"snippet": "...news event extraction. However, existing works on microblog event extraction, which usually use keywords, entities, or selected microblogs to represent events, are not able to extract details of an ev...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [63, 68, 1.0], [69, 79, 1.0], [201, 206, 1.0]]}, "2016.wwwconf_conference-2016c.152": {"snippet": "Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data\n\n\n ABSTRACTNowadays, human movement in urban spaces can be traced digitally in many cases. It can be ob...", "weights": []}, "2020.emnlp-main.49": {"snippet": "Event Extraction by Answering (Almost) Natural Questions\n\n\n The problem of event extraction requires detecting the event trigger and extracting its corresponding arguments. Existing work in event argu...", "weights": [[0, 5, 1.0], [6, 16, 1.0], [75, 80, 1.0], [81, 91, 1.0], [115, 120, 1.0], [190, 195, 1.0]]}, "R17-1031": {"snippet": "...ased Event Extraction from {T}witter", "weights": [[8, 13, 1.0], [14, 24, 1.0]]}, "2020.aacl-main.81": {"snippet": "...ting Event Regions for Event Extraction via Graph Attention Networks\n\n\n Event information is usually scattered across multiple sentences within a document. The local sentence-level event extractors of...", "weights": [[8, 13, 1.0], [26, 31, 1.0], [32, 42, 1.0], [75, 80, 1.0], [184, 189, 1.0]]}, "Q18-1006": {"snippet": "Event Time Extraction with a Decision Tree of Neural Classifiers\n\n\n Extracting the information from text when an event happened is challenging. Documents do not only report on current events, but also...", "weights": [[0, 5, 1.0], [11, 21, 1.0], [113, 118, 1.0]]}, "P11-1114": {"snippet": "...rove event extraction by learning to identify secondary role filler contexts in the absence of event keywords. We propose a multilayered event extraction architecture that progressively \"zooms in\" on ...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [98, 103, 1.0], [140, 145, 1.0], [146, 156, 1.0]]}, "1994.tc-1.10": {"snippet": "...mini-cab drivers. The clue is that these so called experts are usually more interested in demonstrating their skills and playing with the technology than your need to earn a living -they are what I wo...", "weights": [[8, 11, 1.0]]}, "W15-3804": {"snippet": "Event Extraction in pieces:Tackling the partial event identification problem on unseen corpora\n\n\n Biomedical event extraction systems have the potential to provide a reliable means of enhancing knowle...", "weights": [[0, 5, 1.0], [6, 16, 1.0], [48, 53, 1.0], [109, 114, 1.0], [115, 125, 1.0]]}, "W12-2412": {"snippet": "...ical Event Extraction\n\n\n Event extraction is a major focus of recent work in biomedical information extraction. Despite substantial advances, many challenges still remain for reliable automatic extrac...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [28, 33, 1.0], [34, 44, 1.0], [103, 113, 1.0], [197, 207, 1.0]]}, "W16-6308": {"snippet": "...ular Event Extraction using a Stacked Generalization based Classifier\n\n\n In this paper we propose a stacked generalization (or stacking) model for event extraction in bio-medical text. Event extractio...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [150, 155, 1.0], [156, 166, 1.0], [188, 193, 1.0], [194, 204, 1.0]]}, "2022.naacl-main.291": {"snippet": "...evel Event Extraction\n\n\n Event extraction aims to identify an event and then extract the arguments participating in the event. Despite the great success in sentencelevel event extraction, events are m...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [28, 33, 1.0], [34, 44, 1.0], [65, 70, 1.0], [123, 128, 1.0], [173, 178, 1.0], [179, 189, 1.0]]}, "2014.wwwconf_conference-2014c.21": {"snippet": "...same event. It can identify groups of articles in different languages that describe the same event and represent them as a single event. From articles in each event it can then extract event's core in...", "weights": [[8, 13, 1.0], [96, 101, 1.0], [133, 138, 1.0], [162, 167, 1.0], [188, 193, 1.0]]}, "2017.fire_conference-2017w.40": {"snippet": "...e on Event Extraction for Indian language shared task which is conducted in Forum for Information Retrieval Evaluation (FIRE) 2017. For this Event extraction task, organizers release the dataset with ...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [144, 149, 1.0], [150, 160, 1.0]]}, "P16-1116": {"snippet": "... for Event Extraction\n\n\n Event extraction is a particularly challenging information extraction task, which intends to identify and classify event triggers and arguments from raw text. In recent works,...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [28, 33, 1.0], [34, 44, 1.0], [87, 97, 1.0], [143, 148, 1.0]]}, "W15-4502": {"snippet": "...ving Event Detection with {A}bstract {M}eaning {R}epresentation\n\n\n Event Detection (ED) aims to identify instances of specified types of events in text, which is a crucial component in the overall tas...", "weights": [[8, 13, 1.0], [70, 75, 1.0]]}, "2010.airs_conference-2010.47": {"snippet": "Event Recognition from News Webpages through Latent Ingredients Extraction", "weights": [[0, 5, 1.0], [64, 74, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A4.16": {"snippet": "Direction-aware KNN queries for moving objects in a road network\n\n\n AbstractRecently more and more people focus on k-nearest neighbor (KNN) query processing over moving objects in road networks, e.g.,...", "weights": []}, "P19-1522": {"snippet": "...y of event extraction itself, insufficient training data hinders the learning process as well. To promote event extraction, we first propose an event extraction model to overcome the roles overlap pro...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [109, 114, 1.0], [115, 125, 1.0], [147, 152, 1.0], [153, 163, 1.0]]}, "2012.wwwconf_workshop-2012tempweb.4": {"snippet": "Extraction of temporal facts and events from Wikipedia", "weights": [[0, 10, 1.0]]}, "W10-1919": {"snippet": "...ards Event Extraction from Full Texts on Infectious Diseases\n\n\n Event extraction approaches based on expressive structured representations of extracted information have been a significant focus of res...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [67, 72, 1.0], [73, 83, 1.0]]}, "2022.findings-acl.16": {"snippet": "...ning Event Extraction as Type-oriented Binary Decoding\n\n\n Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbo...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [61, 66, 1.0], [67, 77, 1.0], [145, 150, 1.0]]}, "2017.wsdm_conference-2017.39": {"snippet": "...ated Event Discovery\n\n\n ABSTRACTWe consider the problem of discovering local events on the web, where events are entities extracted from webpages. Examples of such local events include small venue con...", "weights": [[8, 13, 1.0]]}, "W09-1403": {"snippet": "Event Extraction from Trimmed Dependency Graphs\n\n\n We describe the approach to event extraction which the JULIELab Team from FSU Jena (Germany) pursued to solve Task 1 in the \"BioNLP'09 Shared Task on...", "weights": [[0, 5, 1.0], [6, 16, 1.0], [79, 84, 1.0], [85, 95, 1.0]]}, "2017.fire_conference-2017w.39": {"snippet": "...ers. Event extraction refers to understanding the events across streams of social media messages. Event extraction helps in taking quicker corrective actions in case of natural calamities and hence po...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [101, 106, 1.0], [107, 117, 1.0]]}, "2020.ccl-1.36": {"snippet": "...evel Event Extraction)\n\n\n Document-level event extraction aims at discovering event mentions and extracting events which contain event arguments and their roles from texts. This paper proposes an end-...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [44, 49, 1.0], [50, 60, 1.0], [81, 86, 1.0], [132, 137, 1.0]]}, "2020.wwwconf_conference-2020.250": {"snippet": "... GPS traces. However, previous methods always modeled each road attribute separately based on intuitive hand-crafted features extracted from GPS traces. This observation motivates us to propose a mach...", "weights": [[8, 14, 1.0], [148, 154, 1.0]]}, "W10-1921": {"snippet": "...with event data with the aim of enhancing event extraction performance. While obtaining promising results, we will argue that an event extraction framework will benefit most from this new data when ta...", "weights": [[8, 13, 1.0], [45, 50, 1.0], [51, 61, 1.0], [132, 137, 1.0], [138, 148, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A5.8": {"snippet": "...nese event extraction\n\n\n Abstract Event extraction is a challenging task in natural language understanding, which aims to recognize event type, subtype and roles of relevant entities from unstructured...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [37, 42, 1.0], [43, 53, 1.0], [135, 140, 1.0]]}, "P14-2114": {"snippet": "...ting event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract stru...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [169, 174, 1.0]]}, "P11-1163": {"snippet": "Event Extraction as Dependency Parsing\n\n\n Nested event structures are a common occurrence in both open domain and domain specific extraction tasks, e.g., a \"crime\" event can cause a \"investigation\" ev...", "weights": [[0, 5, 1.0], [6, 16, 1.0], [49, 54, 1.0], [130, 140, 1.0], [164, 169, 1.0], [198, 203, 1.0]]}, "W10-1904": {"snippet": "...ical Event Extraction to the Entire {P}ub{M}ed\n\n\n We present the first full-scale event extraction experiment covering the titles and abstracts of all PubMed citations. Extraction is performed using a...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [85, 90, 1.0], [91, 101, 1.0], [172, 182, 1.0]]}, "C12-1033": {"snippet": "...nese Event Extraction with Rich Linguistic Features\n\n\n Compared to the amount of research that has been done on English event extraction, there exists relatively little work on Chinese event extractio...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [123, 128, 1.0], [129, 139, 1.0], [188, 193, 1.0], [194, 204, 1.0]]}, "2021.acl-tutorials.2": {"snippet": "...ated extraction, conceptualization, coreference resolution and prediction of events and their relations, (iii) induction of event processes and properties, and (iv) a wide range of NLP and commonsense...", "weights": [[8, 18, 1.0], [127, 132, 1.0]]}, "2021.acl-long.217": {"snippet": "...-end Event Extraction\n\n\n Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [28, 33, 1.0], [34, 44, 1.0], [92, 97, 1.0], [144, 149, 1.0], [187, 192, 1.0]]}, "L06-1313": {"snippet": "{I}-{CAB}: the {I}talian Content Annotation Bank", "weights": [[5, 8, 1.0]]}, "2016.cikm_conference-2016.302": {"snippet": "Routing an Autonomous Taxi with Reinforcement Learning\n\n\n ABSTRACTSingapore's vision of a Smart Nation encompasses the development of effective and efficient means of transportation. The government's ...", "weights": []}, "2016.wwwconf_conference-2016.8": {"snippet": "... and Event Recommendations Using Personal Digital Traces\n\n\n ABSTRACTWe propose a new user-centric recommendation model, called Immersive Recommendation, that incorporates cross-platform and diverse pe...", "weights": [[8, 13, 1.0], [53, 59, 1.0]]}, "W14-6831": {"snippet": "...utes Extraction Based on the Combination of Sentences Classifications and Rules\n\n\n Personal attributes extraction plays a significant role in information mining, event tracing and personal name disamb...", "weights": [[8, 18, 1.0], [106, 116, 1.0], [165, 170, 1.0]]}, "W17-7547": {"snippet": "...ular Event Extraction System towards Knowledge Acquisition\n\n\n This paper presents a robust system for the automatic extraction of bio-molecular events from scientific texts. Event extraction provides ...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [119, 129, 1.0], [177, 182, 1.0], [183, 193, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A6.17": {"snippet": "...data extraction in MVCC-based RDBMS\n\n\n Abstract Account reconciliation is the core business in banks and game companies. It regularly examines the account balance with the bank or expense statement fo...", "weights": [[8, 18, 1.0]]}, "P06-1023": {"snippet": "...hich traces and fillers are co-indexed. The parser is an unlexicalized PCFG parser which is guaranteed to return the most probable parse. The grammar is extracted from a version of the PENN treebank w...", "weights": [[8, 14, 1.0]]}, "W11-1506": {"snippet": "...ical Event Extraction from Text\n\n\n In this paper, we report on how historical events are extracted from text within the Semantics of History research project. The project aims at the creation of resou...", "weights": [[8, 13, 1.0], [14, 24, 1.0]]}, "2008.mir_conference-2008.2": {"snippet": "... GPS traces and visual words for event classification", "weights": [[8, 14, 1.0], [36, 41, 1.0]]}, "2008.cikm_conference-2008.15": {"snippet": "...late CAB and a Call Logging CAB, using a pipeline consisting of audio capture of a telephone conversation, automatic speech recognition, text analysis, and log generation. We developed techniques for ...", "weights": [[8, 11, 1.0], [31, 34, 1.0]]}, "2017.ipm_journal-ir0volumeA53A3.0": {"snippet": "...cial event recommendation\n\n\n a b s t r a c tEvent-based social networks (EBSNs) have experienced increased popularity and rapid growth. Due to the huge volume of events available in EBSNs, event recom...", "weights": [[8, 13, 1.0], [192, 197, 1.0]]}, "2021.nuse-1.4": {"snippet": "...evel event extraction is a challenging task as it requires the extraction of event and entity coreference, and capturing arguments that span across different sentences. Existing works on event extract...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [66, 76, 1.0], [80, 85, 1.0], [190, 195, 1.0], [196, 206, 1.0]]}, "N09-5001": {"snippet": "...tion Extraction (IE) systems identify many unconnected facts. The objective of this paper is to define a new cross-document information extraction task and demonstrate a system which can extract, rank...", "weights": [[8, 18, 1.0], [139, 149, 1.0]]}, "P16-1026": {"snippet": "...ntly Event Extraction and Visualization on {T}witter via Probabilistic Modelling\n\n\n Event extraction from texts aims to detect structured information such as what has happened, to whom, where and when...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [87, 92, 1.0], [93, 103, 1.0]]}, "C16-1215": {"snippet": "...obal Event Extraction\n\n\n Event extraction is a difficult information extraction task. Li et al. (2014)  explore the benefits of modeling event extraction and two related tasks, entity mention and rela...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [28, 33, 1.0], [34, 44, 1.0], [72, 82, 1.0], [140, 145, 1.0], [146, 156, 1.0]]}, "N16-1137": {"snippet": "...enre Event Extraction with Knowledge Enrichment\n\n\n The goal of Event extraction is to extract structured information of events that are of interest from unstructured documents. Existing event extracto...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [66, 71, 1.0], [72, 82, 1.0], [189, 194, 1.0]]}, "2021.naacl-demos.16": {"snippet": "...evel event extraction to cross-document cross-lingual cross-media event extraction, coreference resolution and temporal event tracking; (2) using human curated event schema library to match and enhanc...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [69, 74, 1.0], [75, 85, 1.0], [123, 128, 1.0], [163, 168, 1.0]]}, "C10-1077": {"snippet": "...g in Event Extraction\n\n\n Several researchers have proposed semi-supervised learning methods for adapting event extraction systems to new event types. This paper investigates two kinds of bootstrapping...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [108, 113, 1.0], [114, 124, 1.0], [140, 145, 1.0]]}, "2012.wsdm_conference-2012.55": {"snippet": "...mine event aggregation platforms to extract event features, which are often noisy or missing. We use these features to develop query formulation strategies for retrieving content associated with an ev...", "weights": [[8, 13, 1.0], [47, 52, 1.0], [201, 206, 1.0]]}, "2021.acl-long.274": {"snippet": "...evel Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker\n\n\n Document-level event extraction aims to recognize event information from a whole piece of article. Existing meth...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [106, 111, 1.0], [112, 122, 1.0], [141, 146, 1.0]]}, "2004.sigirconf_conference-2004.56": {"snippet": "...ased event extraction along a timeline\n\n\n ABSTRACTIn this paper, we present a framework and a system that extracts events relevant to a query from a collection C of documents, and places such events a...", "weights": [[8, 13, 1.0], [14, 24, 1.0]]}, "W12-2410": {"snippet": "...cale Event Extraction for Post-Translational Modifications, Epigenetics and Protein Structural Relations\n\n\n Recent efforts in biomolecular event extraction have mainly focused on core event types invo...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [142, 147, 1.0], [148, 158, 1.0], [187, 192, 1.0]]}, "P16-1025": {"snippet": "...eral Event Extraction and Event Schema Induction\n\n\n We propose a brand new \"Liberal\" Event Extraction paradigm to extract events and discover event schemas from any input corpus simultaneously. We inc...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [29, 34, 1.0], [88, 93, 1.0], [94, 104, 1.0], [145, 150, 1.0]]}, "2021.findings-acl.14": {"snippet": "...ping Event Extraction\n\n\n Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Most existing methods assume that events appear in sentences wi...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [28, 33, 1.0], [34, 44, 1.0], [75, 85, 1.0], [112, 117, 1.0]]}, "2020.sigirjournals_journal-ir0volumeA54A1.16": {"snippet": "...tion extraction from digital social trace data with applications to social media and scholarly communication data", "weights": [[8, 18, 1.0]]}, "2021.ecir_conference-20212.20": {"snippet": "...s\n\n\n Event detection involves the identification of instances of specified types of events in text and their classification into event types. In this paper, we approach the event detection task as a r...", "weights": [[8, 13, 1.0], [132, 137, 1.0], [176, 181, 1.0]]}, "2022.acl-long.358": {"snippet": "...ased Event Extraction\n\n\n We consider event extraction in a generative manner with template-based conditional generation. Although there is a rising trend of casting the task of event extraction as a s...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [40, 45, 1.0], [46, 56, 1.0], [180, 185, 1.0], [186, 196, 1.0]]}, "S15-2139": {"snippet": "...olve event coreferences, and to order the events chronologically. We addressed the sub-tasks, in which event mentions were provided, i.e., no additional event extraction was required. For this, we dev...", "weights": [[8, 13, 1.0], [106, 111, 1.0], [156, 161, 1.0], [162, 172, 1.0]]}, "2017.tweb_journal-ir0volumeA11A4.1": {"snippet": "...ream traces (i.e., sequences of click events from users), we seek to achieve two goals: (1) detection: to capture distinct user groups for the detection of malicious accounts, and (2) understanding: t...", "weights": [[8, 14, 1.0]]}, "2021.findings-acl.211": {"snippet": "... the event extraction task (EE) to detect event trigger words and their arguments in a novel domain of historical texts. In particular, we introduce a new EE dataset for a corpus of nineteenth-century...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [45, 50, 1.0]]}, "P19-3006": {"snippet": "...tion extraction tasks that has many applications such as web search and question answering. We present a system for rapidly customizing event extraction capability to find new event types (what happen...", "weights": [[8, 18, 1.0], [139, 144, 1.0], [145, 155, 1.0], [179, 184, 1.0]]}, "2020.lrec-1.258": {"snippet": "... the event from the text. For the event extraction, event arguments have been used. Event arguments identify event triggering words or phrases that clearly express the occurrence of the event. The eve...", "weights": [[8, 13, 1.0], [37, 42, 1.0], [43, 53, 1.0], [55, 60, 1.0], [87, 92, 1.0], [112, 117, 1.0], [189, 194, 1.0], [200, 205, 1.0]]}, "E89-1022": {"snippet": "...sis (CAB) for the potential atomic parts of a complex object is introduced in the following. CABs pose conceptual constraints on the formation of complex RefOs in general. We argue that in cases where...", "weights": [[8, 11, 1.0]]}, "2016.ijirr_journal-ir0volumeA6A1.2": {"snippet": "...atic Extraction from Arabic Texts", "weights": [[8, 18, 1.0]]}, "P10-1081": {"snippet": "...ross-Event Inference to Improve Event Extraction\n\n\n Event extraction is a particularly challenging type of information extraction (IE). Most current event extraction systems rely on local information ...", "weights": [[8, 13, 1.0], [35, 40, 1.0], [41, 51, 1.0], [55, 60, 1.0], [61, 71, 1.0], [122, 132, 1.0], [152, 157, 1.0], [158, 168, 1.0]]}, "R11-1001": {"snippet": "...r an event differ in regular and predictable ways. We describe a novel system that tackles the problem of extracting knowledge from text about how actions and events change the world over time. We lev...", "weights": [[8, 13, 1.0]]}, "C14-1204": {"snippet": "...ying Event Inference to Improve Semi-Supervised {C}hinese Event Extraction\n\n\n Although semi-supervised model can extract the event mentions matching frequent event patterns, it suffers much from those...", "weights": [[8, 13, 1.0], [61, 66, 1.0], [67, 77, 1.0], [128, 133, 1.0], [161, 166, 1.0]]}, "2018.tist_journal-ir0volumeA9A3.7": {"snippet": "... GPS traces are usually unknown, or in other words, we have no idea about people's activities once they leave the taxi. Therefore, there is indeed a dilemma in which we have massive human footprints b...", "weights": [[8, 14, 1.0]]}, "2021.econlp-1.10": {"snippet": "...News Event Extraction\n\n\n Event extraction in commodity news is a less researched area as compared to generic event extraction. However, accurate event extraction from commodity news is useful in a bro...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [28, 33, 1.0], [34, 44, 1.0], [112, 117, 1.0], [118, 128, 1.0], [148, 153, 1.0], [154, 164, 1.0]]}, "2015.tist_journal-ir0volumeA6A3.11": {"snippet": "...cial Event Detection and Evaluation with Taxi Traces\n\n\n A social event is an occurrence that involves lots of people and is accompanied by an obvious rise in human flow. Analysis of social events has ...", "weights": [[8, 13, 1.0], [49, 55, 1.0], [68, 73, 1.0]]}, "W09-1411": {"snippet": "...ular event extraction from Link Grammar parse trees\n\n\n We present an approach for extracting molecular events from literature based on a deep parser, using in a query language for parse trees. Detecte...", "weights": [[8, 13, 1.0], [14, 24, 1.0]]}, "H05-1008": {"snippet": "...y of event extraction is limited by a number of complicating factors, with errors compounded at all sages inside the Information Extraction pipeline. In this paper, we present methods for recovering a...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [132, 142, 1.0]]}, "1992.tc-1.17": {"snippet": "Cab computer-speak language: developing multilingual audio and visual communications systems for Transmanche trains\n\n\n High-speed BR, SNCB and SNCF trains will run through the Channel Tunnel from mid-...", "weights": [[0, 3, 1.0]]}, "2019.ecir_workshop-2019text2story.4": {"snippet": "...ated Event Extraction Model for Linked Portuguese Documents", "weights": [[8, 13, 1.0], [14, 24, 1.0]]}, "L14-1091": {"snippet": "Event Extraction Using Distant Supervision", "weights": [[0, 5, 1.0], [6, 16, 1.0]]}, "2017.wwwconf_conference-2017c.66": {"snippet": "...f an event, we mine the search log data to identify queries that are triggered by it and further characterize the temporal trend of influence created by the same event on user queries. We solve this d...", "weights": [[8, 13, 1.0], [165, 170, 1.0]]}, "2007.wwwconf_conference-2007.125": {"snippet": "...eans event on one query is the causation of some event on the other. We first detect events in query logs by efficient statistical frequency threshold. Then the causal relation of queries is mined by ...", "weights": [[8, 13, 1.0], [52, 57, 1.0]]}, "N12-1008": {"snippet": "...ulti-Event Extraction Guided by Global Constraints\n\n\n This paper addresses the extraction of event records from documents that describe multiple events. Specifically, we aim to identify the fields of ...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [82, 92, 1.0], [96, 101, 1.0]]}, "1996.tois_journal-ir0volumeA14A1.1": {"snippet": "Sequential Patterns in Information Systems Development: An Application of a Social Process Model\n\n\n We trace the process of developingand implementinga materialsmanagementsystem in one company over a ...", "weights": []}, "2021.findings-acl.405": {"snippet": "...-end Event Extraction\n\n\n Event extraction (EE) aims to harvest event instances from plain text, where each instance is composed of a group of event arguments with specific event roles. Existing end-to...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [28, 33, 1.0], [34, 44, 1.0], [66, 71, 1.0], [145, 150, 1.0], [175, 180, 1.0]]}, "C65-1002": {"snippet": "...ibed cab provide useful information that may be employed in syntactic analysis and certain information retrieval applications.", "weights": [[8, 11, 1.0]]}, "W17-2315": {"snippet": "...h an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset 1 (Kim et al., 20...", "weights": [[8, 13, 1.0], [161, 166, 1.0], [167, 177, 1.0]]}, "W06-0901": {"snippet": "...s of event extraction\n\n\n Event detection and recognition is a complex task consisting of multiple sub-tasks of varying difficulty. In this paper, we present a simple, modular approach to event extract...", "weights": [[8, 13, 1.0], [14, 24, 1.0], [28, 33, 1.0], [190, 195, 1.0], [196, 206, 1.0]]}, "2018.fire_conference-2018w.36": {"snippet": "... the event and event span given in the data. Thus further going ahead in this track, along with the identification of event and its span, it is necessary to identify the cause and effects of a given e...", "weights": [[8, 13, 1.0], [18, 23, 1.0], [121, 126, 1.0], [202, 207, 1.0]]}, "2013.tist_journal-ir0volumeA5A1.14": {"snippet": "...ture extraction and classification are obtained by solving corresponding convex optimization problem with trace norm regularization. For feature extraction, robust principle component analysis (robust...", "weights": [[8, 18, 1.0], [148, 158, 1.0]]}, "2014.cikm_workshop-2014webkr.1": {"snippet": "...tion Extraction from Natural Disaster Events on Twitter\n\n\n ABSTRACTAs soon as natural disaster events happen, users are eager to know more about them. However, search engines currently provide a ten b...", "weights": [[8, 18, 1.0]]}, "W15-0808": {"snippet": "Event analysis for information extraction from business-based technical documents\n\n\n Event identification plays a crucial role in several natural language processing applications such as information e...", "weights": [[0, 5, 1.0], [31, 41, 1.0], [85, 90, 1.0], [199, 209, 1.0]]}, "W17-7001": {"snippet": "...hips extraction in order to obtain relevant information about the event in case. Spanish data from Twitter was used as a study case and tested with the developed application.", "weights": [[8, 18, 1.0], [69, 74, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.6": {"snippet": "...rget event, the host group of the event, and the location of the event. Furthermore, our proposed model can extract latent event topics from event text descriptions, and characterize events, groups, a...", "weights": [[8, 13, 1.0], [37, 42, 1.0], [68, 73, 1.0], [126, 131, 1.0], [144, 149, 1.0]]}, "2017.cikm_conference-2017.75": {"snippet": "...ious event sequence discovery, and propose GID, an efficient graph-based intrusion detection technique that can identify abnormal event sequences from massive heterogeneous process traces with high ac...", "weights": [[8, 13, 1.0], [133, 138, 1.0], [184, 190, 1.0]]}, "2022.deeplo-1.11": {"snippet": "... our event trigger detection 1 and event argument extraction 2 models are publicly available for research purposes. We also release a dockerized system connecting the two models into an unified event ...", "weights": [[8, 13, 1.0], [38, 43, 1.0], [53, 63, 1.0], [197, 202, 1.0]]}, "2019.tist_journal-ir0volumeA10A1.1": {"snippet": "RelationLines: Visual Reasoning of Egocentric Relations from Heterogeneous Urban Data\n\n\n The increased accessibility of urban sensor data and the popularity of social network applications is enabling ...", "weights": []}, "P11-1113": {"snippet": "...evel event extraction system. Experiments show that we can get 8.6% gain in trigger (event) identification, and more than 11.8% gain for argument (role) classification in ACE event extraction.", "weights": [[8, 13, 1.0], [14, 24, 1.0], [88, 93, 1.0], [178, 183, 1.0], [184, 194, 1.0]]}, "2016.tweb_journal-ir0volumeA10A3.1": {"snippet": "... for event processing on top of web event ads, consisting of event extraction, complex event processing, and action execution in response to these events. Given a schema of the events to be tracked, t...", "weights": [[8, 13, 1.0], [39, 44, 1.0], [64, 69, 1.0], [70, 80, 1.0], [90, 95, 1.0]]}, "2012.wwwjournals_journal-ir0volumeA15A2.3": {"snippet": "...ging event, which captures the relations among a group of correlated tags where the tags are either bursty or associated with bursty tag co-occurrence. This kind of bursty tagging event generally corr...", "weights": [[8, 13, 1.0], [183, 188, 1.0]]}}}
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Furthe...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 26, 1.0], [166, 170, 1.0], [171, 175, 1.0]]}, "2021.conll-1.12": {"snippet": "Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training\n\n\n Language grounding aims at linking the symbolic representation of language (e.g., words) into the rich perc...", "weights": [[0, 8, 1.0], [9, 13, 1.0], [14, 18, 1.0]]}, "2021.naacl-main.379": {"snippet": "...tend zero-shot stance detection to new topics, highlighting future directions for zero-shot transfer.", "weights": [[8, 12, 1.0], [13, 17, 1.0], [85, 89, 1.0], [90, 94, 1.0]]}, "2019.sigirconf_conference-2019.66": {"snippet": "...Deep Multimodal Learning for Cross-Modal Retrieval", "weights": [[8, 18, 1.0], [19, 27, 1.0]]}, "2020.cikm_conference-2020.355": {"snippet": "Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval", "weights": [[0, 4, 1.0], [5, 9, 1.0], [33, 41, 1.0]]}, "D19-6112": {"snippet": "Few-Shot and Zero-Shot Learning for Historical Text Normalization\n\n\n Historical text normalization often relies on small training datasets. Recent work has shown that multi-task learning can lead to s...", "weights": [[4, 8, 1.0], [13, 17, 1.0], [18, 22, 1.0], [23, 31, 1.0], [178, 186, 1.0]]}, "2021.metanlp-1.2": {"snippet": "...Meta-Learning for Zero-Shot Cross-Lingual Transfer\n\n\n Multilingual pre-trained contextual embedding models (Devlin et al., 2019) have achieved impressive performance on zero-shot cross-lingual transfe...", "weights": [[8, 16, 1.0], [21, 25, 1.0], [26, 30, 1.0], [172, 176, 1.0], [177, 181, 1.0]]}, "2020.acl-main.272": {"snippet": "Zero-shot Text Classification via Reinforced Self-training\n\n\n Zero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes ...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [62, 66, 1.0], [67, 71, 1.0], [72, 80, 1.0]]}, "2020.findings-emnlp.198": {"snippet": "Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering\n\n\n Extracting rationales can help human understand which information the model utilizes and how it makes the predictio...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [49, 57, 1.0]]}, "2020.emnlp-main.235": {"snippet": "... Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs\n\n\n Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise ins...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 26, 1.0], [87, 91, 1.0], [92, 96, 1.0], [97, 105, 1.0]]}, "2020.findings-emnlp.205": {"snippet": "...able Zero-shot {NMT}\n\n\n Using a single encoder and decoder for all directions and training with English-centric data is a popular scheme for multilingual NMT. However, zero-shot translation under this...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [171, 175, 1.0], [176, 180, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.21": {"snippet": "...nce. Zero-shot learning is potential to be applied to solve these issues since it can perform classification without positive example. Nevertheless, current zero-shot learning algorithms have been stu...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 26, 1.0], [160, 164, 1.0], [165, 169, 1.0], [170, 178, 1.0]]}, "2021.mrl-1.18": {"snippet": "...on a zero-shot scenario where the target language is unseen during training. In the context of zero-shot learning, this task is typically approached using representations from pre-trained multilingual...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [98, 102, 1.0], [103, 107, 1.0], [108, 116, 1.0]]}, "2020.acl-main.12": {"snippet": "Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking\n\n\n Zero-shot transfer learning for multi-domain dialogue state tracking can allow us to handle new domains wi...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [19, 27, 1.0], [94, 98, 1.0], [99, 103, 1.0], [113, 121, 1.0]]}, "2021.emnlp-main.126": {"snippet": "...ving Zero-Shot Cross-Lingual Transfer Learning via Robust Training\n\n\n Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [41, 49, 1.0], [179, 183, 1.0], [184, 188, 1.0]]}, "2022.naacl-main.333": {"snippet": "...cult zero-shot setting with no paired audio-text data, our model demonstrates state-of-the-art zero-shot performance on the ESC50 and US8K audio classification tasks, and even surpasses the supervised...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [98, 102, 1.0], [103, 107, 1.0]]}, "D18-1352": {"snippet": "Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces\n\n\n Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (...", "weights": [[4, 8, 1.0], [13, 17, 1.0], [18, 22, 1.0], [35, 43, 1.0]]}, "2021.emnlp-main.746": {"snippet": "...tive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling\n\n\n Zero-shot cross-domain slot filling alleviates the data dependence in the case of data scarcity in the target domai...", "weights": [[8, 16, 1.0], [49, 53, 1.0], [54, 58, 1.0], [59, 67, 1.0], [88, 92, 1.0], [93, 97, 1.0]]}, "2022.acl-long.39": {"snippet": "...ized Zero-Shot Text Classification\n\n\n Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most exi...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [53, 57, 1.0], [58, 62, 1.0]]}, "2021.metanlp-1.3": {"snippet": "Zero-Shot Compositional Concept Learning\n\n\n In this paper, we study the problem of recognizing compositional attribute-object concepts within the zero-shot learning (ZSL) framework. We propose an epis...", "weights": [[0, 4, 1.0], [5, 9, 1.0], [32, 40, 1.0], [146, 150, 1.0], [151, 155, 1.0], [156, 164, 1.0]]}, "2021.acl-long.68": {"snippet": "...sfer learning from a pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and crossmodality tr...", "weights": [[8, 16, 1.0], [160, 164, 1.0], [165, 169, 1.0]]}, "2019.tist_journal-ir0volumeA10A2.1": {"snippet": "...y of zero-shot learning. First of all, we provide an overview of zero-shot learning. According to the data utilized in model optimization, we classify zero-shot learning into three learning settings. ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 26, 1.0], [68, 72, 1.0], [73, 77, 1.0], [78, 86, 1.0], [154, 158, 1.0], [159, 163, 1.0], [164, 172, 1.0], [184, 192, 1.0]]}, "2016.cikm_conference-2016.195": {"snippet": "...tive Zero-Shot Learning", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 26, 1.0]]}, "2022.acl-long.374": {"snippet": "...Task Learning For Zero Shot Performance Prediction of Multilingual Models\n\n\n Massively Multilingual Transformer based Language Models have been observed to be surprisingly effective on zero-shot trans...", "weights": [[8, 16, 1.0], [21, 25, 1.0], [26, 30, 1.0], [188, 192, 1.0], [193, 197, 1.0]]}, "2021.emnlp-main.323": {"snippet": "{CTAL}: Pre-training Cross-modal Transformer for Audio-and-Language Representations\n\n\n Existing approaches for audio-language taskspecific prediction focus on building complicated late-fusion mechanis...", "weights": []}, "2020.coling-main.126": {"snippet": "...tive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack\n\n\n Zero-shot slot filling has widely arisen to cope with data scarcity in target domains. However, previous approaches oft...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 26, 1.0], [84, 88, 1.0], [89, 93, 1.0]]}, "2019.wsdm_conference-2019.43": {"snippet": "...tter learning performance. In this paper, we study the novel problem of cyberbullying detection within a multi-modal context by exploiting social media data in a collaborative way. This task, however,...", "weights": [[8, 16, 1.0]]}, "2021.emnlp-main.614": {"snippet": "...sing multimodal information from text and images available at posting time. For that purpose, we enrich a currently available data set for POI type prediction with the images that accompany the text m...", "weights": [[8, 18, 1.0]]}, "2021.semeval-1.97": {"snippet": "... Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.\n\n\n This paper describes our submission to Se-mEval 2021 Task 2. We compare XLM-RoBERTa Base and Large in the few-shot ...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [198, 202, 1.0]]}, "2021.acl-long.202": {"snippet": "...tive Learning\n\n\n Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other. They can only utilize single-modal data (i.e., text o...", "weights": [[8, 16, 1.0]]}, "2020.wmt-1.64": {"snippet": "...fect Zero-Shot Neural Machine Translation\n\n\n Zero-shot neural machine translation is an attractive goal because of the high cost of obtaining data and building translation systems for new translation ...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [48, 52, 1.0], [53, 57, 1.0]]}, "2017.mir_conference-2017.8": {"snippet": "... for Zero-shot Learning", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 26, 1.0]]}, "2021.wwwconf_conference-2021.293": {"snippet": "...nced Zero-shot Learning", "weights": [[8, 12, 1.0], [13, 17, 1.0], [18, 26, 1.0]]}, "2022.repl4nlp-1.4": {"snippet": "...sive zero-shot ability in various tasks. However, recent work showed that in comparison to unimodal (visual) networks, CLIP's multimodal training does not benefit generalization (e.g. few-shot or tran...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [129, 139, 1.0], [191, 195, 1.0]]}, "2022.findings-acl.187": {"snippet": "...rong zero-shot performance on multimodal generation tasks, such as open-ended visual question answering and image captioning. For example, it achieves 44.5% zero-shot accuracy on the VQAv2 dataset, su...", "weights": [[8, 12, 1.0], [13, 17, 1.0], [33, 43, 1.0], [160, 164, 1.0], [165, 169, 1.0]]}, "2021.naacl-main.195": {"snippet": "...gual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models\n\n\n This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on ...", "weights": [[8, 18, 1.0], [36, 40, 1.0], [41, 45, 1.0], [117, 121, 1.0], [122, 126, 1.0]]}, "2022.acl-long.397": {"snippet": "... low-shot relation extraction. To fill in the gap between zero-shot and few-shot RE, we propose the triplet-paraphrase meta-training, which leverages triplet paraphrase to pre-train zero-shot label ma...", "weights": [[8, 12, 1.0], [61, 65, 1.0], [66, 70, 1.0], [79, 83, 1.0], [185, 189, 1.0], [190, 194, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.13": {"snippet": "...rial learning, we propose a novel Deep Adversarial Metric Learning approach, termed DAML for cross-modal retrieval. DAML nonlinearly maps labeled data pairs of different modalities into a shared laten...", "weights": [[8, 16, 1.0], [61, 69, 1.0]]}, "2022.acl-long.421": {"snippet": "... few-shot learner by leveraging the power of language. We first evaluate CLIP's zero-shot performance on a typical visual question answering task and demonstrate a zero-shot cross-modality transfer ca...", "weights": [[8, 12, 1.0], [83, 87, 1.0], [88, 92, 1.0], [167, 171, 1.0], [172, 176, 1.0]]}}}
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Some existing hashing methods can be used for efficient document similarity search. However, unsupervised hashing methods cannot incorporate prior knowledge for better hashing. Althoug...", "weights": [[8, 17, 1.0], [41, 48, 1.0], [133, 140, 1.0]]}, "W15-0204": {"snippet": "...ured image retrieval, and a mapping of annotated semantic entities and the image's low-level features will likely assist event recognition and description generation tasks.", "weights": [[8, 13, 1.0], [14, 23, 1.0], [78, 83, 1.0]]}, "2021.ipm_journal-ir0volumeA58A1.38": {"snippet": "...Semi-supervised Classification Method based on Dynamic Graph and Self-paced Learning", "weights": [[8, 18, 1.0], [68, 72, 1.0]]}, "2009.tois_journal-ir0volumeA27A3.3": {"snippet": "...with image retrieval using both natural photo images and medical images show that This article is based on the paper \"Semi-Supervised SVM Batch Mode Active Learning for Image Retrieval\" [Hoi et al. 20...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [126, 136, 1.0], [172, 177, 1.0], [178, 187, 1.0]]}, "2022.acl-long.105": {"snippet": "...four self-supervised and supervised subtasks for cross modality learning. A self-supervised speech subtask leverages unlabelled speech data, and a (self-)supervised text to text subtask makes use of a...", "weights": [[8, 12, 1.0], [13, 23, 1.0], [28, 38, 1.0], [79, 83, 1.0], [84, 94, 1.0], [151, 155, 1.0], [157, 167, 1.0]]}, "2010.spire_conference-2010.29": {"snippet": "A Self-Supervised Approach for Extraction of Attribute-Value Pairs from Wikipedia Articles", "weights": [[2, 6, 1.0], [7, 17, 1.0]]}, "2020.sigirconf_conference-2020.113": {"snippet": "...ideo retrieval can be conducted in an efficient way. In this paper, we make a first attempt to combine quantization method with video retrieval called 3D-UVQ, which obtains high retrieval accuracy wit...", "weights": [[8, 17, 1.0], [137, 146, 1.0], [181, 190, 1.0]]}, "P10-1043": {"snippet": "...s in Supervised and Semi-Supervised Sentiment Classification\n\n\n In this paper, we adopt two views, personal and impersonal views, and systematically employ them in both supervised and semi-supervised ...", "weights": [[8, 18, 1.0], [28, 38, 1.0], [172, 182, 1.0], [192, 202, 1.0]]}, "2001.jasis_journal-ir0volumeA52A10.9": {"snippet": "Web image retrieval using self-organizing feature map", "weights": [[4, 9, 1.0], [10, 19, 1.0], [26, 30, 1.0]]}, "2017.mir_conference-2017.24": {"snippet": "Deep Supervised Hashing for Multi-Label and Large-Scale Image Retrieval", "weights": [[5, 15, 1.0], [56, 61, 1.0], [62, 71, 1.0]]}, "2005.clef_workshop-2005.76": {"snippet": "Supervised Machine Learning Based Medical Image Annotation and Retrieval in ImageCLEFmed 2005", "weights": [[0, 10, 1.0], [42, 47, 1.0], [63, 72, 1.0]]}, "2019.mir_conference-2019.28": {"snippet": "Self-Supervised Visual Representations for Cross-Modal Retrieval", "weights": [[0, 4, 1.0], [5, 15, 1.0], [55, 64, 1.0]]}, "1999.sigirconf_conference-99.54": {"snippet": "...with supervised learning; and (2) scene semantics at the image level, recognized from the association of primitive semantics of the patches in a self-organizing manner. Images are classified and retri...", "weights": [[8, 18, 1.0], [60, 65, 1.0], [148, 152, 1.0]]}, "2022.bionlp-1.14": {"snippet": "...able Self-Supervised Pre-Training for Genome-Wide Association Study\n\n\n Self-supervised pre-training methods have brought remarkable breakthroughs in the understanding of text, image, and speech. Recen...", "weights": [[8, 12, 1.0], [13, 23, 1.0], [74, 78, 1.0], [79, 89, 1.0], [103, 110, 1.0], [179, 184, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.22": {"snippet": "Semi-supervised cross-modal learning for cross modal retrieval and image annotation\n\n\n Abstract Multimedia data are usually associated with multiple modalities represented by heterogeneous features. R...", "weights": [[5, 15, 1.0], [53, 62, 1.0], [67, 72, 1.0]]}, "2014.wwwconf_conference-2014c.84": {"snippet": "...semi-supervised method for opinion target extraction\n\n\n ABSTRACTThis paper proposes a semi-supervised self-learning method, which is based on a Naive Bayes classifier exploiting context features and P...", "weights": [[8, 18, 1.0], [94, 104, 1.0], [105, 109, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...ting image retrieval using a form of text based representation. Presumably for commercial reasons, it is not possible to find how these representations were generated or how effective the retrieval me...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [191, 200, 1.0], [201, 208, 1.0]]}, "2015.mir_conference-2015.19": {"snippet": "Semi-Supervised Image Classification by Nonnegative Sparse Neighborhood Propagation", "weights": [[5, 15, 1.0], [16, 21, 1.0]]}, "W16-4205": {"snippet": "Semi-supervised Clustering of Medical Text\n\n\n Semi-supervised clustering is an attractive alternative for traditional (unsupervised) clustering in targeted applications. By using the information of a ...", "weights": [[5, 15, 1.0], [51, 61, 1.0]]}, "2000.sigirconf_conference-2000.49": {"snippet": "...mage retrieval, searching for images, search behavior, image database searching.\nAIMSThe objective of this study is to investigate how different types of queries affect searching behavior in an image ...", "weights": [[8, 17, 1.0], [58, 63, 1.0], [197, 202, 1.0]]}, "D19-1347": {"snippet": "...Semi-Supervised Text Classification using Pretrained Word Embeddings\n\n\n We propose a novel and simple method for semi-supervised text classification. The method stems from the hypothesis that a classi...", "weights": [[8, 18, 1.0], [121, 131, 1.0]]}, "2006.tois_journal-ir0volumeA24A2.2": {"snippet": "...k in image retrieval using unlabeled data\n\n\n Relevance feedback is an effective scheme bridging the gap between high-level semantics and lowlevel features in content-based image retrieval (CBIR). In c...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [175, 180, 1.0], [181, 190, 1.0]]}, "2019.clef_conference-2019w.183": {"snippet": "...ment Retrieval (LMRT) task. We use several different methods to provide a flexible retrieval system based on the huge amounts of multi-modal dataset. The work proposes a supervised learning approach w...", "weights": [[8, 17, 1.0], [56, 63, 1.0], [86, 95, 1.0], [173, 183, 1.0]]}, "2003.sigirconf_conference-2003.84": {"snippet": "...ving image retrieval effectiveness. We present a new approach to classify images based on the combination of image processing techniques and hybrid neural networks. Multiple keywords are assigned to a...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [112, 117, 1.0]]}, "2008.cikm_workshop-2008lsdsir.8": {"snippet": "...ding self-organized image retrieval network\n\n\n ABSTRACTWe propose a self-organized content-based Image Retrieval Network (IRN) that is inspired by a Metric Social Network (MSN) search system. The prop...", "weights": [[8, 12, 1.0], [23, 28, 1.0], [29, 38, 1.0], [71, 75, 1.0], [100, 105, 1.0], [106, 115, 1.0]]}, "P19-1347": {"snippet": "...-art methods. Moreover, ablation studies validate that both methods of incorporating f-GCN for extracting knowledge from multi-modal contexts and our newly proposed self-supervised learning process ar...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [168, 172, 1.0], [173, 183, 1.0]]}, "2006.airs_conference-2006.59": {"snippet": "...semi-supervised learning algorithm to integrate pseudo relevant documents with documents to be re-ranked. Given an initial ranked list of retrieved documents, the document re-ranking approach picks a ...", "weights": [[8, 18, 1.0]]}, "W11-0223": {"snippet": "Self-training and co-training in biomedical word sense disambiguation\n\n\n Word sense disambiguation (WSD) is an intermediate task within information retrieval and information extraction, attempting to ...", "weights": [[0, 4, 1.0], [148, 157, 1.0]]}, "2019.ismir_conference-2019.53": {"snippet": "Self-Supervised Methods for Learning Semantic Similarity in Music", "weights": [[0, 4, 1.0], [5, 15, 1.0], [16, 23, 1.0]]}, "2021.eacl-main.6": {"snippet": "...ing: Self-supervised Meta-learning for Learning from Inference\n\n\n A common approach in many machine learning algorithms involves self-supervised learning on large unlabeled data before fine-tuning on ...", "weights": [[8, 12, 1.0], [13, 23, 1.0], [132, 136, 1.0], [137, 147, 1.0]]}, "2021.wsdm_conference-2021.8": {"snippet": "... via Self-Pretraining\n\n\n We present a neural semi-supervised learning model termed Self-Pretraining. Our model is inspired by the classic self-training algorithm. However, as opposed to self-training,...", "weights": [[8, 12, 1.0], [53, 63, 1.0], [86, 90, 1.0], [141, 145, 1.0], [189, 193, 1.0]]}, "2022.naacl-tutorials.2": {"snippet": "Self-supervised Representation Learning for Speech Processing", "weights": [[0, 4, 1.0], [5, 15, 1.0]]}, "2020.lrec-1.293": {"snippet": "...s by self-supervised tasks on large-scale corpora. Recent studies mainly focus on how to fine-tune different downstream tasks from a general pre-trained model. However, some studies show that customiz...", "weights": [[8, 12, 1.0], [13, 23, 1.0]]}, "W14-5410": {"snippet": "...line image repository (i.e., Flickr), in order to further acquire additional examples of topic-specific iconic relations. To this end, we leverage a combination of visual similarity measures, image cl...", "weights": [[8, 13, 1.0], [195, 200, 1.0]]}, "2013.clef_conference-2013w.29": {"snippet": "...hoto Retrieval subtask of Image-CLEF 2013. In order to solve the subtask, a self-developed multimodal multimedia retrieval system, PythiaSearch, is used. The discussed retrieval approaches focus on tw...", "weights": [[8, 17, 1.0], [29, 34, 1.0], [79, 83, 1.0], [116, 125, 1.0], [171, 180, 1.0]]}, "2006.ipm_journal-ir0volumeA42A2.9": {"snippet": "...g in image databases. Region-based image retrieval is a method of retrieving partially similar images. It has been proposed as a way to accurately process queries in an image database. In region-based...", "weights": [[8, 13, 1.0], [38, 43, 1.0], [44, 53, 1.0], [172, 177, 1.0]]}, "2008.clef_workshop-2008w.23": {"snippet": "...pare retrieval based on text description, to image-only retrieval, and to merged retrieval, and to compare results for the different clustering methods.", "weights": [[8, 17, 1.0], [48, 53, 1.0], [59, 68, 1.0], [84, 93, 1.0], [147, 154, 1.0]]}, "2010.clef_workshop-2010w.100": {"snippet": "...evel image features to identify visually similar images. In this article we present an overview of the application of our methods to the modality detection, ad-hoc image retrieval, and case-based retr...", "weights": [[8, 13, 1.0], [125, 132, 1.0], [167, 172, 1.0], [173, 182, 1.0], [199, 208, 1.0]]}, "2005.ipm_journal-ir0volumeA41A2.1": {"snippet": "...post-retrieval clustering for content-based image retrieval\n\n\n AbstractIn this paper, we propose a re-ranking algorithm using post-retrieval clustering for content-based image retrieval (CBIR). In con...", "weights": [[8, 17, 1.0], [47, 52, 1.0], [53, 62, 1.0], [134, 143, 1.0], [173, 178, 1.0], [179, 188, 1.0]]}, "2009.clef_workshop-2009w.24": {"snippet": "...text retrieval and our own simple block-based visual retrieval engine. The results obtained at image-CLEF 2009 show that our method is robust and promising. However, there is room for improvement on t...", "weights": [[8, 17, 1.0], [56, 65, 1.0], [98, 103, 1.0]]}, "2009.ipm_journal-ir0volumeA45A3.2": {"snippet": "...ment retrieval. The method, which is referred to as SSRANK, aims to use the advantages of both the traditional Information Retrieval (IR) methods and the supervised learning methods for IR proposed re...", "weights": [[8, 17, 1.0], [126, 135, 1.0], [141, 148, 1.0], [157, 167, 1.0], [177, 184, 1.0]]}, "2020.emnlp-main.357": {"snippet": "...ased Image Editing via Self-Supervised Counterfactual Reasoning\n\n\n Iterative Language-Based Image Editing (IL-BIE) tasks follow iterative instructions to edit images step by step. Data scarcity is a s...", "weights": [[8, 13, 1.0], [26, 30, 1.0], [31, 41, 1.0], [95, 100, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.13": {"snippet": "...olor image retrieval technique based on color features and image bitmap\n\n\n AbstractThe field of color image retrieval has been an important research area for several decades. For the purpose of effect...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [62, 67, 1.0], [105, 110, 1.0], [111, 120, 1.0]]}, "2014.clef_conference-2014.12": {"snippet": "Self-supervised Relation Extraction Using UMLS", "weights": [[0, 4, 1.0], [5, 15, 1.0]]}, "W13-4053": {"snippet": "...ines supervised and unsupervised learning models in order to improve classification quality. We have shown that the proposed approach is able to outperform existing methods on a large dataset and do n...", "weights": [[8, 18, 1.0], [168, 175, 1.0]]}, "2015.mir_conference-2015.43": {"snippet": "... for Image Retrieval", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2011.sigirconf_conference-2011.130": {"snippet": "...semi-supervised learning for large-scale image datasets using mapreduce\n\n\n ABSTRACTSemi-supervised learning is to exploit the vast amount of unlabeled data in the world. This paper proposes a scalable...", "weights": [[8, 18, 1.0], [44, 49, 1.0], [91, 101, 1.0]]}, "2020.emnlp-main.162": {"snippet": "...ally-supervised language models show consistent improvements over self-supervised alternatives on multiple purelanguage tasks such as GLUE, SQuAD, and SWAG. 1", "weights": [[8, 18, 1.0], [69, 73, 1.0], [74, 84, 1.0]]}, "2009.cikm_conference-2009.229": {"snippet": "...semi-supervised method called Word-Class Distribution Learning (WCDL), and apply it the task of information extraction (IE) by utilizing unlabeled sentences to improve supervised classification method...", "weights": [[8, 18, 1.0], [171, 181, 1.0], [197, 204, 1.0]]}, "2002.sigirconf_conference-2002.47": {"snippet": "...sing self-supervised word segmentation in Chinese information retrieval\n\n\n ABSTRACTWe propose a self-supervised word-segmentation technique for Chinese information retrieval. This method combines the ...", "weights": [[8, 12, 1.0], [13, 23, 1.0], [65, 74, 1.0], [99, 103, 1.0], [104, 114, 1.0], [167, 176, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "...tion retrieval. Each season is self-contained.\nSession 1: Core Learning Technologies for Information Retrieval.This session of the tutorial will cover the core machine learning methods, basic optimiza...", "weights": [[8, 17, 1.0], [34, 38, 1.0], [104, 113, 1.0], [180, 187, 1.0]]}, "L18-1436": {"snippet": "...ting Image Question Answering Dataset by Exploiting Image Captions\n\n\n Image question answering (IQA) is one of the tasks that need rich resources, i.e. supervised data, to achieve optimal performance....", "weights": [[8, 13, 1.0], [55, 60, 1.0], [73, 78, 1.0], [155, 165, 1.0]]}, "2002.ntcir_workshop-2002.72": {"snippet": "...sing Self-supervised Word Segmentation\n\n\n In this paper, we describe the system we use in the NTCIR-3 CLIR (cross language IR) task. We participate the SLIR (single language IR) track. In our system, ...", "weights": [[8, 12, 1.0], [13, 23, 1.0]]}, "2021.naacl-main.95": {"snippet": "...ully supervised methods and significantly outperforms current unsupervised methods. TTL methods with a smaller model are also competitive with the current state-of-the-art in unsupervised reading comp", "weights": [[8, 18, 1.0], [19, 26, 1.0], [78, 85, 1.0], [91, 98, 1.0]]}, "2016.jasis_journal-ir0volumeA67A2.7": {"snippet": "...ased image retrieval methods and professional image users", "weights": [[8, 13, 1.0], [14, 23, 1.0], [24, 31, 1.0], [49, 54, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A6.4": {"snippet": "...ased image retrieval (CBIR). We propose a novel active learning scheme to utilize the labeled and unlabeled images to build the initial Support Vector Machine (SVM) classifier for image retrieving. In...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [183, 188, 1.0]]}, "2020.emnlp-main.142": {"snippet": "...ning Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection\n\n\n Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to...", "weights": [[8, 12, 1.0], [26, 30, 1.0], [31, 41, 1.0]]}, "2022.acl-short.44": {"snippet": "...CD}: Self-Contrastive Decorrelation of Sentence Embeddings\n\n\n In this paper, we propose Self-Contrastive Decorrelation (SCD), a self-supervised approach. Given an input sentence, it optimizes a joint ...", "weights": [[8, 12, 1.0], [91, 95, 1.0], [131, 135, 1.0], [136, 146, 1.0]]}, "2021.birws_workshop-2021.2": {"snippet": "Self-Supervised Learning for Visual Summary Identification in Scientific Publications", "weights": [[0, 4, 1.0], [5, 15, 1.0]]}}}
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Their success, however, heavily depends on the ava...", "weights": [[8, 18, 1.0], [57, 65, 1.0], [66, 72, 1.0]]}, "2019.wwwconf_conference-2019.273": {"snippet": "A Multimodal Text Matching Model for Obfuscated Language Identification in Adversarial Communication?", "weights": [[2, 12, 1.0], [48, 56, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...ural language processing techniques. Different groups focused on different aspects of the overall problem, ranging from the development of language-independent tools such as stemmers to much work on l...", "weights": [[8, 16, 1.0], [142, 150, 1.0], [202, 210, 1.0]]}, "2018.wwwconf_conference-2018c.158": {"snippet": "...tial models to perform video attractiveness prediction by relying solely on video contents. The sequential models exploit the multimodal relationships between visual and audio components of the video ...", "weights": [[8, 14, 1.0], [110, 116, 1.0], [129, 139, 1.0]]}, "D18-1014": {"snippet": "Multimodal Language Analysis with Recurrent Multistage Fusion\n\n\n Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, ...", "weights": [[0, 10, 1.0], [11, 19, 1.0], [97, 107, 1.0], [108, 116, 1.0], [157, 165, 1.0], [190, 198, 1.0]]}, "2016.mir_conference-2016.75": {"snippet": "Multimodal Event Detection and Summarization in Large Scale Image Collections", "weights": [[0, 10, 1.0], [48, 53, 1.0]]}, "W18-3302": {"snippet": "...evel multimodal fusion of audio, video, and text for recognition of emotions and their intensities for the 2018 First Grand Challenge on Computational Modeling of Human Multimodal Language. Our propos...", "weights": [[8, 18, 1.0], [172, 182, 1.0], [183, 191, 1.0]]}, "W18-3308": {"snippet": "...ent: Multimodal Sequence to Sequence Models for Sentiment Analysis\n\n\n Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge i...", "weights": [[8, 18, 1.0], [40, 46, 1.0], [73, 83, 1.0], [138, 146, 1.0]]}, "L18-1334": {"snippet": "...s on Multimodal Learning\n\n\n Multimodal representations are distributed vectors that map multiple modes of information to a single mathematical space, where distances between instances delineate their ...", "weights": [[8, 18, 1.0], [31, 41, 1.0]]}, "2020.coling-main.168": {"snippet": "...weet Multimodal Model Based on Text-image Relation for Multimodal {NER}\n\n\n Multimodal named entity recognition (MNER) for tweets has received increasing attention recently. 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Overall, we believe that our strong baseline models offer new benchmarking options for futur...", "weights": [[8, 18, 1.0], [86, 92, 1.0], [156, 162, 1.0]]}, "2019.ecir_conference-20192.12": {"snippet": "...l in Multimodal Question Answering?\n\n\n Abstract. Passage retrieval for multimodal question answering, spanning natural language processing and computer vision, is a challenging task, particularly when...", "weights": [[8, 18, 1.0], [74, 84, 1.0], [122, 130, 1.0]]}, "D18-1320": {"snippet": "Multimodal neural pronunciation modeling for spoken languages with logographic origin\n\n\n Graphemes of most languages encode pronunciation, though some are more explicit than others. Languages like Spa...", "weights": [[0, 10, 1.0]]}, "2007.mir_workshop-2007.30": {"snippet": "Large-scale multimodal semantic concept detection for consumer video", "weights": [[0, 5, 1.0], [12, 22, 1.0]]}, "C18-1197": {"snippet": "Multimodal Grounding for Language Processing\n\n\n This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow i...", "weights": [[0, 10, 1.0], [25, 33, 1.0], [97, 107, 1.0], [154, 162, 1.0]]}, "2009.clef_workshop-20092.20": {"snippet": "Multimodal Image Retrieval over a Large Database", "weights": [[0, 10, 1.0], [34, 39, 1.0]]}, "D19-6121": {"snippet": "...irst multimodal model proposed for multilingual g2p. Our approach is highly effective: on our in-domain test set, our multimodal model reduces phoneme error rate to 2.46%, a more than 65% decrease com...", "weights": [[8, 18, 1.0], [121, 131, 1.0]]}, "2021.blackboxnlp-1.10": {"snippet": "...e of large language models by proposing a method for evaluating visual knowledge transfer to text for uni-or multimodal language models. The method is based on two steps, 1) a novel task querying for ...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 29, 1.0], [112, 122, 1.0], [123, 131, 1.0], [132, 138, 1.0]]}, "L06-1189": {"snippet": "... for Multimodal Language-enabled Systems", "weights": [[8, 18, 1.0], [19, 27, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A3.20": {"snippet": "...ning models ignore some data types and only focus on a single modality. This paper presents a new multimodal deep learning framework for event detection from videos by leveraging recent advances in de...", "weights": [[8, 14, 1.0], [101, 111, 1.0]]}, "2021.acl-long.218": {"snippet": "A Large-Scale {C}hinese Multimodal {NER} Dataset with Speech Clues\n\n\n In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both text...", "weights": [[2, 7, 1.0], [24, 34, 1.0], [144, 154, 1.0]]}, "2021.lantern-1.2": {"snippet": "...ural Language Processing\n\n\n Visual grounding is a promising path toward more robust and accurate Natural Language Processing (NLP) models. Many multimodal extensions of BERT (e.g., VideoBERT, LXMERT, ...", "weights": [[8, 16, 1.0], [108, 116, 1.0], [134, 140, 1.0], [147, 157, 1.0]]}, "D18-1012": {"snippet": "...ogue models that should generate relevant temporal and spatial event language from the live video, while also being relevant to the chat history. For strong baselines, we also present several discrimi...", "weights": [[8, 14, 1.0], [72, 80, 1.0]]}, "2021.emnlp-main.326": {"snippet": "...ined Language Models for Multimodal Abstractive Summarization\n\n\n Multimodal abstractive summarization (MAS) models that summarize videos (vision modality) and their corresponding transcripts (text mod...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [28, 38, 1.0], [68, 78, 1.0], [111, 117, 1.0]]}, "D13-1115": {"snippet": "A Multimodal {LDA} Model integrating Textual, Cognitive and Visual Modalities\n\n\n Recent investigations into grounded models of language have shown that holistic views of language and perception can pr...", "weights": [[2, 12, 1.0], [117, 123, 1.0], [127, 135, 1.0], [170, 178, 1.0]]}, "P19-1540": {"snippet": "...in a Multimodal Dialogue System\n\n\n Multimodal dialogue systems have opened new frontiers in the traditional goal-oriented dialogue systems. The state-of-the-art dialogue systems are primarily based on...", "weights": [[8, 18, 1.0], [38, 48, 1.0]]}, "2022.naacl-main.390": {"snippet": "...odal models and multimodal models. Our results indicate that multimodal models better reconstruct attribute distributions, but are still subject to reporting bias. Moreover, increasing model size does...", "weights": [[8, 14, 1.0], [19, 29, 1.0], [30, 36, 1.0], [64, 74, 1.0], [75, 81, 1.0]]}, "W14-0507": {"snippet": "...onal models for (grounded) language acquisition\n\n\n This paper describes the design and acquisition of a German multimodal corpus for the development and evaluation of computational models for (grounde...", "weights": [[8, 14, 1.0], [30, 38, 1.0], [114, 124, 1.0], [184, 190, 1.0]]}, "2020.emnlp-main.162": {"snippet": "...nded language datasets and pure-language corpora. Therefore, we develop a technique named \"vokenization\" that extrapolates multimodal alignments to language-only data by contextually mapping language ...", "weights": [[8, 16, 1.0], [35, 43, 1.0], [126, 136, 1.0], [151, 159, 1.0], [194, 202, 1.0]]}, "P97-1036": {"snippet": "...ased Multimodal Integration\n\n\n Recent empirical research has shown conclusive advantages of multimodal interaction over speech-only interaction for mapbased tasks. This paper describes a multimodal la...", "weights": [[8, 18, 1.0], [95, 105, 1.0], [190, 200, 1.0], [201, 209, 1.0]]}, "2020.sigirconf_conference-2020.147": {"snippet": "...sing Multimodal Deep Learning\n\n\n We address the web table retrieval task, aiming to retrieve and rank web tables as whole answers to a given information need. To this end, we formally define web table...", "weights": [[8, 18, 1.0]]}, "2022.acl-long.65": {"snippet": "...with Multimodal Contrastive Pretraining\n\n\n Large-scale pretrained language models have achieved SOTA results on NLP tasks. However, they have been shown vulnerable to adversarial attacks especially fo...", "weights": [[8, 18, 1.0], [46, 51, 1.0], [69, 77, 1.0], [78, 84, 1.0]]}, "2022.acl-long.308": {"snippet": "... for Multimodal Audio-Visual Speech Recognition\n\n\n Training Transformer-based models demands a large amount of data, while obtaining aligned and labelled data in multimodality is rather cost-demanding...", "weights": [[8, 18, 1.0], [81, 87, 1.0], [98, 103, 1.0]]}, "W03-2307": {"snippet": "...ting Multimodal Referring Expressions", "weights": [[8, 18, 1.0]]}, "2015.cikm_workshop-2015tm.5": {"snippet": "... for Multimodal Topic Modeling of Large Collections", "weights": [[8, 18, 1.0], [37, 42, 1.0]]}, "2021.acl-long.44": {"snippet": "...nced Multimodal {BART} for Visual Commonsense Generation\n\n\n We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformerbased sequence-to-sequence model capable of reasoning about c...", "weights": [[8, 18, 1.0], [93, 103, 1.0]]}, "2022.naacl-srw.26": {"snippet": "Multimodal large language models for inclusive collaboration learning tasks\n\n\n This PhD project leverages advancements in multimodal large language models to build an inclusive collaboration feedback ...", "weights": [[0, 10, 1.0], [11, 16, 1.0], [17, 25, 1.0], [26, 32, 1.0], [122, 132, 1.0], [133, 138, 1.0], [139, 147, 1.0], [148, 154, 1.0]]}, "N09-4002": {"snippet": "...uted Language Models\n\n\n Language models are used in a wide variety of natural language applications, including machine translation, speech recognition, spelling correction, optical character recogniti...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [27, 35, 1.0], [36, 42, 1.0], [81, 89, 1.0]]}, "2022.findings-acl.234": {"snippet": "...ient Multimodal Fusion with Prompting\n\n\n Recent research has made impressive progress in large-scale multimodal pre-training. In the context of the rapid growth of model size, it is necessary to seek ...", "weights": [[8, 18, 1.0], [92, 97, 1.0], [104, 114, 1.0]]}, "2021.wassa-1.14": {"snippet": "...ight Models for Multimodal Sequential Data\n\n\n Human language encompasses more than just text; it also conveys emotions through tone and gestures. We present a case study of three simple and efficient ...", "weights": [[8, 14, 1.0], [19, 29, 1.0], [55, 63, 1.0]]}, "2020.emnlp-main.496": {"snippet": "...g of Large Language Models\n\n\n Large language models have recently achieved state of the art performance across a wide variety of natural language tasks. Meanwhile, the size of these models and their l...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 29, 1.0], [33, 38, 1.0], [39, 47, 1.0], [48, 54, 1.0], [140, 148, 1.0], [185, 191, 1.0]]}, "2021.findings-acl.229": {"snippet": "...ural language tasks, GEM is a largescale vision-language benchmark, which consists of GEM-I for image-language tasks and GEM-V for video-language tasks. Comparing with existing multimodal datasets suc...", "weights": [[8, 16, 1.0], [51, 59, 1.0], [105, 113, 1.0], [140, 148, 1.0], [180, 190, 1.0]]}, "2021.emnlp-main.720": {"snippet": "... for Multimodal Sequence Fusion\n\n\n Effective unimodal representation and complementary crossmodal representation fusion are both important in multimodal representation learning. Prior works often modu...", "weights": [[8, 18, 1.0], [145, 155, 1.0]]}, "2022.findings-acl.196": {"snippet": "...dapt multimodal transformer-based models to become multilingual, and-vice versa-multilingual models to become multimodal. Our proposed methods outperform current state-of-the-art multilingual multimod...", "weights": [[8, 18, 1.0], [37, 43, 1.0], [96, 102, 1.0], [113, 123, 1.0], [195, 205, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.3": {"snippet": "...n by Multimodal Relevance and User Feedback\n\n\n With Internet delivery of video content surging to an unprecedented level, video recommendation, which suggests relevant videos to targeted users accordi...", "weights": [[8, 18, 1.0]]}, "2021.maiworkshop-1.7": {"snippet": "...om a large \"teacher\" model to a smaller \"student\" model. However, KD on multimodal datasets such as vision-language datasets is relatively unexplored and digesting such multimodal information is chall...", "weights": [[8, 13, 1.0], [75, 85, 1.0], [110, 118, 1.0], [172, 182, 1.0]]}, "D18-1448": {"snippet": "...MO}: Multimodal Summarization with Multimodal Output\n\n\n Multimodal summarization has drawn much attention due to the rapid growth of multimedia data. The output of the current multimodal summarization...", "weights": [[8, 18, 1.0], [38, 48, 1.0], [59, 69, 1.0], [179, 189, 1.0]]}, "2020.jasis_journal-ir0volumeA71A1.2": {"snippet": "...d on multimodal features", "weights": [[8, 18, 1.0]]}, "2022.naacl-main.248": {"snippet": "Multimodal Dialogue State Tracking\n\n\n Designed for tracking user goals in dialogues, a dialogue state tracker is an essential component in a dialogue system. However, the research of dialogue state tr...", "weights": [[0, 10, 1.0]]}, "2020.nlpbt-1.7": {"snippet": "... for Multimodal Abstractive Text Summarization that utilizes information from all three modalities -text, audio and video -in a multimodal video. Prior work on multimodal abstractive text summarizatio...", "weights": [[8, 18, 1.0], [131, 141, 1.0], [163, 173, 1.0]]}, "D17-1115": {"snippet": "... for Multimodal Sentiment Analysis\n\n\n Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multim...", "weights": [[8, 18, 1.0], [41, 51, 1.0], [144, 152, 1.0], [197, 207, 1.0]]}, "J09-3002": {"snippet": "...ars, multimodal grammars can be brittle with respect to unexpected, erroneous, or disfluent input. In this article, we show how the finite-state approach to multimodal language processing can be exten...", "weights": [[8, 18, 1.0], [160, 170, 1.0], [171, 179, 1.0]]}, "2020.findings-emnlp.242": {"snippet": "... for Multimodal Speech Recognition\n\n\n Multimodal automatic speech recognition systems integrate information from images to improve speech recognition quality, by grounding the speech in the visual con...", "weights": [[8, 18, 1.0], [41, 51, 1.0]]}, "N15-1016": {"snippet": "...ning Language and Vision with a Multimodal Skip-gram Model\n\n\n We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account. Like SKIP-GRAM, our multimodal models (...", "weights": [[8, 16, 1.0], [35, 45, 1.0], [184, 194, 1.0], [195, 201, 1.0]]}, "2022.acl-srw.19": {"snippet": "...ning language from text alone. Therefore, recent focus has been on developing multimodal models. However, few benchmarks exist that can measure what language models learn about language from multimoda...", "weights": [[8, 16, 1.0], [81, 91, 1.0], [92, 98, 1.0], [152, 160, 1.0], [161, 167, 1.0], [180, 188, 1.0], [194, 204, 1.0]]}, "W17-5602": {"snippet": "...gned Multimodal and Multilingual Corpus\n\n\n Integrating Natural Language Processing (NLP) and computer vision is a promising effort. However, the applicability of these methods directly depends on the ...", "weights": [[8, 18, 1.0], [66, 74, 1.0]]}, "D19-1211": {"snippet": "...s of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established re...", "weights": [[8, 18, 1.0], [19, 27, 1.0], [64, 72, 1.0], [89, 95, 1.0], [104, 112, 1.0]]}, "2011.ecir_conference-2011.33": {"snippet": "...from Large Multimodal Databases", "weights": [[8, 13, 1.0], [14, 24, 1.0]]}, "P19-1656": {"snippet": "Multimodal Transformer for Unaligned Multimodal Language Sequences\n\n\n Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors. Howe...", "weights": [[0, 10, 1.0], [37, 47, 1.0], [48, 56, 1.0], [76, 84, 1.0], [94, 104, 1.0], [145, 153, 1.0]]}, "C00-1053": {"snippet": "...s. A multimodal language processing architecture is needed to integrate semantic content from the different modes. Johnston 1998a proposes a modular approach to multimodal language processing in which...", "weights": [[8, 18, 1.0], [19, 27, 1.0], [164, 174, 1.0], [175, 183, 1.0]]}, "2022.naacl-main.280": {"snippet": "...onal models of child language acquisition, which is believed to rely heavily on cross-modal signals. However, prior studies have been limited by their reliance on vision models trained on large image ...", "weights": [[8, 14, 1.0], [24, 32, 1.0], [173, 179, 1.0], [191, 196, 1.0]]}, "P17-5002": {"snippet": "Multimodal Machine Learning: Integrating Language, Vision and Speech\n\n\n Multimodal machine learning is a vibrant multi-disciplinary research field which addresses some of the original goals of artific...", "weights": [[0, 10, 1.0], [41, 49, 1.0], [72, 82, 1.0]]}, "W16-2359": {"snippet": "...v{A} Multimodal {MT} System Report\n\n\n We present a doubly-attentive multimodal machine translation model. Our model learns to attend to source language and spatial-preserving CONV 5,4 visual features ...", "weights": [[8, 18, 1.0], [71, 81, 1.0], [146, 154, 1.0]]}, "2022.findings-acl.187": {"snippet": "...ling Multimodal Generation on {CLIP} via Vision-Language Knowledge Distillation\n\n\n The recent large-scale vision-language pretraining (VLP) of dual-stream architectures (e.g., CLIP) with a tremendous ...", "weights": [[8, 18, 1.0], [51, 59, 1.0], [97, 102, 1.0], [116, 124, 1.0]]}, "2022.repl4nlp-1.4": {"snippet": "...odal models? When judging human-centric concepts\n\n\n CLIP, a vision-language network trained with a multimodal contrastive learning objective on a large dataset of images and captions, has demonstrated...", "weights": [[8, 14, 1.0], [70, 78, 1.0], [102, 112, 1.0], [149, 154, 1.0]]}, "L04-1196": {"snippet": "Multimodal Meaning Representation for Generic Dialogue Systems Architectures", "weights": [[0, 10, 1.0]]}, "2020.acl-main.214": {"snippet": "...ined models is straightforward for lexical applications (applications with only language modality), it is not trivial for multimodal language (a growing area in NLP focused on modeling face-to-face co...", "weights": [[8, 14, 1.0], [83, 91, 1.0], [125, 135, 1.0], [136, 144, 1.0]]}, "2021.naacl-main.195": {"snippet": "...gual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models\n\n\n This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on ...", "weights": [[8, 18, 1.0], [79, 87, 1.0], [88, 94, 1.0], [160, 168, 1.0], [169, 175, 1.0]]}, "2020.emnlp-main.161": {"snippet": "... for large-scale video+language omnirepresentation learning. HERO  encodes multimodal inputs in a hierarchical structure, where local context of a video frame is captured by a Cross-modal Transformer ...", "weights": [[8, 13, 1.0], [26, 34, 1.0], [78, 88, 1.0]]}}}
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answering requires combining information from multiple sentences. We focus on a natural setting where, unlike typical reading comprehens...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0], [27, 36, 1.0], [40, 45, 1.0], [46, 49, 1.0], [58, 66, 1.0], [67, 76, 1.0]]}, "2017.sigirconf_conference-2017.90": {"snippet": "...ideo Question Answering via Attribute-Augmented Attention Network Learning", "weights": [[8, 16, 1.0], [17, 26, 1.0], [41, 50, 1.0]]}, "2021.acl-long.316": {"snippet": "Generation-Augmented Retrieval for Open-Domain Question Answering\n\n\n We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation...", "weights": [[0, 10, 1.0], [11, 20, 1.0], [21, 30, 1.0], [47, 55, 1.0], [56, 65, 1.0], [80, 90, 1.0], [91, 100, 1.0], [101, 110, 1.0], [121, 130, 1.0], [190, 200, 1.0]]}, "2021.sustainlp-1.7": {"snippet": "...ense Retrieval for Computationally Efficient Multi-hop Question Answering\n\n\n In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the...", "weights": [[8, 17, 1.0], [48, 53, 1.0], [54, 57, 1.0], [58, 66, 1.0], [67, 76, 1.0], [102, 110, 1.0], [111, 120, 1.0], [133, 142, 1.0]]}, "D19-1242": {"snippet": "...main Question Answering with Iterative Retrieval on Knowledge Bases and Text\n\n\n We consider open-domain question answering (QA) where answers are drawn from either a corpus, a knowledge base (KB), or ...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [42, 51, 1.0], [107, 115, 1.0], [116, 125, 1.0]]}, "2021.findings-acl.265": {"snippet": "...urce Question Generation\n\n\n Multi-hop question generation requires complex reasoning and coherent language realization. Learning a generation model for the problem requires extensive multi-hop questio...", "weights": [[8, 16, 1.0], [17, 27, 1.0], [31, 36, 1.0], [37, 40, 1.0], [41, 49, 1.0], [50, 60, 1.0], [134, 144, 1.0], [186, 191, 1.0], [192, 195, 1.0], [196, 204, 1.0]]}, "P19-1262": {"snippet": "... for Multi-Hop {QA}\n\n\n Multi-hop question answering requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. In this paper, we show that in the multi...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [26, 31, 1.0], [32, 35, 1.0], [36, 44, 1.0], [45, 54, 1.0], [153, 161, 1.0]]}, "D19-5816": {"snippet": "Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering\n\n\n Multi-hop question answering (QA) requires an information retrieval (IR) system that can find multiple supporting ev...", "weights": [[0, 5, 1.0], [38, 47, 1.0], [52, 57, 1.0], [58, 61, 1.0], [62, 70, 1.0], [71, 80, 1.0], [84, 89, 1.0], [90, 93, 1.0], [94, 102, 1.0], [103, 112, 1.0], [142, 151, 1.0]]}, "2020.acl-main.414": {"snippet": "...ence Retrieval for Multi-hop Question Answering\n\n\n Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of th...", "weights": [[8, 17, 1.0], [22, 27, 1.0], [28, 31, 1.0], [32, 40, 1.0], [41, 50, 1.0], [63, 72, 1.0], [96, 104, 1.0], [105, 114, 1.0]]}, "P19-1613": {"snippet": "Multi-hop Reading Comprehension through Question Decomposition and Rescoring\n\n\n Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs. We propose a system f...", "weights": [[0, 5, 1.0], [6, 9, 1.0], [40, 48, 1.0], [80, 85, 1.0], [86, 89, 1.0]]}, "2022.acl-long.69": {"snippet": "...ling Multi-hop Question Answering as Single Sequence Prediction\n\n\n Fusion-in-decoder (FID) (Izacard and Grave, 2021) is a generative question answering (QA) model that leverages passage retrieval with...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0], [27, 36, 1.0], [136, 144, 1.0], [145, 154, 1.0], [189, 198, 1.0]]}, "2019.wwwconf_conference-2019.320": {"snippet": "...gual Question Retrieval for Community Question Answering", "weights": [[8, 16, 1.0], [17, 26, 1.0], [41, 49, 1.0], [50, 59, 1.0]]}, "2005.trec_conference-2005.117": {"snippet": "...ds a Multi-Stream Question Answering-As-XML-Retrieval Strategy", "weights": [[8, 13, 1.0], [21, 29, 1.0], [30, 39, 1.0], [47, 56, 1.0]]}, "2020.findings-emnlp.369": {"snippet": "...ense Question Answering\n\n\n Commonsense question answering (QA) requires background knowledge which is not explicitly stated in a given context. Prior works use commonsense knowledge graphs (KGs) to ob...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [42, 50, 1.0], [51, 60, 1.0]]}, "2022.naacl-industry.31": {"snippet": "...ased Multi-hop Question Answering with Knowledge Graph\n\n\n The objective of a Question-Answering system over Knowledge Graph (KGQA) is to respond to natural language queries presented over the KG. A co...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0], [27, 36, 1.0], [80, 88, 1.0], [89, 98, 1.0]]}, "2021.naacl-main.368": {"snippet": "...ense retrieval methods, we propose a new multi-step retrieval approach (BEAMDR) that iteratively forms an evidence chain through beam search in dense representations. When evaluated on multi-hop quest...", "weights": [[8, 17, 1.0], [44, 49, 1.0], [55, 64, 1.0], [188, 193, 1.0], [194, 197, 1.0], [198, 206, 1.0]]}, "2021.naacl-main.469": {"snippet": "...ised Multi-hop Question Answering by Question Generation\n\n\n Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train ...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0], [27, 36, 1.0], [40, 48, 1.0], [49, 59, 1.0], [91, 96, 1.0], [97, 100, 1.0], [101, 109, 1.0], [110, 119, 1.0]]}, "2020.acl-main.91": {"snippet": "...raph Generation for Answering Multi-hop Complex Questions from Knowledge Bases\n\n\n Previous work on answering complex questions from knowledge bases usually separately addresses two types of complexity...", "weights": [[8, 18, 1.0], [23, 32, 1.0], [33, 38, 1.0], [39, 42, 1.0], [102, 111, 1.0]]}, "D19-5309": {"snippet": "...k on Multi-Hop Inference for Explanation Regeneration\n\n\n While automated question answering systems are increasingly able to retrieve answers to natural language questions, their ability to generate d...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [76, 84, 1.0], [85, 94, 1.0]]}, "2021.naacl-main.363": {"snippet": "...ence Retrieval for Multi-hop Question Answering\n\n\n Multi-hop reasoning requires aggregation and inference from multiple facts. To retrieve such facts, we propose a simple approach that retrieves and r...", "weights": [[8, 17, 1.0], [22, 27, 1.0], [28, 31, 1.0], [32, 40, 1.0], [41, 50, 1.0], [54, 59, 1.0], [60, 63, 1.0]]}, "2022.mia-1.8": {"snippet": "...Open-retrieval Question Answering System\n\n\n This paper introduces our proposed system for the MIA Shared Task on Cross-lingual Openretrieval Question Answering (COQA). In this challenging scenario, gi...", "weights": [[8, 17, 1.0], [18, 26, 1.0], [27, 36, 1.0], [144, 152, 1.0], [153, 162, 1.0]]}, "2020.coling-main.610": {"snippet": "... for multi-hop explanation regeneration, that retrieves relevant factual evidence in the form of text snippets, given a natural language question and its answer. Combining multiple sources of evidence...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [140, 148, 1.0]]}, "2021.findings-acl.62": {"snippet": "... for Multi-Hop Retriever\n\n\n In multi-hop QA, answering complex questions entails iterative document retrieval for finding the missing entity of the question. The main steps of this process are sub-que...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [34, 39, 1.0], [40, 43, 1.0], [48, 57, 1.0], [103, 112, 1.0], [151, 159, 1.0], [200, 208, 1.0]]}, "2020.coling-main.580": {"snippet": "...ng A Multi-hop {QA} Dataset for Comprehensive Evaluation of Reasoning Steps\n\n\n A multi-hop question answering (QA) dataset aims to test reasoning and inference skills by requiring a model to read mult...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [84, 89, 1.0], [90, 93, 1.0], [94, 102, 1.0], [103, 112, 1.0]]}, "2022.acl-long.475": {"snippet": "...lled Generation Framework for Multi-hop Question Generation\n\n\n Multi-hop question generation focuses on generating complex questions that require reasoning over multiple pieces of information of the i...", "weights": [[8, 18, 1.0], [33, 38, 1.0], [39, 42, 1.0], [43, 51, 1.0], [52, 62, 1.0], [66, 71, 1.0], [72, 75, 1.0], [76, 84, 1.0], [85, 95, 1.0]]}, "P19-1222": {"snippet": "Multi-Hop Paragraph Retrieval for Open-Domain Question Answering\n\n\n This paper is concerned with the task of multi-hop open-domain Question Answering (QA). This task is particularly challenging since ...", "weights": [[0, 5, 1.0], [6, 9, 1.0], [20, 29, 1.0], [46, 54, 1.0], [55, 64, 1.0], [109, 114, 1.0], [115, 118, 1.0], [131, 139, 1.0], [140, 149, 1.0]]}, "2019.sigirconf_conference-2019.12": {"snippet": "...tive Multi-Attention Network Incorporating Answer Information for Duplicate Question Detection", "weights": [[8, 13, 1.0], [79, 87, 1.0]]}, "2022.acl-long.396": {"snippet": "...raph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering\n\n\n Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. The desired subgraph ...", "weights": [[8, 17, 1.0], [37, 42, 1.0], [43, 46, 1.0], [62, 70, 1.0], [71, 80, 1.0], [115, 123, 1.0], [124, 133, 1.0]]}, "2020.emnlp-main.710": {"snippet": "... for Multi-hop Question Answering\n\n\n In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. To aggregate clues from scattered texts across multiple paragraphs, a ...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0], [27, 36, 1.0], [103, 108, 1.0], [109, 112, 1.0], [113, 121, 1.0], [122, 131, 1.0]]}, "P19-1617": {"snippet": "... for Multi-hop Reasoning\n\n\n Text-based question answering (TBQA) has been studied extensively in recent years. Most existing approaches focus on finding the answer to a question within a single paragr...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [42, 50, 1.0], [51, 60, 1.0], [172, 180, 1.0]]}, "2008.sigirconf_conference-2008.115": {"snippet": "...toid question-answering degrades. However, instead of answering factoid questions, it might be more effective to answer opinion and complex questions. \nCategories and Subject\nOVERVIEWRecently, communi...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [57, 66, 1.0]]}, "2020.acl-main.412": {"snippet": "...ving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings\n\n\n Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes and relations among them as...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [18, 26, 1.0], [27, 36, 1.0], [59, 64, 1.0], [120, 125, 1.0]]}, "2022.fever-1.1": {"snippet": "Retrieval Data Augmentation Informed by Downstream Question Answering Performance\n\n\n Training retrieval models to fetch contexts for Question Answering (QA) over large corpora requires labeling releva...", "weights": [[0, 9, 1.0], [51, 59, 1.0], [60, 69, 1.0], [94, 103, 1.0], [133, 141, 1.0], [142, 151, 1.0]]}, "D19-1261": {"snippet": "... the question seldom contains retrievable clues about the missing entity (here, the author). Answering such a question requires multi-hop reasoning where one must gather information about the missing ...", "weights": [[8, 16, 1.0], [96, 105, 1.0], [113, 121, 1.0], [131, 136, 1.0], [137, 140, 1.0]]}, "2022.bea-1.26": {"snippet": "...onal Multi-Question Generation for Reading Comprehension\n\n\n Automated question generation has made great advances with the help of large NLP generation models. However, typically only one question is ...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 33, 1.0], [73, 81, 1.0], [82, 92, 1.0], [144, 154, 1.0], [191, 199, 1.0]]}, "2020.findings-emnlp.416": {"snippet": "Multi-hop Question Generation with Graph Convolutional Network\n\n\n Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evid...", "weights": [[0, 5, 1.0], [6, 9, 1.0], [10, 18, 1.0], [19, 29, 1.0], [66, 71, 1.0], [72, 75, 1.0], [76, 84, 1.0], [85, 95, 1.0]]}, "P19-1416": {"snippet": "...tate Multi-hop Reasoning\n\n\n Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs. We argue that it can be difficult to con...", "weights": [[8, 13, 1.0], [14, 17, 1.0], [31, 36, 1.0], [37, 40, 1.0]]}, "2021.eacl-main.283": {"snippet": "Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions?\n\n\n Multi-hop question answering (QA) requires a model to retrieve and integrate information from multiple passa...", "weights": [[3, 8, 1.0], [9, 12, 1.0], [13, 21, 1.0], [22, 31, 1.0], [70, 73, 1.0], [92, 97, 1.0], [98, 101, 1.0], [102, 110, 1.0], [111, 120, 1.0]]}, "2021.wwwconf_conference-2021c.129": {"snippet": "...tual-Augmented Data for Multi-Hop Knowledge Base Question Answering", "weights": [[8, 17, 1.0], [27, 32, 1.0], [33, 36, 1.0], [52, 60, 1.0], [61, 70, 1.0]]}, "2022.naacl-main.162": {"snippet": "...ided Generation for Knowledge-Intensive {NLP} Tasks\n\n\n Retrieval-augmented generation models have shown state-of-the-art performance across many knowledge-intensive NLP tasks such as open-domain quest...", "weights": [[8, 18, 1.0], [58, 67, 1.0], [68, 77, 1.0], [78, 88, 1.0], [198, 206, 1.0]]}, "2020.wwwconf_conference-2020.26": {"snippet": "...ting Multi-hop Reasoning Questions to Improve Machine Reading Comprehension", "weights": [[8, 13, 1.0], [14, 17, 1.0]]}, "C04-1063": {"snippet": "Multi-Answer-Focused Multi-Document Summarization Using a Question-Answering Engine", "weights": [[0, 5, 1.0], [21, 26, 1.0], [50, 55, 1.0], [58, 66, 1.0], [67, 76, 1.0]]}, "2010.ntcir_workshop-2010.25": {"snippet": "...tion Retrieval based on Question Decomposition and Question Answering", "weights": [[8, 17, 1.0], [27, 35, 1.0], [54, 62, 1.0], [63, 72, 1.0]]}, "D19-5806": {"snippet": "...main Multi-Hop Question Answering\n\n\n A key challenge of multi-hop question answering (QA)  in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. 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Existing models mainly focused on using one or two sentences as the input. Long text has posed challenges for seq...", "weights": [[8, 16, 1.0], [20, 28, 1.0], [36, 42, 1.0], [99, 105, 1.0], [200, 208, 1.0]]}, "2022.findings-acl.188": {"snippet": "...A} : Answering by Interactive Sub Question Sequence\n\n\n Most existing approaches to Visual Question Answering (VQA) answer questions directly, however, people usually decompose a complex question into ...", "weights": [[8, 17, 1.0], [37, 45, 1.0], [46, 54, 1.0], [93, 101, 1.0], [102, 111, 1.0], [189, 197, 1.0]]}, "W14-3405": {"snippet": "... for question decomposition that rely only on the syntactic parse tree.", "weights": [[8, 16, 1.0]]}, "2022.suki-1.5": {"snippet": "...cial models and training paradigms for table-related tasks such as tablebased question answering and table retrieval. Though effective, they add complexity in both modeling and data acquisition compar...", "weights": [[8, 14, 1.0], [42, 47, 1.0], [81, 89, 1.0], [90, 99, 1.0], [104, 109, 1.0]]}, "2022.tacl-1.3": {"snippet": "...form Table Question Answering\n\n\n Existing table question answering datasets contain abundant factual questions that primarily evaluate a QA system's comprehension of query and tabular data. However, r...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 32, 1.0], [45, 50, 1.0], [51, 59, 1.0], [60, 69, 1.0]]}, "2017.sigirconf_conference-2017.73": {"snippet": "...w-up Question Resolution using Retrieval based Sequence to Sequence Learning\n\n\n ABSTRACTIntelligent personal assistants (IPAs) and interactive question answering (IQA) systems frequently encounter inc...", "weights": [[8, 16, 1.0], [50, 58, 1.0], [62, 70, 1.0], [146, 154, 1.0], [155, 164, 1.0]]}, "S17-1020": {"snippet": "...ased question answering models by comparing them to a question answering baseline that queries the web and extracts the answer only from web snippets, without access to the target knowledge-base. We i...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [27, 33, 1.0], [57, 65, 1.0], [66, 75, 1.0]]}, "P19-1415": {"snippet": "... our question generation models by leveraging the existing reading comprehension dataset. Experimental results show that the pair-to-sequence model performs consistently better compared with the seque...", "weights": [[8, 16, 1.0], [28, 34, 1.0], [136, 144, 1.0], [198, 206, 1.0]]}, "2021.eacl-main.74": {"snippet": "...tive Models for Open Domain Question Answering\n\n\n Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this app...", "weights": [[8, 14, 1.0], [31, 39, 1.0], [40, 49, 1.0], [64, 70, 1.0], [87, 95, 1.0], [96, 105, 1.0]]}, "2008.sigirconf_conference-2008.115": {"snippet": "...toid question-answering degrades. However, instead of answering factoid questions, it might be more effective to answer opinion and complex questions. \nCategories and Subject\nOVERVIEWRecently, communi...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [57, 66, 1.0]]}, "N18-2090": {"snippet": "...ural question generation is to generate a corresponding question given the input passage (fact) and answer. It is useful for enlarging the training set of QA systems. Previous work has adopted sequenc...", "weights": [[8, 16, 1.0], [59, 67, 1.0], [196, 204, 1.0]]}, "2014.cikm_conference-2014.70": {"snippet": "Focusing Decomposition Accuracy by Personalizing Tensor Decomposition (PTD)\n\n\n ABSTRACTTensor decomposition operation is the basis for many data analysis tasks from clustering, trend detection, anomal...", "weights": []}, "2021.eacl-main.253": {"snippet": "...rove question answering from tables by refining table representations based on information from surrounding text. We also present an effective method to combine text and table-based predictions for qu...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [51, 56, 1.0], [173, 178, 1.0], [201, 209, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.21": {"snippet": "... for answering why-questions\n\n\n AbstractThe problem of automatically answering natural language questions by pinpointing exact answers in a large text (web) corpus has been studied since the mid 1990s...", "weights": [[8, 17, 1.0], [72, 81, 1.0]]}, "S19-2227": {"snippet": "... 10: Sequence-to-Sequence Learning and Expression Trees\n\n\n This paper describes our participation in SemEval-2019 shared task \"Math Question Answering\", where the aim is to create a program that could...", "weights": [[8, 16, 1.0], [20, 28, 1.0], [135, 143, 1.0], [144, 153, 1.0]]}, "2021.acl-demo.24": {"snippet": "...evel Table Retrieval and Table Question Answering\n\n\n We present the first end-to-end, transformerbased table question answering (QA) system that takes natural language questions and massive table corp...", "weights": [[8, 13, 1.0], [28, 33, 1.0], [34, 42, 1.0], [43, 52, 1.0], [106, 111, 1.0], [112, 120, 1.0], [121, 130, 1.0], [193, 198, 1.0]]}, "2021.eacl-main.201": {"snippet": "... the Table Parsing (TAPAS) model developed for question answering (Herzig et al., 2020) , we find that modeling table structure improves a language model pre-trained on unstructured text. Pre-training...", "weights": [[8, 13, 1.0], [50, 58, 1.0], [59, 68, 1.0], [115, 120, 1.0]]}, "D19-1317": {"snippet": "...ving Question Generation With to the Point Context\n\n\n Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence. A major challe...", "weights": [[8, 16, 1.0], [57, 65, 1.0], [110, 118, 1.0]]}, "D18-1194": {"snippet": "Cross-lingual Decompositional Semantic Parsing\n\n\n We introduce the task of cross-lingual decompositional semantic parsing: mapping content provided in a source language into a decompositional semantic...", "weights": []}, "P19-1440": {"snippet": "...se a question decomposer which decomposes a complex question into a sequence of subquestions. In the second stage, we design an information extractor to derive the type and predicate information of th...", "weights": [[8, 16, 1.0], [55, 63, 1.0], [71, 79, 1.0]]}, "2020.findings-emnlp.91": {"snippet": "...-Hop Question Answering over Tabular and Textual Data\n\n\n Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. Ho...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [69, 77, 1.0], [78, 87, 1.0], [176, 181, 1.0]]}, "2010.ntcir_workshop-2010.25": {"snippet": "...d on Question Decomposition and Question Answering", "weights": [[8, 16, 1.0], [35, 43, 1.0], [44, 53, 1.0]]}, "N18-1059": {"snippet": "... for Answering Complex Questions\n\n\n Answering complex questions is a timeconsuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made...", "weights": [[8, 17, 1.0], [39, 48, 1.0]]}, "2020.acl-main.746": {"snippet": "Universal Decompositional Semantic Parsing\n\n\n We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language...", "weights": []}, "D18-1427": {"snippet": "...ural Question Generation\n\n\n In this paper, we focus on the problem of question generation (QG). Recent neural networkbased approaches employ the sequence-tosequence model which takes an answer and its...", "weights": [[8, 16, 1.0], [73, 81, 1.0], [148, 156, 1.0]]}, "W18-6518": {"snippet": "...nion Question Generation\n\n\n We study the problem of opinion question generation from sentences with the help of community-based question answering systems. For this purpose, we use a sequence to seque...", "weights": [[8, 16, 1.0], [63, 71, 1.0], [131, 139, 1.0], [140, 149, 1.0], [186, 194, 1.0], [198, 206, 1.0]]}}}
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The system is evaluated with TabMCQ dataset, and gets a huge improvement compared to the state of the art.", "weights": [[8, 13, 1.0]]}, "P15-1142": {"snippet": "...onal Semantic Parsing on Semi-Structured Tables\n\n\n Two important aspects of semantic parsing for question answering are the breadth of the knowledge source and the depth of logical compositionality. W...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [79, 87, 1.0], [88, 95, 1.0]]}, "2021.acl-short.22": {"snippet": "...y in Weakly Supervised Semantic Parsing\n\n\n The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons. Prior wo...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [26, 34, 1.0], [35, 42, 1.0], [75, 81, 1.0], [82, 92, 1.0], [93, 101, 1.0], [102, 109, 1.0]]}, "D19-1391": {"snippet": "...s\n\n\n Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation. W...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [202, 208, 1.0]]}, "2020.wwwconf_conference-2020.94": {"snippet": "Weakly Supervised Attention for Hashtag Recommendation using Graph Data", "weights": [[0, 6, 1.0], [7, 17, 1.0]]}, "E06-1003": {"snippet": "Weakly Supervised Approaches for Ontology Population\n\n\n We present a weakly supervised approach to automatic Ontology Population from text and compare it with other two unsupervised approaches. In our...", "weights": [[0, 6, 1.0], [7, 17, 1.0], [69, 75, 1.0], [76, 86, 1.0]]}, "P10-1113": {"snippet": "...ated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Secondly, semantic information generated by semantic parsing is incorporated into the syntactic pars...", "weights": [[8, 15, 1.0], [50, 58, 1.0], [59, 66, 1.0], [86, 93, 1.0], [113, 121, 1.0], [147, 155, 1.0], [156, 163, 1.0], [199, 206, 1.0]]}, "P15-1128": {"snippet": "Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base\n\n\n We propose a novel semantic parsing framework for question answering using a knowledge base. We define a q...", "weights": [[0, 8, 1.0], [9, 16, 1.0], [113, 121, 1.0], [122, 129, 1.0]]}, "P17-1167": {"snippet": "...ural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are design...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [51, 57, 1.0], [58, 68, 1.0]]}, "N15-1088": {"snippet": "Semantic parsing of speech using grammars learned with weak supervision\n\n\n Semantic grammars can be applied both as a language model for a speech recognizer and for semantic parsing, e.g. in order to ...", "weights": [[0, 8, 1.0], [9, 16, 1.0], [75, 83, 1.0], [165, 173, 1.0], [174, 181, 1.0]]}, "P84-1073": {"snippet": "...uage parsing is discussed. An LR parser is a ~;hift-reduce parser which is doterministically guided by a parsing table. A parsing table can be obtained automatically from a contextfree phrase structur...", "weights": [[8, 15, 1.0], [108, 115, 1.0], [116, 121, 1.0], [125, 132, 1.0], [133, 138, 1.0]]}, "2021.mrqa-1.8": {"snippet": "... and table datasets from related work and compare the retrieval performance of different encoding schemata. We find that dense vector embeddings of transformer models outperform sparse embeddings on f...", "weights": [[8, 13, 1.0]]}, "2021.naacl-main.96": {"snippet": "...e on table QA task without even using any of these specialized pre-training techniques. The first model, called RCI interaction, leverages a transformer based architecture that independently classifie...", "weights": [[8, 13, 1.0]]}, "D17-1160": {"snippet": "...ural Semantic Parsing with Type Constraints for Semi-Structured Tables\n\n\n We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser ...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [94, 102, 1.0], [103, 110, 1.0]]}, "K18-1035": {"snippet": "Weakly-Supervised Neural Semantic Parsing with a Generative Ranker\n\n\n Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent. The task is challe...", "weights": [[0, 6, 1.0], [7, 17, 1.0], [25, 33, 1.0], [34, 41, 1.0], [70, 76, 1.0], [77, 87, 1.0], [88, 96, 1.0]]}, "2007.coria_conference-2007.14": {"snippet": "Vers une indexation personnalis\u00e9e de photographies par apprentissage non supervis\u00e9 de r\u00e9gularit\u00e9s", "weights": []}, "W16-3902": {"snippet": "...s on Semantic Grounding Tasks\n\n\n Entity linking and semantic parsing have been shown to be crucial to important applications such as question answering and document understanding. These tasks often re...", "weights": [[8, 16, 1.0], [55, 63, 1.0], [64, 71, 1.0]]}, "2021.emnlp-main.695": {"snippet": "...m of weakly-supervised conversational Question Answering over large Knowledge Graphs using a neural semantic parsing approach. We introduce a new Logical Form (LF) grammar that can model a wide range ...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [103, 111, 1.0], [112, 119, 1.0]]}, "Q19-1015": {"snippet": "...rate weakly supervised tasks, machine translation and semantic parsing. We show that objectives should actively discourage negative outputs in addition to promoting a surrogate gold structure. This no...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [57, 65, 1.0], [66, 73, 1.0]]}, "N19-1273": {"snippet": "... for Weakly Supervised Semantic Parsing\n\n\n Training semantic parsers from questionanswer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search c...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [26, 34, 1.0], [35, 42, 1.0], [55, 63, 1.0]]}, "2021.adaptnlp-1.25": {"snippet": "...ng a weakly-supervised semantic parser to transform brief, multi-intent natural utterances into logical forms. Our findings suggest a new \"projection and reduction\" method that iteratively performs pr...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [26, 34, 1.0]]}, "2013.sigirconf_conference-2013.83": {"snippet": "A weakly-supervised detection of entity central documents in a stream", "weights": [[2, 8, 1.0], [9, 19, 1.0]]}, "P19-1473": {"snippet": "...fied Semantic Parsing with Weak Supervision\n\n\n Semantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains. However, the ...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [50, 58, 1.0], [59, 66, 1.0]]}, "2015.coria_conference-2015.31": {"snippet": "Regroupement par popularit\u00e9 pour la RI semi-supervis\u00e9e centr\u00e9e sur les entit\u00e9s", "weights": []}, "2021.findings-acl.62": {"snippet": "Weakly Supervised Pre-Training for Multi-Hop Retriever\n\n\n In multi-hop QA, answering complex questions entails iterative document retrieval for finding the missing entity of the question. The main ste...", "weights": [[0, 6, 1.0], [7, 17, 1.0]]}, "W10-2903": {"snippet": "...ving Semantic Parsing from the World{'}s Response\n\n\n Current approaches to semantic parsing, the task of converting text to a formal meaning representation, rely on annotated training data mapping sen...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [78, 86, 1.0], [87, 94, 1.0]]}, "2018.wwwconf_conference-2018.152": {"snippet": "...ther table-based information access scenarios, such as table completion or table mining. The main novel contribution of this work is a method for performing semantic matching between queries and table...", "weights": [[8, 13, 1.0], [58, 63, 1.0], [78, 83, 1.0], [160, 168, 1.0]]}, "Q13-1016": {"snippet": "...that weakly supervised training is competitive with fully supervised training while requiring significantly less annotation effort.", "weights": [[8, 14, 1.0], [15, 25, 1.0], [61, 71, 1.0]]}, "2022.suki-1.5": {"snippet": "...k of table retrieval, and ask: \"is table-specific model design necessary for table retrieval, or can a simpler text-based model be effectively used to achieve a similar result?\" First, we perform an a...", "weights": [[8, 13, 1.0], [38, 43, 1.0], [80, 85, 1.0]]}, "2022.tacl-1.3": {"snippet": "... the table-based question answering task, we introduce FeTaQA, a new dataset with 10K Wikipedia-based {table, question, free-form answer, supporting table cells} pairs. FeTaQA is collected from notewo...", "weights": [[8, 13, 1.0], [106, 111, 1.0], [152, 157, 1.0]]}, "Q14-1030": {"snippet": "...cale Semantic Parsing without Question-Answer Pairs\n\n\n In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or questi...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [93, 101, 1.0], [102, 109, 1.0]]}, "2018.ecir_conference-2018.52": {"snippet": "...h is weakly supervised, trained on question-answer-table triples. It generates human readable logical forms from natural language questions, which are then ranked based on word and character convoluti...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [54, 59, 1.0]]}, "2022.naacl-main.64": {"snippet": "...Semi-weakly Supervised Learning for Natural Language Understanding\n\n\n Building machine learning models for natural language understanding (NLU) tasks relies heavily on labeled data. Weak supervision h...", "weights": [[8, 14, 1.0], [15, 25, 1.0]]}, "2021.wwwconf_conference-2021.172": {"snippet": "Weakly-Supervised Question Answering with Effective Rank and Weighted Loss over Candidates", "weights": [[0, 6, 1.0], [7, 17, 1.0]]}, "N15-1009": {"snippet": "Weakly Supervised Slot Tagging with Partially Labeled Sequences from Web Search Click Logs\n\n\n In this paper, we apply a weakly-supervised learning approach for slot tagging using conditional random fi...", "weights": [[0, 6, 1.0], [7, 17, 1.0], [120, 126, 1.0], [127, 137, 1.0]]}, "2017.jeptalnrecital-court.13": {"snippet": "...sing Weakly Supervised Learning)\n\n\n Plusieurs t\u00e2ches en traitement du langage naturel impliquent de modifier des phrases en conservant au mieux leur sens, comme la reformulation, la compression, la si...", "weights": [[8, 14, 1.0], [15, 25, 1.0]]}, "2015.ictir_conference-2015.28": {"snippet": "...m of table-related context retrieval: given a table and the sentences within the same web page, determine for each sentence whether it is relevant to the table. We define the concept of relevance and ...", "weights": [[8, 13, 1.0], [49, 54, 1.0], [157, 162, 1.0]]}, "2021.emnlp-main.342": {"snippet": "...able Table Question Answering\n\n\n Weakly-supervised table question-answering (TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question...", "weights": [[8, 13, 1.0], [36, 42, 1.0], [43, 53, 1.0], [54, 59, 1.0]]}, "2021.eacl-main.253": {"snippet": "... the table. Using the information from an article as additional context can potentially enrich table representations. In this work we aim to improve question answering from tables by refining table re...", "weights": [[8, 13, 1.0], [98, 103, 1.0], [195, 200, 1.0]]}, "P13-1092": {"snippet": "...ised Semantic Parsing\n\n\n We present the first unsupervised approach for semantic parsing that rivals the accuracy of supervised approaches in translating natural-language questions to database queries...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [75, 83, 1.0], [84, 91, 1.0], [120, 130, 1.0]]}, "2022.acl-long.525": {"snippet": "... for Table Fact Verification via Structure-Aware Semantic Parsing\n\n\n Table fact verification aims to check the correctness of textual statements based on given semi-structured data. Most existing meth...", "weights": [[8, 13, 1.0], [52, 60, 1.0], [61, 68, 1.0], [72, 77, 1.0]]}, "2022.acl-long.180": {"snippet": "...s in table generation: table constraint and table relation embeddings. We consider text-to-table as an inverse problem of the well-studied table-to-text, and make use of four existing table-to-text da...", "weights": [[8, 13, 1.0], [26, 31, 1.0], [47, 52, 1.0], [94, 99, 1.0], [142, 147, 1.0], [187, 192, 1.0]]}, "W13-0111": {"snippet": "...ards Weakly Supervised Resolution of Null Instantiations\n\n\n This paper addresses the task of finding antecedents for locally uninstantiated arguments. To resolve such null instantiations, we develop a...", "weights": [[8, 14, 1.0], [15, 25, 1.0]]}, "D19-1543": {"snippet": "... the table, called table-related tokens. These table-related tokens are troublesome for the downstream neural semantic parser because it brings complex semantics and hinders the sharing across the tra...", "weights": [[8, 13, 1.0], [22, 27, 1.0], [50, 55, 1.0], [113, 121, 1.0]]}, "2019.jeptalnrecital-court.1": {"snippet": "...gue (Weakly supervised dialog act analysis)\n\n\n Nous nous int\u00e9ressons ici \u00e0 l'analyse de conversation par chat dans un contexte orient\u00e9-t\u00e2che avec un conseiller technique s'adressant \u00e0 un client, o\u00f9 l'...", "weights": [[8, 14, 1.0], [15, 25, 1.0]]}, "P96-1056": {"snippet": "...type parsing of Tree Adjoining Languages (TALs). The algorithm presented handles modifications to the input grammar by updating the parser generated so far. In this paper, a lazy generation of LRtype ...", "weights": [[8, 15, 1.0]]}, "2021.acl-demo.24": {"snippet": "...evel Table Retrieval and Table Question Answering\n\n\n We present the first end-to-end, transformerbased table question answering (QA) system that takes natural language questions and massive table corp...", "weights": [[8, 13, 1.0], [28, 33, 1.0], [106, 111, 1.0], [193, 198, 1.0]]}, "2021.acl-long.318": {"snippet": "...m in Weakly Supervised Question Answering\n\n\n Weakly supervised question answering usually has only the final answers as supervision signals while the correct solutions to derive the answers are not pr...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [48, 54, 1.0], [55, 65, 1.0]]}, "2021.eacl-main.201": {"snippet": "... the Table Parsing (TAPAS) model developed for question answering (Herzig et al., 2020) , we find that modeling table structure improves a language model pre-trained on unstructured text. Pre-training...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [115, 120, 1.0]]}, "N15-1077": {"snippet": "...nded Semantic Parsing for Complex Knowledge Extraction\n\n\n Recently, there has been increasing interest in learning semantic parsers with indirect supervision, but existing work focuses almost exclusiv...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [118, 126, 1.0]]}, "2022.acl-long.78": {"snippet": "...ical Table Dataset for Question Answering and Natural Language Generation\n\n\n Tables are often created with hierarchies, but existing works on table reasoning mainly focus on flat tables and neglect hi...", "weights": [[8, 13, 1.0], [145, 150, 1.0]]}, "2021.naacl-main.105": {"snippet": "...ovel weakly supervised Structure-Grounded pretraining framework (STRUG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus. We identify a ...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [134, 139, 1.0], [175, 180, 1.0]]}, "W14-6602": {"snippet": "{BACANAL}: Short Length Random Walks For Lexical Analysis, Application to lexical substitution ({BACANAL} : Balades Al{\\'e}atoires Courtes pour {ANA}lyses Lexicales Application {\\`a} la substitution l...", "weights": []}, "2020.acl-main.398": {"snippet": "...}as: Weakly Supervised Table Parsing via Pre-training\n\n\n Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical f...", "weights": [[8, 14, 1.0], [15, 25, 1.0], [26, 31, 1.0], [32, 39, 1.0], [130, 138, 1.0], [139, 146, 1.0]]}, "W16-0105": {"snippet": "...(KB) table. Unlike previous work on end-to-end training of semantic parsers, NEU-RAL ENQUIRER is fully \"neuralized\": it gives distributed representations of queries and KB tables, and executes queries...", "weights": [[8, 13, 1.0], [62, 70, 1.0]]}, "2022.naacl-main.68": {"snippet": "...shot Table-based Question Answering\n\n\n The information in tables can be an important complement to text, making table-based question answering (QA) systems of great value. The intrinsic complexity of ...", "weights": [[8, 13, 1.0], [115, 120, 1.0]]}, "2020.acl-main.745": {"snippet": "...ural semantic parsers using TABERT as feature representation layers achieve new best results on the challenging weakly-supervised semantic parsing benchmark WIKITABLEQUESTIONS, while performing compet...", "weights": [[8, 16, 1.0], [115, 121, 1.0], [122, 132, 1.0], [133, 141, 1.0], [142, 149, 1.0]]}, "R09-2004": {"snippet": "...ting Semantic Structure using Partial Parsing\n\n\n Partial parsing is an established NLP technique used to perform syntactic analysis without generating a full constituent parse tree. This paper present...", "weights": [[8, 16, 1.0], [41, 48, 1.0], [60, 67, 1.0]]}, "2010.riao_conference-2010.21": {"snippet": "Weakly supervised person naming in news video", "weights": [[0, 6, 1.0], [7, 17, 1.0]]}, "P18-1034": {"snippet": "Semantic Parsing with Syntax- and Table-Aware {SQL} Generation\n\n\n We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically g...", "weights": [[0, 8, 1.0], [9, 16, 1.0], [34, 39, 1.0]]}, "2020.findings-emnlp.167": {"snippet": "... for Semantic Parsing to {SQL} Queries\n\n\n Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches. But can richer supervision help even ...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [57, 65, 1.0], [66, 73, 1.0], [143, 153, 1.0]]}, "2015.coria_conference-2015.22": {"snippet": "Clustering Spectral semi-supervis\u00e9 avec propagation automatique des contraintes par paires", "weights": []}, "2010.wwwconf_conference-2010.122": {"snippet": "...bles semantic triples (knowledge). We then describe a solution to a key problem in extracting semantic triples: protagonist detection, i.e., finding the subject of the table that often is not present ...", "weights": [[8, 16, 1.0], [97, 105, 1.0], [171, 176, 1.0]]}, "2011.jeptalnrecital-long.4": {"snippet": "...e (A weakly supervised approach to large scale relation extraction)\n\n\n Les syst\u00e8mes d'extraction d'information traditionnels se focalisent sur un domaine sp\u00e9cifique et un nombre limit\u00e9 de relations. L...", "weights": [[8, 14, 1.0], [15, 25, 1.0]]}}}
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"score": 20.85944575403421, "doc_id": "2010.sigirconf_conference-2010.162"}, {"rank": 9, "score": 20.373350813484155, "doc_id": "W11-2405"}, {"rank": 10, "score": 20.337740660063286, "doc_id": "2012.wwwconf_conference-2012.51"}]}], "docs": {"2007.ecir_conference-2007.24": {"snippet": "...ting Diversity and Density in Active Learning for Relevance Feedback", "weights": [[8, 17, 1.0], [53, 62, 1.0]]}, "2014.sigirconf_conference-2014.86": {"snippet": "...both relevance and diversity are both taken into consideration. In this short paper, we propose a few data fusion methods to try to improve performance when both relevance and diversity are concerned....", "weights": [[8, 17, 1.0], [22, 31, 1.0], [165, 174, 1.0], [179, 188, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A6.3": {"snippet": "...rity-aware refinement of aggregation queries\n\n\n Abstract Interactive data exploration platforms in Web, business and scientific domains are becoming increasingly popular. Typically, users without prio...", "weights": [[8, 13, 1.0]]}, "2013.wwwconf_conference-2013.62": {"snippet": "...iple relevance and diversity measures to understand whether they have any correlations. Next, we propose a novel measure called expanded relevance which combines both relevance and diversity into a si...", "weights": [[8, 17, 1.0], [22, 31, 1.0], [140, 149, 1.0], [170, 179, 1.0], [184, 193, 1.0]]}, "2013.sigirconf_conference-2013.200": {"snippet": "...only relevance, but also novelty and diversity in tag recommendation. To reach this goal, we intend to propose new metrics that estimate relevance, novelty and diversity of tags, as well as new tag re...", "weights": [[8, 17, 1.0], [40, 49, 1.0], [118, 125, 1.0], [140, 149, 1.0], [163, 172, 1.0]]}, "2015.mir_conference-2015.42": {"snippet": "...ving Diversity in Image Search via Supervised Relevance Scoring", "weights": [[8, 17, 1.0], [49, 58, 1.0]]}, "2008.ir_journal-ir0volumeA11A5.3": {"snippet": "...eval metrics designed for evaluation with incomplete relevance assessments", "weights": [[8, 15, 1.0], [56, 65, 1.0]]}, "2013.wwwconf_conference-2013c.221": {"snippet": "...hese metrics influence in the resulting recommendations. We also propose new metrics for evaluating the recommendations based on social concepts (novelty, diversity and coverage) that have never been ...", "weights": [[8, 15, 1.0], [80, 87, 1.0], [158, 167, 1.0]]}, "2010.sigirconf_conference-2010.162": {"snippet": "...ance relevance and diversity parameters, (b) shows that specific definitions of relevance and diversity metrics appropriate to MMR emerge naturally, and (c) formally derives variants of latent semanti...", "weights": [[8, 17, 1.0], [22, 31, 1.0], [83, 92, 1.0], [97, 106, 1.0], [107, 114, 1.0]]}, "2016.tois_journal-ir0volumeA35A2.6": {"snippet": "...ache-aware approach in terms of both data layout and access patterns and to a control flow that entails very low branch mis-prediction rates.", "weights": [[8, 13, 1.0]]}, "2014.sigirconf_conference-2014.212": {"snippet": "...s to diversity research in Information Retrieval.First, we propose the definition of a new framework of novelty and diversity metrics for Recommender Systems [4]. This framework adapts and unifies sev...", "weights": [[8, 17, 1.0], [119, 128, 1.0], [129, 136, 1.0]]}, "2021.ecir_conference-20212.24": {"snippet": "Diversity Aware Relevance Learning for Argument Search\n\n\n In this work, we focus on retrieving relevant arguments for a query claim covering diverse aspects. State-of-the-art methods rely on explicit ...", "weights": [[0, 9, 1.0], [10, 15, 1.0], [16, 25, 1.0]]}, "2014.wwwconf_conference-2014c.52": {"snippet": "...els, metrics, and learning. We present systematic solutions to address the challenges. This leads to drastic new models and approaches for cost-effective ranking at scale, which will be discussed in d...", "weights": [[8, 15, 1.0]]}, "2016.wwwconf_conference-2016.5": {"snippet": "...four metrics of the social diversity of places which relate to their social brokerage role, their entropy, the homogeneity of their visitors and the amount of serendipitous encounters they are able to...", "weights": [[8, 15, 1.0], [30, 39, 1.0]]}, "2006.cikm_conference-2006.79": {"snippet": "Cache-oblivious nested-loop joins\n\n\n ABSTRACTWe propose to adapt the newly emerged cache-oblivious model to relational query processing. Our goal is to automatically achieve an overall performance com...", "weights": []}, "2010.clef_conference-2010.10": {"snippet": "...tion Metrics for Patent Retrieval with Incomplete Relevance Judgements", "weights": [[8, 15, 1.0], [53, 62, 1.0]]}, "2009.trec_conference-2009.66": {"snippet": "...ical Diversity and Relevance Feedback", "weights": [[8, 17, 1.0], [22, 31, 1.0]]}, "2012.sigirconf_conference-2012.14": {"snippet": "... our metrics agree with the majority user preference more often than the current diversity-based information retrieval metrics. Finally, we demonstrate the flexibility of our framework by showing that...", "weights": [[8, 15, 1.0], [84, 93, 1.0], [122, 129, 1.0]]}, "2010.ntcir_workshop-2010evia.7": {"snippet": "...nary-relevance Diversity Task data from the TREC 2009 Web Track suggest that our metrics corrrelate well with existing metrics but can be more intuitive. Hence, we argue that our metrics are suitable ...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [84, 91, 1.0], [122, 129, 1.0], [182, 189, 1.0]]}, "2010.wwwconf_conference-2010.226": {"snippet": "...over metrics for relevance, comprehensiveness, coverage, diversity, discovery freshness, content freshness, and presentation. We will also describe how these metrics can be mapped to proxy metrics for...", "weights": [[8, 15, 1.0], [20, 29, 1.0], [60, 69, 1.0], [161, 168, 1.0], [192, 199, 1.0]]}, "2013.cikm_conference-2013.73": {"snippet": "...e AS metrics that capture key AS components (e.g., vertical selection) have several advantages over other metrics. This work sheds new lights on the further developments and applications of AS metrics", "weights": [[8, 15, 1.0], [109, 116, 1.0], [196, 203, 1.0]]}, "2015.sigirconf_conference-2015.78": {"snippet": "...iori Relevance Based On Quality and Diversity Of Social Signals", "weights": [[8, 17, 1.0], [39, 48, 1.0]]}, "2020.sigirconf_conference-2020.57": {"snippet": "...licy-aware counterfactual estimator for LTR metrics that can account for the effect of a stochastic logging policy. We prove that the policy-aware estimator is unbiased if every relevant item has a no...", "weights": [[8, 13, 1.0], [47, 54, 1.0], [144, 149, 1.0]]}, "2022.naacl-main.100": {"snippet": "...lity-aware decoding consistently outperforms MAP-based decoding according both to state-of-the-art automatic metrics (COMET and BLEURT) and to human assessments. Our code is available at https://githu...", "weights": [[8, 13, 1.0], [112, 119, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.18": {"snippet": "...eval metrics based on graded relevance\n\n\n AbstractThis paper compares 14 information retrieval metrics based on graded relevance, together with 10 traditional metrics based on binary relevance, in ter...", "weights": [[8, 15, 1.0], [32, 41, 1.0], [98, 105, 1.0], [122, 131, 1.0], [162, 169, 1.0], [186, 195, 1.0]]}, "2015.sigirconf_conference-2015.13": {"snippet": "...inal Relevance Model via Directly Optimizing Diversity Evaluation Measures", "weights": [[8, 17, 1.0], [48, 57, 1.0]]}, "2009.cikm_conference-2009.66": {"snippet": "...aded relevance\n\n\n ABSTRACTWhile numerous metrics for information retrieval are available in the case of binary relevance, there is only one commonly used metric for graded relevance, namely the Discou...", "weights": [[8, 17, 1.0], [44, 51, 1.0], [114, 123, 1.0], [175, 184, 1.0]]}, "W15-4920": {"snippet": "...ally aware or unaware methods, or a combination of both. We designed a flexible and time-efficient framework which applies and combines linguistically unaware or aware metrics in the source and target...", "weights": [[8, 13, 1.0], [165, 170, 1.0], [171, 178, 1.0]]}, "2012.wwwconf_conference-2012.51": {"snippet": "...ther diversity metrics and therefore may be the most intuitive as a metric that emphasises diversity; and (c) In terms of preference agreement with effective precision, DIN -nDCG is superior to other ...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [94, 103, 1.0]]}, "2010.sigirconf_conference-2010.30": {"snippet": "... the relevance prediction stage and ranking decision stage. During retrieval the relevance of documents is not known a priori, and the joint probability of relevance is used to measure the uncertainty...", "weights": [[8, 17, 1.0], [84, 93, 1.0], [159, 168, 1.0]]}, "2021.wwwconf_conference-2021.263": {"snippet": "...ture-Aware Diverse Trends Framework for Recommendation", "weights": [[8, 13, 1.0]]}, "2018.sigirconf_conference-2018.40": {"snippet": "...erse relevance patterns require an ideal retrieval model to be able to assess relevance in the right granularity adaptively. Unfortunately, most existing retrieval models compute relevance at a single...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [182, 191, 1.0]]}, "2004.wwwconf_conference-2004at.87": {"snippet": "... the relevance between queries and documents will not satisfy users if they want the top search results to present a wide coverage of topics by a few representative documents. In this paper, we propos...", "weights": [[8, 17, 1.0]]}, "2013.ecir_conference-2013.73": {"snippet": "...tent-Aware Expected Reciprocal Rank Sufficient to Evaluate Diversity?", "weights": [[8, 13, 1.0], [62, 71, 1.0]]}, "2020.ipm_journal-ir0volumeA57A3.36": {"snippet": "...rity metrics for recommendation", "weights": [[8, 15, 1.0]]}, "2011.sigirconf_conference-2011.189": {"snippet": "...ring relevance, diversity and prestige in information networks\n\n\n ABSTRACTMany network-based ranking approaches have been proposed to rank objects according to different criteria, including relevance,...", "weights": [[8, 17, 1.0], [19, 28, 1.0], [193, 202, 1.0]]}, "2011.sigirconf_conference-2011.175": {"snippet": "... and metrics, with little convergence between both fields. In this poster we explore the adaptation of diversity metrics, techniques, and principles from adhoc IR to the recommendation task, by introd...", "weights": [[8, 15, 1.0], [106, 115, 1.0], [116, 123, 1.0]]}, "2013.ipm_journal-ir0volumeA49A2.10": {"snippet": "...c co-relevance, where the similarity between two documents is proportional to the probability that they are both co-relevant to a specific given query. We further simplify the proposed co-relevance-ba...", "weights": [[8, 17, 1.0], [191, 200, 1.0]]}, "2011.cikm_conference-2011.230": {"snippet": "...ance relevance (i.e., high values of the ranking function) with diversity, which promotes different, yet almost equally relevant, entities in the top-k combinations. This paper explores two different ...", "weights": [[8, 17, 1.0], [67, 76, 1.0]]}, "2010.wwwconf_conference-2010.181": {"snippet": "...iple relevance metrics in web search\n\n\n ABSTRACTSeveral relevance metrics, such as NDCG, precision and pSkip, are proposed to measure search relevance, where different metrics try to characterize sear...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [59, 68, 1.0], [69, 76, 1.0], [144, 153, 1.0], [171, 178, 1.0]]}, "2021.acl-long.521": {"snippet": "...nted Relevance Score\n\n\n Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements. Newer metrics such as BERTScore have addressed many wea...", "weights": [[8, 17, 1.0], [46, 53, 1.0], [154, 161, 1.0]]}, "2011.sigirconf_conference-2011.107": {"snippet": "...d IR metrics by closely examining the actual ranked lists from TREC. We show that a family of metrics called D -measures have several advantages over other metrics such as \u03b1-nDCG and Intent-Aware metr", "weights": [[8, 15, 1.0], [97, 104, 1.0], [159, 166, 1.0], [193, 198, 1.0], [199, 206, 1.0]]}, "2015.wwwconf_conference-2015c.317": {"snippet": "... and Diversity Aware Recommendations\n\n\n ABSTRACTSince the first introduced Collaborative Filtering Recommenders (CFR), there have been many attempts to improve their performance by enhancing the predi...", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "2018.ecir_conference-2018.43": {"snippet": "... how diversity and relevance contribute to the performance metric of each method. In this paper, we use the statistical technique ANOVA to analyse and compare three representative evaluation methods f...", "weights": [[8, 17, 1.0], [22, 31, 1.0]]}, "2020.aacl-main.6": {"snippet": "...ment Diversity-aware Neural News Recommendation\n\n\n Personalized news recommendation is important for online news services. Many news recommendation methods recommend news based on their relevance to u...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [189, 198, 1.0]]}, "2016.wsdm_conference-2016.15": {"snippet": "...tent-aware evaluation metrics that take into account the co-occurrence between intents. Showing many query suggestions simply increases a chance to satisfy users with diverse intents in this model, wh...", "weights": [[8, 13, 1.0], [25, 32, 1.0]]}, "2011.cikm_conference-2011.281": {"snippet": "...ion (diversity), trust, and value. In our analysis we also show how these interact with relevance in different ways. We study the benefits of diversity and also show why guaranteeing diversity is impo...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [145, 154, 1.0], [186, 195, 1.0]]}, "2017.ipm_journal-ir0volumeA53A4.3": {"snippet": "...uare metrics that extend the diversity metrics through incorporating hierarchical subtopics are used as references. The experimental results show that the intentsquare metrics disagree with the divers...", "weights": [[8, 15, 1.0], [32, 41, 1.0], [42, 49, 1.0], [171, 178, 1.0], [197, 206, 1.0]]}, "2018.sigirconf_conference-2018.65": {"snippet": "...s of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility Metric\n\n\n ABSTRACTMany evaluation metrics have been defined to evaluate the effectiveness ad-hoc retrieval and search result dive...", "weights": [[8, 17, 1.0], [29, 36, 1.0], [108, 115, 1.0]]}, "2021.wwwconf_conference-2021.67": {"snippet": "...edge-aware retrieval model for diverse relevance estimation", "weights": [[8, 13, 1.0], [42, 51, 1.0]]}, "2009.airs_conference-2009.5": {"snippet": "... and Relevance Mismatch in Retrieval Evaluation", "weights": [[8, 17, 1.0]]}, "2007.ntcir_workshop-2007evia.9": {"snippet": "...nary-relevance evaluation metrics, even when graded relevance data were available. However, the NTCIR-6 Crosslingual Task has finally started adopting graded-relevance metrics, though only as addition...", "weights": [[8, 17, 1.0], [29, 36, 1.0], [55, 64, 1.0], [161, 170, 1.0], [171, 178, 1.0]]}, "2020.sigirconf_conference-2020.14": {"snippet": "... for relevance feedback that directly optimizes any desired retrieval metric, including precision-oriented, recall-oriented, and even diversity metrics: RML can be easily extended to directly optimize...", "weights": [[8, 17, 1.0], [137, 146, 1.0], [147, 154, 1.0]]}, "2011.sigirconf_conference-2011.162": {"snippet": "...y of relevance and diversity metrics as objective functions for learning a diverse ranking. Our results in the context of the diversity task of the TREC 2009 and 2010 Web tracks show that diversity me...", "weights": [[8, 17, 1.0], [22, 31, 1.0], [32, 39, 1.0], [129, 138, 1.0], [191, 200, 1.0], [201, 208, 1.0]]}, "2007.mir_workshop-2007.9": {"snippet": "Video diver: generic video indexing with diverse features", "weights": []}, "2018.sigirconf_conference-2018.123": {"snippet": "...ient metrics for influence measurement, focusing on a network oblivious approach and show that the graph-based approach used in other OSNs is a poor fit for Instagram. In our study, we consider user s...", "weights": [[8, 15, 1.0]]}, "2018.ecir_conference-2018.27": {"snippet": "... and diversity have attracted attention in recent years, where several metrics have been defined with this goal in mind. However, it is unclear how suitable these metrics are to measure novelty or div...", "weights": [[8, 17, 1.0], [74, 81, 1.0], [166, 173, 1.0], [200, 209, 1.0]]}, "2012.sigirconf_conference-2012.180": {"snippet": "... the relevance vs. diversity trade-off.\nRELEVANCE MODEL AND OBJECTIVEWe review the probabilistic subtopic model of binary relevance from  shown as a directed graphical model in . Shaded nodes represen...", "weights": [[8, 17, 1.0], [22, 31, 1.0], [43, 52, 1.0], [125, 134, 1.0]]}, "2013.sigirconf_conference-2013.43": {"snippet": "... and diversity typically involves an assessor judging each document for relevance against a set of pre-identified subtopics, which may be disambiguations of the query, facets of an information need, o...", "weights": [[8, 17, 1.0], [75, 84, 1.0]]}, "2013.wwwconf_conference-2013c.248": {"snippet": "...file diversity, a novel idea in searching scientific documents. Combining keyword relevance with popularity in a scoring function has been the subject of different forms of social relevance . Content ...", "weights": [[8, 17, 1.0], [85, 94, 1.0], [183, 192, 1.0]]}, "I13-1193": {"snippet": "...ally Aware Coreference Evaluation Metrics\n\n\n Virtually all the commonly-used evaluation metrics for entity coreference resolution are linguistically agnostic, treating the mentions to be clustered as ...", "weights": [[8, 13, 1.0], [37, 44, 1.0], [91, 98, 1.0]]}, "2011.sigirconf_conference-2011.62": {"snippet": "...tent-aware retrieval models when estimating the relevance of documents to different aspects. In this paper, we propose to diversify the results retrieved for a given query, by learning the appropriate...", "weights": [[8, 13, 1.0], [51, 60, 1.0]]}, "2011.cikm_conference-2011.220": {"snippet": "...cial Relevance Score (SRS) is based on two factors. First, the engagement intensity quantifies the effort a user has made during an interaction. Second, users can assign a trust score to each person f...", "weights": [[8, 17, 1.0]]}, "W11-2405": {"snippet": "Diversity-aware Evaluation for Paraphrase Patterns\n\n\n Common evaluation metrics for paraphrase patterns do not necessarily correlate with extrinsic recognition task performance. We propose a metric wh...", "weights": [[0, 9, 1.0], [10, 15, 1.0], [72, 79, 1.0]]}, "2015.sigirconf_conference-2015.140": {"snippet": "Relevance-aware Filtering of Tuples Sorted by an Attribute Value via Direct Optimization of Search Quality Metrics", "weights": [[0, 9, 1.0], [10, 15, 1.0], [107, 114, 1.0]]}, "2019.ecir_conference-20191.39": {"snippet": "...w Do Metrics Capture Topical Relevance, Diversity and User Effort?\n\n\n Abstract. Complex dynamic search tasks typically involve multiaspect information needs and repeated interactions with an informati...", "weights": [[8, 15, 1.0], [32, 41, 1.0], [43, 52, 1.0]]}, "2017.sigirconf_conference-2017.248": {"snippet": "...ning relevance as rigorously as we can is essential to the development of both e ective IR systems and sound evaluation metrics. As a result, modeling relevance has always been a central challenge in ...", "weights": [[8, 17, 1.0], [123, 130, 1.0], [154, 163, 1.0]]}, "2011.cikm_conference-2011.248": {"snippet": "...ords diversity, set-level relevance, maximal marginal relevance\nDIVERSE RETRIEVAL AND SUBTOPICSOne of the basic tenets of set-based information retrieval is to minimize redundancy, hence maximize dive...", "weights": [[8, 17, 1.0], [29, 38, 1.0], [57, 66, 1.0], [199, 208, 1.0]]}, "2007.sigirconf_conference-2007.214": {"snippet": "...rder aware. Referenced documents probably contain semantically unrelated content deserving higher distances than text occurrences in the same document.For structural queries (e.g., /A[t3]//B[t1]) we p...", "weights": [[8, 13, 1.0]]}}}
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The downside is that fine-tuning may cause models to overfit to task-specific data and thereby ...", "weights": [[8, 14, 1.0], [91, 99, 1.0], [100, 106, 1.0], [151, 157, 1.0]]}, "D19-5827": {"snippet": "...ined Language Model Fine-tuning\n\n\n With a large number of datasets being released and new techniques being proposed, Question answering (QA) systems have witnessed great breakthroughs in reading compr...", "weights": [[8, 16, 1.0], [45, 50, 1.0]]}, "2014.eamt-1.28": {"snippet": "... FP7 Language Technologies (ICT-2011.4.2) Small and medium scale focused research project (STREP) 288342 http://www.xlike.org List of partners Jo\u017eef Stefan Institute (JSI), Slovenia (coordinator)", "weights": [[8, 16, 1.0], [45, 50, 1.0]]}, "D19-6606": {"snippet": "...huge language models such as GPT-2 to memorise the answers to factoid questions raises questions about the extent to which knowledge is being embedded directly within these large models. This short pa...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [176, 181, 1.0], [182, 188, 1.0]]}, "P19-1337": {"snippet": "...ware Language Models Using Knowledge Distillation\n\n\n Prior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more su...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [86, 91, 1.0], [135, 143, 1.0], [144, 150, 1.0]]}, "2004.spire_conference-2004.22": {"snippet": "...th a small and systematic modification. Since the child table consumes moderate space and is constructed very fast, the enhanced suffix array is almost as time/space-efficient as the suffix array. How...", "weights": [[8, 13, 1.0]]}, "2013.wwwconf_conference-2013.113": {"snippet": "...y of large graph collections\n\n\n ABSTRACTA growing set of on-line applications are generating data that can be viewed as very large collections of small, dense social graphs -these range from sets of s...", "weights": [[8, 13, 1.0], [128, 133, 1.0], [149, 154, 1.0]]}, "2022.acl-srw.26": {"snippet": "...xact language. This work investigates multilingual and cross-lingual detoxification and the behavior of large multilingual models like in this setting. Unlike previous works we aim to make large langu...", "weights": [[8, 16, 1.0], [107, 112, 1.0], [126, 132, 1.0], [192, 197, 1.0], [198, 206, 1.0]]}, "2022.acl-long.264": {"snippet": "...ural Language Models\n\n\n This work connects language model adaptation with concepts of machine learning theory. We consider a training setup with a large outof-domain set and a small in-domain set. We ...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [46, 54, 1.0], [150, 155, 1.0], [179, 184, 1.0]]}, "2021.findings-acl.387": {"snippet": "...ined Language Model Distillation from Multiple Teachers\n\n\n Pre-trained language models (PLMs) achieve great success in NLP. However, their huge model sizes hinder their applications in many practical ...", "weights": [[8, 16, 1.0], [74, 82, 1.0], [83, 89, 1.0]]}, "1998.jasis_journal-ir0volumeA49A14.2": {"snippet": "...tial Coordination. I. The Best of Pre-Coordination and Post-Coordination", "weights": [[8, 20, 1.0], [41, 53, 1.0], [63, 75, 1.0]]}, "2021.wanlp-1.6": {"snippet": "...pact language models for resource-limited devices or applications. Not to mention, how to efficiently train such models for a low-resource language like Arabic. In this paper, we investigate how such ...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [116, 122, 1.0], [142, 150, 1.0]]}, "2021.wmt-1.62": {"snippet": "...th a small language-specific vocabulary and fine-tuning the new embeddings on the new language's parallel data. Some additional language-specific components may be trained to improve performance (e.g....", "weights": [[8, 13, 1.0], [14, 22, 1.0], [89, 97, 1.0], [131, 139, 1.0]]}, "D19-6403": {"snippet": "...g', 'small') -and explore the impact of varying the training data on the learning behavior of a state-of-art system. In previous work, models have been shown to fail in generalizing to unseen adjectiv...", "weights": [[8, 13, 1.0], [138, 144, 1.0]]}, "D19-1165": {"snippet": "...gual models and one massively multilingual model for most language pairs, paving the way towards universal machine translation.", "weights": [[8, 14, 1.0], [61, 69, 1.0]]}, "2022.naacl-srw.6": {"snippet": "...s of Language Models\n\n\n Despite their outstanding performance, large language models (LLMs) suffer notorious flaws related to their preference for simple, surfacelevel textual relations over full sema...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [66, 71, 1.0], [72, 80, 1.0], [81, 87, 1.0]]}, "N16-2017": {"snippet": "...late language models trained on the target language and on the related language. In our second experiment, we select the sentences most similar to the target language and add them to our training corp...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [46, 54, 1.0], [74, 82, 1.0], [161, 169, 1.0]]}, "2003.wwwconf_conference-2003.75": {"snippet": "...ow a small number of enhancements to the XForms and XML Events vocabularies can facilitate coordination between clients and provide an appropriate level of control to applications. We also describe a ...", "weights": [[8, 13, 1.0], [94, 106, 1.0]]}, "2021.emnlp-main.800": {"snippet": "...gual Language Models to New Scripts\n\n\n Massively multilingual language models such as multilingual BERT offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. However, due ...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [65, 73, 1.0], [74, 80, 1.0]]}, "2021.mrl-1.3": {"snippet": "...gual language models based on clustering of monolingual segments. We show significant improvements over standard multilingual segmentation across nine languages on a question answering task, both in a...", "weights": [[8, 16, 1.0], [17, 23, 1.0]]}, "2020.emnlp-main.496": {"snippet": "...g of Large Language Models\n\n\n Large language models have recently achieved state of the art performance across a wide variety of natural language tasks. Meanwhile, the size of these models and their l...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 29, 1.0], [33, 38, 1.0], [39, 47, 1.0], [48, 54, 1.0], [140, 148, 1.0], [185, 191, 1.0]]}, "D07-1064": {"snippet": "...rget language and domain. To reduce the cost of training data construction, our method accepts training examples in which complete word-by-word alignment labels are missing, but instead only the bound...", "weights": [[8, 16, 1.0]]}, "2020.acl-main.314": {"snippet": "...ined Language Models via {M}onte-{C}arlo Methods\n\n\n It has been a common approach to pre-train a language model on a large corpus and finetune it on task-specific data. In practice, we observe that fi...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [100, 108, 1.0], [120, 125, 1.0]]}, "2014.tois_journal-ir0volumeA32A3.2": {"snippet": "...stic language models trained on Flickr and Twitter can be used to assign coordinates to Wikipedia articles. Our results show that exploiting these language models substantially outperforms both (i) cl...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [150, 158, 1.0], [159, 165, 1.0]]}, "2021.eacl-main.238": {"snippet": "...mely Small {BERT} Models from Mixed-Vocabulary Training\n\n\n Pretrained language models like BERT have achieved good results on NLP tasks, but are impractical on resource-limited devices due to memory f...", "weights": [[8, 13, 1.0], [21, 27, 1.0], [73, 81, 1.0], [82, 88, 1.0]]}, "2014.amta-researchers.17": {"snippet": "...ased language models for machine translation\n\n\n This paper presents two improvements of language models based on Restricted Boltzmann Machine (RBM) for large machine translation tasks. In contrast to ...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [91, 99, 1.0], [100, 106, 1.0], [155, 160, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.12": {"snippet": "...evel coordination is closely linked to multitasking behavior. The implicit cognitive level coordination is related to the task coordination process; this includes the evolution of information problem ...", "weights": [[8, 20, 1.0], [94, 106, 1.0], [130, 142, 1.0]]}, "2020.emnlp-main.394": {"snippet": "...main Language Model Pre-training\n\n\n Pre-training large language models has become a standard in the natural language processing community. Such models are pretrained on generic data (e.g. BookCorpus a...", "weights": [[8, 16, 1.0], [52, 57, 1.0], [58, 66, 1.0], [67, 73, 1.0], [111, 119, 1.0], [147, 153, 1.0]]}, "2020.clef_conference-2020w.77": {"snippet": "... two models: a Logistic Regression Model trained on a small dataset, and a Neural Network based model trained on the large dataset. These models achieved AUCs of 0.939 and 0.953 on the small and large...", "weights": [[8, 14, 1.0], [57, 62, 1.0], [120, 125, 1.0], [141, 147, 1.0], [188, 193, 1.0], [198, 203, 1.0]]}, "2021.wmt-1.55": {"snippet": "...2021 Large-Scale Multilingual Translation Task\n\n\n This paper presents the submission of Huawei Translation Services Center (HW-TSC) to the WMT 2021 Large-Scale Multilingual Translation Task. We partic...", "weights": [[8, 13, 1.0], [151, 156, 1.0]]}, "D19-6114": {"snippet": "...with large-scale language models (LM) has led to dramatic performance improvements across a broad range of natural language understanding tasks. However, the size and memory footprint of these large L...", "weights": [[8, 13, 1.0], [20, 28, 1.0], [29, 35, 1.0], [118, 126, 1.0], [196, 201, 1.0]]}, "D19-1287": {"snippet": "...ural Language Models: Coordination Phrase as a Case Study\n\n\n Neural language models have achieved stateof-the-art performances on many NLP tasks, and recently have been shown to learn a number of hier...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [25, 37, 1.0], [71, 79, 1.0], [80, 86, 1.0]]}, "2022.findings-acl.93": {"snippet": "...nced Language Model with Discourse Relations\n\n\n Large-scale pre-trained language models have demonstrated strong knowledge representation ability. However, recent studies suggest that even though thes...", "weights": [[8, 16, 1.0], [51, 56, 1.0], [75, 83, 1.0], [84, 90, 1.0]]}, "D12-1027": {"snippet": "...poor language to X by adapting a large bi-text for a related resource-rich language and X (the same target language). We assume a small bi-text for the resourcepoor language to X pair, which we use to...", "weights": [[8, 16, 1.0], [36, 41, 1.0], [78, 86, 1.0], [110, 118, 1.0], [133, 138, 1.0], [168, 176, 1.0]]}, "Q16-1031": {"snippet": "...rget language has a large treebank, a small treebank, or no treebank for training.", "weights": [[8, 16, 1.0], [23, 28, 1.0], [41, 46, 1.0]]}, "P13-2119": {"snippet": "...gram language models. Here, we explore the use of neural language models for data selection. We hypothesize that the continuous vector representation of words in neural language models makes them more...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [60, 68, 1.0], [69, 75, 1.0], [172, 180, 1.0], [181, 187, 1.0]]}, "2020.findings-emnlp.429": {"snippet": "... NER models is commonly performed in two steps: i) Pretraining a language model by self-supervised tasks on huge data and ii) fine-tune training with small labelled data. The success of the strategy d...", "weights": [[8, 14, 1.0], [68, 76, 1.0], [153, 158, 1.0]]}, "2021.blackboxnlp-1.27": {"snippet": "...s of language models to investigate the ability of pretrained vision-and-language models to handle negation. We find that these models severely underperform in the presence of negation.", "weights": [[8, 16, 1.0], [17, 23, 1.0], [76, 84, 1.0], [85, 91, 1.0], [131, 137, 1.0]]}, "2022.acl-long.396": {"snippet": "...as a small one may exclude the answer but a large one might introduce more noises. However, the existing retrieval is either heuristic or interwoven with the reasoning, causing reasoning on the partia...", "weights": [[8, 13, 1.0], [47, 52, 1.0]]}, "2020.pam-1.15": {"snippet": "...rmer models (BERT) while older RNN-based models were sensitive to them. (2) All models, both transformers and RNN-based models, are incapable of taking into account small relevant changes in the punct", "weights": [[8, 14, 1.0], [44, 50, 1.0], [83, 89, 1.0], [123, 129, 1.0], [168, 173, 1.0]]}, "2021.findings-acl.40": {"snippet": "...ping Small, Fast and Effective Pretrained Language Models for Domains\n\n\n Large pretrained models have achieved great success in many natural language processing tasks. However, when they are applied i...", "weights": [[8, 13, 1.0], [45, 53, 1.0], [54, 60, 1.0], [76, 81, 1.0], [93, 99, 1.0], [144, 152, 1.0]]}, "J16-2004": {"snippet": "...tion models from a resource-poor language POOR into a target language TGT by adapting and using a large bitext for a related resource-rich language RICH and the same target language TGT. We assume a s...", "weights": [[8, 14, 1.0], [36, 44, 1.0], [64, 72, 1.0], [101, 106, 1.0], [142, 150, 1.0], [176, 184, 1.0], [202, 207, 1.0]]}, "W18-3408": {"snippet": "...ly a Small Corpus\n\n\n Fine-tuning is a popular method to achieve better performance when only a small target corpus is available. However, it requires tuning of a number of metaparameters and thus it m...", "weights": [[8, 13, 1.0], [98, 103, 1.0]]}, "P98-2239": {"snippet": "...ased models are constructed within a window. It is found that proper MI-Trigger modeling is superior to word bigram model and the DD MI-Trigger models have better performance than the DI MI-Trigger mo...", "weights": [[8, 14, 1.0], [147, 153, 1.0], [201, 207, 1.0]]}, "2022.findings-acl.64": {"snippet": "...ined language models have been effective in many NLP tasks. However, these models are often huge and produce large sentence embeddings. Moreover, there is a big performance gap between large and small...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [78, 84, 1.0], [112, 117, 1.0], [188, 193, 1.0], [198, 203, 1.0]]}, "I13-1188": {"snippet": "...nd a large corpus. By restricting to a small subset of semantically strong patterns, i.e., coordinations, we improve results significantly. We show that the restriction to coordinations has several ad...", "weights": [[8, 13, 1.0], [42, 47, 1.0]]}, "2021.emnlp-main.749": {"snippet": "...d in Large Language Model: Towards Effective and Generalizable Fine-tuning\n\n\n Recent pretrained language models extend from millions to billions of parameters. Thus the need to fine-tune an extremely ...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [99, 107, 1.0], [108, 114, 1.0]]}, "2021.findings-acl.26": {"snippet": "...ural language processing tasks. In addition to models being pretrained on large datasets, they can be further trained on intermediate (supervised) tasks that are similar to the target task. For small ...", "weights": [[8, 16, 1.0], [50, 56, 1.0], [77, 82, 1.0], [197, 202, 1.0]]}, "D15-1043": {"snippet": "...ctic Language Models for Word Ordering\n\n\n Syntactic language models and N-gram language models have both been used in word ordering. In this paper, we give an empirical comparison between N-gram and s...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [55, 63, 1.0], [64, 70, 1.0], [82, 90, 1.0], [91, 97, 1.0]]}, "N10-3001": {"snippet": "... the coordination structure is exploited. Generating candidate sentences corresponding to different possible coordination structures and comparing them with a language model is employed to help determ...", "weights": [[8, 20, 1.0], [112, 124, 1.0], [162, 170, 1.0]]}, "2021.naacl-main.38": {"snippet": "...gual Language Models\n\n\n Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-theart performance in NLP. Still, it remains unclea...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [66, 74, 1.0], [75, 81, 1.0], [87, 92, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...ural language processing techniques. Different groups focused on different aspects of the overall problem, ranging from the development of language-independent tools such as stemmers to much work on l...", "weights": [[8, 16, 1.0], [142, 150, 1.0], [202, 210, 1.0]]}, "2021.acl-long.295": {"snippet": "...ller language models for which fine-tuning is computationally efficient. We present LM-BFF-better few-shot fine-tuning of language models 1 -a suite of simple and complementary techniques for finetuni...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [125, 133, 1.0], [134, 140, 1.0]]}, "2021.wmt-1.2": {"snippet": "... (i) SMALL-TASK1 (Central/South-Eastern European Languages), (ii) the SMALL-TASK2 (South East Asian Languages), and (iii) FULL-TASK (all 101 x 100 language pairs). All the tasks used the FLORES-101 da...", "weights": [[8, 13, 1.0], [73, 78, 1.0], [150, 158, 1.0]]}, "D18-1271": {"snippet": "...from large amounts of streaming data for endless language knowledge mining, which makes it promising to be a new paradigm for automatic language knowledge acquisition.", "weights": [[8, 13, 1.0], [52, 60, 1.0], [139, 147, 1.0]]}, "2021.wnut-1.24": {"snippet": "...in a small percentage of tweets, but Twitter removed the option to share these coordinates in mid-2019. Moreover, there is reason to suspect that a large share of the provided coordinates did not corr...", "weights": [[8, 13, 1.0], [151, 156, 1.0]]}, "2020.findings-emnlp.332": {"snippet": "...tion models on the LitCovid dataset, a growing collection of 23,000 research papers regarding the novel 2019 coronavirus. We find that pre-trained language models fine-tuned on this dataset outperform...", "weights": [[8, 14, 1.0], [150, 158, 1.0], [159, 165, 1.0]]}, "N19-1343": {"snippet": "...ocal Models for Coordinate Structure Parsing\n\n\n We propose a simple and accurate model for coordination boundary identification. Our model decomposes the task into three subtasks during training; find...", "weights": [[8, 14, 1.0], [94, 106, 1.0]]}, "W15-1805": {"snippet": "...eudo-coordination\n\n\n We present a case study on supervised classification of Swedish pseudocoordination (SPC). The classification is attempted on the type-level with data collected from two data sets:...", "weights": [[8, 20, 1.0]]}, "2021.naacl-main.341": {"snippet": "...ined Language Models\n\n\n Large-scale language models (LMs) pretrained on massive corpora of text, such as GPT-2, are powerful open-domain text generators. However, as our systematic examination reveals...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [27, 32, 1.0], [39, 47, 1.0], [48, 54, 1.0]]}, "2012.tist_journal-ir0volumeA3A2.3": {"snippet": "...n of large document corpora. In this article, we first argue that the Parallel Coordinates (PC) technique, which can map multidimensional vectors onto a 2D space in such a way that elements with simil...", "weights": [[8, 13, 1.0]]}, "2021.mrl-1.11": {"snippet": "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages\n\n\n Pretrained multilingual language models have been shown to work well on many l...", "weights": [[0, 5, 1.0], [75, 83, 1.0], [84, 90, 1.0], [146, 154, 1.0], [155, 161, 1.0]]}, "D19-5623": {"snippet": "...with Large Language Models\n\n\n Recently, large language models such as GPT-2 have shown themselves to be extremely adept at text generation and have also been able to achieve high-quality results in ma...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 29, 1.0], [43, 48, 1.0], [49, 57, 1.0], [58, 64, 1.0]]}, "2021.wnut-1.47": {"snippet": "...ased Language Models Improve Downstream Task Performances In Low-Resource And Noisy Language Scenarios?\n\n\n Recent impressive improvements in NLP, largely based on the success of contextual neural lang...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [87, 95, 1.0], [199, 207, 1.0]]}, "2020.findings-emnlp.285": {"snippet": "...ined Language Models for Low Resource Fine Tuning\n\n\n Pretrained Language Models (PLMs) have improved the performance of natural language understanding in recent years. Such models are pretrained on la...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [67, 75, 1.0], [76, 82, 1.0], [131, 139, 1.0], [176, 182, 1.0], [201, 206, 1.0]]}, "2022.naacl-main.223": {"snippet": "...ined Models for {A}frican News Translation\n\n\n Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mo...", "weights": [[8, 14, 1.0], [88, 96, 1.0], [97, 103, 1.0], [113, 118, 1.0], [157, 163, 1.0]]}, "2022.findings-acl.20": {"snippet": "...uned Models\n\n\n Several studies have investigated the reasons behind the effectiveness of fine-tuning, usually through the lens of probing. However, these studies often neglect the role of the size of ...", "weights": [[8, 14, 1.0]]}, "2012.tist_journal-ir0volumeA3A4.17": {"snippet": "... all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During performance, each ILR pr...", "weights": [[8, 20, 1.0], [115, 120, 1.0]]}, "2022.fl4nlp-1.2": {"snippet": "...s on small models, due to the serverclient communication and on-device computation bottlenecks. In this work, we leverage various techniques for mitigating these bottlenecks to train larger language m...", "weights": [[8, 13, 1.0], [14, 20, 1.0], [193, 201, 1.0], [202, 208, 1.0]]}, "2021.acl-short.108": {"snippet": "...ined Language Models through Adapters\n\n\n Transfer learning with large pretrained transformer-based language models like BERT has become a dominating approach for most NLP tasks. Simply fine-tuning tho...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [67, 72, 1.0], [102, 110, 1.0], [111, 117, 1.0]]}, "2020.findings-emnlp.41": {"snippet": "...tive Language Model Via Parameter-Efficient Transfer Learning\n\n\n Fine-tuning pre-trained generative language models to down-stream language generation tasks has shown promising results. However, this ...", "weights": [[8, 16, 1.0], [103, 111, 1.0], [112, 118, 1.0], [134, 142, 1.0]]}, "2020.semeval-1.177": {"snippet": "...ning large contemporary Transformer models, we train our own domain-specific embeddings and use them for downstream tasks. We also discuss how this technique provides comparable performance while maki...", "weights": [[8, 13, 1.0], [39, 45, 1.0]]}, "2021.findings-acl.325": {"snippet": "...very small sample of fine-grained examples, and English-language news text. We evaluate several baseline models, including zero-shot results from rule-based and questionanswering models, few-shot mode...", "weights": [[8, 13, 1.0], [59, 67, 1.0], [108, 114, 1.0], [182, 188, 1.0], [199, 205, 1.0]]}, "N04-4012": {"snippet": "...ural language and a small-screen graphical user interface. It adapts to the user's preferences and situation. Multimodal Functional Unification Grammar (MUG) is a unification-based formalism that uses...", "weights": [[8, 16, 1.0], [23, 28, 1.0]]}, "2020.acl-main.89": {"snippet": "...into Language Models\n\n\n Large pre-trained language models (LMs) are known to encode substantial amounts of linguistic information. However, high-level reasoning skills, such as numerical reasoning, ar...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [27, 32, 1.0], [45, 53, 1.0], [54, 60, 1.0]]}, "I11-1051": {"snippet": "...g of Coordination by Factoring Parallelism and Selectional Preferences\n\n\n We present a unified generative model of coordination that considers parallelism of conjuncts and selectional preferences. Par...", "weights": [[8, 20, 1.0], [118, 130, 1.0]]}, "2021.eacl-demos.33": {"snippet": "...ural Language Generation Models\n\n\n It is standard procedure these days to solve Information Extraction task by fine-tuning large pre-trained language models. This is not the case for generation task, ...", "weights": [[8, 16, 1.0], [28, 34, 1.0], [126, 131, 1.0], [144, 152, 1.0], [153, 159, 1.0]]}, "2021.wat-1.23": {"snippet": "...g on small in-domain corpora. Firstly, we observed that a small amount of pretraining followed by fine-tuning on small bilingual corpora can yield large gains over when pre-training is not used. Furth...", "weights": [[8, 13, 1.0], [61, 66, 1.0], [116, 121, 1.0], [150, 155, 1.0]]}, "D09-1078": {"snippet": "...tter Language Models\n\n\n The recent availability of large corpora for training N-gram language models has shown the utility of models of higher order than just trigrams. In this paper, we investigate m...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [54, 59, 1.0], [88, 96, 1.0], [97, 103, 1.0], [129, 135, 1.0]]}, "2022.naacl-industry.25": {"snippet": "...onal language impacts performance of pre-trained models on downstream dialogue tasks, we build compact Transformer-based Language Models from scratch on several large corpora of conversational data. W...", "weights": [[8, 16, 1.0], [52, 58, 1.0], [124, 132, 1.0], [133, 139, 1.0], [164, 169, 1.0]]}, "C12-1134": {"snippet": "...uent Coordination for Grammar Engineering\n\n\n This paper provides an explicit, formal analysis of two not only interesting but also frequent coordination phenomena: the coordination of unlike categorie...", "weights": [[8, 20, 1.0], [143, 155, 1.0], [171, 183, 1.0]]}, "2022.ecnlp-1.10": {"snippet": "...ars, large pretrained models have been used in dialogue systems to improve successful task completion rates. However, lack of reasoning capabilities of dialogue platforms make it difficult to provide ...", "weights": [[8, 13, 1.0], [25, 31, 1.0]]}}}
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23.805675913743, "doc_id": "2005.sigirconf_conference-2005.101"}, {"rank": 5, "score": 23.802357196774384, "doc_id": "2011.fire_workshop-2011.26"}, {"rank": 6, "score": 23.802357196774384, "doc_id": "2000.ipm_journal-ir0volumeA36A2.4"}, {"rank": 7, "score": 23.579123784449635, "doc_id": "2007.clef_workshop-2007.10"}, {"rank": 8, "score": 23.579123784449635, "doc_id": "2001.ntcir_workshop-2001.19"}, {"rank": 9, "score": 23.414817027414653, "doc_id": "P16-2080"}, {"rank": 10, "score": 23.360038711883842, "doc_id": "2011.irfc_conference-2011.4"}]}], "docs": {"2010.airs_conference-2010.17": {"snippet": "... for Cross Lingual Information Retrieval\n\n\n Abstract. The performance of transliteration from a source language to a target language builds the ground work in support of proper name Cross Lingual Info...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [185, 190, 1.0], [191, 198, 1.0], [199, 210, 1.0]]}, "2020.findings-emnlp.249": {"snippet": "Cross-Lingual Training of Neural Models for Document Ranking\n\n\n We tackle the challenge of cross-lingual training of neural document ranking models for mono-lingual retrieval, specifically leveraging ...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [91, 96, 1.0], [97, 104, 1.0], [157, 164, 1.0], [165, 174, 1.0]]}, "P19-3004": {"snippet": "...urce Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage\n\n\n With the increasing democratization of electronic media, vast information resources are available ...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [37, 48, 1.0], [49, 58, 1.0], [167, 178, 1.0]]}, "2004.clef_workshop-2004.12": {"snippet": "... for Cross-Lingual Retrieval", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 31, 1.0]]}, "2021.acl-long.226": {"snippet": "...ight Cross-Lingual Sentence Representation Learning\n\n\n Large-scale models for learning fixeddimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significa...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [107, 112, 1.0], [113, 120, 1.0]]}, "2004.sigirjournals_journal-ir0volumeA38A1.1": {"snippet": "Cross-lingual information retrieval (CLIR) task at the NTCIR workshop 3", "weights": [[0, 5, 1.0], [6, 13, 1.0], [14, 25, 1.0], [26, 35, 1.0]]}, "P19-1227": {"snippet": "...gate cross-lingual OpenQA. In this paper, we construct a novel dataset XQA for cross-lingual OpenQA research. It consists of a training set in English as well as development and test sets in eight oth...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [82, 87, 1.0], [88, 95, 1.0]]}, "I08-6012": {"snippet": "Cross-Lingual Information Retrieval System for {I}ndian Languages\n\n\n This paper describes our first participation in the Indian language sub-task of the main Adhoc monolingual and bilingual track in C...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [14, 25, 1.0], [26, 35, 1.0]]}, "2020.clssts-1.4": {"snippet": "...tive Retrieval\n\n\n We describe an approach to cross lingual information retrieval that does not rely on explicit translation of either document or query terms. Instead, both queries and documents are m...", "weights": [[8, 17, 1.0], [48, 53, 1.0], [54, 61, 1.0], [62, 73, 1.0], [74, 83, 1.0]]}, "2020.clssts-1.6": {"snippet": "... hoc Cross-Lingual Document Retrieval\n\n\n We address the problem of linking related documents across languages in a multilingual collection. We evaluate three diverse unsupervised methods to represent ...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [31, 40, 1.0]]}, "2007.trec_conference-2007.10": {"snippet": "Cross Language Information Retrieval for Biomedical Literature\n\n\n This workshop report discusses the collaborative work of UT, EMC and TNO on the TREC Genomics Track 2007. The biomedical information r...", "weights": [[0, 5, 1.0], [15, 26, 1.0], [27, 36, 1.0], [187, 198, 1.0], [199, 208, 1.0]]}, "2020.clssts-1.10": {"snippet": "...t to cross-lingual text retrieval are quite good relative to the task of cross-lingual speech retrieval. Overall we find that the task of MMCLIR and specifically cross-lingual speech retrieval is quit...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [27, 36, 1.0], [76, 81, 1.0], [82, 89, 1.0], [97, 106, 1.0], [165, 170, 1.0], [171, 178, 1.0], [186, 195, 1.0]]}, "2012.wwwconf_conference-2012.47": {"snippet": "Cross-lingual knowledge linking across wiki knowledge bases", "weights": [[0, 5, 1.0], [6, 13, 1.0]]}, "2021.ecnlp-1.14": {"snippet": "...gual Information Retrieval\n\n\n In this paper, we explored different levels of textual representations for cross-lingual information retrieval. Beyond the traditional token level representation, we adop...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [108, 113, 1.0], [114, 121, 1.0], [122, 133, 1.0], [134, 143, 1.0]]}, "L14-1740": {"snippet": "... for Cross-Lingual Information Retrieval in the Medical Domain", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2020.coling-main.323": {"snippet": "Cross-Lingual Document Retrieval with Smooth Learning\n\n\n Cross-lingual document search is an information retrieval task in which the queries' language differs from the documents' language. In this pap...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [23, 32, 1.0], [57, 62, 1.0], [63, 70, 1.0], [93, 104, 1.0], [105, 114, 1.0]]}, "2001.trec_conference-2001.54": {"snippet": "...2001 Cross-lingual Retrieval at BBN", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 31, 1.0]]}, "2011.fire_workshop-2011.26": {"snippet": "...lish Cross Lingual Information Retrieval", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "N19-1380": {"snippet": "...rent cross-lingual transfer methods: (1) translating the training data, (2) using cross-lingual pre-trained embeddings, and (3) a novel method of using a multilingual machine translation encoder as co...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [85, 90, 1.0], [91, 98, 1.0]]}, "2000.trec_conference-2000.58": {"snippet": "...EC-9 Cross-lingual Retrieval at BBN", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 31, 1.0]]}, "2005.sigirconf_conference-2005.101": {"snippet": "...y of cross lingual information retrieval using on-line translation systems\n\n\n ABSTRACTTypical cross language retrieval requires special linguistic resources, such as bilingual dictionaries and paralle...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [97, 102, 1.0], [112, 121, 1.0]]}, "C12-1057": {"snippet": "Cross-Lingual Topical Relevance Models\n\n\n Cross-lingual relevance modelling (CLRLM) is a state-of-the-art technique for cross-lingual information retrieval (CLIR) which integrates query term disambigu...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [42, 47, 1.0], [48, 55, 1.0], [120, 125, 1.0], [126, 133, 1.0], [134, 145, 1.0], [146, 155, 1.0]]}, "2021.naacl-main.285": {"snippet": "Cross-lingual Cross-modal Pretraining for Multimodal Retrieval\n\n\n Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. Their success...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [14, 19, 1.0], [53, 62, 1.0], [147, 152, 1.0], [159, 168, 1.0]]}, "2021.acl-long.350": {"snippet": "... for Cross-lingual Transfer\n\n\n In recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transf...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [183, 188, 1.0], [189, 196, 1.0]]}, "2020.emnlp-main.340": {"snippet": "... for Cross-Lingual Information Retrieval\n\n\n We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically f...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [142, 147, 1.0], [148, 155, 1.0], [156, 167, 1.0], [168, 177, 1.0]]}, "2021.wwwconf_conference-2021.91": {"snippet": "Cross-lingual Language Model Pretraining for Retrieval", "weights": [[0, 5, 1.0], [6, 13, 1.0], [45, 54, 1.0]]}, "2004.clef_workshop-2004w.54": {"snippet": "... for cross-lingual information retrieval. The performance of cross-lingual information retrieval was poor and that of monolingual information retrieval was not good. We need to check our modules and p", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [64, 69, 1.0], [70, 77, 1.0], [78, 89, 1.0], [90, 99, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2017.sigirconf_conference-2017.252": {"snippet": "...many information retrieval (IR) tasks. It has been shown that heterogeneous information in knowledge graphs and entity annotations can help to signi cantly improve information retrieval tasks. In part...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [79, 90, 1.0], [167, 178, 1.0], [179, 188, 1.0]]}, "2008.amta-papers.8": {"snippet": "... for cross-lingual patent retrieval, which can be used to evaluate the contribution of machine translation to retrieving patent documents across languages. This paper describes our test collection, me...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [29, 38, 1.0]]}, "2008.ntcir_workshop-2008.26": {"snippet": "...m of Cross-lingual Information Retrieval of ICT in NTCIR-7\n\n\n IR4QA is a new task in NTCIR-7, which intends to evaluate which IR techniques are more helpful to a QA system. This paper describes in det...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2008.ntcir_workshop-2008.29": {"snippet": "...in a Cross-Language Patent Access System\n\n\n In this paper, a cross-language patent retrieval and classification system is presented to integrate the query translation using various free web translator...", "weights": [[8, 13, 1.0], [64, 69, 1.0], [86, 95, 1.0]]}, "2011.irfc_conference-2011.4": {"snippet": "...abic Cross-Lingual Retrieval Using Contextual Information", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 31, 1.0], [49, 60, 1.0]]}, "2001.clef_workshop-2000w.8": {"snippet": "Cross-Language Information Retrieval using Dutch Query Translation\n\n\n This paper describes a hastily carried out, rather limited bilingual information retrieval experiment. The experiment takes Dutch ...", "weights": [[0, 5, 1.0], [15, 26, 1.0], [27, 36, 1.0], [139, 150, 1.0], [151, 160, 1.0]]}, "2016.clef_conference-2016.5": {"snippet": "... for Cross-Lingual Information Retrieval\n\n\n Abstract. Machine Translation (MT) systems employed to translate queries for Cross-Lingual Information Retrieval typically produce a single translation with...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [124, 129, 1.0], [130, 137, 1.0], [138, 149, 1.0], [150, 159, 1.0]]}, "R13-1063": {"snippet": "...ulti-lingual systems in order to overcome crucial issues like cross-language and cross-collection retrieval. Our proposal is a system which improves capabilities of language-technology-based informati...", "weights": [[8, 15, 1.0], [65, 70, 1.0], [84, 89, 1.0], [101, 110, 1.0], [194, 205, 1.0]]}, "2013.ecir_conference-2013.106": {"snippet": "...s in cross-lingual information retrieval models.Keywords: Probabilistic topic models, Cross-lingual retrieval, Ranking models, Cross-lingual text mining.\nGoals and ScopeThis half day tutorial presents...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [89, 94, 1.0], [95, 102, 1.0], [103, 112, 1.0], [130, 135, 1.0], [136, 143, 1.0]]}, "2016.sigirconf_conference-2016.230": {"snippet": "...ical information search refers to methodologies and technologies that seek to improve access to medical information archives via a process of information retrieval (IR). Such information is now potent...", "weights": [[8, 19, 1.0], [107, 118, 1.0], [145, 156, 1.0], [157, 166, 1.0], [178, 189, 1.0]]}, "2013.ecir_conference-2013.9": {"snippet": "... is, cross-lingual topics. We show how to integrate the topical knowledge into a unified relevance modeling framework in order to build quality retrieval models in monolingual and cross-lingual contex...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [147, 156, 1.0], [183, 188, 1.0], [189, 196, 1.0]]}, "N18-2073": {"snippet": "Cross-Lingual Learning-to-Rank with Shared Representations\n\n\n Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that ...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [62, 67, 1.0], [68, 75, 1.0], [76, 87, 1.0], [88, 97, 1.0], [119, 128, 1.0]]}, "P14-2080": {"snippet": "... for Cross-Language Retrieval\n\n\n We present an approach to cross-language retrieval that combines dense knowledgebased features and sparse word translations. Both feature types are learned directly fr...", "weights": [[8, 13, 1.0], [23, 32, 1.0], [62, 67, 1.0], [77, 86, 1.0]]}, "C08-1030": {"snippet": "...ting Cross-Language Category Hierarchies\n\n\n This paper presents a method of retrieving bilingual collocations of a verb and its objective noun from cross-lingual documents with similar contents. Relev...", "weights": [[8, 13, 1.0], [151, 156, 1.0], [157, 164, 1.0]]}, "1998.sigirconf_conference-98.10": {"snippet": "Cross-Language Information Retrieval with the UMLS Metathesaurus\n\n\n AbstractWe investigate an automatic method for Cross Language Information Retrieval (CLIR) that utilizes the multilingual UMLS Metat...", "weights": [[0, 5, 1.0], [15, 26, 1.0], [27, 36, 1.0], [115, 120, 1.0], [130, 141, 1.0], [142, 151, 1.0]]}, "1999.ntcir_workshop-1999.17": {"snippet": "...dhoc retrieval and cross-lingual task. This paper explains the systems. key words 2-poisson model, adhoc retrieval, cross lingual retrieval G\u00b0\u00b1\u00ea+HKs\u00de\u00f02 \u2022\u00d9 \u00c0 U4 \u00f5\u00e3 1\u00f9 mE \u00bf5 d \u00be\u00eeM \u00f5\u00af\u00fd'\u00f5S '\u00bf\u00d6\u00f5 m 651-2401", "weights": [[8, 17, 1.0], [22, 27, 1.0], [28, 35, 1.0], [108, 117, 1.0], [119, 124, 1.0], [125, 132, 1.0], [133, 142, 1.0]]}, "W16-2348": {"snippet": "Cross-lingual Pronoun Prediction with Linguistically Informed Features\n\n\n We present the LIMSI's cross-lingual pronoun prediction system for the WMT 2016 shared task. We use high-level linguistic feat...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [97, 102, 1.0], [103, 110, 1.0]]}, "2018.ecir_conference-2018.30": {"snippet": "Cross-Lingual Document Retrieval Using Regularized Wasserstein Distance\n\n\n Abstract. Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare obje...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [23, 32, 1.0], [90, 101, 1.0], [102, 111, 1.0]]}, "P19-1306": {"snippet": "...urce Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations\n\n\n In this paper, we propose to boost lowresource cross-lingual document retrieval performance with deep bilingua...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [31, 40, 1.0], [140, 145, 1.0], [146, 153, 1.0], [163, 172, 1.0]]}, "2022.acl-long.288": {"snippet": "Cross-Lingual Phrase Retrieval\n\n\n Cross-lingual retrieval aims to retrieve relevant text across languages. Current methods typically achieve cross-lingual retrieval by learning language-agnostic text ...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [21, 30, 1.0], [34, 39, 1.0], [40, 47, 1.0], [48, 57, 1.0], [141, 146, 1.0], [147, 154, 1.0], [155, 164, 1.0]]}, "P16-2080": {"snippet": "Cross-Lingual Word Representations via Spectral Graph Embeddings\n\n\n Cross-lingual word embeddings are used for cross-lingual information retrieval or domain adaptations. In this paper, we extend Eigen...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [68, 73, 1.0], [74, 81, 1.0], [111, 116, 1.0], [117, 124, 1.0], [125, 136, 1.0], [137, 146, 1.0]]}, "2005.jasis_journal-ir0volumeA56A2.2": {"snippet": "...eric cross-lingual retrieval of documents and automated summaries", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 31, 1.0]]}, "2000.ipm_journal-ir0volumeA36A2.4": {"snippet": "...ting cross-lingual information retrieval", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "L04-1338": {"snippet": "...ased Cross-Lingual Information Retrieval in the Medical Domain", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2018.sigirconf_conference-2018.184": {"snippet": "...ised Cross-Lingual Information Retrieval Using Monolingual Data Only\n\n\n ABSTRACTWe propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no biling...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [136, 141, 1.0], [142, 149, 1.0], [150, 161, 1.0], [162, 171, 1.0]]}, "2020.clssts-1.8": {"snippet": "...BBN} Cross-lingual Information Retrieval System\n\n\n In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents ...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [83, 88, 1.0], [89, 96, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "C12-2057": {"snippet": "... for Cross-Language Information Retrieval\n\n\n This paper introduces a cross-language information retrieval (CLIR) framework that combines the state-of-the-art keyword-based approach with a latent seman...", "weights": [[8, 13, 1.0], [23, 34, 1.0], [35, 44, 1.0], [72, 77, 1.0], [87, 98, 1.0], [99, 108, 1.0]]}, "2015.ipm_journal-ir0volumeA51A1.6": {"snippet": "... (1) cross-lingual event-centered news clustering, (2) cross-lingual document classification, (3) cross-lingual semantic similarity, and (4) cross-lingual information retrieval. We also briefly review...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [58, 63, 1.0], [64, 71, 1.0], [101, 106, 1.0], [107, 114, 1.0], [144, 149, 1.0], [150, 157, 1.0], [158, 169, 1.0], [170, 179, 1.0]]}, "2020.sigirconf_conference-2020.353": {"snippet": "...ion, cross-lingual information retrieval, and document classification, as well as learning cross-lingual representations. Here, we describe the end-to-end process of searching the web for parallel cro...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [94, 99, 1.0], [100, 107, 1.0], [200, 205, 1.0]]}, "2002.trec_conference-2002.68": {"snippet": "...2002 Cross-lingual Retrieval at BBN", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 31, 1.0]]}, "2008.ntcir_workshop-2008.59": {"snippet": "... for cross-lingual patent retrieval, which can be used to evaluate the contribution of machine translation to retrieving patent documents across languages. This paper describes our test collection, me...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [29, 38, 1.0]]}, "D19-6129": {"snippet": "...-hoc Cross-lingual Information Retrieval\n\n\n We propose a weakly supervised neural model for Ad-hoc Cross-lingual Information Retrieval (CLIR) from low-resource languages. Low resource languages often ...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [102, 107, 1.0], [108, 115, 1.0], [116, 127, 1.0], [128, 137, 1.0]]}, "2007.clef_workshop-2007w.23": {"snippet": "Cross-Lingual Information Retrieval System for Indian Languages\n\n\n This paper describes our first participation in the Indian language sub-task of the main Adhoc monolingual and bilingual track in CLE...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [14, 25, 1.0], [26, 35, 1.0]]}, "2007.ntcir_workshop-2007.6": {"snippet": "... and Cross-Lingual Information Retrieval", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2002.sigirconf_conference-2002.24": {"snippet": "Cross-lingual relevance models\n\n\n ABSTRACTWe propose a formal model of Cross-Language Information Retrieval that does not rely on either query translation or document translation. Our approach leverag...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [71, 76, 1.0], [86, 97, 1.0], [98, 107, 1.0]]}, "2019.ecir_conference-20191.34": {"snippet": "... for Cross-Lingual Sentence Retrieval Using Bidirectional Attention Model\n\n\n Abstract. We present a neural architecture for cross-lingual mate sentence retrieval which encodes sentences in a joint mul...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [31, 40, 1.0], [127, 132, 1.0], [133, 140, 1.0], [155, 164, 1.0]]}, "A97-1049": {"snippet": "...gual Information Browsing and Retrieval System Using Information Extraction\n\n\n In this paper, we describe our multilingual (or cross-linguistic) information browsing and retrieval system, which is aim...", "weights": [[8, 19, 1.0], [33, 42, 1.0], [56, 67, 1.0], [130, 135, 1.0], [148, 159, 1.0], [173, 182, 1.0]]}, "2008.sigirconf_conference-2008.190": {"snippet": "Cross-lingual search over 22 european languages\n\n\n ABSTRACTIn this paper we present a system for cross-lingual information retrieval, which can handle tens of languages and millions of documents. Func...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [97, 102, 1.0], [103, 110, 1.0], [111, 122, 1.0], [123, 132, 1.0]]}, "2017.sigirconf_conference-2017.141": {"snippet": "... for Cross-Language Information Retrieval\n\n\n ABSTRACTOnline learning to rank for information retrieval has shown great promise in optimization of Web search results based on user interactions. However...", "weights": [[8, 13, 1.0], [23, 34, 1.0], [35, 44, 1.0], [84, 95, 1.0], [96, 105, 1.0]]}, "N16-1072": {"snippet": "Cross-lingual Wikification Using Multilingual Embeddings\n\n\n Cross-lingual Wikification is the task of grounding mentions written in non-English documents to entries in the English Wikipedia. This task...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [60, 65, 1.0], [66, 73, 1.0]]}, "E17-2065": {"snippet": "Cross-Lingual Syntactically Informed Distributed Word Representations\n\n\n We develop a novel cross-lingual word representation model which injects syntactic information through dependencybased contexts...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [92, 97, 1.0], [98, 105, 1.0], [156, 167, 1.0]]}, "2008.clef_workshop-2008w.159": {"snippet": "... for Cross-lingual Information Retrieval using only Wikipedia\n\n\n This paper presents WikiTranslate, a system which performs query translation for cross-lingual information retrieval (CLIR) using only ...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [149, 154, 1.0], [155, 162, 1.0], [163, 174, 1.0], [175, 184, 1.0]]}, "2007.mtsummit-tutorials.2": {"snippet": "... for cross-lingual information retrieval\n\n\n Cross-lingual retrieval is a key issue in the development of current search engines, either in the web or in knowledge management systems (like for example ...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [47, 52, 1.0], [53, 60, 1.0], [61, 70, 1.0]]}, "2021.naacl-main.46": {"snippet": "...both information scarcity-where languages have few reference articles-and information asymmetry-where questions reference concepts from other cultures. This work extends open-retrieval question answer...", "weights": [[8, 19, 1.0], [77, 88, 1.0], [178, 187, 1.0]]}, "L18-1616": {"snippet": "...s\n\n\n Cross-lingual word embeddings are the representations of words across languages in a shared continuous vector space. Cross-lingual word embeddings have been shown to be helpful in the development...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [125, 130, 1.0], [131, 138, 1.0]]}, "W00-1312": {"snippet": "Cross-lingual Information Retrieval Using Hidden {M}arkov Models\n\n\n This paper presents empirical results in cross-lingual information retrieval using English queries to access Chinese documents (TREC...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [14, 25, 1.0], [26, 35, 1.0], [109, 114, 1.0], [115, 122, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2021.ecir_conference-20211.23": {"snippet": "... and cross-lingual representation learning and transfer, rendering cross-lingual word embeddings (CLWEs) effectively obsolete. However, questions remain to which extent this finding generalizes 1) to ...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [70, 75, 1.0], [76, 83, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [42, 53, 1.0], [54, 63, 1.0], [92, 103, 1.0], [104, 113, 1.0], [116, 121, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "2007.clef_workshop-2007.10": {"snippet": "Cross-Lingual Information Retrieval System for Indian Languages", "weights": [[0, 5, 1.0], [6, 13, 1.0], [14, 25, 1.0], [26, 35, 1.0]]}, "W19-6602": {"snippet": "... for Cross-Lingual Information Retrieval in Low-Resource Settings\n\n\n The goal of cross-lingual information retrieval (CLIR) is to find relevant documents written in languages different from that of th...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [84, 89, 1.0], [90, 97, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "2014.wsdm_conference-2014.74": {"snippet": "...ains cross-lingual information retrieval starting from simpler query likelihood models  and leading to more elaborate retrieval models which combine cross-lingual topical knowledge with the relevance ...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [121, 130, 1.0], [152, 157, 1.0], [158, 165, 1.0]]}, "2006.clef_workshop-2006.30": {"snippet": "...s in Cross-Lingual Keyword-Based Image Retrieval", "weights": [[8, 13, 1.0], [14, 21, 1.0], [42, 51, 1.0]]}, "W17-1313": {"snippet": "... for Cross-lingual {A}rabic-to-{E}nglish User-generated Speech Search\n\n\n Cross Language Information Retrieval (CLIR) systems are a valuable tool to enable speakers of one language to search for conten...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [76, 81, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "2007.sigirconf_conference-2007.61": {"snippet": "...t to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages. This is very important to scenarios of cross-language information re...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [174, 179, 1.0], [189, 200, 1.0], [201, 210, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...only retrieval.In the third and last research theme we propose a cross-lingual framework for monolingual biomedical IR. In this framework, the integration of a concept-based representation is viewed a...", "weights": [[8, 17, 1.0], [68, 73, 1.0], [74, 81, 1.0]]}, "2020.clssts-1.5": {"snippet": "Cross-lingual Information Retrieval with {BERT}\n\n\n Multiple neural language models have been developed recently, e.g., BERT and XLNet, and achieved impressive results in various NLP tasks including se...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [14, 25, 1.0], [26, 35, 1.0]]}, "2022.naacl-main.329": {"snippet": "...ning Cross-Lingual {IR} from an {E}nglish Retriever\n\n\n We present DR.DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual Representation), a new cross-lingual information retrieval (CLIR) sy...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [84, 93, 1.0], [121, 126, 1.0], [127, 134, 1.0], [158, 163, 1.0], [164, 171, 1.0], [172, 183, 1.0], [184, 193, 1.0]]}, "2018.cikm_conference-2018.178": {"snippet": "...ding cross-lingual information retrieval and cross-lingual text classification. To compute similarity, we focus on neural machine translation models and examine the utility of their intermediate state...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [48, 53, 1.0], [54, 61, 1.0]]}, "N15-1123": {"snippet": "... for Cross-Lingual Information Retrieval\n\n\n Current approaches to cross-lingual information retrieval (CLIR) rely on standard retrieval models into which query translations by statistical machine tran...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [69, 74, 1.0], [75, 82, 1.0], [83, 94, 1.0], [95, 104, 1.0], [129, 138, 1.0]]}, "2003.clef_workshop-2003.14": {"snippet": "...s to Cross-Lingual Text Retrieval", "weights": [[8, 13, 1.0], [14, 21, 1.0], [27, 36, 1.0]]}, "2012.airs_conference-2012.0": {"snippet": "Information Retrieval Technology, 8th Asia Information Retrieval Societies Conference, AIRS 2012, Tianjin, China, December 17-19, 2012. Proceedings\n\n\n The annual AIRS conference has established its re...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [43, 54, 1.0], [55, 64, 1.0]]}, "2001.ntcir_workshop-2001.19": {"snippet": "...lish Cross-lingual Information Retrieval at DLUT", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2020.acl-main.613": {"snippet": "... for Cross-Lingual Information Retrieval in the Medical Domain\n\n\n We present a thorough comparison of two principal approaches to Cross-Lingual Information Retrieval: document translation (DT) and que...", "weights": [[8, 13, 1.0], [14, 21, 1.0], [22, 33, 1.0], [34, 43, 1.0], [133, 138, 1.0], [139, 146, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "W07-0909": {"snippet": "Cross Lingual and Semantic Retrieval for Cultural Heritage Appreciation\n\n\n We describe a system which enhances the experience of museum visits by providing users with language-technology-based informa...", "weights": [[0, 5, 1.0], [6, 13, 1.0], [27, 36, 1.0], [193, 204, 1.0]]}, "2010.cikm_conference-2010.21": {"snippet": "A cross-lingual framework for monolingual biomedical information retrieval\n\n\n ABSTRACTAn important challenge for biomedical information retrieval (IR) is dealing with the complex, inconsistent and amb...", "weights": [[2, 7, 1.0], [8, 15, 1.0], [53, 64, 1.0], [65, 74, 1.0], [124, 135, 1.0], [136, 145, 1.0]]}}}
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"...work/graph, knowledge graph embedding, knowledge base completion/population, link prediction; knowledge-based systems, probabilistic knowledge bases, graph databases. (3) Knowledge Base-related Applic...", "weights": [[8, 13, 1.0], [15, 24, 1.0], [25, 30, 1.0], [42, 51, 1.0], [97, 106, 1.0], [136, 145, 1.0], [153, 158, 1.0], [174, 183, 1.0]]}, "2020.findings-emnlp.207": {"snippet": "...onal knowledge representation learning (KRL) methods usually treat a single triple as a training unit, neglecting the usage of graph contextualized knowledge. To utilize these unexploited graph-level ...", "weights": [[8, 17, 1.0], [130, 135, 1.0], [151, 160, 1.0], [191, 196, 1.0]]}, "2021.naacl-demos.8": {"snippet": "...ture Knowledge Graph Construction and Drug Repurposing Report Generation\n\n\n To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientifi...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0], [181, 190, 1.0]]}, "2020.ecir_conference-20201.10": {"snippet": "... and knowledge graph entity embeddings to address the issues of vocabulary mismatch in ad-hoc document retrieval and knowledge graph noisiness and incompleteness, respectively, have been reported in r...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [120, 129, 1.0], [130, 135, 1.0]]}, "2020.ecir_conference-20201.2": {"snippet": "...phs. Knowledge graph embedding has been an active research area for knowledge graph completion, with great improvement from the early TransE to the current state-of-the-art ConvKB. ConvKB considers a ...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [71, 80, 1.0], [81, 86, 1.0]]}, "2016.ictir_conference-2016.2": {"snippet": "...in a knowledge graph and how to leverage each most effectively.We start the tutorial with a brief overview of different types of knowledge bases, their structure and information contained in popular g...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [132, 141, 1.0]]}, "2019.cikm_workshop-2019kars.1": {"snippet": "...rful graph for news recommendations. Thus we propose an enhanced knowledge graph called news graph. Compared with a generic knowledge graph, the news graph is enhanced from three aspects: (1) adding a...", "weights": [[8, 13, 1.0], [68, 77, 1.0], [78, 83, 1.0], [96, 101, 1.0], [127, 136, 1.0], [137, 142, 1.0], [153, 158, 1.0]]}, "2021.semeval-1.57": {"snippet": "...ding Knowledge Graph for {NLP} Research\n\n\n Research in Natural Language Processing is making rapid advances, resulting in the publication of a large number of research papers. Finding relevant researc...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [29, 32, 1.0]]}, "2020.cikm_conference-2020.325": {"snippet": "...main Knowledge Graph Construction and Application in E-commerce", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0]]}, "2020.emnlp-main.460": {"snippet": "Knowledge Association with Hyperbolic Knowledge Graph Embeddings\n\n\n Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits N...", "weights": [[0, 9, 1.0], [38, 47, 1.0], [48, 53, 1.0], [95, 104, 1.0], [199, 202, 1.0]]}, "D15-1061": {"snippet": "...ming Knowledge Graph Sparsity\n\n\n Automatic construction of knowledge graphs (KGs) from unstructured text has received considerable attention in recent research, resulting in the construction of severa...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [46, 58, 1.0], [62, 71, 1.0], [181, 193, 1.0]]}, "2020.wwwjournals_journal-ir0volumeA23A2.3": {"snippet": "...main knowledge graph construction", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0]]}, "C18-2011": {"snippet": "...zing Graph Measure to Deduce Omitted Entities in Paragraphs\n\n\n This demo deals with the problem of capturing omitted arguments in relation extraction given a proper knowledge base for entities of inte...", "weights": [[8, 13, 1.0], [168, 177, 1.0]]}, "2018.tweb_journal-ir0volumeA12A2.0": {"snippet": "...hole graph.Recently, many research works have been conducted to project a knowledge graph into a continuous vector space, called knowledge graph embedding, to tackle these problems. Generally, knowled...", "weights": [[8, 13, 1.0], [77, 86, 1.0], [87, 92, 1.0], [132, 141, 1.0], [142, 147, 1.0], [196, 205, 1.0]]}, "2019.wwwconf_conference-2019c.117": {"snippet": "...p on Knowledge Graph Technology and Applications", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "C16-1201": {"snippet": "...from knowledge graph, which provides ground truth such as attributes and properties of entities and encodes valuable relations between entities. Specifically, we propose a joint model to combine knowl...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [198, 207, 1.0]]}, "2019.wwwconf_conference-2019c.118": {"snippet": "...p on Knowledge Graph Technology and Applications", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "2017.sigirconf_conference-2017.252": {"snippet": "... to) knowledge graph population and semantic resource construction with a special focus on enabling IR-related techniques and applications. Examples include domain/task-speci c knowledge graph constru...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [57, 69, 1.0], [180, 189, 1.0], [190, 195, 1.0], [196, 208, 1.0]]}, "D19-5302": {"snippet": "...s\n\n\n Knowledge graphs (KGs) are generally used for various NLP tasks. However, as KGs still miss some information, it is necessary to develop Knowledge Graph Completion (KGC) methods. Most KGC researc...", "weights": [[8, 17, 1.0], [62, 65, 1.0], [145, 154, 1.0], [155, 160, 1.0]]}, "2018.sigirconf_conference-2018.234": {"snippet": "...s of knowledge graphs, semantics, and IR. The research on knowledge acquisition and alignment often overlooks their applications in text-centric information retrieval systems. Similarly, in IR researc...", "weights": [[8, 17, 1.0], [61, 70, 1.0]]}, "D12-1098": {"snippet": "...mate Graph Construction for {NLP}\n\n\n Many natural language processing problems involve constructing large nearest-neighbor graphs. We propose a system called FLAG to construct such graphs approximatel...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [32, 35, 1.0]]}, "2015.wwwconf_conference-2015c.23": {"snippet": "...rchy Construction for Freebase\n\n\n ABSTRACTThe hierarchical structure of a knowledge base system can lead to various valuable applications; however, many knowledge base systems do not have such propert...", "weights": [[8, 20, 1.0], [77, 86, 1.0], [156, 165, 1.0]]}, "2020.emnlp-main.90": {"snippet": "...ring Knowledge Graph\n\n\n Previous works on knowledge-to-text generation take as input a few RDF triples or keyvalue pairs conveying the knowledge of some entities to generate a natural language descrip...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [45, 54, 1.0], [138, 147, 1.0]]}, "2017.sigirconf_conference-2017.17": {"snippet": "...h as knowledge graphs link related concepts within a domain. In this paper, we explore a multi-layer extension to knowledge graphs, hierarchical knowledge graphs (HKGs), that combines hierarchical and...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [148, 157, 1.0]]}, "2016.cikm_conference-2016.273": {"snippet": "...stic Knowledge Graph Construction: Compositional and Incremental Approaches", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0]]}, "R19-1057": {"snippet": "...s of knowledge encoded in wordnets. As the use of wordnets becomes extensively widespread, extending the existing ones gets more attention. Manual construction and extension of lexical semantic relati...", "weights": [[8, 17, 1.0], [150, 162, 1.0]]}, "2021.wwwconf_conference-2021.153": {"snippet": "...ient Knowledge Graph Completion", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "2018.sigirconf_workshop-2018profs.11": {"snippet": "...s\n\n\n Knowledge graphs have shown to be effective in improving information retrieval effectiveness, in particular together with entity linking [3, 6, 13, 10], which sets a new standard for the Robust 2...", "weights": [[8, 17, 1.0]]}, "N18-5004": {"snippet": "... for knowledge graph construction. As an additional novel initiative, CL Scholar supports more than 1200 scholarly natural language queries along with standard keywordbased search on constructed knowl...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0], [198, 207, 1.0]]}, "2020.emnlp-main.722": {"snippet": "...onal knowledge underlying the text. This is fulfilled by using a linked knowledge graph to select informative entities and then masking their mentions. In addition, we use knowledge graphs to obtain d...", "weights": [[8, 17, 1.0], [75, 84, 1.0], [85, 90, 1.0], [175, 184, 1.0]]}, "2020.coling-main.397": {"snippet": "...ning knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training sentence pairs, m...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [89, 98, 1.0], [105, 116, 1.0]]}, "L18-1385": {"snippet": "...rom {NLP} Frameworks for Automatic Knowledge Base Construction\n\n\n We present LODeXporter, a novel approach for exporting Natural Language Processing (NLP) results to a graph-based knowledge base, foll...", "weights": [[8, 11, 1.0], [38, 47, 1.0], [53, 65, 1.0], [153, 156, 1.0], [171, 176, 1.0], [183, 192, 1.0]]}, "2018.sigirconf_conference-2018.225": {"snippet": "...zing Knowledge Graphs for Text-Centric Information Retrieval\n\n\n ABSTRACTThe past decade has witnessed the emergence of several publicly available and proprietary knowledge graphs (KGs). The depth and ...", "weights": [[8, 17, 1.0], [165, 174, 1.0]]}, "N19-1104": {"snippet": "...in a knowledge graph is processed and the models can be difficult to interpret. In this paper, we utilize graph patterns in a knowledge graph to overcome such problems. Our proposed model, the graph p...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [109, 114, 1.0], [129, 138, 1.0], [139, 144, 1.0], [196, 201, 1.0]]}, "N18-6004": {"snippet": "...ense knowledge into AI/NLP systems is currently an important research area, with several open problems and challenges. At the same time, processing and storing this knowledge in lexical resources is n...", "weights": [[8, 17, 1.0], [26, 29, 1.0], [168, 177, 1.0]]}, "2020.acl-main.199": {"snippet": "...nomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer\n\n\n Extracting lexico-semantic relations as graphstructured taxonomies, also known as taxonomy construction, has been...", "weights": [[8, 20, 1.0], [43, 48, 1.0], [68, 77, 1.0], [181, 193, 1.0]]}, "W01-0721": {"snippet": "...for {NLP} learning tasks", "weights": [[8, 11, 1.0]]}, "2020.coling-main.84": {"snippet": "...cial Knowledge Graph Construction\n\n\n Motivated by applications such as question answering, fact checking, and data integration, there is significant interest in constructing knowledge graphs by extrac...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0], [177, 186, 1.0]]}, "2021.acl-long.366": {"snippet": "...ated knowledge graphs to control the generation of arguments. In particular, we construct and populate three knowledge graphs, employing several compositions of them to encode various knowledge into t...", "weights": [[8, 17, 1.0], [112, 121, 1.0], [187, 196, 1.0]]}, "D19-1602": {"snippet": "...ural Knowledge Graph-aware Network\n\n\n Leveraging external knowledge is an emerging trend in machine comprehension task. Previous work usually utilizes knowledge graphs such as ConceptNet as external k...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [61, 70, 1.0], [154, 163, 1.0], [202, 211, 1.0]]}, "N18-3011": {"snippet": "Construction of the Literature Graph in Semantic Scholar\n\n\n We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic m...", "weights": [[0, 12, 1.0], [31, 36, 1.0], [167, 172, 1.0]]}, "2020.deelio-1.9": {"snippet": "...ense Knowledge Graph in Pretrained Models for Social Commonsense Tasks\n\n\n Pretrained language models have excelled at many NLP tasks recently; however, their social intelligence is still unsatisfactor...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [126, 129, 1.0]]}, "2022.naacl-main.368": {"snippet": "...rame knowledge defined in FrameNet. In this paper, we propose a Knowledge-guided Incremental semantic parser with Double-graph (KID). We first introduce Frame Knowledge Graph (FKG), a heterogeneous gr...", "weights": [[8, 17, 1.0], [67, 76, 1.0], [124, 129, 1.0], [162, 171, 1.0], [172, 177, 1.0], [201, 206, 1.0]]}, "2019.wwwconf_conference-2019c.125": {"snippet": "...able Knowledge Graph Construction over Text using Deep Learning based Predicate Mapping", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0]]}, "2020.coling-main.369": {"snippet": "...rnal knowledge in the form of knowledge graphs has shown to improve the performance in recommendation and dialogue systems. Information from knowledge graphs aids in enriching those systems by providi...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [144, 153, 1.0]]}, "2020.emnlp-main.595": {"snippet": "...sing knowledge graph embeddings\n\n\n It has been shown that knowledge graph embeddings encode potentially harmful social biases, such as the information that women are more likely to be nurses, and men ...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [61, 70, 1.0], [71, 76, 1.0]]}, "2018.wsdm_conference-2018.107": {"snippet": "...rent knowledge graph has swiftly become a core challenge in machine learning. Applications that rely on structured knowledge representations include digital assistants (e.g., Siri, Alexa, Cortana, and...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [118, 127, 1.0]]}, "J16-4009": {"snippet": "...stic Graph {B}anks\n\n\n Graphs exceeding the formal complexity of rooted trees are of growing relevance to much NLP research. Although formally well understood in graph theory, there is substantial vari...", "weights": [[8, 13, 1.0], [113, 116, 1.0], [164, 169, 1.0]]}, "2021.semeval-1.44": {"snippet": "...rly {NLP} Contributions for a Research Knowledge Graph\n\n\n There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to e...", "weights": [[8, 11, 1.0], [42, 51, 1.0], [52, 57, 1.0]]}, "N07-3010": {"snippet": "...stic knowledge in the system. The claim is that the knowledge needed can largely be acquired by knowledge-free and unsupervised methods. Here, graph models are employed for representing language data....", "weights": [[8, 17, 1.0], [55, 64, 1.0], [99, 108, 1.0], [146, 151, 1.0]]}, "N18-1029": {"snippet": "...orld knowledge on demand. In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks. Our aim...", "weights": [[8, 17, 1.0], [92, 101, 1.0], [117, 126, 1.0], [127, 132, 1.0], [184, 187, 1.0]]}, "2021.wwwconf_conference-2021.124": {"snippet": "...ased Knowledge Graph Exploration", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "2022.acl-srw.27": {"snippet": "...ient Knowledge Embedding Representation for Link Prediction and Question Answering\n\n\n Knowledge Graphs (KGs) are symbolically structured storages of facts. The KG embedding contains concise data used ...", "weights": [[8, 17, 1.0], [89, 98, 1.0]]}, "P18-1024": {"snippet": "...ulti-Graph Representation Learning with Entity Linkage\n\n\n Knowledge graphs have emerged as an important model for studying complex multirelational data. This has given rise to the construction of nume...", "weights": [[8, 13, 1.0], [61, 70, 1.0], [183, 195, 1.0]]}, "2017.sigirconf_conference-2017.243": {"snippet": "...eded knowledge for a task (i.e. text data analysis). Text data retrieval is essential for ltering out non-relevant text data to a particular problem, thus dramatically improving the e ciency since any...", "weights": [[8, 17, 1.0]]}, "2015.wwwconf_conference-2015c.328": {"snippet": "...cale Knowledge Graphs: WWW 2015 Tutorial\n\n\n ABSTRACTRecent years have witnessed a proliferation of large-scale knowledge graphs, such as Freebase, Google's Knowledge Graph, YAGO, Facebook's Entity Gra...", "weights": [[8, 17, 1.0], [114, 123, 1.0], [159, 168, 1.0], [169, 174, 1.0], [200, 205, 1.0]]}, "C18-2004": {"snippet": "...w: a Knowledge Graph-based Question Answering and Infor-mation Retrieval System for Traditional {C}hinese Medicine\n\n\n T-Know is a knowledge service system based on the constructed knowledge graph of T...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [133, 142, 1.0], [183, 192, 1.0], [193, 198, 1.0]]}, "2017.wsdm_conference-2017.89": {"snippet": "...pose knowledge graphs (KGs) such as Wikipedia, DBpedia, Freebase, and WikiData but also proprietary KGs such as Google's Knowledge Graph and Microsoft's Satori are growing in breadth and depth every d...", "weights": [[8, 17, 1.0], [124, 133, 1.0], [134, 139, 1.0]]}, "2022.woah-1.7": {"snippet": "...iven Knowledge Graph Construction For Cultural Knowledge and Stereotypes\n\n\n Analyzing ethnic or religious bias is important for improving fairness, accountability, and transparency of natural language...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0], [50, 59, 1.0]]}, "2021.findings-acl.165": {"snippet": "...-End Construction of {NLP} Knowledge Graph\n\n\n This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers. We focus on extracting four types of relations: eval...", "weights": [[8, 20, 1.0], [25, 28, 1.0], [30, 39, 1.0], [40, 45, 1.0], [83, 95, 1.0], [102, 105, 1.0], [106, 115, 1.0], [116, 121, 1.0]]}, "2020.wwwconf_conference-2020c.100": {"snippet": "...ork, Knowledge Graphs (KG) are used as a standard way to extract and organize the knowledge in social networks. This tutorial focuses on how to build KGs for social networks by developing deep NLP mod...", "weights": [[8, 17, 1.0], [85, 94, 1.0], [196, 199, 1.0]]}, "D19-6607": {"snippet": "...able Knowledge Graph Construction from Text Collections\n\n\n We present a scalable, open-source platform that \"distills\" a potentially large text collection into a knowledge graph. Our platform takes do...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0], [165, 174, 1.0], [175, 180, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.26": {"snippet": "...with Knowledge Graphs\n\n\n AbstractThis dissertation aims to improve text representation, retrieval, and understanding with knowledge graphs. Previous information retrieval systems were mostly built upo...", "weights": [[8, 17, 1.0], [125, 134, 1.0]]}, "2020.cikm_conference-2020.67": {"snippet": "...ers. Knowledge Graphs (KGs) offer offer a good solution for such processing. Natural Language Processing (NLP) offers the possibility for mining and lifting natural language texts to knowledge graphs ...", "weights": [[8, 17, 1.0], [109, 112, 1.0], [186, 195, 1.0]]}, "D19-1428": {"snippet": "...ocal Knowledge Graph Construction to Scale {S}eq2{S}eq Models to Multi-Document Inputs\n\n\n Query-based open-domain NLP tasks require information synthesis from long and diverse web results. Current app...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0], [117, 120, 1.0]]}, "2016.sigirconf_conference-2016.217": {"snippet": "...cale Knowledge Graphs\n\n\n ABSTRACTRecent years have witnessed a proliferation of large-scale knowledge graphs, from purely academic projects such as YAGO to major commercial projects such as Google's K...", "weights": [[8, 17, 1.0], [95, 104, 1.0], [202, 211, 1.0]]}, "2021.acl-long.488": {"snippet": "...rm a knowledge graph containing relevant background knowledge for the the entity mentions in the text. To make the final predictions, KECI fuses the initial span graph and the knowledge graph into a m...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [55, 64, 1.0], [165, 170, 1.0], [179, 188, 1.0], [189, 194, 1.0]]}, "2021.findings-acl.203": {"snippet": "... the knowledge graph. In practice, we utilize Graph Convolutional Network (GCN) to learn the concept representation in the knowledge graph and use a Transformerbased encoder to learn the representatio...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [49, 54, 1.0], [126, 135, 1.0], [136, 141, 1.0]]}, "2020.wwwjournals_journal-ir0volumeA23A5.2": {"snippet": "Knowledge graph construction from multiple online encyclopedias", "weights": [[0, 9, 1.0], [10, 15, 1.0], [16, 28, 1.0]]}, "2022.suki-1.1": {"snippet": "...}: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source\n\n\n As the demands for large-scale information processing have grown, knowledg...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [109, 118, 1.0], [195, 204, 1.0]]}}}
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As a consequence, PageRank and the...", "weights": [[8, 16, 1.0], [187, 195, 1.0]]}, "2007.cikm_conference-2007.59": {"snippet": "...TThe PageRank algorithm is used in Web information retrieval to calculate a single list of popularity scores for each page in the Web. These popularity scores are used to rank query results when prese...", "weights": [[8, 16, 1.0], [17, 26, 1.0]]}, "2007.sigirconf_conference-2007.176": {"snippet": "...n of pagerank", "weights": [[8, 16, 1.0]]}, "2010.tois_journal-ir0volumeA28A3.1": {"snippet": "...n of PageRank, among which the Arnoldi-type algorithm and the GMRES algorithm are competitive candidates. In essence, the former deals with the PageRank problem from an eigenproblem, while the latter ...", "weights": [[8, 16, 1.0], [47, 56, 1.0], [71, 80, 1.0], [147, 155, 1.0]]}, "2002.wwwconf_conference-2002.49": {"snippet": "... the construction of effective search engines for both generic (horizontal) and focused (vertical) search. Ranking schemes for horizontal search like the PageRank algorithm used by Google operate on t...", "weights": [[8, 20, 1.0], [157, 165, 1.0], [166, 175, 1.0]]}, "2002.ecir_conference-2002.5": {"snippet": "...lled PageRank . The sum of all PageRank values should be one. However, we notice that the sum becomes less than one in some cases. We present an improved PageRank algorithm that computes the PageRank ...", "weights": [[8, 16, 1.0], [34, 42, 1.0], [157, 165, 1.0], [166, 175, 1.0], [194, 202, 1.0]]}, "2013.cikm_conference-2013.245": {"snippet": "...ized pagerank computation\n\n\n ABSTRACTPersonalized PageRank (PPR) based measures of node proximity have been shown to be highly effective in many prediction and recommendation applications. The use of ...", "weights": [[8, 16, 1.0], [53, 61, 1.0]]}, "2005.sigirconf_conference-2005.122": {"snippet": "...sful algorithm in ranking web sources. In order to avoid the rank sink problem, PageRank assumes that a surfer, being in a page, jumps to a random page with a certain probability. In the standard Page...", "weights": [[8, 17, 1.0], [83, 91, 1.0], [199, 207, 1.0]]}, "2005.wwwconf_conference-2005si.58": {"snippet": "... the PageRank algorithm called, Predictive Ranking (PreR), in which different classes of dangling pages are analyzed individually so that the link structure can be predicted more accurately. We detail...", "weights": [[8, 16, 1.0], [17, 26, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.40": {"snippet": "...val. PageRank, HITS, and SALSA are well-known page ranking algorithm based on link structure analysis of a seed set, but ranking given by them has not yet been efficient. In this paper, we propose a v...", "weights": [[8, 16, 1.0], [62, 71, 1.0]]}, "2006.ecir_conference-2006.22": {"snippet": "...n of PageRank", "weights": [[8, 16, 1.0]]}, "2008.sigirconf_conference-2008.103": {"snippet": "...ical PageRank based algorithm for recommender systems\n\n\n ABSTRACTIn this paper, we propose a Topical PageRank based algorithm for recommender systems, which aim to rank products by analyzing previous ...", "weights": [[8, 16, 1.0], [23, 32, 1.0], [104, 112, 1.0], [119, 128, 1.0]]}, "W12-2413": {"snippet": "...pply pagerank-derived algorithm to the obtained semantic graph in order to filter out the acquired synonyms. Evaluation performed with two independent experts indicates that the quality of synonyms is...", "weights": [[8, 16, 1.0], [25, 34, 1.0]]}, "P13-3020": {"snippet": "... the PageRank algorithm. Experimenting on Reuters-21578 corpus, we have confirmed that our proposed methods work well for raising the accuracy of a multi-class document categorization.", "weights": [[8, 16, 1.0], [17, 26, 1.0]]}, "2003.sigirconf_conference-2003.10": {"snippet": "...fied PageRank algorithm to re-rank web-pages for small web search. Our experimental results indicate that the proposed method outperforms content-based method by 16%, explicit link-based PageRank by 2...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [190, 198, 1.0]]}, "2005.cikm_conference-2005.132": {"snippet": "...ting PageRank, which causes prohibitive communication cost to achieve accurate results in distributed solution. In this paper, we propose a distributed PageRank computation algorithm based on iterativ...", "weights": [[8, 16, 1.0], [155, 163, 1.0], [176, 185, 1.0]]}, "2004.wwwconf_conference-2004at.103": {"snippet": "... for pagerank\n\n\n ABSTRACTTheoretical analysis of the Web graph is often used to improve the efficiency of search engines. The PageRank algorithm, proposed by , is used by the Google search engine [4] ...", "weights": [[8, 16, 1.0], [129, 137, 1.0], [138, 147, 1.0]]}, "2002.wwwconf_conference-2002.50": {"snippet": "...tive PageRank\n\n\n ABSTRACTIn the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the...", "weights": [[8, 16, 1.0], [44, 52, 1.0], [53, 62, 1.0], [123, 131, 1.0]]}, "2004.wwwconf_conference-2004at.161": {"snippet": "... for pagerank computation under missing data\n\n\n ABSTRACTThe enormity and rapid growth of the web-graph forces quantities such as its pagerank to be computed under missing information consisting of out...", "weights": [[8, 16, 1.0], [136, 144, 1.0]]}, "2017.wsdm_conference-2017.83": {"snippet": "...k of PageRank is that its computation depends only on input graph structures, not considering external information such as the attributes of nodes. This work proposes AttriRank, an unsupervised rankin...", "weights": [[8, 16, 1.0]]}, "2009.cikm_conference-2009.85": {"snippet": "...e.g. PageRank and its variants) express the importance of a page as the linear combination of its in-links and compute page importance scores by solving a linear system in an iterative way. Such linea...", "weights": [[8, 16, 1.0]]}, "2004.sigirconf_conference-2004.102": {"snippet": "... The PageRank algorithm, which is used in Google Search Engine, plays an important role on improving the quality of its resuls by employing the explicit hyperlink structure among the Web pages. The pr...", "weights": [[8, 16, 1.0], [17, 26, 1.0]]}, "2019.wwwconf_conference-2019.101": {"snippet": "...ized PageRank on Large Graphs", "weights": [[8, 16, 1.0]]}, "2004.wwwconf_conference-2004at.45": {"snippet": "...Rank algorithm computes 10%~30% faster than PageRank algorithm. 2) Top web pages in PowerRank algorithm remain similar to that of the PageRank algorithm.", "weights": [[8, 17, 1.0], [47, 55, 1.0], [56, 65, 1.0], [97, 106, 1.0], [137, 145, 1.0], [146, 155, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...ince PageRank is a query-independent document score, the PageRank vector can be partitioned in the same way as the web corpus, and index-serving nodes can determine the PageRank scores of results usin...", "weights": [[8, 16, 1.0], [60, 68, 1.0], [172, 180, 1.0]]}, "2004.airs_conference-2004.13": {"snippet": "...ient PageRank with Same Out-Link Groups\n\n\n Abstract. Traditional PageRank algorithm suffers from heavy computation cost due to the huge number of web pages. In this paper, we propose a more efficient ...", "weights": [[8, 16, 1.0], [68, 76, 1.0], [77, 86, 1.0]]}, "2008.sigirconf_conference-2008.179": {"snippet": "...n of PageRank and reverse PageRank\n\n\n ABSTRACTWe consider the problem of approximating the PageRank of a target node using only local information provided by a link server. We prove that local approxi...", "weights": [[8, 16, 1.0], [29, 37, 1.0], [94, 102, 1.0]]}, "2008.sigirconf_conference-2008.183": {"snippet": "...ovel PageRank based clustering (PRC) algorithm which uses the hypertext structure. The PRC algorithm produces graph partitioning with high modularity and coverage. The comparison of the PRC algorithm ...", "weights": [[8, 16, 1.0], [40, 49, 1.0], [94, 103, 1.0], [193, 202, 1.0]]}, "P06-3007": {"snippet": "...s by PageRank algorithm on the event map constructed from documents. Experimental results are encouraging.", "weights": [[8, 16, 1.0], [17, 26, 1.0]]}, "2003.wwwconf_conference-2003.28": {"snippet": "...ting PageRank computations\n\n\n ABSTRACTWe present a novel algorithm for the fast computation of PageRank, a hyperlink-based estimate of the \"importance\" of Web pages. The original PageRank algorithm us...", "weights": [[8, 16, 1.0], [60, 69, 1.0], [98, 106, 1.0], [182, 190, 1.0], [191, 200, 1.0]]}, "2004.sigirconf_conference-2004.58": {"snippet": "...rch. PageRank and HITS are two of the most popular algorithms. Most of the existing link analysis algorithms treat a web page as a single node in the web graph. However, in most cases, a web page cont...", "weights": [[8, 16, 1.0]]}, "2005.wwwconf_conference-2005si.147": {"snippet": "... for PageRank?\n\n\n ABSTRACTThis paper proposes to extend a previous work, The Effect of the Back Button in a Random Walk: Application for PageRank . We introduce an enhanced version of the PageRank alg...", "weights": [[8, 16, 1.0], [140, 148, 1.0], [191, 199, 1.0], [200, 209, 1.0]]}, "2012.cikm_conference-2012.399": {"snippet": "...ssic PageRank is negative (see ). For this reason, in recent years a number of authors introduce some algorithms of PageRank actualization. We introduce our new algorithm called Actual PageRank, which...", "weights": [[8, 16, 1.0], [119, 127, 1.0], [164, 173, 1.0], [188, 196, 1.0]]}, "2008.cikm_conference-2008.170": {"snippet": "...ered PageRank algorithm, called two-way reordered PageRank algorithm, which exploits both dangling nodes and reversedangling nodes to reduce the computational complexity of the PageRank vector.", "weights": [[8, 16, 1.0], [17, 26, 1.0], [53, 61, 1.0], [62, 71, 1.0], [180, 188, 1.0]]}, "2006.sigirconf_conference-2006.41": {"snippet": "...that PageRank can be approximated with other simpler forms of rankings that may be computed more efficiently, our focus is of more speculative nature, in that it aims at separating the kernel of PageR...", "weights": [[8, 16, 1.0], [198, 206, 1.0]]}, "2006.ir_journal-ir0volumeA9A2.1": {"snippet": "...ient PageRank approximation via graph aggregation", "weights": [[8, 16, 1.0]]}, "2002.spire_conference-2002.13": {"snippet": "...t of PageRank, the algorithm used by the Google search engine, as it is currently proposed in recent works . Then we prove the substantial equivalence between this PageRank formula and the classic for...", "weights": [[8, 16, 1.0], [22, 31, 1.0], [167, 175, 1.0]]}, "2008.tois_journal-ir0volumeA26A2.4": {"snippet": "...m of PageRank\n\n\n Link-based ranking algorithms are among the most important techniques to improve web search. In particular, the PageRank algorithm has been successfully used in the Google search engi...", "weights": [[8, 16, 1.0], [132, 140, 1.0], [141, 150, 1.0]]}, "2016.wwwconf_conference-2016c.3": {"snippet": "...hted PageRank is finally constructed and employed, with HITS algorithm to assess the impact of articles. Through experiments on American Physical Society (APS) dataset, we show that PNCOIRank signific...", "weights": [[8, 16, 1.0], [64, 73, 1.0]]}, "2018.ictir_conference-2018.19": {"snippet": "... the PageRank algorithm is applied on these graphs to assign word importance scores based on centrality.We propose two measures to compute topic quality: (1) the Aggregate PageRank of Top-words of a t...", "weights": [[8, 16, 1.0], [17, 26, 1.0], [175, 183, 1.0]]}, "2019.ipm_journal-ir0volumeA56A1.6": {"snippet": "...UID) algorithm is better than that of CF algorithm and TFIDF algorithm with respect to F-measure, Precision and Recall. Moreover, Precision of the proposed community detection (PCD) algorithm is impro...", "weights": [[8, 17, 1.0], [44, 53, 1.0], [64, 73, 1.0], [185, 194, 1.0]]}, "S10-1087": {"snippet": "...ized PageRank algorithm which was applied to a graph constructed from the whole of WordNet in which nodes are initialized with ranking scores of words and their senses. In the competition, our systems...", "weights": [[8, 16, 1.0], [17, 26, 1.0]]}, "2007.ecir_conference-2007.63": {"snippet": "...Step PageRank for Web Search", "weights": [[8, 16, 1.0]]}, "2005.wwwconf_conference-2005si.72": {"snippet": "...ized PageRank\n\n\n ABSTRACTPersonalized PageRank expresses backlink-based page quality around user-selected pages in a similar way to PageRank over the entire Web. Algorithms for computing personalized ...", "weights": [[8, 16, 1.0], [41, 49, 1.0], [135, 143, 1.0]]}, "2012.jasis_journal-ir0volumeA63A8.7": {"snippet": "...sing PageRank", "weights": [[8, 16, 1.0]]}, "2010.wwwconf_conference-2010.117": {"snippet": "...Rank algorithm [4] employs visual similarity to represent the link structure in a graph so that the classic PageRank algorithm can be applied to select the most relevant images. However, measuring vis...", "weights": [[8, 17, 1.0], [111, 119, 1.0], [120, 129, 1.0]]}, "2014.cikm_conference-2014.106": {"snippet": "...sted PageRank\n\n\n ABSTRACTGraph-based ranking plays a key role in many applications, such as web search and social computing. Pioneering methods of ranking on graphs (e.g., PageRank and HITS) computed ...", "weights": [[8, 16, 1.0], [175, 183, 1.0]]}, "2012.ir_journal-ir0volumeA15A6.0": {"snippet": "...le's PageRank and its derivative, with attention to unreferenced nodes", "weights": [[8, 16, 1.0]]}, "2006.wwwconf_conference-2006.77": {"snippet": "...yond PageRank: machine learning for static ranking\n\n\n ABSTRACTSince the publication of Brin and Page's paper on PageRank, many in the Web community have depended on PageRank for the static (query-inde...", "weights": [[8, 16, 1.0], [115, 123, 1.0], [168, 176, 1.0]]}, "2011.cikm_conference-2011.77": {"snippet": "...ocal algorithm that computes a correct ranking must take into consideration \u2126( \u221a kn) nodes -even when ranking the top k nodes of the graph, even if their PageRank scores are \"well separated\", and even...", "weights": [[8, 17, 1.0], [157, 165, 1.0]]}, "2008.cikm_conference-2008.30": {"snippet": "...fast PageRank convergence. It follows that calculating Reverse PageRank locally is frequently more feasible than computing PageRank locally.We demonstrate that Reverse PageRank is useful for several a...", "weights": [[8, 16, 1.0], [66, 74, 1.0], [126, 134, 1.0], [171, 179, 1.0]]}, "2004.wwwconf_conference-2004at.114": {"snippet": "... our algorithm updates PageRank using just 25% and 14%, respectively, of the time required by the original PageRank algorithm. Our algorithm uses iterative aggregation techniques  to focus on the slow...", "weights": [[8, 17, 1.0], [26, 34, 1.0], [110, 118, 1.0], [119, 128, 1.0], [134, 143, 1.0]]}, "2004.airs_conference-2004.14": {"snippet": "... The PageRank algorithm, used in the Google Search Engine, plays an important role in improving the quality of results by employing an explicit hyperlink structure among the Web pages. The prestige of...", "weights": [[8, 16, 1.0], [17, 26, 1.0]]}, "2008.sigirconf_conference-2008.59": {"snippet": "...nce, PageRank is such an algorithm, which employs a discrete-time Markov process as the model. Unfortunately, the link graph might be incomplete and inaccurate with respect to data for determining pag...", "weights": [[8, 16, 1.0], [28, 37, 1.0]]}, "2002.cikm_conference-2002.70": {"snippet": "...ting pagerank\n\n\n ABSTRACTOver the last few years, most major search engines have integrated link-based ranking techniques in order to provide more accurate search results. One widely known approach is...", "weights": [[8, 16, 1.0]]}, "2005.wwwconf_conference-2005.64": {"snippet": "...ated PageRank computation\n\n\n ABSTRACTIn this note we consider a simple reformulation of the traditional power iteration algorithm for computing the stationary distribution of a Markov chain. Rather th...", "weights": [[8, 16, 1.0], [123, 132, 1.0]]}, "2004.wwwconf_conference-2004at.160": {"snippet": "...t to PageRank. A simplistic implementation of our method required less than half the running time of a highly optimized implementation of PageRank, implying that larger speedup factors are probably po", "weights": [[8, 16, 1.0], [141, 149, 1.0]]}}}
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In this work, we present a simple technique to maxi...", "weights": [[8, 17, 1.0]]}, "2003.cikm_conference-2003.40": {"snippet": "... the challenge of efficiently processing queries on sequentially accessible token-based data. While the automata model is naturally suited for pattern matching on tokenized XML streams, the algebraic ...", "weights": [[8, 17, 1.0]]}, "2022.tacl-1.5": {"snippet": "...ient Tokenization-Free Encoder for Language Representation\n\n\n Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly used models still require an exp...", "weights": [[8, 20, 1.0]]}, "K17-3009": {"snippet": "...ion, tokenization, POS tagging, lemmatization and dependency parsing. We provide models for all 50 languages of UD 2.0, and furthermore, the pipeline can be trained easily using data in CoNLL-U format...", "weights": [[8, 20, 1.0]]}, "2020.emnlp-main.121": {"snippet": "... and multilingual corpora analysis, but it is a challenging machine learning problem since annotation is expensive and hard to scale. This work improves the prediction and annotation of finegrained se...", "weights": [[8, 20, 1.0]]}, "2018.fire_conference-2018w.34": {"snippet": "Multilingual Author Profiling from SMS\n\n\n This paper presents our solution for the Author Profiling task in the FIRE challenge 2018. This task mainly focuses on finding the age and gender of people fr...", "weights": [[0, 12, 1.0], [117, 126, 1.0]]}, "W17-7521": {"snippet": "...s in Multilingual Multidirectional Dictionaries\n\n\n The multilingual multidirectional dictionary gives the linguistic equivalent across the languages. In order to build such a dictionary in electronic ...", "weights": [[8, 20, 1.0], [58, 70, 1.0]]}, "2020.emnlp-main.76": {"snippet": "Token-level Adaptive Training for Neural Machine Translation\n\n\n There exists a token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to diff...", "weights": []}, "2020.vardial-1.11": {"snippet": "A Tokenization System for the {K}urdish Language\n\n\n Tokenization is one of the essential and fundamental tasks in natural language processing. Despite the recent advances in applying unsupervised stat...", "weights": [[2, 14, 1.0], [52, 64, 1.0]]}, "2020.nlposs-1.7": {"snippet": "...cale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, to...", "weights": [[8, 20, 1.0], [201, 213, 1.0]]}, "L10-1632": {"snippet": "Multilingual Question Generation", "weights": [[0, 12, 1.0]]}, "2020.emnlp-main.360": {"snippet": "...from multilingual WordNet that include single-token words and MWEs, and most importantly, include MWEs that correspond to single words in another language. We show that the pre-tokenization of MWEs as...", "weights": [[8, 20, 1.0], [180, 192, 1.0]]}, "D19-6106": {"snippet": "...s of Tokenization\n\n\n Multilingual transfer learning can benefit both high-and low-resource languages, but the source of these improvements is not well understood. Cananical Correlation Analysis (CCA) ...", "weights": [[8, 20, 1.0], [24, 36, 1.0]]}, "2021.emnlp-main.800": {"snippet": "...ting Multilingual Language Models to New Scripts\n\n\n Massively multilingual language models such as multilingual BERT offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. ...", "weights": [[8, 20, 1.0], [65, 77, 1.0], [102, 114, 1.0]]}, "2020.wmt-1.21": {"snippet": "...on a multilingual approach, jointly training a Transformer model on several agglutinative languages. The English-Inuktitut translation task is challenging at every step, from data selection, preparati...", "weights": [[8, 20, 1.0]]}, "L02-1212": {"snippet": "Multilingual Terminology Extraction and Validation", "weights": [[0, 12, 1.0]]}, "W14-2611": {"snippet": "...ng a Multilingual Sentiment Analysis Application for Social Media Mining", "weights": [[8, 20, 1.0]]}, "2021.findings-acl.316": {"snippet": "...s to Multilingual Term Extraction Across Domains\n\n\n Automated Term Extraction (ATE), even though well-investigated, continues to be a challenging task. Approaches conventionally extract terms on corpu...", "weights": [[8, 20, 1.0]]}, "2022.naacl-main.200": {"snippet": "...n in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition\n\n\n Named entity recognition (NER) in a realworld setting remains challenging and is impacted by factors like text g...", "weights": [[8, 20, 1.0]]}, "1998.amta-tutorials.7": {"snippet": "Multilingual text summarization", "weights": [[0, 12, 1.0]]}, "2021.acl-long.243": {"snippet": "...e of Multilingual Language Models\n\n\n In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with ...", "weights": [[8, 20, 1.0], [131, 143, 1.0]]}, "2022.eamt-1.16": {"snippet": "...urce Multilingual Translation\n\n\n We propose a novel technique of combining multiple subword tokenizations of a single source-target language pair for use with multilingual neural translation training ...", "weights": [[8, 20, 1.0], [162, 174, 1.0]]}, "W15-4637": {"snippet": "...tion challenge: Multilingual Singledocument Summarization and Multilingual Multi-document Summarization.", "weights": [[8, 17, 1.0], [19, 31, 1.0], [65, 77, 1.0]]}, "2020.semeval-1.46": {"snippet": "...k 4: Multilingual Commonsense\n\n\n This paper describes work of the BUT-FIT's team at SemEval 2020 Task 4 -Commonsense Validation and Explanation. We participated in all three subtasks. In subtasks A an...", "weights": [[8, 20, 1.0]]}, "K19-1004": {"snippet": "...ning multilingual embeddings with shared vocabulary.", "weights": [[8, 20, 1.0]]}, "2002.clef_workshop-2002.8": {"snippet": "A Multilingual Approach to Multilingual Information Retrieval", "weights": [[2, 14, 1.0], [27, 39, 1.0]]}, "1994.riao_conference-1994.5": {"snippet": "...s in Multilingual Searching", "weights": [[8, 20, 1.0]]}, "2021.emnlp-main.160": {"snippet": "...iece Tokenization\n\n\n Tokenization is a fundamental preprocessing step for almost all NLP tasks. In this paper, we propose efficient algorithms for the Word-Piece tokenization used in BERT, from single...", "weights": [[8, 20, 1.0], [24, 36, 1.0], [165, 177, 1.0]]}, "2021.wnut-1.52": {"snippet": "... for multilingual lexical normalization\n\n\n This paper describes the HEL-LJU submissions to the MultiLexNorm shared task on multilingual lexical normalization. Our system is based on a BERT token class...", "weights": [[8, 20, 1.0], [126, 138, 1.0]]}, "2022.semeval-1.207": {"snippet": "...d or multilingual, among other scenarios. In this paper, we introduce our submitted system to the Multilingual Complex Named Entity Recognition (MultiCoNER) shared task. We approach the complex NER fo...", "weights": [[8, 20, 1.0], [101, 113, 1.0]]}, "2022.semeval-1.27": {"snippet": "... for Multilingual Idiomaticity Detection\n\n\n We propose a multilingual adversarial training model for determining whether a sentence contains an idiomatic expression. Given that a key challenge with th...", "weights": [[8, 20, 1.0], [60, 72, 1.0], [186, 195, 1.0]]}, "2020.wwwconf_conference-2020.127": {"snippet": "Traveling the token world: A graph analysis of Ethereum ERC20 token ecosystem\n\n\n The birth of Bitcoin ushered in the era of cryptocurrency, which has now become a financial market attracted extensive ...", "weights": []}, "2021.naacl-main.40": {"snippet": "...n\n\n\n Multilingual pretrained representations generally rely on subword segmentation algorithms to create a shared multilingual vocabulary. However, standard heuristic algorithms often lead to sub-opti...", "weights": [[8, 20, 1.0], [117, 129, 1.0]]}, "K18-2007": {"snippet": "Multilingual {U}niversal {D}ependency Parsing from Raw Text with Low-Resource Language Enhancement\n\n\n This paper describes the system of our team Phoenix for participating CoNLL 2018 Shared Task: Mult...", "weights": [[0, 12, 1.0], [196, 208, 1.0]]}, "2022.naacl-main.293": {"snippet": "...ased tokenization and learns an embedding for each subword. The tokenizer of the source model (in English) is replaced with a tokenizer in the target language and token embeddings are initialized such...", "weights": [[8, 20, 1.0]]}, "2022.semeval-1.200": {"snippet": "... for Multilingual Named Entity Recognition\n\n\n The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. T...", "weights": [[8, 20, 1.0]]}, "2007.ir_journal-ir0volumeA10A45.0": {"snippet": "...y of tokenization strategies for biomedical information retrieval", "weights": [[8, 20, 1.0]]}, "2020.insights-1.1": {"snippet": "...} in tokenization and sub-word representations of Out-of-Vocabulary words\n\n\n BERT model (Devlin et al., 2019) has achieved significant progress in several Natural Language Processing (NLP) tasks by le...", "weights": [[8, 20, 1.0]]}, "2021.bsnlp-1.15": {"snippet": "...gual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across {S}lavic Languages\n\n\n This paper describes Slav-NER: the 3 rd Multilingual Named Entity Challenge in ...", "weights": [[8, 17, 1.0], [164, 176, 1.0], [190, 199, 1.0]]}, "2021.eacl-demos.10": {"snippet": "... for Multilingual Natural Language Processing\n\n\n We introduce Trankit, a light-weight Transformer-based Toolkit for multilingual Natural Language Processing (NLP). It provides a trainable pipeline for...", "weights": [[8, 20, 1.0], [119, 131, 1.0]]}, "2003.clef_workshop-2003w.23": {"snippet": "...s in Tokenization and Non-Word Translation\n\n\n In the past we have conducted experiments that investigate the benefits and peculiarities attendant to alternative methods for tokenization, particularly ...", "weights": [[8, 20, 1.0], [176, 188, 1.0]]}, "2022.repl4nlp-1.10": {"snippet": "...Free Multilingual Neural Tokenizer for End-to-End Task Learning\n\n\n Subword tokenization is a commonly used input pre-processing step in most recent NLP models. However, it limits the models' ability t...", "weights": [[8, 20, 1.0], [78, 90, 1.0]]}, "2021.ecnlp-1.21": {"snippet": "...in a multilingual country like India. This can be achieved by an in-domain robust machine translation (MT) system. 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Searches on the Web of Data go beyond the retrieval of textual Web sites, and shifts the focus of search engines towards domain...", "weights": [[8, 16, 1.0], [56, 62, 1.0], [174, 180, 1.0]]}, "2007.cikm_workshop-2007pikm.4": {"snippet": "...anet search; search in a P2P network, given that each employee shares some file on her desktop; search in both structured (databases) and unstructured (documents) data; search for not only files (peop...", "weights": [[8, 14, 1.0], [16, 22, 1.0], [99, 105, 1.0], [172, 178, 1.0]]}, "W04-3211": {"snippet": "...s in Semantic Parsin\n\n\n We apply a novel variant of Random Forests (Breiman, 2001) to the shallow semantic parsing problem and show extremely promising results. The final system has a semantic role cl...", "weights": [[8, 16, 1.0], [101, 109, 1.0], [187, 195, 1.0]]}, "2013.cikm_workshop-2013esair.13": {"snippet": "...nted search on the web using semantic annotations\n\n\n ABSTRACTWeb search engines are optimized for early precision, which makes it difficult to perform recall oriented tasks with them. In this article,...", "weights": [[8, 14, 1.0], [32, 40, 1.0], [68, 74, 1.0]]}, "2009.eamt-1.30": {"snippet": "Can Semantic Role Labeling Improve {SMT}?\n\n\n We present a series of empirical studies aimed at illuminating more precisely the likely contribution of semantic roles in improving statistical machine tr...", "weights": [[4, 12, 1.0], [150, 158, 1.0]]}, "1998.sigirconf_conference-98.1": {"snippet": "...time enhancing speed, reliability, and accuracy.", "weights": [[8, 17, 1.0], [42, 50, 1.0]]}, "2005.cikm_conference-2005.97": {"snippet": "...anet search-engines using context information from databases\n\n\n ABSTRACTInformation in enterprises comes in documents and databases. From a semantic viewpoint, both kinds of information are usually ti...", "weights": [[8, 14, 1.0], [143, 151, 1.0]]}, "P10-1141": {"snippet": "...tion accuracy. We show that variants of the classic tf.idf scheme adapted to sentiment analysis provide significant increases in accuracy, especially when using a sublinear function for term frequency...", "weights": [[8, 16, 1.0], [132, 140, 1.0]]}, "2009.cikm_conference-2009.43": {"snippet": "...ext, search query and social annotation), both of which are valuable to indicate the semantic content of the document. However, due to the free nature of the web, the two streams of web data suffer fr...", "weights": [[8, 14, 1.0], [88, 96, 1.0]]}, "2020.cikm_conference-2020.109": {"snippet": "...ized Search", "weights": [[8, 14, 1.0]]}, "W19-6202": {"snippet": "...s in semantic parsing accuracy, and that the magnitude of these improvements varies with the specific combination of the syntactic and the semantic representation used. In contrast, automatically pred...", "weights": [[8, 16, 1.0], [25, 33, 1.0], [142, 150, 1.0]]}, "2014.ipm_journal-ir0volumeA50A1.8": {"snippet": "... and search techniques for Web resources considering semantic relationships\n\n\n a b s t r a c tOn the Semantic Web, the types of resources and the semantic relationships between resources are defined i...", "weights": [[8, 14, 1.0], [56, 64, 1.0], [104, 112, 1.0], [149, 157, 1.0]]}, "P11-4002": {"snippet": "...ured search in scientific digital libraries. The ACL Anthology Searchbench is meant to become a publicly available research tool to query the content of the ACL Anthology. The application provides sea...", "weights": [[8, 14, 1.0], [200, 206, 1.0]]}, "2019.ecir_workshop-2019bir.1": {"snippet": "...emic Search, Semantic Scholar). This editorial presents the 8th iteration of the oneday BIR workshop held at ECIR 2019 in Cologne, Germany.", "weights": [[8, 14, 1.0], [16, 24, 1.0]]}, "2015.ecir_workshop-2015bir.6": {"snippet": "...ding Search Facilities via Bibliometric-Enhanced Stratagems\n\n\n The paper introduces simple bibliometric-enhanced search facilities which are derived from the famous stratagems by Bates. Moves, tactics...", "weights": [[8, 14, 1.0], [116, 122, 1.0]]}, "2016.wwwconf_conference-2016c.170": {"snippet": "Semantic Enrichment for Local Search Engine using Linked Open Data\n\n\n ABSTRACTLocal search engines are a vital part of presence of local businesses on the Internet. Local search engines improvement is...", "weights": [[0, 8, 1.0], [30, 36, 1.0], [84, 90, 1.0], [171, 177, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.7": {"snippet": "...oice Search: Comparing Spoken with Typed-in Mobile Web Search Queries\n\n\n The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web sear...", "weights": [[8, 14, 1.0], [58, 64, 1.0], [109, 115, 1.0], [199, 205, 1.0]]}, "2008.tweb_journal-ir0volumeA2A3.1": {"snippet": "... Web search. Results show that search enhanced by query suggestions outperforms other systems in terms of subject perceptions and search effectiveness for fact-finding search tasks. However, search en...", "weights": [[8, 14, 1.0], [34, 40, 1.0], [133, 139, 1.0], [171, 177, 1.0], [194, 200, 1.0]]}, "2004.spire_conference-2004.22": {"snippet": "...tern search in the enhanced suffix array takes O(m|\u03a3|) time where m is the length of the pattern and \u03a3 is the alphabet, while the pattern search in the suffix tree takes O(m log |\u03a3|) time. In this pap...", "weights": [[8, 14, 1.0], [141, 147, 1.0]]}, "U05-1009": {"snippet": "...ical semantic information from WordNet to the SASH in an attempt to improve the accuracy and efficiency of similarity searches.", "weights": [[8, 16, 1.0], [83, 91, 1.0]]}, "2021.acl-long.442": {"snippet": "...Code Search and Question Answering\n\n\n Finding codes given natural language query is beneficial to the productivity of software developers. Future progress towards better semantic matching between quer...", "weights": [[8, 14, 1.0], [173, 181, 1.0]]}, "2007.cikm_conference-2007.55": {"snippet": "...tion accuracy on semantic search performance\n\n\n ABSTRACTResearchers have shown that various natural language processing techniques can be used in document analysis to impact search performance. For th...", "weights": [[8, 16, 1.0], [20, 28, 1.0], [29, 35, 1.0], [177, 183, 1.0]]}, "2012.irfc_conference-2012.3": {"snippet": "...-Art-Search Based on Keyterm Dependency Relations and Semantic Tags", "weights": [[8, 14, 1.0], [57, 65, 1.0]]}, "S15-2160": {"snippet": "...eNet semantic parsing. In the closed track our system ranked third for the semantic graph accuracy and first for exact labeled match of complete semantic graphs. These results can be attributed to the...", "weights": [[8, 16, 1.0], [78, 86, 1.0], [93, 101, 1.0], [148, 156, 1.0]]}, "2010.ipm_journal-ir0volumeA46A4.1": {"snippet": "Semantic annotation based exploratory search for information analysts\n\n\n a b s t r a c tThe system presented in this article aims to improve information access through the use of semantic annotation u...", "weights": [[0, 8, 1.0], [38, 44, 1.0], [179, 187, 1.0]]}, "2019.sigirjournals_journal-ir0volumeA53A2.13": {"snippet": "Search and analytics using semantic annotations", "weights": [[0, 6, 1.0], [27, 35, 1.0]]}, "L14-1427": {"snippet": "...tive Search for Boosting Frame-Semantic Parsing Accuracy", "weights": [[8, 14, 1.0], [34, 42, 1.0], [51, 59, 1.0]]}, "2018.chiir_conference-2018.62": {"snippet": "...yond accuracy user experience of using recommender system is drawing more and more a ention. For example, the system interface has been shown to associate positively with overall levels of user satisf...", "weights": [[8, 16, 1.0]]}, "2013.tist_journal-ir0volumeA5A1.18": {"snippet": "...cial search engines suggest search terms based on the frequency of prior used terms that match the leading alphabets the user types. In this article, we present a novel mechanism to construct semantic...", "weights": [[8, 14, 1.0], [31, 37, 1.0], [195, 203, 1.0]]}, "2013.sigirconf_conference-2013.166": {"snippet": "...s in semantic search hide the complexity behind an easy-to-use interface, they have not managed to cover common search needs. In this paper, we present X-ENS (eXplore ENtities in Search), a web search...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [115, 121, 1.0], [182, 188, 1.0], [197, 203, 1.0]]}, "W10-0801": {"snippet": "...ics: Enhancing Semantic Representation with Construction Grammar\n\n\n In Construction Grammar, structurally patterned units called constructions are assigned meaning in the same way that words are -via ...", "weights": [[8, 17, 1.0], [18, 26, 1.0]]}, "2021.blackboxnlp-1.19": {"snippet": "Enhancing Interpretable Clauses Semantically using Pretrained Word Representation\n\n\n Tsetlin Machine (TM) is an interpretable pattern recognition algorithm based on propositional logic, which has demo...", "weights": [[0, 9, 1.0]]}, "2013.coria_conference-2013.26": {"snippet": "Semantic Query Structuring to Enhance Precision of an Information Retrieval System: Application to the Medical Domain", "weights": [[0, 8, 1.0]]}, "2003.wwwconf_conference-2003.73": {"snippet": "Semantic search", "weights": [[0, 8, 1.0], [9, 15, 1.0]]}, "D19-1547": {"snippet": "...tive Semantic Parsing: A Unified Framework and A Text-to-{SQL} Case Study\n\n\n As a promising paradigm, interactive semantic parsing has shown to improve both semantic parsing accuracy and user confiden...", "weights": [[8, 16, 1.0], [117, 125, 1.0], [160, 168, 1.0], [177, 185, 1.0]]}, "P99-1054": {"snippet": "...rser accuracy, but also substantially improves the search efficiency.", "weights": [[8, 16, 1.0], [54, 60, 1.0]]}, "2005.wwwconf_conference-2005.50": {"snippet": "... and semantic information for searching in Semantic Portals. The model extends the search capabilities of existing methods and can answer more complex search requests. The ideas in a fuzzy description...", "weights": [[8, 16, 1.0], [46, 54, 1.0], [86, 92, 1.0], [154, 160, 1.0]]}, "2016.airs_conference-2016.3": {"snippet": "...ting Semantic Knowledge into Latent Matching Model in Search\n\n\n Abstract. The relevance between a query and a document in search can be represented as matching degree between the two objects. Latent s...", "weights": [[8, 16, 1.0], [57, 63, 1.0], [125, 131, 1.0]]}, "2018.sigirconf_conference-2018.246": {"snippet": "... the search query.is research focuses on utilizing techniques from semantic search in the next evolution stage of search engines, namely, the support for task completion. Search is usually performed w...", "weights": [[8, 14, 1.0], [70, 78, 1.0], [79, 85, 1.0], [117, 123, 1.0], [174, 180, 1.0]]}, "2011.cikm_conference-2011.370": {"snippet": "Enhancing accessibility of microblogging messages using semantic knowledge", "weights": [[0, 9, 1.0], [56, 64, 1.0]]}, "2017.chiir_conference-2017.67": {"snippet": "... for Enhancing Web Search Literacy\n\n\n ABSTRACTRecent studies reveal that most Web search engine users lack of appropriate search strategies for finding information efficiently and effectively, and ove...", "weights": [[8, 17, 1.0], [22, 28, 1.0], [85, 91, 1.0], [125, 131, 1.0]]}, "2013.cikm_workshop-2013esair.17": {"snippet": "... and semantic annotations in search is that \"standard text search excels at shallow information needs expressed by short keyword queries, and so semantic annotation contributes very little\". I suggest...", "weights": [[8, 16, 1.0], [32, 38, 1.0], [62, 68, 1.0], [148, 156, 1.0]]}, "1991.sigirconf_conference-91.28": {"snippet": "... the accuracy of information retrieval. Our approach is based on the database concept of semantic modeling (particularly entities and relationships among entities).We extend the concept of query-docum...", "weights": [[8, 16, 1.0], [92, 100, 1.0]]}, "2013.cikm_conference-2013.41": {"snippet": "...ient semantic full-text search", "weights": [[8, 16, 1.0], [27, 33, 1.0]]}, "2011.sigirconf_conference-2011.75": {"snippet": "... web search\n\n\n ABSTRACT \"Ten blue links\" have defined web search results for the last fifteen years -snippets of text combined with document titles and URLs. In this paper, we establish the notion of ...", "weights": [[8, 14, 1.0], [61, 67, 1.0]]}, "2021.ecir_conference-20212.57": {"snippet": "...rch: Semantic Search System for Workplace Accident Reports\n\n\n Semantic search engines, which integrate the output of text mining (TM) methods, can significantly increase the ease and efficiency of fin...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [65, 73, 1.0], [74, 80, 1.0]]}, "2014.wwwconf_conference-2014c.110": {"snippet": "...word search for smartphones", "weights": [[8, 14, 1.0]]}, "2007.ecir_conference-2007.81": {"snippet": "Enhancing Expert Search Through Query Modeling", "weights": [[0, 9, 1.0], [17, 23, 1.0]]}, "P94-1016": {"snippet": "... and semantic structure building. Two techniques are presented for reducing search by reducing local ambiguity: Limited leftcontext constraints are used to reduce local syntactic ambiguity, and deferr...", "weights": [[8, 16, 1.0], [79, 85, 1.0]]}, "1994.ipm_journal-ir0volumeA30A2.6": {"snippet": "...g in Search Enhancement", "weights": [[8, 14, 1.0]]}, "2012.trec_conference-2012.5": {"snippet": "...cord Search Accuracy", "weights": [[8, 14, 1.0], [15, 23, 1.0]]}, "2016.sigirconf_workshop-2016gamifir.4": {"snippet": "... web search literacy (WSL) by teaching search strategies and basic and advanced concepts of web search engines in the form of a task based application. Users will be introduced to more complex search ...", "weights": [[8, 14, 1.0], [42, 48, 1.0], [99, 105, 1.0], [196, 202, 1.0]]}, "2021.tois_journal-ir0volumeA39A2.0": {"snippet": "...nces Accuracy", "weights": [[8, 16, 1.0]]}, "2014.ipm_journal-ir0volumeA50A2.9": {"snippet": "...wned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance ...", "weights": [[8, 14, 1.0], [92, 100, 1.0], [101, 107, 1.0], [148, 154, 1.0]]}, "2010.wwwconf_conference-2010.218": {"snippet": "...ess, semantic Web search engines feed existing semantic data into the SIOC framework, where new semantic data are composed by the community and indexed again by those search engines. Compared to exist...", "weights": [[8, 16, 1.0], [21, 27, 1.0], [50, 58, 1.0], [99, 107, 1.0], [170, 176, 1.0]]}, "2020.emnlp-main.29": {"snippet": "Semantic Evaluation for Text-to-{SQL} with Distilled Test Suites\n\n\n We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models. Our method distills a small test suite of da...", "weights": [[0, 8, 1.0], [90, 98, 1.0], [114, 122, 1.0], [123, 131, 1.0]]}, "W14-3611": {"snippet": "...ased search is found to have some limitations. Such as word sense ambiguity, and the query intent ambiguity which can hurt the precision. Semantic search uses the contextual meaning of terms in additi...", "weights": [[8, 14, 1.0], [141, 149, 1.0], [150, 156, 1.0]]}, "W07-1427": {"snippet": "...eper semantic level. We present a machine learning approach to alignment scoring, a stochastic search procedure, and a new tool that finds deeper semantic alignments, allowing rapid development of sem...", "weights": [[8, 16, 1.0], [98, 104, 1.0], [149, 157, 1.0], [200, 208, 1.0]]}, "2007.cikm_conference-2007.47": {"snippet": "Semantic components enhance retrieval of domain-specific documents", "weights": [[0, 8, 1.0]]}, "1997.sigirconf_conference-97.39": {"snippet": "Semantic Search and Semantic Categorization (Abstract)", "weights": [[0, 8, 1.0], [9, 15, 1.0], [20, 28, 1.0]]}, "W10-1203": {"snippet": "...eval Accuracy\n\n\n Text normalization transforms words into a base form so that terms from common equivalent classes match. Traditionally, information retrieval systems employ stemming techniques to rem...", "weights": [[8, 16, 1.0]]}, "2011.sigirconf_conference-2011.170": {"snippet": "Enhancing mobile search using web search log data\n\n\n ABSTRACTMobile search is still in infancy compared with general purpose web search. With limited training data and weak relevance features, the ran...", "weights": [[0, 9, 1.0], [17, 23, 1.0], [34, 40, 1.0], [68, 74, 1.0], [129, 135, 1.0]]}, "N16-1074": {"snippet": "...ng a Semantic Parser Lexicon\n\n\n We introduce several probabilistic models for learning the lexicon of a semantic parser. Lexicon learning is the first step of training a semantic parser for a new appl...", "weights": [[8, 16, 1.0], [107, 115, 1.0], [173, 181, 1.0]]}, "2014.sigirconf_conference-2014.212": {"snippet": "...that accuracy alone might be a sub-optimal strategy for a successful user experience . Properties such as novelty and diversity have been explored in both fields for assessing and enhancing the useful...", "weights": [[8, 16, 1.0], [183, 192, 1.0]]}, "D17-1125": {"snippet": "...ient Semantic Parsing\n\n\n To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denota...", "weights": [[8, 16, 1.0], [39, 47, 1.0], [99, 105, 1.0]]}, "2016.jasis_journal-ir0volumeA67A12.6": {"snippet": "Enhancing information retrieval through concept-based language modeling and semantic smoothing", "weights": [[0, 9, 1.0], [76, 84, 1.0]]}, "1989.jasis_journal-ir0volumeA40A4.1": {"snippet": "...s by semantic and citation searching", "weights": [[8, 16, 1.0]]}, "2020.sigirconf_conference-2020.79": {"snippet": "...cted search intents and linked entities in her search history. The knowledge enhanced user profile and intent representation are then utilized by KEPS for better, knowledge enhanced, personalized sear...", "weights": [[8, 14, 1.0], [50, 56, 1.0], [199, 205, 1.0]]}, "2016.wwwconf_conference-2016c.113": {"snippet": "...able semantic search method for Internet videos that allows for intelligent and flexible search schemes over the video content (text-to-video search and text&video-to-video search). To achieve this am...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [92, 98, 1.0], [145, 151, 1.0], [176, 182, 1.0]]}, "2007.mir_workshop-2007.2": {"snippet": "Semantic search", "weights": [[0, 8, 1.0], [9, 15, 1.0]]}, "2011.tois_journal-ir0volumeA29A3.2": {"snippet": "... and semantic granularity, to improve domain-specific search. In particular, a concept-based computational model is developed to estimate the semantic granularity of documents with reference to a doma...", "weights": [[8, 16, 1.0], [57, 63, 1.0], [145, 153, 1.0]]}, "2005.ipm_journal-ir0volumeA41A4.11": {"snippet": "Enhancing semantic digital library query using a content and service inference model (CSIM)\n\n\n AbstractAlthough digital library (DL) information is becoming increasingly annotated using metadata, sema...", "weights": [[0, 9, 1.0], [10, 18, 1.0], [196, 204, 1.0]]}, "2016.ntcir_conference-2016.99": {"snippet": "...rove search accuracy. The evaluation demonstrated that our approach achieved pretty decent results with querying simple scenarios, while a more sophisticated model is needed for search complicated sce...", "weights": [[8, 14, 1.0], [15, 23, 1.0], [181, 187, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.9": {"snippet": "... Web search engine, such that news-related queries can be satisfied more accurately, comprehensively and in a more timely manner. The aim is to provide end-users with an aggregate search result rankin...", "weights": [[8, 14, 1.0], [183, 189, 1.0]]}, "I13-1115": {"snippet": "...User Search Sessions for the Semantic Categorization of Question-like Informational Search Queries\n\n\n This work proposes to semantically classify question-like search queries (e.g., \"oil based heel cr...", "weights": [[8, 14, 1.0], [32, 40, 1.0], [87, 93, 1.0], [163, 169, 1.0]]}, "L16-1457": {"snippet": "...rpus search via parsing\n\n\n In this paper, we describe an addition to the corpus query system Kontext that enables to enhance the search using syntactic attributes in addition to the existing features,...", "weights": [[8, 14, 1.0], [132, 138, 1.0]]}, "2008.cikm_conference-2008.146": {"snippet": "...d up semantic search in p2p networks", "weights": [[8, 16, 1.0], [17, 23, 1.0]]}, "2014.wwwconf_conference-2014c.138": {"snippet": "Semantic search engine with an intuitive user interface\n\n\n ABSTRACTIt is crucial to enable regular users to explore RDF-compliant data bases in an effective way, regardless their knowledge about the S...", "weights": [[0, 8, 1.0], [9, 15, 1.0]]}}}
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{"rank": 9, "score": 17.559644240776084, "doc_id": "2011.cikm_conference-2011.235"}, {"rank": 10, "score": 17.528275295993172, "doc_id": "W07-1209"}]}], "docs": {"2014.wsdm_conference-2014.3": {"snippet": "...ated search engines\n\n\n ABSTRACTFor many search settings, distributed/replicated search engines deploy a large number of machines to ensure efficient retrieval. This paper investigates how the power co...", "weights": [[8, 14, 1.0], [43, 49, 1.0], [83, 89, 1.0], [152, 161, 1.0]]}, "C94-2169": {"snippet": "...ates retrieval queries from similarities, 2) efficient example retrieval through the tree structure of a thesaurus, 3) binary search along subsumption ordering of retrieval queries. Example retrieval ...", "weights": [[8, 17, 1.0], [66, 75, 1.0], [129, 135, 1.0], [166, 175, 1.0], [193, 202, 1.0]]}, "2017.sigirconf_conference-2017.52": {"snippet": "...owed time. Experiments examining both the e ectiveness and e ciency of the proposed approach demonstrate that a 49% decrease in mean response time (and 62% decrease in 95th-percentile response time) c...", "weights": [[8, 12, 1.0], [119, 127, 1.0], [145, 149, 1.0], [159, 167, 1.0], [196, 200, 1.0]]}, "1998.sigirconf_conference-98.63": {"snippet": "...ient Search Server Assignment in a Disproportionate System Environment\n\n\n AbstractAn efficient method is proposed for assigning jobs to search servers in cluster-type system environments when search s...", "weights": [[8, 14, 1.0], [55, 61, 1.0], [139, 145, 1.0], [170, 176, 1.0], [195, 201, 1.0]]}, "2017.ictir_conference-2017.34": {"snippet": "...tion Retrieval (IIR), a system should minimise the search time by guiding the user appropriately. As a prerequisite, in any search situation, the system must be able to estimate the time the user will...", "weights": [[8, 17, 1.0], [27, 33, 1.0], [54, 60, 1.0], [61, 65, 1.0], [127, 133, 1.0], [149, 155, 1.0], [185, 189, 1.0]]}, "2005.cikm_conference-2005.81": {"snippet": "...xing time vs. query time: trade-offs in dynamic information retrieval systems\n\n\n ABSTRACTWe examine issues in the design of fully dynamic information retrieval systems supporting both document inserti...", "weights": [[8, 12, 1.0], [23, 27, 1.0], [63, 72, 1.0], [153, 162, 1.0]]}, "2011.sigirconf_conference-2011.159": {"snippet": "...ning retrieval strategies\n\n\n ABSTRACT Document-at-a-time (DAAT) dynamic pruning strategies for information retrieval systems such as MaxScore and Wand can increase querying efficiency without decreasi...", "weights": [[8, 17, 1.0], [55, 59, 1.0], [110, 119, 1.0]]}, "2013.chiir_conference-2013.1": {"snippet": "Slow Search: Information Retrieval without Time Constraints", "weights": [[5, 11, 1.0], [25, 34, 1.0], [43, 47, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.17": {"snippet": "...tion retrieval but also complex networks. Understanding network connectivity will provide guidance on how decentralized search and retrieval methods can function in these information spaces.The disser...", "weights": [[8, 17, 1.0], [123, 129, 1.0], [134, 143, 1.0]]}, "1991.tois_journal-ir0volumeA9A3.3": {"snippet": "... the search results, and automatic ranking of the retrieved passages to speed the identification of relevant information. Users' search performance using the expert system was compared with their sear...", "weights": [[8, 14, 1.0], [132, 138, 1.0], [168, 174, 1.0], [199, 205, 1.0]]}, "2006.ipm_journal-ir0volumeA42A6.3": {"snippet": "...t in time on, no new sources are created. Such systems are called mature systems. When time increases in mature systems the expected number of sources with a low number of items strictly decreases. An...", "weights": [[8, 12, 1.0], [90, 94, 1.0]]}, "2010.wwwconf_conference-2010.158": {"snippet": "... and time efficiencies without inserting any additional auxiliary information. Experimental results show that RABI achieves both high space efficiency and search efficiency, and outperforms the existi...", "weights": [[8, 12, 1.0], [158, 164, 1.0]]}, "2007.sigirconf_conference-2007.25": {"snippet": "...ance retrieval systems without resorting to methods that may harm effectiveness.We evaluate our techniques using Galago, a new retrieval system designed for efficient query processing. Our system achi...", "weights": [[8, 17, 1.0], [130, 139, 1.0], [140, 146, 1.0], [192, 198, 1.0]]}, "2013.cikm_conference-2013.160": {"snippet": "...tion retrieval systems. Specifically, the timeliness of a web document can be a significant factor for determining whether it is relevant for a search query. Previous works have proposed time-aware re...", "weights": [[8, 17, 1.0], [147, 153, 1.0], [190, 194, 1.0], [201, 210, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.8": {"snippet": "...tion retrieval problems, similarity search, geographically distributed web search, and optimization techniques for search efficiency.", "weights": [[8, 17, 1.0], [39, 45, 1.0], [78, 84, 1.0], [118, 124, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords retrieval model and the retrieval needs of the QA system. Bag-of-words IR retrieves documents matching a query, but the QA system really needs documents that contain answers. Through question ana...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [58, 64, 1.0], [131, 137, 1.0]]}, "2007.ipm_journal-ir0volumeA43A1.13": {"snippet": "...tion retrieval systems to effectively search for and locate the required information. In this study, we present several improvements over the two main bottlenecks in a distributed information retrieva...", "weights": [[8, 17, 1.0], [41, 47, 1.0], [195, 204, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A2.15": {"snippet": "... XML retrieval techniques to the EAD finding aids, develop system evaluation of EAD retrieval, and study information seeking behavior of archival search. The main information retrieval (IR) contributi...", "weights": [[8, 17, 1.0], [62, 68, 1.0], [87, 96, 1.0], [149, 155, 1.0], [178, 187, 1.0]]}, "W07-1209": {"snippet": "... the Search Space of a Hand-Crafted Parsing System with a Probabilistic Parser\n\n\n The demand for deep linguistic analysis for huge volumes of data means that it is increasingly important that the time...", "weights": [[8, 14, 1.0], [47, 53, 1.0], [199, 203, 1.0]]}, "2014.cikm_conference-2014.58": {"snippet": "...ting Search Task Difficulty at Different Search Stages\n\n\n ABSTRACTKnowing, in real time, whether a current searcher in an information retrieval system finds the search task difficult can be valuable f...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [86, 90, 1.0], [137, 146, 1.0], [147, 153, 1.0], [164, 170, 1.0]]}, "2012.cikm_workshop-2012pikm.9": {"snippet": "... Web search environment. A collaborative web search system needs not only support the interaction between a user and the system, but also support the interaction among users. A successful collaborativ...", "weights": [[8, 14, 1.0], [48, 54, 1.0], [55, 61, 1.0], [124, 130, 1.0]]}, "2002.trec_conference-2002.22": {"snippet": "... web retrieval, we modified our original search system to make it return higher precision result than before. First, we proposed a novel search algorithm to get a base set of highly relevant documents...", "weights": [[8, 17, 1.0], [44, 50, 1.0], [51, 57, 1.0], [140, 146, 1.0]]}, "1986.sigirconf_conference-86.20": {"snippet": "... the search, and the mean time required to retrieve a set of documents for a query. The processing time is further divided into CPU time and L/O time. The measurements are obtained by observing search...", "weights": [[8, 14, 1.0], [29, 33, 1.0], [102, 106, 1.0], [135, 139, 1.0], [148, 152, 1.0]]}, "1984.sigirconf_conference-84.6": {"snippet": "...n of Search Strategies in Document Retrieval Systems", "weights": [[8, 14, 1.0], [38, 47, 1.0]]}, "2007.cikm_conference-2007.13": {"snippet": "... for search within minutes or hours of arrival. Applications such as file system and email search demand even faster turnaround from search systems, requiring new content to become available for searc...", "weights": [[8, 14, 1.0], [77, 83, 1.0], [94, 100, 1.0], [136, 142, 1.0], [198, 204, 1.0]]}, "2012.sigirconf_conference-2012.12": {"snippet": "... the search process that directly impact the quality of retrieval results as experienced by the user. In particular, these measures implicitly model users as working down a list of retrieval results, ...", "weights": [[8, 14, 1.0], [59, 68, 1.0], [184, 193, 1.0]]}, "2013.cikm_conference-2013.240": {"snippet": "...mate retrieval from large corpora\n\n\n ABSTRACTInverted indexing is a ubiquitous technique used in retrieval systems including web search. Despite its popularity, it has a drawback -query retrieval time...", "weights": [[8, 17, 1.0], [100, 109, 1.0], [132, 138, 1.0], [189, 198, 1.0], [199, 203, 1.0]]}, "2020.ecir_conference-20201.47": {"snippet": "...rity Search for Formula Retrieval\n\n\n Formula retrieval systems using substructure matching are effective, but suffer from slow retrieval times caused by the complexity of structure matching. We presen...", "weights": [[8, 14, 1.0], [27, 36, 1.0], [48, 57, 1.0], [130, 139, 1.0]]}, "2004.sigirconf_conference-2004.16": {"snippet": "...SI's retrieval quality is inferior to methods such as Okapi.  The Singular Value Decomposition (SVD) used in LSI is unscalable in terms of both memory consumption and computation time.This paper addre...", "weights": [[8, 17, 1.0], [182, 186, 1.0]]}, "2010.jasis_journal-ir0volumeA61A12.6": {"snippet": "... and retrieval system familiarity on search behavior in online public access catalogs: A mixed methods study", "weights": [[8, 17, 1.0], [18, 24, 1.0], [40, 46, 1.0]]}, "2009.cikm_conference-2009.327": {"snippet": "...t of time (e.g., often more than several days) in the literature search process. This paper proposes RefMed, a novel search system for PubMed, which supports relevance ranking by enabling relevance fe...", "weights": [[8, 12, 1.0], [68, 74, 1.0], [120, 126, 1.0], [127, 133, 1.0]]}, "2008.sigirconf_conference-2008.21": {"snippet": "...ween system performance and search behavior, we control system performance, hoping to elicit adaptive search behaviors. 36 subjects each completed 12 searches using either a standard system or one of ...", "weights": [[8, 14, 1.0], [31, 37, 1.0], [59, 65, 1.0], [105, 111, 1.0], [186, 192, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.3": {"snippet": "...ased retrieval technique, the proposed system could partially bridge lexical chasms between queries and FAQs. In addition, the proposed system outperforms the traditional information retrieval systems", "weights": [[8, 17, 1.0], [42, 48, 1.0], [139, 145, 1.0], [186, 195, 1.0]]}, "2014.ecir_conference-2014.30": {"snippet": "...or a search engine to achieve fast response times to users' queries. While the performance of an information retrieval (IR) system can be enhanced through the compression of its posting lists, there i...", "weights": [[8, 14, 1.0], [112, 121, 1.0], [127, 133, 1.0]]}, "2015.trec_conference-2015.56": {"snippet": "...ased retrieval effectiveness.", "weights": [[8, 17, 1.0]]}, "P09-4006": {"snippet": "... and Retrieval System with Enhanced Query Translation and Document Retrieval\n\n\n In this paper, we introduce a multilingual access and retrieval system with enhanced query translation and multilingual ...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [70, 79, 1.0], [137, 146, 1.0], [147, 153, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...pert System based control system for the control of an Automated Storage and Retrieval System (AS/RS) is created. The control system includes several unique elements, including the hierarchical partit...", "weights": [[8, 14, 1.0], [29, 35, 1.0], [80, 89, 1.0], [90, 96, 1.0], [129, 135, 1.0]]}, "2016.cikm_conference-2016.191": {"snippet": "...ards Time-Discounted Influence Maximization\n\n\n ABSTRACTThe classical influence maximization (IM) problem in social networks does not distinguish between whether a campaign gets viral in a week or in a...", "weights": [[8, 12, 1.0]]}, "2003.mir_workshop-2003.12": {"snippet": "...ing, search & retrieval of time\u2013continuous data on the Web", "weights": [[8, 14, 1.0], [17, 26, 1.0], [30, 34, 1.0]]}, "C16-1196": {"snippet": "...ient Search: A Document Retrieval System for Speech Streams\n\n\n We present Ambient Search, an open source system for displaying and retrieving relevant documents in real time for speech input. The syst...", "weights": [[8, 14, 1.0], [27, 36, 1.0], [37, 43, 1.0], [85, 91, 1.0], [108, 114, 1.0], [172, 176, 1.0], [199, 205, 1.0]]}, "P11-4014": {"snippet": "...t-in-Time Retrieval System using Semantic Search\n\n\n The Automatic Content Linking Device is a just-in-time document retrieval system which monitors an ongoing conversation or a monologue and enriches ...", "weights": [[8, 12, 1.0], [13, 22, 1.0], [23, 29, 1.0], [45, 51, 1.0], [105, 109, 1.0], [119, 128, 1.0], [129, 135, 1.0]]}, "1983.jasis_journal-ir0volumeA34A6.2": {"snippet": "...onse time variations in an online search system", "weights": [[8, 12, 1.0], [37, 43, 1.0], [44, 50, 1.0]]}, "2006.cikm_conference-2006.23": {"snippet": "...d to decrease the size of the index by over 90%, at only a minor decrease in retrieval effectiveness. It thus allows us to make the index small enough to fit entirely into the main memory of a single ...", "weights": [[8, 16, 1.0], [68, 76, 1.0], [80, 89, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.12": {"snippet": "... the time dimension into retrieval and ranking.Leveraging the time dimension in ranking can improve the retrieval effectiveness if information about the creation or publication time of documents is av...", "weights": [[8, 12, 1.0], [28, 37, 1.0], [65, 69, 1.0], [107, 116, 1.0], [180, 184, 1.0]]}, "2005.sigirconf_conference-2005.58": {"snippet": "...tion retrieval be \"good enough\"?\n\n\n ABSTRACTWe describe a user study that examined the relationship between the quality of an Information Retrieval system and the effectiveness of its users in perform...", "weights": [[8, 17, 1.0], [141, 150, 1.0], [151, 157, 1.0]]}, "2013.cikm_conference-2013.148": {"snippet": "...TThe time cost of searching with an inverted index is directly proportional to the number of postings processed and the cost of processing each posting. Dynamic pruning reduces the number of postings ...", "weights": [[8, 12, 1.0]]}, "2018.chiir_conference-2018.35": {"snippet": "...tion Retrieval Using Search Behaviors and Time Constraints\n\n\n ABSTRACTStudies have examined how time constraints influence search behaviors; however, no effort has been spent on how time constraints m...", "weights": [[8, 17, 1.0], [24, 30, 1.0], [45, 49, 1.0], [99, 103, 1.0], [126, 132, 1.0], [185, 189, 1.0]]}, "2010.trec_conference-2010.45": {"snippet": "...cial search systems and academic information retrieval research. Our method of entity retrieval is based on a two-layer retrieval and extraction probability model (TREPM) for integrating document retr...", "weights": [[8, 14, 1.0], [48, 57, 1.0], [89, 98, 1.0], [123, 132, 1.0], [199, 208, 1.0]]}, "2005.ecir_conference-2005.27": {"snippet": "... and decrease the total amount of bits per document pointer. However, approximations developed so far requires great amounts of time or use an uncontrolled memory size. This paper presents an efficien...", "weights": [[8, 16, 1.0], [131, 135, 1.0]]}, "R09-1014": {"snippet": "...r to decrease the parse time and these ambiguities, we use an idea of combining statistical chunker based on TAG formalism, with a heuristically rule-based search method to achieve the full parses. Th...", "weights": [[8, 16, 1.0], [27, 31, 1.0], [159, 165, 1.0]]}, "1990.sigirconf_conference-90.15": {"snippet": "...pert system ranks the retrieved passages in decreasing order of probable relevance.Users' search performance using the expert system was compared with their search performance on their own, and their ...", "weights": [[8, 14, 1.0], [93, 99, 1.0], [129, 135, 1.0], [160, 166, 1.0]]}, "2011.tois_journal-ir0volumeA29A4.0": {"snippet": "...ying time from various statistics of each term to allow on-the-fly adaptation of the applied retrieval strategy. This article, by using uniform notation, formulates the problem of determining a term u...", "weights": [[8, 12, 1.0], [96, 105, 1.0]]}, "2018.cikm_conference-2018.22": {"snippet": "...ment time and effort for high-recall retrieval, we conducted a 50-person, controlled, user study. We designed a highrecall retrieval system using continuous active learning (CAL) that could display ei...", "weights": [[8, 12, 1.0], [40, 49, 1.0], [126, 135, 1.0], [136, 142, 1.0]]}, "W00-0101": {"snippet": "...tion Retrieval\n\n\n In experiments on a natural language information retrieval system that retrieves images based on textual captions, we show that syntactic complexity actually aids retrieval. We compa...", "weights": [[8, 17, 1.0], [70, 79, 1.0], [80, 86, 1.0], [184, 193, 1.0]]}, "2011.cikm_conference-2011.235": {"snippet": "...tent search\n\n\n ABSTRACTTopics in prior-art patent search are typically full patent applications and relevant items are patents often taken from sources in different languages. Cross language patent re...", "weights": [[8, 14, 1.0], [53, 59, 1.0], [201, 210, 1.0]]}, "2010.sigirconf_conference-2010.204": {"snippet": "...ated search\n\n\n Abstract:Federated search refers to the brokered retrieval of content from a set of auxiliary retrieval systems instead of from a single, centralized retrieval system. Federated search ...", "weights": [[8, 14, 1.0], [37, 43, 1.0], [67, 76, 1.0], [112, 121, 1.0], [168, 177, 1.0], [178, 184, 1.0], [196, 202, 1.0]]}, "2007.wwwconf_conference-2007.56": {"snippet": "...d of time may be denoted as continuous search queries. The improvement of continuous search queries may concern not only the quality of retrieved results but also the freshness of results, i.e. the ti...", "weights": [[8, 12, 1.0], [42, 48, 1.0], [88, 94, 1.0], [201, 205, 1.0]]}, "2017.chiir_conference-2017.53": {"snippet": "Time Limits, Information Search and the Use of Search Assistance", "weights": [[0, 4, 1.0], [25, 31, 1.0], [47, 53, 1.0]]}, "2010.wwwconf_conference-2010.34": {"snippet": "... web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However,...", "weights": [[8, 14, 1.0], [29, 38, 1.0], [110, 116, 1.0], [150, 156, 1.0], [187, 193, 1.0]]}, "2002.trec_conference-2002.45": {"snippet": "...ETRI retrieval system's response to an ad hoc search is generally a list of documents ranked by decreasing similarity to the query. A known-item search is similar to an ad hoc search but the target of...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [49, 55, 1.0], [148, 154, 1.0], [179, 185, 1.0]]}, "2008.sigirconf_conference-2008.188": {"snippet": "...eted search with corpus landscapes, to reveal search result clusters and their distribution across a whole corpus. We demonstrate with a portion of a local file system. Our interface shows multiple la...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [164, 170, 1.0]]}, "2017.chiir_conference-2017.16": {"snippet": "...ers' Time Perception during Web Search\n\n\n ABSTRACTDue to the tremendous economic value of search result pages, search engine companies have invested a lot to improve their quality. Recently, much effo...", "weights": [[8, 12, 1.0], [35, 41, 1.0], [93, 99, 1.0], [114, 120, 1.0]]}, "2015.ictir_conference-2015.33": {"snippet": "...en a time budget in milliseconds. Experiments on two web test collections show that our approach can accurately control query evaluation latency and that aggressive limits on execution time lead to mi...", "weights": [[8, 12, 1.0], [188, 192, 1.0]]}, "2016.chiir_conference-2016.41": {"snippet": "...ting Time Perception of Web Search Users", "weights": [[8, 12, 1.0], [31, 37, 1.0]]}, "1995.sigirconf_conference-95.5": {"snippet": "...tion retrieval system. Experimental results show that query evaluation time can be reduced by more than half with little impact on retrieval effectiveness.", "weights": [[8, 17, 1.0], [18, 24, 1.0], [74, 78, 1.0], [134, 143, 1.0]]}, "2017.tois_journal-ir0volumeA36A2.10": {"snippet": "...fast search system are more sensitive to delays than the users of a slow search system. Moreover, the study finds that users are more likely to notice the response latency delays beyond a certain late...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [76, 82, 1.0], [83, 89, 1.0]]}, "2017.wsdm_conference-2017.16": {"snippet": "...tion Retrieval (IR) studies such as search evaluation, user behavior analysis, temporal search result ranking and query understanding. Especially, in search evaluation studies, time is usually adopted...", "weights": [[8, 17, 1.0], [39, 45, 1.0], [91, 97, 1.0], [153, 159, 1.0], [180, 184, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...real-time\" requirement. In every day life, people expect an immediate response during discourse. The work by Coden and Brown focuses on this real-time aspect and the paper \"WASABI: Framework for Real-...", "weights": [[8, 12, 1.0], [149, 153, 1.0]]}, "1996.tois_journal-ir0volumeA14A4.0": {"snippet": "...erm. Retrieval time for inverted lists can be greatly reduced by the use of compression, but this adds to the CPU time required. Here we show that the CPU component of query response time for conjunct...", "weights": [[8, 17, 1.0], [18, 22, 1.0], [117, 121, 1.0], [186, 190, 1.0]]}, "2021.ccl-1.76": {"snippet": "\u57fa\u4e8e\u6539\u8fdb{C}onformer\u7684\u65b0\u95fb\u9886\u57df\u7aef\u5230\u7aef\u8bed\u97f3\u8bc6\u522b(End-to-End Speech Recognition in News Field based on Conformer)\n\n\n At present, the open source Chinese speech recognition data sets are mostly for the general domain, and t...", "weights": []}, "2020.sigirconf_workshop-2020birds.2": {"snippet": "...tion Retrieval (IR) has been traditionally evaluated from the perspective of the relevance of search engine results to people searching for information, i.e., the extent to which the system provides \"...", "weights": [[8, 17, 1.0], [97, 103, 1.0], [186, 192, 1.0]]}, "2016.ecir_conference-2016.69": {"snippet": "...ying Search Results Using Time - An Information Retrieval Method for Historians", "weights": [[8, 14, 1.0], [29, 33, 1.0], [51, 60, 1.0]]}, "1996.jasis_journal-ir0volumeA47A7.7": {"snippet": "...tion Retrieval Systems: From Search Product to Search Process", "weights": [[8, 17, 1.0], [32, 38, 1.0], [50, 56, 1.0]]}, "2015.tois_journal-ir0volumeA33A4.3": {"snippet": "...back Retrieval\n\n\n The inverted index is the dominant indexing method in information retrieval systems. It enables fast return of the list of all documents containing a given query term. However, for r...", "weights": [[8, 17, 1.0], [87, 96, 1.0], [202, 211, 1.0]]}, "2004.ipm_journal-ir0volumeA40A4.4": {"snippet": "...tion retrieval experimentation system prototype\n\n\n AbstractWe introduce WiIRE, a prototype system for conducting interactive information retrieval (IIR) experiments via the Internet. We conceived WiIR...", "weights": [[8, 17, 1.0], [34, 40, 1.0], [94, 100, 1.0], [140, 149, 1.0]]}, "W89-0210": {"snippet": "...sing time. As for the searching strategy for the scored parsing algorithm, it may be either parallel or sequential. But in our system , a time limit is used to stop the parsing process when a sentence...", "weights": [[8, 12, 1.0], [130, 136, 1.0], [141, 145, 1.0]]}, "2014.wwwconf_conference-2014c.65": {"snippet": "...tive search algorithm is proposed to decrease the complexity of the optimal search for the diversified search problem. Experimental results indicate that the proposed algorithm can decrease the comput...", "weights": [[8, 14, 1.0], [40, 48, 1.0], [79, 85, 1.0], [106, 112, 1.0], [184, 192, 1.0]]}, "2005.trec_conference-2005.79": {"snippet": "...irst time. For the Robust track we applied our existing information retrieval system that was originally designed for use with structured (XML) retrieval to the domain of document retrieval. For the T...", "weights": [[8, 12, 1.0], [71, 80, 1.0], [81, 87, 1.0], [147, 156, 1.0], [183, 192, 1.0]]}, "C16-2049": {"snippet": "...ient Search: Implicit Document Retrieval for Speech Streams\n\n\n In this demonstration paper we describe Ambient Search, a system that displays and retrieves documents in real time based on speech input...", "weights": [[8, 14, 1.0], [34, 43, 1.0], [114, 120, 1.0], [124, 130, 1.0], [177, 181, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.17": {"snippet": "...less search time in finding the results. This study did not find a significant correlation among search time, search correctness and search satisfaction. The study concludes with suggestions for futur...", "weights": [[8, 14, 1.0], [15, 19, 1.0], [100, 106, 1.0], [107, 111, 1.0], [113, 119, 1.0], [136, 142, 1.0]]}, "2012.cikm_conference-2012.393": {"snippet": "...imal search for verbose patent search queries\n\n\n ABSTRACTEven though queries received by traditional information retrieval systems are quite short, there are many application scenarios where long natu...", "weights": [[8, 14, 1.0], [34, 40, 1.0], [116, 125, 1.0]]}, "2011.ipm_journal-ir0volumeA47A2.7": {"snippet": "Relations between the shape of a size-frequency distribution and the shape of a rank-frequency distribution\n\n\n a b s t r a c tWe study the dependence of the shape of the rank-frequency distribution g ...", "weights": []}, "1999.sigirconf_conference-99.49": {"snippet": "... the retrieval effectiveness by more than 40% and reduces retrieval time by a factor of 12,000.To try Jester, please visit:http://shadow.ieor.berkeley.edu/humor 1. PROBLEM DEFINTION", "weights": [[8, 17, 1.0], [61, 70, 1.0], [71, 75, 1.0]]}, "2002.jasis_journal-ir0volumeA53A4.5": {"snippet": "...ring search engine performance over time", "weights": [[8, 14, 1.0], [39, 43, 1.0]]}, "1998.sigirconf_conference-98.56": {"snippet": "... the system we developed to reuse information generated by search engines and previously developed retrieval systems. Conceptually, it is similar to a meta-search engine, but with the major difference...", "weights": [[8, 14, 1.0], [62, 68, 1.0], [102, 111, 1.0], [159, 165, 1.0]]}, "2008.clef_workshop-2008w.55": {"snippet": "...hoto Retrieval by Result Fusion\n\n\n This paper considers the strategies of query expansion, relevance feedback and result fusion to increase both relevance and diversity in photo retrieval. In the text...", "weights": [[8, 17, 1.0], [181, 190, 1.0]]}, "2019.wsdm_conference-2019.90": {"snippet": "...alog retrieval where one of them allows users to also input an aspect term specifying particular perspective of a query. The current prototype system permits temporal analog search across two differen...", "weights": [[8, 17, 1.0], [146, 152, 1.0], [177, 183, 1.0]]}, "2009.cikm_conference-2009.278": {"snippet": "...ised retrieval system\n\n\n ABSTRACTWe present a personalised retrieval system that captures explicit relevance feedback to build an evolving user profile with multiple aspects. The user profile is used ...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [62, 71, 1.0], [72, 78, 1.0]]}, "1978.sigirconf_conference-78.8": {"snippet": "...tial search and clustered search. For large collections, the average search time for this algorithm is less than that for a sequential search and greater than that for a clustered search. However, the...", "weights": [[8, 14, 1.0], [29, 35, 1.0], [72, 78, 1.0], [79, 83, 1.0], [138, 144, 1.0], [183, 189, 1.0]]}, "2014.sigirconf_conference-2014.78": {"snippet": "...with time-critical search and retrieval, and little direct support is offered by search systems. Critical challenges with time-critical search include accurately inferring when users have urgent needs...", "weights": [[8, 12, 1.0], [22, 28, 1.0], [33, 42, 1.0], [84, 90, 1.0], [125, 129, 1.0], [139, 145, 1.0]]}, "2014.sigirconf_conference-2014.103": {"snippet": "...what search action information is available. Henceforth, we use the terms \"dwell time\" and \"click dwell time\" interchangeably to refer to the time spent on a search result.Given server-side logs of us...", "weights": [[8, 14, 1.0], [84, 88, 1.0], [107, 111, 1.0], [145, 149, 1.0], [161, 167, 1.0]]}, "2018.chiir_conference-2018.21": {"snippet": "...s of search quality. We had users search for answers to questions as we manipulated the placement of the only relevant document in a ranked list of search results. We show that as the quality of searc...", "weights": [[8, 14, 1.0], [37, 43, 1.0], [151, 157, 1.0], [198, 204, 1.0]]}, "2013.ictir_conference-2013.14": {"snippet": "...tion Retrieval with Time Series Query\n\n\n ABSTRACTWe study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find relevant do...", "weights": [[8, 17, 1.0], [23, 27, 1.0], [81, 90, 1.0], [121, 125, 1.0], [145, 149, 1.0], [166, 175, 1.0]]}, "2010.cikm_conference-2010.207": {"snippet": "...oves search quality by expanding the query using terms from high-ranking documents from an initial retrieval. Although PRF can often result in large gains in effectiveness, running two queries is time...", "weights": [[8, 14, 1.0], [102, 111, 1.0], [199, 203, 1.0]]}}}
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"...ajor impact on performance and determine that some features originally proposed for the task no longer provide performance gains in light of more powerful, recently discovered features. Our results co...", "weights": [[8, 14, 1.0], [18, 29, 1.0], [114, 125, 1.0]]}, "2010.sigirconf_conference-2010.74": {"snippet": "...ver, query reduction on the web is hampered by the lack of accurate query performance predictors and the constraints imposed by search engine architectures and ranking algorithms.In this paper, we pre...", "weights": [[8, 13, 1.0], [71, 76, 1.0], [77, 88, 1.0], [131, 137, 1.0]]}, "2014.jasis_journal-ir0volumeA65A12.6": {"snippet": "...ving query expansion using WordNet", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2006.sigirconf_conference-2006.137": {"snippet": "...onal query terms as a searcher types a search query, in effect offering interactive query expansion (IQE) options while the query is formulated. Analysis of the results shows that offering IQE during ...", "weights": [[8, 13, 1.0], [42, 48, 1.0], [49, 54, 1.0], [87, 92, 1.0], [93, 102, 1.0], [127, 132, 1.0]]}, "2002.sigirconf_conference-2002.22": {"snippet": "...uage query expansion techniques by degrading translation resources\n\n\n ABSTRACTThe quality of translation resources is arguably the most important factor affecting the performance of a cross-language i...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [24, 34, 1.0], [170, 181, 1.0]]}, "2012.trec_conference-2012.64": {"snippet": "...dhoc search task and four automatic runs for the filtering task. In the adhoc search task, we experiment with different pre-processing and query expansion techniques. Our most important finding is hig...", "weights": [[8, 14, 1.0], [81, 87, 1.0], [142, 147, 1.0], [148, 157, 1.0], [158, 168, 1.0]]}, "2007.ipm_journal-ir0volumeA43A3.8": {"snippet": "...Time Query Expansion (RTQE). As a searcher enters their query in a text box at the interface, RTQE provides a list of suggested additional query terms, in effect offering query expansion options while...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [59, 64, 1.0], [142, 147, 1.0], [174, 179, 1.0], [180, 189, 1.0]]}, "2004.cikm_conference-2004.38": {"snippet": "...tive query expansion\n\n\n ABSTRACTQuery expansion is a well-known technique that has been shown to improve average retrieval performance. This technique has not been used in many operational systems bec...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [41, 50, 1.0], [126, 137, 1.0]]}, "2015.clef_conference-2015.32": {"snippet": "...ased Query Term Expansion on Retrieval Effectiveness in Patent Searching", "weights": [[8, 13, 1.0], [19, 28, 1.0]]}, "H05-1121": {"snippet": "Query Expansion with the Minimum User Feedback by Transductive Learning\n\n\n Query expansion techniques generally select new query terms from a set of top ranked documents. Although a user's manual judg...", "weights": [[0, 5, 1.0], [6, 15, 1.0], [75, 80, 1.0], [81, 90, 1.0], [91, 101, 1.0], [123, 128, 1.0]]}, "2019.ecir_conference-20192.26": {"snippet": "... for Query Expansion Using Word Embeddings\n\n\n Abstract. Query expansion is a method for alleviating the vocabulary mismatch problem present in information retrieval tasks. Previous works have shown th...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [59, 64, 1.0], [65, 74, 1.0]]}, "2001.tois_journal-ir0volumeA19A1.0": {"snippet": "...ight expansion terms within Rocchio's framework for query reweighting. We compare ranking with information-theoretic query expansion versus ranking with other query expansion techniques, showing that ...", "weights": [[8, 17, 1.0], [55, 60, 1.0], [120, 125, 1.0], [126, 135, 1.0], [162, 167, 1.0], [168, 177, 1.0], [178, 188, 1.0]]}, "2013.cikm_conference-2013.250": {"snippet": "...r of expansion terms for a query may easily lead to resource consumption overhead. As web search engines apply strict constraints on response time, it is essential to estimate the impact of each expan...", "weights": [[8, 17, 1.0], [30, 35, 1.0], [93, 99, 1.0], [183, 189, 1.0], [198, 207, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.1": {"snippet": "...eral techniques have been developed, such as query expansion, cluster-based retrieval and dimensionality reduction to resolve this issue. Of these techniques, this paper performs an empirical study on...", "weights": [[8, 18, 1.0], [48, 53, 1.0], [54, 63, 1.0], [150, 160, 1.0]]}, "2013.cikm_workshop-2013ueo.7": {"snippet": "...ific query expansion\n\n\n ABSTRACTQueries submitted to a search engine are usually short and ambiguous. Query expansion is an effective way to resolve the ambiguity of a query. We attempt to make a cons...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [58, 64, 1.0], [105, 110, 1.0], [111, 120, 1.0], [171, 176, 1.0]]}, "2018.tois_journal-ir0volumeA36A3.7": {"snippet": "...oice Search: Comparing Spoken with Typed-in Mobile Web Search Queries\n\n\n The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web sear...", "weights": [[8, 14, 1.0], [58, 64, 1.0], [109, 115, 1.0], [199, 205, 1.0]]}, "2004.jasis_journal-ir0volumeA55A1.4": {"snippet": "...atic query expansion", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2007.sigirconf_conference-2007.42": {"snippet": "...bust query expansion technique based on the Markov random field model for information retrieval. The technique, called latent concept expansion, provides a mechanism for modeling term dependencies dur...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [137, 146, 1.0]]}, "P08-1017": {"snippet": "...that query expansion using only hand-crafted lexical resources leads to significant performance improvement. The performance can be further improved if the proposed method is combined with query expan...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [87, 98, 1.0], [116, 127, 1.0], [192, 197, 1.0], [198, 207, 1.0]]}, "2004.trec_conference-2004.16": {"snippet": "... the impact of domain-specific knowledge on the effectiveness of query expansion and analyzed the quality of Google as a source of query expansion terms based on pseudo-relevance feedback. Our results...", "weights": [[8, 14, 1.0], [68, 73, 1.0], [74, 83, 1.0], [134, 139, 1.0], [140, 149, 1.0]]}, "2007.cikm_conference-2007.55": {"snippet": "... the impact of information extraction accuracy on semantic search performance\n\n\n ABSTRACTResearchers have shown that various natural language processing techniques can be used in document analysis to ...", "weights": [[8, 14, 1.0], [62, 68, 1.0], [69, 80, 1.0], [156, 166, 1.0]]}, "2012.cikm_conference-2012.245": {"snippet": "...tric query expansion for enterprise search\n\n\n ABSTRACTEnterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Many information needs of enterpr...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [39, 45, 1.0], [68, 74, 1.0], [97, 103, 1.0], [125, 131, 1.0]]}, "2005.trec_conference-2005.89": {"snippet": "... H1: Query expansion using a \"clarity\"-based approach will increase effectiveness over baseline queries and baseline queries plus pseudo-relevance feedback; H2: Query expansion based on the Web will i...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [164, 169, 1.0], [170, 179, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "...s of query operators on the performance of three major Web search engines. We selected one hundred queries from the transaction log of a Web search service. Each of these original queries contained qu...", "weights": [[8, 13, 1.0], [31, 42, 1.0], [62, 68, 1.0], [144, 150, 1.0], [201, 206, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.2": {"snippet": "...en a query. We apply learning-to-rank approaches for this task. We exploit both the query features and the system configuration features in the learning-to-rank method so that the selection of configu...", "weights": [[8, 13, 1.0], [87, 92, 1.0]]}, "2010.sigirconf_conference-2010.106": {"snippet": "...edia search. Most existing multimedia search engines are able to automatically expand a list of textual query terms based on text search techniques, which can be called textual query expansion (TQE). ...", "weights": [[8, 14, 1.0], [41, 47, 1.0], [107, 112, 1.0], [133, 139, 1.0], [140, 150, 1.0], [180, 185, 1.0], [186, 195, 1.0]]}, "2009.ecir_conference-2009.57": {"snippet": "...ying Query Expansion Effectiveness", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2017.ictir_conference-2017.46": {"snippet": "...n of Query Expansion Bias\n\n\n ABSTRACTQuery expansion is a useful retrieval mechanism for creating more verbose queries from the users initial key word search. Query expansion generally have multiple p...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [46, 55, 1.0], [154, 160, 1.0], [162, 167, 1.0], [168, 177, 1.0]]}, "2018.cikm_conference-2018.168": {"snippet": "...tive Query Expansion for Web Search\n\n\n ABSTRACTQuery Expansion (QE) techniques expand the user queries with additional terms, e.g., synonyms and acronyms, to enhance the system recall. State-of-the-ar...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [32, 38, 1.0], [56, 65, 1.0], [71, 81, 1.0]]}, "2008.sigirconf_conference-2008.104": {"snippet": "The impact of history length on personalized search\n\n\n ABSTRACTPersonalized search is a promising way to better serve different users' information needs. Search history is one of the major information...", "weights": [[4, 10, 1.0], [45, 51, 1.0], [76, 82, 1.0], [154, 160, 1.0]]}, "2012.sigirconf_conference-2012.65": {"snippet": "...hing query results and its impact on search engines\n\n\n ABSTRACTWe investigate the impact of query result prefetching on the efficiency and effectiveness of web search engines. We propose offline and o...", "weights": [[8, 13, 1.0], [30, 36, 1.0], [40, 46, 1.0], [85, 91, 1.0], [95, 100, 1.0], [163, 169, 1.0]]}, "1999.trec_conference-1999.38": {"snippet": "...ovel Query Expansion Technique using Apriori Algorithm", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2005.trec_conference-2005.31": {"snippet": "...ious query expansion techniques can impairs retrieval performance.", "weights": [[8, 13, 1.0], [14, 23, 1.0], [24, 34, 1.0], [57, 68, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "... on. Query-dependent or \"dynamic\" features take the query into account and thus can only be computed at query time, whereas query-independent or \"static\" features do not rely on the query and thus can...", "weights": [[8, 13, 1.0], [55, 60, 1.0], [107, 112, 1.0], [127, 132, 1.0], [185, 190, 1.0]]}, "L08-1136": {"snippet": "... and Performance of Morphological Query Expansion and Language-Filtering Words on {B}asque Web Searching", "weights": [[8, 19, 1.0], [37, 42, 1.0], [43, 52, 1.0]]}, "2000.ir_journal-ir0volumeA1A4.3": {"snippet": "...s of Query Structure and Expansion on Retrieval Performance in Probabilistic Text Retrieval", "weights": [[8, 13, 1.0], [28, 37, 1.0], [51, 62, 1.0]]}, "1998.sigirconf_conference-98.17": {"snippet": "The Impact of Query Structure and Query Expansion on Retrieval Performance\n\n\n Abstract The effects of query structures and query expansion (QE) on retrieval performance were tested with a best match r...", "weights": [[4, 10, 1.0], [14, 19, 1.0], [34, 39, 1.0], [40, 49, 1.0], [63, 74, 1.0], [102, 107, 1.0], [123, 128, 1.0], [129, 138, 1.0], [157, 168, 1.0]]}, "2014.sigirconf_workshop-2014pir.6": {"snippet": "... Web Search: Exploring Search Query Parameters and User Information Privacy Implications-The Case of Google\n\n\n Personalised search adapts search results to the needs and interests of users. This is do...", "weights": [[8, 14, 1.0], [26, 32, 1.0], [33, 38, 1.0], [127, 133, 1.0], [141, 147, 1.0]]}, "1994.trec_conference-1994.19": {"snippet": "Query Expansion/Reduction and its Impact on Retrieval Effectiveness", "weights": [[0, 5, 1.0], [6, 15, 1.0], [34, 40, 1.0]]}, "2003.cikm_conference-2003.2": {"snippet": "Query expansion using associated queries", "weights": [[0, 5, 1.0], [6, 15, 1.0]]}, "2013.sigirconf_conference-2013.10": {"snippet": "...tted query, or reference query, but also information about the current search context, such as the user's recent search interactions. We focus on two important classes of queries that make up search c...", "weights": [[8, 13, 1.0], [28, 33, 1.0], [74, 80, 1.0], [116, 122, 1.0], [195, 201, 1.0]]}, "2015.trec_conference-2015.54": {"snippet": "Query-expansion Approaches for Microblog Retrieval", "weights": [[0, 5, 1.0], [6, 15, 1.0]]}, "2003.sigirconf_conference-2003.30": {"snippet": "...tive query expansion\n\n\n ABSTRACTMuch attention has been paid to the relative effectiveness of interactive query expansion versus automatic query expansion. Although interactive query expansion has the...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [109, 114, 1.0], [115, 124, 1.0], [142, 147, 1.0], [148, 157, 1.0], [180, 185, 1.0], [186, 195, 1.0]]}, "2008.clef_workshop-2008.37": {"snippet": "... the Impact of Thesaurus-Based Expansion Techniques in QA-Centric IR", "weights": [[8, 14, 1.0], [34, 43, 1.0], [44, 54, 1.0]]}, "2016.airs_conference-2016.10": {"snippet": "...act. Query expansion methods have been proven to be effective to improve the average performance of patent retrieval, and most of query expansion methods use single source of information for query exp...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [88, 99, 1.0], [133, 138, 1.0], [139, 148, 1.0], [194, 199, 1.0], [200, 209, 1.0]]}, "2012.ir_journal-ir0volumeA15A34.2": {"snippet": "...ving search via personalized query expansion using social media", "weights": [[8, 14, 1.0], [32, 37, 1.0], [38, 47, 1.0]]}, "2013.sigirconf_conference-2013.58": {"snippet": "Query expansion using path-constrained random walks\n\n\n ABSTRACTThis paper exploits Web search logs for query expansion (QE) by presenting a new QE method based on path-constrained random walks (PCRW),...", "weights": [[0, 5, 1.0], [6, 15, 1.0], [87, 93, 1.0], [103, 108, 1.0], [109, 118, 1.0]]}, "2004.spire_conference-2004.4": {"snippet": "Techniques for Efficient Query Expansion\n\n\n Abstract. Query expansion is a well-known method for improving average effectiveness in information retrieval. However, the most effective query expansion m...", "weights": [[0, 10, 1.0], [25, 30, 1.0], [31, 40, 1.0], [54, 59, 1.0], [60, 69, 1.0], [183, 188, 1.0], [189, 198, 1.0]]}, "2006.trec_conference-2006.49": {"snippet": "...sion search and expert search tasks. For discussion search, our focus was on query expansion techniques, using additional information from the topic statement and from message threads; while the forme...", "weights": [[8, 14, 1.0], [26, 32, 1.0], [55, 61, 1.0], [80, 85, 1.0], [86, 95, 1.0], [96, 106, 1.0]]}, "2003.jasis_journal-ir0volumeA54A5.2": {"snippet": "...s of performance variation using query expansion", "weights": [[8, 19, 1.0], [36, 41, 1.0], [42, 51, 1.0]]}, "2013.clef_conference-2013w.135": {"snippet": "...n of query expansion techniques and features used. Evaluation will be based on the c@1 measure. Best results are obtained when it uses both query expansion techniques and all features with certain wei", "weights": [[8, 13, 1.0], [14, 23, 1.0], [24, 34, 1.0], [143, 148, 1.0], [149, 158, 1.0], [159, 169, 1.0]]}, "2005.clef_workshop-2005.16": {"snippet": "...ng a query expansion technique. However, since our initial retrieval effectiveness was low, the query expansion technique had a negative impact on performance.", "weights": [[8, 13, 1.0], [14, 23, 1.0], [99, 104, 1.0], [105, 114, 1.0], [140, 146, 1.0], [150, 161, 1.0]]}, "2003.sigirconf_conference-2003.86": {"snippet": "... [1] performance. Work on query expansion looks at queries in isolation, and attempts to increase recall by expanding the query . Lau and Horvitz [2] learn to predict general web search behaviors from...", "weights": [[8, 19, 1.0], [29, 34, 1.0], [35, 44, 1.0], [125, 130, 1.0], [182, 188, 1.0]]}, "2002.wwwconf_conference-2002.32": {"snippet": "...stic query expansion using query logs\n\n\n ABSTRACTQuery expansion has long been suggested as an effective way to resolve the short query and word mismatching problems. A number of query expansion metho...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [30, 35, 1.0], [58, 67, 1.0], [133, 138, 1.0], [182, 187, 1.0], [188, 197, 1.0]]}, "2008.wsdm_conference-2008.3": {"snippet": "...y of search results. In particular, our objective is to fetch new pages that have the most impact, where the impact of a page is equal to the number of times the page appears in the top K search resul...", "weights": [[8, 14, 1.0], [94, 100, 1.0], [112, 118, 1.0], [191, 197, 1.0]]}, "2012.wwwconf_conference-2012c.215": {"snippet": "... the query expansion. We implement twodimensional faceted query expansion methods based on the results of the Latent Dirichlet Allocation (LDA) analysis of CQA resources. The question articles derivin...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [61, 66, 1.0], [67, 76, 1.0]]}, "2007.cikm_conference-2007.38": {"snippet": "... and query expansion in expert search\n\n\n ABSTRACTPseudo-relevance feedback, or query expansion, has been shown to improve retrieval performance in the adhoc retrieval task. In such a scenario, a few t...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [34, 40, 1.0], [82, 87, 1.0], [88, 97, 1.0], [135, 146, 1.0]]}, "2007.ipm_journal-ir0volumeA43A3.9": {"snippet": "...ving query precision using semantic expansion\n\n\n AbstractQuery Expansion (QE) is one of the most important mechanisms in the information retrieval field. A typical short Internet query will go through...", "weights": [[8, 13, 1.0], [39, 48, 1.0], [66, 75, 1.0], [182, 187, 1.0]]}, "2009.wwwconf_conference-2009.44": {"snippet": "...oved techniques for result caching in web search engines\n\n\n ABSTRACTQuery processing is a major cost factor in operating large web search engines. In this paper, we study query result caching, one of ...", "weights": [[8, 18, 1.0], [45, 51, 1.0], [134, 140, 1.0], [174, 179, 1.0]]}, "2017.tois_journal-ir0volumeA36A2.10": {"snippet": "... the Impact of Response Latency on User Behaviour in Web Search\n\n\n The interplay between the response latency of web search systems and users' search experience has only recently started to attract re...", "weights": [[8, 14, 1.0], [60, 66, 1.0], [120, 126, 1.0], [146, 152, 1.0]]}, "2020.clef_conference-2020w.101": {"snippet": "...alth search. Our focus for this participation is to study the effectiveness of simple query expansions for health related retrieval. We focused on several query expansions, using knowledge-based or em...", "weights": [[8, 14, 1.0], [89, 94, 1.0], [158, 163, 1.0]]}, "D12-1061": {"snippet": "...from Search Logs for Query Expansion\n\n\n This paper explores log-based query expansion (QE) models for Web search. Three lexicon models are proposed to bridge the lexical gap between Web documents and ...", "weights": [[8, 14, 1.0], [24, 29, 1.0], [30, 39, 1.0], [73, 78, 1.0], [79, 88, 1.0], [109, 115, 1.0]]}, "2009.ecir_conference-2009.33": {"snippet": "Query Expansion Using External Evidence\n\n\n Abstract. Automatic query expansion may be used in document retrieval to improve search effectiveness. Traditional query expansion methods are based on the d...", "weights": [[0, 5, 1.0], [6, 15, 1.0], [63, 68, 1.0], [69, 78, 1.0], [124, 130, 1.0], [158, 163, 1.0], [164, 173, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...e IR techniques rely fundamentally on matching words and phrases in documents to corresponding items in the query, any process that corrupts the document may cause problems. Indeed, if a critical quer...", "weights": [[8, 18, 1.0], [111, 116, 1.0], [199, 204, 1.0]]}, "W03-1110": {"snippet": "...ment Expansion: Untranslatable Cognates and Missegmented Words\n\n\n Query expansion by pseudo-relevance feedback is a well-established technique in both mono-and cross-lingual information retrieval, enr...", "weights": [[8, 17, 1.0], [69, 74, 1.0], [75, 84, 1.0]]}, "2018.sigirconf_conference-2018.147": {"snippet": "...cial search engines and reflected on its impact on research that uses them as basis of algorithmical techniques or for user studies. Search engine volatility refers to the fact that a query posed to a...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [104, 114, 1.0], [136, 142, 1.0], [187, 192, 1.0]]}, "2004.ecir_conference-2004.10": {"snippet": "Query Difficulty, Robustness, and Selective Application of Query Expansion", "weights": [[0, 5, 1.0], [59, 64, 1.0], [65, 74, 1.0]]}, "2014.ecir_conference-2014.47": {"snippet": "The Impact of Semantic Document Expansion on Cluster-Based Fusion for Microblog Search", "weights": [[4, 10, 1.0], [32, 41, 1.0], [80, 86, 1.0]]}, "2004.ipm_journal-ir0volumeA40A6.1": {"snippet": "... the performance of query expansion\n\n\n AbstractThis paper examines the factors affecting the performance of global query expansion based on term cooccurrence data and suggests a way to maximize the re...", "weights": [[8, 19, 1.0], [23, 28, 1.0], [29, 38, 1.0], [96, 107, 1.0], [118, 123, 1.0], [124, 133, 1.0]]}, "2007.clef_workshop-2007.19": {"snippet": "...sing Query Expansion", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "O11-3004": {"snippet": "...eval Performance of Query Expansion Method and On-line Search Engine on General Query) [In {C}hinese]\n\n\n Users might use general terms to query the information in need, when the exact keyword is unkno...", "weights": [[8, 19, 1.0], [23, 28, 1.0], [29, 38, 1.0], [58, 64, 1.0], [83, 88, 1.0], [141, 146, 1.0]]}, "2011.trec_conference-2011.55": {"snippet": "...tive Query Expansion\n\n\n This article describes our experiments during participation in the Legal Track of the 2011 Text REtrieval Conference. We incorporated machine learning, via selective query expa...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [193, 198, 1.0], [199, 208, 1.0]]}, "2019.ecir_conference-20191.23": {"snippet": "...sion performance impacts overall query processing speed, how the choice of query processing algorithm depends on the compression method used, and how performance is impacted by document reordering tec...", "weights": [[8, 19, 1.0], [36, 41, 1.0], [78, 83, 1.0], [153, 164, 1.0], [200, 210, 1.0]]}, "2003.tois_journal-ir0volumeA21A3.0": {"snippet": "...mal. Query optimization based on a relevance feedback or genetic algorithm using dynamic query contexts can help casual users search the Internet. These algorithms can draw on implicit user feedback b...", "weights": [[8, 13, 1.0], [92, 97, 1.0], [129, 135, 1.0]]}, "2006.sigirconf_conference-2006.51": {"snippet": "...hout query expansion, the performance is not satisfactory for short queries. In this paper, we present two novel query expansion techniques that make use of dependency relation analysis to extract con...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [29, 40, 1.0], [116, 121, 1.0], [122, 131, 1.0], [132, 142, 1.0]]}, "1997.sigirconf_conference-97.36": {"snippet": "...inal query. This is called query expansion. If the terms are provided by the system, as a result of relevance feedback for example, then it is called automatic query expansion. The potential of automa...", "weights": [[8, 13, 1.0], [30, 35, 1.0], [36, 45, 1.0], [163, 168, 1.0], [169, 178, 1.0]]}, "U08-1010": {"snippet": "...ovel query expansion method which incorporates text mining techniques into query expansion for improving Chinese information retrieval performance. Unlike most of the existing query expansion strategi...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [62, 72, 1.0], [78, 83, 1.0], [84, 93, 1.0], [138, 149, 1.0], [179, 184, 1.0], [185, 194, 1.0]]}, "2003.sigirconf_conference-2003.62": {"snippet": "...ough query expansion. This technique automatically introduces additional query terms that are statistically likely to match documents on the intended topic. However, query expansion techniques rely on...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [76, 81, 1.0], [169, 174, 1.0], [175, 184, 1.0], [185, 195, 1.0]]}, "2019.ipm_journal-ir0volumeA56A5.6": {"snippet": "Query expansion techniques for information retrieval: A survey", "weights": [[0, 5, 1.0], [6, 15, 1.0], [16, 26, 1.0]]}, "2014.ir_journal-ir0volumeA17A56.1": {"snippet": "...s on query expansion for patent retrieval", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}}}
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First, correctly ranking documents on the top of the result list is crucial for an Information Retrieval system. One mu...", "weights": [[8, 15, 1.0], [73, 82, 1.0], [101, 108, 1.0], [179, 188, 1.0], [189, 195, 1.0]]}, "2003.sigirconf_conference-2003.49": {"snippet": "... and Retrieval]: Retrieval Models.General Terms: Theory, Experimentation. Keywords: Ranking Retrieval Systems.\nINTRODUCTIONSoboroff, Nicholas and Cahan [2] recently proposed a method for evaluating re...", "weights": [[8, 17, 1.0], [20, 29, 1.0], [87, 94, 1.0], [95, 104, 1.0], [201, 210, 1.0]]}, "2008.tois_journal-ir0volumeA27A1.1": {"snippet": "...t of retrieval effectiveness\n\n\n A range of methods for measuring the effectiveness of information retrieval systems has been proposed. These are typically intended to provide a quantitative single-val...", "weights": [[8, 17, 1.0], [101, 110, 1.0]]}, "2020.birws_workshop-2020.5": {"snippet": "... for Ranking Algorithms in Legal Information Retrieval Systems", "weights": [[8, 15, 1.0], [48, 57, 1.0]]}, "S16-1127": {"snippet": "...tion retrieval. Ranking may be used in different modules in natural language processing such as search engines. In this paper, we introduce a competitive ranking system which combines three different ...", "weights": [[8, 17, 1.0], [19, 26, 1.0], [157, 164, 1.0], [165, 171, 1.0]]}, "1988.ipm_journal-ir0volumeA24A3.7": {"snippet": "... for ranking output documents in conventional Boolean retrieval systems", "weights": [[8, 15, 1.0], [57, 66, 1.0]]}, "2008.sigirconf_conference-2008.110": {"snippet": "...nion retrieval performance and required parsing time. We find that opinion finding retrieval performance is improved by integrating OpinionFinder into the retrieval system, and that retrieval performa...", "weights": [[8, 17, 1.0], [86, 95, 1.0], [158, 167, 1.0], [168, 174, 1.0], [185, 194, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords retrieval model and the retrieval needs of the QA system. Bag-of-words IR retrieves documents matching a query, but the QA system really needs documents that contain answers. Through question ana...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [58, 64, 1.0], [131, 137, 1.0]]}, "P92-1014": {"snippet": "...tion retrieval system which uses advanced natural language processing techniques to enhance the effectiveness of traditional key-word based document retrieval. The backbone of our system is a statisti...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [152, 161, 1.0], [183, 189, 1.0]]}, "2010.sigirconf_conference-2010.10": {"snippet": "...king system is the retrieval and ranking of candidate matches for a given user. We present the first in-depth study of information retrieval approaches applied to match-making systems. Specifically, w...", "weights": [[8, 14, 1.0], [22, 31, 1.0], [36, 43, 1.0], [134, 143, 1.0]]}, "2001.jasis_journal-ir0volumeA52A13.15": {"snippet": "...nked retrieval with semantic networks and vector spaces", "weights": [[8, 17, 1.0]]}, "2010.ntcir_workshop-2010evia.5": {"snippet": "Ranking Retrieval Systems without Relevance Assessments: Revisited\n\n\n We reexamine the problem of ranking retrieval systems without relevance assessments in the context of collaborative evaluation for...", "weights": [[0, 7, 1.0], [8, 17, 1.0], [98, 105, 1.0], [106, 115, 1.0]]}, "2011.cikm_conference-2011.406": {"snippet": "...lity Ranking Principle. Recently, there has been a new type of theoretical research that moves beyond the Probability Ranking Principle to determine optimal ranking algorithms that consider the rankin...", "weights": [[8, 15, 1.0], [121, 128, 1.0], [160, 167, 1.0], [197, 204, 1.0]]}, "2019.tois_journal-ir0volumeA37A1.2": {"snippet": "...ment Retrieval System Configurations\n\n\n Modern Information Retrieval (IR) systems have become more and more complex, involving a large number of parameters. For example, a system may choose from a set...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [62, 71, 1.0], [175, 181, 1.0]]}, "2007.cikm_conference-2007.96": {"snippet": "...n of retrieval systems. Using our method, we are able to accurately rank retrieval systems with up to 99% fewer relevance judgments than collected for the TREC conferences, and significantly more accu...", "weights": [[8, 17, 1.0], [76, 85, 1.0]]}, "2003.sigirconf_conference-2003.65": {"snippet": "...ems, retrieval of relevant documents, and generation of metasearch lists.\nCategories and Subject Descriptors:H.3.3 [Information Search and Retrieval]: Retrieval Models.General Terms: Algorithms, Theor...", "weights": [[8, 17, 1.0], [142, 151, 1.0], [154, 163, 1.0]]}, "2003.sigirconf_conference-2003.58": {"snippet": "...atic ranking of retrieval systems in imperfect environments\n\n\n ABSTRACTThe empirical investigation of the effectiveness of information retrieval (IR) systems requires a test collection, a set of query...", "weights": [[8, 15, 1.0], [19, 28, 1.0], [138, 147, 1.0]]}, "2015.spire_conference-2015.8": {"snippet": "...Rank Retrieval Systems Without Relevance Judgments", "weights": [[8, 17, 1.0]]}, "1994.sigirconf_conference-94.18": {"snippet": "...nked Retrieval Systems", "weights": [[8, 17, 1.0]]}, "2009.tois_journal-ir0volumeA27A4.1": {"snippet": "... for retrieval evaluation of ranked outputs. It applies decision theory to model relevance decision preferences and shows that the Probability Ranking Principle (PRP) specifies optimal ranking. It has...", "weights": [[8, 17, 1.0], [146, 153, 1.0], [188, 195, 1.0]]}, "2008.cikm_conference-2008.188": {"snippet": "...ting retrieval effectiveness using rank distributions\n\n\n ABSTRACTIn this paper, we consider the task of estimating query effectiveness, i.e., assessment of the retrieval system performance in absence ...", "weights": [[8, 17, 1.0], [163, 172, 1.0], [173, 179, 1.0]]}, "O01-2005": {"snippet": "...) re-ranking retrieval results, and (3) combining retrieval results.", "weights": [[8, 15, 1.0], [16, 25, 1.0], [53, 62, 1.0]]}, "2008.airs_conference-2008.25": {"snippet": "...raph-Ranking for Text Retrieval", "weights": [[8, 15, 1.0], [25, 34, 1.0]]}, "2007.sigirconf_conference-2007.38": {"snippet": "...ains Ranking SVM as a special case, and MHR can overcome the shortcomings which Ranking SVM suffers from. Experimental results on two information retrieval datasets show that MHR can outperform Rankin", "weights": [[8, 15, 1.0], [83, 90, 1.0], [149, 158, 1.0], [197, 204, 1.0]]}, "2006.sigirconf_conference-2006.57": {"snippet": "...tion retrieval systems aim to maximize the number of relevant documents returned to a user within some window of the top. For that goal, the probability ranking principle, which ranks documents in dec...", "weights": [[8, 17, 1.0], [156, 163, 1.0]]}, "2017.trec_conference-2017.21": {"snippet": "...i, a ranking of relevant entity-passage tuples (E, P). Tow tasks are offered: passage ranking and entity ranking. This paper introduces an algorithm and a system for passage ranking. The retrieval que...", "weights": [[8, 15, 1.0], [89, 96, 1.0], [108, 115, 1.0], [158, 164, 1.0], [177, 184, 1.0], [190, 199, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.13": {"snippet": "... two ranking principles, (2) empirically compare three ranking principles (i. e. PRP, interactive PRP, and qPRP) and two state-of-the-art ranking strategies in two retrieval scenarios, those of ad-hoc...", "weights": [[8, 15, 1.0], [58, 65, 1.0], [141, 148, 1.0], [167, 176, 1.0]]}, "2020.ecir_conference-20202.83": {"snippet": "... the ranking of search results (bibliometricenhanced information retrieval), and user-centred evaluation for ranking improvements. The goal of this project is improve the ranking in legal IR systems. ...", "weights": [[8, 15, 1.0], [68, 77, 1.0], [112, 119, 1.0], [174, 181, 1.0]]}, "1985.sigirconf_conference-85.9": {"snippet": "...tput Ranking Methodology for Document-Clustering-Based Boolean Retrieval Systems", "weights": [[8, 15, 1.0], [66, 75, 1.0]]}, "2008.sigirconf_conference-2008.35": {"snippet": "...tion retrieval, ranking, Gaussian process, learning Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are ...", "weights": [[8, 17, 1.0], [19, 26, 1.0]]}, "2007.sigirconf_conference-2007.47": {"snippet": "...ured retrieval for question answering\n\n\n ABSTRACTBag-of-words retrieval is popular among Question Answering (QA) system developers, but it does not support constraint checking and ranking on the lingu...", "weights": [[8, 17, 1.0], [65, 74, 1.0], [116, 122, 1.0], [183, 190, 1.0]]}, "2011.cikm_conference-2011.38": {"snippet": "...atic retrieval model, we propose a new dynamic ranking approach. In particular, our proposed two-level dynamic ranking model allows users to adapt the ranking through interaction, thus overcoming the ...", "weights": [[8, 17, 1.0], [50, 57, 1.0], [114, 121, 1.0], [154, 161, 1.0]]}, "2010.wsdm_conference-2010.2": {"snippet": "...ency ranking in web search\n\n\n ABSTRACTIn web search, recency ranking refers to ranking documents by relevance which takes freshness into account. In this paper, we propose a retrieval system which aut...", "weights": [[8, 15, 1.0], [64, 71, 1.0], [82, 89, 1.0], [177, 186, 1.0], [187, 193, 1.0]]}, "C18-2010": {"snippet": "... Our system outperforms previous claim retrieval system and argument mining system. Moreover, the claim-oriented ranking module can be easily adapted to new topics without any manual process or extern...", "weights": [[8, 14, 1.0], [42, 51, 1.0], [52, 58, 1.0], [79, 85, 1.0], [116, 123, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.0": {"snippet": "...atic ranking of information retrieval systems using data fusion\n\n\n AbstractMeasuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring se...", "weights": [[8, 15, 1.0], [31, 40, 1.0], [117, 126, 1.0]]}, "2001.sigirconf_conference-2001.9": {"snippet": "Ranking Retrieval Systems without Relevance Judgments\n\n\n ABSTRACTThe most prevalent experimental methodology for comparing the effectiveness of information retrieval systems requires a test collection...", "weights": [[0, 7, 1.0], [8, 17, 1.0], [156, 165, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...tion retrieval (IR) field has a long tradition of using retrieval experiments on test collections to advance the state of the art [2, 10, 131. In a common experimental scenario, a particular retrieval...", "weights": [[8, 17, 1.0], [59, 68, 1.0], [194, 203, 1.0]]}, "2007.ipm_journal-ir0volumeA43A4.14": {"snippet": "...each system is selected an equal number of times. It is shown that the average percentage of a system's documents that are only found by it and no other systems is strongly and negatively correlated w...", "weights": [[8, 14, 1.0], [98, 104, 1.0]]}, "2008.clef_workshop-2008w.47": {"snippet": "... the system developed by the Language Technologies Laboratory of INAOE for the Geographical Information Retrieval task of CLEF 2008. The presented system focuses on the problem of ranking documents in...", "weights": [[8, 14, 1.0], [107, 116, 1.0], [150, 156, 1.0], [183, 190, 1.0]]}, "2008.cikm_conference-2008.182": {"snippet": "...cery retrieval system, introduced in our earlier work. We also compare our system against an off-the-shelf retrieval tool, and show that our system is significantly better for top-ranked retrieval res", "weights": [[8, 17, 1.0], [18, 24, 1.0], [78, 84, 1.0], [110, 119, 1.0], [144, 150, 1.0], [190, 199, 1.0]]}, "2003.trec_conference-2003.98": {"snippet": "Ranking Function Discovery by Genetic Programming for Robust Retrieval\n\n\n Ranking functions are instrumental for the success of an information retrieval (search engine) system. However nearly all exis...", "weights": [[0, 7, 1.0], [61, 70, 1.0], [74, 81, 1.0], [143, 152, 1.0], [169, 175, 1.0]]}, "L18-1328": {"snippet": "...nked retrieval systems. The resulting TREC style test collection can be used to compare some alternative retrieval system designs.", "weights": [[8, 17, 1.0], [108, 117, 1.0], [118, 124, 1.0]]}, "2009.cikm_conference-2009.259": {"snippet": "... for system ranking estimation\n\n\n ABSTRACTRanking a number of retrieval systems according to their retrieval effectiveness without relying on costly relevance judgments was first explored by Soboroff ...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [65, 74, 1.0], [102, 111, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...hose retrieval system anafyses the content of images to locate images of people in a particular state. By integrating the two representation schemes into a single retrieval system, it would be hoped t...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [166, 175, 1.0], [176, 182, 1.0]]}, "2022.naacl-industry.39": {"snippet": "...tion System for Natural-Language Queries\n\n\n We present a system for document retrieval that combines direct classification with standard content-based retrieval approaches to significantly improve the...", "weights": [[8, 14, 1.0], [60, 66, 1.0], [80, 89, 1.0], [154, 163, 1.0]]}, "2018.wsdm_conference-2018.91": {"snippet": "...atic Ranking of Information Retrieval Systems\n\n\n ABSTRACTTypical information retrieval system evaluation requires expensive manually-collected relevance judgments of documents, which are used to rank ...", "weights": [[8, 15, 1.0], [31, 40, 1.0], [80, 89, 1.0], [90, 96, 1.0]]}, "2013.cikm_workshop-2013livinglab.8": {"snippet": "...llow retrieval systems to optimize their own performance directly from interactions with users via click feedback. In the software package Lerot, presented in this paper, we have bundled all ingredien...", "weights": [[8, 17, 1.0]]}, "2006.ecir_conference-2006.30": {"snippet": "...ning Ranking for Structured Information Retrieval\n\n\n Abstract. We consider the Structured Information Retrieval task which consists in ranking nested textual units according to their relevance for a g...", "weights": [[8, 15, 1.0], [43, 52, 1.0], [105, 114, 1.0], [138, 145, 1.0]]}, "2002.trec_conference-2002.45": {"snippet": "... the retrieval system's response is usually a ranked list of documents, and the system is evaluated by the rank at which the target document is retrieved.", "weights": [[8, 17, 1.0], [18, 24, 1.0], [83, 89, 1.0]]}, "2010.mir_conference-2010.33": {"snippet": "...odal retrieval and ranking: more than waveforms", "weights": [[8, 17, 1.0], [22, 29, 1.0]]}, "2006.airs_conference-2006.59": {"snippet": "...t re-ranking and standard Rocchio's relevance feedback. The document re-ranking method ranks the top retrieved documents based on the intrinsic manifold structure collectively revealed by a great amou...", "weights": [[8, 15, 1.0], [75, 82, 1.0]]}, "2017.sigirconf_conference-2017.49": {"snippet": "...tion Retrieval Meets Game Theory: The Ranking Competition Between Documents? Authors\n\n\n ABSTRACTIn competitive search se ings as the Web, there is an ongoing ranking competition between document autho...", "weights": [[8, 17, 1.0], [41, 48, 1.0], [161, 168, 1.0]]}, "2012.wwwconf_conference-2012c.163": {"snippet": "...tion Retrieval problem, where we consider retrieval as a stochastic, controllable process. The ranking action continuously controls the retrieval system's dynamics and an optimal ranking policy is fou...", "weights": [[8, 17, 1.0], [45, 54, 1.0], [98, 105, 1.0], [139, 148, 1.0], [149, 155, 1.0], [182, 189, 1.0]]}, "1991.ipm_journal-ir0volumeA27A5.2": {"snippet": "...tion retrieval system that uses statistical ranking", "weights": [[8, 17, 1.0], [18, 24, 1.0], [47, 54, 1.0]]}, "2017.sigirconf_conference-2017.141": {"snippet": "...tion Retrieval (CLIR) based on implicit feedback inferred from user interactions. We show that ranking models for CLIR with acceptable performance can be learned in an online se ing, although ranking ...", "weights": [[8, 17, 1.0], [98, 105, 1.0], [195, 202, 1.0]]}, "2011.ecir_conference-2011.30": {"snippet": "Text Retrieval Methods for Item Ranking in Collaborative Filtering", "weights": [[5, 14, 1.0], [32, 39, 1.0]]}, "1994.ipm_journal-ir0volumeA30A1.5": {"snippet": "Ranking Documents in Thesaurus-Based Boolean Retrieval Systems", "weights": [[0, 7, 1.0], [45, 54, 1.0]]}, "2008.cikm_conference-2008.92": {"snippet": "... hoc retrieval\n\n\n ABSTRACTWe introduce the Ranked Feature Fusion framework for information retrieval system design. Typical information retrieval formalisms such as the vector space model, the bestmat...", "weights": [[8, 17, 1.0], [94, 103, 1.0], [104, 110, 1.0], [139, 148, 1.0]]}, "2007.ipm_journal-ir0volumeA43A4.13": {"snippet": "... and Ranking Effects as the result list depth used in data fusion is varied\n\n\n AbstractThe Authority and Ranking Effects play a key role in data fusion. The former refers to the fact that the potentia...", "weights": [[8, 15, 1.0], [108, 115, 1.0]]}, "2010.jasis_journal-ir0volumeA61A6.0": {"snippet": "... for ranking retrieval systems without relevance judgments", "weights": [[8, 15, 1.0], [16, 25, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...tion Retrieval and Speech 3 to money or rivers, does fly refer to airplanes, insects, or trousers? In theory, if a system could automatically figure out the sense of the word intended by the user, ret...", "weights": [[8, 17, 1.0], [118, 124, 1.0], [200, 209, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...tion retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC -required s...", "weights": [[8, 17, 1.0], [42, 51, 1.0], [92, 101, 1.0], [147, 156, 1.0]]}, "2020.sigirconf_workshop-2020birds.2": {"snippet": "...y in Ranking\n\n\n Ranking in Information Retrieval (IR) has been traditionally evaluated from the perspective of the relevance of search engine results to people searching for information, i.e., the ext...", "weights": [[8, 15, 1.0], [19, 26, 1.0], [42, 51, 1.0]]}, "2005.trec_conference-2005.95": {"snippet": "...d Re-Ranking: A Two-phase Approach for Genomic Information Retrieval\n\n\n We describe in this paper the design and evaluation of the system built at Simon Fraser University for the TREC 2005 adhoc retri...", "weights": [[8, 15, 1.0], [62, 71, 1.0], [134, 140, 1.0], [198, 207, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...rive ranking models for shot and video segment retrieval. The shot retrieval and the video segment model re-use the probability of relevance and a language modeling ranking function respectively, basi...", "weights": [[8, 15, 1.0], [50, 59, 1.0], [70, 79, 1.0], [168, 175, 1.0]]}, "2008.ecir_conference-2008.74": {"snippet": "...l Re-ranking with Cluster-Based Retrieval", "weights": [[8, 15, 1.0], [35, 44, 1.0]]}, "2011.cikm_conference-2011.274": {"snippet": "Re-ranking by local re-scoring for video indexing and retrieval\n\n\n ABSTRACTVideo retrieval can be done by ranking the samples according to their probability scores that were predicted by classifiers. ...", "weights": [[3, 10, 1.0], [54, 63, 1.0], [81, 90, 1.0], [106, 113, 1.0]]}, "2004.sigirconf_conference-2004.13": {"snippet": "... and ranking for XML information retrieval\n\n\n ABSTRACTIndexing and ranking are two key factors for efficient and effective XML information retrieval. Inappropriate indexing may result in false negativ...", "weights": [[8, 15, 1.0], [36, 45, 1.0], [70, 77, 1.0], [142, 151, 1.0]]}, "2009.clef_workshop-2009w.60": {"snippet": "...bled retrieval system constructed at Southampton for the ImageCLEFphoto 2009 task. The retrieval system used Terrier as the underlying textual indexing and retrieval system, and combined it with a tec...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [90, 99, 1.0], [100, 106, 1.0], [159, 168, 1.0], [169, 175, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.9": {"snippet": "...type Retrieval System Using Statistical Ranking", "weights": [[8, 17, 1.0], [18, 24, 1.0], [43, 50, 1.0]]}, "1995.sigirconf_conference-95.30": {"snippet": "...Text retrieval systems typically produce a ranking of documents and let a user decide how far down that ranking to go. In contrast, programs that filter text streams, software that categorizes documen...", "weights": [[8, 17, 1.0], [46, 53, 1.0], [107, 114, 1.0]]}, "1999.sigirconf_conference-99.29": {"snippet": "...tion Retrieval System\n\n\n AbstractWe p r e s e n t a new method for information retrieval using hidden Markov models (HMMs). We d e v elop a general framework for incorporating multiple word generation...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [82, 91, 1.0]]}, "2007.wwwconf_conference-2007.133": {"snippet": "...tion retrieval\n\n\n ABSTRACTA number of existing information retrieval systems propose the notion of query context to combine the knowledge of query and user into retrieval to reveal the most exact desc...", "weights": [[8, 17, 1.0], [62, 71, 1.0], [164, 173, 1.0]]}, "C10-2026": {"snippet": "...obal Ranking via Data Fusion\n\n\n Global ranking, a new information retrieval (IR) technology, uses a ranking model for cases in which there exist relationships between the objects to be ranked. In the ...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [69, 78, 1.0], [103, 110, 1.0]]}}}
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"score": 12.057664421895604, "doc_id": "2013.ijmir_journal-ir0volumeA2A4.5"}]}], "docs": {"C94-2169": {"snippet": "...ased Efficient Example Retrieval by Generating Retrieval Queries from Similarities\n\n\n In example-based NLP, the problem of eoml)utational cost of example retrieval is severe, since the retrieval time ...", "weights": [[8, 17, 1.0], [26, 35, 1.0], [50, 59, 1.0], [157, 166, 1.0], [188, 197, 1.0]]}, "2012.mir_conference-2012.41": {"snippet": "Efficient graffiti image retrieval", "weights": [[0, 9, 1.0], [25, 34, 1.0]]}, "2011.sigirconf_conference-2011.13": {"snippet": "... for efficient ranked retrieval\n\n\n ABSTRACTThere is a fundamental tradeoff between effectiveness and efficiency when designing retrieval models for large-scale document collections. Effectiveness tend...", "weights": [[8, 17, 1.0], [25, 34, 1.0], [130, 139, 1.0], [140, 146, 1.0]]}, "2012.sigirconf_conference-2012.25": {"snippet": "Efficient in-memory top-k document retrieval", "weights": [[0, 9, 1.0], [35, 44, 1.0]]}, "2017.sigirconf_conference-2017.58": {"snippet": "...tion retrieval model that is optimized for learning outcomes instead of generic relevance. We do this by formulating an optimization problem that incorporates a cognitive learning model into a retriev...", "weights": [[8, 17, 1.0], [196, 205, 1.0]]}, "2021.acl-short.123": {"snippet": "...such retrieval models often require large memory to run because of the massive size of their passage index. In this paper, we introduce Binary Passage Retriever (BPR), a memory-efficient neural retrie...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [180, 189, 1.0], [197, 206, 1.0]]}, "P06-2052": {"snippet": "Efficient Sentence Retrieval Based on Syntactic Structure\n\n\n This paper proposes an efficient method of sentence retrieval based on syntactic structure. Collins proposed Tree Kernel to calculate struc...", "weights": [[0, 9, 1.0], [19, 28, 1.0], [84, 93, 1.0], [113, 122, 1.0]]}, "2022.findings-acl.117": {"snippet": "...ader models. This paper proposes a two-step question retrieval model, SQuID (Sequential Question-Indexed Dense retrieval) and distant supervision for training. SQuID uses two bi-encoders for question ...", "weights": [[8, 14, 1.0], [56, 65, 1.0], [114, 123, 1.0]]}, "2015.ictir_conference-2015.2": {"snippet": "...y of Retrieval: The Retrievability of Information\n\n\n ABSTRACTRetrievability is an important and interesting indicator that can be used in a number of ways to analyse Information Retrieval systems and ...", "weights": [[8, 17, 1.0], [181, 190, 1.0]]}, "2021.ecir_conference-20211.10": {"snippet": "...ical Retrieval Model with Semantic Residual Embeddings\n\n\n This paper presents clear, a retrieval model that seeks to complement classical lexical exact-match models such as BM25 with semantic matching...", "weights": [[8, 17, 1.0], [90, 99, 1.0], [161, 167, 1.0]]}, "2012.ijirr_journal-ir0volumeA2A2.3": {"snippet": "Efficient Retrieval Technique for Microarray Gene Expression", "weights": [[0, 9, 1.0], [10, 19, 1.0]]}, "1991.riao_conference-1991.33": {"snippet": "... for efficient full-text retrieval", "weights": [[8, 17, 1.0], [28, 37, 1.0]]}, "2021.naacl-main.292": {"snippet": "... and efficient retrieval in this paper. Unlike most existing BERT-style retrieval models, which only focus on the ranking phase in retrieval systems, our model makes considerable improvements to the r...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [75, 84, 1.0], [85, 91, 1.0], [134, 143, 1.0], [202, 211, 1.0]]}, "2001.jasis_journal-ir0volumeA52A5.6": {"snippet": "... for efficient retrieval", "weights": [[8, 17, 1.0], [18, 27, 1.0]]}, "2014.ecir_conference-2014.82": {"snippet": "Efficiently Estimating Retrievability Bias", "weights": []}, "2016.wsdm_conference-2016.10": {"snippet": "... how efficient are social media users at selecting their information sources.In this work, we propose a computational framework to quantify users' efficiency at selecting information sources. Our fram...", "weights": [[8, 17, 1.0]]}, "2017.ir_journal-ir0volumeA20A3.0": {"snippet": "...tion retrieval: introduction to special issue", "weights": [[8, 17, 1.0]]}, "2014.trec_conference-2014.70": {"snippet": "... and Efficient Web Search Framework\n\n\n The Web Track of 2014 Text REtrieval Conference (TREC) addresses the most fundamental problem of Information Retrieval. We did not intend to craft a system that ...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 160, 1.0]]}, "2012.cikm_conference-2012.269": {"snippet": "...op-k retrieval using conditional preference networks\n\n\n ABSTRACTThis paper considers top-k retrieval using Conditional Preference Network (CP-Net). As a model for expressing user preferences on multip...", "weights": [[8, 17, 1.0], [94, 103, 1.0]]}, "2008.ecir_conference-2008.84": {"snippet": "...tion Retrieval Workshop", "weights": [[8, 17, 1.0]]}, "2021.acl-short.35": {"snippet": "...ural retrieval model. The resulting retrieval model with additional data significantly outperforms retrieval models directly trained with gold annotations on Precision at N (P@N) and Mean Reciprocal R", "weights": [[8, 17, 1.0], [39, 48, 1.0], [102, 111, 1.0], [112, 118, 1.0]]}, "2008.sigirconf_conference-2008.47": {"snippet": "...mage retrieval problem, by optimizing the leave-one-out retrieval performance on the training data. 2) To be scalable, millions of images together with rich textual information have been crawled from ...", "weights": [[8, 17, 1.0], [59, 68, 1.0]]}, "1994.ipm_journal-ir0volumeA30A1.6": {"snippet": "...n an Efficient Document Retrieval System", "weights": [[8, 17, 1.0], [27, 36, 1.0]]}, "2006.sigirconf_conference-2006.24": {"snippet": "...ment models for ad-hoc retrieval\n\n\n ABSTRACTSearch algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied...", "weights": [[8, 14, 1.0], [26, 35, 1.0], [139, 148, 1.0], [177, 186, 1.0]]}, "2020.sigirconf_conference-2020.110": {"snippet": "...ment Retrieval\n\n\n Many top-k document retrieval strategies have been proposed based on the WAND and MaxScore heuristics and yet, from recent work, it is surprisingly difficult to identify the \"fastest...", "weights": [[8, 17, 1.0], [41, 50, 1.0]]}, "1991.sigirconf_conference-91.30": {"snippet": "An Efficient Directory System for Document Retrieval", "weights": [[3, 12, 1.0], [43, 52, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.13": {"snippet": "...tion retrieval: bridging the theory-effectiveness gap\n\n\n AbstractImproving the effectiveness of general retrieval models has been a long-standing difficult challenge in information retrieval research,...", "weights": [[8, 17, 1.0], [107, 116, 1.0], [117, 123, 1.0], [184, 193, 1.0]]}, "2021.repl4nlp-1.17": {"snippet": "...ense Retrieval\n\n\n We present an efficient training approach to text retrieval with dense representations that applies knowledge distillation using the Col-BERT late-interaction ranking model. Specific...", "weights": [[8, 17, 1.0], [35, 44, 1.0], [71, 80, 1.0]]}, "2004.mir_workshop-2004.26": {"snippet": "Efficient 3D object retrieval using depth images", "weights": [[0, 9, 1.0], [20, 29, 1.0]]}, "2013.wsdm_conference-2013.93": {"snippet": "...tion retrieval (LSDS-IR 2013)\n\n\n ABSTRACTThe LSDS-IR'13 workshop aims to bring together both information retrieval practitioners from industry, as well as academic researchers concerned with efficient...", "weights": [[8, 17, 1.0], [108, 117, 1.0], [194, 203, 1.0]]}, "2013.ijmir_journal-ir0volumeA2A4.5": {"snippet": "... for efficient content based video shot classification and retrieval", "weights": [[8, 17, 1.0], [62, 71, 1.0]]}, "2008.sigirconf_conference-2008.128": {"snippet": "...uage models on web data\n\n\n ABSTRACTIn this paper we explore the use of parsimonious language models for web retrieval. These models are smaller thus more efficient than the standard language models an...", "weights": [[8, 14, 1.0], [96, 102, 1.0], [111, 120, 1.0], [128, 134, 1.0], [157, 166, 1.0], [194, 200, 1.0]]}, "1995.ipm_journal-ir0volumeA31A3.6": {"snippet": "Efficient Retrieval of Partial Documents", "weights": [[0, 9, 1.0], [10, 19, 1.0]]}, "2007.wwwconf_conference-2007.123": {"snippet": "...ence retrieval model\n\n\n ABSTRACTThis paper makes an intensive investigation of the application of Bayesian network in sentence retrieval and introduces three Bayesian network based sentence retrieval ...", "weights": [[8, 17, 1.0], [130, 139, 1.0], [193, 202, 1.0]]}, "2011.cikm_conference-2011.278": {"snippet": "Efficient retrieval of 3D building models using embeddings of attributed subgraphs", "weights": [[0, 9, 1.0], [10, 19, 1.0], [35, 41, 1.0]]}, "2010.cikm_conference-2010.127": {"snippet": "...r of retrieval models to improve effectiveness.In this paper, we propose new early termination techniques for efficient query processing for the case where term proximity is integrated into the retrie...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [113, 122, 1.0], [197, 206, 1.0]]}, "2016.ictir_conference-2016.6": {"snippet": "Efficient and Effective Higher Order Proximity Modeling\n\n\n ABSTRACTBag-of-words retrieval models are widely used, and provide a robust trade-off between efficiency and effectiveness. These models ofte...", "weights": [[0, 9, 1.0], [80, 89, 1.0], [90, 96, 1.0], [189, 195, 1.0]]}, "2020.sigirconf_conference-2020.21": {"snippet": "...odel retrieval, the intrinsic correlation and the degree of view discrimination among multiple views in a 3D model have not been effectively exploited. To obtain a more efficient feature descriptor fo...", "weights": [[8, 17, 1.0], [172, 181, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...tion retrieval skills by (a) comparing student use of printed and electronic forms of an encyclopedia, (b) examining the effects of menu or command driven interfaces and task difficulty on students' s...", "weights": [[8, 17, 1.0]]}, "2015.sigirconf_conference-2015.110": {"snippet": "...ting Retrieval Models through Histogram Analysis\n\n\n ABSTRACTWe present a novel approach for efficiently evaluating the performance of retrieval models and introduce two evaluation metrics: Distributio...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [137, 146, 1.0], [147, 153, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2007.cikm_conference-2007.40": {"snippet": "...edia retrieval\n\n\n ABSTRACTAs one of the most effective query expansion approaches, local feedback is able to automatically discover new query terms and improve retrieval accuracy for different retriev...", "weights": [[8, 17, 1.0], [163, 172, 1.0], [196, 205, 1.0]]}, "2008.ecir_conference-2008.49": {"snippet": "Efficient Multimedia Time Series Data Retrieval Under Uniform Scaling and Normalisation", "weights": [[0, 9, 1.0], [38, 47, 1.0]]}, "2021.naacl-main.241": {"snippet": "...tion Retrieval with Contextualized Inverted List\n\n\n Classical information retrieval systems such as BM25 rely on exact lexical match and carry out search efficiently with inverted list index. Recent n...", "weights": [[8, 17, 1.0], [77, 86, 1.0]]}, "W03-1113": {"snippet": "...tion retrieval. In this paper, we propose a method of dynamic programming matching for information retrieval. This method is as effective as a conventional information retrieval system, even though it...", "weights": [[8, 17, 1.0], [102, 111, 1.0], [171, 180, 1.0]]}, "1993.trec_conference-1993.15": {"snippet": "... and Efficient Retrieval from Large and Dynamic Document Collections", "weights": [[8, 17, 1.0], [18, 27, 1.0]]}, "1991.riao_conference-1991.36": {"snippet": "Efficient probabilistic Inference for text retrieval", "weights": [[0, 9, 1.0], [43, 52, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A3.18": {"snippet": "...tion Retrieval Systems", "weights": [[8, 17, 1.0]]}, "1998.ecir_conference-1998.4": {"snippet": "...tion Retrieval with Information Extraction for Efficient Retrieval of Calls for Papers", "weights": [[8, 17, 1.0], [50, 59, 1.0], [60, 69, 1.0]]}, "1969.ipm_journal-ir0volumeA5A3.1": {"snippet": "...tion retrieval system", "weights": [[8, 17, 1.0]]}, "2011.airs_conference-2011.27": {"snippet": "Efficient Top-k Document Retrieval Using a Term-Document Binary Matrix", "weights": [[0, 9, 1.0], [25, 34, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...mage Retrieval by Hypertext Links\n\n\n This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, th...", "weights": [[8, 17, 1.0], [72, 81, 1.0]]}, "2015.tois_journal-ir0volumeA33A4.5": {"snippet": "...tion Retrieval\n\n\n The multinomial language model has been one of the most effective models of retrieval for more than a decade. However, the multinomial distribution does not model one important lingu...", "weights": [[8, 17, 1.0], [87, 93, 1.0], [97, 106, 1.0]]}, "2010.sigirconf_workshop-2010lsdsir.7": {"snippet": "Efficient Dynamic Pruning with Proximity Support\n\n\n Modern retrieval approaches apply not just single-term weighting models when ranking documents-instead, proximity weighting models are in common use...", "weights": [[0, 9, 1.0], [59, 68, 1.0], [117, 123, 1.0], [176, 182, 1.0]]}, "2012.cikm_conference-2012.57": {"snippet": "...m of efficient retrieval of recommendations in a MF framework. We reduce the retrieval in a MF model to an apparently simple task of finding the maximum dot-product for the user vector over the set of...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [80, 89, 1.0]]}, "2020.sigirconf_conference-2020.375": {"snippet": "...m of efficient contextualization in two common scenarios of ad-hoc ranking: passage retrieval and document retrieval. As part of this PhD we already proposed a novel efficient Transformer based passag...", "weights": [[8, 17, 1.0], [87, 96, 1.0], [110, 119, 1.0], [169, 178, 1.0]]}, "2016.cikm_conference-2016.74": {"snippet": "...tion Retrieval\n\n\n ABSTRACTA common limitation of many information retrieval (IR) models is that relevance scores are solely based on exact (i.e., syntactic) matching of words in queries and documents ...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [84, 90, 1.0]]}, "2022.tacl-1.29": {"snippet": "... and efficient crossmodal retrieval, we propose a novel fine-tuning framework that turns any pretrained text-image multi-modal model into an efficient retrieval model. The framework is based on a coop...", "weights": [[8, 17, 1.0], [29, 38, 1.0], [144, 153, 1.0], [154, 163, 1.0]]}, "2021.eacl-main.293": {"snippet": "...ased models in real-world ad-hoc retrieval applications by comparison to a \"greener and more sustainable\" alternative, comprising only 620 trainable parameters. We present an analysis of their efficac...", "weights": [[8, 14, 1.0], [36, 45, 1.0]]}, "1995.sigirconf_conference-95.6": {"snippet": "Efficient Recompression Techniques for Dynamic Full-Text Retrieval Systems", "weights": [[0, 9, 1.0], [57, 66, 1.0]]}, "1981.sigirconf_conference-81.6": {"snippet": "...arch Models into One Document Retrieval System\n\n\n AbstractMany effective search strategies derived from different models are available for document retrieval systems.However, it does not appear that t...", "weights": [[8, 14, 1.0], [33, 42, 1.0], [117, 123, 1.0], [151, 160, 1.0]]}, "2014.wsdm_conference-2014.79": {"snippet": "...tion retrieval (LSDS-IR 2014)\n\n\n ABSTRACTThe LSDS-IR'14 workshop aims to bring together information retrieval practitioners from industry and academic researchers concerned with efficient and distribu...", "weights": [[8, 17, 1.0], [103, 112, 1.0], [181, 190, 1.0]]}, "2020.emnlp-main.550": {"snippet": "...sage Retrieval for Open-Domain Question Answering\n\n\n Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, su...", "weights": [[8, 17, 1.0], [97, 106, 1.0], [115, 124, 1.0], [193, 199, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.22": {"snippet": "... (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective o...", "weights": [[8, 17, 1.0], [152, 161, 1.0]]}, "2021.emnlp-main.78": {"snippet": "... for Efficient Text Retrieval\n\n\n The semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of symbolic approaches. However, neural models' dense ...", "weights": [[8, 17, 1.0], [23, 32, 1.0], [93, 102, 1.0], [189, 195, 1.0]]}, "2020.sigirconf_conference-2020.175": {"snippet": "...ural models is dramatically different from the one used by a Transformer model, e.g., word vs. contextual embeddings. CCS CONCEPTS \u2022 Information systems \u2192 Retrieval models and ranking; Question answer", "weights": [[8, 14, 1.0], [158, 167, 1.0], [168, 174, 1.0]]}, "2022.naacl-main.272": {"snippet": "... and Efficient Retrieval via Lightweight Late Interaction\n\n\n Neural information retrieval (IR) has greatly advanced search and other knowledgeintensive language tasks. While many neural IR methods enc...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [83, 92, 1.0]]}, "2022.nlp4convai-1.15": {"snippet": "...LU}: Retrieval Augmented Task-Oriented Semantic Parsing\n\n\n While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with n...", "weights": [[8, 17, 1.0], [95, 101, 1.0]]}, "2020.sustainlp-1.14": {"snippet": "...hese models for specific domains in a data and computationally efficient manner. Typically, researchers fine-tune models using corpus-specific labeled data from sources such as TREC. We first answer t...", "weights": [[8, 14, 1.0], [66, 75, 1.0], [117, 123, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...uage models. ASR systems generally contain a language model that provides word sequence probabilities. Some information retrieval systems also rely on language models for retrieval. It is likely that ...", "weights": [[8, 14, 1.0], [123, 132, 1.0], [163, 169, 1.0], [174, 183, 1.0]]}, "2018.wsdm_conference-2018.83": {"snippet": "...ting models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applic...", "weights": [[8, 14, 1.0], [172, 181, 1.0]]}, "2021.sustainlp-1.7": {"snippet": "...ense Retrieval for Computationally Efficient Multi-hop Question Answering\n\n\n In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the...", "weights": [[8, 17, 1.0], [38, 47, 1.0], [133, 142, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.6": {"snippet": "...data retrieval, distinguished from current retrieval methods. In this model, an answer to a keyword query is a connected subgraph that contains all the query keywords. In addition, the answer is minim...", "weights": [[8, 17, 1.0], [46, 55, 1.0]]}, "2014.sigirconf_conference-2014.222": {"snippet": "...rmal models for Information Retrieval, where his research interests include: (i) Models for the retrieval of documents, sentences, experts and other information objects ; (ii) Probabilistic models of ...", "weights": [[8, 14, 1.0], [31, 40, 1.0], [84, 90, 1.0], [99, 108, 1.0], [193, 199, 1.0]]}, "2014.cikm_conference-2014.208": {"snippet": "...tion Retrieval\n\n\n ABSTRACTIncorporating semantic information into document representation is effective and potentially significant to improve retrieval performance. Recently, log-bilinear language mod...", "weights": [[8, 17, 1.0], [145, 154, 1.0]]}, "D19-1527": {"snippet": "On Efficient Retrieval of Top Similarity Vectors\n\n\n Retrieval of relevant vectors produced by representation learning critically influences the efficiency in natural language processing (NLP) tasks. I...", "weights": [[3, 12, 1.0], [13, 22, 1.0], [52, 61, 1.0]]}, "2018.sigirconf_conference-2018.242": {"snippet": "...rned Models for Information Retrieval", "weights": [[8, 14, 1.0], [31, 40, 1.0]]}, "2010.sigirconf_conference-2010.19": {"snippet": "...ning models that directly optimize the tradeoff metrics. Experiments indicate that models learned in this way provide a good balance between retrieval effectiveness and efficiency. With specific loss ...", "weights": [[8, 14, 1.0], [86, 92, 1.0], [144, 153, 1.0]]}, "2005.sigirconf_conference-2005.30": {"snippet": "...text retrieval systems has dealt primarily with methods that consider inverted lists in sequence; these methods are known as term-at-a-time methods. 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ASR systems generally contain a language model that provides word sequence probabilities. Some information retrieval systems also rely on language models for retrieval. It is likely that some informa...", "weights": [[8, 15, 1.0], [111, 120, 1.0], [121, 128, 1.0], [162, 171, 1.0]]}, "W10-4154": {"snippet": "...tion results. Evaluations show that the proposed system, which incorporates extraction and clustering technique, achieves encouraging recall and good overall performance.", "weights": [[8, 15, 1.0]]}, "2010.cikm_conference-2010.101": {"snippet": "... 2 , personalized search becomes more important and challenging, as users demand higher retrieval quality. To achieve this goal, personalized search needs to take users' personalized profiles and info...", "weights": [[8, 20, 1.0], [91, 100, 1.0], [132, 144, 1.0], [173, 185, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...tion retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC -required s...", "weights": [[8, 17, 1.0], [42, 51, 1.0], [92, 101, 1.0], [147, 156, 1.0]]}, "O06-1015": {"snippet": "...dern retrieval systems usually lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. In this study, we propose the LLSF method based o...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [131, 140, 1.0]]}, "2018.ipm_journal-ir0volumeA54A4.9": {"snippet": "...tion retrieval, there is a big lack of standard datasets and methodologies for evaluating personalized information retrieval (PIR) systems, due to the costly process of producing such datasets. Subseq...", "weights": [[8, 17, 1.0], [93, 105, 1.0], [118, 127, 1.0], [134, 141, 1.0]]}, "2008.cikm_workshop-2008ssm.5": {"snippet": "... and personalized information retrieval", "weights": [[8, 20, 1.0], [33, 42, 1.0]]}, "2013.wwwconf_conference-2013.46": {"snippet": "...arch results. While conceptually simple, there are numerous details that our methodology must handle in order to accurately attribute differences in search results to personalization. Second, we apply...", "weights": [[8, 15, 1.0], [159, 166, 1.0]]}, "2019.clef_conference-2019w.106": {"snippet": "...elog Retrieval System\n\n\n This paper describes the participation of the Organiser Team in the ImageCLEFlifelog 2019 Solve My Life Puzzle (Puzzle) and Lifelog Moment Retrieval (LMRT) tasks. We proposed ...", "weights": [[8, 17, 1.0], [167, 176, 1.0]]}, "2017.sigirconf_conference-2017.58": {"snippet": "...to a retrieval objective, and then give an algorithm for an efficient approximate solution to find the search results that represent the best 'training set' for a human learner. Our model can personal...", "weights": [[8, 17, 1.0], [113, 120, 1.0]]}, "1998.sigirconf_conference-98.67": {"snippet": "...yzed results are all recorded in a chunk for future information retrieval.Users can assign a priority to each information chunk. In the prioritized retrieval, Personal Browser will pull relevant web p...", "weights": [[8, 15, 1.0], [67, 76, 1.0], [151, 160, 1.0]]}, "1979.sigirjournals_journal-ir0volumeA13A4.3": {"snippet": "...onal Retrieval System - Annotated Bibliography", "weights": [[8, 17, 1.0]]}, "2003.sigirconf_conference-2003.49": {"snippet": "...y of retrieval systems by assessing the similarity of their retrieved results. Then, given a collection of retrieval systems and their retrieved results, we use this measure to assess the average simi...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [73, 80, 1.0], [110, 119, 1.0], [120, 127, 1.0], [148, 155, 1.0]]}, "2002.ntcir_workshop-2002.58": {"snippet": "... Our results show that if information retrieval systems use the OKAPI method, results can be used for Web retrieval that is based on scored results without score normalization. On the other hand, if t...", "weights": [[8, 15, 1.0], [41, 50, 1.0], [51, 58, 1.0], [81, 88, 1.0], [109, 118, 1.0], [143, 150, 1.0]]}, "2011.wsdm_conference-2011.9": {"snippet": "...arch results has long been recognized as an avenue to greatly improve the search experience. We present a personalization approach that builds a user interest profile using users' complete browsing be...", "weights": [[8, 15, 1.0]]}, "D19-1193": {"snippet": "... for Personalized Response Selection in Retrieval-Based Chatbots\n\n\n This paper proposes a dually interactive matching network (DIM) for presenting the personalities of dialogue agents in retrievalbase...", "weights": [[8, 20, 1.0], [43, 52, 1.0]]}, "2013.clef_conference-2013w.93": {"snippet": "...hoto Retrieval\n\n\n In this paper, we describe our methods for ImageCLEF 2013 Personal Photo Retrieval Task. We devote our attention to making our system efficient in retrieving documents which have the...", "weights": [[8, 17, 1.0], [94, 103, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...mage retrieval from other hypermedia collections. The paper initially develops the model of retrieval and then presents the results of a series of experiments with a large Web based collection.\nThe mo...", "weights": [[8, 17, 1.0], [95, 104, 1.0], [127, 134, 1.0]]}, "2013.wwwconf_conference-2013.122": {"snippet": "...cing personalized search by mining and modeling task behavior\n\n\n ABSTRACTPersonalized search systems tailor search results to the current user intent using historic search interactions. This relies on...", "weights": [[8, 20, 1.0], [96, 103, 1.0], [118, 125, 1.0]]}, "2010.ipm_journal-ir0volumeA46A1.4": {"snippet": "...ging systems\n\n\n a b s t r a c tSocial media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaini...", "weights": [[8, 15, 1.0], [47, 54, 1.0]]}, "2011.sigirconf_conference-2011.229": {"snippet": "...n in personalized multilingual information retrieval\n\n\n AbstractA general characteristic of Information Retrieval (IR) and Multilingual IR (MIR)  systems is that if the same query was submitted by dif...", "weights": [[8, 20, 1.0], [46, 55, 1.0], [107, 116, 1.0], [149, 156, 1.0]]}, "I08-1068": {"snippet": "... for Personalized Search\n\n\n Web search personalization has been well studied in the recent few years. Relevance feedback has been used in various ways to improve relevance of search results. In this p...", "weights": [[8, 20, 1.0], [185, 192, 1.0]]}, "2010.cikm_conference-2010.181": {"snippet": "...ntal results demonstrate that using re-finding predictions can improve retrieval performance for personalized search.", "weights": [[8, 15, 1.0], [74, 83, 1.0], [100, 112, 1.0]]}, "2000.riao_conference-2000.130": {"snippet": "...of a Personalized Filtering Information Retrieval System - Methodology and Experimental Results", "weights": [[8, 20, 1.0], [43, 52, 1.0], [91, 98, 1.0]]}, "2012.cikm_workshop-2012shb.6": {"snippet": "... for personalized health information retrieval\n\n\n ABSTRACTWith the rapid proliferation of the Internet, traditional Information Retrieval (IR) techniques need to address challenges that stem from info...", "weights": [[8, 20, 1.0], [40, 49, 1.0], [131, 140, 1.0]]}, "2013.sigirconf_conference-2013.193": {"snippet": "...tive retrieval and recommender systems: BARS 2013\n\n\n ABSTRACTEvaluating adaptive and personalized information retrieval techniques is known to be a difficult endeavor. The rapid evolution of novel tec...", "weights": [[8, 17, 1.0], [34, 41, 1.0], [88, 100, 1.0], [113, 122, 1.0]]}, "2010.cikm_conference-2010.59": {"snippet": "...such personalized retrieval systems. Thorough comparative evaluation of such complex systems has been difficult due to the lack of appropriate publicly available datasets that provide such diverse fea...", "weights": [[8, 20, 1.0], [21, 30, 1.0], [31, 38, 1.0], [88, 95, 1.0]]}, "2020.sigirconf_conference-2020.352": {"snippet": "...more personalized to different users for the same search query. In this paper, we present a novel approach called DPSR, which stands for Deep Personalized and Semantic Retrieval, to tackle this proble...", "weights": [[8, 20, 1.0], [145, 157, 1.0], [171, 180, 1.0]]}, "2010.cikm_conference-2010.230": {"snippet": "...or a personalized information retrieval system. We introduce two new ontologies for metadata creation and a novel semi-automatic topic vocabulary extraction algorithm. We demonstrate and evaluate our ...", "weights": [[8, 20, 1.0], [33, 42, 1.0]]}, "W17-2708": {"snippet": "Inference of Fine-Grained Event Causality from Blogs and Films\n\n\n Human understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a ke...", "weights": []}, "2019.tois_journal-ir0volumeA37A2.5": {"snippet": "... the personalized product search. To this end, we propose a novel Attentive Long Short-Term Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks app...", "weights": [[8, 20, 1.0], [134, 146, 1.0]]}, "2005.cikm_conference-2005.163": {"snippet": "... for personalized search\n\n\n ABSTRACTInformation retrieval systems (e.g., web search engines) are critical for overcoming information overload. A major deficiency of existing retrieval systems is that ...", "weights": [[8, 20, 1.0], [51, 60, 1.0], [61, 68, 1.0], [177, 186, 1.0], [187, 194, 1.0]]}, "1979.sigirconf_conference-79.13": {"snippet": "...phic Retrieval System", "weights": [[8, 17, 1.0]]}, "2021.nlp4convai-1.17": {"snippet": "...from personalized factors, such as user's speech pattern, dialect, or preferences. In this work, we propose a personalized search-based QR framework, which focuses on automatic reduction of user defec...", "weights": [[8, 20, 1.0], [113, 125, 1.0]]}, "P06-1074": {"snippet": "...h to Personalized Search\n\n\n General information retrieval systems are designed to serve all users without considering individual needs. In this paper, we propose a novel approach to personalized searc...", "weights": [[8, 20, 1.0], [51, 60, 1.0], [61, 68, 1.0], [185, 197, 1.0]]}, "2018.coria_conference-2018.8": {"snippet": "...tion Retrieval System Results", "weights": [[8, 17, 1.0], [25, 32, 1.0]]}, "2017.chiir_conference-2017.72": {"snippet": "... the retrieval engine that looks for matching video segments, and then browses the results, which are typically lists of keyframes extracted from the result segments.Retrieval approaches commonly use ...", "weights": [[8, 17, 1.0], [86, 93, 1.0], [169, 178, 1.0]]}, "2006.cikm_conference-2006.45": {"snippet": "...tion retrieval is now moving into a personalized scenario where a retrieval or filtering system maintains a separate user profile for each user. In this framework, information delivered to the user ca...", "weights": [[8, 17, 1.0], [39, 51, 1.0], [69, 78, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...tion systems have indicated that the content of the knowledge base and the reasoning behavior of systems analysts are two important factors in the development of computer based information systems. Th...", "weights": [[8, 15, 1.0], [100, 107, 1.0], [192, 199, 1.0]]}, "2011.cikm_conference-2011.26": {"snippet": "...ized retrieval models aim at capturing user interests to provide personalized results that are tailored to the respective information needs. User interests are however widely spread, subject to change...", "weights": [[8, 17, 1.0], [68, 80, 1.0], [81, 88, 1.0]]}, "2008.sigirconf_conference-2008.106": {"snippet": "...ards personalized distributed information retrieval\n\n\n ABSTRACTOur aim is to investigate if and how the performance of Distributed Information Retrieval (DIR) systems can be improved through personali...", "weights": [[8, 20, 1.0], [45, 54, 1.0], [146, 155, 1.0], [162, 169, 1.0]]}, "2005.cikm_conference-2005.24": {"snippet": "...arch results: WebHawk\n\n\n ABSTRACTFinding information about people on the Web using a search engine is difficult because there is a many-to-many mapping between person names and specific persons (i.e. ...", "weights": [[8, 15, 1.0]]}, "2018.clef_conference-2018w.50": {"snippet": "...n of Personalized Information Retrieval\n\n\n Personalized Information Retrieval (PIR) is an effective solution when purposes of queries are issued but users receive the same results. The PIR-CLEF 2018 t...", "weights": [[8, 20, 1.0], [33, 42, 1.0], [46, 58, 1.0], [71, 80, 1.0], [175, 182, 1.0]]}, "2020.jasis_journal-ir0volumeA71A3.7": {"snippet": "...tion retrieval: A survey", "weights": [[8, 17, 1.0]]}, "2012.sigirconf_conference-2012.23": {"snippet": "...arch results using interaction behaviors in search sessions\n\n\n ABSTRACTPersonalization of search results offers the potential for significant improvement in information retrieval performance. User int...", "weights": [[8, 15, 1.0], [100, 107, 1.0], [172, 181, 1.0]]}, "2018.wwwconf_conference-2018.189": {"snippet": "...cks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads ...", "weights": [[8, 20, 1.0], [42, 51, 1.0], [127, 136, 1.0], [163, 172, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [117, 126, 1.0], [127, 134, 1.0]]}, "2020.sigirconf_conference-2020.143": {"snippet": "... for Personalized Search\n\n\n Personalized search is a task to tailor the general document ranking list based on user interests to better satisfy the user's information need. Many personalized search mo...", "weights": [[8, 20, 1.0], [31, 43, 1.0], [181, 193, 1.0]]}, "2017.clef_conference-2017w.44": {"snippet": "...tion Retrieval. A Position Paper\n\n\n Two key, but usually ignored, issues for the evaluation of methods of personalization for information retrieval are: that such evaluation must be of a search sessio...", "weights": [[8, 17, 1.0], [141, 150, 1.0]]}, "2013.wwwconf_conference-2013.75": {"snippet": "... Our results indicate that LTP achieves high accuracy (R-pre = 84%) in discovering personalized topics. For Google data, our qualitative results demonstrate that the topics determined by LTP for a use...", "weights": [[8, 15, 1.0], [86, 98, 1.0], [140, 147, 1.0]]}, "1987.sigirconf_conference-87.37": {"snippet": "...tion Retrieval Systems\n\n\n AbstractInformation retrieval (IR) systems provide individual remote access to centrally managed data. The current proliferation of personal computer systems, as well as adva...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [49, 58, 1.0], [64, 71, 1.0], [179, 186, 1.0]]}, "2013.clef_conference-2013w.29": {"snippet": "...hoto Retrieval Runs at ImageCLEF 2013\n\n\n This paper summarizes the results of the BTU DBIS research group's participation in the Personal Photo Retrieval subtask of Image-CLEF 2013. In order to solve ...", "weights": [[8, 17, 1.0], [70, 77, 1.0], [147, 156, 1.0]]}, "2016.mir_conference-2016.68": {"snippet": "Personalized Retrieval and Browsing of Classical Music and Supporting Multimedia Material", "weights": [[0, 12, 1.0], [13, 22, 1.0]]}, "2013.ipm_journal-ir0volumeA49A5.11": {"snippet": "... hoc retrieval paradigm. Among these approaches, personalized search systems and exploratory search systems attracted many followers. Personalized search explored the power of artificial intelligence ...", "weights": [[8, 17, 1.0], [52, 64, 1.0], [72, 79, 1.0], [103, 110, 1.0], [137, 149, 1.0]]}, "2009.cikm_conference-2009.278": {"snippet": "...ised retrieval system issue fewer queries and interact with fewer results than subjects using a baseline system. The interaction data also shows a trend of subjects interacting with the proactively re...", "weights": [[8, 17, 1.0], [69, 76, 1.0]]}, "2012.ecir_conference-2012.60": {"snippet": "...tion Retrieval", "weights": [[8, 17, 1.0]]}, "C18-1292": {"snippet": "Personalized Text Retrieval for Learners of {C}hinese as a Foreign Language\n\n\n This paper describes a personalized text retrieval algorithm that helps language learners select the most suitable readin...", "weights": [[0, 12, 1.0], [18, 27, 1.0], [102, 114, 1.0], [120, 129, 1.0]]}, "2012.ipm_journal-ir0volumeA48A4.6": {"snippet": "...tion Retrieval and Adaptive Hypermedia techniques\n\n\n a b s t r a c tA key driver for next generation web information retrieval systems is becoming the degree to which a user's search and presentation ...", "weights": [[8, 17, 1.0], [120, 129, 1.0], [130, 137, 1.0]]}, "2020.wsdm_conference-2020.15": {"snippet": "... the results from search ranking experiments performed on two large-scale personal search systems. 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It is necessary to understand the new progress of NLP and their potential impact to a search engine.", "weights": [[8, 14, 1.0], [15, 21, 1.0], [81, 87, 1.0], [171, 177, 1.0], [183, 189, 1.0], [190, 196, 1.0]]}, "2011.ipm_journal-ir0volumeA47A3.5": {"snippet": "...pert search\n\n\n a b s t r a c tThe retrieval effectiveness of the underlying document search component of an expert search engine can have an important impact on the effectiveness of the generated expe...", "weights": [[8, 14, 1.0], [47, 60, 1.0], [88, 94, 1.0], [118, 124, 1.0], [125, 131, 1.0], [154, 160, 1.0], [168, 181, 1.0]]}, "2010.sigirconf_conference-2010.74": {"snippet": "... the search engine's architecture for rank-time query reduction. 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Most prior research of federated search focused on...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [22, 35, 1.0], [50, 56, 1.0], [78, 84, 1.0], [100, 106, 1.0], [138, 144, 1.0], [186, 192, 1.0]]}, "2005.wwwconf_conference-2005si.25": {"snippet": "How search engines shape the web\n\n\n AbstractThe state of the web today has been and continues to be greatly influenced by the existence of web-search engines. This panel will discuss the ways in which...", "weights": [[4, 10, 1.0], [143, 149, 1.0]]}, "2018.sigirconf_conference-2018.147": {"snippet": "...cial search engines and reflected on its impact on research that uses them as basis of algorithmical techniques or for user studies. Search engine volatility refers to the fact that a query posed to a...", "weights": [[8, 14, 1.0], [44, 50, 1.0], [136, 142, 1.0], [143, 149, 1.0]]}, "2009.ir_journal-ir0volumeA12A3.4": {"snippet": "...eval effectiveness of search engines for diacritisized Arabic documents", "weights": [[8, 21, 1.0], [25, 31, 1.0]]}, "2020.sdp-1.23": {"snippet": "...ural Search and Insights Platform for Navigating and Organizing {AI} Research\n\n\n To provide AI researchers with modern tools for dealing with the explosive growth of the research literature in their f...", "weights": [[8, 14, 1.0], [68, 70, 1.0], [95, 97, 1.0]]}, "2015.cikm_conference-2015.183": {"snippet": "...pid\" Search Components on User Interaction with Search Engines", "weights": [[8, 14, 1.0], [51, 57, 1.0]]}, "2010.wsdm_conference-2010.36": {"snippet": "...ored search\n\n\n ABSTRACTSponsored search is a multi-billion dollar business that generates most of the revenue for search engines. Predicting the probability that users click on ads is crucial to spons...", "weights": [[8, 14, 1.0], [36, 42, 1.0], [117, 123, 1.0]]}, "2005.sigirconf_conference-2005.57": {"snippet": "... the search results.What techniques do people use to defeat IR algorithms? What are the evaluation challenges for a web search engine? How much impact has IR had on search engines? How does Google ser...", "weights": [[8, 14, 1.0], [123, 129, 1.0], [130, 136, 1.0], [147, 153, 1.0], [168, 174, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.8": {"snippet": "...of a search engine depends on the speed with which it answers queries (efficiency) and the quality of its answers (effectiveness). These two metrics have a large impact on the operational costs of the...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [118, 131, 1.0], [165, 171, 1.0]]}, "2019.ecir_conference-20192.20": {"snippet": "... the Impact of Storing Query Frequency History for Search Engine Result Caching\n\n\n Abstract.We investigate the impact of the size of query frequency history and its compact representation in memory fo...", "weights": [[8, 14, 1.0], [54, 60, 1.0], [61, 67, 1.0], [114, 120, 1.0]]}, "2018.tois_journal-ir0volumeA36A4.1": {"snippet": "... The effectiveness of a search engine is typically evaluated using hand-labeled datasets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too larg...", "weights": [[8, 21, 1.0], [27, 33, 1.0], [34, 40, 1.0]]}, "2004.wwwconf_conference-2004at.133": {"snippet": "...ars, search engine research has grown rapidly in areas such as algorithms, strategies and architecture, increasing both effectiveness and quality of results. However, a very important aspect that is o...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [123, 136, 1.0]]}, "2012.sigirconf_conference-2012.65": {"snippet": "... its impact on search engines\n\n\n ABSTRACTWe investigate the impact of query result prefetching on the efficiency and effectiveness of web search engines. We propose offline and online strategies for s...", "weights": [[8, 14, 1.0], [18, 24, 1.0], [63, 69, 1.0], [120, 133, 1.0], [141, 147, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.12": {"snippet": "... the effectiveness of search engines in the retrieval of relevant ecommerce links. The study examines the effectiveness of five different types of search engines in response to ecommerce queries by co...", "weights": [[8, 21, 1.0], [25, 31, 1.0], [109, 122, 1.0], [150, 156, 1.0]]}, "2010.ipm_journal-ir0volumeA46A3.4": {"snippet": "...uage search engines provide multimedia searching, and what multimedia search functionalities are available in Chinese language Web search engines. Specifically, we examine each Web search engine's (1)...", "weights": [[8, 14, 1.0], [73, 79, 1.0], [134, 140, 1.0], [184, 190, 1.0], [191, 197, 1.0]]}, "2007.jasis_journal-ir0volumeA58A12.8": {"snippet": "...ving search engines by query clustering", "weights": [[8, 14, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.37": {"snippet": "... Web search behavior\n\n\n A B S T R A C TAn individual's Web search behavior can be influenced by a number of factors, including features and functions of a search engine as well as search education. In...", "weights": [[8, 14, 1.0], [62, 68, 1.0], [158, 164, 1.0], [165, 171, 1.0], [183, 189, 1.0]]}, "2007.sigirconf_conference-2007.26": {"snippet": "The impact of caching on search engines\n\n\n ABSTRACTIn this paper we study the trade-offs in designing efficient caching systems for Web search engines. We explore the impact of different approaches, s...", "weights": [[4, 10, 1.0], [25, 31, 1.0], [136, 142, 1.0], [167, 173, 1.0]]}, "2011.sigirconf_conference-2011.227": {"snippet": "...in a search engine, (2) devising efficient and effective solutions to minimise or eliminate the bottlenecks and (2) adopting the solutions for distributed IR.As shown previously , a large portion of t...", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2018.wwwconf_conference-2018c.88": {"snippet": "...very Engine for AI Research", "weights": [[8, 14, 1.0], [19, 21, 1.0]]}, "2003.sigirconf_conference-2003.102": {"snippet": "... the search engine.(3) Search Engine Discovery: If a user submits a URL that does not have a search engine on the Webpage, this component will crawl Webpages \"nearby\" to find search engine interfaces....", "weights": [[8, 14, 1.0], [15, 21, 1.0], [26, 32, 1.0], [33, 39, 1.0], [96, 102, 1.0], [103, 109, 1.0], [178, 184, 1.0], [185, 191, 1.0]]}, "2013.sigirconf_conference-2013.71": {"snippet": "The impact of solid state drive on search engine cache management\n\n\n ABSTRACTCaching is an important optimization in search engine architectures. Existing caching techniques for search engine optimiza...", "weights": [[4, 10, 1.0], [35, 41, 1.0], [42, 48, 1.0], [117, 123, 1.0], [124, 130, 1.0], [178, 184, 1.0], [185, 191, 1.0]]}, "2011.jasis_journal-ir0volumeA62A7.5": {"snippet": "...y of search engine results", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2012.spire_conference-2012.17": {"snippet": "Impact of Regionalization on Performance of Web Search Engine Result Caches", "weights": [[0, 6, 1.0], [48, 54, 1.0], [55, 61, 1.0]]}, "2017.jasis_journal-ir0volumeA68A8.3": {"snippet": "...n of search engine results", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2003.tois_journal-ir0volumeA21A1.3": {"snippet": "... WIN search engine, a sibling to INQUERY, run against one or more authority sources when search is required. We compare our approach to one that does not recognize or utilize distinct features associa...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [92, 98, 1.0]]}, "2012.wwwconf_conference-2012.18": {"snippet": "...ting search engine overload, and implement the two-level cache scheme on a very busy web server. Our experimental results show that the proposed caching framework can effectively reduce the impact of ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [193, 199, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.16": {"snippet": "....com search engine (the first set was from Dogpile, the second was from across the Infospace Network of search properties were submitted to the four single Web search engines). Findings show that the ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [107, 113, 1.0], [163, 169, 1.0]]}, "2020.jeptalnrecital-jep.56": {"snippet": "...e. L'impact du d\u00e9veloppement lexical sur la production des deux fricatives a \u00e9t\u00e9 investigu\u00e9. Les r\u00e9sultats sugg\u00e8rent, d'une part, un contraste \u00e9mergeant plus pr\u00e9cocement chez les bilingues fran\u00e7ais-ar...", "weights": [[8, 14, 1.0]]}, "2018.sigirconf_conference-2018.231": {"snippet": "... and Search, under the background of Explainable AI in a more general sense. Early recommendation and search systems adopted intuitive yet easily explainable models to generate recommendation and sear...", "weights": [[8, 14, 1.0], [52, 54, 1.0], [105, 111, 1.0], [199, 205, 1.0]]}, "2019.ipm_journal-ir0volumeA56A1.7": {"snippet": "...lear impact on the user behavior in web search. Such impact is reflected both in users' subjective perception of the usability of a search engine and in their interaction with the search engine in ter...", "weights": [[8, 14, 1.0], [43, 49, 1.0], [56, 62, 1.0], [135, 141, 1.0], [142, 148, 1.0], [183, 189, 1.0], [190, 196, 1.0]]}, "2022.trustnlp-1.4": {"snippet": "...ced {AI}\n\n\n Artificial Intelligence (AI) and Machine Learning (ML) are rapidly becoming musthave capabilities. According to a 2019 Forbes Insights Report, \"seventy-nine percent [of executives] agree t...", "weights": [[8, 10, 1.0], [40, 42, 1.0]]}, "2018.ipm_journal-ir0volumeA54A3.5": {"snippet": "... the impact of domain and cognitive complexity on query formulation and reformulation\n\n\n A B S T R A C TThe purpose of this analysis was to evaluate an existing set of search tasks in terms of their e...", "weights": [[8, 14, 1.0], [171, 177, 1.0], [202, 215, 1.0]]}, "2019.ir_journal-ir0volumeA22A5.3": {"snippet": "... the impact of group size on collaborative search effectiveness", "weights": [[8, 14, 1.0], [46, 52, 1.0], [53, 66, 1.0]]}, "2018.sigirconf_conference-2018.229": {"snippet": "...efit effectiveness of information retrieval (IR) systems have a negative impact on efficiency. Yet, with the large scale of Web search engines, there is a need to deploy efficient query processing tec...", "weights": [[8, 21, 1.0], [76, 82, 1.0], [131, 137, 1.0]]}, "2010.sigirconf_conference-2010.17": {"snippet": "...ttle impact on effectiveness, while the size of the collection has a substantial impact on the quantity, quality and effectiveness of anchor text. We also compare the diversity of the search results o...", "weights": [[8, 14, 1.0], [18, 31, 1.0], [84, 90, 1.0], [120, 133, 1.0], [187, 193, 1.0]]}, "2006.cikm_conference-2006.35": {"snippet": "...s of search systems, it is unclear how individual and task differences impact the effectiveness of such algorithms. In this paper we describe a study on the effect on retrieval performance of using ad...", "weights": [[8, 14, 1.0], [74, 80, 1.0], [85, 98, 1.0]]}, "2015.jasis_journal-ir0volumeA66A9.1": {"snippet": "...eval effectiveness of web search engines using a representative query sample", "weights": [[8, 21, 1.0], [29, 35, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.14": {"snippet": "... may impact webpage visibility in a search engine. Four hypotheses are presented relating to the impact of selected content characteristics on webpage visibility in search engine results lists. Webpag...", "weights": [[8, 14, 1.0], [39, 45, 1.0], [46, 52, 1.0], [100, 106, 1.0], [168, 174, 1.0], [175, 181, 1.0]]}, "2008.tois_journal-ir0volumeA26A4.2": {"snippet": "... the search engine on search usage was limited and inconsistent. Third, search was used mainly as a last resort when users could not remember file location. Finally, there was no evidence that using i...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [25, 31, 1.0], [75, 81, 1.0]]}, "2014.trec_conference-2014.64": {"snippet": "...e of search engine in itself. In this work, we propose a Search engine Impact Factor (SEIF) estimation approach to improve the performance of vertical and resource selection. The officially released r...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [60, 66, 1.0], [67, 73, 1.0], [74, 80, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.14": {"snippet": "...r on search and evaluation behavior using Google\n\n\n AbstractTo improve search engine effectiveness, we have observed an increased interest in gathering additional feedback about users\u00d5 information nee...", "weights": [[8, 14, 1.0], [74, 80, 1.0], [81, 87, 1.0], [88, 101, 1.0]]}, "2009.sigirconf_conference-2009.75": {"snippet": "The impact of crawl policy on web search effectiveness", "weights": [[4, 10, 1.0], [34, 40, 1.0], [41, 54, 1.0]]}, "2016.sigirconf_conference-2016.224": {"snippet": "... the search engine.This tutorial aims to provide a fairly comprehensive overview of the scalability and efficiency challenges in largescale web search engines. In particular, the tutorial provides an ...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [147, 153, 1.0]]}, "2018.wwwconf_conference-2018c.298": {"snippet": "...brid.AI: A Learning Search Engine for Large-scale Structured Data\n\n\n ABSTRACTVariety of Big data ] is a significant impediment for anyone who wants to search inside a large-scale structured dataset. F...", "weights": [[8, 10, 1.0], [23, 29, 1.0], [30, 36, 1.0], [154, 160, 1.0]]}, "2009.jasis_journal-ir0volumeA60A8.3": {"snippet": "...n of search engine performance", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2005.ecir_conference-2005.44": {"snippet": "...ting Search Engines", "weights": [[8, 14, 1.0]]}, "2005.sigirconf_conference-2005.116": {"snippet": "...eval effectiveness of Web search engines can be expensive if human relevance judgments are required to evaluate search results. Using implicit user feedback for search engine evaluation provides a cos...", "weights": [[8, 21, 1.0], [29, 35, 1.0], [115, 121, 1.0], [164, 170, 1.0], [171, 177, 1.0]]}, "2020.nl4xai-1.2": {"snippet": "... the impact of the technology in our lives. The area of responsible AI has recently emerged in an attempt to put humans at the center of AI-based systems by considering aspects, such as fairness, reli...", "weights": [[8, 14, 1.0], [71, 73, 1.0], [140, 142, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.15": {"snippet": "...y in search engine results (Part II)\n\n\n AbstractThis paper discusses the impact of metadata implementation in a webpage on its visibility performance in a search engine results list. Influential inter...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [76, 82, 1.0], [158, 164, 1.0], [165, 171, 1.0]]}, "2014.sigirconf_conference-2014.12": {"snippet": "Impact of response latency on user behavior in web search\n\n\n ABSTRACTTraditionally, the efficiency and effectiveness of search systems have both been of great interest to the information retrieval com...", "weights": [[0, 6, 1.0], [51, 57, 1.0], [103, 116, 1.0], [120, 126, 1.0]]}, "2008.sigirconf_conference-2008.8": {"snippet": "... web search by promoting multiple search engine use\n\n\n ABSTRACTAny given Web search engine may provide higher quality results than others for certain queries. Therefore, it is in users' best interest ...", "weights": [[8, 14, 1.0], [37, 43, 1.0], [44, 50, 1.0], [80, 86, 1.0], [87, 93, 1.0]]}, "1999.cikm_conference-99.43": {"snippet": "...rove search effectiveness. Today, many me&search engines exist which combine the results of various search engines in the hopes of improving overall effectiveness. However, the combination of results ...", "weights": [[8, 14, 1.0], [15, 28, 1.0], [45, 51, 1.0], [103, 109, 1.0], [152, 165, 1.0]]}, "2019.wsdm_conference-2019.110": {"snippet": "...mage Search\n\n\n ABSTRACTWeb-based image search engines di er from Web search engines greatly. The intents or goals behind human interactions with image search engines are di erent. In image search, use...", "weights": [[8, 14, 1.0], [42, 48, 1.0], [72, 78, 1.0], [154, 160, 1.0], [192, 198, 1.0]]}, "2017.tois_journal-ir0volumeA36A2.10": {"snippet": "... the search engine in the long term . This, in turn, can have a negative impact on the advertising revenue of the search engine. On the system side, commercial web search companies are known to make m...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [76, 82, 1.0], [117, 123, 1.0], [124, 130, 1.0], [167, 173, 1.0]]}, "2009.cikm_conference-2009.182": {"snippet": "...cant impact on the performance of ranking functions in the context of learning to rank for web search. Due to resource constraints, training data for smaller search engine markets are scarce and we ne...", "weights": [[8, 14, 1.0], [98, 104, 1.0], [161, 167, 1.0], [168, 174, 1.0]]}, "2012.sigirconf_conference-2012.147": {"snippet": "...ding effectiveness. On the other hand, in a modern search engine setting, dynamic pruning strategies can be used to efficiently obtain the set of documents to be re-ranked by the application of a lear...", "weights": [[8, 21, 1.0], [54, 60, 1.0], [61, 67, 1.0]]}, "2022.jeptalnrecital-taln.12": {"snippet": "Impact du fran{\\c{c}}ais inclusif sur les outils du {TAL} (Impact of {F}rench Inclusive Language on {NLP} Tools)\n\n\n Le fran\u00e7ais inclusif est une vari\u00e9t\u00e9 du fran\u00e7ais standard mise en avant pour t\u00e9moign...", "weights": [[0, 6, 1.0], [59, 65, 1.0]]}, "C14-1091": {"snippet": "...that search engines for common usage have a positive impact on writing performance. Our contribution is a large-scale user study with 121 participants using the Netspeak search engine to shed light on...", "weights": [[8, 14, 1.0], [56, 62, 1.0], [173, 179, 1.0], [180, 186, 1.0]]}, "2005.wwwconf_conference-2005si.46": {"snippet": "...ng a search engine's efficiency and effectiveness, each user has his/her own preference and is highly subjective in judging the relevance of the search results.We have proposed a formal evaluation mod...", "weights": [[8, 14, 1.0], [15, 21, 1.0], [39, 52, 1.0], [148, 154, 1.0]]}, "2003.tois_journal-ir0volumeA21A4.2": {"snippet": "... The impact of query operators on Web search engine results\n\n\n Research has reported that about 10% of Web searchers utilize advanced query operators, with the other 90% using extremely simple queries...", "weights": [[8, 14, 1.0], [41, 47, 1.0], [48, 54, 1.0]]}, "2012.sigirconf_workshop-2012osir.6": {"snippet": "...tive Search Engine", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A1.3": {"snippet": "...ving Search Engine Results", "weights": [[8, 14, 1.0], [15, 21, 1.0]]}, "2017.sigirconf_conference-2017.103": {"snippet": "...ving Search Engines via Large-Scale Physiological Sensing", "weights": [[8, 14, 1.0]]}}}
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The baseline approach is based on the development of named entity recognition and geospatial information retrieval tools and on it...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0], [166, 177, 1.0], [178, 187, 1.0]]}, "2008.clef_workshop-2008w.91": {"snippet": "...mage Retrieval\n\n\n In this paper, we present our solutions for the WikipediaMM task at ImageCLEF 2008. The aim of this task is to investigate effective retrieval approaches in the context of a large-sc...", "weights": [[8, 17, 1.0], [154, 163, 1.0], [164, 174, 1.0]]}, "2001.jasis_journal-ir0volumeA52A10.6": {"snippet": "...mage retrieval and information theory: A general approach", "weights": [[8, 17, 1.0], [22, 33, 1.0]]}, "2000.sigirconf_conference-2000.52": {"snippet": "...s of information needs and information retrieval. We have the goal of developing new approaches to information retrieval which are based on user modelling techniques for building and managing the repr...", "weights": [[8, 19, 1.0], [30, 41, 1.0], [42, 51, 1.0], [88, 98, 1.0], [102, 113, 1.0], [114, 123, 1.0]]}, "2010.ntcir_workshop-2010.21": {"snippet": "...OCCC Approaches in Document Re-ranking\n\n\n In this paper, we describe our approach for information retrieval for question answering (IR4QA) of NTCIR-8 tasks. For improving information retrieval perform...", "weights": [[8, 18, 1.0], [89, 100, 1.0], [101, 110, 1.0], [174, 185, 1.0], [186, 195, 1.0]]}, "1994.ipm_journal-ir0volumeA30A2.3": {"snippet": "Information Retrieval Evaluation in Practice: A Case Study Approach", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords Information Retrieval (IR) solutions to retrieve passages matching a set of terms extracted from the question.There is a fundamental disconnect between the capabilities of the bag-of-words retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [197, 206, 1.0]]}, "2003.cikm_conference-2003.28": {"snippet": "... for information retrieval systems\n\n\n ABSTRACTWe present a novel approach to detect misuse within an information retrieval system by gathering and maintaining knowledge of the behavior of the user rat...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [104, 115, 1.0], [116, 125, 1.0]]}, "2015.jasis_journal-ir0volumeA66A11.9": {"snippet": "...tive information retrieval behavior to inform design", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...n of retrieval models particularly for recall oriented retrieval domains, where the focus is on retrieving all relevant information but not just retrieving a subset of relevant information. Available ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [123, 134, 1.0], [180, 191, 1.0]]}, "1998.sigirconf_conference-98.34": {"snippet": "...h to Information Retrieval\n\n\n AbstractModels of document indexing and document retrieval have been extensively studied. The integration of these two classes of models has been the goal of several rese...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [82, 91, 1.0]]}, "2003.sigirconf_conference-2003.73": {"snippet": "XML retrieval: what to retrieve?\n\n\n ABSTRACTThe fundamental difference between standard information retrieval and XML retrieval is the unit of retrieval. In traditional IR, the unit of retrieval is fi...", "weights": [[4, 13, 1.0], [88, 99, 1.0], [100, 109, 1.0], [118, 127, 1.0], [143, 152, 1.0], [185, 194, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.0": {"snippet": "...rds: Information retrieval system; Evaluation; Information search process; Decision making; Multi-criteria model; Analytic hierarchy process\nBackgroundInformation retrieval (IR) systems play an import...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [166, 175, 1.0]]}, "W10-0504": {"snippet": "An Information-Retrieval Approach to Language Modeling: Applications to Social Data\n\n\n In this paper we propose the IR-LM (Information Retrieval Language Model) which is an approach to carrying out la...", "weights": [[3, 14, 1.0], [15, 24, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2005.sigirconf_conference-2005.101": {"snippet": "...gual information retrieval using on-line translation systems\n\n\n ABSTRACTTypical cross language retrieval requires special linguistic resources, such as bilingual dictionaries and parallel corpus. In t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [98, 107, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.1": {"snippet": "...uage information retrieval: a review\n\n\n AbstractThis paper reviews state-of-the-art techniques and methods for enhancing effectiveness of cross-language information retrieval (CLIR). The following res...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [156, 167, 1.0], [168, 177, 1.0]]}, "1979.sigirjournals_journal-ir0volumeA13A4.4": {"snippet": "...ased Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "P13-2027": {"snippet": "... Hoc Information Retrieval?\n\n\n The current topic modeling approaches for Information Retrieval do not allow to explicitly model query-oriented latent topics. More, the semantic coherence of the topics...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [61, 71, 1.0], [76, 87, 1.0], [88, 97, 1.0]]}, "P98-1066": {"snippet": "...ased Information Retrieval\n\n\n A layered approach to information retrieval permits the inclusion of multiple search engines as well as multiple databases, with a natural language layer to convert Engli...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [55, 66, 1.0], [67, 76, 1.0]]}, "2018.trec_conference-2018.34": {"snippet": "...ment retrieval with modified queries, and combine the results by formulating re-ranking as a text classification task. We evaluate our proposed framework to retrieve biomedical research abstracts. Our...", "weights": [[8, 17, 1.0]]}, "2006.sigirjournals_journal-ir0volumeA40A2.11": {"snippet": "...tive information retrieval: (a conceptual graph based approach)", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1974.ipm_journal-ir0volumeA10A1112.6": {"snippet": "...h to information retrieval - I", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2019.ictir_conference-2019.44": {"snippet": "...E: A Retrieval Approach for Information Extraction", "weights": [[8, 17, 1.0], [31, 42, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A2.13": {"snippet": "...ling approaches to information retrieval: bridging the theory-effectiveness gap\n\n\n AbstractImproving the effectiveness of general retrieval models has been a long-standing difficult challenge in infor...", "weights": [[8, 18, 1.0], [22, 33, 1.0], [34, 43, 1.0], [133, 142, 1.0], [198, 209, 1.0]]}, "2021.naacl-main.363": {"snippet": "...ence retrieval performance when fed to the same supervised reranker. In particular, our joint retrieval and then reranking approach achieves new state-of-the-art evidence retrieval performance on two ...", "weights": [[8, 17, 1.0], [97, 106, 1.0], [174, 183, 1.0]]}, "O01-2005": {"snippet": "...s of Information Retrieval with Clustering and Fusion\n\n\n Fusion and clustering are two approaches to improving the effectiveness of information retrieval. In fusion, ranked lists are combined together...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [90, 100, 1.0], [135, 146, 1.0], [147, 156, 1.0]]}, "R09-2015": {"snippet": "...XML} Retrieval\n\n\n This paper presents a data-centric approach to XML information retrieval which benefits from XML document structure and adapts traditional text-centric information retrieval techniqu...", "weights": [[8, 17, 1.0], [72, 83, 1.0], [84, 93, 1.0], [173, 184, 1.0], [185, 194, 1.0]]}, "1987.ipm_journal-ir0volumeA23A4.0": {"snippet": "Approaches to Intelligent Information Retrieval", "weights": [[0, 10, 1.0], [26, 37, 1.0], [38, 47, 1.0]]}, "2007.jasis_journal-ir0volumeA58A7.5": {"snippet": "... two approaches in information retrieval: From frameworks to study designs", "weights": [[8, 18, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2001.cikm_conference-2001.52": {"snippet": "...h to Information Retrieval\n\n\n ABSTRACTThe language modeling approach to retrieval has been shown to perform well empirically. One advantage of this new approach is its statistical foundations. However...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [75, 84, 1.0]]}, "2017.fire_conference-2017w.19": {"snippet": "... the Information Retrieval task. The distributed representation of text attained 3rd position in subtask 1 and attained satisfactory score in subtask 2. CCS CONCEPTS \u2022 Information systems \u2192 Informatio...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [171, 182, 1.0], [193, 204, 1.0]]}, "W09-4632": {"snippet": "...e\n\n\n Information retrieval is a field of research reaching from computer and information science to lingusitics. As a linguist in the information retrieval field, I leave the quest for effective searc...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [137, 148, 1.0], [149, 158, 1.0]]}, "1993.ipm_journal-ir0volumeA29A3.2": {"snippet": "Information Retrieval from Hypertext: An Approach Using Plausible Inference", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2007.ecir_conference-2007.59": {"snippet": "... XML Retrieval\n\n\n Abstract. The objective of XML retrieval is to return relevant XML document fragments that answer a given user information need, by exploiting the document structure. The focus in th...", "weights": [[8, 17, 1.0], [52, 61, 1.0], [132, 143, 1.0]]}, "2004.ntcir_workshop-2004.57": {"snippet": "...onal Retrieval Task 2 that was conducted from 2003 to 2004 as a subtask of the WEB Task at the Fourth NTCIR Workshop ('NTCIR-4 WEB'). In the Informational Retrieval Task, we attempted to assess the re...", "weights": [[8, 17, 1.0], [158, 167, 1.0], [201, 210, 1.0]]}, "2014.ecir_conference-2014.56": {"snippet": "An Information Retrieval-Based Approach to Determining Contextual Opinion Polarity of Words", "weights": [[3, 14, 1.0], [15, 24, 1.0]]}, "T78-1026": {"snippet": "... for retrieval only, and not for retrieval and updating simultaneously. A related assumption (or constraint) that in questions descriptions are used referentially only (i.e., a question cannot be used...", "weights": [[8, 17, 1.0], [36, 45, 1.0]]}, "1999.ntcir_workshop-1999.17": {"snippet": "Information Retrieval Based on Stochastic Models\n\n\n Our method for information retrieval uses Robertson's 2-poisson model which is one type of probablistic approach. This method achieved a comparative...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [67, 78, 1.0], [79, 88, 1.0]]}, "2008.tois_journal-ir0volumeA27A1.0": {"snippet": "... XML retrieval\n\n\n In information retrieval research, comparing retrieval approaches requires test collections consisting of documents, user requests and relevance assessments. Obtaining relevance asse...", "weights": [[8, 17, 1.0], [24, 35, 1.0], [36, 45, 1.0], [66, 75, 1.0], [76, 86, 1.0]]}, "2000.ismir_conference-2000.1": {"snippet": "...Text Information Retrieval Approaches to Monophonic Music Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 40, 1.0], [61, 70, 1.0]]}, "2010.wsdm_conference-2010.29": {"snippet": "...ent, retrieval, and sharing. The approach is based on multi-modal Bayesian models which allow us to integrate spatial semantics of social media in a well-formed, probabilistic manner. We systematicall...", "weights": [[8, 17, 1.0]]}, "2007.sigirconf_conference-2007.47": {"snippet": "...ntic information of interest to the QA system. We present an approach to retrieval for QA, applying structured retrieval techniques to the types of text annotations that QA systems use. We demonstrate...", "weights": [[8, 19, 1.0], [76, 85, 1.0], [114, 123, 1.0]]}, "1973.ipm_journal-ir0volumeA9A5.3": {"snippet": "...ting information storage and retrieval system - A decision theory approach", "weights": [[8, 19, 1.0], [32, 41, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2006.jasis_journal-ir0volumeA57A4.6": {"snippet": "Soft approaches to information retrieval and information access on the Web: An introduction to the special topic section", "weights": [[5, 15, 1.0], [19, 30, 1.0], [31, 40, 1.0], [45, 56, 1.0]]}, "2017.sigirconf_conference-2017.243": {"snippet": "... the approaches that work well for text retrieval and text analysis tend to be statistical approaches. In the past decade, a special kind of statistical approaches, called probabilistic topic models, ...", "weights": [[8, 18, 1.0], [43, 52, 1.0], [94, 104, 1.0], [156, 166, 1.0]]}, "O00-3003": {"snippet": "...uage Information Retrieval: An Interlingua Approach\n\n\n Electronically available multilingual information can be divided into two major categories: (1) alphabetic language information (English-like alp...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [96, 107, 1.0], [174, 185, 1.0]]}, "2016.sigirconf_conference-2016.89": {"snippet": "...This information retrieval test collection is the first to combine multilingual search, passage retrieval, and informal online genre text.", "weights": [[8, 19, 1.0], [20, 29, 1.0], [99, 108, 1.0]]}, "2006.spire_conference-2006.20": {"snippet": "... for Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2003.trec_conference-2003.54": {"snippet": "...h of Information Retrieval with Reference Corpus to Novelty Detection", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.10": {"snippet": "... XML retrieval. XML retrieval deals with retrieving those document parts that specifically answer a query. It is concerned with using the document structure to improve the retrieval of information fro...", "weights": [[8, 17, 1.0], [23, 32, 1.0], [175, 184, 1.0], [188, 199, 1.0]]}, "1992.sigirconf_conference-92.3": {"snippet": "...of a Retrieval Process", "weights": [[8, 17, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2008.sigirconf_conference-2008.55": {"snippet": "...ment retrieval algorithm and a language model approach for passage-based retrieval. Furthermore, our combined approach has been shown to provide better results than both document retrieval and passage...", "weights": [[8, 17, 1.0], [76, 85, 1.0], [182, 191, 1.0]]}, "2007.jasis_journal-ir0volumeA58A14.15": {"snippet": "... for information retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "C12-2057": {"snippet": "...uage Information Retrieval\n\n\n This paper introduces a cross-language information retrieval (CLIR) framework that combines the state-of-the-art keyword-based approach with a latent semantic-based retri...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [72, 83, 1.0], [84, 93, 1.0], [198, 207, 1.0]]}, "2004.riao_conference-2004.67": {"snippet": "Information Retrieval and Extraction from the Web: the CROSSMARC approach", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "2004.riao_conference-2004.51": {"snippet": "... Pre-retrieval Model Selection Approach to Information Retrieval", "weights": [[8, 17, 1.0], [46, 57, 1.0], [58, 67, 1.0]]}, "1988.riao_conference-1988.72": {"snippet": "...gual Information Retrieval Mechanism Using VLSI. Requirements and Approaches for Information Retrieval Systems in the Computer-Aided Software Engineering and Document Processing Environment", "weights": [[8, 19, 1.0], [20, 29, 1.0], [69, 79, 1.0], [84, 95, 1.0], [96, 105, 1.0]]}, "2013.ipm_journal-ir0volumeA49A6.5": {"snippet": "...ural information has been shown to be an effective source of evidence in textual information retrieval, only a few works investigated its interest in multimedia retrieval. More precisely, the task we ...", "weights": [[8, 19, 1.0], [84, 95, 1.0], [96, 105, 1.0], [164, 173, 1.0]]}, "2005.ir_journal-ir0volumeA8A4.4": {"snippet": "...ment Retrieval", "weights": [[8, 17, 1.0]]}, "2006.ecir_conference-2006.3": {"snippet": "...h to Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1973.ipm_journal-ir0volumeA9A6.1": {"snippet": "Information retrieval, statistical analysis and graphics: An integrated approach", "weights": [[0, 11, 1.0], [12, 21, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...eral approaches to combining hypertext networks with information retrieval (IR) engines (/Agosti 96], [Frisse 88], [Croji 93], [Dunlop 93], & [Frei 92]), there has been little exploitation or testing ...", "weights": [[8, 18, 1.0], [56, 67, 1.0], [68, 77, 1.0]]}, "2005.ntcir_workshop-2005.47": {"snippet": "...tent Retrieval Experiments at Hitachi\n\n\n In NTCIR-5, we used five retrieval methods proposed in NTCIR-4: (1) query term weighting using only document frequency, (2) stopword deletion, (3) two-stage pa...", "weights": [[8, 17, 1.0], [69, 78, 1.0]]}, "2011.sigirconf_conference-2011.220": {"snippet": "...tive approaches to learning to rank at Cornell University in 2010. His recent research focuses on machine learning approaches to leveraging user feedback, diversified retrieval, and interactive inform...", "weights": [[8, 18, 1.0], [118, 128, 1.0], [170, 179, 1.0], [197, 208, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.31": {"snippet": "... and Information  Retrieval\" (1960). This was the first paper to present a probabilistic approach to information retrieval, and perhaps the first paper on ranked retrieval. Although it is one of the m...", "weights": [[8, 19, 1.0], [21, 30, 1.0], [104, 115, 1.0], [116, 125, 1.0], [165, 174, 1.0]]}, "2005.clef_workshop-2005w.119": {"snippet": "... All retrieval results rely heavily on two retrieval systems: for visual retrieval we use the GNU Image Finding Tool (GIFT), and for textual retrieval the EasyIR retrieval system. For the ad-hoc retri...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [76, 85, 1.0], [144, 153, 1.0], [165, 174, 1.0], [198, 207, 1.0]]}, "2003.clef_workshop-2003.37": {"snippet": "...s in information retrieval had been on monolingual (mainly English) collections; and there had been few experiments in multilingual information retrieval systems 2 . \nThe GIRT Data in the Evaluation o...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [135, 146, 1.0], [147, 156, 1.0]]}, "1995.tois_journal-ir0volumeA13A3.3": {"snippet": "...stic Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1975.ipm_journal-ir0volumeA11A57.2": {"snippet": "...h to information retrieval - II", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2002.sigirconf_conference-2002.6": {"snippet": "...ay's information retrieval systems that are not very well explained by the current state of information retrieval theory, including stop words, mandatory terms, coordination level ranking and retrieva", "weights": [[8, 19, 1.0], [20, 29, 1.0], [95, 106, 1.0], [107, 116, 1.0], [195, 204, 1.0]]}, "C98-1063": {"snippet": "...ased Information Retrieval\n\n\n A layered approach to information retrieval permits the inclusion of multiple search engines as well as nmltiple databases, with a natural language layer to convert Engli...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [55, 66, 1.0], [67, 76, 1.0]]}, "2004.ntcir_workshop-2004.14": {"snippet": "...tion Approaches in Information Retrieval: Words vs. N-grams and Query Translation vs. Document Translation\n\n\n This paper reports our proposal and experimental results at the NTCIR-4 CLIR task. For mon...", "weights": [[8, 18, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "1987.sigirjournals_journal-ir0volumeA21A34.1": {"snippet": "... for Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2017.ictir_conference-2017.36": {"snippet": "...post-retrieval QPP method predicts the query's performance based on the quality of its retrieved result list .A common approach utilized by many state-of-the art post-retrieval QPP methods is the anal...", "weights": [[8, 17, 1.0], [170, 179, 1.0]]}, "2018.trec_conference-2018.22": {"snippet": "...rial retrieval subtasks. Recalling that our submissions using pseudo relevance feedback and Markov Random Field information retrieval models are also inferior to those using simple IR approaches in th...", "weights": [[8, 17, 1.0], [115, 126, 1.0], [127, 136, 1.0], [187, 197, 1.0]]}, "2013.mir_conference-2013.29": {"snippet": "An information retrieval approach to identifying infrequent events in surveillance video", "weights": [[3, 14, 1.0], [15, 24, 1.0]]}, "2020.sigirconf_workshop-2020birds.5": {"snippet": "...ween Information Science, Information Retrieval, and Data Science) at SIGIR 2020. This talk reflects well my research area, which uniquely connects Information Retrieval (IR), Information Science (IS)...", "weights": [[8, 19, 1.0], [29, 40, 1.0], [41, 50, 1.0], [151, 162, 1.0], [163, 172, 1.0], [179, 190, 1.0]]}, "2002.tois_journal-ir0volumeA20A2.3": {"snippet": "...sing information. Thus, the generalization of information retrieval from the text domain to the image domain is non-trivial. This paper presents a framework for information retrieval in the image doma...", "weights": [[8, 19, 1.0], [49, 60, 1.0], [61, 70, 1.0], [164, 175, 1.0], [176, 185, 1.0]]}, "1999.trec_conference-1999.11": {"snippet": "...uage retrieval track. Our objective was to continue our investigation of our conceptual interlingua approach to cross-language retrieval, specifically by measuring the contribution of conceptual retri...", "weights": [[8, 17, 1.0], [130, 139, 1.0], [198, 207, 1.0]]}, "2006.tois_journal-ir0volumeA24A4.0": {"snippet": "...e of information, which can potentially be exploited by retrieval methods for better information discovery. In addition to structural information, XML tagging is commonly used to indicate semantic inf...", "weights": [[8, 19, 1.0], [59, 68, 1.0], [88, 99, 1.0], [137, 148, 1.0], [200, 211, 1.0]]}, "1999.cikm_conference-99.43": {"snippet": "...n of Information Retrieval Approaches\n\n\n ABSTRACTPrior work has shown that combining results of various retrieval approaches and query representations can improve search effectiveness. Today, many me&...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 40, 1.0], [107, 116, 1.0], [117, 127, 1.0]]}, "2012.ecir_conference-2012.15": {"snippet": "...eech Retrieval", "weights": [[8, 17, 1.0]]}, "2005.sigirconf_conference-2005.64": {"snippet": "...atic approaches to information retrieval\n\n\n ABSTRACTExisting retrieval models generally do not offer any guarantee for optimal retrieval performance. Indeed, it is even difficult, if not impossible, t...", "weights": [[8, 18, 1.0], [22, 33, 1.0], [34, 43, 1.0], [64, 73, 1.0], [130, 139, 1.0]]}, "2008.jasis_journal-ir0volumeA59A5.5": {"snippet": "...h to information retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2004.cikm_conference-2004.59": {"snippet": "...n of retrieval approaches. Whereas the evaluation methodology for flat text retrieval approaches is well established, the evaluation of XML retrieval approaches is a research issue. This is because XM...", "weights": [[8, 17, 1.0], [18, 28, 1.0], [79, 88, 1.0], [89, 99, 1.0], [143, 152, 1.0], [153, 163, 1.0]]}, "2018.sigirconf_conference-2018.227": {"snippet": "...eral approaches to text data retrieval and analysis, probabilistic topic models-notably Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocations (LDA), and their many extensions-hav...", "weights": [[8, 18, 1.0], [32, 41, 1.0]]}, "2019.ecir_conference-20192.55": {"snippet": "...nced Information Retrieval: 8th International BIR Workshop\n\n\n Abstract. The Bibliometric-enhanced Information Retrieval workshop series (BIR) at ECIR tackles issues related to academic search, at the ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [101, 112, 1.0], [113, 122, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2008.sigirconf_conference-2008.170": {"snippet": "...ment retrieval, highlights the relative strengths and weaknesses of both approaches, and explains the relative effectiveness of document retrieval approaches at element retrieval tasks.", "weights": [[8, 17, 1.0], [76, 86, 1.0], [140, 149, 1.0], [150, 160, 1.0], [172, 181, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.15": {"snippet": "...sual information for cross-language image retrieval: A trans-media dictionary approach\n\n\n AbstractThis paper explores the integration of textual and visual information for cross-language image retriev...", "weights": [[8, 19, 1.0], [45, 54, 1.0], [159, 170, 1.0], [196, 205, 1.0]]}, "1985.riao_conference-1985.32": {"snippet": "...hree approaches to information retrieval", "weights": [[8, 18, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ment retrieval. The shot retrieval and the video segment model re-use the probability of relevance and a language modeling ranking function respectively, basing themselves on decades of text retrieval...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [194, 203, 1.0]]}, "2008.ir_journal-ir0volumeA11A4.3": {"snippet": "... for information retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2019.ecir_conference-20191.32": {"snippet": "...h to information retrieval (IR) has shown that the effectiveness of a retrieval method is connected to its fulfillment of axioms. 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The aim of XML element retrieval is to identify key points in structured documents, and prop...", "weights": [[8, 17, 1.0], [88, 97, 1.0], [134, 143, 1.0]]}, "2002.cikm_conference-2002.54": {"snippet": "...usic information retrieval approaches\n\n\n ABSTRACTIn this paper, we describe the Ultima project which aims to construct a platform for evaluating various approaches of music information retrieval. Two ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [30, 40, 1.0], [156, 166, 1.0], [176, 187, 1.0], [188, 197, 1.0]]}, "2009.trec_conference-2009.7": {"snippet": "...atic retrieval models on the large data collection. Axiomatic approaches to information retrieval have been recently proposed and studied. The basic idea is to search for retrieval functions that can ...", "weights": [[8, 17, 1.0], [65, 75, 1.0], [79, 90, 1.0], [91, 100, 1.0], [174, 183, 1.0]]}, "N07-1044": {"snippet": "...e an information retrieval-based method for sense ranking that does not require annotated data. The method queries an information retrieval engine to estimate the degree of association between a word ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [121, 132, 1.0], [133, 142, 1.0]]}, "2020.sigirconf_conference-2020.363": {"snippet": "...ween Information Science, Information Retrieval and Data Science\n\n\n The proposed BIRDS workshop aimed to foster the cross-fertilization of Information Science (IS), Information Retrieval (IR) and Data...", "weights": [[8, 19, 1.0], [29, 40, 1.0], [41, 50, 1.0], [142, 153, 1.0], [168, 179, 1.0], [180, 189, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2014.cikm_conference-2014.187": {"snippet": "...uage Information Retrieval (CLIR) addresses the problem of finding documents in some language that are relevant to a question posed in a different language. Retrieving answers to questions written usi...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "D09-1163": {"snippet": "...tial retrieval results exploring the intrinsic structure of documents is widely researched in information retrieval (IR) and has attracted a considerable amount of time and study. However, one of the ...", "weights": [[8, 17, 1.0], [97, 108, 1.0], [109, 118, 1.0]]}, "2020.acl-main.613": {"snippet": "...gual Information Retrieval in the Medical Domain\n\n\n We present a thorough comparison of two principal approaches to Cross-Lingual Information Retrieval: document translation (DT) and query translation...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [105, 115, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}}}
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In theory, if a system could automatically figure out the sense of the word intended by the user, ret...", "weights": [[8, 17, 1.0], [118, 124, 1.0], [200, 209, 1.0]]}, "2020.sigirconf_conference-2020.278": {"snippet": "...tual Speed from Search Behaviour\n\n\n Perceptual Speed (PS) is a cognitive ability that is known to affect multiple factors in Information Retrieval (IR) such as a user's search performance and subjecti...", "weights": [[8, 13, 1.0], [50, 55, 1.0], [140, 149, 1.0]]}, "1994.sigirconf_conference-94.37": {"snippet": "...tive Retrieval Systems", "weights": [[8, 17, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...tion retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC -required s...", "weights": [[8, 17, 1.0], [42, 51, 1.0], [92, 101, 1.0], [147, 156, 1.0]]}, "1994.sigirconf_conference-94.8": {"snippet": "...tual Speed, Learning and Information Retrieval Performance", "weights": [[8, 13, 1.0], [40, 49, 1.0]]}, "1996.jasis_journal-ir0volumeA47A6.3": {"snippet": "...tion Retrieval System", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "2013.cikm_conference-2013.240": {"snippet": "...mate retrieval from large corpora\n\n\n ABSTRACTInverted indexing is a ubiquitous technique used in retrieval systems including web search. Despite its popularity, it has a drawback -query retrieval time...", "weights": [[8, 17, 1.0], [100, 109, 1.0], [189, 198, 1.0]]}, "2007.clef_workshop-2007.77": {"snippet": "...n of Retrieval Quality", "weights": [[8, 17, 1.0]]}, "2008.clef_workshop-2008w.133": {"snippet": "...only retrieval increased to 0.1081, but the MAP for the combined text and image retrieval decreased to 0.1140. By implementing an interface to the PostgreSQL database the retrieval speed and compariso...", "weights": [[8, 17, 1.0], [83, 92, 1.0], [174, 183, 1.0], [184, 189, 1.0]]}, "2014.wwwconf_conference-2014c.52": {"snippet": "...odel speed and effectiveness, and how such tradeoffs influence ranking system operational costs. Second, we introduce the audience to three important challenges in breaking through the tradeoff barrie...", "weights": [[8, 13, 1.0], [74, 80, 1.0]]}, "2020.acl-main.85": {"snippet": "...rase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models. In this paper, we a...", "weights": [[8, 17, 1.0], [71, 76, 1.0]]}, "2010.wwwconf_conference-2010.204": {"snippet": "...mage retrieval in image-rich information networks\n\n\n ABSTRACTIn this demo, we present a system called iRIN designed for performing image retrieval in image-rich information networks. We first introduc...", "weights": [[8, 17, 1.0], [91, 97, 1.0], [140, 149, 1.0]]}, "2010.cikm_conference-2010.128": {"snippet": "...tion Retrieval systems use inverted index structures for efficient query processing. Due to the extremely large size of many data sets, these index structures are usually kept in compressed form, and ...", "weights": [[8, 17, 1.0]]}, "2007.sigirconf_conference-2007.82": {"snippet": "...that retrieval effectiveness is significantly improved. Finally, the effect of pruning in recognition is analyzed, showing that when recognition speed is increased, the reduction in retrieval performa...", "weights": [[8, 17, 1.0], [148, 153, 1.0], [185, 194, 1.0]]}, "2014.ipm_journal-ir0volumeA50A5.11": {"snippet": "...ines retrieval speed of the matrix form with compactness of the list form. LOUDS is a succinct data structure using bit-string. Retrieval speed of LOUDS is not faster than that of the double array, bu...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [131, 140, 1.0], [141, 146, 1.0]]}, "2003.sigirconf_conference-2003.49": {"snippet": "... and Retrieval]: Retrieval Models.General Terms: Theory, Experimentation. Keywords: Ranking Retrieval Systems.\nINTRODUCTIONSoboroff, Nicholas and Cahan [2] recently proposed a method for evaluating re...", "weights": [[8, 17, 1.0], [20, 29, 1.0], [95, 104, 1.0], [201, 210, 1.0]]}, "2020.sigirconf_conference-2020.235": {"snippet": "...mage retrieval. Different from the classical work that tries to reduce the dimension of the data for faster retrieval speed, the model projects dense input samples into a higher-dimensional space and ...", "weights": [[8, 17, 1.0], [111, 120, 1.0], [121, 126, 1.0]]}, "2010.clef_workshop-2010w.21": {"snippet": "...ReMo System (Contextual Reference Monotony) - Lab Report for PAN at CLEF 2010\n\n\n In this paper a new approach is shown for a very fast monolingual external plagiarism detection system based on an alte...", "weights": [[8, 14, 1.0], [180, 186, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...hose retrieval system anafyses the content of images to locate images of people in a particular state. By integrating the two representation schemes into a single retrieval system, it would be hoped t...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [166, 175, 1.0], [176, 182, 1.0]]}, "1985.sigirconf_conference-85.28": {"snippet": "...tion Retrieval Research: The Utah Retrieval System Architecture\n\n\n AbstractThe Utah Retrieval System Architecture provides an excellent testbed for the development and testing of new algorithms or tec...", "weights": [[8, 17, 1.0], [37, 46, 1.0], [47, 53, 1.0], [87, 96, 1.0], [97, 103, 1.0]]}, "1990.jasis_journal-ir0volumeA41A7.2": {"snippet": "...l of retrieval system performance", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "2022.findings-acl.117": {"snippet": "...tion Retrieval for Open-Domain {QA}\n\n\n The retriever-reader pipeline has shown promising performance in open-domain QA but suffers from a very slow inference speed. Recently proposed question retrieva...", "weights": [[8, 17, 1.0], [161, 166, 1.0], [195, 204, 1.0]]}, "2005.ipm_journal-ir0volumeA41A2.6": {"snippet": "...tion retrieval systems use the inverted file as indexing structure. The inverted file, however, requires inefficient reorganization when new documents are to be added to an existing collection. Most s...", "weights": [[8, 17, 1.0]]}, "W01-1202": {"snippet": "...d QA system can save response time because it is not necessary for the QA system to extract answer candidates with scores on retrieval time. If the QA system is combined with a traditional Information...", "weights": [[8, 14, 1.0], [77, 83, 1.0], [128, 137, 1.0], [154, 160, 1.0]]}, "N16-1100": {"snippet": "...tion system that maximize BLEU scores while ensuring that decoding speed exceeds a minimum value. We propose the use of Bayesian Optimization to efficiently tune the speed-related decoding parameters ...", "weights": [[8, 14, 1.0], [70, 75, 1.0], [169, 174, 1.0]]}, "1991.ipm_journal-ir0volumeA27A4.3": {"snippet": "High speed text retrieval from large databases on a massively parallel processor", "weights": [[5, 10, 1.0], [16, 25, 1.0]]}, "2018.cikm_conference-2018.261": {"snippet": "...tion retrieval (IR) systems are unable to represent formulae and they are therefore ill-suited for math information retrieval (MIR). To fill the gap, we have developed, and open-sourced the MIaS MIR s...", "weights": [[8, 17, 1.0], [119, 128, 1.0], [202, 208, 1.0]]}, "2020.findings-emnlp.14": {"snippet": "...136x speed-up over the state of the art in neural speed reading); and that (b) any claims that neural speed reading is \"human-inspired\", are ill-founded.", "weights": [[8, 13, 1.0], [53, 58, 1.0], [105, 110, 1.0]]}, "2016.sigirconf_conference-2016.119": {"snippet": "...s to Speed-up Document Scoring with Tree Ensembles\n\n\n ABSTRACTScoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees is currently deemed one of the best solu...", "weights": [[8, 13, 1.0]]}, "1989.ipm_journal-ir0volumeA25A4.2": {"snippet": "...s of retrieval systems by information structures", "weights": [[8, 17, 1.0]]}, "1991.tois_journal-ir0volumeA9A3.3": {"snippet": "...s to speed the identification of relevant information. Users' search performance using the expert system was compared with their search performance on their own, and their search performance using an ...", "weights": [[8, 13, 1.0], [101, 107, 1.0]]}, "J14-3004": {"snippet": "...ween speed and accuracy. It is trivial to improve the training speed via sacrificing accuracy or to improve the accuracy via sacrificing speed. Nevertheless, it is nontrivial to improve the training s...", "weights": [[8, 13, 1.0], [66, 71, 1.0], [140, 145, 1.0], [202, 207, 1.0]]}, "2001.sigirconf_conference-2001.63": {"snippet": "...tion retrieval system implemented on a PC cluster system. We used the Web-TREC dataset under a left-deep query execution plan. We achieved satisfactory speed up.", "weights": [[8, 17, 1.0], [18, 24, 1.0], [53, 59, 1.0], [155, 160, 1.0]]}, "1998.sigirconf_conference-98.2": {"snippet": "...tion Retrieval\n\n\n AbstractThis paper presents an investigation into the utility of document summarisation in the context of information retrieval, more specifically in the application of so called que...", "weights": [[8, 17, 1.0], [139, 148, 1.0]]}, "1998.sigirconf_conference-98.59": {"snippet": "... the retrieval speed. This paper proposes two optimizations of query evaluation: one selects a combination of n-grams whose processing cost is the minimum among all the possible combinations, and the ...", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "2020.chiir_conference-2020.26": {"snippet": "...tual Speed Tests in Information Retrieval: Limitations, Challenges, and Recommendations", "weights": [[8, 13, 1.0], [35, 44, 1.0]]}, "2010.iir_workshop-2010.17": {"snippet": "...of a System for Image Retrieval", "weights": [[8, 14, 1.0], [25, 34, 1.0]]}, "Y99-1028": {"snippet": "...High-speed Partial Matching\n\n\n The telephone directory retrieval task requires high-speed partial matching, and many processes run concurrently so there is a need to reduce resource requirements such ...", "weights": [[8, 13, 1.0], [58, 67, 1.0], [87, 92, 1.0]]}, "2007.sigirconf_conference-2007.212": {"snippet": "...tion retrieval system?\n\n\n ABSTRACTThis research explores the relationship between Information Retrieval (IR) systems' effectiveness and users' performance (accuracy and speed) and their satisfaction w...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [97, 106, 1.0], [172, 177, 1.0]]}, "2020.acl-main.537": {"snippet": "...ovel speed-tunable FastBERT with adaptive inference time. The speed at inference can be flexibly adjusted under varying demands, while redundant calculation of samples is avoided. Moreover, this model...", "weights": [[8, 13, 1.0], [65, 70, 1.0]]}, "1971.sigirconf_conference-71.14": {"snippet": "... and retrieval system utilizing the combinatorial file structure is developed.Storage and retrieval results derived from a simulated document library of 4000 items are presented.The new file organizat...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [93, 102, 1.0]]}, "1999.trec_conference-1999.35": {"snippet": "...pace retrieval system's involvement in TREC8. The main goal for this year's work was to speed up document indexing and query processing compared to previous years. This goal was achieved, but retrieva...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [91, 96, 1.0], [195, 204, 1.0]]}, "2021.naacl-main.77": {"snippet": "...Text Retrieval\n\n\n Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow inferen...", "weights": [[8, 17, 1.0]]}, "2005.trec_conference-2005.49": {"snippet": "...ment retrieval systems. I describe how to overcome some of these issues and in particular improvements to the IXE search engine in order to achieve higher precision while maintaining good retrieval pe...", "weights": [[8, 17, 1.0], [191, 200, 1.0]]}, "1987.sigirconf_conference-87.38": {"snippet": "...text retrieval systems, which accommodate metrical as well as Boolean operators, the traditional approach to query processing uses a \"concordance\", from which large sets of coordinates are retrieved a...", "weights": [[8, 17, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...pert System based control system for the control of an Automated Storage and Retrieval System (AS/RS) is created. The control system includes several unique elements, including the hierarchical partit...", "weights": [[8, 14, 1.0], [29, 35, 1.0], [80, 89, 1.0], [90, 96, 1.0], [129, 135, 1.0]]}, "I05-2042": {"snippet": "...ding Speed-based Procedure\n\n\n This paper proposes a unified evaluation method for multiple reading support systems such as a sentence translation system and a word translation system. In reading a non...", "weights": [[8, 13, 1.0], [149, 155, 1.0], [179, 185, 1.0]]}, "2009.mtsummit-posters.20": {"snippet": "...ting Speed, and Some Other Factors\n\n\n This paper summarises the results of a pilot project conducted to investigate the correlation between automatic evaluation metric scores and post-editing speed on...", "weights": [[8, 13, 1.0], [195, 200, 1.0]]}, "2005.mtsummit-posters.14": {"snippet": "...ding Speed and Effective Speed\n\n\n This paper reports the result of our experiment, the aim of which is to examine the efficiency of reading support systems such as a sentencemachine translation system...", "weights": [[8, 13, 1.0], [28, 33, 1.0], [197, 203, 1.0]]}, "2007.clef_workshop-2007w.100": {"snippet": "...n of Retrieval Quality\n\n\n The Image Distortion Model (IDM) has shown good retrieval quality in previous runs of the medical automatic annotation task of previous ImageCLEF workshops. However, one of i...", "weights": [[8, 17, 1.0], [77, 86, 1.0]]}, "1997.sigirconf_conference-97.10": {"snippet": "...y to speed up clustering is to speed up the distance calculations at the heart of clustering routines. We study two techniques for improving the cost ofdistance calculations, LSI and trrmcation, and d...", "weights": [[8, 13, 1.0], [34, 39, 1.0]]}, "1993.ipm_journal-ir0volumeA29A5.4": {"snippet": "...tion Retrieval System for Reusable Software", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "2007.ntcir_workshop-2007.71": {"snippet": "...tent retrieval task. In this task, large numbers of patent applications are classified into Fterm categories. Therefore, a patent classification system requires high classification speed, as well as h...", "weights": [[8, 17, 1.0], [148, 154, 1.0], [184, 189, 1.0]]}, "1989.sigirconf_conference-89.7": {"snippet": "...tion Retrieval in a Knowledge Worker Support System\n\n\n AbstractThis paper describes the design of the information retrieval facilities of an integrated information system called EUROMATH. EUROMATH is ...", "weights": [[8, 17, 1.0], [48, 54, 1.0], [117, 126, 1.0], [167, 173, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords retrieval model and the retrieval needs of the QA system. Bag-of-words IR retrieves documents matching a query, but the QA system really needs documents that contain answers. Through question ana...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [58, 64, 1.0], [131, 137, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [196, 202, 1.0]]}, "1982.sigirconf_conference-82.4": {"snippet": "...ment Retrieval System", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "2010.irfc_conference-2010.7": {"snippet": "...of a Retrieval System", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "1995.sigirconf_conference-95.5": {"snippet": "...tion Retrieval\n\n\n AbstractInformation retrieval systems are being challenged to manage larger and larger document collections.In an effort to provide better retrieval performance on large collections,...", "weights": [[8, 17, 1.0], [41, 50, 1.0], [160, 169, 1.0]]}, "2008.sigirconf_conference-2008.101": {"snippet": "...tion retrieval (IIR) systems. This paper proposes to use document titles as an alternative to queries in the generation of summaries. The use of document titles allows us to pre-generate summaries sta...", "weights": [[8, 17, 1.0]]}, "2011.sigirconf_conference-2011.153": {"snippet": "...d to speed-up music retrieval\n\n\n ABSTRACTComputations in most music retrieval systems strongly depend on the size of data compared. We propose to enhance performances of a music retrieval system, name...", "weights": [[8, 13, 1.0], [23, 32, 1.0], [71, 80, 1.0], [181, 190, 1.0], [191, 197, 1.0]]}, "2011.ntcir_workshop-2011.25": {"snippet": "... and retrieval methods have been proposed. This paper describes a Japanese spoken document retrieval system that is robust for considering OOV words and mis-recognition of subunits. Additionally our s...", "weights": [[8, 17, 1.0], [94, 103, 1.0], [104, 110, 1.0], [202, 208, 1.0]]}, "2021.acl-long.389": {"snippet": "...mage retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In this paper, we propose VisualSp...", "weights": [[8, 17, 1.0], [66, 75, 1.0]]}, "1985.tois_journal-ir0volumeA3A3.2": {"snippet": "...type System for the Electronic Storage and Retrieval of Document Images\n\n\n A prototype system has been implemented for electronic scanning, digitization, storage, retrieval, and display of images of b...", "weights": [[8, 14, 1.0], [46, 55, 1.0], [90, 96, 1.0], [166, 175, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...tion retrieval (IR) field has a long tradition of using retrieval experiments on test collections to advance the state of the art [2, 10, 131. In a common experimental scenario, a particular retrieval...", "weights": [[8, 17, 1.0], [59, 68, 1.0], [194, 203, 1.0]]}, "D18-1460": {"snippet": "...tion speed. The direct consequence is that a trade-off has to be made between translation quality and speed, thus its performance can not come into full play. We apply cube pruning, a popular techniqu...", "weights": [[8, 13, 1.0], [105, 110, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A1.9": {"snippet": "... and retrieval systems", "weights": [[8, 17, 1.0]]}, "2021.emnlp-main.461": {"snippet": "...a 6x speed-up in inference speed while retaining comparable performance. The empirical analysis we present may provide guidelines for future research seeking to develop or deploy more efficient non-pa", "weights": [[8, 13, 1.0], [30, 35, 1.0]]}, "1992.ipm_journal-ir0volumeA28A4.1": {"snippet": "...e of Retrieval System Evaluation", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "2021.ranlp-1.78": {"snippet": "...tion retrieval search engine, we conclude that it is possible to achieve real-time retrieval speed of about tens of microseconds even for a large translation memory with 5 million segment pairs.", "weights": [[8, 17, 1.0], [86, 95, 1.0], [96, 101, 1.0]]}, "1985.tois_journal-ir0volumeA3A1.1": {"snippet": "...rsor speed did not seem to act as a pacing device for the entire editing task. Since cursor speed is a form of system response, this finding is in contrast with the generally found positive relation b...", "weights": [[8, 13, 1.0], [95, 100, 1.0], [114, 120, 1.0]]}}}
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And t...", "weights": [[8, 16, 1.0], [38, 46, 1.0]]}, "1981.sigirconf_conference-81.10": {"snippet": "...phic Retrieval Systems\n\n\n Abs t ractThe general model and simulation algorithms for bibliographic retrieval systems presented in an earlier paper I are expanded.The new model integrates the physical a...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [101, 110, 1.0], [111, 118, 1.0]]}, "N06-5004": {"snippet": "...ment retrieval, issues of automatic transcription of long audio files, and techniques for the indexing and retrieval of spoken audio files. * TF-IDF/vector space methods * Probabilistic methods * Larg...", "weights": [[8, 17, 1.0], [97, 105, 1.0], [110, 119, 1.0], [165, 172, 1.0], [189, 196, 1.0]]}, "1988.sigirconf_conference-88.43": {"snippet": "...mple indexing of documents based on signatures. Here, texts are treated as character strings. We call this method Textual Search. 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Distributing different aspects of search (e.g., crawling, indexing, and query processing) is essential to achieve scalability in large-scale information retrieval systems. The ...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [61, 67, 1.0], [85, 93, 1.0], [180, 189, 1.0], [190, 197, 1.0]]}, "2007.sigirconf_conference-2007.37": {"snippet": "...rage precision\n\n\n ABSTRACTMachine learning is commonly used to improve ranked retrieval systems. Due to computational difficulties, few learning techniques have been developed to directly optimize for...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [91, 98, 1.0], [191, 199, 1.0]]}, "1998.sigirconf_conference-98.68": {"snippet": "...d on indexing and interrogation functionalities.\nl Indexing facilitiesImage indexing is performed with a computer-assisted pmcess, according to a particular image indexing model [Mec95]. A basic part ...", "weights": [[8, 16, 1.0], [54, 62, 1.0], [79, 87, 1.0], [166, 174, 1.0]]}, "2005.trec_conference-2005.27": {"snippet": "...tion Retrieval\n\n\n We describe indexing and retrieval techniques that are suited to perform terabyte-scale information retrieval tasks on a standard desktop PC. Starting from an Okapi-BM25-based defaul...", "weights": [[8, 17, 1.0], [33, 41, 1.0], [46, 55, 1.0], [121, 130, 1.0]]}, "2013.cikm_conference-2013.240": {"snippet": "...tion indexing: fast approximate retrieval from large corpora\n\n\n ABSTRACTInverted indexing is a ubiquitous technique used in retrieval systems including web search. Despite its popularity, it has a dra...", "weights": [[8, 16, 1.0], [35, 44, 1.0], [84, 92, 1.0], [127, 136, 1.0], [137, 144, 1.0], [159, 165, 1.0]]}, "2018.sigirconf_conference-2018.11": {"snippet": "...tive Search\n\n\n ABSTRACTSelective search architectures use resource selection algorithms such as Rank-S or Taily to rank index shards and determine how many to search for a given query. Most prior rese...", "weights": [[8, 14, 1.0], [36, 42, 1.0], [162, 168, 1.0]]}, "C96-1087": {"snippet": "...noun indexing that is about segmenting or decomposing compound nouns into promising index terms. Compound nouns as index terms that usually subscribe to specific notions tend to increase the precision...", "weights": [[8, 16, 1.0], [194, 203, 1.0]]}, "2014.sigirconf_conference-2014.18": {"snippet": "...high precision and high recall from an interactive retrieval process. Such scenarios are very common in real life, exemplified by medical search, legal search, market research, and literature review. ...", "weights": [[8, 17, 1.0], [54, 63, 1.0], [141, 147, 1.0], [155, 161, 1.0]]}, "W01-1202": {"snippet": "...s on retrieval time. If the QA system is combined with a traditional Information Retrieval system, it can improve the document retrieval precision for closed-class questions after minimum loss of retr", "weights": [[8, 17, 1.0], [84, 93, 1.0], [130, 139, 1.0], [140, 149, 1.0], [199, 208, 1.0]]}, "2006.ipm_journal-ir0volumeA42A3.3": {"snippet": "...ased retrieval technique, the proposed system could partially bridge lexical chasms between queries and FAQs. In addition, the proposed system outperforms the traditional information retrieval systems", "weights": [[8, 17, 1.0], [186, 195, 1.0], [196, 203, 1.0]]}, "1989.sigirconf_conference-89.8": {"snippet": "Retrieval System Evaluation Using Recall and Precision: Problems and Answers", "weights": [[0, 9, 1.0], [45, 54, 1.0]]}, "2007.sigirconf_conference-2007.212": {"snippet": "... web search engines, in the context of simple search task, are more concerned with precision than completeness of the search. This stemmed from the stronger correlation between users' satisfaction wit...", "weights": [[8, 14, 1.0], [49, 55, 1.0], [86, 95, 1.0], [121, 127, 1.0]]}, "2006.clef_workshop-2006w.1": {"snippet": "...high precision, but low recall information retrieval approach with a greedy sentence ranking algorithm. The high precision retrieval is ensured by querying the search engine with the exact topic, in t...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [116, 125, 1.0], [126, 135, 1.0], [163, 169, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...this indexing, three information pharmacists selected 98 queries for an assessment of retrieval effectiveness; they and the author formulated sets of search statements that were used to search the fil...", "weights": [[8, 16, 1.0], [89, 98, 1.0], [153, 159, 1.0], [189, 195, 1.0]]}, "2011.sigirconf_conference-2011.153": {"snippet": "...s an indexing method to speed-up music retrieval\n\n\n ABSTRACTComputations in most music retrieval systems strongly depend on the size of data compared. We propose to enhance performances of a music ret...", "weights": [[8, 16, 1.0], [42, 51, 1.0], [90, 99, 1.0], [100, 107, 1.0], [200, 209, 1.0]]}, "2010.sigirconf_conference-2010.13": {"snippet": "...uted search engines\n\n\n ABSTRACTQuery forwarding is an important technique for preserving the result quality in distributed search engines where the index is geographically partitioned over multiple se...", "weights": [[8, 14, 1.0], [126, 132, 1.0], [201, 207, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...rent indexing methods when evaluated using four independent sets of judgments, but the correlation between the methods' rankings was always very high and the absolute difference in performance of the ...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [114, 121, 1.0]]}, "2010.clef_workshop-2010w.54": {"snippet": "...ving Retrieval Using External Annotations: OHSU at ImageCLEF 2010\n\n\n Over the past several years, our team has focused its efforts on improving retrieval precision performance by mixing visual and tex...", "weights": [[8, 17, 1.0], [147, 156, 1.0], [157, 166, 1.0]]}, "2010.wwwconf_conference-2010.11": {"snippet": "... its precision is suboptimal as well. We investigate various strategies for compact, hierarchy-aware indexing of sponsored search ads through adaptation of standard IR indexing techniques. We also pro...", "weights": [[8, 17, 1.0], [104, 112, 1.0], [126, 132, 1.0], [171, 179, 1.0]]}, "2008.ecir_conference-2008.23": {"snippet": "Book Search Experiments: Investigating IR Methods for the Indexing and Retrieval of Books", "weights": [[5, 11, 1.0], [42, 49, 1.0], [58, 66, 1.0], [71, 80, 1.0]]}, "1982.sigirjournals_journal-ir0volumeA17A3.2": {"snippet": "...ment retrieval so as to facilitate searches on any content word.The characteristics of a retrieval system based on word fragment indexing can be precisely predicted from a multinomial model of text.Co...", "weights": [[8, 17, 1.0], [92, 101, 1.0], [132, 140, 1.0]]}, "2011.sigirconf_conference-2011.58": {"snippet": "...tion retrieval. For each unique word occurring in a document collection, the inverted index stores a list of the documents in which this word occurs. Compression techniques are often applied to furthe...", "weights": [[8, 17, 1.0]]}, "1997.sigirconf_conference-97.5": {"snippet": "...gram indexing works surprisingly well. Bigrarn indexing leads to a large index term space, three times that of short-word indexing, but is as good as short-word indexing in precision, and about 5% bet...", "weights": [[8, 16, 1.0], [50, 58, 1.0], [125, 133, 1.0], [164, 172, 1.0], [176, 185, 1.0]]}, "2001.sigirconf_conference-2001.6": {"snippet": "...tion Retrieval Systems\n\n\n ABSTRACTWe introduce static index pruning methods that significantly reduce the index size in information retrieval systems. We investigate uniform and term-based methods tha...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [71, 78, 1.0], [135, 144, 1.0], [145, 152, 1.0], [192, 199, 1.0]]}, "2014.cikm_conference-2014.186": {"snippet": "...dern search engines. Beyond that, they increasingly serve as an important building block for applications such as entity-oriented search, text analytics, and plagiarism detection. Processing phrase qu...", "weights": [[8, 14, 1.0], [133, 139, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.8": {"snippet": "...mage indexing systems. The automatic indexing task is formulated as one of image annotation, or automatic metadata generation for images. The evaluation is composed of two individual methods. First, t...", "weights": [[8, 16, 1.0], [17, 24, 1.0], [40, 48, 1.0], [186, 193, 1.0]]}, "1986.jasis_journal-ir0volumeA37A1.2": {"snippet": "Interdependence of PRECIS role operators: A quantitiative analysis of their associations", "weights": []}, "2010.sigirconf_conference-2010.204": {"snippet": "...ated search\n\n\n Abstract:Federated search refers to the brokered retrieval of content from a set of auxiliary retrieval systems instead of from a single, centralized retrieval system. Federated search ...", "weights": [[8, 14, 1.0], [37, 43, 1.0], [67, 76, 1.0], [112, 121, 1.0], [122, 129, 1.0], [168, 177, 1.0], [196, 202, 1.0]]}, "2002.sigirconf_conference-2002.70": {"snippet": "...gher precision for two-word queries\n\n\n ABSTRACTQueries have specific properties, and may need individualized methods and parameters to optimize retrieval. Length is one property. We look at how two-wo...", "weights": [[8, 17, 1.0], [112, 119, 1.0], [138, 146, 1.0], [147, 156, 1.0]]}, "1967.ipm_journal-ir0volumeA3A4.9": {"snippet": "Indexing and classification for interactive retrieval systems", "weights": [[0, 8, 1.0], [44, 53, 1.0], [54, 61, 1.0]]}, "2007.sigirconf_conference-2007.68": {"snippet": "...text search\n\n\n ABSTRACTText search over temporally versioned document collections such as web archives has received little attention as a research problem. As a consequence, there is no scalable and p...", "weights": [[8, 14, 1.0], [31, 37, 1.0]]}, "Y95-1033": {"snippet": "...High Precision Rate\n\n\n Many information retrieval(IR) systems retrieve relevant documents based on exact matching of keywords between a query and documents. This method degrades precision rate. In ord...", "weights": [[8, 17, 1.0], [43, 52, 1.0], [57, 64, 1.0], [181, 190, 1.0]]}, "2010.trec_conference-2010.26": {"snippet": "...tion retrieval to improve retrieval quality. The integration of such proximity scoring models into efficient query processing, however, has not been equally well studied. Existing methods make use of ...", "weights": [[8, 17, 1.0], [29, 38, 1.0], [183, 190, 1.0]]}, "2022.spanlp-1.5": {"snippet": "... and Precision Reduction\n\n\n Recently neural network based approaches to knowledge-intensive NLP tasks, such as question answering, started to rely heavily on the combination of neural retrievers and r...", "weights": [[8, 17, 1.0]]}, "2016.sigirconf_conference-2016.224": {"snippet": "... web search engines. In particular, the tutorial provides an in-depth architectural overview of a web search engine, mainly focusing on the web crawling, indexing, and query processing components. The...", "weights": [[8, 14, 1.0], [105, 111, 1.0], [157, 165, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.22": {"snippet": "...oved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indi...", "weights": [[8, 17, 1.0], [49, 55, 1.0], [89, 98, 1.0], [172, 179, 1.0]]}, "2008.cikm_conference-2008.69": {"snippet": "...dard indexing method for supporting search queries in a variety of content-based applications. Examples of such applications include enterprise document management, e-mail, web search, and social netw...", "weights": [[8, 16, 1.0], [39, 45, 1.0], [180, 186, 1.0]]}, "2000.sigirconf_conference-2000.12": {"snippet": "...ment retrieval\n\n\n ABSTRACTCombined word-based indexes and phonetic indexes have been used to improve the performance of spoken document retrieval systems primarily by addressing the out-of-vocabulary ...", "weights": [[8, 17, 1.0], [139, 148, 1.0], [149, 156, 1.0]]}, "1992.trec_conference-1992.6": {"snippet": "...ment Indexing and Search Term Weighting Based on Probabilistic Models", "weights": [[8, 16, 1.0], [21, 27, 1.0]]}, "2003.sigirconf_conference-2003.13": {"snippet": "...ully search on the basis of colour, and were able to formulate visual queries, their ability to do so was affected by search task type. Search task type was also shown to be related to search tool cho...", "weights": [[8, 14, 1.0], [121, 127, 1.0], [139, 145, 1.0], [188, 194, 1.0]]}, "2005.ntcir_workshop-2005.3": {"snippet": "...uted Retrieval System for NTCIR-5 Patent Retrieval Task\n\n\n We developed a distributed search system with the corresponding very large scale corpora from NTCIR-5 Patent Retrieval Task. And we developed...", "weights": [[8, 17, 1.0], [44, 53, 1.0], [89, 95, 1.0], [171, 180, 1.0]]}, "2013.wsdm_conference-2013.13": {"snippet": "...ment retrieval strategies for block-max indexes\n\n\n ABSTRACTLarge web search engines use significant hardware and energy resources to process hundreds of millions of queries each day, and a lot of rese...", "weights": [[8, 17, 1.0], [72, 78, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "... ASR systems generally contain a language model that provides word sequence probabilities. Some information retrieval systems also rely on language models for retrieval. It is likely that some informa...", "weights": [[8, 15, 1.0], [111, 120, 1.0], [121, 128, 1.0], [162, 171, 1.0]]}, "1996.jasis_journal-ir0volumeA47A6.3": {"snippet": "...tion Retrieval System", "weights": [[8, 17, 1.0]]}, "2018.wsdm_conference-2018.47": {"snippet": "...ming methods, or retrieval models, to model parameters, such as k1 and b in BM25, or the number of query expansion terms. Grid and random search, the dominant methods to search for the optimal system ...", "weights": [[8, 15, 1.0], [20, 29, 1.0], [141, 147, 1.0], [162, 169, 1.0], [173, 179, 1.0]]}, "1996.jasis_journal-ir0volumeA47A7.7": {"snippet": "...ring Methods for Information Retrieval Systems: From Search Product to Search Process", "weights": [[8, 15, 1.0], [32, 41, 1.0], [42, 49, 1.0], [56, 62, 1.0], [74, 80, 1.0]]}, "2015.tois_journal-ir0volumeA33A4.3": {"snippet": "...ndex Methods for Pseudo-Relevance Feedback Retrieval\n\n\n The inverted index is the dominant indexing method in information retrieval systems. It enables fast return of the list of all documents contain...", "weights": [[8, 15, 1.0], [46, 55, 1.0], [94, 102, 1.0], [125, 134, 1.0], [135, 142, 1.0]]}, "2009.cikm_conference-2009.60": {"snippet": "...osed methods retrieve a smaller candidate set than previous methods. For example, using the same number of features, our method improve the precision for the query candidate set by 4%-13% in compariso...", "weights": [[8, 15, 1.0], [63, 70, 1.0], [143, 152, 1.0]]}, "2004.sigirconf_conference-2004.13": {"snippet": "...able indexing and ranking for XML information retrieval\n\n\n ABSTRACTIndexing and ranking are two key factors for efficient and effective XML information retrieval. Inappropriate indexing may result in ...", "weights": [[8, 16, 1.0], [49, 58, 1.0], [155, 164, 1.0], [180, 188, 1.0]]}, "2009.clef_workshop-2009w.60": {"snippet": "...bled retrieval system constructed at Southampton for the ImageCLEFphoto 2009 task. The retrieval system used Terrier as the underlying textual indexing and retrieval system, and combined it with a tec...", "weights": [[8, 17, 1.0], [90, 99, 1.0], [146, 154, 1.0], [159, 168, 1.0]]}, "R09-1045": {"snippet": "...tion retrieval system for wikis. Instead of a full-text index, our system indexes only link texts and document titles. Nevertheless, its precision exceeds that of a popular full-text search engine, an...", "weights": [[8, 17, 1.0], [140, 149, 1.0], [186, 192, 1.0]]}, "1988.ipm_journal-ir0volumeA24A2.2": {"snippet": "...ring indexing systems for retrieval using proximity operators", "weights": [[8, 16, 1.0], [17, 24, 1.0], [29, 38, 1.0]]}, "1997.sigirjournals_journal-ir0volumeA31A1.7": {"snippet": "...lean search methods that operate with controlled indexing and search languages are difficult to master and remain inaccessible to normal end-users.The current situation relating to the use of retrieva...", "weights": [[8, 14, 1.0], [15, 22, 1.0], [52, 60, 1.0], [65, 71, 1.0], [195, 204, 1.0]]}, "W04-2907": {"snippet": "...ires indexing weighted automata. We present a general algorithm for the indexation of weighted automata. The resulting index is represented by a deterministic weighted transducer that is optimal for s...", "weights": [[8, 16, 1.0], [202, 208, 1.0]]}, "2014.sigirconf_conference-2014.219": {"snippet": "... the methods for many IR applications.\nCategories and Subject Descriptors: H.3.3 [Information Search and Retrieval]: Retrieval modelsGeneral Terms: Algorithms Keywords: axiomatic analysis; information...", "weights": [[8, 15, 1.0], [97, 103, 1.0], [108, 117, 1.0], [120, 129, 1.0]]}, "1995.sigirconf_conference-95.15": {"snippet": "...ased Indexing Organization using Frequency Data for Japanese Documents\n\n\n AbstractA character based indexing is preferable for Japanese IR systems since Japanese words are not segmented. This paper pr...", "weights": [[8, 16, 1.0], [103, 111, 1.0], [142, 149, 1.0]]}, "1998.sigirconf_conference-98.56": {"snippet": "...r of search engines and user profiling in tandem.Namely, we attempt to optimize:l recall by aggregating hits from major search engines and other previously developed retrieval systems, l precision by ...", "weights": [[8, 14, 1.0], [74, 82, 1.0], [123, 129, 1.0], [169, 178, 1.0], [179, 186, 1.0], [190, 199, 1.0]]}, "2016.ipm_journal-ir0volumeA52A4.4": {"snippet": "...mage retrieval system for hierarchical image databases\n\n\n Available online xxxKeywords: Content based image retrieval Semantic assignment Clustering Visual search space Indexing of visual features a b...", "weights": [[8, 17, 1.0], [111, 120, 1.0], [159, 165, 1.0], [172, 180, 1.0]]}, "2005.wwwconf_conference-2005.26": {"snippet": "...real search engine show that our technique can reduce the indices' storage costs by up to 60% over traditional lossless compression methods, while keeping the loss in retrieval precision to a minimum....", "weights": [[8, 14, 1.0], [135, 142, 1.0], [170, 179, 1.0], [180, 189, 1.0]]}, "2007.riao_conference-2007.31": {"snippet": "...tion retrieval systems: a machine learning based solution", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2004.ntcir_workshop-2004.75": {"snippet": "... Web Retrieval Task\n\n\n We evaluated the Relevance-based Superimposition Model at NTCIR 4 Web task A (survey retrieval) and B (target retrieval). We developed a distributed indexing / searching engine ...", "weights": [[8, 17, 1.0], [111, 120, 1.0], [136, 145, 1.0], [175, 183, 1.0]]}, "2006.clef_workshop-2006.69": {"snippet": "...High Precision Information Retrieval Method for WiQA", "weights": [[8, 17, 1.0], [30, 39, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "...tion Retrieval Models\n\n\n ABSTRACTThe accuracy of a search engine is mostly determined by the optimality of the retrieval model used in the search engine. Develoing optimal retrieval models has always ...", "weights": [[8, 17, 1.0], [54, 60, 1.0], [114, 123, 1.0], [142, 148, 1.0], [175, 184, 1.0]]}, "2000.sigirconf_conference-2000.42": {"snippet": "...tion retrieval model, Boolean query formulation can help users to make precise representation of their information search needs. Retrieval experiments on a number of test collections seem to show that...", "weights": [[8, 17, 1.0], [118, 124, 1.0], [132, 141, 1.0]]}, "2011.wwwconf_conference-2011c.164": {"snippet": "...ream indexing for spoken web search\n\n\n ABSTRACTThis paper presents two-stream processing of audio to index the audio content for Spoken Web search. The first stream indexes the meta-data associated wi...", "weights": [[8, 16, 1.0], [32, 38, 1.0], [143, 149, 1.0]]}, "L10-1107": {"snippet": "Indexing Methods for Faster and More Effective Person Name Search", "weights": [[0, 8, 1.0], [9, 16, 1.0], [59, 65, 1.0]]}, "2010.ir_journal-ir0volumeA13A1.0": {"snippet": "...ring methods for content-based retrieval on indexed symbolic music databases", "weights": [[8, 15, 1.0], [34, 43, 1.0]]}, "2011.cikm_conference-2011.248": {"snippet": "...opic retrieval that have been used to motivate diverse retrieval. \nCategories and Subject Descriptors\nGeneral TermsAlgorithms Keywords diversity, set-level relevance, maximal marginal relevance\nDIVERS...", "weights": [[8, 17, 1.0], [58, 67, 1.0]]}}}
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Tools used currently by Web ...", "weights": [[8, 16, 1.0], [20, 23, 1.0], [41, 44, 1.0], [102, 105, 1.0], [165, 168, 1.0], [199, 202, 1.0]]}, "2014.wwwconf_conference-2014c.159": {"snippet": "... the web - revisited: a trick of the heavy tail\n\n\n ABSTRACTKnowledge about the general graph structure of the World Wide Web is important for understanding the social mechanisms that govern its growth...", "weights": [[8, 11, 1.0], [124, 127, 1.0]]}, "2001.tois_journal-ir0volumeA19A3.3": {"snippet": "...used crawling which is able to crawl particular topical portions of the World Wide Web quickly, without having to explore all web pages. In this paper, we propose the novel concept of intelligent craw...", "weights": [[8, 16, 1.0], [86, 89, 1.0], [129, 132, 1.0], [199, 207, 1.0]]}, "2020.wwwconf_conference-2020.37": {"snippet": "... for Crawling Commercial Web Content\n\n\n The rapid growth of commercial web content has driven the development of shopping search services to help users find product offers. Due to the dynamic nature o...", "weights": [[8, 16, 1.0], [28, 31, 1.0], [74, 77, 1.0]]}, "2019.wwwconf_conference-2019.60": {"snippet": "...tive Crawling for Commercial Web Content", "weights": [[8, 16, 1.0], [32, 35, 1.0]]}, "2007.cikm_conference-2007.58": {"snippet": "...ased web crawling policies for search engine crawlers\n\n\n ABSTRACTThe World Wide Web is growing and changing at an astonishing rate. Web information systems such as search engines have to keep up with ...", "weights": [[8, 11, 1.0], [12, 20, 1.0], [83, 86, 1.0], [135, 138, 1.0]]}, "2020.wwwconf_conference-2020.16": {"snippet": "...cale Web crawls have emerged as the state of the art for studying characteristics of the Web. In particular, they are a core tool for online tracking research. Web crawling is an attractive approach t...", "weights": [[8, 11, 1.0], [92, 95, 1.0], [163, 166, 1.0], [167, 175, 1.0]]}, "2005.tois_journal-ir0volumeA23A4.2": {"snippet": "... The crawling process is modeled as a parallel best-first search over a graph defined by the Web. The classifiers provide heuristics to the crawler thus biasing it towards certain portions of the Web ...", "weights": [[8, 16, 1.0], [96, 99, 1.0], [199, 202, 1.0]]}, "2014.wwwconf_conference-2014c.60": {"snippet": "...sive crawling for the masses\n\n\n ABSTRACTAlthough web crawlers have been around for twenty years by now, there is virtually no freely available, open-source crawling software that guarantees high throu...", "weights": [[8, 16, 1.0], [52, 55, 1.0], [159, 167, 1.0]]}, "2010.tois_journal-ir0volumeA28A4.0": {"snippet": "...ntal web crawling\n\n\n When crawling resources, for example, number of machines, crawl-time, and so on, are limited, so a crawler has to decide an optimal order in which to crawl and recrawl Web pages. ...", "weights": [[8, 11, 1.0], [12, 20, 1.0], [29, 37, 1.0], [192, 195, 1.0]]}, "W19-8907": {"snippet": "...cial Web Observatory: An entity-driven, holistic information summarization platform across sources\n\n\n The Social Web Observatory is an entitydriven, sentiment-aware, event summarization web platform, ...", "weights": [[8, 11, 1.0], [116, 119, 1.0], [189, 192, 1.0]]}, "2007.wwwconf_conference-2007.46": {"snippet": "...ndom web crawls\n\n\n ABSTRACTThis paper proposes a random Web crawl model. A Web crawl is a (biased and partial) image of the Web. This paper deals with the hyperlink structure, i.e. a Web crawl is a gr...", "weights": [[8, 11, 1.0], [59, 62, 1.0], [78, 81, 1.0], [127, 130, 1.0], [186, 189, 1.0]]}, "2018.wwwconf_conference-2018.127": {"snippet": "... all Web pages already in 2016. While this constitutes an important information source for tasks such as Web search, Web page classification or knowledge graph augmentation, individual markup nodes ar...", "weights": [[8, 11, 1.0], [108, 111, 1.0], [120, 123, 1.0]]}, "2008.wwwconf_conference-2008.46": {"snippet": "... for web forums\n\n\n ABSTRACTWe study in this paper the Web forum crawling problem, which is a very fundamental step in many Web applications, such as search engine and Web data mining. As a typical use...", "weights": [[8, 11, 1.0], [57, 60, 1.0], [67, 75, 1.0], [126, 129, 1.0], [170, 173, 1.0]]}, "2022.humeval-1.4": {"snippet": "...n of web-crawled parallel corpora for machine translation\n\n\n Quality assessment has been an ongoing activity of the series of ParaCrawl efforts to crawl massive amounts of parallel data from multiling...", "weights": [[8, 11, 1.0]]}, "E14-3002": {"snippet": "...used web crawling system that automatically collects relevant documents and minimizes the amount of irrelevant web content. The collected web data is semantically processed in order to acquire rich in...", "weights": [[8, 11, 1.0], [12, 20, 1.0], [114, 117, 1.0], [141, 144, 1.0]]}, "2008.wwwconf_workshop-2008l.4": {"snippet": "...vant Web pages. For an effective retrieval of such content, this paper examines the application of focused Web crawling to the geospatial domain. We describe our approach for a geo-aware focused crawl...", "weights": [[8, 11, 1.0], [110, 113, 1.0], [114, 122, 1.0], [198, 206, 1.0]]}, "2009.wwwjournals_journal-ir0volumeA12A3.1": {"snippet": "...used crawling that exploits not only content-related information but also genre information present in Web pages to guide the crawling process. This approach has been designed to address situations in...", "weights": [[8, 16, 1.0], [106, 109, 1.0], [129, 137, 1.0]]}, "2004.wwwconf_conference-2004at.152": {"snippet": "...ware web crawling\n\n\n ABSTRACTDistributed crawling has shown that it can overcome important limitations of the today's crawling paradigm. However, the optimal benefits of this approach are usually limi...", "weights": [[8, 11, 1.0], [12, 20, 1.0], [44, 52, 1.0], [121, 129, 1.0]]}, "2014.tweb_journal-ir0volumeA8A4.2": {"snippet": "...used Web Crawling", "weights": [[8, 11, 1.0], [12, 20, 1.0]]}, "2009.sigirconf_workshop-2009lsdsir.2": {"snippet": "...ized web search paradigm introduces several problems, such as large data traffic requirements for crawling, index freshness problems and problems to index everything. In this study, we look at collect...", "weights": [[8, 11, 1.0], [101, 109, 1.0]]}, "W14-0402": {"snippet": "...used Web Corpus Crawling\n\n\n In web corpus construction, crawling is a necessary step, and it is probably the most costly of all, because it requires expensive bandwidth usage, and excess crawling incr...", "weights": [[8, 11, 1.0], [19, 27, 1.0], [34, 37, 1.0], [59, 67, 1.0], [190, 198, 1.0]]}, "2001.sigirconf_conference-2001.30": {"snippet": "...iven Web Crawlers", "weights": [[8, 11, 1.0]]}, "2007.wwwconf_conference-2007.15": {"snippet": "... for web crawling", "weights": [[8, 11, 1.0], [12, 20, 1.0]]}, "2007.wwwconf_conference-2007.134": {"snippet": "...used crawling\n\n\n ABSTRACTThis paper reports a new general framework of focused web crawling based on \"relational subgroup discovery\". Predicates are used explicitly to represent the relevance clues of...", "weights": [[8, 16, 1.0], [82, 85, 1.0], [86, 94, 1.0]]}, "2004.jasis_journal-ir0volumeA55A14.2": {"snippet": "Web-crawling reliability", "weights": [[0, 3, 1.0], [4, 12, 1.0]]}, "2001.wwwconf_conference-2001.11": {"snippet": "...used crawling which i s a b l e t o c r a wl particular topical portions of the world wide web quickly without having to explore all web pages. In this paper, we propose the novel concept of intellige...", "weights": [[8, 16, 1.0], [94, 97, 1.0], [136, 139, 1.0]]}, "2011.cikm_conference-2011.12": {"snippet": "...iven web crawling\n\n\n ABSTRACTTo optimize the performance of web crawlers, various measures of page importance have been studied to select and order URLs in crawling. Most sophisticated measures (e.g. ...", "weights": [[8, 11, 1.0], [12, 20, 1.0], [63, 66, 1.0], [159, 167, 1.0]]}, "P13-1135": {"snippet": "... The Web is a comprehensive source of preexisting parallel text, but crawling the entire web is impossible for all but the largest companies. We bring web-scale parallel text to the masses by mining t...", "weights": [[8, 11, 1.0], [72, 80, 1.0], [92, 95, 1.0], [154, 157, 1.0]]}, "2014.tweb_journal-ir0volumeA8A3.6": {"snippet": "... for Crawling Rich Internet Applications\n\n\n New Web technologies, like AJAX, result in more responsive and interactive Web applications, sometimes called Rich Internet Applications (RIAs). Crawling te...", "weights": [[8, 16, 1.0], [51, 54, 1.0], [122, 125, 1.0], [192, 200, 1.0]]}, "2005.gir_workshop-2005.10": {"snippet": "...uted web crawling", "weights": [[8, 11, 1.0], [12, 20, 1.0]]}, "2006.wwwconf_conference-2006.180": {"snippet": "...iven web crawlers\n\n\n ABSTRACTWe present GoGetIt!, a tool for generating structure-driven crawlers that requires a minimum effort from the users. The tool takes as input a sample page and an entry poin...", "weights": [[8, 11, 1.0]]}, "L04-1476": {"snippet": "Data Driven Ontology Evaluation", "weights": []}, "2011.mir_conference-2011.33": {"snippet": "Social media driven image retrieval", "weights": []}, "2012.tweb_journal-ir0volumeA6A1.2": {"snippet": "Crawling Ajax-Based Web Applications through Dynamic Analysis of User Interface State Changes", "weights": [[0, 8, 1.0], [20, 23, 1.0]]}, "2016.sigirconf_conference-2016.79": {"snippet": "...tric Web crawls exist, existing public Arabic Web crawls are quite limited, limiting research and development. To remedy this, we present ArabicWeb16, a new public Web crawl of roughly 150M Arabic Web...", "weights": [[8, 11, 1.0], [49, 52, 1.0], [167, 170, 1.0], [200, 203, 1.0]]}, "2013.cikm_conference-2013.354": {"snippet": "... the Web pages crawled, which results in a Web archive of richer value to an archive user. In our demonstration scenario, we invite a user to compare application-aware crawling to regular Web crawling...", "weights": [[8, 11, 1.0], [46, 49, 1.0], [171, 179, 1.0], [191, 194, 1.0], [195, 203, 1.0]]}, "2005.wwwconf_conference-2005.45": {"snippet": "...tric Web crawling\n\n\n ABSTRACTSearch engines are the primary gateways of information access on the Web today. Behind the scenes, search engines crawl the Web to populate a local indexed repository of W...", "weights": [[8, 11, 1.0], [12, 20, 1.0], [101, 104, 1.0], [156, 159, 1.0], [202, 205, 1.0]]}, "2012.cikm_conference-2012.258": {"snippet": "... the Web. To this end, we design a sentiment-focused web crawling framework for faster discovery and retrieval of such content. In particular, we propose different sentimentfocused web crawling strate...", "weights": [[8, 11, 1.0], [56, 59, 1.0], [60, 68, 1.0], [184, 187, 1.0], [188, 196, 1.0]]}, "L06-1125": {"snippet": "...used Web Crawling", "weights": [[8, 11, 1.0], [12, 20, 1.0]]}, "2016.ijirr_journal-ir0volumeA6A2.0": {"snippet": "... for Crawling the Opinions from World Wide Web", "weights": [[8, 16, 1.0], [46, 49, 1.0]]}}}
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Online visitors often do not find the content they were expecting on specific pages of a large enterprise website, and subsequently search for it in si...", "weights": [[8, 15, 1.0], [30, 38, 1.0], [90, 97, 1.0]]}, "2005.wwwconf_conference-2005si.116": {"snippet": "Detection of phishing webpages based on visual similarity\n\n\n ABSTRACTAn approach to detection of phishing webpages based on visual similarity is proposed, which can be utilized as a part of an enterpr...", "weights": [[0, 9, 1.0], [22, 30, 1.0], [84, 93, 1.0], [106, 114, 1.0]]}, "2010.cikm_workshop-2010smuc.2": {"snippet": "...ated content such as the tag information that is associated with the webpages. In this paper, we present a subspace based feature extraction approach which leverages tag information to complement the ...", "weights": [[8, 15, 1.0], [72, 80, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A4.9": {"snippet": "...ages detection. 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This boilerplate text typically is no...", "weights": [[8, 17, 1.0], [83, 90, 1.0]]}, "2013.ecir_conference-2013.47": {"snippet": "...s of webpages. Specifically, we first identify features from textual content, link structure, webpages design, as well as their social popularity learned from popular social media sites (e.g., Faceboo...", "weights": [[8, 16, 1.0], [72, 79, 1.0], [97, 105, 1.0]]}, "2005.wwwjournals_journal-ir0volumeA8A2.4": {"snippet": "...ting Content Extraction of HTML Documents\n\n\n AbstractWeb pages often contain clutter (such as unnecessary images and extraneous links) around the body of an article that distracts a user from actual c...", "weights": [[8, 15, 1.0], [202, 209, 1.0]]}, "2013.cikm_conference-2013.17": {"snippet": "...page content incorporating the structured content representation. Different from existing language models for retrieval, our proposed model considers both the proximity and the structured webpage cont...", "weights": [[8, 15, 1.0], [45, 52, 1.0], [199, 206, 1.0]]}, "2016.sigirconf_conference-2016.207": {"snippet": "...yond Relevance: Incorporating Effort in Information Retrieval\n\n\n ABSTRACTPrimary focus of Information retrieval (IR) systems has been to optimize for Relevance. Existing approaches used to rank docume...", "weights": [[8, 17, 1.0], [153, 162, 1.0]]}, "2012.cikm_conference-2012.208": {"snippet": "...ding relevance signal. Thus, devising content-based relevance estimation (ranking) approaches becomes highly challenging. We address this challenge using two methods that utilize inter-document simila...", "weights": [[8, 17, 1.0], [41, 48, 1.0], [55, 64, 1.0]]}, "2019.wwwconf_conference-2019.344": {"snippet": "Semi-supervised Multi-view Individual and Sharable Feature Learning for Webpage Classification", "weights": []}, "2007.cikm_conference-2007.58": {"snippet": "...ocal webpages into different clusters such that each cluster contains webpages with similar change pattern. We then sample webpages from each cluster to estimate the change frequency of all the webpag...", "weights": [[8, 16, 1.0], [73, 81, 1.0], [126, 134, 1.0], [197, 205, 1.0]]}, "2013.wwwconf_conference-2013c.36": {"snippet": "Content extraction using diverse feature sets\n\n\n ABSTRACTThe goal of content extraction or boilerplate detection is to separate the main content from navigation chrome, advertising blocks, copyright n...", "weights": [[0, 7, 1.0], [69, 76, 1.0], [103, 112, 1.0], [137, 144, 1.0]]}, "2003.wwwjournals_journal-ir0volumeA6A2.1": {"snippet": "Relevance Feedback and Learning in Content-Based Image Search", "weights": [[0, 9, 1.0], [35, 42, 1.0]]}, "2011.ijirr_journal-ir0volumeA1A4.1": {"snippet": "...g of Webpages", "weights": [[8, 16, 1.0]]}, "2001.trec_conference-2001.32": {"snippet": "... the webpages returned by a standard vector space IR system. In the second phase, a logistic regression analysis is used to combine multiple sources of evidence on the remaining webpages to predict wh...", "weights": [[8, 16, 1.0], [181, 189, 1.0]]}, "2007.wwwconf_conference-2007.128": {"snippet": "... the webpages. 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Many calculation redundancies exist when processing similar webpages, thus it is possible to cache and reuse previously calculated intermediate results to improve web browser performanc...", "weights": [[8, 16, 1.0], [78, 86, 1.0]]}, "2019.sigirconf_conference-2019.64": {"snippet": "...ling Relevance and Sensitivity for Search Among Sensitive Content", "weights": [[8, 17, 1.0], [61, 68, 1.0]]}, "2020.fnp-1.29": {"snippet": "...ents Detection in Financial Documents\n\n\n Title Detection and Table of Contents Generation are important components in detecting document structure. In particular, these two elements serve to provide t...", "weights": [[8, 17, 1.0], [50, 59, 1.0]]}, "2012.ipm_journal-ir0volumeA48A2.6": {"snippet": "... the relevance ranking algorithm named PolarityRank. 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However, most such algorithms rely solely on document content to detect duplication, ignoring the fact that a primary goal of duplicate detection is to identify documents th...", "weights": [[8, 17, 1.0], [84, 91, 1.0], [166, 175, 1.0]]}, "2008.wwwconf_conference-2008.170": {"snippet": "...ance detection algorithm, which leverages structural, content, and visual information. The structural and content information is leveraged via template, a generalized regular expression learnt over se...", "weights": [[8, 17, 1.0], [57, 64, 1.0], [109, 116, 1.0]]}, "2011.wwwconf_conference-2011c.68": {"snippet": "...ment content quality. We also describe how to use segment content quality estimates as weights in the BM25F formulation. Experimental results show our method improves relevance of retrieved results by...", "weights": [[8, 15, 1.0], [61, 68, 1.0], [170, 179, 1.0]]}, "W14-5153": {"snippet": "...ides relevance. 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Instead of depending on the malicious content used by previous methods, our algorithm first identifies and then leverages the URLs of the MDN's central servers, where a central ...", "weights": [[8, 17, 1.0], [64, 71, 1.0]]}, "1999.ipm_journal-ir0volumeA35A5.1": {"snippet": "A relevance feedback mechanism for content-based image retrieval", "weights": [[2, 11, 1.0], [35, 42, 1.0]]}, "2015.wwwconf_conference-2015.117": {"snippet": "...tual content, but also other elements such as branding banners, navigational elements, advertisements, copyright etc. This noisy content is typically not related to the main subjects of the webpages. ...", "weights": [[8, 15, 1.0], [132, 139, 1.0], [193, 201, 1.0]]}, "2012.cikm_conference-2012.220": {"snippet": "...ther webpages are present on the web in multiple languages. Standard search engines evaluate the relevance of the candidate documents to the given query. However, when considering documents with overl...", "weights": [[8, 16, 1.0], [100, 109, 1.0]]}, "2020.argmining-1.3": {"snippet": "...vant content and structure, they also include text that is irrelevant, or even detrimental, to their purpose. In this paper, we present a precision-oriented approach to detecting such irrelevant text ...", "weights": [[8, 15, 1.0]]}, "2012.irfc_conference-2012.4": {"snippet": "... and Detection of Web Spam by Means of Web Content", "weights": [[8, 17, 1.0], [46, 53, 1.0]]}, "2010.cikm_workshop-2010smuc.7": {"snippet": "Spam detection with a content-based random-walk algorithm\n\n\n ABSTRACTIn this work we tackle the problem of the spam detection on the Web. Spam web pages have become a problem for Web search engines, d...", "weights": [[5, 14, 1.0], [22, 29, 1.0], [116, 125, 1.0]]}, "2013.sigirconf_conference-2013.116": {"snippet": "...ying relevance scores for search results\n\n\n ABSTRACTInternet search engines typically compute a relevance score for webpages given the query terms, and then rank the pages by decreasing relevance scor...", "weights": [[8, 17, 1.0], [99, 108, 1.0], [119, 127, 1.0], [189, 198, 1.0]]}}}
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Web crawler is a kind of tool to retrieve web pages. Previous researches focused on English web crawlers and the web crawler is always optimized for English web pages. We found that the performance o...", "weights": [[8, 15, 1.0], [121, 128, 1.0]]}, "2012.sigirconf_workshop-2012osir.9": {"snippet": "... Web Crawler", "weights": [[8, 15, 1.0]]}, "2020.acl-main.164": {"snippet": "...atic Detection of Generated Text is Easiest when Humans are Fooled\n\n\n Recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. 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To evaluate the performance of an incremental crawler for a Web search engine, we measure ...", "weights": [[8, 15, 1.0], [159, 166, 1.0]]}, "L10-1415": {"snippet": "...ards Optimal {TTS} Corpora", "weights": [[8, 15, 1.0]]}, "2010.tois_journal-ir0volumeA28A4.0": {"snippet": "...so a crawler has to decide an optimal order in which to crawl and recrawl Web pages. Ideally, crawlers should request only those Web pages that have changed since the last crawl; in practice, a crawle...", "weights": [[8, 15, 1.0], [33, 40, 1.0], [197, 204, 1.0]]}, "2005.tois_journal-ir0volumeA23A4.2": {"snippet": "... the crawler thus biasing it towards certain portions of the Web graph. 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Web Crawler", "weights": [[8, 15, 1.0]]}, "2008.wwwconf_conference-2008.46": {"snippet": "...gent crawler for web forums\n\n\n ABSTRACTWe study in this paper the Web forum crawling problem, which is a very fundamental step in many Web applications, such as search engine and Web data mining. As a...", "weights": [[8, 15, 1.0]]}, "2010.jasis_journal-ir0volumeA61A6.9": {"snippet": "...used crawler for Dark Web forums", "weights": [[8, 15, 1.0]]}, "2003.wwwconf_conference-2003p.16": {"snippet": "...used Crawler", "weights": [[8, 15, 1.0]]}, "2006.ismir_conference-2006.81": {"snippet": "...udio crawler focused on weblogs", "weights": [[8, 15, 1.0]]}, "W05-1725": {"snippet": "{C}hunking: an unsupervised method to find errors in text\n\n\n We describe a method to use a chunker for grammar checking. Once a chunker is available the method is fully unsupervised, only unannotated ...", "weights": []}, "2009.wwwjournals_journal-ir0volumeA12A3.1": {"snippet": "...e or genre on that topic to be retrieved. In this article, we describe an approach to focused crawling that exploits not only content-related information but also genre information present in Web page...", "weights": [[8, 13, 1.0], [166, 171, 1.0]]}, "2021.ranlp-1.8": {"snippet": "... for genre analysis in the corpus were undertaken. A technique based on logistic regression and BERT has been found to perform genre analysis highly satisfactorily with an average of 88.25 on F-score ...", "weights": [[8, 13, 1.0], [130, 135, 1.0]]}, "2004.wwwconf_conference-2004at.152": {"snippet": "... the optimal benefits of this approach are usually limited to the sites hosting the crawler. In this work, we propose a location-aware method, called IPMicra, that utilizes an IP address hierarchy, an...", "weights": [[8, 15, 1.0], [87, 94, 1.0]]}, "2001.sigirconf_conference-2001.30": {"snippet": "Evaluating Topic-Driven Web Crawlers", "weights": []}, "2011.cikm_conference-2011.11": {"snippet": "... Web crawler. We show that a high fraction of URLs that appear in toolbar logs are not discovered by the crawler. We also reveal that a certain fraction of URLs are discovered by the crawler later tha...", "weights": [[8, 15, 1.0], [108, 115, 1.0], [186, 193, 1.0]]}, "2008.sigirconf_conference-2008.125": {"snippet": "...used crawler efficiently and effectively using on-line topical importance estimation\n\n\n ABSTRACTFocused crawling is a critical technique for topical resource discovery on the Web. We propose a new fro...", "weights": [[8, 15, 1.0]]}, "2007.sigirconf_conference-2007.28": {"snippet": "...opic detection and alignment of multiple documents\n\n\n ABSTRACTTopic detection and tracking  and topic segmentation  play an important role in capturing the local and sequential information of document...", "weights": [[8, 17, 1.0], [71, 80, 1.0]]}, "2006.ismir_conference-2006.75": {"snippet": "...ient Genre Classification using Qualitative Representations", "weights": [[8, 13, 1.0]]}, "Y18-1055": {"snippet": "...pose Genre-Oriented Content Extraction (GOCE), a novel content extraction framework consisting of two steps: web genre classification and content extraction algorithms based on the detected genre. GOC...", "weights": [[8, 13, 1.0], [116, 121, 1.0], [193, 198, 1.0]]}, "2008.sigirconf_conference-2008.60": {"snippet": "... the detection of the page-flipping links. The skeleton links instruct the crawler to only crawl valuable pages and meanwhile avoid duplicate and uninformative ones; and the page-flipping links tell t...", "weights": [[8, 17, 1.0], [78, 85, 1.0]]}, "2009.wwwconf_conference-2009.159": {"snippet": "... web genre detection\n\n\n ABSTRACTThe term web genre denotes the type of a given web resource, in contrast to the topic of its content. In this research, we focus on recognizing the web genres blog, wik...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [48, 53, 1.0]]}, "W09-3826": {"snippet": "...rror Detection with Best Approximated Parse\n\n\n In this paper, we propose that grammar error detection be disambiguated in generating the connected parse(s) of optimal merit for the full input utteranc...", "weights": [[8, 17, 1.0], [95, 104, 1.0], [162, 169, 1.0]]}, "2016.sigirconf_conference-2016.224": {"snippet": "...nts: crawler, indexer, and query processor.\nWeb CrawlingThe web crawler is responsible for discovering new web pages and downloading their content while refreshing the content of previously downloaded...", "weights": [[8, 15, 1.0], [67, 74, 1.0]]}, "2016.wwwconf_conference-2016.79": {"snippet": "... web crawler that tracks and stores the cookies as it navigates to websites. We use this crawler to collect over 3.2M cookies from the two crawls, separated by 18 months, of the top 100K Alexa web sit...", "weights": [[8, 15, 1.0], [92, 99, 1.0]]}, "2020.tist_journal-ir0volumeA11A2.10": {"snippet": "... 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The approach is compare...", "weights": [[8, 15, 1.0], [98, 105, 1.0]]}, "2001.wwwconf_conference-2001.12": {"snippet": "... web crawler", "weights": [[8, 15, 1.0]]}, "2018.wwwconf_conference-2018c.371": {"snippet": "...usic Genre Using Extreme Gradient Boosting\n\n\n ABSTRACTThis paper summarizes our contribution to the CrowdAI music genre classification challenge \"Learning to Recognise Musical Genre from Audio on the ...", "weights": [[8, 13, 1.0], [117, 122, 1.0], [179, 184, 1.0]]}, "2019.ipm_journal-ir0volumeA56A1.6": {"snippet": "...nity detection using hierarchical clustering based on edge-weighted similarity in cloud environment\n\n\n Recently, social network has been paid more and more attention by people. 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In this paper, we present FairScholar, a novel scientific paper recommendation system that aims at balancing both relevance and diversity while search...", "weights": [[8, 18, 1.0], [100, 110, 1.0]]}, "2020.ecir_conference-20201.17": {"snippet": "...n of Scientific Concepts from Research Articles\n\n\n We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions. Fir...", "weights": [[8, 18, 1.0], [102, 112, 1.0]]}, "2011.wwwconf_conference-2011c.9": {"snippet": "...f an optimal solution in linear time.", "weights": [[8, 15, 1.0]]}, "1993.sigirconf_conference-93.36": {"snippet": "...TREC topics were produced by ten different expert online searchers. These different formulations were grouped, and the groups, and combinations of them, were used as searches against the TREC test col...", "weights": [[8, 14, 1.0], [169, 177, 1.0]]}, "2008.sigirconf_conference-2008.71": {"snippet": "... for optimal query-specific clusters\n\n\n ABSTRACTTo improve the precision at the very top ranks of a document list presented in response to a query, researchers suggested to exploit information induced...", "weights": [[8, 15, 1.0], [22, 30, 1.0]]}, "2014.wwwjournals_journal-ir0volumeA17A6.4": {"snippet": "...s on scientific topic formation and development using the citation network structure among scientific papers. Experimental results on two real-world data sets show that the division of a core group br...", "weights": [[8, 18, 1.0], [94, 104, 1.0], [105, 111, 1.0]]}, "2010.wwwconf_conference-2010.112": {"snippet": "...s in scientific articles\n\n\n ABSTRACTAlgorithms are an integral part of computer science literature. However, none of the current search engines offer specialized algorithm search facility. We describe...", "weights": [[8, 18, 1.0]]}, "C08-1087": {"snippet": "Scientific Paper Summarization Using Citation Summary Networks\n\n\n Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of s...", "weights": [[0, 10, 1.0], [160, 170, 1.0]]}, "2002.tois_journal-ir0volumeA20A1.1": {"snippet": "...thin specific topics with encapsulation of topic-specific information in a persistent repository. One key characteristic of these persistent repositories is that their footprint is small enough to fit...", "weights": [[8, 16, 1.0], [17, 23, 1.0], [52, 60, 1.0]]}, "2016.sigirconf_conference-2016.194": {"snippet": "...erse topics) and exploitation (moving towards more specific topics). Where other RL-based systems su\u21b5er from the \"cold start\" problem, requiring sufficient time to adjust to a user's information needs...", "weights": [[8, 14, 1.0], [54, 62, 1.0], [63, 69, 1.0]]}, "P16-1082": {"snippet": "...in a Scientific Corpus\n\n\n Our goal is to generate reading lists for students that help them optimally learn technical material. Existing retrieval algorithms return items directly relevant to a query ...", "weights": [[8, 18, 1.0]]}, "C80-1089": {"snippet": "... for specific topics (for a given corpus) without a priori semantic information. Information contained in the thesaurus lead to new search formulations via automatic and/or user feedback. This present...", "weights": [[8, 16, 1.0], [17, 23, 1.0]]}, "2019.ecir_conference-20192.33": {"snippet": "... the scientific community as well as by researchers studying citations. \nMotivationResearchers in all scientific fields are nowadays confronted with vast amounts of scientific papers published within ...", "weights": [[8, 18, 1.0], [105, 115, 1.0], [168, 178, 1.0], [179, 185, 1.0]]}, "2017.sigirconf_workshop-2017birndl.6": {"snippet": "...ated scientific studies. Even though the publications of a single author can be easily retrieved and explored, understanding how authors have collaborated with each other on specific research topics a...", "weights": [[8, 18, 1.0], [177, 185, 1.0], [195, 201, 1.0]]}, "2015.tois_journal-ir0volumeA33A4.1": {"snippet": "... and searches only a few shards that are estimated to contain relevant documents for the query. We propose shard creation techniques that are scalable, efficient, self-reliant, and create topic-based ...", "weights": [[8, 16, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A2.20": {"snippet": "...this specific public. The thesis behind our work is that consumer health information retrieval is affected by context features, which can be used in query formulation support to improve retrieval perf...", "weights": [[8, 16, 1.0]]}, "2014.ecir_workshop-2014bir.3": {"snippet": "...l of Scientific Literature based on Citation Relations: Introducing the CitNetExplorer Tool\n\n\n We consider the problem of scientific literature search, and we suggest that citation relations between p...", "weights": [[8, 18, 1.0], [125, 135, 1.0]]}, "2008.ipm_journal-ir0volumeA44A6.3": {"snippet": "Using genetic algorithms to evolve a population of topical queries\n\n\n AbstractSystems for searching the Web based on thematic contexts can be built on top of a conventional search engine and benefit f...", "weights": []}, "Q17-1014": {"snippet": "...g of Topics, Citations, and Topical Authority in Academic Corpora\n\n\n Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications...", "weights": [[8, 14, 1.0], [80, 90, 1.0], [180, 190, 1.0]]}, "S10-1004": {"snippet": "...from Scientific Articles\n\n\n This paper describes Task 5 of the Workshop on Semantic Evaluation 2010 (SemEval-2010). Systems are to automatically assign keyphrases or keywords to given scientific artic...", "weights": [[8, 18, 1.0], [187, 197, 1.0]]}, "2016.clef_conference-2016.7": {"snippet": "...dico-scientific terminology. To assess how the language and terminology impact the use of suggestions, we conducted a user study with 40 subjects considering their English proficiency, health literacy...", "weights": [[8, 18, 1.0]]}, "2018.trec_conference-2018.14": {"snippet": "...ical scientific literature that can be accessed online represents a valuable source of information useful to tailor medical decisions to a specific clinical case. With this respect, Information Retrie...", "weights": [[8, 18, 1.0], [142, 150, 1.0]]}, "2018.desires_conference-2018.20": {"snippet": "...opic-Specific Off-line Web Search", "weights": [[8, 16, 1.0]]}, "2010.sigirconf_workshop-2010lsdsir.5": {"snippet": "...ning topics on a subset of the collection. Experiments with three large collections demonstrate that selective search using topic-based shards reduces search costs by at least an order of magnitude wi...", "weights": [[8, 14, 1.0]]}, "2018.tweb_journal-ir0volumeA12A3.4": {"snippet": "... the optimal page presentation to render heterogeneous results onto search result page (SERP). Page presentation is broadly defined as the strategy to present a set of items on SERP, much more express...", "weights": [[8, 15, 1.0]]}, "2021.findings-acl.167": {"snippet": "...n of Scientific Topics and their Relationships\n\n\n We propose a study of the development of scientific topics through time, as well as the relations between them within the scientific field of computat...", "weights": [[8, 18, 1.0], [19, 25, 1.0], [94, 104, 1.0], [105, 111, 1.0], [175, 185, 1.0]]}, "2018.sigirconf_workshop-2018birndl.4": {"snippet": "...used Scientific Paper Summarization with Localized Sentence Representation\n\n\n Query-focused summarization enables the extraction of information on a specific \"aspect\" such as a \"proposed approach\" or ...", "weights": [[8, 18, 1.0], [152, 160, 1.0]]}, "2009.sigirconf_conference-2009.173": {"snippet": "...arch topics", "weights": [[8, 14, 1.0]]}, "2001.trec_conference-2001.72": {"snippet": "...opic-Specific Optimization and Structuring", "weights": [[8, 16, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...to a specific functional area of business (such as accounting, manufacturing, or marketing) and the effect of the presence or absence of such function specific domain knowledge on how systems analysts...", "weights": [[8, 16, 1.0], [154, 162, 1.0]]}, "2016.jasis_journal-ir0volumeA67A11.5": {"snippet": "Predicting information searchers' topic knowledge at different search stages", "weights": []}, "2011.sigirconf_conference-2011.38": {"snippet": "...from optimal search tips\n\n\n ABSTRACTWeb search performance can be improved by either improving the search engine itself or by educating the user to search more efficiently. There is a large amount of ...", "weights": [[8, 15, 1.0]]}, "1999.sigirconf_conference-99.60": {"snippet": "...rnet Searches (poster abstract)\n\n\n ABSTRACTResults of searches using a variety of query formulations with several Internet search engines show that strategies intended to give narrower and more precis...", "weights": [[8, 16, 1.0], [57, 65, 1.0]]}, "2014.cikm_workshop-2014webkr.4": {"snippet": "...r of scientific papers has risen sharply, there is an urgent need to assist researchers in locating the candidate cited papers they are looking for. Classical relation-based and text-based approaches ...", "weights": [[8, 18, 1.0], [19, 25, 1.0], [123, 129, 1.0]]}, "2012.sigirconf_conference-2012.159": {"snippet": "... the specific task that scientists are searching for worth-reading articles beneficial for their research. Our study finds: users' perception of relevance and preference of reading are only moderately...", "weights": [[8, 16, 1.0]]}, "P13-2102": {"snippet": "...zing Scientific Topics Starting from Keywords\n\n\n In this paper, we investigate the problem of automatic generation of scientific surveys starting from keywords provided by a user. We present a system ...", "weights": [[8, 18, 1.0], [19, 25, 1.0], [121, 131, 1.0]]}, "P18-3007": {"snippet": "... the scientific publications, requiring a substantial amount of time and effort. Due to the exponential growth of scientific literature, it has become almost impossible for a scientist to keep track o...", "weights": [[8, 18, 1.0], [117, 127, 1.0]]}, "2018.sigirconf_conference-2018.10": {"snippet": "...rent topics and information needs. By using scientifically formulated knowledge tests, we calibrated the knowledge of users before and after their search sessions, quantifying their knowledge gain. Ou...", "weights": [[8, 14, 1.0]]}, "W16-1504": {"snippet": "... for Scientific Paper Searches\n\n\n To improve the search performance of retrieval methods using cocitation linkages, this study proposes a technique to enlarge a co-citation network by incorporating sa...", "weights": [[8, 18, 1.0], [25, 33, 1.0]]}, "2014.cikm_conference-2014.62": {"snippet": "... are optimal or close to the optimal solution. Moreover, a manually tuned parameter (say, \u03bb) is often required to trade off relevance and diversity. This makes it difficult to know whether the failure...", "weights": [[8, 15, 1.0], [32, 39, 1.0]]}, "2019.jasis_journal-ir0volumeA70A9.9": {"snippet": "...ween scientific publications and patents", "weights": [[8, 18, 1.0]]}, "2018.chiir_conference-2018.2": {"snippet": "...rent topics and information needs. By using scientifically formulated knowledge tests we calibrated the knowledge of users before and after their search sessions, quantifying their knowledge gain. We ...", "weights": [[8, 14, 1.0]]}, "W16-6114": {"snippet": "...tity Searches in {P}ub{M}ed\n\n\n Identifying relevant studies from the entire scientific literature is an important task in biomedical research. Past efforts have incorporated semantically recognized bi...", "weights": [[8, 16, 1.0], [79, 89, 1.0]]}, "2017.ecir_workshop-2017bir.5": {"snippet": "...ajor Scientific Events\n\n\n In this paper, we look at the search behavior of users of an academic search engine and in particular, their query patterns following the occurrence of major scientific event...", "weights": [[8, 18, 1.0], [187, 197, 1.0]]}, "2013.cikm_conference-2013.365": {"snippet": "...more specific search queries. The SE is designed to discover similar but generally infrequent words with surrounding texts in browser web documents and then suggest found words as possible query keywo...", "weights": [[8, 16, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.7": {"snippet": "... the optimal way of informing the user about their contents; and second, that in an interactive setting the optimal way of formulating and refining the query is helping the user to choose appropriate ...", "weights": [[8, 15, 1.0], [111, 118, 1.0], [126, 137, 1.0]]}, "2017.wwwconf_conference-2017c.103": {"snippet": "... all papers (blue) and for a specific field (Neuroscience, the most frequent field in our data, consisting of 3,500 papers). Both have a slight positive slope for a linear regression line fit (Slope 0...", "weights": [[8, 14, 1.0], [32, 40, 1.0], [119, 125, 1.0]]}, "2009.wsdm_workshop-2009wscd.6": {"snippet": "...opic-specific analysis of search queries", "weights": [[8, 16, 1.0]]}, "2018.tois_journal-ir0volumeA36A4.6": {"snippet": "...as a specific type of extra-topical dimension (i.e., a type of constraint that is independent from the information need's topic). We focus on six extra-topical dimensions: (1) domain knowledge, (2) vi...", "weights": [[8, 16, 1.0]]}, "A97-1042": {"snippet": "...ying Topics by Position\n\n\n This paper addresses the problem of identifying likely topics of texts by their position in the text. It describes the automated training and evaluation of an Optimal Positi...", "weights": [[8, 14, 1.0], [85, 91, 1.0], [189, 196, 1.0]]}, "2019.ecir_workshop-2019bir.8": {"snippet": "... for Scientific Literature Search: Proposal and Algorithmic Specification", "weights": [[8, 18, 1.0]]}, "D14-1170": {"snippet": "...s in Scientific Papers: An Optimization Approach\n\n\n In this paper, we investigate a challenging task of automatic related work generation. Given multiple reference papers as input, the task aims to ge...", "weights": [[8, 18, 1.0], [19, 25, 1.0], [167, 173, 1.0]]}, "2010.airs_conference-2010.44": {"snippet": "...main-Specific Term Rankings Using Topic Models", "weights": [[8, 16, 1.0]]}, "2021.eacl-demos.31": {"snippet": "...hose optimal hyper-parameters are estimated using a Bayesian Optimization approach. The proposed solution integrates several state-of-the-art topic models and evaluation metrics. These metrics can be ...", "weights": [[8, 15, 1.0]]}, "W18-2318": {"snippet": "...ng a specific document in databases is called a Single Citation search. Single citation searches are particularly important for scholarly databases, such as PubMed \u00ae , because users are frequently sea...", "weights": [[8, 16, 1.0], [91, 99, 1.0]]}, "2009.cikm_conference-2009.129": {"snippet": "Beyond hyperlinks: organizing information footprints in search logs to support effective browsing\n\n\n ABSTRACTWhile current search engines serve known-item search such as homepage finding very well, th...", "weights": []}, "2013.wwwconf_conference-2013c.291": {"snippet": "...urse-specific search engines: semi-automated methods for identifying high quality topic-specific corpora", "weights": [[8, 16, 1.0], [91, 99, 1.0]]}, "2018.ipm_journal-ir0volumeA54A4.2": {"snippet": "...ally optimal choice at each round. As a result, it is difficult to know whether the failures are caused by the optimization criterion or the setting of parameters. Different from previous studies, we ...", "weights": [[8, 15, 1.0]]}, "2019.sigirconf_workshop-2019birndl.2": {"snippet": "...cent papers that are specific and relevant to each user's scientific interests by leveraging state of the art embeddings and clustering techniques. Meta also calculates article-level inferred Eigenfac...", "weights": [[8, 14, 1.0], [24, 32, 1.0], [61, 71, 1.0]]}, "2003.wwwconf_conference-2003.27": {"snippet": "...ging topics appear constantly and rapidly. There is often not enough time for someone to write a book on such topics. To learn such emerging topics, one can resort to research papers. However, researc...", "weights": [[8, 14, 1.0], [113, 119, 1.0], [144, 150, 1.0], [179, 185, 1.0]]}, "2001.wwwconf_conference-2001.61": {"snippet": "...ular topics are usually linked to by enthusiasts in the same domain which can be exploited. In this paper, we propose a novel ranking scheme for popular topics that places the most authoritative pages...", "weights": [[8, 14, 1.0], [156, 162, 1.0]]}, "P04-2003": {"snippet": "... for Topics in a Large Collection of Texts\n\n\n We describe an original method that automatically finds specific topics in a large collection of texts. Each topic is first identified as a specific clust...", "weights": [[8, 14, 1.0], [105, 113, 1.0], [114, 120, 1.0], [189, 197, 1.0]]}, "2011.cikm_workshop-2011books.4": {"snippet": "...swer specific needs such as finding the definition of a concept, finding results for a particular problem, checking whether an idea has already been tested, or comparing the scientific conclusions of ...", "weights": [[8, 16, 1.0], [177, 187, 1.0]]}, "2014.wwwconf_conference-2014c.65": {"snippet": "... the optimal solution for diversified search is NP-hard. Therefore, several algorithms have been proposed to obtain a local optimal ranking with greedy approximations. In this paper, a pruned exhausti...", "weights": [[8, 15, 1.0], [127, 134, 1.0]]}, "D17-1227": {"snippet": "...ably optimal beam search algorithm that will always return the optimal-score complete hypothesis (modulo beam size), and finish as soon as the optimality is established (finishing no later than the ba...", "weights": [[8, 15, 1.0], [66, 73, 1.0]]}, "2017.ir_journal-ir0volumeA20A5.5": {"snippet": "Optimizing search results for human learning goals", "weights": []}, "2004.sigirconf_conference-2004.69": {"snippet": "...opic-specific page relevance bias over a collection of documents. Subwebs may be automatically identified using a simple algorithm we describe, and used to provide highly-relevant topic-specific infor...", "weights": [[8, 16, 1.0], [189, 197, 1.0]]}, "2012.sigirconf_conference-2012.219": {"snippet": "...ient-specific information (e.g. electronic health records) and knowledge-based information (e.g. scientific papers). The increasing emphasis on the use of evidence-based medicine (using the best evide...", "weights": [[8, 16, 1.0], [100, 110, 1.0], [111, 117, 1.0]]}, "2008.wwwconf_conference-2008.42": {"snippet": "Learning to rank relational objects and its application to web search\n\n\n ABSTRACTLearning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technolo...", "weights": []}, "2002.tois_journal-ir0volumeA20A1.2": {"snippet": "...ular topics are usually linked to by enthusiasts in the same domain. In this paper, we propose a novel ranking scheme for popular topics that places the most authoritative pages on the query topic at ...", "weights": [[8, 14, 1.0], [133, 139, 1.0]]}, "N10-2001": {"snippet": "... for scientific literature. The Camtology system allows for sentence-level searches of PDF files and combines text and image searches, thus facilitating the retrieval of information present in tables ...", "weights": [[8, 18, 1.0], [78, 86, 1.0], [128, 136, 1.0]]}, "2018.cikm_conference-2018.6": {"snippet": "... for Scientific Article Analysis\n\n\n ABSTRACTTopic modelling and citation recommendation of scientific articles are important yet challenging research problems in scientific article analysis. In partic...", "weights": [[8, 18, 1.0], [94, 104, 1.0], [165, 175, 1.0]]}, "2019.trec_conference-2019.18": {"snippet": "...e to scientific abstracts and clinical trials. 40 topics where patient data are given (demographic data, disease, gene and genetic variant) were available for this competition. The aim was to retrieve...", "weights": [[8, 18, 1.0], [53, 59, 1.0]]}, "2010.tweb_journal-ir0volumeA4A2.2": {"snippet": "Optimal distance bounds for fast search on compressed time-series query logs\n\n\n Consider a database of time-series, where each datapoint in the series records the total number of users who asked for a...", "weights": [[0, 7, 1.0]]}, "W19-2602": {"snippet": "...from Scientific Literature\n\n\n As scientific communities grow and evolve, there is a high demand for improved methods for finding relevant papers, comparing papers on similar topics and studying trends...", "weights": [[8, 18, 1.0], [36, 46, 1.0], [141, 147, 1.0], [159, 165, 1.0], [177, 183, 1.0]]}, "2012.cikm_workshop-2012esair.9": {"snippet": "...ting scientific papers for mathematical formula search\n\n\n ABSTRACTIn recent years, growing numbers of scientific papers have been published in XML format generating a large published base of MathML-st...", "weights": [[8, 18, 1.0], [19, 25, 1.0], [105, 115, 1.0], [116, 122, 1.0]]}, "2010.sigirconf_conference-2010.96": {"snippet": "...ated topics in web search\n\n\n ABSTRACTSuggesting topics that are related to user's goal or interest is very important in web search. However, search engines today focus on suggesting mainly reformulati...", "weights": [[8, 14, 1.0], [51, 57, 1.0]]}, "2012.cikm_conference-2012.255": {"snippet": "A unified optimization framework for auction and guaranteed delivery in online advertising\n\n\n ABSTRACTThis paper proposes a new unified optimization framework combining pay-per-click auctions and guar...", "weights": []}, "2006.trec_conference-2006.31": {"snippet": "...s in Scientific Articles: Fusion of Automatic Approaches vs. an Interactive Team Effort\n\n\n This paper presents our approach to retargeting the information retrieval systems designed and/or optimized f...", "weights": [[8, 18, 1.0]]}, "W16-1510": {"snippet": "... for specific research topics which have been named by them in an interview before the experiment. Our results show clearly that the presented search term, journal name and author name recommendations...", "weights": [[8, 16, 1.0], [26, 32, 1.0]]}, "2016.wwwconf_conference-2016.51": {"snippet": "...user searches the same topic, and big search tasks, where a user searches a series of related topics. We extensively evaluate the proposed approach and compare with several state-of-the-art search tas...", "weights": [[8, 16, 1.0], [68, 76, 1.0], [97, 103, 1.0]]}, "C16-3003": {"snippet": "...s to scientific information. Extracting information from scientific papers, for example, can contribute to the development of rich scientific knowledge bases which can be leveraged to support intellig...", "weights": [[8, 18, 1.0], [60, 70, 1.0], [71, 77, 1.0], [134, 144, 1.0]]}, "1999.cikm_conference-99.19": {"snippet": "...l of Scientific Literature\n\n\n AbstractThe web has greatly improved access to scientific literature. However, scientific articles on the web are largely disorganized, with research articles being sprea...", "weights": [[8, 18, 1.0], [80, 90, 1.0], [112, 122, 1.0]]}}}
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few years very little as been made of the usefulness of Connectivity Analysis to Information R...", "weights": [[8, 16, 1.0], [53, 62, 1.0], [178, 186, 1.0], [190, 201, 1.0], [202, 211, 1.0]]}, "2008.ntcir_workshop-2008.65": {"snippet": "... via Link Analysis of Wikipedia for CLIR\n\n\n In this paper, we report how we do the query expansion in the NTCIR-7 IR4QA subtask. 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These engines return, as the query's result, a list o...", "weights": [[8, 12, 1.0], [23, 31, 1.0], [61, 72, 1.0]]}, "2005.sigirconf_conference-2005.41": {"snippet": "... hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider generation links, which indicate that ...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2011.ecir_conference-2011.11": {"snippet": "... for Retrieval?", "weights": [[8, 17, 1.0]]}, "2018.tweb_journal-ir0volumeA12A4.4": {"snippet": "...site information aggregation that uses crosssite links to stitch together site-unique information fields for OSN users. As an example application of cross-site information aggregation, we develop a ge...", "weights": [[8, 19, 1.0], [89, 100, 1.0], [163, 174, 1.0]]}, "2013.ictir_conference-2013.12": {"snippet": "...nced Information Retrieval by Exploiting Recommender Techniques in Cluster-Based Link Analysis\n\n\n ABSTRACTInspired by the use of PageRank algorithms in document ranking, we develop and evaluate a clus...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [84, 88, 1.0], [89, 97, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.27": {"snippet": "... (3) information retrieval researchers for further study of symbolic links in web directories.", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "C12-1028": {"snippet": "Analysis and Enhancement of Wikification for Microblogs with Context Expansion\n\n\n Disambiguation to Wikipedia (D2W) is the task of linking mentions of concepts in text to their corresponding Wikipedia...", "weights": [[0, 8, 1.0]]}, "2013.sigirconf_conference-2013.187": {"snippet": "... and retrieval\n\n\n ABSTRACTThis full-day tutorial presents a comprehensive introduction to entity linking and retrieval. Part I provides a detailed overview of entity linking: identifying and disambigu...", "weights": [[8, 17, 1.0], [112, 121, 1.0]]}, "2009.wwwconf_conference-2009.133": {"snippet": "...g to link selling sites and authorities -to link-promoted sites. However, the main purpose of the algorithm is to detect every paid rather than link selling pages or paid link promoted sites.\nALGORITH...", "weights": [[8, 12, 1.0], [47, 51, 1.0], [147, 151, 1.0], [174, 178, 1.0]]}, "2005.ecir_conference-2005.48": {"snippet": "...s in Link Structure Analysis\n\n\n Abstract. Much research has been performed investigating how links between web pages can be exploited in an Information Retrieval setting . In this poster, we investiga...", "weights": [[8, 12, 1.0], [23, 31, 1.0], [143, 154, 1.0], [155, 164, 1.0]]}, "2011.wwwconf_workshop-2011ldow.11": {"snippet": "...by a link traversal based execution alone. However, no systematic analysis exist that justifies the application of caching strategies based on these assumptions. In this paper we evaluate the potentia...", "weights": [[8, 12, 1.0], [69, 77, 1.0]]}, "2007.cikm_conference-2007.62": {"snippet": "... for information retrieval\n\n\n ABSTRACTLink and graph analysis tools are important devices to boost the richness of information retrieval systems. Internet and the existing social networking portals ar...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [56, 64, 1.0], [118, 129, 1.0], [130, 139, 1.0]]}, "2004.ipm_journal-ir0volumeA40A1.1": {"snippet": "...d of information retrieval evaluation: the \"missing\" link M and the universal IR surface", "weights": [[8, 19, 1.0], [20, 29, 1.0], [56, 60, 1.0]]}, "2006.sigirconf_conference-2006.28": {"snippet": "...d of information retrieval, stimulated to a great extent by the importance of link analysis in the development of Web search techniques . This body of work has focused primarily on the network that is...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [81, 85, 1.0], [86, 94, 1.0]]}, "2002.cikm_conference-2002.26": {"snippet": "... for information about a specific topic. A method for combining textual estimates of authoritativeness with link analysis is also presented. The types of textual cues to authoritativeness that are eas...", "weights": [[8, 19, 1.0], [111, 115, 1.0], [116, 124, 1.0]]}, "2009.ecir_conference-2009.93": {"snippet": "Information Extraction and Linking in a Retrieval Context", "weights": [[0, 11, 1.0], [40, 49, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "2003.tois_journal-ir0volumeA21A1.1": {"snippet": "...o as link information, is considered important since it can be used to effectively improve document retrieval. Depending on the retrieval strategy, link information can be local or global. Local link ...", "weights": [[8, 12, 1.0], [13, 24, 1.0], [103, 112, 1.0], [131, 140, 1.0], [151, 155, 1.0], [156, 167, 1.0], [198, 202, 1.0]]}, "2014.wwwconf_workshop-2014ldow.12": {"snippet": "...erse Link Traversal?\n\n\n Query execution using link-traversal is a promising approach for retrieving and accessing data on the web. However, this approach finds its limitation when it comes to query pa...", "weights": [[8, 12, 1.0], [49, 53, 1.0]]}, "N03-2005": {"snippet": "...tory link detection has been regarded as a core technology for other Topic Detection and Tracking tasks such as new event detection. In this paper we analyze story link detection and new event detecti...", "weights": [[8, 12, 1.0], [167, 171, 1.0]]}, "2002.cikm_conference-2002.73": {"snippet": "...lize information entropy for representing the knowledge that corresponds to the amount of information in a link or a page in the link analysis. Experiments on several real news Web sites show that the...", "weights": [[8, 19, 1.0], [93, 104, 1.0], [110, 114, 1.0], [132, 136, 1.0], [137, 145, 1.0]]}, "2014.wsdm_conference-2014.75": {"snippet": "...heir information needs, rather than links to documents. Semantic search and its recent applications enabled search engines to organize their wealth of information around entities. Entity linking and r...", "weights": [[8, 19, 1.0], [154, 165, 1.0], [202, 211, 1.0]]}, "2003.sigirconf_conference-2003.10": {"snippet": "...icit link analysis for small web search\n\n\n ABSTRACTCurrent Web search engines generally impose link analysis-based re-ranking on web-page retrieval. However, the same techniques, when applied directly...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [98, 102, 1.0], [103, 111, 1.0], [141, 150, 1.0]]}, "2002.trec_conference-2002.37": {"snippet": "...s of information that can be used while finding the expected page, content information and link information. We exploited both of them. That is to say, our system is content-based and link-based. As t...", "weights": [[8, 19, 1.0], [78, 89, 1.0], [94, 98, 1.0], [99, 110, 1.0], [187, 191, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.15": {"snippet": "...asks.Link information can be derived from the link graph of the entire collection-giving global, query-independent information-or from a subset of the link graph derived from the top results for a giv...", "weights": [[8, 12, 1.0], [13, 24, 1.0], [49, 53, 1.0], [118, 129, 1.0], [154, 158, 1.0]]}, "2017.ipm_journal-ir0volumeA53A1.10": {"snippet": "...cept analysis phase that utilizes formal concept analysis to discover the conceptual links among portal and attributes. Finally, the information retrieval phase where we propose a portal ranking metho...", "weights": [[8, 16, 1.0], [52, 60, 1.0], [136, 147, 1.0], [148, 157, 1.0]]}, "2011.wwwconf_workshop-2011ldow.13": {"snippet": "...ical analysis assumes prominence. So there is need of a good model with efficient UI design to perform the Linked Data Mining. We proposed a model that helps to effectively interact with linked data p...", "weights": [[8, 16, 1.0]]}, "R09-1045": {"snippet": "... for Information Retrieval in Wikis\n\n\n Good hypertext writing style mandates that link texts clearly indicate the nature of the link target. While this guideline is routinely ignored in HTML, the ligh...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [85, 89, 1.0], [131, 135, 1.0]]}, "1990.ipm_journal-ir0volumeA26A3.7": {"snippet": "...text information retrieval: Further analysis and clarification", "weights": [[8, 19, 1.0], [20, 29, 1.0], [39, 47, 1.0]]}, "2002.tois_journal-ir0volumeA20A1.0": {"snippet": "...y on link analysis, compares well with dedicated WWW image retrieval systems. We conclude that link analysis, a proven effective technique for Web page search, can improve the performance of Web image...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [62, 71, 1.0], [98, 102, 1.0], [103, 111, 1.0]]}, "2003.wwwconf_conference-2003.56": {"snippet": "...s in information retrieval over hyperlinked corpora have convincingly demonstrated that links carry less noisy information than text. We investigate the feasibility of applying link-based methods in n...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [114, 125, 1.0], [180, 184, 1.0]]}, "2003.sigirconf_conference-2003.9": {"snippet": "...ding information retrieval, natural language processing, and question answering. In this paper, we propose a novel approach to automatically constructing a domain-specific thesaurus from the Web using...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2004.wwwconf_conference-2004.32": {"snippet": "...most link analysis research only considers one type of link. In this paper, we propose a unified link analysis framework, called \"link fusion\", which considers both the inter-and intra-type link struc...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [58, 62, 1.0], [100, 104, 1.0], [105, 113, 1.0], [133, 137, 1.0], [193, 197, 1.0]]}, "2005.wwwconf_conference-2005si.14": {"snippet": "...ying link farm spam pages\n\n\n ABSTRACTWith the increasing importance of search in guiding today's web traffic, more and more effort has been spent to create search engine spam. Since link analysis is o...", "weights": [[8, 12, 1.0], [185, 189, 1.0], [190, 198, 1.0]]}, "2004.ntcir_workshop-2004.57": {"snippet": "...onal Retrieval Task 2 that was conducted from 2003 to 2004 as a subtask of the WEB Task at the Fourth NTCIR Workshop ('NTCIR-4 WEB'). In the Informational Retrieval Task, we attempted to assess the re...", "weights": [[8, 17, 1.0], [158, 167, 1.0], [201, 210, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...ng), information retrieval (information retrieval models; indexing; ranking and filtering; querying and interface design), natural language processing (text analysis; text mining; machine learning; in...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [31, 42, 1.0], [43, 52, 1.0], [160, 168, 1.0], [201, 212, 1.0]]}, "2005.wwwconf_conference-2005si.154": {"snippet": "... for information retrieval using key resource page selection\n\n\n ABSTRACTWith the page explosion of WWW, how to cover more useful information with limited storage and computation resources becomes more...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [132, 143, 1.0]]}, "2011.iir_workshop-2011.22": {"snippet": "Web Information Retrieval for Complex Not-Informational Intents: An Introduction to User Intent Analysis", "weights": [[4, 15, 1.0], [16, 25, 1.0], [96, 104, 1.0]]}, "1992.sigirjournals_journal-ir0volumeA26A2.7": {"snippet": "DR-LINK: Document Retrieval Using Linguistic Knowledge\n\n\n Overview DR-LINK is a multi-stage document detection system (see  being developed under the auspices of DARPA's TIPSTER Project. DR-LINK's ove...", "weights": [[3, 7, 1.0], [18, 27, 1.0], [70, 74, 1.0], [190, 194, 1.0]]}, "2001.wwwconf_conference-2001.43": {"snippet": "...such analysis is usually combined with content analysis, there is a sense in which some algorithms are deemed to be \"more balanced\" and others \"more focused\". We undertake a comparative study of hyper...", "weights": [[8, 16, 1.0], [50, 58, 1.0]]}, "2014.wwwconf_conference-2014c.335": {"snippet": "...of a link between every node pair in the network based on the past observed networks arising in many practical applications such as recommender systems, information retrieval, and the marketing analys...", "weights": [[8, 12, 1.0], [156, 167, 1.0], [168, 177, 1.0], [197, 205, 1.0]]}, "2002.ntcir_workshop-2002.69": {"snippet": "... Web Retrieval Task\n\n\n In NTCIR-3 Web Task, we introduced new approaches in (1) similarity retrieval using one known relevant document and pseudo-relevance feedback and (2) topic and target retrieval ...", "weights": [[8, 17, 1.0], [94, 103, 1.0], [193, 202, 1.0]]}, "2005.cikm_conference-2005.102": {"snippet": "...t in Information Retrieval systems. In this work, we propose to use hyperlink information to derive a similarity measure that can then be applied to compare any text documents, with or without hyperli...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [81, 92, 1.0]]}, "2004.wwwconf_conference-2004at.94": {"snippet": "...ovel link-based ranking algorithm RBS, which may be viewed as an extension of PageRank by back-step feature.\nCategories and Subject Descriptors\nH.3.3 [Information Search and Retrieval]: Information fi...", "weights": [[8, 12, 1.0], [154, 165, 1.0], [177, 186, 1.0], [189, 200, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.20": {"snippet": "...all, Link Analysis: An Information Science Approach, 2004, Elsevier Academic Press, 269 pp., ISBN: 0-12-088553-0", "weights": [[8, 12, 1.0], [13, 21, 1.0], [26, 37, 1.0]]}, "2010.ecir_conference-2010.6": {"snippet": "...zing Information Retrieval Methods to Recover Broken Web Links", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2014.wsdm_conference-2014.32": {"snippet": "...t as link prediction problems (e.g., social link and location link prediction). Traditional link prediction researches on LBSNs mostly focus on predicting either social links or location links, assumi...", "weights": [[8, 12, 1.0], [47, 51, 1.0], [65, 69, 1.0], [95, 99, 1.0]]}, "2002.ecir_conference-2002.4": {"snippet": "...ork. Information extracted from the content of Web documents and derived from the analysis of the Web link structure is used as source of evidence in support of the ranking algorithm. This content and...", "weights": [[8, 19, 1.0], [85, 93, 1.0], [105, 109, 1.0]]}, "2006.jasis_journal-ir0volumeA57A13.15": {"snippet": "Link analysis: An information science approach", "weights": [[0, 4, 1.0], [5, 13, 1.0], [18, 29, 1.0]]}, "1995.ipm_journal-ir0volumeA31A1.0": {"snippet": "...text Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2004.ipm_journal-ir0volumeA40A5.2": {"snippet": "...ext, link-based evidence is most commonly used in conjunction with content-based evidential information in order to improve retrieval effectiveness. This paper examines the impact the various types of...", "weights": [[8, 12, 1.0], [95, 106, 1.0], [127, 136, 1.0]]}, "2001.trec_conference-2001.8": {"snippet": "... and Link-based Retrieval Methods for Web IR", "weights": [[8, 12, 1.0], [19, 28, 1.0]]}, "2004.ntcir_workshop-2004.45": {"snippet": "...m of retrieval results is based on a traditional probabilistic model. We report on the result of gram-based indexing and the retrieval, and propose a scoring method based on link structure analysis.", "weights": [[8, 17, 1.0], [128, 137, 1.0], [177, 181, 1.0], [192, 200, 1.0]]}, "2001.ipm_journal-ir0volumeA37A6.2": {"snippet": "...y of information retrieval research by using co-word analysis", "weights": [[8, 19, 1.0], [20, 29, 1.0], [56, 64, 1.0]]}, "W15-4207": {"snippet": "...h to link four different datasets to each other: two terminologies, the European Migration Network (EMN) glossary as well as the Interactive Terminology for Europe (IATE), BabelNet, and the Manually A...", "weights": [[8, 12, 1.0]]}, "2001.trec_conference-2001.30": {"snippet": "Link-based Approaches for Text Retrieval", "weights": [[0, 4, 1.0], [31, 40, 1.0]]}, "2010.ecir_conference-2010.76": {"snippet": "...Data Analysis and Modelling Framework for the Evaluation of Interactive Information Retrieval", "weights": [[8, 16, 1.0], [75, 86, 1.0], [87, 96, 1.0]]}, "2007.cikm_conference-2007.130": {"snippet": "Link analysis using time series of web graphs\n\n\n ABSTRACTLink analysis is a key technology in contemporary web search engines. Most of the previous work on link analysis only used information from one...", "weights": [[0, 4, 1.0], [5, 13, 1.0], [62, 70, 1.0], [156, 160, 1.0], [161, 169, 1.0], [180, 191, 1.0]]}, "2004.ntcir_workshop-2004.90": {"snippet": "...n of link analysis which employs literal matching between information units of the NTCIR-4 Web data. The experiment results show: Our Web searching system that uses our proposed ranking function works...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [61, 72, 1.0]]}, "2006.airs_conference-2006.55": {"snippet": "An Analysis on Topic Features and Difficulties Based on Web Navigational Retrieval Experiments", "weights": [[3, 11, 1.0], [73, 82, 1.0]]}, "2002.trec_conference-2002.92": {"snippet": "... Web Information Retrieval, which integrates content and link analyses. The link analysis component of the framework employs a new probabilistic approach, called the Absorbing Model, for calculating a...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [60, 64, 1.0], [79, 83, 1.0], [84, 92, 1.0]]}, "2001.sigirconf_conference-2001.56": {"snippet": "...s of link information in a single unified framework. Experiments with a web collection show benefits for link information especially for general queries.", "weights": [[8, 12, 1.0], [13, 24, 1.0], [108, 112, 1.0], [113, 124, 1.0]]}, "2018.wwwconf_conference-2018c.269": {"snippet": "...work analysis, recommender systems, and information retrieval.", "weights": [[8, 16, 1.0], [43, 54, 1.0], [55, 64, 1.0]]}, "2004.wwwconf_conference-2004at.144": {"snippet": "...ning link and content analysis to estimate semantic similarity\n\n\n ABSTRACTSearch engines use content and link information to crawl, index, retrieve, and rank Web pages. The correlations between simila...", "weights": [[8, 12, 1.0], [25, 33, 1.0], [108, 112, 1.0], [113, 124, 1.0]]}, "2005.ecir_conference-2005.40": {"snippet": "...work Analysis for Distributed Information Retrieval Architectures\n\n\n Abstract. In this study, we present the analysis of the interconnection network of a distributed Information Retrieval (IR) system,...", "weights": [[8, 16, 1.0], [33, 44, 1.0], [45, 54, 1.0], [112, 120, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "1994.sigirconf_conference-94.28": {"snippet": "Information Filtering Based on User Behaviour Analysis and Best Match Text Retrieval", "weights": [[0, 11, 1.0], [46, 54, 1.0], [75, 84, 1.0]]}, "2005.ntcir_workshop-2005.49": {"snippet": "...s of link analyses, that is, in-link count and inter site and inter page link analysis. We merged score from word search for three indices and score from link analyses variously. We found that anchor ...", "weights": [[8, 12, 1.0], [35, 39, 1.0], [76, 80, 1.0], [81, 89, 1.0], [157, 161, 1.0]]}, "M93-1024": {"snippet": "...the {LINK} System Used for {MUC}-5\n\n\n BACKGROUND Over the past five years, we have developed a natural language processing (NLP) syste m called LINK . LINK is a unification-based system, in which all ...", "weights": [[8, 12, 1.0], [147, 151, 1.0], [154, 158, 1.0]]}}}
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Automatic query expansion may be used in document retrieval to improve search effectiveness. Traditional query expansion methods are based on the d...", "weights": [[0, 5, 1.0], [6, 15, 1.0], [63, 68, 1.0], [69, 78, 1.0], [131, 144, 1.0], [158, 163, 1.0], [164, 173, 1.0]]}, "2016.jasis_journal-ir0volumeA67A6.4": {"snippet": "... for query expansion", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "W12-1614": {"snippet": "Improving Implicit Discourse Relation Recognition Through Feature Set Optimization\n\n\n We provide a systematic study of previously proposed features for implicit discourse relation identification, iden...", "weights": []}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...e IR techniques rely fundamentally on matching words and phrases in documents to corresponding items in the query, any process that corrupts the document may cause problems. Indeed, if a critical quer...", "weights": [[8, 18, 1.0], [111, 116, 1.0], [199, 204, 1.0]]}, "1998.sigirconf_conference-98.15": {"snippet": "...r is query expansion. Both techniques enhance the discriminatory power of typical queries for choosing the right collections and hence significantly improve retrieval results. Query expansion, in part...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [30, 40, 1.0], [179, 184, 1.0], [185, 194, 1.0]]}, "2010.sigirconf_conference-2010.106": {"snippet": "...tual query terms based on text search techniques, which can be called textual query expansion (TQE). However, the annotations (title and tag) around web videos are generally noisier for text-only quer...", "weights": [[8, 13, 1.0], [41, 51, 1.0], [81, 86, 1.0], [87, 96, 1.0], [199, 204, 1.0]]}, "2014.sigirconf_conference-2014.39": {"snippet": "...eval effectiveness. Most previous query expansion approaches focus on text, mainly using unigram concepts. In this paper, we propose a new technique, called entity query feature expansion (EQFE) which...", "weights": [[8, 21, 1.0], [37, 42, 1.0], [43, 52, 1.0], [167, 172, 1.0], [181, 190, 1.0]]}, "2009.ecir_conference-2009.57": {"snippet": "...ying Query Expansion Effectiveness", "weights": [[8, 13, 1.0], [14, 23, 1.0], [24, 37, 1.0]]}, "2001.sigirconf_conference-2001.1": {"snippet": "...tion Techniques for Term Selection in Relevance Feedback\n\n\n ABSTRACTQuery-expansion is an effective Relevance Feedback technique for improving performance in Information Retrieval. In general query-ex...", "weights": [[8, 18, 1.0], [77, 86, 1.0], [195, 200, 1.0], [201, 210, 1.0]]}, "2006.sigirconf_conference-2006.137": {"snippet": "...onal query terms as a searcher types a search query, in effect offering interactive query expansion (IQE) options while the query is formulated. 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State-of-the-ar...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [56, 65, 1.0], [71, 81, 1.0]]}, "2016.sigirconf_conference-2016.29": {"snippet": "...ised Query Expansion via Two-stage Feature Selection\n\n\n ABSTRACTQuery expansion (QE) is a well known technique to improve retrieval effectiveness, which expands original queries with extra terms that ...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [73, 82, 1.0], [135, 148, 1.0]]}, "2002.sigirconf_conference-2002.22": {"snippet": "...e of query expansion techniques to combat errors induced by translation, no study has yet examined the effectiveness of these techniques across resources of varying quality. This paper presents result...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [24, 34, 1.0], [106, 119, 1.0], [129, 139, 1.0]]}, "2004.spire_conference-2004.4": {"snippet": "Techniques for Efficient Query Expansion\n\n\n Abstract. Query expansion is a well-known method for improving average effectiveness in information retrieval. However, the most effective query expansion m...", "weights": [[0, 10, 1.0], [25, 30, 1.0], [31, 40, 1.0], [54, 59, 1.0], [60, 69, 1.0], [115, 128, 1.0], [183, 188, 1.0], [189, 198, 1.0]]}, "2014.ipm_journal-ir0volumeA50A4.4": {"snippet": "...cept query suggestions for literature search. These suggestions explicitly specify important phrasal concepts related to an initial detailed query. The merits of phrasal-concept query suggestions for ...", "weights": [[8, 13, 1.0], [144, 149, 1.0], [181, 186, 1.0]]}, "2010.sigirconf_conference-2010.39": {"snippet": "...bose query processing techniques\n\n\n ABSTRACTVerbose or long queries are a small but significant part of the query stream in web search, and are common in other applications such as collaborative quest...", "weights": [[8, 13, 1.0], [25, 35, 1.0], [111, 116, 1.0]]}, "2005.ntcir_workshop-2005.58": {"snippet": "... WEB Query Term Expansion Subtask\n\n\n The query term expansion subtask was conducted to establish an evaluation framework for information retrieval (IR) systems that focus on the effectiveness of query...", "weights": [[8, 13, 1.0], [19, 28, 1.0], [44, 49, 1.0], [55, 64, 1.0], [181, 194, 1.0], [198, 203, 1.0]]}, "2006.clef_workshop-2006.18": {"snippet": "...s of Expansion Techniques and Retrieval Measures", "weights": [[8, 17, 1.0], [18, 28, 1.0]]}, "2000.tois_journal-ir0volumeA18A1.2": {"snippet": "...ocal techniques emphasize analysis of the top-ranked documents retrieved for a query. While local techniques have shown to be more effective than global techniques in general, existing local technique...", "weights": [[8, 18, 1.0], [82, 87, 1.0], [101, 111, 1.0], [156, 166, 1.0], [194, 204, 1.0]]}, "2015.clef_conference-2015w.60": {"snippet": "...ying Query Expansion Models to help Patients Diagnose Themselves\n\n\n In the paper we use two query expansion models, Kullback-Liebler Divergence(KLD) model and parameter-free Bose-Einstein statistics-b...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [95, 100, 1.0], [101, 110, 1.0]]}, "2007.ipm_journal-ir0volumeA43A3.8": {"snippet": "... the effectiveness of real-time query expansion\n\n\n AbstractInteractive query expansion (IQE) (c.f. . Query expansion. Annual Review of Information Systems and Technology, 31,) is a potentially useful ...", "weights": [[8, 21, 1.0], [35, 40, 1.0], [41, 50, 1.0], [74, 79, 1.0], [80, 89, 1.0], [104, 109, 1.0], [110, 119, 1.0]]}, "2005.clef_workshop-2005w.16": {"snippet": "...ment expansion and query expansion using blind relevance feedback were effective, although optimal parameter choices differed somewhat between the training and evaluation sets. Document expansion in w...", "weights": [[8, 17, 1.0], [22, 27, 1.0], [28, 37, 1.0], [189, 198, 1.0]]}, "2008.clef_workshop-2008.110": {"snippet": "Query Expansion for Effective Geographic Information Retrieval", "weights": [[0, 5, 1.0], [6, 15, 1.0]]}, "2002.sigirconf_conference-2002.2": {"snippet": "... the effectiveness of retrieval when short queries are applied to large document collections. In conjunction with techniques called quantization and thresholding, impact transformation allows improved...", "weights": [[8, 21, 1.0], [117, 127, 1.0]]}, "2001.cikm_conference-2001.24": {"snippet": "...uery expansion can give boosts in a single database environment, and that when more information is available, query expansion can work in distributed environments. We also show that most of the benefi...", "weights": [[8, 17, 1.0], [113, 118, 1.0], [119, 128, 1.0]]}, "1999.trec_conference-1999.38": {"snippet": "...ovel Query Expansion Technique using Apriori Algorithm", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "H05-1121": {"snippet": "Query Expansion with the Minimum User Feedback by Transductive Learning\n\n\n Query expansion techniques generally select new query terms from a set of top ranked documents. Although a user's manual judg...", "weights": [[0, 5, 1.0], [6, 15, 1.0], [75, 80, 1.0], [81, 90, 1.0], [91, 101, 1.0], [123, 128, 1.0]]}, "2016.clef_conference-2016w.13": {"snippet": "The Effectiveness of Query Expansion when searching for Health related Content: InfoLab at CLEF eHealth 2016\n\n\n In this paper we describe the participation of InfoLab in the patient-centred informatio...", "weights": [[4, 17, 1.0], [21, 26, 1.0], [27, 36, 1.0]]}, "2001.tois_journal-ir0volumeA19A1.0": {"snippet": "...ight expansion terms within Rocchio's framework for query reweighting. We compare ranking with information-theoretic query expansion versus ranking with other query expansion techniques, showing that ...", "weights": [[8, 17, 1.0], [55, 60, 1.0], [120, 125, 1.0], [126, 135, 1.0], [162, 167, 1.0], [168, 177, 1.0], [178, 188, 1.0]]}, "2002.wwwconf_conference-2002.32": {"snippet": "...stic query expansion using query logs\n\n\n ABSTRACTQuery expansion has long been suggested as an effective way to resolve the short query and word mismatching problems. A number of query expansion metho...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [30, 35, 1.0], [58, 67, 1.0], [133, 138, 1.0], [182, 187, 1.0], [188, 197, 1.0]]}, "2004.trec_conference-2004.101": {"snippet": "...h to query expansion, which extracts additional query terms from a clustering of the snippets from the first query. The technique proved effective, almost doubling the Mean Average Precision. However,...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [51, 56, 1.0], [112, 117, 1.0]]}, "2007.clef_workshop-2007.19": {"snippet": "...sing Query Expansion", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2001.clef_workshop-2001.15": {"snippet": "Query Expansion Techniques for the CLEF Bilingual Track", "weights": [[0, 5, 1.0], [6, 15, 1.0], [16, 26, 1.0]]}, "2007.ipm_journal-ir0volumeA43A2.1": {"snippet": "...eral techniques have been developed, such as query expansion, cluster-based retrieval and dimensionality reduction to resolve this issue. Of these techniques, this paper performs an empirical study on...", "weights": [[8, 18, 1.0], [48, 53, 1.0], [54, 63, 1.0], [150, 160, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "... on. Query-dependent or \"dynamic\" features take the query into account and thus can only be computed at query time, whereas query-independent or \"static\" features do not rely on the query and thus can...", "weights": [[8, 13, 1.0], [55, 60, 1.0], [107, 112, 1.0], [127, 132, 1.0], [185, 190, 1.0]]}, "2013.cikm_workshop-2013ueo.7": {"snippet": "...ific query expansion\n\n\n ABSTRACTQueries submitted to a search engine are usually short and ambiguous. Query expansion is an effective way to resolve the ambiguity of a query. We attempt to make a cons...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [105, 110, 1.0], [111, 120, 1.0], [171, 176, 1.0]]}, "C10-2007": {"snippet": "Query Expansion based on Pseudo Relevance Feedback from Definition Clusters\n\n\n Query expansion consists in extending user queries with related terms in order to solve the lexical gap problem in Inform...", "weights": [[0, 5, 1.0], [6, 15, 1.0], [79, 84, 1.0], [85, 94, 1.0]]}, "1998.sigirconf_conference-98.17": {"snippet": "...t of Query Structure and Query Expansion on Retrieval Performance\n\n\n Abstract The effects of query structures and query expansion (QE) on retrieval performance were tested with a best match retrieval ...", "weights": [[8, 13, 1.0], [28, 33, 1.0], [34, 43, 1.0], [96, 101, 1.0], [117, 122, 1.0], [123, 132, 1.0]]}, "2005.sigirconf_conference-2005.61": {"snippet": "... for query expansion\n\n\n ABSTRACTIn this paper we investigate the effectiveness of a documentindependent technique for eliciting feedback from users about their information problems. We propose that su...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [68, 81, 1.0]]}, "2010.wsdm_conference-2010.5": {"snippet": "... the effectiveness of a range of query reformulation techniques (including log-based stemming, substitution, and expansion) using standard TREC collections. Our results show that logbased query reform...", "weights": [[8, 21, 1.0], [36, 41, 1.0], [56, 66, 1.0], [116, 125, 1.0], [191, 196, 1.0]]}, "2010.sigirconf_conference-2010.149": {"snippet": "...rank query reformulations\n\n\n ABSTRACTQuery reformulation techniques based on query logs have recently proven to be effective for web queries. However, when initial queries have reasonably good quality...", "weights": [[8, 13, 1.0], [60, 70, 1.0], [80, 85, 1.0]]}, "2003.sigirconf_conference-2003.29": {"snippet": "... the effectiveness of a particular query elicitation technique in increasing initial searcher query length, and which tested the effectiveness of queries elicited using this technique, and the relatio...", "weights": [[8, 21, 1.0], [38, 43, 1.0], [97, 102, 1.0], [132, 145, 1.0]]}, "2007.clef_workshop-2007w.56": {"snippet": "...eval effectiveness using a query expansion technique. The result shows that identifying locations in the queries and applying the query expansion technique can help improve the retrieval effectiveness...", "weights": [[8, 21, 1.0], [30, 35, 1.0], [36, 45, 1.0], [133, 138, 1.0], [139, 148, 1.0], [190, 203, 1.0]]}, "2018.ipm_journal-ir0volumeA54A1.0": {"snippet": "...obal query expansion strategy using word embeddings\n\n\n a b s t r a c tThe effectiveness of query expansion methods depends essentially on identifying good candidates, or prospects, semantically relate...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [77, 90, 1.0], [94, 99, 1.0], [100, 109, 1.0]]}, "1994.trec_conference-1994.19": {"snippet": "Query Expansion/Reduction and its Impact on Retrieval Effectiveness", "weights": [[0, 5, 1.0], [6, 15, 1.0], [54, 67, 1.0]]}, "2007.sigirconf_conference-2007.42": {"snippet": "...bust query expansion technique based on the Markov random field model for information retrieval. The technique, called latent concept expansion, provides a mechanism for modeling term dependencies dur...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [137, 146, 1.0]]}, "2006.sigirconf_conference-2006.51": {"snippet": "... for query expansion in passage retrieval\n\n\n ABSTRACTClassical query expansion techniques such as the local context analysis (LCA) make use of term co-occurrence statistics to incorporate additional c...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [66, 71, 1.0], [72, 81, 1.0], [82, 92, 1.0]]}, "1993.sigirconf_conference-93.16": {"snippet": "...ased Query Expansion\n\n\n AbstractQuery expansion methods have been studied for a long time -with debatable success in many instances. In this paper we present a probabilistic query expansion model base...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [41, 50, 1.0], [177, 182, 1.0], [183, 192, 1.0]]}, "2015.trec_conference-2015.54": {"snippet": "Query-expansion Approaches for Microblog Retrieval", "weights": [[0, 5, 1.0], [6, 15, 1.0]]}, "P08-1017": {"snippet": "...n of Query Expansion Using Lexical Resources\n\n\n Query expansion is an effective technique to improve the performance of information retrieval systems. Although hand-crafted lexical resources, such as ...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [51, 56, 1.0], [57, 66, 1.0]]}, "2003.sigirconf_conference-2003.30": {"snippet": "...tial effectiveness of interactive query expansion\n\n\n ABSTRACTMuch attention has been paid to the relative effectiveness of interactive query expansion versus automatic query expansion. Although intera...", "weights": [[8, 21, 1.0], [37, 42, 1.0], [43, 52, 1.0], [109, 122, 1.0], [138, 143, 1.0], [144, 153, 1.0], [171, 176, 1.0], [177, 186, 1.0]]}, "2019.ipm_journal-ir0volumeA56A5.6": {"snippet": "Query expansion techniques for information retrieval: A survey", "weights": [[0, 5, 1.0], [6, 15, 1.0], [16, 26, 1.0]]}, "2003.sigirconf_conference-2003.62": {"snippet": "When query expansion fails\n\n\n AbstractThe effectiveness of queries in information retrieval can be improved through query expansion. This technique automatically introduces additional query terms that...", "weights": [[5, 10, 1.0], [11, 20, 1.0], [42, 55, 1.0], [116, 121, 1.0], [122, 131, 1.0], [184, 189, 1.0]]}, "2005.trec_conference-2005.89": {"snippet": "... H1: Query expansion using a \"clarity\"-based approach will increase effectiveness over baseline queries and baseline queries plus pseudo-relevance feedback; H2: Query expansion based on the Web will i...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [71, 84, 1.0], [164, 169, 1.0], [170, 179, 1.0]]}, "2013.cikm_conference-2013.140": {"snippet": "...s in query expansion: a structural linguistic perspective\n\n\n ABSTRACTMany successful query expansion techniques ignore information about the term dependencies that exist within natural language. Howev...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [88, 93, 1.0], [94, 103, 1.0], [104, 114, 1.0]]}, "2001.clef_workshop-2001w.24": {"snippet": "Query Expansion Techniques for the CLEF Bilingual Track\n\n\n This paper evaluates the effectiveness of a query translation and disambiguation as well as expansion techniques on CLEF Collections, using S...", "weights": [[0, 5, 1.0], [6, 15, 1.0], [16, 26, 1.0], [84, 97, 1.0], [103, 108, 1.0], [151, 160, 1.0], [161, 171, 1.0]]}, "1997.sigirconf_conference-97.36": {"snippet": "...tual Effectiveness of Interactive Query Expansion\n\n\n In query expansion, terms from a source such as relevance feedback are added to the query. This often improves retrieval effectiveness but results ...", "weights": [[8, 21, 1.0], [37, 42, 1.0], [43, 52, 1.0], [59, 64, 1.0], [65, 74, 1.0], [140, 145, 1.0], [177, 190, 1.0]]}, "2010.cikm_conference-2010.29": {"snippet": "...eval effectiveness. In this paper, we propose a novel query expansion technique that models the various types of dependencies that exist between original query terms and expansion terms within a robus...", "weights": [[8, 21, 1.0], [57, 62, 1.0], [63, 72, 1.0], [157, 162, 1.0], [173, 182, 1.0]]}, "2016.airs_conference-2016.10": {"snippet": "...act. Query expansion methods have been proven to be effective to improve the average performance of patent retrieval, and most of query expansion methods use single source of information for query exp...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [133, 138, 1.0], [139, 148, 1.0], [194, 199, 1.0], [200, 209, 1.0]]}, "U08-1010": {"snippet": "...ovel query expansion method which incorporates text mining techniques into query expansion for improving Chinese information retrieval performance. Unlike most of the existing query expansion strategi...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [62, 72, 1.0], [78, 83, 1.0], [84, 93, 1.0], [179, 184, 1.0], [185, 194, 1.0]]}}}
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Thus, researchers have started exploring the use of social med...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [106, 112, 1.0], [113, 118, 1.0], [193, 199, 1.0], [200, 205, 1.0]]}, "2017.jasis_journal-ir0volumeA68A3.1": {"snippet": "...ting social influence", "weights": [[8, 14, 1.0], [15, 24, 1.0]]}, "2018.wwwconf_conference-2018c.290": {"snippet": "...test political elections. Indeed, during the 2014 Italian mayoral elections, the 2016 US presidential elections, the 2016 UK Brexit referendum, and the 2017 French presidential elections, social media...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [69, 78, 1.0], [105, 114, 1.0], [180, 189, 1.0], [191, 197, 1.0], [198, 203, 1.0]]}, "C16-1208": {"snippet": "...ased Social Influence Model to study the implicit mechanism underlying the change of opinions. We then apply the learned model to predict users' future opinions. 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M...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [110, 116, 1.0], [188, 194, 1.0], [195, 200, 1.0]]}, "S18-2009": {"snippet": "...ays, social media have become a platform where people can easily express their opinions and emotions about any topic such as politics, movies, music, electronic products and many others. On the other ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [82, 90, 1.0]]}, "2013.wsdm_conference-2013.55": {"snippet": "...lar\" political sentiment in social media\n\n\n ABSTRACTAs mainstream news media and political campaigns start to pay attention to the political discourse online, a systematic analysis of political speech...", "weights": [[8, 17, 1.0], [31, 37, 1.0], [38, 43, 1.0], [74, 79, 1.0], [84, 93, 1.0], [134, 143, 1.0], [187, 196, 1.0]]}, "W12-0607": {"snippet": "... for political elections? Merely counting Twitter messages mentioning political party names is no guarantee for obtaining good election predictions. By improving the quality of the document collection...", "weights": [[8, 17, 1.0], [18, 27, 1.0], [73, 82, 1.0]]}, "2015.tweb_journal-ir0volumeA9A3.5": {"snippet": "...n of Political Manipulation in Online Communities through Measures of Effort and Collaboration\n\n\n Online social media allow users to interact with one another by sharing opinions, and these opinions h...", "weights": [[8, 17, 1.0], [108, 114, 1.0], [115, 120, 1.0], [173, 181, 1.0], [193, 201, 1.0]]}, "2014.wwwconf_conference-2014c.152": {"snippet": "...s of political parties to attack the opposing party. We demonstrate a lack of political maturity as evidenced through high percentage of political attacks in a developing region such as Pakistan.", "weights": [[8, 17, 1.0], [81, 90, 1.0], [140, 149, 1.0]]}, "R17-1032": {"snippet": "...g in Social Networks versus Electoral Polls", "weights": [[8, 14, 1.0]]}, "2011.cikm_conference-2011.123": {"snippet": "...ore, social media has a very strong impact in many aspects -at personal level, in business, and in politics, among many others. In spite of its fast adoption, little is known about social media usage ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [184, 190, 1.0], [191, 196, 1.0]]}, "2020.restup-1.2": {"snippet": "...\n In social media, people express themselves every day on issues that affect their lives. During the parliamentary elections, people's interaction with the candidates in social media posts reflects a ...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [118, 127, 1.0], [173, 179, 1.0], [180, 185, 1.0]]}, "2018.wwwconf_conference-2018.81": {"snippet": "...duce Social Mirror, a social network visualization tool that enables a sample of Twitter users to explore the politically-active parts of their social network. We use Social Mirror to recruit Twitter ...", "weights": [[8, 14, 1.0], [25, 31, 1.0], [147, 153, 1.0], [170, 176, 1.0]]}, "E17-1070": {"snippet": "...s on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people ma...", "weights": [[8, 14, 1.0], [19, 28, 1.0], [57, 63, 1.0], [64, 69, 1.0], [148, 153, 1.0]]}, "W19-2112": {"snippet": "...s of Social Media Discourse for Moral Classification\n\n\n Political discourse on social media microblogs, specifically Twitter, has become an undeniable part of mainstream U.S. politics. Given the lengt...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [59, 68, 1.0], [82, 88, 1.0], [89, 94, 1.0]]}, "2013.cikm_workshop-2013plead.3": {"snippet": "...iven political party; the dynamics between explicitly positive or explicitly negative mentions of a given political party; the total number of hashtags mentioning one of the leading candidates, Angela...", "weights": [[8, 17, 1.0], [109, 118, 1.0]]}, "2021.wnut-1.15": {"snippet": "...ular media for information propagation among the masses during any socially engaging event. In the recent past, several countries have witnessed its effectiveness and influence in political and social...", "weights": [[8, 13, 1.0], [170, 179, 1.0], [183, 192, 1.0], [197, 203, 1.0]]}, "2009.sigirconf_workshop-2009uiir.14": {"snippet": "... the social and political sciences. Using the three months prior to the 2008 election as a test scenario, the analysis here assesses daily fluctuations in search coverage of candidates and issues as p...", "weights": [[8, 14, 1.0], [19, 28, 1.0]]}, "W11-3712": {"snippet": "...n of political parties. News media covering an election may be involved in endorsing political parties, attempting to set aspects of election agenda, and may have gender bias. Using Rocksteady, an aff...", "weights": [[8, 17, 1.0], [32, 37, 1.0], [88, 97, 1.0]]}, "2018.wwwjournals_journal-ir0volumeA21A3.4": {"snippet": "...line social networks", "weights": [[8, 14, 1.0]]}, "2016.wsdm_conference-2016.6": {"snippet": "...s on Social Media\n\n\n ABSTRACTSocial media is increasingly being used to access and disseminate information on sociopolitical issues like gun rights and general elections. The popularity and openness o...", "weights": [[8, 14, 1.0], [15, 20, 1.0], [39, 44, 1.0], [163, 172, 1.0]]}, "2013.wsdm_conference-2013.85": {"snippet": "...e of social influence in various social networks. Second, we present computational models for quantifying social influence. Third, we describe how social influence can help real applications. 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We discuss the need for a fast and accurate lammatization to enhance Arabic Information Retrieval results. We also introduce a new datase...", "weights": [[8, 16, 1.0], [154, 163, 1.0]]}, "2008.clef_workshop-2008w.154": {"snippet": "...tion Retrieval\n\n\n With the emergence of vast resources of information, it is necessary to develop methods that retrieve most relevant information according to the users needs. These retrieval methods ...", "weights": [[8, 17, 1.0], [185, 194, 1.0]]}, "2015.ipm_journal-ir0volumeA51A1.4": {"snippet": "...s of stemming has resulted, in the past few years, in the development of methods that are reliable and perform well. Our approach further shifts the boundaries of the state of the art by providing mor...", "weights": [[8, 16, 1.0]]}, "2003.sigirconf_conference-2003.49": {"snippet": "...n of retrieval systems and their retrieved results, we use this measure to assess the average similarity of a system to the other systems in the collection. We demonstrate that evaluating retrieval sy...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [58, 61, 1.0], [133, 140, 1.0], [191, 200, 1.0], [201, 208, 1.0]]}, "2013.airs_conference-2013.24": {"snippet": "Stemming for Kurdish Information Retrieval", "weights": [[0, 8, 1.0], [33, 42, 1.0]]}, "1981.ipm_journal-ir0volumeA17A5.5": {"snippet": "...tion retrieval systems", "weights": [[8, 17, 1.0], [18, 25, 1.0]]}, "2008.ipm_journal-ir0volumeA44A5.5": {"snippet": "...tion retrieval and text mining systems -conflates morphological variants of a word to a single representative form. In this paper, we describe an approach to lexicon-based inflectional normalisation. ...", "weights": [[8, 17, 1.0], [34, 41, 1.0]]}, "W79-0117": {"snippet": "Lemmatisering {--} hvilke af de ideelle krav til lemmatisering er opfyldelige eller opfyldte? (Lemmatization {--} which of the ideal requirements of lemmatization are fulfillable or fulfilled?) [In {D...", "weights": []}, "2017.ecir_conference-2017.53": {"snippet": "...ogs. Stemming is a common step for improving retrieval performance by unifying different morphological variants of a word. In this study, we show that rule-based stemming meant for formal text often c...", "weights": [[8, 16, 1.0], [48, 57, 1.0], [165, 173, 1.0]]}, "2010.airs_conference-2010.27": {"snippet": "...abic Stemming Using N-Grams\n\n\n Abstract. In languages with high word inflation such as Arabic, stemming improves text retrieval performance by reducing words variants. We propose a change in the corpu...", "weights": [[8, 16, 1.0], [98, 106, 1.0], [121, 130, 1.0]]}, "2014.airs_conference-2014.27": {"snippet": "...age. Stemming improve the retrieval performance by reducing words variants, and in lcrease the similarity between related words. However, an Arabic Information Retrieval (AIR) can use stemming algorit...", "weights": [[8, 16, 1.0], [29, 38, 1.0], [163, 172, 1.0], [183, 186, 1.0], [187, 195, 1.0]]}, "P12-2043": {"snippet": "...abic Retrieval Revisited: Morphological Hole Filling\n\n\n Due to Arabic's morphological complexity, Arabic retrieval benefits greatly from morphological analysis -particularly stemming. However, the bes...", "weights": [[8, 17, 1.0], [108, 117, 1.0], [177, 185, 1.0]]}, "2002.sigirconf_conference-2002.36": {"snippet": "...pus. Retrieval results show that the thesaurus can significantly improve Arabic monolingual retrieval. For cross-lingual retrieval (CLIR), we found that spelling normalization and stemming have little", "weights": [[8, 17, 1.0], [95, 104, 1.0], [124, 133, 1.0], [183, 191, 1.0]]}, "2002.sigirconf_conference-2002.37": {"snippet": "...ving stemming for Arabic information retrieval: light stemming and co-occurrence analysis\n\n\n ABSTRACTArabic, a highly inflected language, requires good stemming for effective information retrieval, ye...", "weights": [[8, 16, 1.0], [40, 49, 1.0], [57, 65, 1.0], [155, 163, 1.0], [190, 199, 1.0]]}, "W01-1703": {"snippet": "...much stemming improves precision in information retrieval for Swedish texts. To perform this, we built an information retrieval tool with optional stemming and created a tagged corpus in Swedish. We k...", "weights": [[8, 16, 1.0], [51, 60, 1.0], [121, 130, 1.0], [150, 158, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...tion Retrieval (IR) solutions to retrieve passages matching a set of terms extracted from the question.There is a fundamental disconnect between the capabilities of the bag-of-words retrieval model an...", "weights": [[8, 17, 1.0], [185, 194, 1.0]]}, "W17-7613": {"snippet": "...atic lemmatisation procedure for treebanks. {O}ld {E}nglish strong and weak verbs\n\n\n The aim of this paper is to present a semiautomatic lemmatisation procedure implemented on database software that i...", "weights": [[8, 21, 1.0], [140, 153, 1.0]]}, "2006.clef_workshop-2006.13": {"snippet": "...ased Stemming in Hungarian Information Retrieval", "weights": [[8, 16, 1.0], [42, 51, 1.0]]}, "W79-0113": {"snippet": "Automatisk lemmatisering utan stamlexikon (Automatic lemmatization without stem lexica) [In {S}wedish]\n\n\n AUTOMATISK LEMMATISERING UTAN STAMLEXIKON N\u00e5gra synpunkter tio \u00e5r efter\u00e5t En tillbakablick F\u00f6r...", "weights": []}, "2019.tois_journal-ir0volumeA37A1.2": {"snippet": "...tive systems.CCS Concepts: \u2022 Information systems \u2192 Retrieval effectiveness; Learning to rank; Information retrieval query processing;Additional Key Words and Phrases: Information systems, information ...", "weights": [[8, 15, 1.0], [44, 51, 1.0], [54, 63, 1.0], [109, 118, 1.0], [182, 189, 1.0]]}, "L00-1164": {"snippet": "... and Lemmatisation for the Spoken {D}utch Corpus", "weights": [[8, 21, 1.0]]}, "W19-3704": {"snippet": "... and Lemmatisation of {S}lovenian, {C}roatian and {S}erbian\n\n\n We present experiments on Slovenian, Croatian and Serbian morphosyntactic annotation and lemmatisation between the former stateof-the-art...", "weights": [[8, 21, 1.0], [155, 168, 1.0]]}, "2006.clef_workshop-2006w.5": {"snippet": "... the use of Stemming for Monolingual Ad-Hoc Portuguese Information Retrieval\n\n\n For UFRGS's first participation on CLEF our goal was to compare the performance of heavier and lighter stemming strategi...", "weights": [[8, 11, 1.0], [15, 23, 1.0], [70, 79, 1.0], [186, 194, 1.0]]}, "2004.sigirconf_conference-2004.112": {"snippet": "...tion retrieval\n\n\n ABSTRACTDue to the high number of inflectional variations of Arabic words, empirical results suggest that stemming is essential for Arabic information retrieval. However, current lig...", "weights": [[8, 17, 1.0], [127, 135, 1.0], [172, 181, 1.0]]}, "2003.spire_conference-2003.18": {"snippet": "...kish Stemming Algorithm\n\n\n Abstract. In this paper, we evaluate the effectiveness of a new stemming algorithm, FINDSTEM, for use with Turkish documents and queries, and compare the use of this algorit...", "weights": [[8, 16, 1.0], [94, 102, 1.0], [128, 131, 1.0], [184, 187, 1.0]]}, "2014.airs_conference-2014.28": {"snippet": "... and retrieval systems. These technological advancements have greatly benefited English language information search and retrieval. However, it is regrettable that Arabic language has not received sign...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [123, 132, 1.0]]}, "2001.clef_workshop-2000w.14": {"snippet": "Retrieval of Spanish-English Bilingual Information by Means of a Standard Automatic Translation System\n\n\n This paper describes our participation in bilingual retrieval (queries in Spanish on documents...", "weights": [[0, 9, 1.0], [158, 167, 1.0]]}, "2002.spire_conference-2002.9": {"snippet": "Stemming Galician Texts", "weights": [[0, 8, 1.0]]}, "2021.ranlp-srw.13": {"snippet": "...h as lemmatisation, part-of-speech tagging and parsing, which is often included in preprocessing. In the literature, the term 'preprocessing' is used to refer to a wide range of procedures, from filte...", "weights": [[8, 21, 1.0]]}, "2006.clef_workshop-2006.12": {"snippet": "... the Use of Stemming for Monolingual Ad-Hoc Portuguese Information Retrieval", "weights": [[8, 11, 1.0], [15, 23, 1.0], [70, 79, 1.0]]}, "2020.iwclul-1.2": {"snippet": "...f in lemmatisation for Uralic languages\n\n\n Lemmatisers in Uralic languages are required for dictionary lookup, an important task for language learners. We explore how to decide which of the rule-based...", "weights": [[8, 21, 1.0]]}, "2007.sigirconf_conference-2007.83": {"snippet": "...tive stemming for web search\n\n\n ABSTRACTTraditionally, stemming has been applied to Information Retrieval tasks by transforming words in documents to the their root form before indexing, and applying ...", "weights": [[8, 16, 1.0], [58, 66, 1.0], [99, 108, 1.0]]}, "1994.jasis_journal-ir0volumeA45A8.1": {"snippet": "...tion Retrieval System", "weights": [[8, 17, 1.0]]}, "2005.ipm_journal-ir0volumeA41A1.7": {"snippet": "... the retrieval effectiveness of information retrieval systems, however the designing and the implementation of stemmers requires a laborious amount of effort due to the fact that documents and queries...", "weights": [[8, 17, 1.0], [47, 56, 1.0], [57, 64, 1.0]]}, "2008.sigirconf_conference-2008.153": {"snippet": "...uent retrieval will be. IR systems commonly use rule-based stemmers to normalize surface word forms to combat the problem of not finding documents that contain words related to query terms by inflecti...", "weights": [[8, 17, 1.0], [30, 37, 1.0], [47, 50, 1.0]]}, "W15-4917": {"snippet": "...tive Stemming in {SMT} Systems\n\n\n In this paper we present an approach to reduce data sparsity problems when translating from morphologically rich languages into less inflected languages by selectivel...", "weights": [[8, 16, 1.0], [26, 33, 1.0]]}, "2013.tois_journal-ir0volumeA31A4.1": {"snippet": "...ased Stemming\n\n\n Stemming is a widely used technique in information retrieval systems to address the vocabulary mismatch problem arising out of morphological phenomena. The major shortcoming of the co...", "weights": [[8, 16, 1.0], [20, 28, 1.0], [71, 80, 1.0], [81, 88, 1.0]]}, "1994.riao_conference-1994.13": {"snippet": "...tion Retrieval System", "weights": [[8, 17, 1.0]]}, "1973.ipm_journal-ir0volumeA9A7.1": {"snippet": "...tion retrieval system using keyword dialog", "weights": [[8, 17, 1.0]]}, "W04-3232": {"snippet": "...ight-stemming algorithm that conflates both regular and broken plurals with their singular forms. We also evaluated the new light-stemming algorithm within the context of information retrieval, compar...", "weights": [[8, 16, 1.0], [133, 141, 1.0], [186, 195, 1.0]]}, "P16-1108": {"snippet": "... for Stemming and Lemmatization.\n\n\n We present several methods for stemming and lemmatization based on discriminative string transduction. We exploit the paradigmatic regularity of semi-structured inf...", "weights": [[8, 16, 1.0], [70, 78, 1.0]]}, "2019.gwc-1.17": {"snippet": "...t\n\n\n Stemming is a technique that reduces any inflected word to its root form. Assamese is a morphologically rich, scheduled Indian language. There are various forms of suffixes applied to a word in v...", "weights": [[8, 16, 1.0]]}, "C96-2209": {"snippet": "A tagger/lemmatiser for {D}utch medical language\n\n\n In this paper, we want to describe a tagger/lemmatiser for Dutch medical vocabulary, which consists of a full-form dictionary and a morphological re...", "weights": []}, "2007.clef_workshop-2007w.70": {"snippet": "... for Retrieval\n\n\n Stemming, an approximation to morphological analysis, is a commonly used technique to improve performance in information retrieval systems. In the MorphoChallenge 2007 evaluation we ...", "weights": [[8, 17, 1.0], [21, 29, 1.0], [142, 151, 1.0], [152, 159, 1.0]]}, "2012.cikm_conference-2012.349": {"snippet": "...blog retrieval\n\n\n ABSTRACTThe use of social media has profoundly affected social and political dynamics in the Arab world. In this paper, we explore the Arabic microblogs retrieval. We illustrate some...", "weights": [[8, 17, 1.0], [33, 36, 1.0], [174, 183, 1.0]]}, "2004.ir_journal-ir0volumeA7A34.3": {"snippet": "...e is Stemming and Decompounding for German Text Retrieval?", "weights": [[8, 16, 1.0], [51, 60, 1.0]]}, "2002.cikm_conference-2002.43": {"snippet": "... the retrieval effectiveness via a light stemming approach\n\n\n ABSTRACTThe inflectional structure of a word impacts the retrieval accuracy of information retrieval systems of Latin-based languages. We ...", "weights": [[8, 17, 1.0], [44, 52, 1.0], [122, 131, 1.0], [156, 165, 1.0], [166, 173, 1.0]]}, "2003.ecir_conference-2003.13": {"snippet": "Stemming and Decompounding for German Text Retrieval", "weights": [[0, 8, 1.0], [43, 52, 1.0]]}, "1994.sigirconf_conference-94.5": {"snippet": "... for Stemming Algorithms", "weights": [[8, 16, 1.0]]}, "2009.ipm_journal-ir0volumeA45A6.5": {"snippet": "...gual retrieval work in a non-normalized index?\n\n\n a b s t r a c tMany operational IR indexes are non-normalized, i.e. no lemmatization or stemming techniques, etc. have been employed in indexing. This...", "weights": [[8, 17, 1.0], [141, 149, 1.0]]}, "P12-2072": {"snippet": "Lemmatisation as a Tagging Task\n\n\n We present a novel approach to the task of word lemmatisation. We formalise lemmatisation as a category tagging task, by describing how a word-to-lemma transformatio...", "weights": [[0, 13, 1.0], [83, 96, 1.0], [111, 124, 1.0]]}, "1998.tois_journal-ir0volumeA16A1.2": {"snippet": "...ased Stemming Using Cooccurrence of Word Variants\n\n\n Stemming is used in many information retrieval (IR) systems to reduce variant word forms to common roots. It is one of the simplest applications of...", "weights": [[8, 16, 1.0], [56, 64, 1.0], [93, 102, 1.0], [108, 115, 1.0]]}, "W15-1808": {"snippet": "...atic Lemmatisation of {L}ithuanian {MWE}s\n\n\n This article presents a study of lemmatisation of flexible multiword expressions in Lithuanian. An approach based on syntactic analysis designed for multiw...", "weights": [[8, 21, 1.0], [81, 94, 1.0]]}, "1999.jasis_journal-ir0volumeA50A6.4": {"snippet": "Stemming Methodologies Over Individual Query Words for an Arabic Information Retrieval System", "weights": [[0, 8, 1.0], [77, 86, 1.0]]}, "1998.ecir_conference-1998.11": {"snippet": "...tion Retrieval with Probabilistic Argumentation Systems", "weights": [[8, 17, 1.0], [51, 58, 1.0]]}, "2020.clef_conference-2020w.189": {"snippet": "...tion Retrieval Systems\n\n\n We report on the systems that the Math Information Retrieval group at Masaryk University (MIRMU) prepared for tasks 1 (find answers) and 2 (formula search) of the ARQMath lab...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [46, 53, 1.0], [80, 89, 1.0]]}, "L12-1411": {"snippet": "Lemmatising {S}erbian as Category Tagging with Bidirectional Sequence Classification", "weights": []}, "P13-1069": {"snippet": "...rase lemmatisation where the main phase is cast as a tagging problem. The idea draws on the observation that the lemmatisation of almost all Polish noun phrases may be decomposed into transformation o...", "weights": [[8, 21, 1.0], [116, 129, 1.0]]}, "C12-3043": {"snippet": "...tion retrieval. We used a hybrid approach for stemming that combines rule based stemming which removes affixes in successively applied steps and dictionary based stemming which reduces stemming errors...", "weights": [[8, 17, 1.0], [49, 57, 1.0], [83, 91, 1.0], [165, 173, 1.0], [188, 196, 1.0]]}, "W10-1203": {"snippet": "...nced Retrieval Accuracy\n\n\n Text normalization transforms words into a base form so that terms from common equivalent classes match. Traditionally, information retrieval systems employ stemming techniq...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [172, 179, 1.0], [187, 195, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...\"The Use of Speech Retrieval Systems: A Study Design,\" proposes a user study whose goal is to determine the user expectation in this emerging field.The paper \"Speech-Driven Text Retrieval: Using Targe...", "weights": [[8, 11, 1.0], [22, 31, 1.0], [32, 39, 1.0], [181, 190, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...tion retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC -required s...", "weights": [[8, 17, 1.0], [42, 51, 1.0], [92, 101, 1.0], [147, 156, 1.0]]}, "1996.sigirconf_conference-96.6": {"snippet": "...wing Stemming as Recall Enhancement\n\n\n AbstractPrevious research on stemming has shown both positive and negative effects on retrieval performance. 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The subject categories are populated by using the category label as a query on the collection, and assigning ...", "weights": [[8, 17, 1.0], [63, 71, 1.0]]}, "2006.trec_conference-2006.103": {"snippet": "...h we use for document retrieval. Various other tools are used, such the GoogleAPI for web boosting, fnTBL Chunker for text chunking, Lingua::Stem for stemming, Lingpipe for Named Entity Recognition, e...", "weights": [[8, 11, 1.0], [25, 34, 1.0], [153, 161, 1.0]]}, "2008.clef_workshop-2008.10": {"snippet": "...tion Retrieval Systems Using Stemming and Part of Speech Tagging\n\n\n Abstract. With the emergence of vast resources of information, it is necessary to develop methods that retrieve the most relevant in...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [32, 40, 1.0]]}, "2008.ntcir_workshop-2008evia.1": {"snippet": "...s in retrieval effectiveness, many problems remain. The results using stemming and stoplist show that the stemming can potentially lead to some gain in retrieval effectiveness; The stoplist slightly i...", "weights": [[8, 17, 1.0], [73, 81, 1.0], [109, 117, 1.0], [155, 164, 1.0]]}, "2008.sigirconf_workshop-2008and.16": {"snippet": "...tion retrieval, text mining, and natural language processing. Tokenization is a languagedependent approach, including normalization, stop words removal, lemmatization and stemming. Both stemming and l...", "weights": [[8, 17, 1.0], [174, 182, 1.0], [189, 197, 1.0]]}, "2006.trec_conference-2006.119": {"snippet": "...\n We use the Okapi retrieval system to conduct the email discussion search. The following issues are investigated. First, we make use of the thread structure in the emails to re-rank the documents ret...", "weights": [[8, 11, 1.0], [22, 31, 1.0], [133, 136, 1.0]]}, "2013.ecir_conference-2013.58": {"snippet": "...sage Retrieval track of CLEF-IP 2012. 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The main features of th...", "weights": [[8, 16, 1.0], [43, 52, 1.0], [97, 105, 1.0], [141, 150, 1.0]]}, "2002.clef_workshop-2002w.39": {"snippet": "...ical Stemming Algorithm\n\n\n In Information Retrieval (IR), stemming is used to reduce variant word forms to common root. The assumption is that if two words have the same root, then they represent the ...", "weights": [[8, 16, 1.0], [45, 54, 1.0], [61, 69, 1.0]]}, "U07-1018": {"snippet": "...ing, stemming/lemmatisation, and ontology expansion.", "weights": [[8, 16, 1.0], [17, 30, 1.0]]}, "2001.clef_workshop-2001w.28": {"snippet": "Stemming in Spanish: A First Approach to its Impact on Information Retrieval\n\n\n Most models and techniques employed in Information Retireval at some time or other use frecuency counts of the terms app...", "weights": [[0, 8, 1.0], [67, 76, 1.0], [163, 166, 1.0]]}, "2002.clef_workshop-2002w.36": {"snippet": "...ased stemming [17] or lexical stemming [11] consistently yields significant improvements. As to the effect of stemming on retrieval performance for languages that are morphologically richer than Engli...", "weights": [[8, 16, 1.0], [33, 41, 1.0], [113, 121, 1.0], [125, 134, 1.0]]}, "W15-4914": {"snippet": "...{'}s lemmatisation for automatic error classification\n\n\n This paper demonstrates the possibility to make an existing automatic error classifier for machine translations independent from the requiremen...", "weights": [[8, 21, 1.0]]}}}
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The evaluation of self-report questionnaires is important not on...", "weights": [[8, 19, 1.0], [46, 57, 1.0]]}, "2013.sigirconf_conference-2013.25": {"snippet": "...lity information. However, the information overload and diversity of UGC sources limit their potential uses. In this research, we propose a framework to organize information from multiple UGC sources ...", "weights": [[8, 19, 1.0], [34, 45, 1.0], [165, 176, 1.0]]}, "2009.wwwconf_conference-2009.6": {"snippet": "... for information retrieval and automatic question answering. Unfortunately, the quality of the submitted questions and answers varies widely -increasingly so that a large fraction of the content is no...", "weights": [[8, 19, 1.0], [190, 197, 1.0]]}, "2014.wwwconf_conference-2014c.176": {"snippet": "...wing content. In this work, we analyze content in the music category created by the nonprofessionals, which we refer to as user-generated content (UGCs). Non-professional users frequently upload conte...", "weights": [[8, 15, 1.0], [42, 49, 1.0], [141, 148, 1.0], [198, 205, 1.0]]}, "2013.sigirconf_conference-2013.41": {"snippet": "...tion reliability\n\n\n ABSTRACTThe reliability of a test collection is proportional to the number of queries it contains. But building a collection with many queries is expensive, so researchers have to ...", "weights": [[8, 19, 1.0], [35, 46, 1.0]]}, "2015.clef_conference-2015.10": {"snippet": "...uage Information Retrieval for User-Generated Internet Video\n\n\n Abstract. Increasing amounts of user-generated video content are being uploaded to online repositories. This content is often very uneve...", "weights": [[8, 19, 1.0], [120, 127, 1.0], [176, 183, 1.0]]}, "2009.wwwconf_workshop-2009wicow.1": {"snippet": "...ated content: how good is it?\n\n\n ABSTRACTUser Generated Content (UGC) is one of the main current trends in the Web. This trend has allowed all people that can access the Internet to publish content in...", "weights": [[8, 15, 1.0], [59, 66, 1.0], [193, 200, 1.0]]}, "2008.mir_conference-2008.11": {"snippet": "...ated content", "weights": [[8, 15, 1.0]]}, "2006.wwwconf_conference-2006.61": {"snippet": "...duct information on e-commerce sites or stock data on financial sites. A large amount of research has focused on the problem of generating wrappers, i.e., software tools that allow easy and robust ext...", "weights": [[8, 19, 1.0]]}, "2016.mir_conference-2016.76": {"snippet": "...ated Content in Support of Social Media Applications", "weights": [[8, 15, 1.0]]}, "2021.ecir_conference-20212.4": {"snippet": "Reliability Prediction for Health-Related Content: A Replicability Study\n\n\n Determining reliability of online data is a challenge that has recently received increasing attention. In particular, unreli...", "weights": [[0, 11, 1.0], [42, 49, 1.0], [88, 99, 1.0]]}, "2015.wwwconf_conference-2015c.64": {"snippet": "... the Reliability of Facebook User Profiling\n\n\n ABSTRACTUser profiling is an essential component of most modern online services offered upon user registration. Profiling typically involves the tracking...", "weights": [[8, 19, 1.0]]}, "2012.eamt-1.11": {"snippet": "...ated Content: A pilot study based on User Ratings\n\n\n This paper presents the results of an experimental pilot user study, focusing on the evaluation of machine-translated user-generated content by use...", "weights": [[8, 15, 1.0], [189, 196, 1.0]]}, "2004.cikm_conference-2004.22": {"snippet": "...ting information and the item content information are utilized to build accurate clusters. Specifically, content information helps us build more accurate clusters for items, and more accurate item clu...", "weights": [[8, 19, 1.0], [33, 40, 1.0], [41, 52, 1.0], [108, 115, 1.0], [116, 127, 1.0]]}, "2013.cikm_conference-2013.363": {"snippet": "...g an information explosion. Determining the reliability of such a large amount of information is a major area of research. Information provenance (aka, sources or origin) provides a way to measure the...", "weights": [[8, 19, 1.0], [47, 58, 1.0], [85, 96, 1.0], [126, 137, 1.0]]}, "2011.cikm_conference-2011.37": {"snippet": "...ated content\n\n\n ABSTRACTWe study the problem of online classification of user generated content, with the goal of efficiently learning to categorize content generated by individual user. This problem ...", "weights": [[8, 15, 1.0], [91, 98, 1.0], [152, 159, 1.0]]}, "2011.sigirconf_conference-2011.236": {"snippet": "... new content types and new usergenerated content constantly added to the system, the candidate object set and underlying data distribution change rapidly; 3) different types of content usually have ve...", "weights": [[8, 15, 1.0], [44, 51, 1.0], [180, 187, 1.0]]}, "2010.cikm_workshop-2010smuc.14": {"snippet": "... the content being published online. This increases the need to develop means to gauge the support available for content posted online. In this work, we aim to make use of the web-content to calculate...", "weights": [[8, 15, 1.0], [116, 123, 1.0], [183, 190, 1.0]]}, "2021.wnut-1.22": {"snippet": "...ated content automatic translation, the well-known specificities of which raise many challenges for MT. Our analyses show that measuring the average-case performance using a standard metric on a UGC t...", "weights": [[8, 15, 1.0]]}, "2012.sigirconf_conference-2012.100": {"snippet": "...ated content: the case of wikipedia", "weights": [[8, 15, 1.0]]}, "2014.wwwconf_conference-2014c.230": {"snippet": "... the content, the contributors of the content and the underlying context. We further propose an approach for deriving a single metric that considers, in a unified manner, the quality of a contributor ...", "weights": [[8, 15, 1.0], [41, 48, 1.0]]}, "J96-2004": {"snippet": "...s in content analysis have already experienced the same difficulties and come up with a solution in the kappa statistic. We discuss what is wrong with reliability measures as they are currently used f...", "weights": [[8, 15, 1.0], [154, 165, 1.0]]}, "N15-1112": {"snippet": "...only information from traditional media, ignoring the rich social context provided by user-generated content that reveals representative public interests or insightful opinions. We instead aim to gene...", "weights": [[8, 19, 1.0], [104, 111, 1.0]]}, "N19-1295": {"snippet": "... and reliability in a domain. Previous studies have mostly focused on estimating each annotator's overall reliability on the entire annotation task. However, in practice, the reliability of an annotat...", "weights": [[8, 19, 1.0], [109, 120, 1.0], [178, 189, 1.0]]}, "2013.wwwconf_conference-2013c.258": {"snippet": "...edia content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques ov...", "weights": [[8, 15, 1.0], [143, 154, 1.0], [165, 172, 1.0]]}, "2006.amta-users.3": {"snippet": "...line content into a familiar language. In addition, confidence ratings given by users to the reliability and accuracy of the information they find are significantly higher (with increases between 5 an...", "weights": [[8, 15, 1.0], [96, 107, 1.0], [128, 139, 1.0]]}, "2018.fire_conference-2018.1": {"snippet": "...ack: Information Retrieval from Microblogs during Disasters (IRMiDis)\n\n\n ABSTRACTIn countries like the US, European countries, Australia and Japan, user-generated content from microblogging sites is e...", "weights": [[8, 19, 1.0], [166, 173, 1.0]]}, "2008.cikm_workshop-2008wicow.2": {"snippet": "...ital content, and users have to retrieve a variety of content using different keywords. The need for an information analysis technology that helps find credible and valuable information from large amo...", "weights": [[8, 15, 1.0], [57, 64, 1.0], [107, 118, 1.0], [177, 188, 1.0]]}, "1999.cikm_conference-99.9": {"snippet": "... the content managed by a database system. Rather, the whole web site is also pre-generated out of the extremely dynamic content and synchronized with the database on the basis of metadata. Thus, topi...", "weights": [[8, 15, 1.0], [124, 131, 1.0]]}, "2013.wwwconf_conference-2013c.24": {"snippet": "... the information near certain geographical locations. Particularly, those users usually want to find interesting information about what is happening nearby. In this poster, we introduce the localized ...", "weights": [[8, 19, 1.0], [116, 127, 1.0]]}, "2002.wwwconf_conference-2002.35": {"snippet": "...n of information management services for processing dynamic web content\n\n\n ABSTRACTDynamic Web content provides us with time-sensitive and continuously changing data. To glean up-to-date information, ...", "weights": [[8, 19, 1.0], [67, 74, 1.0], [98, 105, 1.0], [190, 201, 1.0]]}, "2019.wwwconf_conference-2019.357": {"snippet": "Discovering Product Defects and Solutions from Online User Generated Contents", "weights": []}, "2022.acl-short.42": {"snippet": "...data reliability is under-reported, and a proposed k-rater reliability (kRR) should be used as the correct data reliability for aggregated datasets. It is a multi-rater generalization of inter-rater r...", "weights": [[8, 19, 1.0], [62, 73, 1.0], [115, 126, 1.0], [202, 213, 1.0]]}, "N13-1098": {"snippet": "Differences in User Responses to a {W}izard-of-{O}z versus Automated System\n\n\n Wizard-of-Oz experimental setup in a dialogue system is commonly used to gather data for informing an automated version o...", "weights": []}, "W19-4452": {"snippet": "...bric Reliability and Annotation of Content and Argument in Source-Based Argument Essays\n\n\n We present a unique dataset of student sourcebased argument essays to facilitate research on the relations be...", "weights": [[8, 19, 1.0], [38, 45, 1.0]]}, "2016.wwwconf_conference-2016c.157": {"snippet": "...sed: content of disclosed items. These methods fail to acquire personality when there is a lack of content (non-disclosure). In this study we do not look at the disclosed content, but whether disclosu...", "weights": [[8, 15, 1.0], [102, 109, 1.0], [174, 181, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A1.9": {"snippet": "...ated content component finds and ranks relevant content for news-related user queries from multiple streams of news and user-generated content. Finally, the news-related content integration component ...", "weights": [[8, 15, 1.0], [51, 58, 1.0], [138, 145, 1.0], [173, 180, 1.0]]}, "2018.wwwconf_conference-2018c.305": {"snippet": "...e of content  . Filter bubbles are the result of engagement-based content filtering. The underlying principle is to show the user content that relates to the content the user has previously engaged on...", "weights": [[8, 15, 1.0], [69, 76, 1.0], [133, 140, 1.0], [161, 168, 1.0]]}, "2014.wwwconf_conference-2014c.229": {"snippet": "...edia content as fake or real. We created a data corpus of tweets around big events focusing on the ones linking to images (fake or real) of which the reliability could be verified by independent onlin...", "weights": [[8, 15, 1.0], [153, 164, 1.0]]}, "2003.wwwconf_conference-2003p.73": {"snippet": "...bile Content Delivery by Exploiting User Interest Correlation", "weights": [[8, 15, 1.0]]}, "2014.cikm_conference-2014.187": {"snippet": "...rmal content are often intermingled, CLIR systems that excel at finding formal content may tend to select formal over informal content. To measure this effect, a test collection annotated for both rel...", "weights": [[8, 15, 1.0], [82, 89, 1.0], [130, 137, 1.0]]}, "2021.naacl-demos.4": {"snippet": "...tive content-planning elements to guide the generation model in producing engaging and informative responses. Two primary modules in DiSCoL's pipeline are conditional generators trained for 1) predict...", "weights": [[8, 15, 1.0]]}, "1993.tmi-1.21": {"snippet": "Better Translation with Knowledge Extracted from Source Text\n\n\n This paper presents a framework in which a source text is translated using not only given knowledge but knowledge which is extracted fro...", "weights": []}, "N18-1160": {"snippet": "...ovie Content Analysis\n\n\n This work takes a first step toward movie content analysis by tackling the novel task of movie overview generation. Overviews are natural language texts that give a first impr...", "weights": [[8, 15, 1.0], [70, 77, 1.0]]}}}
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({C}orsican questions: is there a prosodic transfer from {C}orsican to {F}rench?) [in {F}ren...", "weights": []}, "J90-1015": {"snippet": "...y in AI are examined, especially the thorny notion of \"models\" in AI theories: what does it mean to use human intellect as a model in an AI theory, or in an AI program? Then, as is so often the case, ...", "weights": [[8, 10, 1.0], [69, 71, 1.0], [103, 106, 1.0], [140, 142, 1.0], [160, 162, 1.0]]}, "2020.coling-main.166": {"snippet": "...tant application of AI in education. In this work, we focus on the task of personalized multimodal feedback generation, which aims to generate personalized feedback for teachers to evaluate students' ...", "weights": [[8, 19, 1.0], [23, 25, 1.0]]}, "2019.wwwconf_conference-2019c.43": {"snippet": "AI for Good at Scale: Challenges in Real Time Emergency Response", "weights": [[0, 2, 1.0]]}, "2022.hcinlp-1.3": {"snippet": "... the use of Artificial Intelligence (AI) in facilitating human communication will grow. This, in turn, will encourage development of intuitive, adaptive, and effective empathic AI interfaces that bett...", "weights": [[8, 11, 1.0], [40, 42, 1.0], [180, 182, 1.0]]}, "2018.sigirconf_conference-2018.221": {"snippet": "...onal AI\n\n\n ABSTRACTThis tutorial surveys neural approaches to conversational AI that were developed in the last few years. We group conversational systems into three categories: (1) question answering...", "weights": [[8, 10, 1.0], [80, 82, 1.0]]}, "2017.jasis_journal-ir0volumeA68A9.17": {"snippet": "AI Anxiety", "weights": [[0, 2, 1.0]]}, "W19-8401": {"snippet": "...hese AI systems in how they derive their decisions or outputs has led to an increasing call for transparency with increasing concerns for the potential of bias to occur in areas such as finance and cr...", "weights": [[8, 10, 1.0]]}, "2020.acl-main.466": {"snippet": "...both AI researchers and legal professionals, as LegalAI is beneficial to the legal system for liberating legal professionals from a maze of paperwork. Legal professionals often think about how to solv...", "weights": [[8, 10, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.8": {"snippet": "...ving AI What Is It and How Do We Achieve It?\n\n\n Many AI applications need to process huge amounts of sensitive information for model training, evaluation, and real-world integration. These tasks inclu...", "weights": [[8, 10, 1.0], [56, 58, 1.0]]}, "F12-1095": {"snippet": "Masques acoustiques et masques linguistiques de diff{\\'e}rentes langues sur la reconnaissance de mots en fran{\\c{c}}ais (Acoustical and linguistic masking effects of different languages on the compreh...", "weights": []}, "2020.chiir_conference-2020.52": {"snippet": "... the AI Devil: Investigating the Partnership Between Lawyers and AI", "weights": [[8, 10, 1.0], [68, 70, 1.0]]}, "2020.blackboxnlp-1.29": {"snippet": "... an {AI} Context\n\n\n With the increase in the use of AI systems, a need for explanation systems arises. Building an explanation system requires a definition of explanation. However, the natural languag...", "weights": [[8, 10, 1.0], [48, 51, 1.0], [55, 57, 1.0]]}, "2021.rocling-1.4": {"snippet": "...uces AI Clerk Platform, which aims to accelerate and improve the entire process and convenience of the development of information extraction tools. AI Clerk Platform provides a friendly and intuitive ...", "weights": [[8, 10, 1.0], [151, 153, 1.0]]}, "2011.jeptalnrecital-long.22": {"snippet": "...F} : application {\\`a} l{'}apprentissage d{'}un segmenteur-{\\'e}tiqueteur du fran{\\c{c}}ais (Integrating linguistic knowledge in a {CRF}: application to learning a segmenter-tagger of {F}rench)\n\n\n Dan...", "weights": [[8, 19, 1.0], [141, 152, 1.0]]}, "W18-1501": {"snippet": "... of {AI} in Human Storytelling and Character Creation\n\n\n In this opinion piece, we argue that there is a need for alternative design directions to complement existing AI efforts in narrative and chara...", "weights": [[8, 10, 1.0], [170, 172, 1.0]]}, "2021.jeptalnrecital-taln.26": {"snippet": "Extraction d{'}arguments bas{\\'e}e sur les transformateurs pour des applications dans le domaine de la sant{\\'e} (Transformer-based Argument Mining for Healthcare Applications)\n\n\n Nous pr\u00e9sentons des ...", "weights": []}, "W12-6301": {"snippet": "...s of AI researchers. In the past century, AI changed tremendously from its theories to its applications, while with this goal unchanged. Especially in the past 20 years, along with the development of ...", "weights": [[8, 10, 1.0], [45, 47, 1.0]]}, "W18-6701": {"snippet": "...onal AI in the Flow.ai conversational AI platform. I will discuss training an end-to-end recurrent neural network on various data sets to build conversational systems, give an overview of the evaluati...", "weights": [[8, 10, 1.0], [23, 25, 1.0], [41, 43, 1.0]]}, "1995.cikm_conference-95.28": {"snippet": "... the AI literature to supply the necessary techniques for the creation of an adaptive system. The system, called INFOrmer, is based on art intelligent agent approach and embodies machine learning, ada...", "weights": [[8, 10, 1.0]]}, "2020.jeptalnrecital-jep.56": {"snippet": "{\\'E}mergence du contraste entre les fricatives sibilantes /s/ - /\u0283/ du fran{\\c{c}}ais en contexte d{'}acquisition bilingue (Emergence of the contrast between the {F}rench sibilant fricatives /s/ - /\u0283...", "weights": []}, "2021.dash-1.15": {"snippet": "...nce (AI) Hub, a novel proof-of-concept framework for enhancing human health and wellbeing via a combination of tailored wearable and Conversational Agent (CA) solutions for non-invasive monitoring of ...", "weights": [[8, 10, 1.0]]}, "2022.trustnlp-1.4": {"snippet": "...ced {AI}\n\n\n Artificial Intelligence (AI) and Machine Learning (ML) are rapidly becoming musthave capabilities. According to a 2019 Forbes Insights Report, \"seventy-nine percent [of executives] agree t...", "weights": [[8, 10, 1.0], [40, 42, 1.0]]}, "2019.cikm_conference-2019.390": {"snippet": "...ion (AI in transportation)", "weights": [[8, 10, 1.0]]}, "2021.gebnlp-1.5": {"snippet": "...nce (AI) are increasingly being used in the real world to provide critical decisions about who is going to be hired, granted a loan, admitted to college, etc. However, the main pillars of AI, Natural ...", "weights": [[8, 10, 1.0], [191, 193, 1.0]]}, "2020.wwwconf_conference-2020c.98": {"snippet": "...able AI in Industry: Practical Challenges and Lessons Learned", "weights": [[8, 10, 1.0]]}, "2022.in2writing-1.8": {"snippet": "... The application of artificial intelligence (AI) for text generation in creative domains raises questions regarding the credibility of AI-generated content. In two studies, we explored if readers can ...", "weights": [[8, 19, 1.0], [48, 50, 1.0], [138, 140, 1.0]]}, "2016.jeptalnrecital-demo.16": {"snippet": "... une application web d\u00e9di\u00e9e \u00e0 l'\u00e9tiquetage syntaxique de l'ancien fran\u00e7ais. Cette plateforme est une d\u00e9monstration permettant d'appliquer sur n'importe quel texte, ou sur un des textes d'ancien fran\u00e7a...", "weights": [[8, 19, 1.0]]}, "W14-5306": {"snippet": "The Varitext platform and the Corpus des vari{\\'e}t{\\'e}s nationales du fran{\\c{c}}ais ({C}o{V}a{N}a-{FR}) as resources for the study of {F}rench from a pluricentric perspective\n\n\n This paper reports ...", "weights": []}, "W15-4646": {"snippet": "...nce (AI) Planning techniques. However, this intertwining of user-centered dialog and AI planning systems, often called mixed-initiative planning (MIP), does not only facilitate more intelligent and co...", "weights": [[8, 10, 1.0], [88, 90, 1.0]]}, "2021.triton-1.6": {"snippet": "...sive use of artificial intelligence (AI) tools to monitor postings of hate speech, celebrations of violence or profanity. Since AI software requires massive volumes of data to train computers, Machine...", "weights": [[8, 11, 1.0], [40, 42, 1.0], [131, 133, 1.0]]}, "2020.cikm_conference-2020.13": {"snippet": "...mble Application of Symbolic and Subsymbolic AI for Sentiment Analysis", "weights": [[8, 19, 1.0], [48, 50, 1.0]]}, "P17-4007": {"snippet": "...w an application of the API in a Japanese online news service for Japanese language learners.", "weights": [[8, 19, 1.0]]}, "2018.wwwconf_conference-2018c.298": {"snippet": "...brid.AI: A Learning Search Engine for Large-scale Structured Data\n\n\n ABSTRACTVariety of Big data ] is a significant impediment for anyone who wants to search inside a large-scale structured dataset. F...", "weights": [[8, 10, 1.0]]}, "2020.wsdm_workshop-2020privatenlp.7": {"snippet": "...e of AI?\n\n\n Artificial Intelligence (AI) hopes to provide a positive paradigm shift in technology by providing new features and personalized experience to our digital and physical world. In the future...", "weights": [[8, 10, 1.0], [40, 42, 1.0]]}, "J07-1004": {"snippet": "...iest AI applications. Computing power has increased since the first such systems were developed, and the general methodology has changed from the use of hand-encoded knowledge bases about simple domai...", "weights": [[8, 10, 1.0], [149, 152, 1.0]]}, "W19-8403": {"snippet": "...ble {AI} Terminology\n\n\n The field of Explainable Artificial Intelligence attempts to solve the problem of algorithmic opacity. 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increased number of free p...", "weights": [[8, 14, 1.0], [64, 70, 1.0], [143, 152, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...uage models. ASR systems generally contain a language model that provides word sequence probabilities. Some information retrieval systems also rely on language models for retrieval. It is likely that ...", "weights": [[8, 14, 1.0], [123, 132, 1.0], [163, 169, 1.0], [174, 183, 1.0]]}, "2017.sigirconf_conference-2017.183": {"snippet": "...d in retrieval functions. However, previous studies have shown that the performance of retrieval models varies for di erent query lengths, and it usually degrades when query length increases. A possib...", "weights": [[8, 17, 1.0], [90, 99, 1.0], [100, 106, 1.0]]}, "2006.sigirconf_conference-2006.90": {"snippet": "...-hoc retrieval on very large text collections\n\n\n ABSTRACTWe propose an integration of term proximity scoring into Okapi BM25. The relative retrieval effectiveness of our retrieval method, compared to ...", "weights": [[8, 17, 1.0], [142, 151, 1.0], [173, 182, 1.0]]}, "2019.trec_conference-2019.50": {"snippet": "...tion retrieval in a large training data regime. It includes two tasks: the document ranking task (1) and the passage ranking task (2). Both of these tasks had a full ranking (a) and reranking (b) subt...", "weights": [[8, 17, 1.0]]}, "2007.riao_conference-2007.28": {"snippet": "...ting Retrieval Performance with Manually-Built Topic Models", "weights": [[8, 17, 1.0], [56, 62, 1.0]]}, "2009.ipm_journal-ir0volumeA45A3.2": {"snippet": "...ment retrieval\n\n\n a b s t r a c tThis paper proposes a new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSRANK, aims to use the ad...", "weights": [[8, 17, 1.0], [111, 117, 1.0], [130, 139, 1.0]]}, "2015.sigirconf_conference-2015.116": {"snippet": "...tion retrieval. Particularly, we propose a novel Term Location (TEL) retrieval model based on BM25 to enhance probabilistic information retrieval, where a kernel-based method is used to capture term p...", "weights": [[8, 17, 1.0], [72, 81, 1.0], [139, 148, 1.0]]}, "2011.cikm_conference-2011.4": {"snippet": "...rent retrieval models: the component of term frequency (TF) normalization by document length is not lower-bounded properly; as a result, very long documents tend to be overly penalized. In order to an...", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "2021.ecir_conference-20212.1": {"snippet": "...uage models-such as BERT-revolutionized web and news search. Naturally, the community aims to adapt these advancements to cross-domain transfer of retrieval models for domain specific search. In the c...", "weights": [[8, 14, 1.0], [150, 159, 1.0], [160, 166, 1.0]]}, "2015.cikm_conference-2015.188": {"snippet": "... for retrieval. Our constraint analysis shows that current stateof-the-art retrieval functions, including BM25 and language models, fail to satisfy the constraint, and that, in order to solve this pro...", "weights": [[8, 17, 1.0], [78, 87, 1.0], [127, 133, 1.0]]}, "2018.sigirconf_conference-2018.224": {"snippet": "...tion Retrieval. The goal of fusion methods is to merge different sources of information so as to address a retrieval task. For example, in the adhoc retrieval setting, fusion methods have been applied...", "weights": [[8, 17, 1.0], [110, 119, 1.0], [152, 161, 1.0]]}, "2018.sigirconf_conference-2018.180": {"snippet": "...mize retrieval performance. Following this intuition, we first propose a new term frequency normalization with query length for probabilistic information retrieval, namely BM25QL. Then BM25QL is incor...", "weights": [[8, 17, 1.0], [157, 166, 1.0]]}, "2019.trec_conference-2019.26": {"snippet": "...swer Retrieval Track\n\n\n Model Description 2.1 Overview We participate in the Complex Answer Retrieval(CAR) track at TREC 2019. We applied several useful models in this work. In the rough ranking, we a...", "weights": [[8, 17, 1.0], [95, 104, 1.0], [156, 162, 1.0]]}, "1989.ipm_journal-ir0volumeA25A1.5": {"snippet": "Models for retrieval with probabilistic indexing", "weights": [[0, 6, 1.0], [11, 20, 1.0]]}, "2020.clef_conference-2020w.205": {"snippet": "...ment Retrieval and Comparative Argument Retrieval. We submitted a run to both tasks. For the first task, we used a combination of the traditional BM25 model and learning to rank models. BM25 model hel...", "weights": [[8, 17, 1.0], [43, 52, 1.0], [181, 187, 1.0]]}, "2005.sigirconf_conference-2005.65": {"snippet": "...tion retrieval\n\n\n ABSTRACTThis paper proposes GBM (gravitation-based model), a physical model for information retrieval inspired by Newton's theory of gravitation. A mapping is built in this model fro...", "weights": [[8, 17, 1.0], [113, 122, 1.0]]}, "2005.ecir_conference-2005.15": {"snippet": "... DFR Models\n\n\n Abstract. The term frequency normalisation parameter tuning is a crucial issue in information retrieval (IR), which has an important impact on the retrieval performance. The classical p...", "weights": [[8, 14, 1.0], [112, 121, 1.0], [165, 174, 1.0]]}, "2011.sigirconf_conference-2011.20": {"snippet": "...tion retrieval (IR) models, e.g. BM25, deal with document length based on a trade-off between the Verbosity hypothesis, which assumes the independence of a document's relevance of its length, and the ...", "weights": [[8, 17, 1.0], [23, 29, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...mage Retrieval by Hypertext Links\n\n\n This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, th...", "weights": [[8, 17, 1.0], [72, 81, 1.0]]}, "1990.jasis_journal-ir0volumeA41A7.2": {"snippet": "...l of retrieval system performance", "weights": [[8, 17, 1.0]]}, "2009.cikm_conference-2009.247": {"snippet": "... for retrieval. In response, we develop a machine learning approach to BM25-style retrieval that learns, using LambdaRank, from the input attributes of BM25. Our model significantly improves retrieval...", "weights": [[8, 17, 1.0], [85, 94, 1.0], [194, 203, 1.0]]}, "2012.ecir_conference-2012.21": {"snippet": "A Log-Logistic Model-Based Interpretation of TF Normalization of BM25", "weights": []}, "2011.wwwconf_conference-2011c.152": {"snippet": "...nion retrieval\n\n\n ABSTRACTExisting opinion retrieval techniques do not provide contextdependent relevant results. Most of the approaches used by state-ofthe-art techniques are based on frequency of qu...", "weights": [[8, 17, 1.0], [46, 55, 1.0]]}, "2021.ecir_conference-20211.10": {"snippet": "...ical Retrieval Model with Semantic Residual Embeddings\n\n\n This paper presents clear, a retrieval model that seeks to complement classical lexical exact-match models such as BM25 with semantic matching...", "weights": [[8, 17, 1.0], [90, 99, 1.0], [161, 167, 1.0]]}, "2021.mrl-1.12": {"snippet": "...ense Retrieval\n\n\n We present Mr. TYDI, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense represent...", "weights": [[8, 17, 1.0], [93, 102, 1.0]]}, "2018.desires_conference-2018.13": {"snippet": "Retrieval and Richness when Querying by Document\n\n\n A single relevant document can be viewed as a long query for ad hoc retrieval, or a tiny training set for supervised learning. We tested techniques ...", "weights": [[0, 9, 1.0], [120, 129, 1.0]]}, "2015.iir_workshop-2015.14": {"snippet": "Geometric Perspectives of the BM25", "weights": []}, "2019.ntcir_conference-2019.5": {"snippet": "...king Models for Question Retrieval", "weights": [[8, 14, 1.0], [28, 37, 1.0]]}, "2010.sigirconf_conference-2010.199": {"snippet": "...-art retrieval models, including BM25 and language modeling, are grounded in probabilistic principles. Having a working understanding of these principles can help researchers understand existing retri...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [198, 207, 1.0]]}, "2016.ictir_conference-2016.6": {"snippet": "...ords retrieval models are widely used, and provide a robust trade-off between efficiency and effectiveness. These models often make simplifying assumptions about relations between query terms, and tre...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [117, 123, 1.0]]}, "2004.trec_conference-2004.8": {"snippet": "...tter Retrieval Function From Machine?\n\n\n The quality of an information retrieval system heavily depends on its retrieval function, which returns a similarity measurement between the query and each doc...", "weights": [[8, 17, 1.0], [74, 83, 1.0], [114, 123, 1.0]]}, "2004.cikm_conference-2004.6": {"snippet": "Simple BM25 extension to multiple weighted fields\n\n\n ABSTRACTThis paper describes a simple way of adapting the BM25 ranking formula to deal with structured documents. In the past it has been common to...", "weights": []}, "2014.ecir_conference-2014.7": {"snippet": "Optimizing Base Rankers Using Clicks - A Case Study Using BM25", "weights": []}, "2013.cikm_conference-2013.169": {"snippet": "...ides retrieval effectiveness approaching that of the best learning-to-rank methods over equivalent features sets. Other measures based on language modeling and divergence from randomness can outperfor...", "weights": [[8, 17, 1.0]]}, "2012.sigirconf_conference-2012.94": {"snippet": "...onal retrieval refrain from taking positional information into account, due to the hurdles of developing a sound model while avoiding an explosion in the number of parameters. Nonetheless, the well-kn...", "weights": [[8, 17, 1.0]]}, "2021.ecir_conference-20211.22": {"snippet": "...ence Retrieval for Question Answering\n\n\n Evidence retrieval is a key component of explainable question answering (QA). We argue that, despite recent progress, transformer network-based approaches such...", "weights": [[8, 17, 1.0], [53, 62, 1.0]]}, "2011.ir_journal-ir0volumeA14A5.1": {"snippet": "Improving document clustering using Okapi BM25 feature weighting", "weights": []}, "2011.sigirconf_conference-2011.147": {"snippet": "...o IR models satisfy the TDC retrieval constraint", "weights": [[8, 14, 1.0], [31, 40, 1.0]]}, "2009.clef_workshop-2009w.134": {"snippet": "...ting models used in text retrieval domain, such as BM25, TFIDF and Language Model (LM), for context-based image retrieval. Second, we will evaluate statistical-based feedback models and ontology-based...", "weights": [[8, 14, 1.0], [28, 37, 1.0], [115, 124, 1.0], [178, 184, 1.0]]}, "2019.sigirconf_conference-2019.125": {"snippet": "...ural Retrieval Models using DeepSHAP", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "2019.fire_conference-2019w.7": {"snippet": "...tion Retrieval Evaluation in 2019(AILA@Fire2019). We formalize the two tasks as the issue of information retrieval, and present the improved BM25 models to retrieve the prior cases and identify the re...", "weights": [[8, 17, 1.0], [108, 117, 1.0], [149, 155, 1.0]]}, "2014.sigirconf_conference-2014.219": {"snippet": "...tion retrieval models; retrieval constraints; optimization\nOVERVIEWOne of the most important research problems in Information Retrieval (IR) is to develop optimal general IR models. Since IR models de...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [26, 35, 1.0], [129, 138, 1.0], [177, 183, 1.0], [194, 200, 1.0]]}, "2021.mrqa-1.16": {"snippet": "...ense retrieval has been shown to be effective for Open Domain Question Answering, surpassing sparse retrieval methods like BM25. One such model, REALM, (Guu et al., 2020) is an end-to-end dense retrie...", "weights": [[8, 17, 1.0], [103, 112, 1.0], [197, 206, 1.0]]}, "2018.ictir_conference-2018.36": {"snippet": "...tion Retrieval and Web Search, BM25 is one of the most influential probabilistic retrieval formulas for document weighting and ranking. BM25 involves three parameters k 1 , k 3 and b, which provide sc...", "weights": [[8, 17, 1.0], [84, 93, 1.0]]}, "2020.emnlp-main.550": {"snippet": "...sage Retrieval for Open-Domain Question Answering\n\n\n Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, su...", "weights": [[8, 17, 1.0], [115, 124, 1.0], [193, 199, 1.0]]}, "2021.naacl-main.331": {"snippet": "...tion retrieval mitigate their computational costs by using a multi-stage ranking pipeline. In the first stage, a number of potentially relevant candidates are retrieved using an efficient retrieval mo...", "weights": [[8, 17, 1.0], [191, 200, 1.0]]}, "2012.trec_conference-2012.15": {"snippet": "...ific Retrieval\n\n\n This paper describes the first participation of DCU in the TREC Medical Records Track (TRECMed) 2012. We performed initial experiments on the the 2011 TRECMed data based on the BM25 ...", "weights": [[8, 17, 1.0]]}, "2011.cikm_conference-2011.250": {"snippet": "...mize retrieval performance, this parameter should be set in a term-specific way. Following this intuition, we propose an information gain measure to directly estimate the contributions of repeated ter...", "weights": [[8, 17, 1.0]]}, "2006.trec_conference-2006.34": {"snippet": "...tial retrieval before passage retrieval. Passages are retrieved based on the concepts defining in topics and concept coverage. Results of submitted runs are listed and discussed.", "weights": [[8, 17, 1.0], [33, 42, 1.0]]}, "2021.acl-long.89": {"snippet": "...Task Retrieval for Knowledge-Intensive Tasks\n\n\n Retrieving relevant contexts from a large corpus is a crucial step for tasks such as opendomain question answering and fact checking. Although neural re...", "weights": [[8, 17, 1.0], [201, 210, 1.0]]}, "2021.acl-short.77": {"snippet": "...tion Retrieval for Large Index Sizes\n\n\n Information Retrieval using dense lowdimensional representations recently became popular and showed out-performance to traditional sparse-representations like B...", "weights": [[8, 17, 1.0], [55, 64, 1.0]]}, "2011.sigirconf_conference-2011.121": {"snippet": "... and Retrieval]: Retrieval models\nGeneral TermsAlgorithms Keywords BM25, BM25L, term frequency, very long documents\nMOTIVATIONThe Okapi BM25 retrieval function  has been the state-of-the-art for nearl...", "weights": [[8, 17, 1.0], [20, 29, 1.0], [30, 36, 1.0], [144, 153, 1.0]]}, "2016.ntcir_conference-2016.12": {"snippet": "...sage retrieval effectiveness. Furthermore, results indicate that no significant gains in retrieval effectiveness can be obtained by using query expansion in combination with our retrieval models over ...", "weights": [[8, 17, 1.0], [92, 101, 1.0], [181, 190, 1.0], [191, 197, 1.0]]}, "2021.naacl-main.241": {"snippet": "...tion Retrieval with Contextualized Inverted List\n\n\n Classical information retrieval systems such as BM25 rely on exact lexical match and carry out search efficiently with inverted list index. Recent n...", "weights": [[8, 17, 1.0], [77, 86, 1.0]]}, "2007.tois_journal-ir0volumeA25A3.3": {"snippet": "...tion retrieval\n\n\n The setting of the term frequency normalization hyper-parameter suffers from the query dependence and collection dependence problems, which remarkably hurt the robustness of the retr...", "weights": [[8, 17, 1.0], [199, 208, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "...tion Retrieval Models\n\n\n ABSTRACTThe accuracy of a search engine is mostly determined by the optimality of the retrieval model used in the search engine. Develoing optimal retrieval models has always ...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [114, 123, 1.0], [175, 184, 1.0], [185, 191, 1.0]]}, "1998.sigirconf_conference-98.34": {"snippet": "...tion Retrieval\n\n\n AbstractModels of document indexing and document retrieval have been extensively studied. 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Information retrieval query processing;Additional Key Words and Phrases: Information systems, information retrieval, learning to rank, retrieval system ...", "weights": [[8, 17, 1.0], [63, 72, 1.0], [157, 166, 1.0], [186, 195, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...ment retrieval, long enough spans of speech can be readily handled.However, information retrieval is not just about document retrieval. There are other problems in and around IR where ASR is still lik...", "weights": [[8, 17, 1.0], [91, 100, 1.0], [128, 137, 1.0]]}, "2004.ismir_conference-2004.76": {"snippet": "...ing: Music Retrieval For The DJ", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "C94-2169": {"snippet": "...mple Retrieval by Generating Retrieval Queries from Similarities\n\n\n In example-based NLP, the problem of eoml)utational cost of example retrieval is severe, since the retrieval time increases in propo...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [139, 148, 1.0], [170, 179, 1.0]]}, "2004.coria_conference-2004.21": {"snippet": "...mage Retrieval", "weights": [[8, 17, 1.0]]}, "2002.cikm_conference-2002.55": {"snippet": "...onic music retrieval\n\n\n ABSTRACTMost work in the ad hoc music retrieval field has focused on the retrieval of monophonic documents using monophonic queries. Polyphony adds considerably more complexity...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [59, 64, 1.0], [65, 74, 1.0], [100, 109, 1.0]]}, "2008.mir_conference-2008.24": {"snippet": "...mage retrieval using query by contextual example", "weights": [[8, 17, 1.0]]}, "2002.ismir_conference-2002.20": {"snippet": "... for Music Information Retrieval", "weights": [[8, 13, 1.0], [26, 35, 1.0]]}, "2010.ismir_conference-2010.79": {"snippet": "...ical-music Interpretations by Real-time Tempo Input", "weights": [[8, 13, 1.0]]}, "2016.chiir_conference-2016.50": {"snippet": "...ples Retrieval\n\n\n ABSTRACTThis paper explores the potential benefits of using similarity-based two-dimensional classifications and their corresponding GUIs, for drum samples retrieval in a creativity-...", "weights": [[8, 17, 1.0], [177, 186, 1.0]]}, "2007.sigirconf_conference-2007.59": {"snippet": "... Web Retrieval and Music Information Retrieval. Based on the ID3 tags of a collection of mp3 files, we retrieve relevant Web pages via Google queries and use the contents of these pages to characteriz...", "weights": [[8, 17, 1.0], [22, 27, 1.0], [40, 49, 1.0]]}, "2007.sigirconf_conference-2007.58": {"snippet": "...ween music and words. We adapt the supervised multi-class labeling (SML) model, which has shown good performance on the task of image retrieval, and use the CAL500 data to learn a model for music retr...", "weights": [[8, 13, 1.0], [137, 146, 1.0], [193, 198, 1.0], [199, 208, 1.0]]}, "2012.tois_journal-ir0volumeA30A2.1": {"snippet": "...e in music information retrieval is how to combine such noisy and heterogeneous information sources in order to improve retrieval effectiveness. With this aim in mind, the article explores a novel mus...", "weights": [[8, 13, 1.0], [26, 35, 1.0], [123, 132, 1.0], [200, 205, 1.0]]}, "2011.ipm_journal-ir0volumeA47A3.8": {"snippet": "...bout music artists and bands. After searching, retrieval, and indexing of Web pages that are related to a music artist or band, Web content mining and music information retrieval techniques were appli...", "weights": [[8, 13, 1.0], [50, 59, 1.0], [109, 114, 1.0], [154, 159, 1.0], [172, 181, 1.0]]}, "2003.cikm_conference-2003.10": {"snippet": "... for music information retrieval\n\n\n ABSTRACTHere we discuss how to look for similar melody in music databases by giving monophonic melody in sheet. In this work, we utilize text expression (or sheet m...", "weights": [[8, 13, 1.0], [26, 35, 1.0], [97, 102, 1.0], [202, 207, 1.0]]}, "2008.ismir_conference-2008.92": {"snippet": "... for Music Information Retrieval", "weights": [[8, 13, 1.0], [26, 35, 1.0]]}, "2004.ismir_conference-2004.103": {"snippet": "... for Music Information Retrieval", "weights": [[8, 13, 1.0], [26, 35, 1.0]]}, "2019.ismir_conference-2019.73": {"snippet": "...odal Music Retrieval", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2010.sigirconf_conference-2010.81": {"snippet": "... for music information retrieval, and most of them take advantage of MDCT coefficients or derived energy type of features. As a first attempt, we propose in this paper utilizing compressed-domain spec...", "weights": [[8, 13, 1.0], [26, 35, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...mage Retrieval by Hypertext Links\n\n\n This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, th...", "weights": [[8, 17, 1.0], [72, 81, 1.0]]}, "2012.tist_journal-ir0volumeA3A3.1": {"snippet": "...very music piece is created to convey emotion, music organization and retrieval by emotion is a reasonable way of accessing music information. A good deal of effort has been made in the music informat...", "weights": [[8, 13, 1.0], [50, 55, 1.0], [73, 82, 1.0], [127, 132, 1.0], [189, 194, 1.0]]}, "2015.sigirconf_conference-2015.193": {"snippet": "...t in music information retrieval (MIR). The tutorial particularly spotlights the question of music similarity, which is an essential aspect in music retrieval and recommendation. Three factors play a ...", "weights": [[8, 13, 1.0], [26, 35, 1.0], [96, 101, 1.0], [146, 151, 1.0], [152, 161, 1.0]]}, "2005.ismir_conference-2005.26": {"snippet": "Query-By-Example Technique for Retrieving Cover Versions of Popular Songs with Similar Melodies", "weights": []}, "2017.ijmir_journal-ir0volumeA6A2.3": {"snippet": "...mple music information retrieval by score-based genre prediction and similarity measure", "weights": [[8, 13, 1.0], [26, 35, 1.0]]}, "2010.mir_conference-2010.35": {"snippet": "Music retrieval from everything", "weights": [[0, 5, 1.0], [6, 15, 1.0]]}, "2004.jasis_journal-ir0volumeA55A12.0": {"snippet": "...e of music information retrieval approaches", "weights": [[8, 13, 1.0], [26, 35, 1.0]]}, "2016.ipm_journal-ir0volumeA52A1.9": {"snippet": "...d by music through a crowdsourcing game\n\n\n Keywords:Music information retrieval Game with a purpose Music induced emotion Crowdsourcing a b s t r a c t One of the major reasons why people find music s...", "weights": [[8, 13, 1.0], [55, 60, 1.0], [73, 82, 1.0], [103, 108, 1.0], [196, 201, 1.0]]}, "2009.ismir_conference-2009.40": {"snippet": "...tage Retrieval Approach for a Melodic Query by Example System", "weights": [[8, 17, 1.0]]}, "2006.airs_conference-2006.34": {"snippet": "A Music Retrieval System Based on Query-by-Singing for Karaoke Jukebox", "weights": [[2, 7, 1.0], [8, 17, 1.0]]}, "2000.sigirconf_conference-2000.11": {"snippet": "...tive music information retrieval method\n\n\n AbstractWe developed, and then evaluated, a music information retrieval (MIR) system based upon the intervals found within the melodies of a collection of 93...", "weights": [[8, 13, 1.0], [26, 35, 1.0], [90, 95, 1.0], [108, 117, 1.0]]}, "2015.sigirconf_conference-2015.132": {"snippet": "...ory, music descriptors, and music popularity trends. Wang et al.  propose a mobile music recommender that predicts user activity from time, acceleration, and ambient noise, and selects music from a se...", "weights": [[8, 13, 1.0], [31, 36, 1.0], [86, 91, 1.0], [188, 193, 1.0]]}, "2013.sigirconf_conference-2013.185": {"snippet": "Music similarity and retrieval\n\n\n ABSTRACTThis tutorial serves as an introductory course to the field of and state-of-the-art in music information retrieval (MIR) and in particular to music similarity...", "weights": [[0, 5, 1.0], [21, 30, 1.0], [129, 134, 1.0], [147, 156, 1.0], [184, 189, 1.0]]}, "L16-1312": {"snippet": "...ased Music Retrieval and Recommendation System\n\n\n In this paper, we present a music retrieval and recommendation system using machine learning techniques. We propose a query by humming system for musi...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [81, 86, 1.0], [87, 96, 1.0], [199, 204, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...tion retrieval (information retrieval models; indexing; ranking and filtering; querying and interface design), natural language processing (text analysis; text mining; machine learning; information ex...", "weights": [[8, 17, 1.0], [31, 40, 1.0]]}, "2017.ipm_journal-ir0volumeA53A5.10": {"snippet": "...osed music descriptors with other conventional features for music retrieval. The results showed that the performance of the proposed music descriptors was competitive with conventional features, there...", "weights": [[8, 13, 1.0], [63, 68, 1.0], [69, 78, 1.0], [136, 141, 1.0]]}, "2008.ismir_conference-2008.22": {"snippet": "...mple Retrieval: Improving Sound Source Separation Based on Integrated Harmonic and Inharmonic Models", "weights": [[8, 17, 1.0]]}, "2010.sigirconf_conference-2010.138": {"snippet": "...tion retrieval system . However, most post-retrieval query performance predictors are expensive to compute, and not well-suited for the web.The key idea behind RAPP is to use retrieval scores and feat...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [178, 187, 1.0]]}, "2002.sigirconf_conference-2002.45": {"snippet": "...lody retrieval. While most of previous studies on music retrieval from melodies were performed with either symbolic (e.g. MIDI) data or with monophonic (single instrument) performances, we performed e...", "weights": [[8, 17, 1.0], [53, 58, 1.0], [59, 68, 1.0]]}, "2017.chiir_conference-2017.72": {"snippet": "...ideo retrieval tools and methods, which allow for automatic search in videos, still the performance of automatic video retrieval is far from optimal.At the same time, the organization of personal data...", "weights": [[8, 17, 1.0], [122, 131, 1.0]]}, "2001.ismir_conference-2001.15": {"snippet": "...ART: Music Retrieval Via Aural Queries", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2017.tist_journal-ir0volumeA8A2.4": {"snippet": "...sing Music-Related Visual Stereotypes for Music Information Retrieval\n\n\n Over decades, music labels have shaped easily identifiable genres to improve recognition value and subsequently market sales of...", "weights": [[8, 13, 1.0], [45, 50, 1.0], [63, 72, 1.0], [90, 95, 1.0]]}, "1999.sigirconf_conference-99.24": {"snippet": "...ased Retrieval for Music Collections\n\n\n AbstractA content-based retrieval model for tackling the mismatch problems specific to music data is proposed and implemented. The system uses a pitch profile e...", "weights": [[8, 17, 1.0], [22, 27, 1.0], [67, 76, 1.0], [130, 135, 1.0]]}, "2010.coria_conference-2010.31": {"snippet": "Querying by examples", "weights": []}, "2010.sigirconf_conference-2010.200": {"snippet": "...tion retrieval\n\n\n Abstract:This tutorial is concerned with creating the best possible multimedia search experience. The intriguing bit here is that the query itself can be a multimedia excerpt: For ex...", "weights": [[8, 17, 1.0]]}, "2013.sigirconf_conference-2013.89": {"snippet": "...brid retrieval approaches to geospatial music recommendation\n\n\n ABSTRACTRecent advances in music retrieval and recommendation algorithms highlight the necessity to follow multimodal approaches in orde...", "weights": [[8, 17, 1.0], [43, 48, 1.0], [94, 99, 1.0], [100, 109, 1.0]]}, "2004.cikm_conference-2004.21": {"snippet": "...ased music retrieval\n\n\n ABSTRACTIn this paper, we present a novel feature extraction method facilitating efficient content-based music retrieval and classification, called InMAF. The goal of our appro...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [132, 137, 1.0], [138, 147, 1.0]]}, "2001.ismir_conference-2001.18": {"snippet": "Music Information Retrieval as Music Understanding", "weights": [[0, 5, 1.0], [18, 27, 1.0], [31, 36, 1.0]]}, "2002.cikm_conference-2002.54": {"snippet": "...ious music information retrieval approaches\n\n\n ABSTRACTIn this paper, we describe the Ultima project which aims to construct a platform for evaluating various approaches of music information retrieval...", "weights": [[8, 13, 1.0], [26, 35, 1.0], [176, 181, 1.0], [194, 203, 1.0]]}, "2002.ismir_conference-2002.54": {"snippet": "...y of Music Information Retrieval Queries: System Design Implications", "weights": [[8, 13, 1.0], [26, 35, 1.0]]}, "2008.sigirconf_conference-2008.48": {"snippet": "...ment retrieval\n\n\n ABSTRACTRecent efforts on the task of spoken document retrieval (SDR) have made use of speech lattices: speech lattices contain information about alternative speech transcription hyp...", "weights": [[8, 17, 1.0], [75, 84, 1.0]]}, "2003.ismir_conference-2003.29": {"snippet": "...ased music retrieval", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2017.tist_journal-ir0volumeA8A2.3": {"snippet": "...like Music Information Research (MIR), Sound and Music Computing (SMC), but also parts of neighbouring fields like musical informatics, computational music theory, performance research, and even music...", "weights": [[8, 13, 1.0], [52, 57, 1.0], [153, 158, 1.0], [198, 203, 1.0]]}, "2003.wwwconf_conference-2003.40": {"snippet": "...ased music retrieval on acoustic data\n\n\n ABSTRACTIn traditional peer-to-peer search networks, operations focus on properly labeled files such as music or video, and the actual search is often limited ...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [148, 153, 1.0]]}, "2006.sigirconf_conference-2006.10": {"snippet": "Music structure based vector space retrieval\n\n\n ABSTRACTThis paper proposes a novel framework for music content indexing and retrieval. The music structure information, i.e., timing, harmony and music...", "weights": [[0, 5, 1.0], [35, 44, 1.0], [98, 103, 1.0], [125, 134, 1.0], [140, 145, 1.0], [195, 200, 1.0]]}, "2017.sigirconf_conference-2017.68": {"snippet": "...n in Music Retrieval\n\n\n ABSTRACTWith the advancement of mobile computing technology and cloud-based streaming music service, user-centered music retrieval has become increasingly important. User-speci...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [113, 118, 1.0], [142, 147, 1.0], [148, 157, 1.0]]}, "2007.ismir_conference-2007.111": {"snippet": "...onic Music Retrieval: The Hausdorff Metric and Geometric Hashing", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2005.sigirconf_conference-2005.128": {"snippet": "...ent, music information retrieval, contentbased audio retrieval, music recommendation.Nowadays access to online music is possible by querying artist or song names (or other types of editorial data such...", "weights": [[8, 13, 1.0], [26, 35, 1.0], [56, 65, 1.0], [67, 72, 1.0], [114, 119, 1.0]]}, "2019.ismir_conference-2019.15": {"snippet": "...g: A Music Exploration System Blending Latent Vector Representations of Lyric Word, Song Audio, and Artist", "weights": [[8, 13, 1.0]]}, "2008.sigirconf_conference-2008.202": {"snippet": "...n of retrieval system. However, general web users are more familiar with unstructured query such as natural language or keywords, which contains no structural information. Therefore, a retrieval metho...", "weights": [[8, 17, 1.0], [188, 197, 1.0]]}, "2013.ijmir_journal-ir0volumeA2A1.4": {"snippet": "... for music retrieval: from version identification to query-by-humming", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2020.sigirconf_conference-2020.181": {"snippet": "...vent Retrieval\n\n\n We propose a Query by Example (QBE) setting for cross-lingual event retrieval. In this setting, a user describes a query event using example sentences in one language, and a retrieva...", "weights": [[8, 17, 1.0], [89, 98, 1.0], [195, 204, 1.0]]}, "2003.ismir_conference-2003.43": {"snippet": "... for music information retrieval", "weights": [[8, 13, 1.0], [26, 35, 1.0]]}, "2013.wwwconf_conference-2013c.216": {"snippet": "...sion retrieval from twitter\n\n\n ABSTRACTWhile social media receive a lot of attention from the scientific community in general, there is little work on high recall retrieval of messages relevant to a d...", "weights": [[8, 17, 1.0], [166, 175, 1.0]]}}}
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F...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 32, 1.0], [120, 125, 1.0], [126, 134, 1.0], [152, 161, 1.0], [184, 193, 1.0]]}, "1999.ntcir_workshop-1999.5": {"snippet": "Cross Language Information Retrieval Based on Comparable Corpora", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0]]}, "2002.sigirjournals_journal-ir0volumeA36A2.10": {"snippet": "Cross language information retrieval: a research roadmap", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0]]}, "I08-6012": {"snippet": "Cross-Lingual Information Retrieval System for {I}ndian Languages\n\n\n This paper describes our first participation in the Indian language sub-task of the main Adhoc monolingual and bilingual track in C...", "weights": [[0, 5, 1.0], [14, 25, 1.0], [26, 35, 1.0], [128, 136, 1.0]]}, "2009.clef_workshop-2009.7": {"snippet": "Cross-Language Information Retrieval Using Meta-language Index Construction and Structural Queries", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0], [48, 56, 1.0]]}, "2006.clef_workshop-2006w.98": {"snippet": "...eech Retrieval Experiments using XML Information Retrieval\n\n\n This report presents the University of Twente's first cross-language speech retrieval experiments in Cross-Language Evaluation Forum (CLEF...", "weights": [[8, 17, 1.0], [40, 51, 1.0], [52, 61, 1.0], [119, 124, 1.0], [125, 133, 1.0], [141, 150, 1.0], [166, 171, 1.0], [172, 180, 1.0]]}, "2007.trec_conference-2007.10": {"snippet": "Cross Language Information Retrieval for Biomedical Literature\n\n\n This workshop report discusses the collaborative work of UT, EMC and TNO on the TREC Genomics Track 2007. The biomedical information r...", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0], [187, 198, 1.0], [199, 208, 1.0]]}, "2014.cikm_conference-2014.203": {"snippet": "...s of Cross-Language Information Retrieval\n\n\n ABSTRACTA major challenge in Cross-Language Information Retrieval (CLIR) is the adoption of translation knowledge in retrieval models, as it affects the te...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [77, 82, 1.0], [83, 91, 1.0], [92, 103, 1.0], [104, 113, 1.0], [165, 174, 1.0]]}, "2007.jasis_journal-ir0volumeA58A3.2": {"snippet": "...ased cross-language information retrieval in retrieval of highly relevant documents", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [48, 57, 1.0]]}, "2002.clef_workshop-2002.42": {"snippet": "...p 3: Cross-Language and Cross-Genre Retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [27, 32, 1.0], [39, 48, 1.0]]}, "L14-1740": {"snippet": "... for Cross-Lingual Information Retrieval in the Medical Domain", "weights": [[8, 13, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "O08-2009": {"snippet": "...in a Cross-Language Patent Retrieval System) [In {C}hinese]\n\n\n In this paper, we introduced a cross-language patent retrieval system which combined the various free web translators on the internet. Th...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [30, 39, 1.0], [97, 102, 1.0], [103, 111, 1.0], [119, 128, 1.0]]}, "2004.riao_conference-2004.38": {"snippet": "Cross-Language Image Retrieval via Spoken Query", "weights": [[0, 5, 1.0], [6, 14, 1.0], [21, 30, 1.0]]}, "2000.clef_workshop-2000.7": {"snippet": "... for Cross-Language Information Retrieval: Multimedia Data and the User Experience", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "2020.coling-main.323": {"snippet": "Cross-Lingual Document Retrieval with Smooth Learning\n\n\n Cross-lingual document search is an information retrieval task in which the queries' language differs from the documents' language. In this pap...", "weights": [[0, 5, 1.0], [23, 32, 1.0], [57, 62, 1.0], [93, 104, 1.0], [105, 114, 1.0], [142, 150, 1.0], [179, 187, 1.0]]}, "2008.clef_workshop-2008w.29": {"snippet": "Cross-language Information Retrieval with Explicit Semantic Analysis", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0]]}, "2003.clef_workshop-2003w.57": {"snippet": "...eves cross-language summaries with the source-language query. The performance of both systems is very similar, confirming that UNED summaries are viable for cross-language indexing. This approach is t...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [49, 57, 1.0], [160, 165, 1.0], [166, 174, 1.0]]}, "2005.sigirconf_conference-2005.101": {"snippet": "...y of cross lingual information retrieval using on-line translation systems\n\n\n ABSTRACTTypical cross language retrieval requires special linguistic resources, such as bilingual dictionaries and paralle...", "weights": [[8, 13, 1.0], [22, 33, 1.0], [34, 43, 1.0], [97, 102, 1.0], [103, 111, 1.0], [112, 121, 1.0]]}, "C12-1057": {"snippet": "... use information from documents in the source language. We overcome this limitation by using information from retrieved documents in the source language to improve the retrieval quality of the target ...", "weights": [[8, 19, 1.0], [49, 57, 1.0], [96, 107, 1.0], [147, 155, 1.0], [171, 180, 1.0]]}, "2001.sigirconf_conference-2001.12": {"snippet": "...ving Cross Language Information Retrieval with Triangulated Translation\n\n\n ABSTRACTMost approaches to cross language information retrieval assume that resources providing a direct translation between ...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [105, 110, 1.0], [111, 119, 1.0], [120, 131, 1.0], [132, 141, 1.0]]}, "A00-1003": {"snippet": "Cross-Language Multimedia Information Retrieval\n\n\n Simple measures can achieve high-accuracy cross-language retrieval in carefully chosen applications. Image retrieval is one of those applications, wi...", "weights": [[0, 5, 1.0], [6, 14, 1.0], [26, 37, 1.0], [38, 47, 1.0], [93, 98, 1.0], [99, 107, 1.0], [108, 117, 1.0], [158, 167, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.1": {"snippet": "...s of cross-language information retrieval: a review\n\n\n AbstractThis paper reviews state-of-the-art techniques and methods for enhancing effectiveness of cross-language information retrieval (CLIR). Th...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [156, 161, 1.0], [162, 170, 1.0], [171, 182, 1.0], [183, 192, 1.0]]}, "2002.sigirconf_conference-2002.64": {"snippet": "... for cross-language information retrieval\n\n\n ABSTRACTThis paper reports on the user-centered design methodology and techniques used for the elicitation of user requirements and how these requirements ...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "2020.emnlp-main.340": {"snippet": "... for Cross-Lingual Information Retrieval\n\n\n We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically f...", "weights": [[8, 13, 1.0], [22, 33, 1.0], [34, 43, 1.0], [142, 147, 1.0], [156, 167, 1.0], [168, 177, 1.0]]}, "2010.ntcir_workshop-2010.15": {"snippet": "...n in Cross-language Information Retrieval\n\n\n This paper presents the technique details and experimental results of the information retrieval system with which we participated at the NTCIR-8 ACLIA (Adv...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [122, 133, 1.0], [134, 143, 1.0]]}, "2004.clef_workshop-2004w.54": {"snippet": "... for cross-lingual information retrieval. The performance of cross-lingual information retrieval was poor and that of monolingual information retrieval was not good. We need to check our modules and p", "weights": [[8, 13, 1.0], [22, 33, 1.0], [34, 43, 1.0], [64, 69, 1.0], [78, 89, 1.0], [90, 99, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2010.clef_workshop-2010w.61": {"snippet": "...ural language used in the queries. Language identification is in fact a key task within Cross Language Information Retrieval systems and the challenge is particularly difficult in the case of search q...", "weights": [[8, 16, 1.0], [38, 46, 1.0], [91, 96, 1.0], [97, 105, 1.0], [106, 117, 1.0], [118, 127, 1.0]]}, "2006.clef_workshop-2006w.137": {"snippet": "...-and cross-language information retrieval (IR) are offered. The intention is to encourage experimentation with all kinds of multilingual information access-from the development of systems for monoling...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [140, 151, 1.0]]}, "2001.ir_journal-ir0volumeA4A34.2": {"snippet": "...ased Cross-Language Information Retrieval: Problems, Methods, and Research Findings", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "2019.ecir_conference-20191.33": {"snippet": "...ical Cross-Lingual Information Retrieval\n\n\n Abstract. We present a method for automatic query expansion for cross-lingual information retrieval in the medical domain. The method employs machine transl...", "weights": [[8, 13, 1.0], [22, 33, 1.0], [34, 43, 1.0], [111, 116, 1.0], [125, 136, 1.0], [137, 146, 1.0]]}, "2005.clef_workshop-2005w.18": {"snippet": "...s of language models and the smoothing strategies for cross-language image retrieval. The second is the evaluations of cross-media image retrieval, i.e., combining text and visual content for image re...", "weights": [[8, 16, 1.0], [57, 62, 1.0], [63, 71, 1.0], [78, 87, 1.0], [122, 127, 1.0], [140, 149, 1.0], [201, 210, 1.0]]}, "2006.bcs-1.9": {"snippet": "...sing Cross-language Information Retrieval for Sentence Alignment", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.0": {"snippet": "Cross-Language Information Retrieval: the way ahead\n\n\n AbstractThis introductory paper covers not only the research content of the articles in this special issue of IP&M but attempts to characterize t...", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0]]}, "2008.ntcir_workshop-2008.29": {"snippet": "...in a Cross-Language Patent Access System\n\n\n In this paper, a cross-language patent retrieval and classification system is presented to integrate the query translation using various free web translator...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [64, 69, 1.0], [70, 78, 1.0], [86, 95, 1.0]]}, "2002.ntcir_workshop-2002.24": {"snippet": "...rean Cross-Language Information Retrieval\n\n\n This paper describes KUNLP system for the English-Korean cross-language information retrieval track in NTCIR-3 workshop and some experiments after the work...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [105, 110, 1.0], [111, 119, 1.0], [120, 131, 1.0], [132, 141, 1.0]]}, "2001.clef_workshop-2000w.8": {"snippet": "Cross-Language Information Retrieval using Dutch Query Translation\n\n\n This paper describes a hastily carried out, rather limited bilingual information retrieval experiment. The experiment takes Dutch ...", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0], [139, 150, 1.0], [151, 160, 1.0]]}, "1999.trec_conference-1999.18": {"snippet": "Cross-Language Information Retrieval (CLIR) Track Overview\n\n\n A cross-language retrieval track was offered for the third time at TREC-8. The main task was the same as that of the previous year: the go...", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0], [64, 69, 1.0], [70, 78, 1.0], [79, 88, 1.0]]}, "1999.ntcir_workshop-1999.6": {"snippet": "Cross-Language Information Retrieval at ULIS", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0]]}, "2013.ecir_conference-2013.106": {"snippet": "...s in cross-lingual information retrieval models.Keywords: Probabilistic topic models, Cross-lingual retrieval, Ranking models, Cross-lingual text mining.\nGoals and ScopeThis half day tutorial presents...", "weights": [[8, 13, 1.0], [22, 33, 1.0], [34, 43, 1.0], [89, 94, 1.0], [103, 112, 1.0], [130, 135, 1.0]]}, "2005.clef_workshop-2005w.64": {"snippet": "... and Cross-language QA using a QA-oriented Passage Retrieval System\n\n\n This report describes the work done by the RFIA group at the Departamento de Sistemas Inform\u00e1ticos y Computaci\u00f3n of the Universid...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [54, 63, 1.0]]}, "1998.trec_conference-1998.1": {"snippet": "Cross-Language Information Retrieval (CLIR) Track Overview", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0]]}, "2001.sigirjournals_journal-ir0volumeA35A1.0": {"snippet": "...tive cross-language information retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "P14-2080": {"snippet": "... for Cross-Language Retrieval\n\n\n We present an approach to cross-language retrieval that combines dense knowledgebased features and sparse word translations. Both feature types are learned directly fr...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 32, 1.0], [62, 67, 1.0], [68, 76, 1.0], [77, 86, 1.0]]}, "1998.sigirconf_conference-98.10": {"snippet": "Cross-Language Information Retrieval with the UMLS Metathesaurus\n\n\n AbstractWe investigate an automatic method for Cross Language Information Retrieval (CLIR) that utilizes the multilingual UMLS Metat...", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0], [115, 120, 1.0], [121, 129, 1.0], [130, 141, 1.0], [142, 151, 1.0]]}, "1997.trec_conference-1997.3": {"snippet": "Cross-Language Information Retrieval (CLIR) Track Overview", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0]]}, "2001.trec_conference-2001.73": {"snippet": "...s in Cross-Language Retrieval\n\n\n In TREC-10 the Berkeley group participated only in the English-Arabic cross-language retrieval (CLIR) track. One Arabic monolingual run and four English-Arabic cross-l...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 32, 1.0], [106, 111, 1.0], [112, 120, 1.0], [121, 130, 1.0], [196, 201, 1.0], [202, 210, 1.0]]}, "1999.trec_conference-1999.12": {"snippet": "...EC-8 cross-language information retrieval (CLIR) track, we adopted the approach of using machine translation to prepare a source-language query for use in a target-language retrieval task. We empirica...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [132, 140, 1.0], [167, 175, 1.0], [176, 185, 1.0]]}, "2000.riao_conference-2000.44": {"snippet": "...n in Cross-Language Information Retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "P19-1306": {"snippet": "...urce Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations\n\n\n In this paper, we propose to boost lowresource cross-lingual document retrieval performance with deep bilingua...", "weights": [[8, 13, 1.0], [31, 40, 1.0], [140, 145, 1.0], [163, 172, 1.0]]}, "O12-1015": {"snippet": "...ment retrieval model for cross-language document retrieval. Given a certain document in the source language, it will be translated into the target language of statistical machine translation model. Th...", "weights": [[8, 17, 1.0], [28, 33, 1.0], [34, 42, 1.0], [52, 61, 1.0], [102, 110, 1.0], [150, 158, 1.0]]}, "O00-3003": {"snippet": "...nese Cross-Language Information Retrieval: An Interlingua Approach\n\n\n Electronically available multilingual information can be divided into two major categories: (1) alphabetic language information (E...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [111, 122, 1.0], [180, 188, 1.0], [189, 200, 1.0]]}, "2022.acl-long.288": {"snippet": "Cross-Lingual Phrase Retrieval\n\n\n Cross-lingual retrieval aims to retrieve relevant text across languages. Current methods typically achieve cross-lingual retrieval by learning language-agnostic text ...", "weights": [[0, 5, 1.0], [21, 30, 1.0], [34, 39, 1.0], [48, 57, 1.0], [141, 146, 1.0], [155, 164, 1.0], [177, 185, 1.0]]}, "W99-0605": {"snippet": "Cross-Language Information Retrieval for Technical Documents\n\n\n This paper proposes a Japanese/English crosslanguage information retrieval (CLIR) system targeting technical documents. Our system first...", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0], [117, 128, 1.0], [129, 138, 1.0]]}, "2005.clef_workshop-2005.67": {"snippet": "...005: Cross-Language and Cross-Media Image Retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [27, 32, 1.0], [45, 54, 1.0]]}, "2006.sigirconf_conference-2006.27": {"snippet": "... for cross-language information retrieval\n\n\n ABSTRACTThis paper introduces a general framework for the use of translation probabilities in cross-language information retrieval based on the notion that...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [142, 147, 1.0], [148, 156, 1.0], [157, 168, 1.0], [169, 178, 1.0]]}, "2009.spire_conference-2009.16": {"snippet": "...tive Cross-Language Information Retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "W00-0504": {"snippet": "... for cross-media and cross-language retrieval. The Mandarin Chinese news audio are indexed with word and subword units by speech recognition. Translation of these multiscale units can effect cross-lan...", "weights": [[8, 13, 1.0], [24, 29, 1.0], [30, 38, 1.0], [39, 48, 1.0], [194, 199, 1.0], [200, 208, 1.0]]}, "1997.sigirconf_conference-97.13": {"snippet": "...IDER information retrieval system, namely the query pseudo-translation module and the speech retrieval module. Our approach to cross-language retrieval uses an automatically constructed corpus-based i...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [96, 105, 1.0], [130, 135, 1.0], [136, 144, 1.0], [145, 154, 1.0], [202, 213, 1.0]]}, "C12-2057": {"snippet": "... for Cross-Language Information Retrieval\n\n\n This paper introduces a cross-language information retrieval (CLIR) framework that combines the state-of-the-art keyword-based approach with a latent seman...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [72, 77, 1.0], [78, 86, 1.0], [87, 98, 1.0], [99, 108, 1.0]]}, "2018.tweb_journal-ir0volumeA12A4.4": {"snippet": "...t of cross-site linking might lead to privacy risks. Based on cross-site links between Foursquare and external OSN sites, we formulate cross-site information aggregation as a problem that uses cross-s...", "weights": [[8, 13, 1.0], [65, 70, 1.0], [138, 143, 1.0], [149, 160, 1.0], [196, 201, 1.0]]}, "2020.sigirconf_conference-2020.353": {"snippet": "...llel cross-lingual data plays a crucial role in a variety of tasks in natural language processing with applications in machine translation, cross-lingual information retrieval, and document classifica...", "weights": [[8, 13, 1.0], [81, 89, 1.0], [143, 148, 1.0], [157, 168, 1.0], [169, 178, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.22": {"snippet": "...many information retrieval tasks are not only restricted to the case of a single modal and the contend-based cross modal retrieval has become one of the popular research fields. The premise of cross m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [112, 117, 1.0], [124, 133, 1.0], [196, 201, 1.0]]}, "2007.clef_workshop-2007.9": {"snippet": "Cross-Language Retrieval with Wikipedia", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 24, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.7": {"snippet": "... the information displayed in the cross-language document summaries. Our results challenge two implicit assumptions in most of cross-language Information Retrieval research: first, that once documents...", "weights": [[8, 19, 1.0], [37, 42, 1.0], [43, 51, 1.0], [130, 135, 1.0], [136, 144, 1.0], [145, 156, 1.0], [157, 166, 1.0]]}, "1998.amta-tutorials.5": {"snippet": "Cross language information retrieval", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0]]}, "2007.clef_workshop-2007w.23": {"snippet": "Cross-Lingual Information Retrieval System for Indian Languages\n\n\n This paper describes our first participation in the Indian language sub-task of the main Adhoc monolingual and bilingual track in CLE...", "weights": [[0, 5, 1.0], [14, 25, 1.0], [26, 35, 1.0], [126, 134, 1.0]]}, "2007.clef_workshop-2007.67": {"snippet": "Cross-Language and Cross-Media Image Retrieval: An Empirical Study at ImageCLEF2007", "weights": [[0, 5, 1.0], [6, 14, 1.0], [19, 24, 1.0], [37, 46, 1.0]]}, "2005.ntcir_workshop-2005.50": {"snippet": "...lish Cross-Language Question Answering System SAIQA-J/E. The system performed best among eight systems that participated in the Japanese-English subtask of the NTCIR Cross-Language Question Answering ...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [169, 174, 1.0], [175, 183, 1.0]]}, "2008.sigirconf_conference-2008.204": {"snippet": "...ical cross-language information retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "2003.ipm_journal-ir0volumeA39A1.4": {"snippet": "Cross-language information retrieval: experiments based on CLEF 2000 corpora", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0]]}, "2000.clef_workshop-2000.22": {"snippet": "Cross-Language Information Retrieval Using Dutch Query Translation", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0]]}, "2010.cikm_workshop-2010esair.6": {"snippet": "Cross language information retrieval based on concept base and language grid", "weights": [[0, 5, 1.0], [6, 14, 1.0], [15, 26, 1.0], [27, 36, 1.0], [63, 71, 1.0]]}, "2009.clef_workshop-2009w.33": {"snippet": "Cross-lingual Information Retrieval based on Multiple Indexes\n\n\n In this paper we present the technical details of the retrieval system with which we participated at the CLEF09 Ad-hoc TEL task. We pre...", "weights": [[0, 5, 1.0], [14, 25, 1.0], [26, 35, 1.0], [119, 128, 1.0]]}, "2008.sigirconf_conference-2008.190": {"snippet": "Cross-lingual search over 22 european languages\n\n\n ABSTRACTIn this paper we present a system for cross-lingual information retrieval, which can handle tens of languages and millions of documents. Func...", "weights": [[0, 5, 1.0], [97, 102, 1.0], [111, 122, 1.0], [123, 132, 1.0]]}, "2017.sigirconf_conference-2017.141": {"snippet": "... for Cross-Language Information Retrieval\n\n\n ABSTRACTOnline learning to rank for information retrieval has shown great promise in optimization of Web search results based on user interactions. However...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [84, 95, 1.0], [96, 105, 1.0]]}, "2017.chiir_conference-2017.28": {"snippet": "...ixed Language Queries in Different Search Intents\n\n\n ABSTRACTWith the increasing number of multilingual resources on the Internet, cross-language information retrieval has become an important research...", "weights": [[8, 16, 1.0], [134, 139, 1.0], [140, 148, 1.0], [149, 160, 1.0], [161, 170, 1.0]]}, "L00-1051": {"snippet": "... for Cross-language Information Retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "1999.trec_conference-1999.11": {"snippet": "... the cross-language retrieval track. Our objective was to continue our investigation of our conceptual interlingua approach to cross-language retrieval, specifically by measuring the contribution of c...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 32, 1.0], [130, 135, 1.0], [136, 144, 1.0], [145, 154, 1.0]]}, "W00-1312": {"snippet": "Cross-lingual Information Retrieval Using Hidden {M}arkov Models\n\n\n This paper presents empirical results in cross-lingual information retrieval using English queries to access Chinese documents (TREC...", "weights": [[0, 5, 1.0], [14, 25, 1.0], [26, 35, 1.0], [109, 114, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2014.ecir_conference-2014.22": {"snippet": "Cross-Language Pseudo-Relevance Feedback Techniques for Informal Text", "weights": [[0, 5, 1.0], [6, 14, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...odic information to isolate sarcasm. -Language models. ASR systems generally contain a language model that provides word sequence probabilities. Some information retrieval systems also rely on languag...", "weights": [[8, 19, 1.0], [41, 49, 1.0], [90, 98, 1.0], [153, 164, 1.0], [165, 174, 1.0], [196, 204, 1.0]]}, "2009.ipm_journal-ir0volumeA45A2.5": {"snippet": "...tive language as one index unit. For Cross-Language Information Retrieval (CLIR), one of the major hindrances to achieving retrieval performance at the level of Mono-Lingual Information Retrieval (MLI...", "weights": [[8, 16, 1.0], [40, 45, 1.0], [46, 54, 1.0], [55, 66, 1.0], [67, 76, 1.0], [126, 135, 1.0], [177, 188, 1.0], [189, 198, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [42, 53, 1.0], [54, 63, 1.0], [92, 103, 1.0], [104, 113, 1.0], [116, 121, 1.0], [122, 130, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "2000.trec_conference-2000.9": {"snippet": "...uage retrieval experiments at Berkeley for TREC-9 Cross-Language Information Retrieval track. We present a simple and effective Chinese word segmentation method and compare the cross-language retrieva...", "weights": [[8, 17, 1.0], [53, 58, 1.0], [59, 67, 1.0], [68, 79, 1.0], [80, 89, 1.0], [180, 185, 1.0], [186, 194, 1.0], [195, 204, 1.0]]}, "2004.sigirjournals_journal-ir0volumeA38A1.13": {"snippet": "...s in cross-language information retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "2012.ipm_journal-ir0volumeA48A4.2": {"snippet": "... for cross-language information retrieval\n\n\n a b s t r a c tThis article describes a framework for cross-language information retrieval that efficiently leverages statistical estimation of translation...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [102, 107, 1.0], [108, 116, 1.0], [117, 128, 1.0], [129, 138, 1.0]]}, "2014.wsdm_conference-2014.74": {"snippet": "...mine information across multiple different languages and jargons, that is, we desire effective and cheap tools for cross-lingual information retrieval and search, semantic similarity, and for cross-la...", "weights": [[8, 19, 1.0], [118, 123, 1.0], [132, 143, 1.0], [144, 153, 1.0], [195, 200, 1.0], [201, 209, 1.0]]}, "2003.clef_workshop-2003w.20": {"snippet": "...lish Cross-Language Image Retrieval\n\n\n In this paper we proposed an approach to deal with the Chinese-English cross-language image retrieval problem. Text-based image retrieval and query translation w...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [29, 38, 1.0], [113, 118, 1.0], [119, 127, 1.0], [134, 143, 1.0], [170, 179, 1.0]]}, "2000.amta-papers.2": {"snippet": "...tage cross-language information retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "J11-2009": {"snippet": "...iew: Cross-Language Information Retrieval by Jian-Yun Nie\n\n\n Cross-Language Information Retrieval is a compact book introducing a branch of information retrieval that has gained considerable research ...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [64, 69, 1.0], [70, 78, 1.0], [79, 90, 1.0], [91, 100, 1.0], [143, 154, 1.0], [155, 164, 1.0]]}, "2004.clef_workshop-2004.59": {"snippet": "...CLEF cross-language image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use of both text and content-based retrieval methods for cross-language image retrieval. Three ta...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [29, 38, 1.0], [141, 150, 1.0], [163, 168, 1.0], [169, 177, 1.0], [184, 193, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ical information retrieval\n\n\n AbstractIn this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 153, 1.0], [154, 163, 1.0]]}, "2004.clef_workshop-2004.66": {"snippet": "...ward Cross-Language and Cross-Media Image Retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [27, 32, 1.0], [45, 54, 1.0]]}, "1997.trec_conference-1997.17": {"snippet": "...ic 3-Language Cross-Language Information Retrieval with Latent Semantic Indexing", "weights": [[8, 16, 1.0], [17, 22, 1.0], [23, 31, 1.0], [32, 43, 1.0], [44, 53, 1.0]]}, "2002.ecir_conference-2002.21": {"snippet": "... for Cross-Language Retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 32, 1.0]]}, "2001.ntcir_workshop-2001.19": {"snippet": "...lish Cross-lingual Information Retrieval at DLUT", "weights": [[8, 13, 1.0], [22, 33, 1.0], [34, 43, 1.0]]}, "2003.clef_workshop-2003w.48": {"snippet": "... the information contained in these documents. This paper describes the CLEF 2003 track investigation of Cross-Language Spoken Document Retrieval (CLSDR) combining information retrieval, cross-languag...", "weights": [[8, 19, 1.0], [108, 113, 1.0], [114, 122, 1.0], [139, 148, 1.0], [167, 178, 1.0], [179, 188, 1.0], [190, 195, 1.0], [196, 204, 1.0]]}, "2020.signlang-1.35": {"snippet": "...able information retrieval from sign language with the queries from the translated written language. With the written language translations as labels, we train a weakly supervised keyword search model...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [40, 48, 1.0], [94, 102, 1.0], [121, 129, 1.0]]}, "2014.cikm_conference-2014.187": {"snippet": "...d of Cross-Language Information Retrieval (CLIR) addresses the problem of finding documents in some language that are relevant to a question posed in a different language. Retrieving answers to questi...", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0], [103, 111, 1.0], [165, 173, 1.0]]}, "2000.riao_conference-2000.91": {"snippet": "... - A cross-language Information retrieval", "weights": [[8, 13, 1.0], [14, 22, 1.0], [23, 34, 1.0], [35, 44, 1.0]]}, "2020.acl-main.613": {"snippet": "... for Cross-Lingual Information Retrieval in the Medical Domain\n\n\n We present a thorough comparison of two principal approaches to Cross-Lingual Information Retrieval: document translation (DT) and que...", "weights": [[8, 13, 1.0], [22, 33, 1.0], [34, 43, 1.0], [133, 138, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "D13-1175": {"snippet": "...zing cross-lingual rankings rivals and complements machine translation-based cross-language information retrieval (CLIR). We propose an efficient boosting algorithm that deals with very large cross-pr...", "weights": [[8, 13, 1.0], [80, 85, 1.0], [86, 94, 1.0], [95, 106, 1.0], [107, 116, 1.0], [195, 200, 1.0]]}}}
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At the sa...", "weights": [[8, 19, 1.0], [81, 92, 1.0]]}, "2011.wsdm_conference-2011.59": {"snippet": "...-way interaction between advertisers, users, and the search engine. The basic ad selection in sponsored search, lets the advertiser choose the exact queries where the ad is to be shown. To increase ad...", "weights": [[8, 19, 1.0]]}, "2020.sigirconf_conference-2020.377": {"snippet": "...en a user and a system can be seen as conversational search where both sides of interaction can take an initiative to achieve a search task. Previous work in the area of user modeling focused on user ...", "weights": [[8, 12, 1.0], [83, 94, 1.0], [173, 177, 1.0], [198, 202, 1.0]]}, "A97-1008": {"snippet": "...2840 user utterances, the Circuit Fix-It Shop natural language dialog system misinterpreted 18.5% of user utterances. These miscommunications created various problems for the dialog interaction, rangi...", "weights": [[8, 12, 1.0], [104, 108, 1.0], [185, 196, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.6": {"snippet": "...n of interaction. The main conclusion and general consensus was that simulation offers great potential for the field of IR; and that simulations of user interaction can make explicit the user and the ...", "weights": [[8, 19, 1.0], [151, 155, 1.0], [156, 167, 1.0], [190, 194, 1.0]]}, "A92-1042": {"snippet": "... the User{'}s Knowledge in Guidance Interactions", "weights": [[8, 12, 1.0]]}, "2008.wwwjournals_journal-ir0volumeA11A2.3": {"snippet": "...e, a user experiment to obtain the user's perception and evaluate the hit-rate of our system is also presented.Keywords intelligent web-based user interfaces . end-user development . web-based program...", "weights": [[8, 12, 1.0], [38, 42, 1.0], [145, 149, 1.0], [167, 171, 1.0]]}, "2008.sigirconf_conference-2008.207": {"snippet": "...m of interaction data. Most of it is linguistic data. Lots of it is captured in logs. Some of it is used to guess what the user is searching for. But to most information retrieval systems, each user i...", "weights": [[8, 19, 1.0], [126, 130, 1.0], [197, 201, 1.0], [202, 213, 1.0]]}, "2020.wwwconf_conference-2020c.108": {"snippet": "...s of user behavior. This work describes the challenges we encountered while attempting to forecast user activity on two popular social interaction sites: Twitter and GitHub.", "weights": [[8, 12, 1.0], [102, 106, 1.0], [138, 149, 1.0]]}, "2016.sigirconf_conference-2016.215": {"snippet": "User Interaction in Mobile Web Search", "weights": [[0, 4, 1.0], [5, 16, 1.0]]}, "2015.wsdm_conference-2015.17": {"snippet": "...from User Interactions\n\n\n ABSTRACTThe ability to learn from user interactions can give systems access to unprecedented amounts of world knowledge. This is already evident in search engines, recommende...", "weights": [[8, 12, 1.0], [63, 67, 1.0]]}, "2017.wsdm_conference-2017.85": {"snippet": "...zing user interaction data for improving search over personal, rather than public, content is a challenging problem. First, the documents (e.g., emails or private files) are not shared across users. S...", "weights": [[8, 12, 1.0], [13, 24, 1.0]]}, "L02-1236": {"snippet": "Advanced Tools for the Study of Natural Interactivity", "weights": []}, "1984.sigirconf_conference-84.28": {"snippet": "...l of User Stopping Behaviour for Scanning the Output of an Information Retrieval System", "weights": [[8, 12, 1.0]]}, "2020.sigirconf_conference-2020.367": {"snippet": "...from interaction becomes a crucial machine learning paradigm for interactive IR, which is based on reinforcement learning. With recent great advances in deep reinforcement learning (DRL), there have b...", "weights": [[8, 19, 1.0]]}, "2013.mtsummit-user.9": {"snippet": "User Evaluation of Advanced Interaction Features for a Computer-Assisted Translation Workbench\n\n\n This paper reports on the results of a user satisfaction survey carried out among 16 translators using...", "weights": [[0, 4, 1.0], [28, 39, 1.0], [137, 141, 1.0]]}, "2020.sigirconf_conference-2020.361": {"snippet": "...onal interaction systems like Siri, and Cortana; news feed recommender systems, and interactive search engines, etc.), are ubiquitous in real-world applications. These systems either converse with use...", "weights": [[8, 19, 1.0]]}, "2020.intellang-1.3": {"snippet": "... for user interaction through AI Chatbots. In this work, we present the voice-based AI chatbot \"FitChat\" developed to deliver behaviour change interventions that encourage physical activities among ol...", "weights": [[8, 12, 1.0], [13, 24, 1.0]]}, "2018.chiir_conference-2018.19": {"snippet": "...uter interaction (HCI)    \nAuthor KeywordsInformation retrieval; Human-computer interaction; Behavioral log analysis; Interdisciplinary research BIOGRAPHY Susan Dumais a Distinguished Scientist and De...", "weights": [[8, 19, 1.0], [83, 94, 1.0]]}, "2020.ir_journal-ir0volumeA23A6.0": {"snippet": "...from user interactions", "weights": [[8, 12, 1.0]]}, "2019.ipm_journal-ir0volumeA56A3.20": {"snippet": "...s as User Interaction Components for Digital Library Evaluation\n\n\n A B S T R A C TThis study aims to explore the relationships between user interaction and digital libraries (DLs) evaluation. User int...", "weights": [[8, 12, 1.0], [13, 24, 1.0], [138, 142, 1.0], [143, 154, 1.0], [195, 199, 1.0], [200, 211, 1.0]]}, "2017.wwwconf_conference-2017c.241": {"snippet": "...om a user's interaction history with Web content. As an ever-increasing portion of Web interaction occurs in a mobile context, the physical and social environment in which the interaction occurs is a ...", "weights": [[8, 12, 1.0], [15, 26, 1.0], [90, 101, 1.0], [179, 190, 1.0]]}, "2016.ecir_conference-2016.22": {"snippet": "...ween User Perception and User Behaviour in Interactive Information Retrieval Evaluation", "weights": [[8, 12, 1.0], [28, 32, 1.0]]}, "W18-5706": {"snippet": "...uman user and an intelligent virtual assistant. This provides a potential foundation for developing artificial personal shoppers for e-commerce websites, such as Alibaba, Amazon, and eBay. Personal sh...", "weights": [[8, 12, 1.0]]}, "2008.sigirconf_conference-2008.4": {"snippet": "... how user interaction can be used to achieve this improvement. Most interaction techniques present users with a fixed number of options for all queries. We achieve improvements by interacting less wit...", "weights": [[8, 12, 1.0], [13, 24, 1.0], [71, 82, 1.0]]}, "C00-1083": {"snippet": "... the user on dismantling some parts of a car. First, in order to change the content of the instruction dialogue to match the user's view, we extend the re nement-driven planning algorithm by using the...", "weights": [[8, 12, 1.0], [128, 132, 1.0]]}, "W13-5601": {"snippet": "...onal User Interface\n\n\n Work on both the graphical user interface (GUI) and the conversational user interface (CUI) started at about the same time, about 40 years ago. The GUI was a lot easier to imple...", "weights": [[8, 12, 1.0], [53, 57, 1.0], [97, 101, 1.0]]}, "2004.ismir_conference-2004.34": {"snippet": "...usic Interaction Concepts: A User Study", "weights": [[8, 19, 1.0], [32, 36, 1.0]]}, "2017.wwwconf_conference-2017c.10": {"snippet": "... the user interaction paradigm by keeping users updated without checking applications. Conversational agents are posed to be the next revolution in user interaction paradigms. However, without intelli...", "weights": [[8, 12, 1.0], [13, 24, 1.0], [151, 155, 1.0], [156, 167, 1.0]]}, "O00-1006": {"snippet": "... the user's query are extracted from similar query sessions, rather than from the contents of the retrieved documents. The experiment results demonstrate that this approach has a great potential in de...", "weights": [[8, 12, 1.0]]}, "2020.chiir_conference-2020.18": {"snippet": "...s on User Experience with Recommended Documents", "weights": [[8, 12, 1.0]]}, "2018.tist_journal-ir0volumeA9A5.10": {"snippet": "...tive interaction techniques. It is designed to allow users to quickly explore and understand graph data via intuitive visual representations and easy-to-use interaction techniques to explore it in a f...", "weights": [[8, 19, 1.0], [160, 171, 1.0]]}, "2020.wwwconf_conference-2020.320": {"snippet": "...deo, user-generated content, and social networks, there is a massive opportunity for disruptive innovations in the media and content industry. It is now a fast-changing landscape with rapid advances i...", "weights": [[8, 12, 1.0]]}, "2010.wsdm_conference-2010.8": {"snippet": "...nced user models", "weights": [[8, 12, 1.0]]}, "2011.cikm_conference-2011.325": {"snippet": "...wide user desktop interactions. Next; filtered user actions are fed to the time-based switch detection algorithm. In the last step the detected activity switches are presented to the user through a re...", "weights": [[8, 12, 1.0], [50, 54, 1.0], [186, 190, 1.0]]}, "2009.airs_conference-2009.23": {"snippet": "...tive User Interactions in Content-Based Image Retrieval", "weights": [[8, 12, 1.0]]}, "2009.ictir_conference-2009.29": {"snippet": "...ctor User Interaction Model for Content-Based Image Retrieval", "weights": [[8, 12, 1.0], [13, 24, 1.0]]}, "2014.wwwconf_conference-2014c.42": {"snippet": "...upon user interface issues and interaction experiences. A specific focus in e-commerce involves returning a small set of results to users, as well as requiring minimal intervention or input from them....", "weights": [[8, 12, 1.0], [34, 45, 1.0]]}, "2004.clef_workshop-2004.34": {"snippet": "...ving Interaction with the User in Cross-Language Question Answering Through Relevant Domains and Syntactic Semantic Patterns\n\n\n Abstract.The iCLEF 2004 experiment at the University of Alicante has foc...", "weights": [[8, 19, 1.0], [29, 33, 1.0]]}, "2006.jasis_journal-ir0volumeA57A5.11": {"snippet": "...hich user interaction for cross-language information retrieval? Design issues and reflections", "weights": [[8, 12, 1.0], [13, 24, 1.0]]}, "2014.sigirconf_conference-2014.143": {"snippet": "...ding user interaction with QAC. Seeking to model user behavior, Kharitonov et al.  [10]  proposed a model of users as interacting with QAC suggestions from top to bottom, looking for a specific sugges...", "weights": [[8, 12, 1.0], [13, 24, 1.0], [52, 56, 1.0]]}, "2018.ictir_conference-2018.2": {"snippet": "User Interactions with Search Systems", "weights": [[0, 4, 1.0]]}, "1999.mtsummit-1.16": {"snippet": "... the interaction for speech communication, pragmatics in speech, and arbitrariness of speech usage. General characteristics of speech communication are discussed. Also the various viewpoints regarding...", "weights": [[8, 19, 1.0]]}, "C04-1144": {"snippet": "...s of user interaction in our system. With user interaction, our system attained both higher coverage and precision than that without user interaction.", "weights": [[8, 12, 1.0], [13, 24, 1.0], [45, 49, 1.0], [50, 61, 1.0], [136, 140, 1.0], [141, 152, 1.0]]}, "2007.cikm_conference-2007.108": {"snippet": "...tive user interaction", "weights": [[8, 12, 1.0], [13, 24, 1.0]]}, "D18-1445": {"snippet": "...real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at https://github.com/UKPLab/ emnlp2018-april.", "weights": [[8, 12, 1.0]]}, "W14-3107": {"snippet": "...ng a User-centered Approach to Design a Visual Text Analytic System\n\n\n Exploring an online conversation can be very difficult for a user, especially when it becomes a long complex thread. We follow a ...", "weights": [[8, 12, 1.0], [135, 139, 1.0]]}, "2019.tois_journal-ir0volumeA37A2.8": {"snippet": "... is, user and item latent vectors are learned during the training phase without considering the interaction between the user and the item. It is intuitive that not all words in the reviews written by ...", "weights": [[8, 12, 1.0], [99, 110, 1.0], [123, 127, 1.0]]}, "2018.wsdm_conference-2018.111": {"snippet": "...s of interaction pa erns. e ability to understand user behavior, record and interpret user interaction signals, gauge user satisfaction and incorporate user feedback gives online systems a vast treasu...", "weights": [[8, 19, 1.0], [53, 57, 1.0], [89, 93, 1.0], [94, 105, 1.0], [121, 125, 1.0], [155, 159, 1.0]]}, "H90-1103": {"snippet": "Opportunities for Advanced Speech Processing in Military Computer-Based Systems*\n\n\n This paper presents a study of military applications of advanced speech processing technology which includes three m...", "weights": []}, "2003.wwwconf_conference-2003p.172": {"snippet": "User Interaction Experience for Semantic Web Information", "weights": [[0, 4, 1.0], [5, 16, 1.0]]}, "2005.jasis_journal-ir0volumeA56A8.5": {"snippet": "...ting user interaction with a prototype visualization-based information retrieval system", "weights": [[8, 12, 1.0], [13, 24, 1.0]]}, "N18-5018": {"snippet": "...et a user's communicative intent from both their verbal utterances and non-verbal behaviors, such as gestures. The agent is also itself able to communicate both with natural language and gestures, thr...", "weights": [[8, 12, 1.0]]}, "2020.cikm_conference-2020.106": {"snippet": "...sure user performance and learning. Our experimental results reveal that conversational interfaces can improve user engagement, augment user long-term memorability, and alleviate user cognitive load. ...", "weights": [[8, 12, 1.0], [114, 118, 1.0], [139, 143, 1.0], [182, 186, 1.0]]}, "2010.sigirconf_conference-2010.210": {"snippet": "...ging user interaction and collaboration for improving multilingual information access in digital libraries", "weights": [[8, 12, 1.0], [13, 24, 1.0]]}, "2018.wsdm_conference-2018.87": {"snippet": "...acro interaction between a user and an item, the user actually performs a sequence of micro behaviors, which indicate how the user locates the item, what activities the user conducts on the item (e.g....", "weights": [[8, 19, 1.0], [30, 34, 1.0], [52, 56, 1.0], [129, 133, 1.0], [172, 176, 1.0]]}, "1988.jasis_journal-ir0volumeA39A1.4": {"snippet": "... the user interface, strategies for effective human-computer interaction", "weights": [[8, 12, 1.0], [64, 75, 1.0]]}, "2018.sigirconf_conference-2018.7": {"snippet": "...g of user behavior is important for advancing information retrieval systems. Existing work focuses on modeling and predicting single interaction events, such as clicks. In this paper, we for the first...", "weights": [[8, 12, 1.0], [136, 147, 1.0]]}, "2007.sigirconf_conference-2007.35": {"snippet": "... the interaction logs of advanced search engine users (and those not so advanced) to better understand how these user groups search. The results show that there are marked differences in the queries, ...", "weights": [[8, 19, 1.0], [116, 120, 1.0]]}, "2018.chiir_conference-2018.53": {"snippet": "... and Interaction System (IRIS): A Toolkit for Investigating Information Retrieval and Interaction Activities\n\n\n ABSTRACT\n\uf020In this demo we present IRIS, an open-source framework that provides a set of ...", "weights": [[8, 19, 1.0], [89, 100, 1.0]]}, "L02-1323": {"snippet": "... and User Interaction: Building a {H}ungarian {W}ord{N}et", "weights": [[8, 12, 1.0], [13, 24, 1.0]]}, "2008.sigirconf_conference-2008.5": {"snippet": "...with user simulations, which characterize the relevance of documents that a user might encounter with different browsing strategies. With broader implications to IR, this work provides a case study of...", "weights": [[8, 12, 1.0], [79, 83, 1.0]]}, "N13-1041": {"snippet": "...ning User Relations from Online Discussions using Sentiment Analysis and Probabilistic Matrix Factorization\n\n\n Advances in sentiment analysis have enabled extraction of user relations implied in onlin...", "weights": [[8, 12, 1.0], [172, 176, 1.0]]}, "P03-2021": {"snippet": "...nced user interface. Three main goals of the system are: (1) provide a user with control over the summarization process, (2) support exploration of the document set with the summary as the staring poi...", "weights": [[8, 12, 1.0], [74, 78, 1.0]]}, "2020.lrec-1.44": {"snippet": "... the user's environment using a deep learning method based on Convolutional Neural Networks (CNN) and names this objects accordingly. Using Augmented Reality (AR) the objects are superimposed with 3D ...", "weights": [[8, 12, 1.0]]}, "2004.clef_workshop-2004w.32": {"snippet": "...ving Interaction with the User in Cross-Language Question Answering through Relevant Domains and Syntactic Semantic Patterns\n\n\n The iCLEF 2004 experiment at the University of Alicante has focused on h...", "weights": [[8, 19, 1.0], [29, 33, 1.0]]}, "2010.jasis_journal-ir0volumeA61A2.1": {"snippet": "...zing user interaction with the ViewFinder video retrieval system", "weights": [[8, 12, 1.0], [13, 24, 1.0]]}, "2016.sigirconf_conference-2016.7": {"snippet": "...sive user study to measure user satisfaction with intelligent assistants, and simultaneously record all user interactions. Second, we show that the dialogue style of interaction makes it necessary to ...", "weights": [[8, 12, 1.0], [30, 34, 1.0], [107, 111, 1.0], [169, 180, 1.0]]}, "2001.jasis_journal-ir0volumeA52A1.4": {"snippet": "...nced user interaction and value-adding of electronic documents: An empirical evaluation", "weights": [[8, 12, 1.0], [13, 24, 1.0]]}}}
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We show that current systems for FEVER are vulnerable to three categories of realistic challenges for fact-checking -multip...", "weights": [[8, 17, 1.0], [167, 177, 1.0], [197, 205, 1.0]]}, "2004.sigirconf_conference-2004.74": {"snippet": "...uage retrieval quality when translation resources have poor lexical coverage. We demonstrate that crosslanguage retrieval is viable for European languages with no translation resources at all; that tr...", "weights": [[8, 17, 1.0], [115, 124, 1.0], [148, 157, 1.0]]}, "N15-3023": {"snippet": "...ring System using Multiple Information Source and Open Type Answer Merge\n\n\n This paper presents a multi-strategy and multisource question answering (QA) system that can use multiple strategies to both...", "weights": [[8, 14, 1.0], [21, 29, 1.0], [156, 162, 1.0], [176, 184, 1.0]]}, "2006.ipm_journal-ir0volumeA42A4.0": {"snippet": "...tion retrieval system; Evaluation; Information search process; Decision making; Multi-criteria model; Analytic hierarchy process\nBackgroundInformation retrieval (IR) systems play an important role in ...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [154, 163, 1.0]]}, "1999.jasis_journal-ir0volumeA50A7.4": {"snippet": "...L: A Multiple Engine Information Retrieval and Visualization System", "weights": [[8, 16, 1.0], [36, 45, 1.0], [64, 70, 1.0]]}, "2001.sigirconf_conference-2001.12": {"snippet": "...ross multiple intermediate languages and fusing the results. Such a technique removes the error, raising the effectiveness of the tested retrieval system, up to and possibly above the level expected, ...", "weights": [[8, 16, 1.0], [30, 39, 1.0], [140, 149, 1.0], [150, 156, 1.0]]}, "2020.sigirconf_conference-2020.42": {"snippet": "...n On Multiple IR Systems\n\n\n Information retrieval (IR) ranking models in production systems continually evolve in response to user feedback, insights from research, and new developments. Rather than i...", "weights": [[8, 16, 1.0], [43, 52, 1.0]]}, "2004.clef_workshop-2004.5": {"snippet": "...ased retrieval is highly effective; that fusion of multiple retrievals is helpful in bilingual retrieval; and, that reliance on language similarity in lieu of translation can outperform a high perform...", "weights": [[8, 17, 1.0], [54, 62, 1.0], [98, 107, 1.0]]}, "2007.ntcir_workshop-2007evia.4": {"snippet": "... the challenges to both Geographic Information Retrieval from text and of evaluation of the results of geographic information retrieval. This poster enumerates some of these challenges to evaluation a...", "weights": [[8, 18, 1.0], [50, 59, 1.0], [129, 138, 1.0], [177, 187, 1.0]]}, "N03-5002": {"snippet": "...tion Retrieval Systems as Integration Platforms for Language Technologies", "weights": [[8, 17, 1.0]]}, "2002.ntcir_workshop-2002.58": {"snippet": "...tion Retrieval System\n\n\n We participated in Web retrieval tasks in order to investigate the integration of multiple retrieval results. We also examined the possibility of using a general retrieval sys...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [51, 60, 1.0], [110, 118, 1.0], [119, 128, 1.0], [190, 199, 1.0], [200, 206, 1.0]]}, "1994.sigirconf_conference-94.18": {"snippet": "...n of Multiple Ranked Retrieval Systems", "weights": [[8, 16, 1.0], [24, 33, 1.0]]}, "2020.ictir_conference-2020.7": {"snippet": "...ment Retrieval with Multiple Queries\n\n\n The merits of using multiple queries representing the same information need to improve retrieval effectiveness have recently been demonstrated in several studie...", "weights": [[8, 17, 1.0], [23, 31, 1.0], [63, 71, 1.0], [130, 139, 1.0]]}, "2016.sigirconf_conference-2016.230": {"snippet": "...s or challenges on the topics of the workshop. The programme aims to encourage interaction and discussion to develop a shared understanding of the research challenges facing the area of medical inform...", "weights": [[8, 18, 1.0], [159, 169, 1.0]]}, "H01-1033": {"snippet": "...uage Retrieval using Backoff Translation\n\n\n The limited coverage of available translation lexicons can pose a serious challenge in some cross-language information retrieval applications. We present tw...", "weights": [[8, 17, 1.0], [166, 175, 1.0]]}, "2014.ntcir_conference-2014.13": {"snippet": "...tion retrieval is desirable. In the studies on spoken document retrieval systems, much research has been presented focusing on the task of spoken term detection (STD), which locates a given search ter...", "weights": [[8, 17, 1.0], [66, 75, 1.0]]}, "2012.mir_conference-2012.69": {"snippet": "...h: a multiple search strategy-supportive multimedia retrieval system", "weights": [[8, 16, 1.0], [55, 64, 1.0], [65, 71, 1.0]]}, "1989.sigirjournals_journal-ir0volumeA23A34.11": {"snippet": "...pert System based control system for the control of an Automated Storage and Retrieval System (AS/RS) is created. The control system includes several unique elements, including the hierarchical partit...", "weights": [[8, 14, 1.0], [29, 35, 1.0], [80, 89, 1.0], [90, 96, 1.0], [129, 135, 1.0]]}, "1974.sigirjournals_journal-ir0volumeA9A1.4": {"snippet": "...User-System Interface in Retrieval System", "weights": [[8, 14, 1.0], [28, 37, 1.0], [38, 44, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0], [196, 202, 1.0]]}, "2022.semeval-1.200": {"snippet": "...ased System for Multilingual Named Entity Recognition\n\n\n The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple l...", "weights": [[8, 14, 1.0], [193, 201, 1.0], [202, 211, 1.0]]}, "2006.spire_conference-2006.20": {"snippet": "A Multiple Criteria Approach for Information Retrieval", "weights": [[2, 10, 1.0], [45, 54, 1.0]]}, "N06-2016": {"snippet": "...eech Retrieval for a Spontaneous Conversational Speech Collection\n\n\n Cross-language retrieval of spontaneous speech combines the challenges of working with noisy automated transcription and language t...", "weights": [[8, 17, 1.0], [87, 96, 1.0], [132, 142, 1.0]]}, "2021.sdp-1.2": {"snippet": "...tion Retrieval with Stochastic Text Generation\n\n\n One of the challenges in information retrieval (IR) is the vocabulary mismatch problem, which happens when the terms between queries and documents are...", "weights": [[8, 17, 1.0], [64, 74, 1.0], [90, 99, 1.0]]}, "W08-0708": {"snippet": "...tion system, Pa-raMor. Our improved segmentation model permits multiple morpheme boundaries in a single word. To prepare ParaMor to effectively apply the new agglutinative segmentation model, two heur...", "weights": [[8, 14, 1.0], [66, 74, 1.0]]}, "2020.ecir_conference-20202.87": {"snippet": "...tion Retrieval for Health from Heterogeneous Content Types and Languages (SIIRH)\n\n\n The application of Information Retrieval (IR) and deep learning strategies to explore the vast amount of rapidly gro...", "weights": [[8, 17, 1.0], [66, 75, 1.0], [118, 127, 1.0]]}, "2001.sigirconf_conference-2001.52": {"snippet": "...s of Multiple-Evidence Combinations for Retrieval Strategies", "weights": [[8, 16, 1.0], [43, 52, 1.0]]}, "2020.coling-main.232": {"snippet": "...tive Retrieval over Multiple Knowledge Sources\n\n\n In order to facilitate natural language understanding, the key is to engage commonsense or background knowledge. However, how to engage commonsense ef...", "weights": [[8, 17, 1.0], [23, 31, 1.0]]}, "D19-5816": {"snippet": "...tion Retrieval for Multi-Hop Question Answering\n\n\n Multi-hop question answering (QA) requires an information retrieval (IR) system that can find multiple supporting evidence needed to answer the quest...", "weights": [[8, 17, 1.0], [112, 121, 1.0], [127, 133, 1.0], [148, 156, 1.0]]}, "2004.clef_workshop-2004w.12": {"snippet": "...ased retrieval is highly effective; that fusion of multiple retrievals is helpful in bilingual retrieval; and, that reliance on language similarity in lieu of translation can outperform a high perform...", "weights": [[8, 17, 1.0], [54, 62, 1.0], [98, 107, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...hose retrieval system anafyses the content of images to locate images of people in a particular state. By integrating the two representation schemes into a single retrieval system, it would be hoped t...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [166, 175, 1.0], [176, 182, 1.0]]}, "2003.clef_workshop-2003.37": {"snippet": "...tion retrieval system evaluations is carried out by the TREC initiative (TREC = Text REtrieval Conference) 1 and has been repeated every year since 1992, up until 1998 it was not possible to directly ...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [88, 97, 1.0]]}, "2016.ntcir_conference-2016.90": {"snippet": "...tent Retrieval (SCR) subtask. We investigated the use of multiple ASR hypotheses (words) and subword units (syllables) for improving retrieval effectiveness. We also compared the retrieval effectivene...", "weights": [[8, 17, 1.0], [60, 68, 1.0], [136, 145, 1.0], [182, 191, 1.0]]}, "2010.ipm_journal-ir0volumeA46A5.4": {"snippet": "...ines multiple evidence to address challenges in answer ranking and answer merging. The hypotheses of the paper are that: (1) the framework effectively combines multiple evidence for identifying answer...", "weights": [[8, 16, 1.0], [37, 47, 1.0], [163, 171, 1.0]]}, "2002.ntcir_workshop-2002.34": {"snippet": "...tent Retrieval Experiments at ULIS\n\n\n Given the growing number of patents filed in multiple countries, users are interested in retrieving patents across languages. We propose a multilingual patent ret...", "weights": [[8, 17, 1.0], [86, 94, 1.0], [156, 165, 1.0], [200, 209, 1.0]]}, "2009.clef_workshop-2009w.33": {"snippet": "...tion Retrieval based on Multiple Indexes\n\n\n In this paper we present the technical details of the retrieval system with which we participated at the CLEF09 Ad-hoc TEL task. We present a retrieval appr...", "weights": [[8, 17, 1.0], [27, 35, 1.0], [101, 110, 1.0], [111, 117, 1.0], [189, 198, 1.0]]}, "A97-1049": {"snippet": "... and Retrieval System Using Information Extraction\n\n\n In this paper, we describe our multilingual (or cross-linguistic) information browsing and retrieval system, which is aimed at monolingual users w...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [148, 157, 1.0], [158, 164, 1.0]]}, "2017.sigirconf_conference-2017.239": {"snippet": "...arch challenges that need to be addressed for effectively utilising social media data (e.g., microblogs) for aiding disaster relief operations.\nResearch challenges:We have identified the following cha...", "weights": [[8, 18, 1.0], [156, 166, 1.0], [200, 210, 1.0]]}, "2003.sigirjournals_journal-ir0volumeA37A1.2": {"snippet": "Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002\n\n\n Summary...", "weights": [[0, 10, 1.0], [26, 35, 1.0], [127, 136, 1.0]]}, "2009.clef_workshop-2009w.88": {"snippet": "Multiple Retrieval Models and Regression Models for Prior Art Search\n\n\n This paper presents the system called PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS) realized at the Humboldt Un...", "weights": [[0, 8, 1.0], [9, 18, 1.0], [96, 102, 1.0], [149, 158, 1.0]]}, "2013.cikm_conference-2013.368": {"snippet": "...from multiple disciplines, including database systems, data mining, information retrieval, human-computer interaction, and knowledge or information management. The commonly raised questions may includ...", "weights": [[8, 16, 1.0], [83, 92, 1.0]]}, "2016.sigirconf_conference-2016.8": {"snippet": "... for Retrieval-Based Human-Computer Conversation System\n\n\n ABSTRACTTo establish an automatic conversation system between humans and computers is regarded as one of the most hardcore problems in comput...", "weights": [[8, 17, 1.0], [52, 58, 1.0], [109, 115, 1.0]]}, "2015.sigirconf_conference-2015.23": {"snippet": "...rate multiple social networks. Towards this end, we first proposed a novel model for data missing completion by seamlessly exploring the knowledge from multiple sources. We then developed a robust mul...", "weights": [[8, 16, 1.0], [155, 163, 1.0], [200, 208, 1.0]]}, "2015.wwwconf_conference-2015c.203": {"snippet": "...lity Challenges in the Multilingual Web for Visually-Impaired Users\n\n\n ABSTRACTThe Web is an open network accessed by people across countries, languages and cultures, irrespective of their functional ...", "weights": [[8, 18, 1.0], [146, 155, 1.0]]}, "2015.fire_workshop-2015w.6": {"snippet": "...mary challenges in process the short message is identifying languages. Therefore, the language identification is not restricted to a language but also to multiple languages. The task is to label the w...", "weights": [[8, 18, 1.0], [63, 72, 1.0], [157, 165, 1.0], [166, 175, 1.0]]}, "1985.sigirconf_conference-85.5": {"snippet": "...ling Multiple Databases in Document Retrieval", "weights": [[8, 16, 1.0], [39, 48, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "...tion Retrieval and Speech 3 to money or rivers, does fly refer to airplanes, insects, or trousers? In theory, if a system could automatically figure out the sense of the word intended by the user, ret...", "weights": [[8, 17, 1.0], [118, 124, 1.0], [200, 209, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...text retrieval systems. CLEF is a continuation and expansion of the cross-language system evaluation activity for European languages begun in 1997 with the track for Cross-Language Information Retriev...", "weights": [[8, 17, 1.0], [86, 92, 1.0], [126, 135, 1.0], [196, 205, 1.0]]}, "2004.clef_workshop-2004.22": {"snippet": "...tion retrieval system to document languages other than English, and completing the multilingual task comprising four languages: English, French, Russian and Finnish. Our retrieval system is based on t...", "weights": [[8, 17, 1.0], [18, 24, 1.0], [37, 46, 1.0], [120, 129, 1.0], [173, 182, 1.0], [183, 189, 1.0]]}, "2015.fire_workshop-2015w.12": {"snippet": "...ased system with voting approach for code-mixed query word labeling at word-level as part of our participation in the shared task on Mixed Script Information Retrieval at Forum for Information Retriev...", "weights": [[8, 14, 1.0], [161, 170, 1.0], [196, 205, 1.0]]}, "W19-6602": {"snippet": "...tion Retrieval in Low-Resource Settings\n\n\n The goal of cross-lingual information retrieval (CLIR) is to find relevant documents written in languages different from that of the query. Robustness to tra...", "weights": [[8, 17, 1.0], [84, 93, 1.0], [142, 151, 1.0]]}, "2018.sigirconf_conference-2018.224": {"snippet": "...tion Retrieval, specifically adhoc (query-based) retrieval, that allows multiple sources of information to be combined into a single result set . Fusion can be collection-based, system-based ( multipl...", "weights": [[8, 17, 1.0], [52, 61, 1.0], [75, 83, 1.0], [181, 187, 1.0], [196, 204, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ased retrieval problem. Similarly to the distribution of future wins, the retrieval framework considers multiple possible concept-based document representations for each document resulting in multiple...", "weights": [[8, 17, 1.0], [77, 86, 1.0], [107, 115, 1.0], [195, 203, 1.0]]}, "R19-1049": {"snippet": "...tion Retrieval?\n\n\n We present a novel approach to automatic question answering that does not depend on the performance of an information retrieval (IR) system and does not require training data. We ev...", "weights": [[8, 17, 1.0], [140, 149, 1.0], [155, 161, 1.0]]}, "2003.ismir_conference-2003.48": {"snippet": "... for multiple pitch candidates in a music information retrieval system", "weights": [[8, 16, 1.0], [57, 66, 1.0], [67, 73, 1.0]]}, "2019.ecir_conference-20192.12": {"snippet": "...sage Retrieval in Multimodal Question Answering?\n\n\n Abstract. Passage retrieval for multimodal question answering, spanning natural language processing and computer vision, is a challenging task, part...", "weights": [[8, 17, 1.0], [73, 82, 1.0]]}, "1992.trec_conference-1992.15": {"snippet": "...Text Retrieval System", "weights": [[8, 17, 1.0], [18, 24, 1.0]]}, "2006.cikm_conference-2006.24": {"snippet": "...tion retrieval systems face a range of unique challenges, not the least of which is the sheer scale of the data that must be handled. Also specific to web retrieval is that queries may be a mix of Boo...", "weights": [[8, 17, 1.0], [49, 59, 1.0], [158, 167, 1.0]]}, "2002.cikm_conference-2002.1": {"snippet": "...tion retrieval systems\n\n\n AbstractImplementing scalable information retrieval systems requires the design and development of efficient methods to ingest data from multiple sources, search and retrieve...", "weights": [[8, 17, 1.0], [71, 80, 1.0], [166, 174, 1.0]]}, "2000.clef_workshop-2000.11": {"snippet": "...uage Retrieval for the CLEF Collections - Comparing Multiple Methods of Retrieval", "weights": [[8, 17, 1.0], [55, 63, 1.0], [75, 84, 1.0]]}, "2017.chiir_conference-2017.72": {"snippet": "...ilar challenges, namely interactive search in video and lifelogging repositories. In Section 1.1, we first introduce current challenges in the video retrieval domain. Section 1.2 then introduces open ...", "weights": [[8, 18, 1.0], [128, 138, 1.0], [152, 161, 1.0]]}, "2004.ir_journal-ir0volumeA7A12.6": {"snippet": "...ning Multiple Strategies for Effective Monolingual and Cross-Language Retrieval", "weights": [[8, 16, 1.0], [73, 82, 1.0]]}, "L08-1002": {"snippet": "...ning Multiple Models for Speech Information Retrieval\n\n\n In this article we present a method for combining different information retrieval models in order to increase the retrieval performance in a Sp...", "weights": [[8, 16, 1.0], [47, 56, 1.0], [132, 141, 1.0], [174, 183, 1.0]]}, "1973.sigirconf_conference-73.10": {"snippet": "...n of Languages for the Implementation of Information Storage and Retrieval Systems", "weights": [[8, 17, 1.0], [68, 77, 1.0]]}, "2022.mia-1.11": {"snippet": "...erse languages. In this task, we adapted two large-scale cross-lingual openretrieval QA datasets in 14 typologically diverse languages, and newly annotated openretrieval QA data in 2 underrepresented ...", "weights": [[8, 17, 1.0], [128, 137, 1.0]]}, "1997.trec_conference-1997.56": {"snippet": "...uage Retrieval with the Twenty-One system", "weights": [[8, 17, 1.0], [38, 44, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "1998.jasis_journal-ir0volumeA49A13.3": {"snippet": "...from Multiple Collection Information Retrieval Systems", "weights": [[8, 16, 1.0], [40, 49, 1.0]]}, "2008.clef_workshop-2008w.92": {"snippet": "...tion system, Morfessor, the combined system identifies the morphemes of all five Challenge languages at recall scores higher than those of any other system which competed in Morpho Challenge. In Turki...", "weights": [[8, 14, 1.0], [40, 46, 1.0], [94, 103, 1.0], [152, 158, 1.0]]}, "2009.wsdm_conference-2009.1": {"snippet": "Challenges in building large-scale information retrieval systems: invited talk", "weights": [[0, 10, 1.0], [47, 56, 1.0]]}}}
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One of the many challenges facing those involved in publishing is t...", "weights": [[8, 13, 1.0]]}, "L06-1261": {"snippet": "{F}re{P}: An electronic tool for extracting frequency information of phonological units from {P}ortuguese written text", "weights": []}, "L14-1239": {"snippet": "...tten Texts of {G}erman School Students for the Purpose of Automatic Spelling Error Classification", "weights": [[8, 13, 1.0]]}, "L06-1274": {"snippet": "Reconsidering Language Identification for Written Language Resources", "weights": []}, "2020.coling-main.208": {"snippet": "Automatic Detection of Machine Generated Text: A Critical Survey\n\n\n Text generative models (TGMs) excel in producing text that matches the style of human language reasonably well. 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Discerning ironic intent exclusively from the words and syntax comprising texts (e.g., tweets, forum posts) is therefore not always possible: additional contextual information about the speaker a...", "weights": [[8, 18, 1.0], [82, 87, 1.0]]}, "2004.riao_conference-2004.60": {"snippet": "Information retrieval on mixed written and spoken documents", "weights": []}, "P11-1100": {"snippet": "Automatically Evaluating Text Coherence Using Discourse Relations\n\n\n We present a novel model to represent and assess the discourse coherence of text. Our model assumes that coherent text implicitly f...", "weights": []}, "2012.wsdm_conference-2012.11": {"snippet": "...ust: discerning multi-faceted trust in a connected world\n\n\n ABSTRACTTraditionally, research about trust assumes a single type of trust between users. However, trust, as a social concept, inherently ha...", "weights": [[8, 18, 1.0]]}, "2020.emnlp-main.191": {"snippet": "Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading\n\n\n Document interpretation and dialog understanding are the two major challenges for conversational machine rea...", "weights": []}, "2022.in2writing-1.8": {"snippet": "...tten texts (generated based on the first line of texts and poems of classic authors) and how the stylistic qualities of these texts are rated. Participants read 9 AI-based continuations and either 9 h...", "weights": [[8, 13, 1.0], [52, 57, 1.0], [129, 134, 1.0]]}, "C69-1601": {"snippet": "...utch texts written by bilinguals I) (speaking French and Dutch), with Dutch texts regarded as STANDARD WRITTEN DUTCH. The attention was focussed on French loan-words appearing in both types of texts a...", "weights": [[8, 13, 1.0], [79, 84, 1.0], [196, 201, 1.0]]}, "2020.emnlp-main.736": {"snippet": "{UNION}: {A}n {U}nreferenced {M}etric for {E}valuating {O}pen-ended {S}tory {G}eneration\n\n\n Despite the success of existing referenced metrics (e.g., BLEU and MoverScore), they correlate poorly with h...", "weights": []}, "2022.in2writing-1.11": {"snippet": "Unmet Creativity Support Needs in Computationally Supported Creative Writing\n\n\n Large language models (LLMs) enabled by the datasets and computing power of the last decade have recently gained popular...", "weights": []}, "2016.ipm_journal-ir0volumeA52A3.0": {"snippet": "...sive texts written by university students. Results show that students improved the quality of their written texts using this system, and also rated the experience very highly. 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Furthermore,...", "weights": [[8, 13, 1.0], [34, 39, 1.0]]}, "C12-2029": {"snippet": "Token Level Identification of Linguistic Code Switching\n\n\n Typically native speakers of Arabic mix dialectal Arabic and Modern Standard Arabic in the same utterance. This phenomenon is known as lingui...", "weights": []}, "2022.naacl-main.113": {"snippet": "{SKILL}: Structured Knowledge Infusion for Large Language Models\n\n\n Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, it is ...", "weights": []}, "2020.lrec-1.779": {"snippet": "Parallel Corpus for {J}apanese Spoken-to-Written Style Conversion\n\n\n With the increase of automatic speech recognition (ASR) applications, spoken-to-written style conversion that transforms spoken-sty...", "weights": []}, "X98-1028": {"snippet": "A Text-Extraction Based Summarizer\n\n\n We present an automated method of generating human-readable summaries from a variety of text documents including newspaper articles, business reports, government ...", "weights": []}, "R15-1012": {"snippet": "...nown texts and only the text of unknown authorship would be considered as written by the author, if it exceeds the average of similarity obtained between texts written by him. The experiments were rea...", "weights": [[8, 13, 1.0], [157, 162, 1.0]]}, "2017.jasis_journal-ir0volumeA68A6.12": {"snippet": "Don't be deceived: Using linguistic analysis to learn how to discern online review authenticity", "weights": []}, "L12-1362": {"snippet": "...tten Texts: a Corpus-based Analysis", "weights": [[8, 13, 1.0]]}, "2021.ranlp-1.82": {"snippet": "...ixed texts to a standard language. Most of the user written code-mixed content in social media remains unprocessed due to the unavailability of linguistic resource such as parallel corpus. This paper ...", "weights": [[8, 13, 1.0]]}, "N19-1068": {"snippet": "...hort Texts\n\n\n Authorship verification is the problem of inferring whether two texts were written by the same author. For this task, unmasking is one of the most robust approaches as of today with the ...", "weights": [[8, 13, 1.0], [81, 86, 1.0]]}, "2022.ltedi-1.4": {"snippet": "Measuring Harmful Sentence Completion in Language Models for {LGBTQIA}+ Individuals\n\n\n Warning: This paper contains examples of language that some people may find offensive or upsetting. Current langu...", "weights": []}, "2020.cl-2.8": {"snippet": "...tten texts. However, in this work, we show that stylometry is limited against machine-generated misinformation. Whereas humans speak differently when trying to deceive, LMs generate stylistically cons...", "weights": [[8, 13, 1.0]]}, "N19-1396": {"snippet": "...mary texts using a language model conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarization datasets. We investigate how they achieve this performan...", "weights": [[8, 13, 1.0]]}, "C04-1048": {"snippet": "Generating Discourse Structures for Written Text\n\n\n This paper presents a system for automatically generating discourse structures from written text. The system is divided into two levels: sentence-le...", "weights": []}, "2013.ipm_journal-ir0volumeA49A1.13": {"snippet": "... The texts are written for different uses and thus for people having different levels of expertise on the domain. Texts intended for professionals may not be understandable at all by a lay person, and...", "weights": [[8, 13, 1.0], [117, 122, 1.0]]}, "2008.jasis_journal-ir0volumeA59A10.2": {"snippet": "Resistance and the underlife: Informal written literacies and their relationship to human information behavior", "weights": []}, "W17-1602": {"snippet": "...d in texts with relatively high accuracy. However, it is difficult to explain what gives rise to these results and there are many possible confounding factors, such as the domain, genre, and target au...", "weights": [[8, 13, 1.0]]}, "2022.findings-acl.263": {"snippet": "A Natural Diet: Towards Improving Naturalness of Machine Translation Output\n\n\n Machine translation (MT) evaluation often focuses on accuracy and fluency, without paying much attention to translation s...", "weights": []}, "W16-3617": {"snippet": "The Role of Discourse Units in Near-Extractive Summarization\n\n\n Although human-written summaries of documents tend to involve significant edits to the source text, most automated summarizers are extra...", "weights": []}, "2022.insights-1.10": {"snippet": "What {GPT} Knows About Who is Who\n\n\n Coreference resolution -which is a crucial task for understanding discourse and language at large -has yet to witness widespread benefits from large language model...", "weights": []}}}
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We use u...", "weights": [[8, 20, 1.0], [35, 45, 1.0], [46, 54, 1.0], [121, 133, 1.0], [154, 164, 1.0], [174, 184, 1.0], [185, 193, 1.0], [202, 214, 1.0]]}, "P19-1322": {"snippet": "...ding Learning\n\n\n Unsupervised word embeddings have become a popular approach of word representation in NLP tasks. However there are limitations to the semantics represented by unsupervised embeddings,...", "weights": [[8, 16, 1.0], [20, 32, 1.0], [106, 109, 1.0], [179, 191, 1.0]]}, "P10-1040": {"snippet": "...Semi-Supervised Learning\n\n\n If we take an existing supervised NLP system, a simple and general way to improve accuracy is to use unsupervised word representations as extra word features. We evaluate B...", "weights": [[8, 18, 1.0], [19, 27, 1.0], [54, 64, 1.0], [65, 68, 1.0], [132, 144, 1.0]]}, "2015.mir_conference-2015.8": {"snippet": "...able Unsupervised Distance Learning in Image Retrieval Tasks", "weights": [[8, 20, 1.0], [30, 38, 1.0]]}, "W10-2904": {"snippet": "...ect, Unsupervised Learning for Context-Sensitive Languages\n\n\n A central problem for NLP is grammar induction: the development of unsupervised learning algorithms for syntax. In this paper we present a...", "weights": [[8, 20, 1.0], [21, 29, 1.0], [87, 90, 1.0], [132, 144, 1.0], [145, 153, 1.0]]}, "2021.emnlp-main.716": {"snippet": "...f an unsupervised as well as a weakly supervised variant of our framework on STS, BUCC and Tatoeba benchmark tasks. The proposed unsupervised sentence embedding framework outperforms even supervised s...", "weights": [[8, 20, 1.0], [41, 51, 1.0], [132, 144, 1.0], [191, 201, 1.0]]}, "D19-1067": {"snippet": "...sing Unsupervised Open Information Extraction Models\n\n\n We propose a novel supervised open information extraction (Open IE) framework that leverages an ensemble of unsupervised Open IE systems and a s...", "weights": [[8, 20, 1.0], [78, 88, 1.0], [167, 179, 1.0]]}, "Q15-1010": {"snippet": "...ined unsupervised model. When the induced constraints are combined with a fully unsupervised model, the resulting model challenges existing lightly supervised featurebased models as well as unsupervis...", "weights": [[8, 20, 1.0], [83, 95, 1.0], [151, 161, 1.0], [193, 205, 1.0]]}, "S19-2137": {"snippet": "...k 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection\n\n\n We present a neural network based approach of transfer learning for offensive language detection. For our system,...", "weights": [[8, 18, 1.0], [23, 35, 1.0], [45, 53, 1.0], [145, 153, 1.0]]}, "2020.cikm_conference-2020.480": {"snippet": "...s in Unsupervised Learning", "weights": [[8, 20, 1.0], [21, 29, 1.0]]}, "W18-3010": {"snippet": "...ious NLP tasks. While common sentence representation methods are unsupervised in nature, recently, an approach for learning universal sentence representation in a supervised setting was presented in (...", "weights": [[8, 11, 1.0], [68, 80, 1.0], [118, 126, 1.0], [166, 176, 1.0]]}, "C12-1175": {"snippet": "Semi-supervised Representation Learning for Domain Adaptation using Dynamic Dependency Networks\n\n\n Recently, various unsupervised representation learning approaches have been investigated to produce a...", "weights": [[5, 15, 1.0], [31, 39, 1.0], [117, 129, 1.0], [145, 153, 1.0]]}, "2010.ismir_conference-2010.90": {"snippet": "Supervised and Unsupervised Web Document Filtering Techniques to Improve Text-Based Music Retrieval", "weights": [[0, 10, 1.0], [15, 27, 1.0]]}, "2021.acl-long.154": {"snippet": "...bust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual {NLP}\n\n\n Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks. However, annotated da...", "weights": [[8, 20, 1.0], [82, 85, 1.0], [99, 107, 1.0], [160, 170, 1.0], [171, 174, 1.0]]}, "2010.coria_conference-2010.10": {"snippet": "Classification Supervis\u00e9e de Questions : R\u00f4le de l'Expansion S\u00e9mantique", "weights": []}, "2021.eacl-main.299": {"snippet": "...sing Unsupervised and Semi-supervised Learning\n\n\n Spoken language is different from the written language in its style and structure. Disfluencies that appear in transcriptions from speech recognition ...", "weights": [[8, 20, 1.0], [30, 40, 1.0], [41, 49, 1.0]]}, "2012.coria_conference-2012.17": {"snippet": "Regroupement de relations pour l'extraction d'information non supervis\u00e9e", "weights": []}, "D14-1012": {"snippet": "...Semi-supervised Learning\n\n\n Recent work has shown success in using continuous word embeddings learned from unlabeled data as features to improve supervised NLP systems, which is regarded as a simple s...", "weights": [[8, 18, 1.0], [19, 27, 1.0], [148, 158, 1.0], [159, 162, 1.0]]}, "2021.sustainlp-1.17": {"snippet": "Unsupervised Contextualized Document Representation\n\n\n Several NLP tasks need the effective representation of text documents. Arora et al., 2017 demonstrate that simple weighted averaging of word vect...", "weights": [[0, 12, 1.0], [63, 66, 1.0]]}, "2022.naacl-main.49": {"snippet": "...meta-learning and the common approaches. Then we summarize task construction settings and application of meta-learning for various NLP problems and review the development of meta-learning in NLP commu", "weights": [[8, 16, 1.0], [113, 121, 1.0], [134, 137, 1.0], [182, 190, 1.0], [194, 197, 1.0]]}, "2006.spire_conference-2006.9": {"snippet": "...lied supervised and unsupervised learning techniques. The results obtained show that for a considerable amount of cases supervised learning is a good option, however through unsupervised learning we f...", "weights": [[8, 18, 1.0], [23, 35, 1.0], [36, 44, 1.0], [123, 133, 1.0], [134, 142, 1.0], [177, 189, 1.0], [190, 198, 1.0]]}, "2014.coria_conference-2014.11": {"snippet": "Techniques d'apprentissage supervis\u00e9 pour l'extraction d'\u00e9v\u00e9nements TimeML en anglais et fran\u00e7ais", "weights": []}, "2004.coria_conference-2004.13": {"snippet": "Apprentissage non-supervis\u00e9 pour la segmentation automatique de textes", "weights": []}, "2016.jeptalnrecital-poster.21": {"snippet": "...brid unsupervised classification tool)\n\n\n Inbenta d\u00e9veloppe un outil de classification non-supervis\u00e9e hybride qui allie \u00e0 la fois les statistiques et la puissance de notre lexique inspir\u00e9 de la Th\u00e9ori...", "weights": [[8, 20, 1.0]]}, "2018.mir_conference-2018.9": {"snippet": "...g by Learning from Very Few Examples: Recent Progress on Unsupervised, Semi-Supervised and Supervised Deep Learning Approaches", "weights": [[8, 16, 1.0], [60, 72, 1.0], [79, 89, 1.0], [94, 104, 1.0], [110, 118, 1.0]]}, "F12-3001": {"snippet": "Segmentation non supervis{\\'e}e : le cas du mandarin (Unsupervized Word Segmentation) [in {F}rench]", "weights": []}, "2021.eacl-main.20": {"snippet": "...dard supervised training is performed on the resulting training set. For several tasks and languages, PET outperforms supervised training and strong semi-supervised approaches in lowresource settings ", "weights": [[8, 18, 1.0], [121, 131, 1.0], [157, 167, 1.0]]}, "N16-1162": {"snippet": "Learning Distributed Representations of Sentences from Unlabelled Data\n\n\n Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is...", "weights": [[0, 8, 1.0], [74, 86, 1.0], [99, 107, 1.0], [171, 174, 1.0]]}, "N19-1255": {"snippet": "...tive Learning for Unsupervised Document Embedding\n\n\n Unsupervised document representation learning is an important task providing pre-trained features for NLP applications. 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We quantified their ...", "weights": [[8, 21, 1.0], [149, 158, 1.0]]}, "2006.sigirconf_conference-2006.90": {"snippet": "...eval effectiveness of our retrieval method, compared to pure BM25, varies from collection to collection. We present an experimental evaluation of our method and show that the gains achieved over BM25 ...", "weights": [[8, 21, 1.0]]}, "2010.spire_conference-2010.2": {"snippet": "Incremental Algorithms for Effective and Efficient Query Recommendation", "weights": []}, "2016.clef_conference-2016w.112": {"snippet": "...eval effectiveness.", "weights": [[8, 21, 1.0]]}, "2001.sigirconf_conference-2001.31": {"snippet": "...ding effectiveness begins to explain why many search engines have adopted link methods. It also opens a rich new area for effectiveness improvement, where traditional methods fail.", "weights": [[8, 21, 1.0], [125, 138, 1.0]]}, "2015.sigirconf_conference-2015.116": {"snippet": "Using Term Location Information to Enhance Probabilistic Information Retrieval\n\n\n ABSTRACTNouns are more important than other parts of speech in information retrieval and are more often found near the...", "weights": []}, "2012.wwwconf_workshop-2012tempweb.3": {"snippet": "...ring algorithm for web page terms extraction that, assuming a recent browsing history per user, takes into account the freshness of keywords in the current page as means of shifting users interests. W...", "weights": [[8, 17, 1.0]]}, "2015.cikm_conference-2015.188": {"snippet": "...sion algorithm to adapt BM25 to query length, and demonstrate its effectiveness on several representative TREC collections.", "weights": [[8, 17, 1.0], [69, 82, 1.0]]}, "2018.sigirconf_conference-2018.224": {"snippet": "...eval effectiveness by exploiting the chorus effect .In this tutorial, we will show that advances in fusion are directly applicable to current open problems in Information Retrieval, and that much can ...", "weights": [[8, 21, 1.0]]}, "2020.ecir_conference-20202.4": {"snippet": "...cant effectiveness differences between them, including Lucene's often maligned approximation of document length. As an added benefit, our empirical approach takes advantage of databases for rapid IR p...", "weights": [[8, 21, 1.0]]}, "2018.wsdm_conference-2018.47": {"snippet": "...eval effectiveness of configurations that have been examined so far. 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Term weighting is responsible for computing the relevance scores ...", "weights": [[8, 21, 1.0]]}, "2013.cikm_conference-2013.169": {"snippet": "...eval effectiveness approaching that of the best learning-to-rank methods over equivalent features sets. Other measures based on language modeling and divergence from randomness can outperform BM25 in ...", "weights": [[8, 21, 1.0]]}, "2007.cikm_conference-2007.19": {"snippet": "... the effectiveness of hits and salsa\n\n\n ABSTRACTThis paper compares the effectiveness of two well-known query-dependent link-based ranking algorithms,\"HyperlinkInduced Topic Search\" (HITS) and the \"St...", "weights": [[8, 21, 1.0], [75, 88, 1.0]]}, "2011.ir_journal-ir0volumeA14A5.1": {"snippet": "Improving document clustering using Okapi BM25 feature weighting", "weights": []}, "2021.mrqa-1.16": {"snippet": "Simple and Efficient ways to Improve {REALM}\n\n\n Dense retrieval has been shown to be effective for Open Domain Question Answering, surpassing sparse retrieval methods like BM25. One such model, REALM,...", "weights": []}, "2018.ictir_conference-2018.36": {"snippet": "... and effectiveness compared to BM25. This means, in our belief, that StatBM25 as a statistically smoothed and normalized variant to BM25, might eventually lead to discoveries of useful new statistic m...", "weights": [[8, 21, 1.0]]}, "2015.clef_conference-2015w.172": {"snippet": "... the effectiveness of information retrieval system and develop search engines for searching on the web looking for health related documents[1].", "weights": [[8, 21, 1.0]]}, "2009.clef_workshop-2009w.147": {"snippet": "...ming algorithm and we normalized words removing some characters that made more evident the word mismatch problem. The use of these simple heuristics allowed us to increases of 106% the MAP value, comp...", "weights": [[8, 17, 1.0]]}, "2012.trec_conference-2012.15": {"snippet": "DCU@TRECMed 2012: Using adhoc Baselines for Domain-Specific Retrieval\n\n\n This paper describes the first participation of DCU in the TREC Medical Records Track (TRECMed) 2012. We performed initial expe...", "weights": []}, "2007.sigirconf_conference-2007.78": {"snippet": "Enhancing relevance scoring with chronological term rank\n\n\n ABSTRACTWe introduce a new relevance scoring technique that enhances existing relevance scoring schemes with term position information. This...", "weights": []}, "2008.clef_workshop-2008w.138": {"snippet": "UFRGS@CLEF2008: Using Association Rules for Cross-Language Information Retrieval\n\n\n For UFRGS's participation on the TEL task at CLEF2008, our aim was to assess the validity of using algorithms for mi...", "weights": []}, "2011.sigirconf_conference-2011.117": {"snippet": "Ad hoc IR: not much room for improvement\n\n\n ABSTRACTRanking function performance reached a plateau in 1994. The reason for this is investigated. 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The scale and shape of this T...", "weights": []}, "2020.sigirconf_workshop-2020birds.11": {"snippet": "BM25-FIC: Information Content-based Field Weighting for BM25F", "weights": []}, "2019.spire_conference-2019.5": {"snippet": "BM25 Beyond Query-Document Similarity", "weights": []}, "2014.sigirconf_conference-2014.95": {"snippet": "...eves effectiveness and efficiency on par with custom-built retrieval engines, but provides many additional advantages, including cleaner query semantics, a simpler architecture, built-in support for e...", "weights": [[8, 21, 1.0]]}, "2011.sigirconf_conference-2011.121": {"snippet": "When documents are very long, BM25 fails!\n\n\n ABSTRACTWe reveal that the Okapi BM25 retrieval function tends to overly penalize very long documents. To address this problem, we present a simple yet eff...", "weights": []}, "2008.spire_conference-2008.7": {"snippet": "Term Impacts as Normalized Term Frequencies for BM25 Similarity Scoring", "weights": []}, "2005.ipm_journal-ir0volumeA41A2.4": {"snippet": "...etic algorithm. The learned weights are then tested on an evaluation set of queries. Structure weighted vector space inner product and structure weighted probabilistic retrieval show an about 5% impro...", "weights": [[8, 17, 1.0]]}, "2017.ecir_workshop-2017smerp.10": {"snippet": "Sensing Microblog for Effective Information Extractions\n\n\n The SMERP 2017 data challenge track given a set of tweets posted during Italy earthquake. For retrieving more relevance information respect t...", "weights": []}, "2017.ictir_conference-2017.52": {"snippet": "... the effectiveness of these two implementations on several standard test collections. Positive, significant results are obtained. Potentials of this BN-based framework in addition to its verified effe...", "weights": [[8, 21, 1.0], [199, 212, 1.0]]}, "2013.cikm_conference-2013.113": {"snippet": "...ning algorithm with a tree representation derived from the syntactic trees of questions and passages connected by relational tags, where the latter are again provided by the means of automatic classif...", "weights": [[8, 17, 1.0]]}, "2005.trec_conference-2005.27": {"snippet": "... vs. Effectiveness in Terabyte-Scale Information Retrieval\n\n\n We describe indexing and retrieval techniques that are suited to perform terabyte-scale information retrieval tasks on a standard desktop ...", "weights": [[8, 21, 1.0]]}, "2013.sigirconf_conference-2013.120": {"snippet": "...rall effectiveness of most modern scoring functions. Constraints on the term frequency result in successive normalizations on the raw TF, each one satisfying one or more properties. 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The purpose of our paper is ...", "weights": [[8, 13, 1.0], [63, 72, 1.0], [100, 105, 1.0], [106, 115, 1.0]]}, "1994.riao_conference-1994.45": {"snippet": "...ntic Retrieval of Pictures and Video", "weights": [[8, 17, 1.0], [34, 39, 1.0]]}, "2008.clef_workshop-2008.122": {"snippet": "...tion Retrieval System for Video Classification", "weights": [[8, 17, 1.0], [29, 34, 1.0]]}, "2017.mir_conference-2017.67": {"snippet": "...odal Video Retrieval with the 2017 IMOTION System", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2022.naacl-main.247": {"snippet": "...MN}: Video-grounded Neural Module Networks for Video-Grounded Dialogue Systems\n\n\n Neural module networks (NMN) have achieved success in image-grounded tasks such as Visual Question Answering (VQA) on ...", "weights": [[8, 13, 1.0], [50, 55, 1.0], [139, 144, 1.0]]}, "2012.mir_conference-2012.40": {"snippet": "...ased image and video retrieval", "weights": [[8, 13, 1.0], [18, 23, 1.0], [24, 33, 1.0]]}, "2013.sigirconf_conference-2013.1": {"snippet": "...M on image and video analysis and retrieval, including IBM Multimedia Analysis and Retrieval System (IMARS), and show recent results for semanticbased classification and retrieval. We conclude with fu...", "weights": [[8, 13, 1.0], [18, 23, 1.0], [37, 46, 1.0], [86, 95, 1.0], [173, 182, 1.0]]}, "2004.sigirconf_conference-2004.124": {"snippet": "... for image and video retrieval\n\n\n ABSTRACTThis paper investigates the level of metadata accuracy required for image filters to be valuable to users. Access to large digital image and video collections...", "weights": [[8, 13, 1.0], [18, 23, 1.0], [24, 33, 1.0], [113, 118, 1.0], [176, 181, 1.0], [186, 191, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "... and video segment retrieval. The shot retrieval and the video segment model re-use the probability of relevance and a language modeling ranking function respectively, basing themselves on decades of ...", "weights": [[8, 13, 1.0], [22, 31, 1.0], [42, 51, 1.0], [60, 65, 1.0]]}, "O01-3002": {"snippet": "... new image compression formats like MPEG2, there still are many video programs encoded in older formats. Thus, video OCR is indispensable for content-based video retrieval. This paper proposes a simpl...", "weights": [[8, 13, 1.0], [67, 72, 1.0], [114, 119, 1.0], [159, 164, 1.0], [165, 174, 1.0]]}, "2011.cikm_conference-2011.274": {"snippet": "... for video indexing and retrieval\n\n\n ABSTRACTVideo retrieval can be done by ranking the samples according to their probability scores that were predicted by classifiers. It is often possible to improv...", "weights": [[8, 13, 1.0], [27, 36, 1.0], [54, 63, 1.0]]}, "2008.mir_conference-2008.61": {"snippet": "... for image and video annotation", "weights": [[8, 13, 1.0], [18, 23, 1.0]]}, "2014.wwwconf_conference-2014c.243": {"snippet": "...ough image flows extracted from blogs and TV to understand societal behaviors. Both blogs and TV generate a huge amount of image flows every day. Comparing such large image flows is one of our biggest...", "weights": [[8, 13, 1.0], [126, 131, 1.0], [170, 175, 1.0]]}, "2006.jasis_journal-ir0volumeA57A4.12": {"snippet": "Image clustering for the exploration of video sequences", "weights": [[0, 5, 1.0], [40, 45, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "Image Retrieval by Hypertext Links\n\n\n This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, t...", "weights": [[0, 5, 1.0], [6, 15, 1.0], [70, 79, 1.0]]}, "2003.sigirconf_conference-2003.20": {"snippet": "...stic image retrieval model\n\n\n ABSTRACTThe main conclusion from the metrics-based evaluation of video retrieval systems at TREC's video track is that non-interactive image retrieval from general collec...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [98, 103, 1.0], [104, 113, 1.0], [132, 137, 1.0], [168, 173, 1.0], [174, 183, 1.0]]}, "2012.sigirconf_conference-2012.220": {"snippet": "...n an image's visual contents and its semantic interpretation) required a clever use of textual metadata (in addition to information extracted from the image's pixel contents) to make image and video r...", "weights": [[8, 13, 1.0], [154, 159, 1.0], [186, 191, 1.0], [196, 201, 1.0], [202, 211, 1.0]]}, "2009.tois_journal-ir0volumeA27A3.4": {"snippet": "...time video clip search\n\n\n Recently, video clips have become very popular online. The massive influx of video clips has created an urgent need for video search engines to facilitate retrieving relevant...", "weights": [[8, 13, 1.0], [39, 44, 1.0], [106, 111, 1.0], [149, 154, 1.0]]}, "2010.sigirconf_conference-2010.204": {"snippet": "...tion retrieval approaches to web search, interactive and faceted retrieval, mining of temporal patterns from news and query logs, cross-lingual information retrieval, graph-based retrieval methods, an...", "weights": [[8, 17, 1.0], [68, 77, 1.0], [159, 168, 1.0], [182, 191, 1.0]]}, "2000.sigirconf_conference-2000.49": {"snippet": "...mage retrieval, searching for images, search behavior, image database searching.\nAIMSThe objective of this study is to investigate how different types of queries affect searching behavior in an image ...", "weights": [[8, 17, 1.0], [58, 63, 1.0], [197, 202, 1.0]]}, "2020.sigirconf_conference-2020.8": {"snippet": "...ding image search, video search, cross lingual retrieval, and QA based on machine reading and comprehension (MRC). We will also share in this talk the core technologies that are crucial to the develop...", "weights": [[8, 13, 1.0], [22, 27, 1.0], [50, 59, 1.0]]}, "2001.cikm_conference-2001.65": {"snippet": "...ased retrieval of image and video data, less attention was received to the content-based retrieval of audio data. In this paper, we propose an approach to retrieve MP3 music objects based on their con...", "weights": [[8, 17, 1.0], [21, 26, 1.0], [31, 36, 1.0], [92, 101, 1.0]]}, "1999.sigirconf_conference-99.68": {"snippet": "... the video or use is to go to desired point in the video. The video summarization starts with a shot-boundary detection, which is used to create several video summaries such as storyboard with its cor...", "weights": [[8, 13, 1.0], [54, 59, 1.0], [65, 70, 1.0], [156, 161, 1.0]]}, "2003.sigirconf_conference-2003.66": {"snippet": "... the video; and OND (Object Number Deviation), the standard deviation of the number of objects per frame across the video. Each of these indexes provides a unique perspective on video content. A novel...", "weights": [[8, 13, 1.0], [119, 124, 1.0], [181, 186, 1.0]]}, "2006.mir_workshop-2006.34": {"snippet": "...king image and video retrieval: an overview", "weights": [[8, 13, 1.0], [18, 23, 1.0], [24, 33, 1.0]]}, "2013.ijmir_journal-ir0volumeA2A3.4": {"snippet": "...s by video", "weights": [[8, 13, 1.0]]}, "2011.sigirconf_conference-2011.72": {"snippet": "...hile image or video contents are more appropriate. In this paper, we introduce a scheme that is able to enrich text answers with image and video information. Our scheme investigates a rich set of tech...", "weights": [[8, 13, 1.0], [17, 22, 1.0], [132, 137, 1.0], [142, 147, 1.0]]}, "2002.trec_conference-2002.96": {"snippet": "Video Indexing and Retrieval at UMD\n\n\n Our team from the University of Maryland and INSA de Lyon participated in the feature extraction evaluation with overlay text features and in the search evaluati...", "weights": [[0, 5, 1.0], [19, 28, 1.0]]}, "2005.sigirjournals_journal-ir0volumeA39A1.11": {"snippet": "Video information retrieval using objects and ostensive relevance feedback", "weights": [[0, 5, 1.0], [18, 27, 1.0]]}, "1999.ecir_conference-1999.1": {"snippet": "... for Video Content Representation and Retrieval", "weights": [[8, 13, 1.0], [41, 50, 1.0]]}, "W12-0105": {"snippet": "...\n As video contents continue to expand, it is increasingly important to properly annotate videos for effective search, mining and retrieval purposes. While the idea of annotating images with keywords ...", "weights": [[8, 13, 1.0], [133, 142, 1.0]]}, "2010.iir_workshop-2010.17": {"snippet": "... for Image Retrieval", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2012.cikm_conference-2012.133": {"snippet": "... for video retrieval\n\n\n ABSTRACTMost of the state-of-art approaches to Query-by-Example (QBE) video retrieval are based on the Bag-of-visual-Words (BovW) representation of visual content. It, however,...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [97, 102, 1.0], [103, 112, 1.0]]}, "2020.ijmir_journal-ir0volumeA9A2.0": {"snippet": "...g in image and video retrieval", "weights": [[8, 13, 1.0], [18, 23, 1.0], [24, 33, 1.0]]}, "1999.sigirconf_conference-99.23": {"snippet": "... and video systems.\nKeywordsMMIR, content-based indexing/retrieval, image indexing/retrieval\nINTRODUCTIONEffective content-based retrieval of imagery and video can be performed at three abstraction le...", "weights": [[8, 13, 1.0], [60, 69, 1.0], [71, 76, 1.0], [86, 95, 1.0], [132, 141, 1.0], [157, 162, 1.0]]}, "2011.ecir_conference-2011.71": {"snippet": "Video Retrieval Based on Words-of-Interest Selection", "weights": [[0, 5, 1.0], [6, 15, 1.0]]}, "2011.tist_journal-ir0volumeA2A2.1": {"snippet": "... and retrieval. We mainly focus on two application domains: image/video annotation and content-based image retrieval. We first briefly introduce the principle of active learning and then we analyze th...", "weights": [[8, 17, 1.0], [63, 68, 1.0], [69, 74, 1.0], [104, 109, 1.0], [110, 119, 1.0]]}, "2020.wwwconf_conference-2020c.21": {"snippet": "...nail Image and Video Clip from a Video via Bullet Screen", "weights": [[8, 13, 1.0], [18, 23, 1.0], [36, 41, 1.0]]}, "2006.sigirconf_conference-2006.119": {"snippet": "...ased video retrieval: does video's semantic visual feature matter?\n\n\n ABSTRACTA new shot level video browsing method based on semantic visual features (e.g., car, mountain, and fire) is proposed to fa...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [30, 35, 1.0], [98, 103, 1.0]]}, "2018.mir_conference-2018.67": {"snippet": "...cale Image-to-Video Retrieval", "weights": [[8, 13, 1.0], [17, 22, 1.0], [23, 32, 1.0]]}, "2016.sigirconf_conference-2016.203": {"snippet": "...mula retrieval, math expressions in text formats like L A T E X are parsed to generate Symbol Layout Trees . For image-based formula retrieval, image processing techniques are used to create a graph-b...", "weights": [[8, 17, 1.0], [116, 121, 1.0], [136, 145, 1.0], [147, 152, 1.0]]}, "2014.wwwconf_conference-2014.14": {"snippet": "...als (image, video,  news, etc.)  are returned to users. For instance, many such search engines return to users both images and web documents as answers to the query \"flower\". Aggregated search has bec...", "weights": [[8, 13, 1.0], [15, 20, 1.0]]}, "2013.cikm_conference-2013.105": {"snippet": "...f an image or a frame. In this paper, we propose a multimodal feature fusion framework which can model any given image-description pair using semantically meaningful features. This framework is traine...", "weights": [[8, 13, 1.0], [116, 121, 1.0]]}, "2006.sigirconf_conference-2006.151": {"snippet": "...ased video retrieval\n\n\n ABSTRACTA new shot level video retrieval system that supports semantic visual features (e.g., car, mountain, and fire) browsing is developed to facilitate content-based retriev...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [52, 57, 1.0], [58, 67, 1.0], [196, 205, 1.0]]}, "P18-3005": {"snippet": "...rch, retrieval, annotation and description of medical images.", "weights": [[8, 17, 1.0]]}, "2013.cikm_conference-2013.258": {"snippet": "...shot video retrieval using content and concepts\n\n\n ABSTRACTRecent research in video retrieval has been successful at finding videos when the query consists of tens or hundreds of sample relevant video...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [81, 86, 1.0], [87, 96, 1.0]]}, "2017.chiir_conference-2017.72": {"snippet": "...r an image (\"An image is worth a thousand words.\") but much worse for a video segment consisting of many images. In fact, most video retrieval tools operate on images (i.e., keyframes of shots) and pr...", "weights": [[8, 13, 1.0], [19, 24, 1.0], [75, 80, 1.0], [130, 135, 1.0], [136, 145, 1.0]]}, "2002.sigirconf_conference-2002.46": {"snippet": "Video retrieval using an MPEG-7 based inference network\n\n\n ABSTRACTThis work proposes a model for video retrieval based upon the inference network model. The document network is constructed using vide...", "weights": [[0, 5, 1.0], [6, 15, 1.0], [98, 103, 1.0], [104, 113, 1.0], [196, 201, 1.0]]}, "2005.mir_workshop-2005.31": {"snippet": "...ased image retrieval", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.16": {"snippet": "...ised video retrieval: application of implicit feedback and semantic user profiles\n\n\n AbstractA challenging problem in the user profiling domain is to create profiles of users of retrieval systems. Thi...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [181, 190, 1.0]]}, "2000.riao_conference-2000.23": {"snippet": "Image Retrieval: Content versus Context", "weights": [[0, 5, 1.0], [6, 15, 1.0]]}, "2005.sigirconf_conference-2005.72": {"snippet": "...ased retrieval of images and video", "weights": [[8, 17, 1.0], [32, 37, 1.0]]}, "2020.mir_conference-2020.2": {"snippet": "...ical Image Retrieval: Applications and Resources", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "1995.tois_journal-ir0volumeA13A4.0": {"snippet": "A Video Retrieval and Sequencing System", "weights": [[2, 7, 1.0], [8, 17, 1.0]]}, "2007.riao_conference-2007.37": {"snippet": "Image Retrieval Using a Multilingual Ontology", "weights": [[0, 5, 1.0], [6, 15, 1.0]]}, "2011.mir_conference-2011.63": {"snippet": "Image and video browsing with a cylindrical 3D storyboard", "weights": [[0, 5, 1.0], [10, 15, 1.0]]}, "2022.acl-long.241": {"snippet": "...nge, Image Retrieval from Contextual Descriptions (IMAGECODE). In particular, models are tasked with retrieving the correct image from a set of 10 minimally contrastive candidates based on a contextua...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [127, 132, 1.0]]}, "2015.mir_conference-2015.2": {"snippet": "Image Classification and Retrieval are ONE", "weights": [[0, 5, 1.0], [25, 34, 1.0]]}, "2011.tois_journal-ir0volumeA29A4.4": {"snippet": "...cate video retrieval. Different from existing nearduplicate video retrieval approaches which are based on video-content similarity, we explore the correlation between two videos. The intuition is that...", "weights": [[8, 13, 1.0], [14, 23, 1.0], [63, 68, 1.0], [69, 78, 1.0], [109, 114, 1.0]]}, "2002.trec_conference-2002.97": {"snippet": "Video Retrieval Using Global Features in Keyframes", "weights": [[0, 5, 1.0], [6, 15, 1.0]]}, "2012.mir_conference-2012.1": {"snippet": "... and video retrieval: towards multimedia summarization", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2000.wwwjournals_journal-ir0volumeA3A4.4": {"snippet": "...dio, video and image Web queries", "weights": [[8, 13, 1.0], [18, 23, 1.0]]}, "T75-2030": {"snippet": "On Retrieving Information From Visual Images", "weights": []}, "2015.ijmir_journal-ir0volumeA4A1.0": {"snippet": "...e on video retrieval", "weights": [[8, 13, 1.0], [14, 23, 1.0]]}, "2014.ecir_conference-2014.34": {"snippet": "Video Clip Retrieval by Graph Matching", "weights": [[0, 5, 1.0], [11, 20, 1.0]]}}}
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It consists of approx. 67,000 sentences with over 2 milli...", "weights": [[8, 17, 1.0], [28, 34, 1.0], [35, 46, 1.0], [92, 98, 1.0], [99, 110, 1.0]]}, "2015.ipm_journal-ir0volumeA51A2.2": {"snippet": "...amed entity recognition and linking for tweets", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2008.cikm_conference-2008.14": {"snippet": "Web-scale named entity recognition\n\n\n ABSTRACTAutomatic recognition of named entities such as people, places, organizations, books, and movies across the entire web presents a number of challenges, bo...", "weights": [[0, 3, 1.0], [16, 22, 1.0], [23, 34, 1.0], [56, 67, 1.0], [161, 164, 1.0]]}, "I13-1160": {"snippet": "...ical documents, based on clinical entity recognition techniques. 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The approach is based on named-entity recognition applied in a set of search results, and on a graph of documents and identi...", "weights": [[8, 11, 1.0], [110, 116, 1.0], [117, 128, 1.0], [183, 192, 1.0]]}, "2020.tist_journal-ir0volumeA11A5.3": {"snippet": "... for Entity Recommendation and Document Ranking in Web Search", "weights": [[8, 14, 1.0], [54, 57, 1.0]]}, "N15-1024": {"snippet": "Entity Linking for Spoken Language\n\n\n Research on entity linking has considered a broad range of text, including newswire, blogs and web documents in multiple languages. However, the problem of entity...", "weights": [[0, 6, 1.0], [50, 56, 1.0], [133, 136, 1.0], [137, 146, 1.0], [194, 200, 1.0]]}, "W04-1217": {"snippet": "...ical Entity Recognition: From Syntax to the Web\n\n\n We describe a machine learning system for the recognition of names in biomedical texts. The system makes extensive use of local and syntactic feature...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [47, 50, 1.0], [100, 111, 1.0]]}, "W17-1412": {"snippet": "...amed Entity Challenge in Slavic Languages. The Challenge targets recognizing mentions of named entities in web documents, their normalization/lemmatization, and cross-lingual matching. The Challenge w...", "weights": [[8, 14, 1.0], [110, 113, 1.0], [114, 123, 1.0]]}, "L10-1310": {"snippet": "...amed Entity Recognition", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "W19-3709": {"snippet": "...amed Entity Challenge in Slavic languages. The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking. The Challenge was organized as part of t...", "weights": [[8, 14, 1.0], [100, 103, 1.0], [104, 113, 1.0]]}, "2014.sigirjournals_journal-ir0volumeA48A2.4": {"snippet": "...'14: Entity Recognition and Disambiguation Challenge\n\n\n AbstractIn this paper we overview the 2014 Entity Recognition and Disambiguation Challenge (ERD'14), which took place from March to June 2014 an...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [102, 108, 1.0], [109, 120, 1.0]]}, "E12-2015": {"snippet": "...amed Entity Recognition and Disambiguation Extraction Tools\n\n\n Named Entity Extraction is a mature task in the NLP field that has yielded numerous services gaining popularity in the Semantic Web commu...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [72, 78, 1.0], [194, 197, 1.0]]}, "2020.aacl-main.22": {"snippet": "...amed Entity Recognition in Multi-level Contexts\n\n\n Named entity recognition is a critical task in the natural language processing field. Most existing methods for this task can only exploit contextual...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [60, 66, 1.0], [67, 78, 1.0]]}, "D07-1074": {"snippet": "...amed Entity Disambiguation Based on {W}ikipedia Data\n\n\n This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a...", "weights": [[8, 14, 1.0], [108, 119, 1.0]]}, "W02-2035": {"snippet": "...amed Entity Recognition", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...Wide Web based collection. Rather than considering complex visual recognition algorithms, the model presented is based on combining evidence of the text content and hypertext structure of the Web. The...", "weights": [[8, 11, 1.0], [69, 80, 1.0], [195, 198, 1.0]]}, "I08-5015": {"snippet": "...amed Entity Recognition(NER) is the task of identifying and classifying tokens in a text document into predefined set of classes. In this paper we show our experiments with various feature combination...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2009.cikm_workshop-2009pavlad.8": {"snippet": "...amed entity recognition\n\n\n ABSTRACTThe preservation of the privacy of persons mentioned in text requires the ability to automatically recognize and identify names. Named entity recognition is a mature...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [173, 179, 1.0], [180, 191, 1.0]]}, "I13-1162": {"snippet": "...from Web Text\n\n\n Recognizing Chinese Named entities from the Web is challenging, due to the lack of labeled data and differences between Chinese and English. We propose a semisupervised approach which...", "weights": [[8, 11, 1.0], [64, 67, 1.0]]}, "2020.textgraphs-1.3": {"snippet": "...amed Entity Recognition from Visual Documents\n\n\n Named entity recognition (NER) from visual documents, such as invoices, receipts or business cards, is a critical task for visual document understandin...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [39, 48, 1.0], [58, 64, 1.0], [65, 76, 1.0], [95, 104, 1.0]]}, "2011.sigirjournals_journal-ir0volumeA45A1.11": {"snippet": "...fine Entity Retrieval by generalizing the expert finding problem to any entity. In Entity Retrieval the goal is to rank entities according to their relevance to a query (e.g., \"Countries where I can p...", "weights": [[8, 14, 1.0], [75, 81, 1.0], [86, 92, 1.0]]}, "2010.trec_conference-2010.16": {"snippet": "...T at Entity Track TREC 2010\n\n\n This paper gives an overview of our work for related entity finding which is proposed in TREC 2010 Entity Track. The goal of the Entity Track is to find the entities rel...", "weights": [[8, 14, 1.0], [87, 93, 1.0], [133, 139, 1.0], [163, 169, 1.0]]}, "2014.wwwconf_conference-2014.38": {"snippet": "...amed entity recognition for idiosyncratic web collections\n\n\n ABSTRACTNamed Entity Recognition (NER) plays an important role in a variety of online information management tasks including text categoriz...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [45, 48, 1.0], [78, 84, 1.0], [85, 96, 1.0]]}, "2016.cikm_conference-2016.309": {"snippet": "...s in Web Document with DBLP", "weights": [[8, 11, 1.0]]}, "2016.clef_conference-2016w.3": {"snippet": "... For entity recognition, the highest performance was achieved on the EMEA corpus, with an overall F-measure of 0.702 for plain entities recognition and 0.529 for normalized entity recognition. For ent...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [139, 150, 1.0], [176, 182, 1.0], [183, 194, 1.0], [200, 206, 1.0]]}, "2004.cikm_conference-2004.16": {"snippet": "...t of Web documents. The method is a departure from traditional approaches to named entity recognition in that: 1) it does not require any start-up seed names or training; 2) it does not encode any dom...", "weights": [[8, 11, 1.0], [12, 21, 1.0], [86, 92, 1.0], [93, 104, 1.0]]}, "2014.sigirconf_conference-2014.223": {"snippet": "...'14: entity recognition and disambiguation challenge", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "C12-1004": {"snippet": "...s on web-scale has been been hampered by the computational cost of NLP over massive text corpora. We present SpeedRead (SR), a named entity recognition pipeline that runs at least 10 times faster than...", "weights": [[8, 11, 1.0], [136, 142, 1.0], [143, 154, 1.0]]}, "I05-1058": {"snippet": "...amed entity recognition is how to control annotation errors introduced at every iteration. In this paper, we present several heuristics for reducing such errors using external resources such as WordNe...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2022.findings-acl.329": {"snippet": "...shot Entity Recognition in Document Images: A Label-aware Sequence-to-Sequence Framework\n\n\n Entity recognition is a fundamental task in understanding document images. Traditional sequence labeling fra...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [95, 101, 1.0], [102, 113, 1.0]]}, "2014.airs_conference-2014.24": {"snippet": "...amed entity recognition from the newspaper articles. Thus, this study designs and develops a crime named entity recognition based on machine learning approaches that extract nationalities, weapons, an...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [108, 114, 1.0], [115, 126, 1.0]]}, "2015.wwwconf_conference-2015c.71": {"snippet": "...m of entity recognition and disambiguation. In this paper, we focus on the task of entity recognition in Web text to address the challenges of entity correctness, completeness and emergence that exist...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [86, 92, 1.0], [93, 104, 1.0], [108, 111, 1.0], [146, 152, 1.0]]}, "W16-1302": {"snippet": "...uate entity mentions on the web. We propose a method for discovering web KBs and our preliminary results suggest that web KBs allow linking to entities that can be found on the web, but may not merit ", "weights": [[8, 14, 1.0], [31, 34, 1.0], [72, 75, 1.0], [121, 124, 1.0], [180, 183, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "...d to web search, some of these assumptions became less tenable (the web is not curated; pages are more variable in length and quality; and users of web search engines are less experienced and tend to ...", "weights": [[8, 11, 1.0], [71, 74, 1.0], [151, 154, 1.0]]}, "2014.sigirconf_conference-2014.192": {"snippet": "...ying documents by means of keywords, entities, and categories. Users simply type words, which are automatically mapped onto appropriate suggestions for entities and categories. Based on named-entity d...", "weights": [[8, 17, 1.0], [195, 201, 1.0]]}, "W19-3644": {"snippet": "...amed Entity Recognition with Commonsense Knowledge", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "W03-0430": {"snippet": "...amed Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons", "weights": [[8, 14, 1.0], [15, 26, 1.0], [81, 84, 1.0]]}, "2021.findings-acl.158": {"snippet": "...amed Entity Evaluation on {E}nglish Web Texts\n\n\n Named Entity Recognition (NER) is a key Natural Language Processing task. However, most existing work on NER targets flat named entities (NEs) and igno...", "weights": [[8, 14, 1.0], [39, 42, 1.0], [58, 64, 1.0], [65, 76, 1.0]]}, "W12-6327": {"snippet": "...amed Entity Recognition and Disambiguation System\n\n\n In this paper we describe an integrated approach for named entity recognition and disambiguation in Chinese. The proposed method relies on named en...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [115, 121, 1.0], [122, 133, 1.0], [201, 207, 1.0]]}, "I11-1096": {"snippet": "...amed entity recognition in Chinese news comments on the Web. We propose to leverage the entity information in the referred news article to improve named entity recognition in the news comments. Three ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [59, 62, 1.0], [91, 97, 1.0], [156, 162, 1.0], [163, 174, 1.0]]}, "2006.clef_workshop-2006.57": {"snippet": "...t of Entity Recognition on Answer Validation", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "O15-1015": {"snippet": "...stic documents such as news articles. Since they have not been trained to deal with informal documents, the performance drops on Web documents, which may lack sentence structure and contain colloquial...", "weights": [[8, 17, 1.0], [96, 105, 1.0], [132, 135, 1.0], [136, 145, 1.0]]}, "2019.sigirconf_conference-2019.185": {"snippet": "Name Entity Recognition with Policy-Value Networks", "weights": [[5, 11, 1.0], [12, 23, 1.0]]}, "W16-3924": {"snippet": "...amed Entity Recognition in Tweets\n\n\n In this paper, we describe the DeepNNNER entry to The 2nd Workshop on Noisy User-generated Text (WNUT) Shared Task #2: Named Entity Recognition in Twitter. Our sha...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [165, 171, 1.0], [172, 183, 1.0]]}, "2014.sigirconf_workshop-2014erd.19": {"snippet": "...n in Entity Recognition and Disambiguation Challenge 2014. We present WebSAIL Wikifier, an entity recognition and disambiguation system that identifies and links textual mentions to their referent ent...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [94, 100, 1.0], [101, 112, 1.0]]}, "L08-1253": {"snippet": "...amed Entity Recognition for Digitised Historical Texts", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2019.sigirconf_conference-2019.253": {"snippet": "...icit Entity Recognition, Classification and Linking in Tweets", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "2020.coling-main.36": {"snippet": "...Text Web Named Entity Recognition\n\n\n HTML tags are typically discarded in free text Named Entity Recognition from Web pages. We investigate whether these discarded tags might be used to improve NER pe...", "weights": [[8, 11, 1.0], [18, 24, 1.0], [25, 36, 1.0], [93, 99, 1.0], [100, 111, 1.0], [117, 120, 1.0]]}, "C18-1168": {"snippet": "... for Entity Recognition of Novel Classes\n\n\n In this reproduction paper, we replicate and extend several past studies on transfer learning for entity recognition. In particular, we are interested in en...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [145, 151, 1.0], [152, 163, 1.0], [201, 207, 1.0]]}, "L14-1328": {"snippet": "...amed Entity Recognition on {T}urkish Tweets", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "L16-1525": {"snippet": "... the Web TextFull linkage to Linked Open Data (WTF-LOD) dataset intended for large-scale evaluation of named entity recognition (NER) systems. First, we present the process of collecting data from the...", "weights": [[8, 11, 1.0], [112, 118, 1.0], [119, 130, 1.0]]}, "2017.clef_conference-2017w.49": {"snippet": "Entity Recognition and Language Identification with FELTS", "weights": [[0, 6, 1.0], [7, 18, 1.0]]}, "2013.sigirconf_conference-2013.164": {"snippet": "...s on web 2.0 e-commerce web sites, even though it is effective in some cases. Additionally, it does not involve entity resolution in information retrieval for products.To apply entity resolution to en...", "weights": [[8, 11, 1.0], [27, 30, 1.0], [115, 121, 1.0], [180, 186, 1.0]]}, "W07-1712": {"snippet": "...amed Entity Recognition for {U}krainian: A Resource-Light Approach\n\n\n Named entity recognition (NER) is a subtask of information extraction (IE) which can be used further on for different purposes. In...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [79, 85, 1.0], [86, 97, 1.0]]}, "2004.sigirconf_conference-2004.37": {"snippet": "...amed entity recognition using machine learning\n\n\n ABSTRACTIn this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named...", "weights": [[8, 14, 1.0], [15, 26, 1.0]]}, "W11-0810": {"snippet": "The Web is not a {PERSON}, Berners-Lee is not an {ORGANIZATION}, and {A}frican-{A}mericans are not {LOCATIONS}: An Analysis of the Performance of Named-Entity Recognition\n\n\n Most work on evaluation of...", "weights": [[4, 7, 1.0], [152, 158, 1.0], [159, 170, 1.0]]}, "I08-1020": {"snippet": "...ther documents potentially referring to the same individual. The algorithm is informed by the results of two different summarization strategies and an offthe-shelf named entity recognition component. ...", "weights": [[8, 17, 1.0], [173, 179, 1.0], [180, 191, 1.0]]}, "2021.acl-long.216": {"snippet": "...amed Entity Recognition\n\n\n Named entity recognition (NER) is a wellstudied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The ...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [36, 42, 1.0], [43, 54, 1.0]]}, "2021.bsnlp-1.15": {"snippet": "...amed Entity Challenge in Slavic languages. The tasks involve recognizing mentions of named entities in Web documents, normalization of the names, and crosslingual linking. The Challenge covers six lan...", "weights": [[8, 14, 1.0], [106, 109, 1.0], [110, 119, 1.0]]}, "S15-1028": {"snippet": "...icit entity mentions and introduce the problem of implicit entity recognition (IER) in clinical documents. We propose a solution to IER that leverages entity definitions from a knowledge base to creat...", "weights": [[8, 14, 1.0], [62, 68, 1.0], [69, 80, 1.0], [99, 108, 1.0], [154, 160, 1.0]]}, "2008.wwwconf_conference-2008.102": {"snippet": "... the web entities of a given entity type based on their web instances, although the global attribute schema is usually highly desired in web entity instances integration and web object extraction. To ...", "weights": [[8, 11, 1.0], [32, 38, 1.0], [59, 62, 1.0], [140, 143, 1.0], [144, 150, 1.0], [177, 180, 1.0]]}, "2021.bsnlp-1.14": {"snippet": "...gual Web documents in Slavic languages with rich inflection. Our solution takes advantage of large collections of both unstructured and structured documents. The former serve as data for unsupervised ...", "weights": [[8, 11, 1.0], [12, 21, 1.0], [150, 159, 1.0]]}, "P03-2031": {"snippet": "...amed Entity (NE) tagged corpus from the web to be used for learning of Named Entity Recognition systems. We use an NE list and an web search engine to collect web documents which contain the NE instan...", "weights": [[8, 14, 1.0], [43, 46, 1.0], [80, 86, 1.0], [87, 98, 1.0], [133, 136, 1.0], [162, 165, 1.0], [166, 175, 1.0]]}, "2010.trec_conference-2010.45": {"snippet": "...side documents instead of documents or web pages themselves has become an active topic in both commercial search systems and academic information retrieval research. Our method of entity retrieval is ...", "weights": [[8, 17, 1.0], [29, 38, 1.0], [42, 45, 1.0], [183, 189, 1.0]]}, "2011.wwwconf_conference-2011c.32": {"snippet": "...dent entity extraction from web search query logs\n\n\n ABSTRACTQuery logs of a Web search engine have been increasingly used as a vital source for data mining. This paper presents a study on largescale ...", "weights": [[8, 14, 1.0], [31, 34, 1.0], [80, 83, 1.0]]}, "D15-1058": {"snippet": "...amed entity recognition with document-specific {KB} tag gazetteers\n\n\n We consider a novel setting for Named Entity Recognition (NER) where we have access to document-specific knowledge base tags. Thes...", "weights": [[8, 14, 1.0], [15, 26, 1.0], [111, 117, 1.0], [118, 129, 1.0]]}, "2013.cikm_conference-2013.334": {"snippet": "...s in web pages\n\n\n ABSTRACTWe propose a system that determines the salience of entities within web documents. Many recent advances in commercial search engines leverage the identification of entities i...", "weights": [[8, 11, 1.0], [97, 100, 1.0], [101, 110, 1.0]]}}}
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It is intrinsically related to retrieval performance because a document needs to be retrieved before it can b...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [125, 134, 1.0]]}, "C94-2169": {"snippet": "...mple Retrieval by Generating Retrieval Queries from Similarities\n\n\n In example-based NLP, the problem of eoml)utational cost of example retrieval is severe, since the retrieval time increases in propo...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [139, 148, 1.0], [170, 179, 1.0]]}, "2008.clef_workshop-2008w.154": {"snippet": "...sian Information Retrieval\n\n\n With the emergence of vast resources of information, it is necessary to develop methods that retrieve most relevant information according to the users needs. These retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [73, 84, 1.0], [149, 160, 1.0], [197, 206, 1.0]]}, "2018.sigirjournals_journal-ir0volumeA52A2.22": {"snippet": "... the information available to us has been notably increasing. In this overload of information, Information Retrieval (IR) has been playing a prominent role by developing systems capable of separating ...", "weights": [[8, 19, 1.0], [85, 96, 1.0], [98, 109, 1.0], [110, 119, 1.0]]}, "2012.tist_journal-ir0volumeA3A4.10": {"snippet": "...edia retrieval. The first three studies deal primarily with comment-targeting retrieval, while the last one deals with commentexploiting retrieval. Throughout the article, connections to information r...", "weights": [[8, 17, 1.0], [81, 90, 1.0], [140, 149, 1.0], [190, 201, 1.0], [202, 211, 1.0]]}, "2015.clef_conference-2015.34": {"snippet": "...s an information overload and motivates progression.\nMotivationAn information retrieval system can be described as a pipeline of different components for indexing and retrieval like stemmers, indexing...", "weights": [[8, 19, 1.0], [69, 80, 1.0], [81, 90, 1.0], [170, 179, 1.0]]}, "2004.sigirconf_conference-2004.132": {"snippet": "...Most retrieval and database, even those tailored to geographic information, provide only nascent approaches to the technologically and conceptually difficult challenges of Geographic Information Retri...", "weights": [[8, 17, 1.0], [66, 77, 1.0], [186, 197, 1.0], [198, 207, 1.0]]}, "1986.sigirjournals_journal-ir0volumeA20A14.4": {"snippet": "... the retrieval processes of document retrieval systems\u00b0 Two basic categories of models that have been employed in information retrieval are the vector processing models and the Boolean retrieval model", "weights": [[8, 17, 1.0], [40, 49, 1.0], [117, 128, 1.0], [129, 138, 1.0], [188, 197, 1.0]]}, "2001.cikm_conference-2001.24": {"snippet": "...uted Information Retrieval\n\n\n ABSTRACTQuery expansion has been shown effective for both single database retrieval and for distributed information retrieval where complete collection information is ava...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [107, 116, 1.0], [137, 148, 1.0], [149, 158, 1.0], [185, 196, 1.0]]}, "2000.jasis_journal-ir0volumeA51A11.5": {"snippet": "...ling information retrieval system", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2012.wsdm_conference-2012.2": {"snippet": "...ject retrieval\n\n\n ABSTRACTThe success of image object retrieval systems relies on the visual bag-of-words paradigm, which allows image retrieval systems to adopt a retrieval strategy analogous to text...", "weights": [[8, 17, 1.0], [57, 66, 1.0], [138, 147, 1.0], [167, 176, 1.0]]}, "2015.ictir_conference-2015.2": {"snippet": "...y of Retrieval: The Retrievability of Information\n\n\n ABSTRACTRetrievability is an important and interesting indicator that can be used in a number of ways to analyse Information Retrieval systems and ...", "weights": [[8, 17, 1.0], [41, 52, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2001.ipm_journal-ir0volumeA37A4.5": {"snippet": "...ting information retrieval - introduce a visual retrieval model", "weights": [[8, 19, 1.0], [20, 29, 1.0], [51, 60, 1.0]]}, "2014.clef_conference-2014.5": {"snippet": "An Information Retrieval Ontology for Information Retrieval Nanopublications", "weights": [[3, 14, 1.0], [15, 24, 1.0], [38, 49, 1.0], [50, 59, 1.0]]}, "2017.cikm_conference-2017.301": {"snippet": "...thin Information Retrieval. Long has it been known that certain algorithms favour particular documents due to a ributes of these documents that are not directly related to relevance. e evaluation of b...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2008.ipm_journal-ir0volumeA44A1.2": {"snippet": "...tive information retrieval\n\n\n AbstractThis paper discusses the role of user-centred evaluations as an essential method for researching interactive information retrieval. It draws mainly on the work ca...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [150, 161, 1.0], [162, 171, 1.0]]}, "2018.ecir_conference-2018.10": {"snippet": "...sage Retrieval\n\n\n Abstract. Answer passage retrieval is an increasingly important information retrieval task as queries become more precise and mobile and audio interfaces more prevalent. In this task...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [85, 96, 1.0], [97, 106, 1.0]]}, "2005.sigirconf_conference-2005.7": {"snippet": "...back information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several contextsensitive retrieval ...", "weights": [[8, 19, 1.0], [65, 76, 1.0], [89, 98, 1.0], [126, 137, 1.0], [138, 147, 1.0], [193, 202, 1.0]]}, "2005.clef_workshop-2005w.54": {"snippet": "...ical information retrieval techniques cause to information retrieval. The baseline approach is based on the development of named entity recognition and geospatial information retrieval tools and on it...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0], [166, 177, 1.0], [178, 187, 1.0]]}, "2004.sigirconf_conference-2004.18": {"snippet": "...tive information retrieval is appealing since it provides a rigorous theoretical foundation to model partiality and uncertainty inherent in any information retrieval (IR) processes. In particular, a r...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [147, 158, 1.0], [159, 168, 1.0], [202, 211, 1.0]]}, "2010.ipm_journal-ir0volumeA46A4.4": {"snippet": "...oved information retrieval\n\n\n a b s t r a c tOntologies are frequently used in information retrieval being their main applications the expansion of queries, semantic indexing of documents and the orga...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [82, 93, 1.0], [94, 103, 1.0]]}, "2004.sigirconf_conference-2004.25": {"snippet": "...ased retrieval using language models\n\n\n ABSTRACTPrevious research on cluster-based retrieval has been inconclusive as to whether it does bring improved retrieval effectiveness over document-based retr...", "weights": [[8, 17, 1.0], [86, 95, 1.0], [155, 164, 1.0], [199, 208, 1.0]]}, "2004.clef_workshop-2004.30": {"snippet": "...gual information. While monolingual information retrieval was improved by using the analyzer, bi-lingual information retrieval still has room for improvement.", "weights": [[8, 19, 1.0], [39, 50, 1.0], [51, 60, 1.0], [108, 119, 1.0], [120, 129, 1.0]]}, "1991.sigirconf_conference-91.24": {"snippet": "...ssor Information Retrieval Systems\n\n\n Abstract.Information retrieval is the selection of documents that are potentially relevant to a user's information need. Given the vast volume of data stored in m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 61, 1.0], [62, 71, 1.0], [144, 155, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.8": {"snippet": "...cale information retrieval systems. Distributing different aspects of search (e.g., crawling, indexing, and query processing) is essential to achieve scalability in large-scale information retrieval s...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [180, 191, 1.0], [192, 201, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.15": {"snippet": "...ords Information Retrieval (IR) solutions to retrieve passages matching a set of terms extracted from the question.There is a fundamental disconnect between the capabilities of the bag-of-words retrie...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [197, 206, 1.0]]}, "2014.cikm_conference-2014.9": {"snippet": "...ween Retrieval Bias and Retrieval Performance\n\n\n ABSTRACTRetrievability provides an alternative way to assess an Information Retrieval (IR) system by measuring how easily documents can be retrieved. R...", "weights": [[8, 17, 1.0], [27, 36, 1.0], [116, 127, 1.0], [128, 137, 1.0]]}, "2015.jasis_journal-ir0volumeA66A11.9": {"snippet": "...tive information retrieval behavior to inform design", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...n of retrieval models particularly for recall oriented retrieval domains, where the focus is on retrieving all relevant information but not just retrieving a subset of relevant information. Available ...", "weights": [[8, 17, 1.0], [58, 67, 1.0], [123, 134, 1.0], [180, 191, 1.0]]}, "P92-1014": {"snippet": "Information Retrieval Using Robust Natural Language Processing\n\n\n We developed a prototype information retrieval system which uses advanced natural language processing techniques to enhance the effect...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [91, 102, 1.0], [103, 112, 1.0]]}, "W98-0704": {"snippet": "...l in information retrieval applications. Experiments using several standard information retrieval test collections show that our method results in a significant improvement of information retrieval pe", "weights": [[8, 19, 1.0], [20, 29, 1.0], [79, 90, 1.0], [91, 100, 1.0], [179, 190, 1.0], [191, 200, 1.0]]}, "2003.sigirconf_conference-2003.73": {"snippet": "XML retrieval: what to retrieve?\n\n\n ABSTRACTThe fundamental difference between standard information retrieval and XML retrieval is the unit of retrieval. In traditional IR, the unit of retrieval is fi...", "weights": [[4, 13, 1.0], [88, 99, 1.0], [100, 109, 1.0], [118, 127, 1.0], [143, 152, 1.0], [185, 194, 1.0]]}, "2005.sigirconf_conference-2005.9": {"snippet": "... hoc information retrieval\n\n\n ABSTRACTInformation retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information nee...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [53, 62, 1.0], [164, 173, 1.0], [188, 199, 1.0]]}, "2006.jasis_journal-ir0volumeA57A10.13": {"snippet": "Information retrieval design: Principles and options for information description, organization, display, and access in information retrieval databases, digital libraries, catalogs, and indexes", "weights": [[0, 11, 1.0], [12, 21, 1.0], [57, 68, 1.0], [119, 130, 1.0], [131, 140, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.1": {"snippet": "...uage information retrieval: a review\n\n\n AbstractThis paper reviews state-of-the-art techniques and methods for enhancing effectiveness of cross-language information retrieval (CLIR). The following res...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [156, 167, 1.0], [168, 177, 1.0]]}, "2013.clef_conference-2013w.110": {"snippet": "...mage retrieval task and case retrieval task. We are developing a system to integrate textual and visual retrieval into a framework for multimodal retrieval. Our approach for multimodal case retrieval ...", "weights": [[8, 17, 1.0], [32, 41, 1.0], [107, 116, 1.0], [149, 158, 1.0], [193, 202, 1.0]]}, "2004.sigirconf_conference-2004.112": {"snippet": "...abic information retrieval\n\n\n ABSTRACTDue to the high number of inflectional variations of Arabic words, empirical results suggest that stemming is essential for Arabic information retrieval. However,...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [172, 183, 1.0], [184, 193, 1.0]]}, "2004.clef_workshop-2004w.54": {"snippet": "...gual information retrieval. The performance of cross-lingual information retrieval was poor and that of monolingual information retrieval was not good. We need to check our modules and procedures.", "weights": [[8, 19, 1.0], [20, 29, 1.0], [64, 75, 1.0], [76, 85, 1.0], [119, 130, 1.0], [131, 140, 1.0]]}, "2006.clef_workshop-2006.108": {"snippet": "...Page Retrieval", "weights": [[8, 17, 1.0]]}, "I11-1117": {"snippet": "...sage Retrieval for Information Extraction using Distant Supervision\n\n\n In this paper, we propose a keyword-based passage retrieval algorithm for information extraction, trained by distant supervision....", "weights": [[8, 17, 1.0], [22, 33, 1.0], [124, 133, 1.0], [148, 159, 1.0]]}, "2002.ntcir_workshop-2002.59": {"snippet": "...s-DB Retrieval\n\n\n In cross-DB retrieval, the domain of queries differs from the retrieval target in the distribution of that of term occurrences. This causes incorrect term weighting in the retrieval ...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [83, 92, 1.0], [193, 202, 1.0]]}, "2020.sigirconf_conference-2020.360": {"snippet": "...tive Information Retrieval: Models, Algorithms, and Evaluation\n\n\n Since Information Retrieval (IR) is an interactive process in general, it is important to study Interactive Information Retrieval (IIR...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [75, 86, 1.0], [87, 96, 1.0], [177, 188, 1.0], [189, 198, 1.0]]}, "1990.jasis_journal-ir0volumeA41A2.10": {"snippet": "...s of information retrieval and automatic text processing: The transformation analysis, and retrieval of information by computer", "weights": [[8, 19, 1.0], [20, 29, 1.0], [94, 103, 1.0], [107, 118, 1.0]]}, "2013.wsdm_conference-2013.93": {"snippet": "... for information retrieval (LSDS-IR 2013)\n\n\n ABSTRACTThe LSDS-IR'13 workshop aims to bring together both information retrieval practitioners from industry, as well as academic researchers concerned wi...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [108, 119, 1.0], [120, 129, 1.0]]}, "2015.ecir_conference-2015.22": {"snippet": "... and Retrieval Bias: A Comparison of Inequality Measures", "weights": [[8, 17, 1.0]]}, "2005.ipm_journal-ir0volumeA41A5.11": {"snippet": "... web retrieval effectiveness\n\n\n AbstractThis paper talks about several schemes for improving retrieval effectiveness that can be used in the named page finding tasks of web information retrieval (Over...", "weights": [[8, 17, 1.0], [96, 105, 1.0], [176, 187, 1.0], [188, 197, 1.0]]}, "2017.fire_conference-2017w.24": {"snippet": "...017: Information Retrieval From Legal Documents\n\n\n This paper details the approach of implementing the Catchphrase Extraction and Precedence Retrieval tasks to be presented at Information Retrieval fr...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [144, 153, 1.0], [179, 190, 1.0], [191, 200, 1.0]]}, "2020.sigirconf_conference-2020.367": {"snippet": "... for Information Retrieval: Fundamentals and Advances\n\n\n Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users' information needs by suggesting...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [60, 71, 1.0], [72, 81, 1.0], [172, 183, 1.0]]}, "2014.sigirconf_conference-2014.221": {"snippet": "...amic information retrieval modeling\n\n\n ABSTRACTDynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [69, 80, 1.0], [81, 90, 1.0]]}, "W09-4632": {"snippet": "...e\n\n\n Information retrieval is a field of research reaching from computer and information science to lingusitics. As a linguist in the information retrieval field, I leave the quest for effective searc...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [80, 91, 1.0], [137, 148, 1.0], [149, 158, 1.0]]}, "2017.fire_conference-2017w.19": {"snippet": "... the Information Retrieval task. The distributed representation of text attained 3rd position in subtask 1 and attained satisfactory score in subtask 2. CCS CONCEPTS \u2022 Information systems \u2192 Informatio...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [171, 182, 1.0], [193, 204, 1.0]]}, "2004.jasis_journal-ir0volumeA55A10.2": {"snippet": "...tive retrieval strategies in the same information retrieval system", "weights": [[8, 17, 1.0], [41, 52, 1.0], [53, 62, 1.0]]}, "2004.ntcir_workshop-2004.57": {"snippet": "...onal Retrieval Task 2 that was conducted from 2003 to 2004 as a subtask of the WEB Task at the Fourth NTCIR Workshop ('NTCIR-4 WEB'). In the Informational Retrieval Task, we attempted to assess the re...", "weights": [[8, 17, 1.0], [158, 167, 1.0], [201, 210, 1.0]]}, "2015.airs_conference-2015.0": {"snippet": "Information Retrieval Technology - 11th Asia Information Retrieval Societies Conference, AIRS 2015, Brisbane, QLD, Australia, December 2-4, 2015. Proceedings", "weights": [[0, 11, 1.0], [12, 21, 1.0], [45, 56, 1.0], [57, 66, 1.0]]}, "2017.trec_conference-2017.21": {"snippet": "...ular information retrieval application is web search engine such as Google [2] , Baidu [3] , Bing [4] and Sogou [5]. These application will return top-N best retrieval result to users. Information Ret...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [161, 170, 1.0], [188, 199, 1.0], [200, 209, 1.0]]}, "T78-1026": {"snippet": "... for retrieval only, and not for retrieval and updating simultaneously. A related assumption (or constraint) that in questions descriptions are used referentially only (i.e., a question cannot be used...", "weights": [[8, 17, 1.0], [36, 45, 1.0]]}, "1999.ntcir_workshop-1999.17": {"snippet": "Information Retrieval Based on Stochastic Models\n\n\n Our method for information retrieval uses Robertson's 2-poisson model which is one type of probablistic approach. This method achieved a comparative...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [67, 78, 1.0], [79, 88, 1.0]]}, "1985.sigirconf_conference-85.25": {"snippet": "...heir retrieval experlrnent. FIZ4 IS using the Information retrieval system GRIPS \u2022 (General Relation based Information l~oceeeing System) which was developed by DIMDI (Deutsches Institut flLer Medlzin...", "weights": [[8, 17, 1.0], [49, 60, 1.0], [61, 70, 1.0], [110, 121, 1.0]]}, "2016.sigirconf_conference-2016.211": {"snippet": "...tion Retrieval systems have traditionally been evaluated in terms of efficiency and performance. These aspects of retrieval systems, whilst very important, do not cover a crucial aspect of the system,...", "weights": [[8, 17, 1.0], [117, 126, 1.0]]}, "2012.ipm_journal-ir0volumeA48A1.9": {"snippet": "... and information science, the same cannot be said about the subfield of information retrieval. We address that in this work by sketching the information retrieval intellectual landscape through visual...", "weights": [[8, 19, 1.0], [75, 86, 1.0], [87, 96, 1.0], [144, 155, 1.0], [156, 165, 1.0]]}, "2016.sigirconf_conference-2016.89": {"snippet": "...This information retrieval test collection is the first to combine multilingual search, passage retrieval, and informal online genre text.", "weights": [[8, 19, 1.0], [20, 29, 1.0], [99, 108, 1.0]]}, "2020.sigirconf_conference-2020.5": {"snippet": "... for Information Retrieval\n\n\n Information retrieval (IR) is the science of search, the search of user query relevant pieces of information from a collection of unstructured resources. Information in t...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [33, 44, 1.0], [45, 54, 1.0], [130, 141, 1.0], [187, 198, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.10": {"snippet": "... XML retrieval. XML retrieval deals with retrieving those document parts that specifically answer a query. It is concerned with using the document structure to improve the retrieval of information fro...", "weights": [[8, 17, 1.0], [23, 32, 1.0], [175, 184, 1.0], [188, 199, 1.0]]}, "1992.sigirconf_conference-92.3": {"snippet": "...of a Retrieval Process", "weights": [[8, 17, 1.0]]}, "1998.sigirconf_conference-98.39": {"snippet": "...t of Retrieval Effectiveness\n\n\n AbstractTest collections have traditionally been used by information retrieval researchers to improve their retrieval strategies. To be viable as a laboratory tool, a c...", "weights": [[8, 17, 1.0], [92, 103, 1.0], [104, 113, 1.0], [143, 152, 1.0]]}, "2016.ictir_conference-2016.20": {"snippet": "...t of retrieval systems that performance and efficiency measures do not. Retrievability is often used to calculate the retrievability bias, an indication of how accessible a system makes all the docume...", "weights": [[8, 17, 1.0]]}, "2004.tois_journal-ir0volumeA22A3.4": {"snippet": "...uted information retrieval, which aims to reduce the network traffic of query passing in the network. We carry out experiments to show the distributed image retrieval system and the Firework informati...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [160, 169, 1.0], [194, 205, 1.0]]}, "2010.trec_conference-2010.45": {"snippet": "...emic information retrieval research. Our method of entity retrieval is based on a two-layer retrieval and extraction probability model (TREPM) for integrating document retrieval and entity extraction ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [61, 70, 1.0], [95, 104, 1.0], [171, 180, 1.0]]}, "2003.sigirconf_conference-2003.57": {"snippet": "...tive information retrieval\n\n\n ABSTRACTIn this poster,we incorporate user query history, as context information, to improve the retrieval performance in interactive retrieval. Experiments using the TRE...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [102, 113, 1.0], [130, 139, 1.0], [167, 176, 1.0]]}, "2006.ipm_journal-ir0volumeA42A1.2": {"snippet": "... for information retrieval\n\n\n AbstractThis paper presents a probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [77, 88, 1.0], [89, 98, 1.0], [122, 131, 1.0]]}, "1998.ecir_conference-1998.4": {"snippet": "...ning Information Retrieval with Information Extraction for Efficient Retrieval of Calls for Papers", "weights": [[8, 19, 1.0], [20, 29, 1.0], [35, 46, 1.0], [72, 81, 1.0]]}, "2005.spire_conference-2005.25": {"snippet": "Retrieval Status Values in Information Retrieval Evaluation", "weights": [[0, 9, 1.0], [27, 38, 1.0], [39, 48, 1.0]]}, "1995.jasis_journal-ir0volumeA46A9.12": {"snippet": "Information Retrieval: New Systems and Current Research. Proceedings of the 15th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, edited by Ruben Leon", "weights": [[0, 11, 1.0], [12, 21, 1.0], [133, 144, 1.0], [145, 154, 1.0]]}, "2017.sigirconf_workshop-2017birndl.8": {"snippet": "... of \"Information Retrieval\" - A Tale of Three Databases\n\n\n Coverage is an important criterion when evaluating information systems. This exploratory study investigates this issue by submitting the same...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [113, 124, 1.0]]}, "2005.ntcir_workshop-2005.47": {"snippet": "...tent Retrieval Experiments at Hitachi\n\n\n In NTCIR-5, we used five retrieval methods proposed in NTCIR-4: (1) query term weighting using only document frequency, (2) stopword deletion, (3) two-stage pa...", "weights": [[8, 17, 1.0], [69, 78, 1.0]]}, "W03-2005": {"snippet": "...tent Retrieval\n\n\n In cross-database retrieval, the domain of queries di ers from that of the retrieval target in the distribution of term occurrences. This causes incorrect term weighting in the retri...", "weights": [[8, 17, 1.0], [39, 48, 1.0], [96, 105, 1.0], [198, 207, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "... the retrieval effectiveness of directly indexing each record's content. These results confirmed that these representations are of suitable quality for use in retrieval (particularly when no content i...", "weights": [[8, 17, 1.0], [162, 171, 1.0], [202, 213, 1.0]]}, "2007.wwwconf_conference-2007.9": {"snippet": "...onal information retrieval models, documents are taken as the retrieval units and the content of a document is considered reliable. However, this reliability assumption is no longer valid in the objec...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [65, 74, 1.0]]}, "2002.ntcir_workshop-2002.1": {"snippet": "...run\" retrieval topics of the task. In this report we will give a brief description about our basic method for web information retrieval, our web information retrieval system and some retrieval experim", "weights": [[8, 17, 1.0], [117, 128, 1.0], [129, 138, 1.0], [148, 159, 1.0], [160, 169, 1.0], [186, 195, 1.0]]}, "2003.ecir_conference-2003.1": {"snippet": "...ment Retrieval: Shallow Data, Deep Theories; Historical Reflections, Potential Directions\n\n\n Abstract. This paper reviews the development of statistically-based retrieval. Since the 1950s statistical ...", "weights": [[8, 17, 1.0], [164, 173, 1.0]]}, "2009.cikm_conference-2009.134": {"snippet": "...opic retrieval\n\n\n ABSTRACTTraditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval...", "weights": [[8, 17, 1.0], [51, 62, 1.0], [63, 72, 1.0], [167, 178, 1.0], [194, 203, 1.0]]}, "2005.clef_workshop-2005w.119": {"snippet": "... All retrieval results rely heavily on two retrieval systems: for visual retrieval we use the GNU Image Finding Tool (GIFT), and for textual retrieval the EasyIR retrieval system. For the ad-hoc retri...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [76, 85, 1.0], [144, 153, 1.0], [165, 174, 1.0], [198, 207, 1.0]]}, "W03-1115": {"snippet": "...resh information. However, fast indexing alone is not enough to retrieve fresh information, as support for temporal information based retrieval is also required. In this paper, we describe temporal in...", "weights": [[8, 19, 1.0], [82, 93, 1.0], [119, 130, 1.0], [137, 146, 1.0], [201, 212, 1.0]]}, "1987.sigirjournals_journal-ir0volumeA21A34.1": {"snippet": "... for Information Retrieval", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "I05-2024": {"snippet": "Information Retrieval Capable of Visualization and High Precision\n\n\n We present a neural-network based selforganizing approach that enables visualization of the information retrieval while at the same...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [161, 172, 1.0], [173, 182, 1.0]]}, "2007.sigirconf_conference-2007.76": {"snippet": "...n of information retrieval systems is one of the core tasks in information retrieval. Problems include the inability to exhaustively label all documents for a topic, nongeneralizability from a small n...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [66, 77, 1.0], [78, 87, 1.0]]}, "C12-3040": {"snippet": "...tion Retrieval System\n\n\n We introduce a translation retrieval system THUTR, which casts translation as a retrieval problem. Translation retrieval aims at retrieving a list of target-language translati...", "weights": [[8, 17, 1.0], [55, 64, 1.0], [108, 117, 1.0], [139, 148, 1.0]]}, "2014.wsdm_conference-2014.79": {"snippet": "... for information retrieval (LSDS-IR 2014)\n\n\n ABSTRACTThe LSDS-IR'14 workshop aims to bring together information retrieval practitioners from industry and academic researchers concerned with efficient ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [103, 114, 1.0], [115, 124, 1.0]]}, "2011.tois_journal-ir0volumeA29A2.0": {"snippet": "...n of Information Retrieval Models\n\n\n Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is nece...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [61, 70, 1.0], [118, 129, 1.0], [130, 139, 1.0]]}, "I11-1168": {"snippet": "...ying Information Needs in Results of Question Retrieval\n\n\n Information need is an important factor in question retrieval. This paper proposes a method to diversify the results of question retrieval in...", "weights": [[8, 19, 1.0], [49, 58, 1.0], [62, 73, 1.0], [114, 123, 1.0], [191, 200, 1.0]]}, "2019.ictir_conference-2019.39": {"snippet": "...sage Retrieval for Entity Retrieval", "weights": [[8, 17, 1.0], [29, 38, 1.0]]}, "2005.sigirconf_conference-2005.64": {"snippet": "...s to information retrieval\n\n\n ABSTRACTExisting retrieval models generally do not offer any guarantee for optimal retrieval performance. Indeed, it is even difficult, if not impossible, to predict a mo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [50, 59, 1.0], [116, 125, 1.0]]}, "2012.ecir_conference-2012.15": {"snippet": "...eech Retrieval", "weights": [[8, 17, 1.0]]}, "2010.jasis_journal-ir0volumeA61A11.15": {"snippet": "Information Retrieval: Searching in the 21st Century; Human Information Retrieval", "weights": [[0, 11, 1.0], [12, 21, 1.0], [60, 71, 1.0], [72, 81, 1.0]]}, "2007.wwwconf_conference-2007.40": {"snippet": "...ided retrieval\n\n\n ABSTRACTUsers searching for information in hypermedia environments often perform querying followed by manual navigation. Yet, the conventional text/hypertext retrieval paradigm does ...", "weights": [[8, 17, 1.0], [49, 60, 1.0], [179, 188, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "1981.sigirconf_conference-81.7": {"snippet": "...stic Retrieval\n\n\n ABSTRACTThe objective is to relate the effectiveness of retrieval, the fuzzy set concept and the processing of Boolean query. The use of a probabilistic retrieval scheme is motivated...", "weights": [[8, 17, 1.0], [77, 86, 1.0], [174, 183, 1.0]]}, "2020.ictir_conference-2020.28": {"snippet": "...s on Retrieval Bias and Effectiveness\n\n\n Prior work on using retrievability measures in the evaluation of information retrieval (IR) systems has laid out the foundations for investigating the relation...", "weights": [[8, 17, 1.0], [109, 120, 1.0], [121, 130, 1.0]]}, "1987.jasis_journal-ir0volumeA38A2.2": {"snippet": "...ment retrieval: II. The retrieval process", "weights": [[8, 17, 1.0], [27, 36, 1.0]]}, "2008.sigirconf_conference-2008.170": {"snippet": "...ment retrieval has shown to be a competitive approach in XML element retrieval, which is counter-intuitive since the element retrieval task requests all and only relevant document parts to be retrieve...", "weights": [[8, 17, 1.0], [72, 81, 1.0], [128, 137, 1.0]]}, "2016.ictir_conference-2016.14": {"snippet": "...y of Information Retrieval Models\n\n\n ABSTRACTDeveloping effective information retrieval models has been a long standing challenge in Information Retrieval (IR), and significant progresses have been ma...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [69, 80, 1.0], [81, 90, 1.0], [136, 147, 1.0], [148, 157, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.14": {"snippet": "...ment retrieval. The shot retrieval and the video segment model re-use the probability of relevance and a language modeling ranking function respectively, basing themselves on decades of text retrieval...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [194, 203, 1.0]]}, "2014.sigirconf_conference-2014.222": {"snippet": "... for Information Retrieval, where his research interests include: (i) Models for the retrieval of documents, sentences, experts and other information objects ; (ii) Probabilistic models of user intera...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [88, 97, 1.0], [141, 152, 1.0]]}, "1976.sigirjournals_journal-ir0volumeA11A2.4": {"snippet": "... of \"Information Retrieval and Processing by Lauren B. Doyle\"; Los Angeles, Melville Publishing Company, 1975\n\n\n While the titles of these two recent books on information retrieval suggest that they m...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [162, 173, 1.0], [174, 183, 1.0]]}, "2006.ipm_journal-ir0volumeA42A5.13": {"snippet": "...y of retrieval modalities for video shot retrieval", "weights": [[8, 17, 1.0], [44, 53, 1.0]]}, "2004.sigirconf_conference-2004.8": {"snippet": "...y of information retrieval heuristics\n\n\n ABSTRACTEmpirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [73, 84, 1.0], [85, 94, 1.0], [118, 127, 1.0], [181, 190, 1.0]]}, "2009.sigirconf_conference-2009.203": {"snippet": "...tive retrieval: study of emotion in adaptive retrieval", "weights": [[8, 17, 1.0], [48, 57, 1.0]]}, "2014.airs_conference-2014.0": {"snippet": "Information Retrieval Technology - 10th Asia Information Retrieval Societies Conference, AIRS 2014, Kuching, Malaysia, December 3-5, 2014. Proceedings", "weights": [[0, 11, 1.0], [12, 21, 1.0], [45, 56, 1.0], [57, 66, 1.0]]}, "2020.ictir_conference-2020.29": {"snippet": "...g in information retrieval (IR), and propose a framework called PyTerrier that allows advanced retrieval pipelines to be expressed, and evaluated, in a declarative manner close to their conceptual des...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [98, 107, 1.0]]}, "2008.ipm_journal-ir0volumeA44A3.9": {"snippet": "...sing information gain to improve multi-modal information retrieval systems\n\n\n AbstractNowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval ...", "weights": [[8, 19, 1.0], [48, 59, 1.0], [60, 69, 1.0], [109, 120, 1.0], [193, 202, 1.0]]}, "2010.sigirjournals_journal-ir0volumeA44A2.20": {"snippet": "...ical information retrieval\n\n\n AbstractIn this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 153, 1.0], [154, 163, 1.0]]}, "1987.sigirconf_conference-87.21": {"snippet": "...near Information Retrieval Models\n\n\n AbstractLinear decision (retrieval)functions have been widely adopted in information retrieval systems such as in Boolean, vector, and probabilistic", "weights": [[8, 19, 1.0], [20, 29, 1.0], [65, 74, 1.0], [113, 124, 1.0], [125, 134, 1.0]]}, "2011.iir_workshop-2011.22": {"snippet": "Web Information Retrieval for Complex Not-Informational Intents: An Introduction to User Intent Analysis", "weights": [[4, 15, 1.0], [16, 25, 1.0]]}, "2002.cikm_conference-2002.1": {"snippet": "... the Information Retrieval Laboratory at the Illinois Institute of Technology. His research area is scalable information systems spanning communication networks, parallel processing, and information r...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [112, 123, 1.0], [190, 201, 1.0], [202, 211, 1.0]]}, "2011.sigirconf_conference-2011.216": {"snippet": "... for information retrieval\n\n\n ABSTRACTIn recent years, we have witnessed successful application of machine learning techniques to a wide range of information retrieval problems, including Web search e...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [149, 160, 1.0], [161, 170, 1.0]]}, "2008.cikm_conference-2008.59": {"snippet": "... the retrieval system. In this paper, we outline some emerging information access related scenarios that require knowledge about how the retrieval system affects the users' ability to access informati...", "weights": [[8, 17, 1.0], [66, 77, 1.0], [140, 149, 1.0], [194, 205, 1.0]]}, "2014.sigirconf_conference-2014.219": {"snippet": "...3.3 [Information Search and Retrieval]: Retrieval modelsGeneral Terms: Algorithms Keywords: axiomatic analysis; information retrieval models; retrieval constraints; optimization\nOVERVIEWOne of the mos...", "weights": [[8, 19, 1.0], [31, 40, 1.0], [43, 52, 1.0], [115, 126, 1.0], [127, 136, 1.0], [145, 154, 1.0]]}, "2005.ecir_conference-2005.37": {"snippet": "...rier Information Retrieval Platform\n\n\n Abstract. Terrier is a modular platform for the rapid development of large-scale Information Retrieval (IR) applications. It can index various document collectio...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [123, 134, 1.0], [135, 144, 1.0]]}, "2021.emnlp-main.297": {"snippet": "...rase Retrieval Learns Passage Retrieval, Too\n\n\n Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems. Among them, dense phrase retrieval-the most f...", "weights": [[8, 17, 1.0], [33, 42, 1.0], [57, 66, 1.0], [112, 121, 1.0], [183, 192, 1.0]]}, "1993.sigirconf_conference-93.34": {"snippet": "...user Information Retrieval System for Semistructured and Dynamic Data\n\n\n AbstractThe access structure, the retrieval model, and the system architecture of the SPIDER information retrieval system are d...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [110, 119, 1.0], [169, 180, 1.0], [181, 190, 1.0]]}, "2009.trec_conference-2009.7": {"snippet": "...atic retrieval models on the large data collection. Axiomatic approaches to information retrieval have been recently proposed and studied. The basic idea is to search for retrieval functions that can ...", "weights": [[8, 17, 1.0], [79, 90, 1.0], [91, 100, 1.0], [174, 183, 1.0]]}, "1997.sigirjournals_journal-ir0volumeA31A1.8": {"snippet": "...ing. Information Processing and  Management, 24, 3 (1988), 269-280.    Historical note: the past thirty years in information retrieval. Journal of the American Society for Information Science (JASIS) ...", "weights": [[8, 19, 1.0], [116, 127, 1.0], [128, 137, 1.0], [175, 186, 1.0]]}, "L08-1002": {"snippet": "...eech Information Retrieval\n\n\n In this article we present a method for combining different information retrieval models in order to increase the retrieval performance in a Speech Information Retrieval ...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [93, 104, 1.0], [105, 114, 1.0], [147, 156, 1.0], [181, 192, 1.0], [193, 202, 1.0]]}, "1999.sigirconf_conference-99.29": {"snippet": "...odel Information Retrieval System\n\n\n AbstractWe p r e s e n t a new method for information retrieval using hidden Markov models (HMMs). We d e v elop a general framework for incorporating multiple wor...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [82, 93, 1.0], [94, 103, 1.0]]}, "1988.riao_conference-1988.41": {"snippet": "...Text Retrieval for Legal Information Retrieval Systems", "weights": [[8, 17, 1.0], [28, 39, 1.0], [40, 49, 1.0]]}, "2001.clef_workshop-2001w.21": {"snippet": "...gual Information Retrieval\n\n\n This paper reports the work of NTU on bilingual-retrieval task at CLEF 2001. We proposed five models. Model 1 used co-occurrence information to disambiguate translation e...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [81, 90, 1.0], [162, 173, 1.0]]}, "2013.ictir_conference-2013.3": {"snippet": "... the retrieval model used in the search engine. Develoing optimal retrieval models has always been a very important fundamental research problem in information retrieval because an improved general re...", "weights": [[8, 17, 1.0], [69, 78, 1.0], [151, 162, 1.0], [163, 172, 1.0], [201, 210, 1.0]]}, "2017.sigirjournals_journal-ir0volumeA51A2.8": {"snippet": "...tive Information Retrieval: Using a Connectionist Representation to Retrieve and Leasrn about Documents", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "1988.sigirconf_conference-88.34": {"snippet": "... for Information Retrieval Systems\n\n\n This paper is a contribution to the construction of a general model for information retrieval.As in the paper of Van Rijsbergen ([RIJSG]), the implicit base in al...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [113, 124, 1.0], [125, 134, 1.0]]}, "2014.cikm_conference-2014.187": {"snippet": "...uage Information Retrieval (CLIR) addresses the problem of finding documents in some language that are relevant to a question posed in a different language. Retrieving answers to questions written usi...", "weights": [[8, 19, 1.0], [20, 29, 1.0]]}, "2008.ipm_journal-ir0volumeA44A2.31": {"snippet": "... and Information  Retrieval\" (1960). This was the first paper to present a probabilistic approach to information retrieval, and perhaps the first paper on ranked retrieval. Although it is one of the m...", "weights": [[8, 19, 1.0], [21, 30, 1.0], [104, 115, 1.0], [116, 125, 1.0], [165, 174, 1.0]]}, "2018.chiir_conference-2018.70": {"snippet": "...n in Information Retrieval\n\n\n ABSTRACTThe purpose of the WEPIR 2018 workshop is to bring together researchers from different backgrounds, interested in advancing the evaluation of personalisation in i...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [202, 213, 1.0]]}}}
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They are: ...", "weights": [[8, 17, 1.0], [162, 171, 1.0]]}, "L08-1330": {"snippet": "Influence of Text Type and Text Length on Anaphoric Annotation", "weights": [[0, 9, 1.0], [32, 38, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "... the query and not the document-at least, not when the query is shorter than about 30 words. For document retrieval, long enough spans of speech can be readily handled.However, information retrieval i...", "weights": [[8, 13, 1.0], [58, 63, 1.0], [109, 118, 1.0], [192, 201, 1.0]]}, "2004.clef_workshop-2004w.63": {"snippet": "...tion Retrieval with Terrier\n\n\n This paper describes our participation in the CLEF 2004 French monolingual task. We used our Terrier Information Retrieval platform and experimented with query expansion...", "weights": [[8, 17, 1.0], [147, 156, 1.0], [188, 193, 1.0]]}, "2015.ictir_conference-2015.40": {"snippet": "...ment length distribution in a collection.", "weights": [[8, 14, 1.0]]}, "2017.sigirconf_conference-2017.183": {"snippet": "...in a query is a query-speci c constant that is typically ignored in retrieval functions. However, previous studies have shown that the performance of retrieval models varies for di erent query lengths...", "weights": [[8, 13, 1.0], [19, 24, 1.0], [71, 80, 1.0], [153, 162, 1.0], [190, 195, 1.0]]}, "2011.sigirconf_conference-2011.114": {"snippet": "...oved query performance prediction using standard deviation\n\n\n ABSTRACTQuery performance prediction (QPP) is an important task in information retrieval (IR). In this paper, we (1) develop a new predict...", "weights": [[8, 13, 1.0], [144, 153, 1.0]]}, "2004.sigirconf_conference-2004.60": {"snippet": "...ixed-length page segmentation, DOM-based page segmentation, vision-based page segmentation, and a combined method which integrates both semantic and fixed-length properties. Experiments on block-level...", "weights": [[8, 14, 1.0], [158, 164, 1.0]]}, "2007.sigirconf_conference-2007.100": {"snippet": "...arch query length", "weights": [[8, 13, 1.0], [14, 20, 1.0]]}, "2015.cikm_conference-2015.188": {"snippet": "...y of Query Length Heuristics in Information Retrieval\n\n\n ABSTRACTQuery length has generally been regarded as a query-specific constant that does not affect document ranking. In this paper, we reveal t...", "weights": [[8, 13, 1.0], [14, 20, 1.0], [47, 56, 1.0], [74, 80, 1.0], [114, 119, 1.0]]}, "2008.sigirconf_conference-2008.104": {"snippet": "...tory length on the effectiveness of personalized ranking. We carried out task-based user study for Web search, and obtained ranked relevance judgments for all queries. Query contexts derived from prev...", "weights": [[8, 14, 1.0], [171, 176, 1.0]]}, "2018.sigirconf_conference-2018.180": {"snippet": "...with query length in order to optimize retrieval performance. Following this intuition, we first propose a new term frequency normalization with query length for probabilistic information retrieval, n...", "weights": [[8, 13, 1.0], [14, 20, 1.0], [42, 51, 1.0], [148, 153, 1.0], [154, 160, 1.0], [191, 200, 1.0]]}, "2011.sigirconf_conference-2011.20": {"snippet": "...tion retrieval (IR) models, e.g. BM25, deal with document length based on a trade-off between the Verbosity hypothesis, which assumes the independence of a document's relevance of its length, and the ...", "weights": [[8, 17, 1.0], [61, 67, 1.0], [187, 193, 1.0]]}, "1999.ntcir_workshop-1999.25": {"snippet": "...text retrieval. Experiments on the effects of supplemental noun phrase indexing in view of the effect of various length of queries are reported. The results show that the noun phrase indexing outperfo...", "weights": [[8, 17, 1.0], [116, 122, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...mage Retrieval by Hypertext Links\n\n\n This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, th...", "weights": [[8, 17, 1.0], [72, 81, 1.0]]}, "2014.ipm_journal-ir0volumeA50A2.5": {"snippet": "...that query modification should be refined when topic difficulty and query verbosity are considered since these two properties interact and query verbosity is not straightforwardly related to query len", "weights": [[8, 13, 1.0], [71, 76, 1.0], [142, 147, 1.0], [194, 199, 1.0], [200, 206, 1.0]]}, "2008.sigirconf_conference-2008.152": {"snippet": "...y of query length\n\n\n ABSTRACTWe analyse query length, and fit power-law and Poisson distributions to four different query sets. We provide a practical model for query length, based on the truncation o...", "weights": [[8, 13, 1.0], [14, 20, 1.0], [43, 48, 1.0], [49, 55, 1.0], [119, 124, 1.0], [164, 169, 1.0], [170, 176, 1.0]]}, "2018.cikm_conference-2018.189": {"snippet": "... hoc Retrieval\n\n\n ABSTRACTNeural embeddings have been effectively integrated into information retrieval tasks including ad hoc retrieval. One of the benefits of neural embeddings is they allow for the...", "weights": [[8, 17, 1.0], [97, 106, 1.0], [130, 139, 1.0]]}, "2021.blackboxnlp-1.14": {"snippet": "How Length Prediction Influence the Performance of Non-Autoregressive Translation?\n\n\n Length prediction is a special task in a series of NAT models where target length has to be determined before gene...", "weights": [[4, 10, 1.0], [22, 31, 1.0], [86, 92, 1.0], [161, 167, 1.0]]}, "2017.jasis_journal-ir0volumeA68A8.6": {"snippet": "...that influence query reformulations and search performance in health information retrieval: A multilevel modeling approach", "weights": [[8, 17, 1.0], [18, 23, 1.0], [84, 93, 1.0]]}, "2004.sigirconf_conference-2004.12": {"snippet": "Length normalization in XML retrieval\n\n\n ABSTRACTXML retrieval is a departure from standard document retrieval in which each individual XML element, ranging from italicized words or phrases to full bl...", "weights": [[0, 6, 1.0], [28, 37, 1.0], [53, 62, 1.0], [101, 110, 1.0]]}, "2002.sigirconf_conference-2002.70": {"snippet": "...mize retrieval. Length is one property. We look at how two-word queries may attain higher precision by re-ranking using word co-occurrence evidence in retrieved documents. Co-occurrence within documen...", "weights": [[8, 17, 1.0], [19, 25, 1.0]]}, "2018.tois_journal-ir0volumeA36A4.8": {"snippet": "...ware Influence Maximization over Dynamic Social Streams\n\n\n Influence maximization (IM), which selects a set of k seed users (a.k.a., a seed set) to maximize the influence spread over a social network,...", "weights": [[8, 17, 1.0], [62, 71, 1.0], [164, 173, 1.0]]}, "2018.ictir_conference-2018.9": {"snippet": "...nded Query Performance Prediction Framework Utilizing Passage-Level Information\n\n\n ABSTRACTWe show that document-level post-retrieval query performance prediction (QPP) methods are mostly suited for s...", "weights": [[8, 13, 1.0], [127, 136, 1.0], [137, 142, 1.0]]}, "2016.wwwconf_conference-2016.71": {"snippet": "...y of Retrieval Models for Long Documents and Queries in Information Retrieval\n\n\n ABSTRACTRecent research has shown that long documents are unfairly penalised by a number of current retrieval methods. ...", "weights": [[8, 17, 1.0], [71, 80, 1.0], [184, 193, 1.0]]}, "2005.ir_journal-ir0volumeA8A4.5": {"snippet": "...e of Length Normalization for XML Retrieval", "weights": [[8, 14, 1.0], [37, 46, 1.0]]}, "2017.ictir_conference-2017.7": {"snippet": "...that query formulation could a ect retrieval performance. Empirical observations suggested that a query may contain extraneous terms that could harm the retrieval e ectiveness. is is true for both ver...", "weights": [[8, 13, 1.0], [38, 47, 1.0], [101, 106, 1.0], [156, 165, 1.0]]}, "2010.spire_conference-2010.0": {"snippet": "... on. Query-dependent or \"dynamic\" features take the query into account and thus can only be computed at query time, whereas query-independent or \"static\" features do not rely on the query and thus can...", "weights": [[8, 13, 1.0], [55, 60, 1.0], [107, 112, 1.0], [127, 132, 1.0], [185, 190, 1.0]]}, "2000.ir_journal-ir0volumeA1A4.3": {"snippet": "...s of Query Structure and Expansion on Retrieval Performance in Probabilistic Text Retrieval", "weights": [[8, 13, 1.0], [41, 50, 1.0], [85, 94, 1.0]]}, "2011.jasis_journal-ir0volumeA62A8.5": {"snippet": "...ween query characteristics and IR functions retrieval bias", "weights": [[8, 13, 1.0], [47, 56, 1.0]]}, "2000.trec_conference-2000.44": {"snippet": "...text retrieval. Experiments on the effects of supplemental noun phrase indexing, pseudo-relevance feedback and reference database feedback in view of the effect of various length of queries are report...", "weights": [[8, 17, 1.0], [175, 181, 1.0]]}, "2010.sigirconf_conference-2010.138": {"snippet": "...eral retrieval features such as BM25F-based features, click-based features, query length, and other query-independent features such as variants of PageRank. For each of these retrieval features we cre...", "weights": [[8, 17, 1.0], [79, 84, 1.0], [85, 91, 1.0], [103, 108, 1.0], [178, 187, 1.0]]}, "2017.sigirconf_conference-2017.42": {"snippet": "Retrieval Consistency in the Presence of Query Variations", "weights": [[0, 9, 1.0], [41, 46, 1.0]]}, "2011.cikm_conference-2011.24": {"snippet": "... can influence standard retrieval models and propose methods to overcome the problems they pose. We consider the sparsity's effect on document length normalization and introduce interestingness as sta...", "weights": [[8, 17, 1.0], [27, 36, 1.0], [146, 152, 1.0]]}, "2016.cikm_conference-2016.73": {"snippet": "Query Variations and their Effect on Comparing Information Retrieval Systems", "weights": [[0, 5, 1.0], [59, 68, 1.0]]}, "2005.sigirconf_conference-2005.61": {"snippet": "...oved retrieval performance. Our results further demonstrate a strong relationship between query length and performance.", "weights": [[8, 17, 1.0], [93, 98, 1.0], [99, 105, 1.0]]}, "P08-1080": {"snippet": "... the query. However, recent research suggests that longer summaries can be preferable for certain types of queries. This paper presents empirical evidence that judges can predict appropriate search re...", "weights": [[8, 13, 1.0]]}, "2013.wsdm_conference-2013.85": {"snippet": "...cial influence analysis\n\n\n ABSTRACTSocial influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence...", "weights": [[8, 17, 1.0], [45, 54, 1.0], [194, 203, 1.0]]}, "L10-1102": {"snippet": "The Influence of the Utterance Length on the Recognition of Aged Voices", "weights": [[4, 13, 1.0], [31, 37, 1.0]]}, "1999.trec_conference-1999.12": {"snippet": "...uage query for use in a target-language retrieval task. We empirically evaluated (1) the effect of pseudo relevance feedback on retrieval performance with two feedback vector length control methods in...", "weights": [[8, 13, 1.0], [43, 52, 1.0], [131, 140, 1.0], [178, 184, 1.0]]}, "2003.sigirconf_conference-2003.29": {"snippet": "Query length in interactive information retrieval\n\n\n ABSTRACTQuery length in best-match information retrieval (IR) systems is well known to be positively related to effectiveness in the IR task, when ...", "weights": [[0, 5, 1.0], [6, 12, 1.0], [40, 49, 1.0], [67, 73, 1.0], [100, 109, 1.0]]}, "2007.sigirconf_conference-2007.20": {"snippet": "...erms query terms, the readability of the snippet, and the length of the URL shown in the caption, can significantly influence users' Web search behavior.", "weights": [[8, 13, 1.0], [61, 67, 1.0], [119, 128, 1.0]]}, "2018.ipm_journal-ir0volumeA54A2.6": {"snippet": "...tion retrieval\n\n\n A B S T R A C TDocument length normalization is one of the fundamental components in a retrieval model because term frequencies can readily be increased in long documents. The key hy...", "weights": [[8, 17, 1.0], [45, 51, 1.0], [108, 117, 1.0]]}, "1994.trec_conference-1994.19": {"snippet": "Query Expansion/Reduction and its Impact on Retrieval Effectiveness", "weights": [[0, 5, 1.0], [44, 53, 1.0]]}, "1998.trec_conference-1998.34": {"snippet": "...e of Query Concepts and Information Extraction to Improve Information Retrieval Effectiveness", "weights": [[8, 13, 1.0], [73, 82, 1.0]]}, "W00-1105": {"snippet": "... and Retrieval Effectiveness of Phrasal Indexing Terms\n\n\n In spite of long controversy, effectiveness of phrasal indexing is not yet clear. Recently, correlation between query length and effect of phr...", "weights": [[8, 17, 1.0], [173, 178, 1.0], [179, 185, 1.0]]}, "O12-5002": {"snippet": "...ment Retrieval\n\n\n This paper proposed an integrated approach for Cross-Language Information Retrieval (CLIR), which integrated with four statistical models: Translation model, Query generation model, ...", "weights": [[8, 17, 1.0], [95, 104, 1.0], [179, 184, 1.0]]}, "2004.spire_conference-2004.27": {"snippet": "...ment Length in Logical Retrieval", "weights": [[8, 14, 1.0], [26, 35, 1.0]]}, "2004.clef_workshop-2004.26": {"snippet": "...tion Retrieval platform, Terrier, and experimented with query expansion and query length normalisation.", "weights": [[8, 17, 1.0], [59, 64, 1.0], [79, 84, 1.0], [85, 91, 1.0]]}, "1985.ipm_journal-ir0volumeA21A4.1": {"snippet": "... for query evaluation in weighted information retrieval", "weights": [[8, 13, 1.0], [49, 58, 1.0]]}, "2004.sigirconf_conference-2004.40": {"snippet": "...ment retrieval service by processing huge Inverted File indexes. Inverted File indexes allow fast query resolution and good memory utilization since their d-gaps representation can be effectively and ...", "weights": [[8, 17, 1.0], [101, 106, 1.0]]}, "2010.ipm_journal-ir0volumeA46A4.2": {"snippet": "The influence of personalization on tag query length in social media search\n\n\n a b s t r a c tSocial content systems contain enormous collections of unstructured user-generated content, annotated by t...", "weights": [[4, 13, 1.0], [40, 45, 1.0], [46, 52, 1.0]]}, "2012.cikm_conference-2012.353": {"snippet": "... the retrieval. Document length normalisation is one such aspect that requires tuning on a per-query and per-collection basis for many retrieval functions.In this paper, we develop an approach that re...", "weights": [[8, 17, 1.0], [28, 34, 1.0], [98, 103, 1.0], [138, 147, 1.0]]}, "2017.sigirconf_conference-2017.128": {"snippet": "... the query box has a halo or glow e ect, that changes as the query becomes longer, has been shown to increase query length by one term, on average. In this paper, we test whether a similar increase is...", "weights": [[8, 13, 1.0], [64, 69, 1.0], [113, 118, 1.0], [119, 125, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.9": {"snippet": "...what influence the given retrieval models have on accessing all the relevant information in the collection. This is particularly important for recall oriented retrieval applications where we want to e...", "weights": [[8, 17, 1.0], [28, 37, 1.0], [162, 171, 1.0]]}, "2007.ipm_journal-ir0volumeA43A1.3": {"snippet": "...oved retrieval performance. Our results further demonstrate a strong relationship between query length and performance.", "weights": [[8, 17, 1.0], [93, 98, 1.0], [99, 105, 1.0]]}, "W17-2328": {"snippet": "...to a query, applied to biomedical literature. Instead of mapping each document and query to a common semantic space, we compute a variable-length difference vector between the query and document which...", "weights": [[8, 13, 1.0], [86, 91, 1.0], [142, 148, 1.0], [179, 184, 1.0]]}, "2010.clef_workshop-2010w.29": {"snippet": "...heir length (number of terms) and position (first query or further reformulations) was examined in a session with respect to query performance estimators such as query scope, IDF-based measures, simpl...", "weights": [[8, 14, 1.0], [53, 58, 1.0], [128, 133, 1.0], [165, 170, 1.0]]}, "2015.cikm_conference-2015.198": {"snippet": "Query Length, Retrievability Bias and Performance", "weights": [[0, 5, 1.0], [6, 12, 1.0]]}, "2008.sigirconf_conference-2008.202": {"snippet": "...n of retrieval model.Hence, we propose a solution that automatically adds structural information to the unstructured query, and represents it as a Mediated Query. The mediated query is an intermediate...", "weights": [[8, 17, 1.0], [120, 125, 1.0], [159, 164, 1.0], [179, 184, 1.0]]}, "2003.trec_conference-2003.84": {"snippet": "...d to query metadata requirements; implemented clarification forms based on query expansion, passage retrieval, and clustering; and retrieved variable length passages deemed most likely to be relevant....", "weights": [[8, 13, 1.0], [78, 83, 1.0], [103, 112, 1.0], [153, 159, 1.0]]}, "2020.coling-main.319": {"snippet": "...sing length constraints based on length-aware positional encoding (PE). Since length constraints with exact target sentence lengths degrade translation performance, we add random noise within a certai...", "weights": [[8, 14, 1.0], [36, 42, 1.0], [81, 87, 1.0]]}}}
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expressions in each document and the temporal information of previous...", "weights": [[8, 16, 1.0], [52, 60, 1.0], [98, 106, 1.0], [125, 133, 1.0], [154, 162, 1.0], [171, 179, 1.0], [180, 191, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "... the document-at least, not when the query is shorter than about 30 words. For document retrieval, long enough spans of speech can be readily handled.However, information retrieval is not just about d...", "weights": [[8, 16, 1.0], [82, 90, 1.0], [91, 100, 1.0], [162, 173, 1.0], [174, 183, 1.0], [202, 210, 1.0]]}, "P13-2145": {"snippet": "...with temporal constraints extracted from eventrelated sentences. 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We employ a fully automated temporal processing system to gener...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [54, 62, 1.0], [63, 74, 1.0], [125, 138, 1.0], [168, 176, 1.0]]}, "2000.clef_workshop-2000.0": {"snippet": "...gual information retrieval \u2022 bilingual information retrieval \u2022 monolingual (non-English) information retrieval \u2022 cross-language domain-specific information retrieval The main task -inherited from TREC...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [42, 53, 1.0], [54, 63, 1.0], [92, 103, 1.0], [104, 113, 1.0], [147, 158, 1.0], [159, 168, 1.0]]}, "2007.ipm_journal-ir0volumeA43A6.16": {"snippet": "... for summarization-inspired temporal-relation extraction\n\n\n AbstractThis paper presents a model that incorporates contemporary theories of tense and aspect and develops a new framework for extracting ...", "weights": [[8, 21, 1.0], [31, 39, 1.0]]}, "2010.ntcir_workshop-2010.24": {"snippet": "...oral Retrieval aims at to improve retrieval results using Geographic and Temporal dimensions of relevance. To accomplish that task, systems need to extract geographic and temporal information from the...", "weights": [[8, 17, 1.0], [37, 46, 1.0], [76, 84, 1.0], [135, 142, 1.0], [174, 182, 1.0], [183, 194, 1.0]]}, "2014.trec_conference-2014.68": {"snippet": "The Information Extraction systems of BUPT_PRIS at TREC2014 Temporal Summarization Track\n\n\n This paper describes the information extraction systems of BUPT_PRIS at Temporal Summarization Track, Which ...", "weights": [[4, 15, 1.0], [27, 34, 1.0], [60, 68, 1.0], [69, 82, 1.0], [117, 128, 1.0], [140, 147, 1.0], [164, 172, 1.0], [173, 186, 1.0]]}, "S10-1062": {"snippet": "...ing, information extraction, and document summarization. In this paper, we describe two systems we submitted to the TempEval 2 challenge, for extracting temporal information from raw text. The systems...", "weights": [[8, 19, 1.0], [36, 44, 1.0], [45, 58, 1.0], [91, 98, 1.0], [156, 164, 1.0], [165, 176, 1.0], [196, 203, 1.0]]}, "2013.ictir_conference-2013.29": {"snippet": "...s to temporal bounding have assumed the provision of a reference document from which to extract temporal bounds. We argue that a real-world setting does not include a reference document and that an in...", "weights": [[8, 16, 1.0], [68, 76, 1.0], [99, 107, 1.0], [180, 188, 1.0], [201, 212, 1.0]]}, "2004.ntcir_workshop-2004.37": {"snippet": "...ulti-Document Summarization Using Document Set Type Classification\n\n\n In this paper, we propose a summarization system which automatically classifies type of document set and summarizes a document set...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [37, 45, 1.0], [101, 114, 1.0], [161, 169, 1.0], [191, 199, 1.0]]}, "2013.trec_conference-2013.11": {"snippet": "...TREC Temporal Summarization Track 2013. Since this is the first track which is held on temporal summarization, the traditional text retrieval framework is introduced to solve the newly emerging tempor...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [90, 98, 1.0], [99, 112, 1.0], [135, 144, 1.0], [197, 205, 1.0]]}, "2013.clef_conference-2013w.177": {"snippet": "...ulti-document summarization, ii) Focused IR and iii) online multi-document Summarization. The Offline multi-document summarization system is based on document graph, clustering and sentence compressio...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [69, 77, 1.0], [78, 91, 1.0], [111, 119, 1.0], [120, 133, 1.0], [153, 161, 1.0]]}, "D11-1040": {"snippet": "...rans-Temporal Summarization\n\n\n We investigate an important and challenging problem in summary generation, i.e., Evolutionary Trans-Temporal Summarization (ETTS), which generates news timelines from ma...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [134, 142, 1.0], [143, 156, 1.0]]}, "2017.ecir_conference-2017.33": {"snippet": "...tes. Systems developed typically combine in an ad-hoc fashion traditional retrieval and document summarization algorithms to filter sentences inside documents. Retrieval and summarization algorithms h...", "weights": [[8, 15, 1.0], [77, 86, 1.0], [91, 99, 1.0], [100, 113, 1.0], [163, 172, 1.0], [177, 190, 1.0]]}, "1997.sigirconf_conference-97.33": {"snippet": "...each document by traditional statistical analysis of the words in the document and then a second representation for each document based on the cluster technique using the text of documents citing, and...", "weights": [[8, 16, 1.0], [73, 81, 1.0], [124, 132, 1.0]]}, "2016.ecir_conference-2016.27": {"snippet": "...ulti-document Summarization Based on Atomic Semantic Events and Their Temporal Relationships", "weights": [[8, 16, 1.0], [17, 30, 1.0], [73, 81, 1.0]]}, "2006.mir_workshop-2006.6": {"snippet": "...ring temporal consistency for video analysis and retrieval", "weights": [[8, 16, 1.0], [52, 61, 1.0]]}, "2016.sigirjournals_journal-ir0volumeA50A1.16": {"snippet": "Temporal Dynamics in Information Retrieval", "weights": [[0, 8, 1.0], [21, 32, 1.0], [33, 42, 1.0]]}, "2014.trec_conference-2014.34": {"snippet": "...TREC Temporal Summarization 2014\n\n\n This paper describes the IRIT lab participation to the 2014 TREC Temporal Summarization track. The goal of the Temporal Summarization track is to develop systems th...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [104, 112, 1.0], [113, 126, 1.0], [150, 158, 1.0], [159, 172, 1.0], [193, 200, 1.0]]}, "2016.sigirconf_conference-2016.227": {"snippet": "Temporal Information Retrieval\n\n\n ABSTRACTThe study of temporal dynamics and its impact can be framed within the so-called temporal IR approaches, which explain how user behavior, document content and...", "weights": [[0, 8, 1.0], [9, 20, 1.0], [21, 30, 1.0], [55, 63, 1.0], [123, 131, 1.0], [180, 188, 1.0]]}, "2011.wwwconf_workshop-2011ldow.1": {"snippet": "...e of information retrieval tasks such as document exploration, similarity search, summarization, and clustering. Traditionally, information retrieval applications do not take full advantage of all the...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [44, 52, 1.0], [85, 98, 1.0], [131, 142, 1.0], [143, 152, 1.0]]}, "W03-1115": {"snippet": "...resh information. However, fast indexing alone is not enough to retrieve fresh information, as support for temporal information based retrieval is also required. In this paper, we describe temporal in...", "weights": [[8, 19, 1.0], [82, 93, 1.0], [110, 118, 1.0], [119, 130, 1.0], [137, 146, 1.0], [192, 200, 1.0], [201, 212, 1.0]]}, "H01-1022": {"snippet": "... For Temporal Summarization", "weights": [[8, 16, 1.0], [17, 30, 1.0]]}, "P06-1076": {"snippet": "...n of Document, Sentence, and Term Event Spaces\n\n\n The trend in information retrieval systems is from document to sub-document retrieval, such as sentences in a summarization system and words or phrase...", "weights": [[8, 16, 1.0], [66, 77, 1.0], [78, 87, 1.0], [88, 95, 1.0], [104, 112, 1.0], [120, 128, 1.0], [129, 138, 1.0], [163, 176, 1.0]]}, "2011.sigirconf_conference-2011.230": {"snippet": "...oral Information Retrieval (T-IR) has been a topic of great interest in recent years. Its purpose is to improve the information retrieval of documents by exploiting temporal information. However and d...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [119, 130, 1.0], [131, 140, 1.0], [168, 176, 1.0], [177, 188, 1.0]]}, "2012.cikm_conference-2012.79": {"snippet": "...ased retrieval systems allow efficient access to individual documents in archives, we still lack methods for understanding a corpus as a whole. In this paper, we explore methods that provide a tempora...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [196, 204, 1.0]]}, "2015.trec_conference-2015.8": {"snippet": "...2015 Temporal Summarization Track\n\n\n In this paper, we describe our efforts for TREC Temporal Summarization Track 2015. Since this is the third time we participate in this track,we adopt a different n...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [88, 96, 1.0], [97, 110, 1.0]]}, "2015.ipm_journal-ir0volumeA51A6.4": {"snippet": "...a of Information Retrieval, the task of automatic text summarization usually assumes a static underlying collection of documents, disregarding the temporal dimension of each document. However, in real...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [58, 71, 1.0], [150, 158, 1.0], [177, 185, 1.0]]}, "Y14-1054": {"snippet": "...atic summarization has also been increasing to help people grasp the essential points of the documents. Many summarization techniques dealing with single document and multi-documents have been studied...", "weights": [[8, 21, 1.0], [112, 125, 1.0], [157, 165, 1.0]]}, "2004.ntcir_workshop-2004.67": {"snippet": "...ment Summarization for Information Retrieval\n\n\n This paper presents a genre-independent method of generating a single summary from a set of the retrieved documents for information retrieval. The propo...", "weights": [[8, 21, 1.0], [26, 37, 1.0], [38, 47, 1.0], [171, 182, 1.0], [183, 192, 1.0]]}, "C10-1111": {"snippet": "...ulti-Document Summarization via the Minimum Dominating Set\n\n\n Multi-document summarization has been an important problem in information retrieval. It aims to distill the most important information fro...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [71, 79, 1.0], [80, 93, 1.0], [127, 138, 1.0], [139, 148, 1.0], [188, 199, 1.0]]}, "P98-2205": {"snippet": "Summarization-based Query Expansion in Information Retrieval\n\n\n We discuss a seml-interactive approach to information retrieval which consists of two tasks performed in a sequence. First, the system a...", "weights": [[0, 13, 1.0], [39, 50, 1.0], [51, 60, 1.0], [106, 117, 1.0], [118, 127, 1.0]]}, "2015.trec_conference-2015.33": {"snippet": "...2015 Temporal Summarization Track\n\n\n The goal of Temporal Summarization task is to develop systems which can detect useful, new, and timely sentence-length updates about a developing event. This paper...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [52, 60, 1.0], [61, 74, 1.0], [94, 101, 1.0]]}, "2005.ipm_journal-ir0volumeA41A3.12": {"snippet": "Temporal Document Retrieval Model for business news archives\n\n\n AbstractTemporal expressions occurring in business news, such as ''last week'' or ''at the end of this month,'' carry important informat...", "weights": [[0, 8, 1.0], [9, 17, 1.0], [18, 27, 1.0], [192, 203, 1.0]]}, "2010.gir_workshop-2010.7": {"snippet": "...atio-temporal information in documents", "weights": [[8, 16, 1.0], [17, 28, 1.0]]}, "2013.trec_conference-2013.31": {"snippet": "...T at Temporal Summarization Track TREC 2013\n\n\n This paper describes our participation in temporal summarization track of TREC2013. All runs are submitted for both two tasks, namely sequential update s...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [92, 100, 1.0], [101, 114, 1.0], [202, 215, 1.0]]}, "P18-1149": {"snippet": "...s\n\n\n Document date is essential for many important tasks, such as document retrieval, summarization, event detection, etc. While existing approaches for these tasks assume accurate knowledge of the do...", "weights": [[8, 16, 1.0], [69, 77, 1.0], [78, 87, 1.0], [89, 102, 1.0], [201, 209, 1.0]]}, "2007.ipm_journal-ir0volumeA43A6.11": {"snippet": "...tion retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave acr...", "weights": [[8, 17, 1.0], [18, 25, 1.0], [99, 106, 1.0]]}, "2002.ntcir_workshop-2002.61": {"snippet": "...om a document and itemizes them to generate a summary. We applied this mechanism for Task A (single document summarization). We also utilized this mechanism for multi-document summarization (Task B) e...", "weights": [[8, 16, 1.0], [103, 111, 1.0], [112, 125, 1.0], [170, 178, 1.0], [179, 192, 1.0]]}, "2007.sigirconf_conference-2007.179": {"snippet": "... the temporal dimension to multi-document summarization\n\n\n ABSTRACTGraph-ranking based algorithms (e.g. TextRank) have been proposed for multi-document summarization in recent years. However, these al...", "weights": [[8, 16, 1.0], [36, 44, 1.0], [45, 58, 1.0], [146, 154, 1.0], [155, 168, 1.0]]}, "2013.trec_conference-2013.70": {"snippet": "...2013 Temporal Summarization Track\n\n\n The University of Waterloo participated in the Temporal Summarization Track at TREC 2013 and submitted 8 runs for the Sequential Update Summarization Task. Methods...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [87, 95, 1.0], [96, 109, 1.0], [176, 189, 1.0]]}, "2010.spire_conference-2010.30": {"snippet": "Temporal Analysis of Document Collections: Framework and Applications", "weights": [[0, 8, 1.0], [21, 29, 1.0]]}, "2012.sigirconf_conference-2012.109": {"snippet": "...uses temporal topics as part of user interest to retrieve documents. We believe that temporal topic-based retrieval is a one solution to improve user experience of present IR systems, as well as to be...", "weights": [[8, 16, 1.0], [88, 96, 1.0], [109, 118, 1.0], [178, 185, 1.0]]}, "2009.airs_conference-2009.8": {"snippet": "...tive Summarization Based on Event Term Temporal Relation Graph and Critical Chain\n\n\n Abstract. In this paper, we investigate whether temporal relations among event terms can help improve event-based e...", "weights": [[8, 21, 1.0], [42, 50, 1.0], [136, 144, 1.0]]}, "2003.wwwconf_conference-2003p.157": {"snippet": "...e V2 Temporal Document Database System", "weights": [[8, 16, 1.0], [17, 25, 1.0]]}, "2020.sigirconf_conference-2020.323": {"snippet": "...TS \u2022 Information systems \u2192 Information systems applications; \u2022 Applied computing \u2192 Document management and text processing.", "weights": [[8, 19, 1.0], [20, 27, 1.0], [30, 41, 1.0], [42, 49, 1.0], [86, 94, 1.0]]}, "2007.sigirjournals_journal-ir0volumeA41A2.3": {"snippet": "...e of temporal information in information retrieval", "weights": [[8, 16, 1.0], [17, 28, 1.0], [32, 43, 1.0], [44, 53, 1.0]]}, "D18-1213": {"snippet": "...from document content itself is an important task. In this paper, we propose Attentive Deep Document Dater (AD3), an attention-based neural document dating system which utilizes both context and tempo...", "weights": [[8, 16, 1.0], [95, 103, 1.0], [143, 151, 1.0], [198, 206, 1.0]]}, "K18-1023": {"snippet": "...line Summarization\n\n\n Timeline summarization (TLS) creates an overview of long-running events via dated daily summaries for the most important dates. TLS differs from standard multi-document summariza...", "weights": [[8, 21, 1.0], [34, 47, 1.0], [185, 193, 1.0], [194, 207, 1.0]]}, "2015.trec_conference-2015.60": {"snippet": "...t an information situation where it is essential to give users access to important and non-redundant information as fast as possible. In this paper, we introduce SeqCluSum, a temporal summarization sy...", "weights": [[8, 19, 1.0], [104, 115, 1.0], [178, 186, 1.0], [187, 200, 1.0]]}, "2021.ecir_conference-20212.53": {"snippet": "...vant temporal happenings of a story through multiple components, including temporal annotation, storylines or temporal clustering. In contrast to classical timeline multi-document summarization tasks,...", "weights": [[8, 16, 1.0], [78, 86, 1.0], [113, 121, 1.0], [174, 182, 1.0], [183, 196, 1.0]]}, "2015.trec_conference-2015.32": {"snippet": "...TREC Temporal Summarization 2015\n\n\n This paper describes the IRIT lab participation to the TREC 2015 Temporal Summarization track. The goal of the Temporal Summarization track is to develop systems th...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [104, 112, 1.0], [113, 126, 1.0], [150, 158, 1.0], [159, 172, 1.0], [193, 200, 1.0]]}, "2017.wwwjournals_journal-ir0volumeA20A2.5": {"snippet": "...cial-temporal context for Twitter summarization. In particular, we provide a methodology to model temporal context globally and locally, and propose a novel unsupervised summarization framework with s...", "weights": [[8, 16, 1.0], [37, 50, 1.0], [101, 109, 1.0], [173, 186, 1.0]]}, "2012.spire_conference-2012.21": {"snippet": "Temporal Web Image Retrieval", "weights": [[0, 8, 1.0], [19, 28, 1.0]]}, "2013.trec_conference-2013.57": {"snippet": "The Information Extraction Systems of PRIS at Temporal Summarization Track\n\n\n This paper describes the information extraction systems of PRIS at Temporal Summarization Track. The Temporal Summarizatio...", "weights": [[4, 15, 1.0], [27, 34, 1.0], [46, 54, 1.0], [55, 68, 1.0], [103, 114, 1.0], [126, 133, 1.0], [145, 153, 1.0], [154, 167, 1.0], [179, 187, 1.0], [188, 201, 1.0]]}, "2012.sigirjournals_journal-ir0volumeA46A1.12": {"snippet": "...s to information retrieval\n\n\n AbstractIn this thesis, we address major challenges in searching temporal document collections. In such collections, documents are created and/or edited over time. Exampl...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [98, 106, 1.0], [107, 115, 1.0]]}, "2015.wwwconf_conference-2015c.302": {"snippet": "...n of temporal information in documents provide an important ingredient in many of today's frameworks for text analysis. Methods range from basic techniques, primarily the extraction of temporal expres...", "weights": [[8, 16, 1.0], [17, 28, 1.0], [188, 196, 1.0]]}, "C98-2200": {"snippet": "Summarization-based Query Expansion in Information Retrieval\n\n\n We discuss a semi-interactive approach to information retrieval which consists of two tasks performed in a sequence. First, the system a...", "weights": [[0, 13, 1.0], [39, 50, 1.0], [51, 60, 1.0], [106, 117, 1.0], [118, 127, 1.0]]}, "2011.spire_conference-2011.4": {"snippet": "...cing Document Snippets Using Temporal Information", "weights": [[8, 16, 1.0], [32, 40, 1.0], [41, 52, 1.0]]}, "2013.sigirjournals_journal-ir0volumeA47A2.17": {"snippet": "...icit temporal queries for temporal information retrieval applications\n\n\n Time is an important dimension of the information retrieval area that can be very useful in helping to meet the users informati...", "weights": [[8, 16, 1.0], [29, 37, 1.0], [38, 49, 1.0], [50, 59, 1.0], [114, 125, 1.0], [126, 135, 1.0], [194, 205, 1.0]]}, "2015.trec_conference-2015.71": {"snippet": "...2015 Temporal Summarization Track\n\n\n In this paper we report on our participation in the TREC 2015 Temporal Summarization track, aimed at encouraging the development of systems able to detect, emit, t...", "weights": [[8, 16, 1.0], [17, 30, 1.0], [102, 110, 1.0], [111, 124, 1.0], [172, 179, 1.0]]}}}
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The proposed model, which repre...", "weights": [[8, 12, 1.0], [27, 35, 1.0], [36, 49, 1.0]]}, "2020.mir_conference-2020.4": {"snippet": "...n of Deep Learning - Theory and Practice", "weights": [[8, 12, 1.0], [13, 21, 1.0]]}, "W16-5906": {"snippet": "...rent deep learning architectures for these structural learning tasks.", "weights": [[8, 12, 1.0], [13, 21, 1.0], [22, 35, 1.0], [57, 65, 1.0]]}, "D17-1144": {"snippet": "...sing deep learning\n\n\n We propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate. ...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [38, 42, 1.0], [43, 51, 1.0]]}, "W18-0539": {"snippet": "Deep Learning Architecture for Complex Word Identification\n\n\n We describe a system for the CWI-task that includes information on 5 aspects of the (complex) lexical item, namely distributional informat...", "weights": [[0, 4, 1.0], [5, 13, 1.0]]}, "W18-2411": {"snippet": "A Deep Learning Based Approach to Transliteration\n\n\n In this paper, we propose different architectures for language independent machine transliteration which is extremely important for natural languag...", "weights": [[2, 6, 1.0], [7, 15, 1.0], [89, 102, 1.0]]}, "2021.emnlp-main.465": {"snippet": "...rmer Modifications Transfer Across Implementations and Applications?\n\n\n The research community has proposed copious modifications to the Transformer architecture since it was introduced over three yea...", "weights": [[8, 21, 1.0], [119, 132, 1.0]]}, "2002.wwwconf_conference-2002.63": {"snippet": "...hese modifications enable multimedia content to be delivered to the mobile clients from the best surrogates in the CDN. 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In particular, we propose a general deep architecture for CF by inte...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [23, 27, 1.0], [28, 36, 1.0], [78, 86, 1.0], [171, 175, 1.0]]}, "2017.clef_conference-2017w.83": {"snippet": "Learning with Noisy and Trusted Labels for Fine-Grained Plant Recognition\n\n\n The paper describes the deep learning approach to automatic visual recognition of 10 000 plant species submitted to the Pla...", "weights": [[0, 8, 1.0], [101, 105, 1.0], [106, 114, 1.0]]}, "W17-4710": {"snippet": "...ding deep transition RNNs, and we vary how attention is used in the deep decoder. We introduce a novel \"BiDeep\" RNN architecture that combines deep transition RNNs and stacked RNNs. Our evaluation is ...", "weights": [[8, 12, 1.0], [71, 75, 1.0], [146, 150, 1.0]]}, "2018.sigirconf_workshop-2018ecom.17": {"snippet": "...hine Learning Approach\n\n\n Rakuten Data Challenge suggests tackling the Large-Scale Taxonomy Challenge. Given a large amount of product titles and category paths leading to these products, we would lik...", "weights": [[8, 16, 1.0]]}, "W18-2303": {"snippet": "...y of deep learning, a re-emerged data-driven technique, in the context of clinical text classification. Various deep learning architectures were devised and evaluated for extracting heart disease risk...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [115, 119, 1.0], [120, 128, 1.0], [129, 142, 1.0]]}, "D16-1012": {"snippet": "Deep Multi-Task Learning with Shared Memory for Text Classification\n\n\n Neural network based models have achieved impressive results on various specific tasks. However, in previous works, most models a...", "weights": [[0, 4, 1.0], [16, 24, 1.0]]}, "2012.airs_conference-2012.10": {"snippet": "...asic modifications and 4 search algorithms across 5 publicly availably available datasets, and give comparisons to similarly optimized Multiclass Support Vector Machine (SVM) classifiers. The use of o...", "weights": [[8, 21, 1.0]]}, "W14-2405": {"snippet": "...ovel deep learning architecture which provides a semantic parsing system through the union of two neural models of language semantics. It allows for the generation of ontology-specific queries from na...", "weights": [[8, 12, 1.0], [13, 21, 1.0]]}, "1999.cikm_conference-99.28": {"snippet": "...what modifications will be made to the query, and how to score the results. These decisions are typically made by considering only the user's keyword query, neglecting the larger information need. Use...", "weights": [[8, 21, 1.0]]}, "2020.coling-main.324": {"snippet": "... for modifications to SANs that enable faster learning, i.e., higher accuracies after fewer update steps. We investigate three modifications to SANs: direct position interactions, learnable temperatur...", "weights": [[8, 21, 1.0], [49, 57, 1.0], [130, 143, 1.0]]}, "2021.findings-acl.289": {"snippet": "...odel architectures are typically deep, resulting in slow training and inference, especially for long inputs. To improve efficiency while maintaining a high accuracy, we propose a new architecture, DoT...", "weights": [[8, 21, 1.0], [36, 40, 1.0]]}, "2018.wsdm_conference-2018.10": {"snippet": "Deep Neural Architecture for Multi-Modal Retrieval based on Joint Embedding Space for Text and Images\n\n\n ABSTRACTRecent advances in deep learning and distributed representations of images and text hav...", "weights": [[0, 4, 1.0], [132, 136, 1.0], [137, 145, 1.0]]}, "N18-1117": {"snippet": "...gned deep neural networks into its encoder-decoder framework. From the optimization perspective, residual connections are adopted to improve learning performance for both encoder and decoder in most o...", "weights": [[8, 12, 1.0], [144, 152, 1.0]]}, "D18-1185": {"snippet": "...ural Architectures with Alignment Factorization for Natural Language Inference\n\n\n This paper presents a new deep learning architecture for Natural Language Inference (NLI). Firstly, we introduce a new...", "weights": [[8, 21, 1.0], [111, 115, 1.0], [116, 124, 1.0]]}, "W11-0215": {"snippet": "...tein Modifications\n\n\n Protein modifications, in particular posttranslational modifications, have a central role in bringing about the full repertoire of protein functions, and the identification of sp...", "weights": [[8, 21, 1.0], [33, 46, 1.0], [80, 93, 1.0]]}, "2012.ismir_conference-2012.68": {"snippet": "...ign: Deep Architectures and Automatic Feature Learning in Music Informatics", "weights": [[8, 12, 1.0], [13, 26, 1.0], [49, 57, 1.0]]}, "S15-2037": {"snippet": "...}: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge\n\n\n In this paper, we present a comment labeling system based on a deep learning strategy. We treat the answer s...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [158, 162, 1.0], [163, 171, 1.0]]}, "W19-7603": {"snippet": "A Deep Learning Curve for Post-Editing 2", "weights": [[2, 6, 1.0], [7, 15, 1.0]]}, "P17-5003": {"snippet": "Deep Learning for Semantic Composition", "weights": [[0, 4, 1.0], [5, 13, 1.0]]}, "2020.mir_conference-2020.40": {"snippet": "... via Deep Reinforcement Learning", "weights": [[8, 12, 1.0], [27, 35, 1.0]]}, "D19-5707": {"snippet": "Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in {S}panish clinical text\n\n\n In this work, we introduce a Deep Learning architecture for pharmaceutical...", "weights": [[0, 4, 1.0], [155, 159, 1.0], [160, 168, 1.0]]}, "2021.emnlp-main.59": {"snippet": "...rmer architectures tend to include positional knowledge, by (for instance) adding positional encodings to token embeddings. Several modifications have been proposed over the sinusoidal positional enco...", "weights": [[8, 21, 1.0], [135, 148, 1.0]]}, "2020.sigirconf_conference-2020.233": {"snippet": "...vice deep learning necessitates the deployment of deep models on mobile devices. However, directly deploying deep models on mobile devices presents both capacity bottleneck and prohibitive privacy ris...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [53, 57, 1.0], [112, 116, 1.0]]}, "W19-5211": {"snippet": "...gh a deep network of hidden layers. We argue that the need to represent and propagate lexical features in each layer limits the model's capacity for learning and representing other information relevan...", "weights": [[8, 12, 1.0], [152, 160, 1.0]]}, "W18-2506": {"snippet": "...t of deep learning architectures. It also provides implementations for simple, high-performance, deep learning models for various NLP tasks, against which newly developed models can be compared. Deep ...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [22, 35, 1.0], [100, 104, 1.0], [105, 113, 1.0], [198, 202, 1.0]]}, "2020.acl-main.561": {"snippet": "...h in deep learning architectures for natural language understanding.", "weights": [[8, 12, 1.0], [13, 21, 1.0], [22, 35, 1.0]]}, "2021.emnlp-demo.27": {"snippet": "...e in Deep Learning\n\n\n We demonstrate a library for the integration of domain knowledge in deep learning architectures. Using this library, the structure of the data is expressed symbolically via graph...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [93, 97, 1.0], [98, 106, 1.0], [107, 120, 1.0]]}, "D17-1191": {"snippet": "Deep Residual Learning for Weakly-Supervised Relation Extraction\n\n\n Deep residual learning (ResNet) (He et al., 2016) is a new method for training very deep neural networks using identity mapping for ...", "weights": [[0, 4, 1.0], [14, 22, 1.0], [68, 72, 1.0], [82, 90, 1.0], [152, 156, 1.0]]}, "W15-1705": {"snippet": "...ture learning and deep learning over labeled and unlabeled tweets, we are able to learn in a more generalizable way than N-gram techniques. Our proposed hidden layer sharing approach makes it possible...", "weights": [[8, 16, 1.0], [21, 25, 1.0], [26, 34, 1.0]]}, "2015.mir_conference-2015.97": {"snippet": "...sing Deep Learning Representations", "weights": [[8, 12, 1.0], [13, 21, 1.0]]}, "N19-1337": {"snippet": "...rial Modifications\n\n\n Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on imp...", "weights": [[8, 21, 1.0], [122, 130, 1.0]]}, "2018.wwwconf_conference-2018c.107": {"snippet": "Learning Procedures from Text: Codifying How-to Procedures in Deep Neural Networks\n\n\n ABSTRACTA lot of knowledge about procedures and how-tos are described in text. Recently, extracting semantic relat...", "weights": [[0, 8, 1.0], [62, 66, 1.0]]}, "2018.clef_conference-2018w.79": {"snippet": "...sing Deep Convolutional Neural Networks\n\n\n Deep learning techniques have significantly improved plant species classification in recent years. The goal of the 2018 ExpertLife-CLEF challenge was to comp...", "weights": [[8, 12, 1.0], [46, 50, 1.0], [51, 59, 1.0]]}, "2019.trec_conference-2019.7": {"snippet": "...2019 Deep Learning Track", "weights": [[8, 12, 1.0], [13, 21, 1.0]]}, "D18-1479": {"snippet": "...is a deep architecture, involving stacked (multi-layered) recurrent encoders. Stacked/Deep architectures are traditionally difficult to train, due to the inherent weaknesses such as difficulty with fe...", "weights": [[8, 12, 1.0], [89, 93, 1.0], [94, 107, 1.0]]}, "C18-1201": {"snippet": "...odal deep learning in human spoken language understanding. We present a deep multimodal network with both feature attention and modality attention to classify utterance-level speech data. The proposed...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [75, 79, 1.0]]}, "2020.acl-srw.42": {"snippet": "...s in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution. However...", "weights": [[8, 12, 1.0], [13, 21, 1.0]]}, "W18-5304": {"snippet": "...s to deep learning architectures that operate solely over the text of the query and candidate document/snippets. Our systems scored at the top or near the top for all batches of the challenge, highlig...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [22, 35, 1.0]]}, "2017.clef_conference-2017w.39": {"snippet": "...sing Deep Residual Learning Network\n\n\n Medical images are often used in clinical diagnosis. However, interpreting the insights gained from them is often a time-consuming task even for experts. For thi...", "weights": [[8, 12, 1.0], [22, 30, 1.0]]}, "2022.semeval-1.137": {"snippet": "...ning deep learning with language models has also resulted in acceptable accuracy. Inspired by this, we propose a novel deep learning model on top of language models. On top of T5, the architecture use...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [122, 126, 1.0], [127, 135, 1.0]]}, "2020.trec_conference-2020.1": {"snippet": "2020 Deep Learning Track", "weights": [[5, 9, 1.0], [10, 18, 1.0]]}}}
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Examples include domain/task-speci c knowledge graph constru...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [57, 69, 1.0], [180, 189, 1.0], [190, 195, 1.0], [196, 208, 1.0]]}, "2018.wwwconf_conference-2018c.293": {"snippet": "...oned graph data models through the foundational RDF standard, the recent hype around industry-driven initiativessuch as Google's Knowledge Graph, Facebook's Graph API or the Open Graph Protocol -indic...", "weights": [[8, 13, 1.0], [132, 141, 1.0], [142, 147, 1.0], [160, 165, 1.0], [182, 187, 1.0]]}, "2020.ccl-1.87": {"snippet": "...rism Knowledge Graph Construction based on Heterogeneous Information\n\n\n The increasing amount of semi-structured and unstructured data on tourism websites brings a need for information extraction (IE)...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0]]}, "2018.sigirconf_conference-2018.234": {"snippet": "...s of knowledge graphs, semantics, and IR. The research on knowledge acquisition and alignment often overlooks their applications in text-centric information retrieval systems. Similarly, in IR researc...", "weights": [[8, 17, 1.0], [61, 70, 1.0]]}, "2015.wwwconf_conference-2015c.23": {"snippet": "...rchy Construction for Freebase\n\n\n ABSTRACTThe hierarchical structure of a knowledge base system can lead to various valuable applications; however, many knowledge base systems do not have such propert...", "weights": [[8, 20, 1.0], [77, 86, 1.0], [156, 165, 1.0]]}, "2019.wwwconf_conference-2019c.243": {"snippet": "...s to Knowledge Graphs", "weights": [[8, 17, 1.0]]}, "2016.cikm_conference-2016.273": {"snippet": "...stic Knowledge Graph Construction: Compositional and Incremental Approaches", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0]]}, "2019.wwwconf_conference-2019c.124": {"snippet": "...ng a Knowledge Graph for the Air Traffic Management Community", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "2002.cikm_conference-2002.96": {"snippet": "...tual knowledge base and discovering knowledge therefrom. Defining a novel representation called the Concept Frame Graph (CFG), we propose a learning algorithm for constructing a CFG knowledge base fro...", "weights": [[8, 17, 1.0], [39, 48, 1.0], [117, 122, 1.0], [185, 194, 1.0]]}, "2017.cikm_conference-2017.227": {"snippet": "...s on Knowledge Graph Queries using Provenance Polynomials\n\n\n ABSTRACTCritical business applications in domains ranging from technical support to healthcare increasingly rely on large-scale, automatica...", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "2021.wwwconf_conference-2021.153": {"snippet": "...ient Knowledge Graph Completion", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "N18-5004": {"snippet": "... for knowledge graph construction. As an additional novel initiative, CL Scholar supports more than 1200 scholarly natural language queries along with standard keywordbased search on constructed knowl...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0], [198, 207, 1.0]]}, "2020.wwwconf_conference-2020.234": {"snippet": "Graph-Query Suggestions for Knowledge Graph Exploration\n\n\n We consider the task of exploratory search through graph queries on knowledge graphs. We propose to assist the user by expanding the query wi...", "weights": [[0, 5, 1.0], [28, 37, 1.0], [38, 43, 1.0], [110, 115, 1.0], [127, 136, 1.0]]}, "N19-1104": {"snippet": "...in a knowledge graph is processed and the models can be difficult to interpret. In this paper, we utilize graph patterns in a knowledge graph to overcome such problems. Our proposed model, the graph p...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [109, 114, 1.0], [129, 138, 1.0], [139, 144, 1.0], [196, 201, 1.0]]}, "W16-2814": {"snippet": "... the construction of an Expert Stance Graph, a novel, large-scale knowledge resource that encodes the stance of more than 100,000 experts towards a variety of controversial topics. We suggest that thi...", "weights": [[8, 20, 1.0], [41, 46, 1.0], [69, 78, 1.0]]}, "2020.acl-main.199": {"snippet": "...nomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer\n\n\n Extracting lexico-semantic relations as graphstructured taxonomies, also known as taxonomy construction, has been...", "weights": [[8, 20, 1.0], [43, 48, 1.0], [68, 77, 1.0], [181, 193, 1.0]]}, "2020.coling-main.84": {"snippet": "...cial Knowledge Graph Construction\n\n\n Motivated by applications such as question answering, fact checking, and data integration, there is significant interest in constructing knowledge graphs by extrac...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0], [177, 186, 1.0]]}, "D19-1602": {"snippet": "...ural Knowledge Graph-aware Network\n\n\n Leveraging external knowledge is an emerging trend in machine comprehension task. Previous work usually utilizes knowledge graphs such as ConceptNet as external k...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [61, 70, 1.0], [154, 163, 1.0], [202, 211, 1.0]]}, "2019.wwwconf_conference-2019c.125": {"snippet": "...able Knowledge Graph Construction over Text using Deep Learning based Predicate Mapping", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0]]}, "D18-1360": {"snippet": "...ific Knowledge Graph Construction\n\n\n We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SCIERC, a dataset th...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0]]}, "2021.chiir_workshop-2021birds.6": {"snippet": "...uery Construction and Result Presentation based on Graph Codes", "weights": [[8, 20, 1.0], [54, 59, 1.0]]}, "2020.textgraphs-1.1": {"snippet": "...ause knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be tr...", "weights": [[8, 17, 1.0], [75, 84, 1.0], [85, 90, 1.0], [169, 178, 1.0], [179, 184, 1.0]]}, "2018.wwwconf_conference-2018c.208": {"snippet": "... RDF Graph Data and Embedding Models for an Augmented Knowledge Graph\n\n\n ABSTRACTVector embedding models have recently become popular for encoding both structured and unstructured data. In the context...", "weights": [[8, 13, 1.0], [57, 66, 1.0], [67, 72, 1.0]]}, "2018.wsdm_conference-2018.107": {"snippet": "...rent knowledge graph has swiftly become a core challenge in machine learning. Applications that rely on structured knowledge representations include digital assistants (e.g., Siri, Alexa, Cortana, and...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [118, 127, 1.0]]}, "D19-5321": {"snippet": "... for Graph Merging: Case of Knowledge Graph Construction\n\n\n Combining two graphs requires merging the nodes which are counterparts of each other. In this process errors occur, resulting in incorrect m...", "weights": [[8, 13, 1.0], [31, 40, 1.0], [41, 46, 1.0], [47, 59, 1.0]]}, "W11-0318": {"snippet": "...p in graph-based semi-supervised classification is to construct a graph from input data. While the k-nearest neighbor graphs have been the de facto standard method of graph construction, this paper ad...", "weights": [[8, 13, 1.0], [69, 74, 1.0], [170, 175, 1.0], [176, 188, 1.0]]}, "2022.jeptalnrecital-recital.3": {"snippet": "Construction de Graphes de Connaissance {\\`a} partir de textes avec une {I}.{A}. centr{\\'e}e-utilisateur (Knowledge Graph Construction from Texts with an User-Centric A)\n\n\n Avec l'essor du Web s\u00e9manti...", "weights": [[0, 12, 1.0], [106, 115, 1.0], [116, 121, 1.0], [122, 134, 1.0]]}, "S19-1016": {"snippet": "... for Knowledge Graph Completion\n\n\n Knowledge graphs, which provide numerous facts in a machine-friendly format, are incomplete. Information that we induce from such graphs -e.g. entity embeddings, rel...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [38, 47, 1.0]]}, "P18-1024": {"snippet": "...ulti-Graph Representation Learning with Entity Linkage\n\n\n Knowledge graphs have emerged as an important model for studying complex multirelational data. This has given rise to the construction of nume...", "weights": [[8, 13, 1.0], [61, 70, 1.0], [183, 195, 1.0]]}, "2019.cikm_conference-2019.230": {"snippet": "Knowledge-aware Textual Entailment with Graph Attention Network", "weights": [[0, 9, 1.0], [40, 45, 1.0]]}, "2015.wwwconf_conference-2015c.328": {"snippet": "...cale Knowledge Graphs: WWW 2015 Tutorial\n\n\n ABSTRACTRecent years have witnessed a proliferation of large-scale knowledge graphs, such as Freebase, Google's Knowledge Graph, YAGO, Facebook's Entity Gra...", "weights": [[8, 17, 1.0], [114, 123, 1.0], [159, 168, 1.0], [169, 174, 1.0], [200, 205, 1.0]]}, "N09-2023": {"snippet": "...enda Graph Construction from Human-Human Dialogs using Clustering Method\n\n\n Various knowledge sources are used for spoken dialog systems such as task model, domain model, and agenda. An agenda graph i...", "weights": [[8, 13, 1.0], [14, 26, 1.0], [87, 96, 1.0], [196, 201, 1.0]]}, "P19-1470": {"snippet": "...atic Knowledge Graph Construction\n\n\n We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and Con...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0], [94, 103, 1.0], [109, 121, 1.0], [152, 161, 1.0]]}, "2022.woah-1.7": {"snippet": "...iven Knowledge Graph Construction For Cultural Knowledge and Stereotypes\n\n\n Analyzing ethnic or religious bias is important for improving fairness, accountability, and transparency of natural language...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0], [50, 59, 1.0]]}, "2017.sigirconf_conference-2017.173": {"snippet": "Graph Summarization for Entity Relatedness Visualization\n\n\n ABSTRACTIn modern search engines, Knowledge Graphs have become a key component for knowledge discovery. When a user searches for an entity, ...", "weights": [[0, 5, 1.0], [94, 103, 1.0], [143, 152, 1.0]]}, "2019.sigirconf_workshop-2019ecom.23": {"snippet": "...ised Construction of a Product Knowledge Graph\n\n\n We describe techniques for generating a commercial knowledge graph with the goal of discovering relationships between brands, products, and categories...", "weights": [[8, 20, 1.0], [34, 43, 1.0], [44, 49, 1.0], [104, 113, 1.0], [114, 119, 1.0]]}, "2020.wwwconf_conference-2020c.100": {"snippet": "...ting Knowledge Graph for Social Networks in A Deep and Holistic Way\n\n\n Online social networks such as Facebook and LinkedIn have been an integrated part of everyday life. To improve the user experienc...", "weights": [[8, 17, 1.0], [18, 23, 1.0]]}, "D19-6607": {"snippet": "...able Knowledge Graph Construction from Text Collections\n\n\n We present a scalable, open-source platform that \"distills\" a potentially large text collection into a knowledge graph. Our platform takes do...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0], [165, 174, 1.0], [175, 180, 1.0]]}, "D19-1428": {"snippet": "...ocal Knowledge Graph Construction to Scale {S}eq2{S}eq Models to Multi-Document Inputs\n\n\n Query-based open-domain NLP tasks require information synthesis from long and diverse web results. Current app...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [24, 36, 1.0]]}, "2013.cikm_workshop-2013akbc.4": {"snippet": "...ges. Knowledge graph identification (KGI) is a technique for knowledge graph construction that jointly reasons about entities, attributes and relations in the presence of uncertain inputs and ontologi...", "weights": [[8, 17, 1.0], [18, 23, 1.0], [64, 73, 1.0], [74, 79, 1.0], [80, 92, 1.0]]}, "2016.sigirconf_conference-2016.217": {"snippet": "...cale Knowledge Graphs\n\n\n ABSTRACTRecent years have witnessed a proliferation of large-scale knowledge graphs, from purely academic projects such as YAGO to major commercial projects such as Google's K...", "weights": [[8, 17, 1.0], [95, 104, 1.0], [202, 211, 1.0]]}, "2020.wwwjournals_journal-ir0volumeA23A5.2": {"snippet": "Knowledge graph construction from multiple online encyclopedias", "weights": [[0, 9, 1.0], [10, 15, 1.0], [16, 28, 1.0]]}}}
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In this paper we introduce a deep neural network architecture to perform information extraction on character-based sequences, e.g....", "weights": [[0, 4, 1.0], [5, 13, 1.0], [34, 45, 1.0], [99, 103, 1.0], [143, 154, 1.0]]}, "2018.sigirconf_conference-2018.238": {"snippet": "...p on Learning from Limited or Noisy Data for Information Retrieval\n\n\n ABSTRACTIn recent years, machine learning approaches, and in particular deep neural networks, have yielded significant improvement...", "weights": [[8, 16, 1.0], [48, 59, 1.0], [60, 69, 1.0], [106, 114, 1.0], [115, 125, 1.0], [145, 149, 1.0]]}, "2019.sigirconf_conference-2019.66": {"snippet": "...able Deep Multimodal Learning for Cross-Modal Retrieval", "weights": [[8, 12, 1.0], [24, 32, 1.0], [49, 58, 1.0]]}, "W19-4301": {"snippet": "...tion learning (Deep CCA, (Andrew et al., 2013) ) and linear many-view representation learning (Generalized CCA (Horst, 1961) ) exist, DGCCA combines the flexibility of nonlinear (deep) representation ...", "weights": [[8, 16, 1.0], [18, 22, 1.0], [88, 96, 1.0], [182, 186, 1.0]]}, "2018.ecir_conference-2018.10": {"snippet": "...sage Retrieval\n\n\n Abstract. Answer passage retrieval is an increasingly important information retrieval task as queries become more precise and mobile and audio interfaces more prevalent. In this task...", "weights": [[8, 17, 1.0], [46, 55, 1.0], [85, 96, 1.0], [97, 106, 1.0]]}, "2017.ictir_conference-2017.4": {"snippet": "...M: A Deep Neural Information Retrieval Model Enhanced by a Knowledge Resource Driven Representation of Documents\n\n\n ABSTRACT e state-of-the-art solutions to the vocabulary mismatch in information retr...", "weights": [[8, 12, 1.0], [20, 31, 1.0], [32, 41, 1.0], [187, 198, 1.0], [199, 208, 1.0]]}, "2016.sigirconf_conference-2016.1": {"snippet": "... and Deep Learning Help?\n\n\n ABSTRACTThere is a lot of overlap between the core problems of information retrieval (IR) and natural language processing (NLP). An IR system gains from understanding a use...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2019.cikm_conference-2019.166": {"snippet": "...with Deep Reinforcement Learning in Retrieval-based QA System", "weights": [[8, 12, 1.0], [27, 35, 1.0], [39, 48, 1.0]]}, "2015.wwwconf_conference-2015c.329": {"snippet": "...s of deep learning as well as its key applications. We give the motivation and underlying ideas of deep learning and describe the architectures and learning algorithms for various deep learning models...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [102, 106, 1.0], [107, 115, 1.0], [151, 159, 1.0], [183, 187, 1.0], [188, 196, 1.0]]}, "2016.cikm_conference-2016.7": {"snippet": "...s of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typicall...", "weights": [[8, 12, 1.0], [30, 39, 1.0], [131, 140, 1.0], [178, 182, 1.0]]}, "2016.clef_conference-2016w.24": {"snippet": "...ask. Deep learning has now been used for several of the ImageCLEF tasks and by many of the participants obtaining very good results. A majority of runs was submitting using deep learning and this foll...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [176, 180, 1.0], [181, 189, 1.0]]}, "2019.sigirconf_conference-2019.222": {"snippet": "...ntic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search", "weights": [[8, 17, 1.0], [48, 52, 1.0], [53, 61, 1.0]]}, "2009.ir_journal-ir0volumeA12A2.3": {"snippet": "...ased learning approach to teaching information retrieval", "weights": [[8, 16, 1.0], [38, 49, 1.0], [50, 59, 1.0]]}, "2012.ijirr_journal-ir0volumeA2A4.3": {"snippet": "Information Retrieval from Deep Web Based on Visual Query Interpretation", "weights": [[0, 11, 1.0], [12, 21, 1.0], [27, 31, 1.0]]}, "2020.clef_conference-2020.14": {"snippet": "...g by Deep Learning and Retrieval", "weights": [[8, 12, 1.0], [13, 21, 1.0], [26, 35, 1.0]]}, "W18-5303": {"snippet": "... the deep learning and reinforcement learning approaches used in the submitted runs. The best run used a deep learning model under a regression-based framework. The deep learning architecture used fea...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [40, 48, 1.0], [49, 59, 1.0], [108, 112, 1.0], [113, 121, 1.0], [168, 172, 1.0], [173, 181, 1.0]]}, "2020.wnut-1.66": {"snippet": "...k 2: Deep Learning Model {R}o{BERT}a for Identify Informative {COVID}-19 {E}nglish Tweets\n\n\n This paper presents the model submitted by NIT COVID-19 team for identified informative COVID-19 English tw...", "weights": [[8, 12, 1.0], [13, 21, 1.0]]}, "2019.cikm_conference-2019.19": {"snippet": "...ture Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment", "weights": [[8, 17, 1.0], [46, 50, 1.0], [51, 59, 1.0]]}, "2017.sigirconf_conference-2017.244": {"snippet": "... for Information Retrieval\n\n\n Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modernday research has given rise to man...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [41, 49, 1.0], [105, 109, 1.0], [110, 118, 1.0]]}, "2019.wwwjournals_journal-ir0volumeA22A2.13": {"snippet": "Deep adversarial metric learning for cross-modal retrieval\n\n\n Abstract Cross-modal retrieval has become a highlighted research topic, to provide flexible retrieval experience across multimedia data su...", "weights": [[0, 4, 1.0], [24, 32, 1.0], [49, 58, 1.0], [83, 92, 1.0], [154, 163, 1.0]]}, "W19-4614": {"snippet": "...pose deep co-learning, a semi-supervised end-toend deep learning approach to assess the credibility of Arabic blogs. In deep co-learning, multiple weak deep neural network classifiers are trained usin...", "weights": [[8, 12, 1.0], [16, 24, 1.0], [54, 58, 1.0], [59, 67, 1.0], [123, 127, 1.0], [131, 139, 1.0], [155, 159, 1.0]]}, "2020.wwwjournals_journal-ir0volumeA23A1.3": {"snippet": "...d on deep reinforcement learning", "weights": [[8, 12, 1.0], [27, 35, 1.0]]}, "2006.wwwjournals_journal-ir0volumeA9A4.9": {"snippet": "...mong Deep Web Databases: A Source-Biased Approach\n\n\n Abstract The escalation of deep web databases has been phenomenal over the last decade, spawning a growing interest in automated discovery of inter...", "weights": [[8, 12, 1.0], [83, 87, 1.0]]}, "2020.sigirconf_conference-2020.367": {"snippet": "Deep Reinforcement Learning for Information Retrieval: Fundamentals and Advances\n\n\n Information retrieval (IR) techniques, such as search, recommendation and online advertising, satisfying users' info...", "weights": [[0, 4, 1.0], [19, 27, 1.0], [32, 43, 1.0], [44, 53, 1.0], [84, 95, 1.0], [96, 105, 1.0], [196, 207, 1.0]]}, "2020.wnut-1.75": {"snippet": "...Deep Learning and Expert rules\n\n\n In the scope of WNUT-2020 Task 2, we developed various text classification systems, using deep learning models and one using linguistically informed rules. While both...", "weights": [[8, 16, 1.0], [127, 131, 1.0], [132, 140, 1.0]]}, "2017.cikm_conference-2017.30": {"snippet": "... New Deep Architecture for Relevance Ranking in Information Retrieval\n\n\n ABSTRACT is paper concerns a deep learning approach to relevance ranking in information retrieval (IR). Existing deep IR models...", "weights": [[8, 12, 1.0], [51, 62, 1.0], [63, 72, 1.0], [105, 109, 1.0], [110, 118, 1.0], [152, 163, 1.0], [164, 173, 1.0], [189, 193, 1.0]]}, "2019.wsdm_conference-2019.119": {"snippet": "... two information objects is the core of many different information retrieval (IR) applications including Web search, question answering, and recommendation. Recently, deep learning methods have yielde...", "weights": [[8, 19, 1.0], [58, 69, 1.0], [70, 79, 1.0], [170, 174, 1.0], [175, 183, 1.0]]}, "2020.coling-main.548": {"snippet": "A Deep Metric Learning Method for Biomedical Passage Retrieval\n\n\n Passage retrieval is the task of identifying text snippets that are valid answers for a natural language posed question. One way to ad...", "weights": [[2, 6, 1.0], [14, 22, 1.0], [53, 62, 1.0], [74, 83, 1.0]]}, "2020.lrec-1.237": {"snippet": "...lish Information Retrieval Dataset\n\n\n Over the past years, deep learning methods allowed for new state-of-the-art results in ad-hoc information retrieval. However such methods usually require large am...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [62, 66, 1.0], [67, 75, 1.0], [135, 146, 1.0], [147, 156, 1.0]]}, "2015.sigirconf_conference-2015.39": {"snippet": "Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks\n\n\n ABSTRACTLearning a similarity function between pairs of objects is at the core of learning to rank approaches. In informati...", "weights": [[0, 8, 1.0], [53, 57, 1.0], [159, 167, 1.0], [176, 186, 1.0], [191, 202, 1.0]]}, "W17-2703": {"snippet": "...s on Deep Learning for automatic feature generation and feature ranking. However, most of those approaches fail to exploit rich semantic information, which results in relatively poor recall. This pape...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [99, 109, 1.0], [140, 151, 1.0]]}, "N15-1092": {"snippet": "...tion Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval\n\n\n Methods of deep neural networks (DNNs) have recently demonstrated superior performance on a...", "weights": [[8, 16, 1.0], [34, 38, 1.0], [87, 98, 1.0], [99, 108, 1.0], [123, 127, 1.0]]}, "2020.sigirconf_conference-2020.5": {"snippet": "How Deep Learning Works for Information Retrieval\n\n\n Information retrieval (IR) is the science of search, the search of user query relevant pieces of information from a collection of unstructured reso...", "weights": [[4, 8, 1.0], [9, 17, 1.0], [28, 39, 1.0], [40, 49, 1.0], [53, 64, 1.0], [65, 74, 1.0], [150, 161, 1.0]]}, "2019.wsdm_conference-2019.83": {"snippet": "Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching\n\n\n ABSTRACTDeep text matching approaches have been widely studied for many applications including question answeri...", "weights": [[0, 8, 1.0], [54, 62, 1.0], [67, 71, 1.0], [116, 126, 1.0]]}, "W17-2328": {"snippet": "Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs\n\n\n We describe a Deep Learning approach to modeling the relevance of a document's text to a query, applie...", "weights": [[0, 4, 1.0], [5, 13, 1.0], [29, 40, 1.0], [41, 50, 1.0], [52, 60, 1.0], [112, 116, 1.0], [117, 125, 1.0]]}, "2020.ipm_journal-ir0volumeA57A5.19": {"snippet": "...oint deep feature learning and unsupervised visual domain adaptation for cross-domain 3D object retrieval", "weights": [[8, 12, 1.0], [21, 29, 1.0], [99, 108, 1.0]]}, "2020.emnlp-main.343": {"snippet": "...-end retrieval system that starts with traditional information retrieval methods, followed by two deep learning re-rankers. We evaluate our method on three different datasets: a COVID-19 related scien...", "weights": [[8, 17, 1.0], [54, 65, 1.0], [66, 75, 1.0], [101, 105, 1.0], [106, 114, 1.0]]}, "2020.sigirconf_conference-2020.147": {"snippet": "...able Retrieval using Multimodal Deep Learning\n\n\n We address the web table retrieval task, aiming to retrieve and rank web tables as whole answers to a given information need. To this end, we formally ...", "weights": [[8, 17, 1.0], [35, 39, 1.0], [40, 48, 1.0], [77, 86, 1.0], [160, 171, 1.0]]}, "2020.ecir_conference-20202.87": {"snippet": "... and Information Retrieval for Health from Heterogeneous Content Types and Languages (SIIRH)\n\n\n The application of Information Retrieval (IR) and deep learning strategies to explore the vast amount of...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [118, 129, 1.0], [130, 139, 1.0], [149, 153, 1.0], [154, 162, 1.0]]}, "2020.sigirconf_conference-2020.380": {"snippet": "...ical information retrieval systems. Recently, deep learning-based biomedical information retrieval systems have been adopted to address this problem, which has the potential improvements in the perfor...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [49, 53, 1.0], [54, 62, 1.0], [80, 91, 1.0], [92, 101, 1.0]]}, "2019.trec_conference-2019.50": {"snippet": "...2019 Deep Learning Track: Working Note\n\n\n The TREC 2019 Deep Learning task aims at studying information retrieval in a large training data regime. It includes two tasks: the document ranking task (1) ...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [59, 63, 1.0], [64, 72, 1.0], [95, 106, 1.0], [107, 116, 1.0]]}, "2020.sigirconf_conference-2020.306": {"snippet": "...ious retrieval systems, but until recently this did not include deep learning systems. This raises a major challenge for reusable evaluation: Since deep learning based models use external resources (e...", "weights": [[8, 17, 1.0], [67, 71, 1.0], [72, 80, 1.0], [151, 155, 1.0], [156, 164, 1.0]]}, "2018.wwwconf_conference-2018.3": {"snippet": "... for Deep Active Learning An Application: Learning Intents in Alexa\n\n\n ABSTRACTThis paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted cro...", "weights": [[8, 12, 1.0], [20, 28, 1.0], [45, 53, 1.0], [148, 152, 1.0], [153, 161, 1.0]]}, "C12-2057": {"snippet": "Learning Semantics with Deep Belief Network for Cross-Language Information Retrieval\n\n\n This paper introduces a cross-language information retrieval (CLIR) framework that combines the state-of-the-art...", "weights": [[0, 8, 1.0], [24, 28, 1.0], [63, 74, 1.0], [75, 84, 1.0], [127, 138, 1.0], [139, 148, 1.0]]}, "2018.wwwconf_conference-2018c.381": {"snippet": "...s of deep learning approaches.", "weights": [[8, 12, 1.0], [13, 21, 1.0], [22, 32, 1.0]]}, "2021.wsdm_conference-2021.145": {"snippet": "...s in deep learning techniques specifically designed for anomaly detection (deep anomaly detection for short). Deep learning has gained tremendous success in transforming many data mining and machine l...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [78, 82, 1.0], [113, 117, 1.0], [118, 126, 1.0], [202, 210, 1.0]]}, "2016.sigirconf_conference-2016.219": {"snippet": "Deep Learning for Information Retrieval\n\n\n ABSTRACTRecent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being succ...", "weights": [[0, 4, 1.0], [5, 13, 1.0], [18, 29, 1.0], [30, 39, 1.0], [104, 115, 1.0], [116, 125, 1.0], [163, 167, 1.0], [168, 176, 1.0]]}, "2020.ipm_journal-ir0volumeA57A6.20": {"snippet": "Deep Learning-based Extraction of Algorithmic Metadata in Full-Text Scholarly Documents", "weights": [[0, 4, 1.0], [5, 13, 1.0]]}, "2015.mir_conference-2015.71": {"snippet": "...hing Retrieval via Deep Learning of Binary Codes and Hierarchical Search", "weights": [[8, 17, 1.0], [22, 26, 1.0], [27, 35, 1.0]]}, "C16-1229": {"snippet": "...tual information they usually contain. In recent years, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to text sentiment analy...", "weights": [[8, 19, 1.0], [59, 63, 1.0], [64, 72, 1.0]]}, "2019.mir_conference-2019.2": {"snippet": "...ideo Retrieval in the Age of Deep Learning", "weights": [[8, 17, 1.0], [32, 36, 1.0], [37, 45, 1.0]]}, "2016.sigirconf_conference-2016.231": {"snippet": "...ural Information Retrieval\n\n\n KeywordsNeural networks; deep learning; information retrieval Website: http://research.microsoft.com/neuir2016\nMOTIVATIONIn recent years, deep neural networks have yielde...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [58, 62, 1.0], [63, 71, 1.0], [73, 84, 1.0], [85, 94, 1.0], [171, 175, 1.0]]}, "2018.sigirconf_conference-2018.219": {"snippet": "...hine learning methods have been exploited to address the problem, which learns a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning has been ...", "weights": [[8, 16, 1.0], [142, 150, 1.0], [180, 184, 1.0], [185, 193, 1.0]]}, "W19-2603": {"snippet": "...eral deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a f...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [132, 136, 1.0], [137, 145, 1.0]]}, "2018.wwwconf_conference-2018.8": {"snippet": "...Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval\n\n\n ABSTRACTPublications in the life sciences are characterized by a large technical vocabulary, with many lexical and...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [64, 75, 1.0], [76, 85, 1.0]]}, "2020.wnut-1.54": {"snippet": "...ined Deep Learning Models and Feature Enrichment for Informative Tweet Identification\n\n\n This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: \"Identification of Inform...", "weights": [[8, 12, 1.0], [13, 21, 1.0]]}, "2020.wnut-1.50": {"snippet": "...}-19 Information on {T}witter by Combining Deep Learning and Transfer Learning Models\n\n\n The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, ...", "weights": [[8, 19, 1.0], [46, 50, 1.0], [51, 59, 1.0], [73, 81, 1.0]]}, "2016.sigirconf_conference-2016.8": {"snippet": "... and learning of how to respond. In this paper, we propose a retrieval-based conversation system with the deep learning-torespond schema through a deep neural network framework driven by web data. The...", "weights": [[8, 16, 1.0], [64, 73, 1.0], [109, 113, 1.0], [114, 122, 1.0], [150, 154, 1.0]]}, "2021.ecir_conference-20212.49": {"snippet": "Deep Query Likelihood Model for Information Retrieval\n\n\n The query likelihood model (QLM) for information retrieval has been thoroughly investigated and utilised. At the basis of this method is the re...", "weights": [[0, 4, 1.0], [32, 43, 1.0], [44, 53, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2001.sigirconf_workshop-2001w1.0": {"snippet": "Information Retrieval Techniques for Speech Applications [this book is based on the workshop \"Information Retrieval Techniques for Speech Applications\", held as part of the 24th Annual International A...", "weights": [[0, 11, 1.0], [12, 21, 1.0], [94, 105, 1.0], [106, 115, 1.0]]}, "2020.lrec-1.259": {"snippet": "...hine Learning and Deep Learning Approaches on {NLP} Tasks for the {I}talian Language\n\n\n We present a comparison between deep learning and traditional machine learning methods for various NLP tasks in ...", "weights": [[8, 16, 1.0], [21, 25, 1.0], [26, 34, 1.0], [35, 45, 1.0], [123, 127, 1.0], [128, 136, 1.0], [161, 169, 1.0]]}, "2019.fire_conference-2019w.58": {"snippet": "...hree approaches namely deep learning using transformers, deep learning using Recurrent Neural Networks (RNN) and a features-based approach for detecting the irony in Arabic tweets. Among these approac...", "weights": [[8, 18, 1.0], [26, 30, 1.0], [31, 39, 1.0], [60, 64, 1.0], [65, 73, 1.0], [196, 206, 1.0]]}, "2018.sigirconf_conference-2018.148": {"snippet": "...cale information retrieval systems. One solution is semantic hashing which designs binary codes to accelerate similarity search. Recently, deep learning has been successfully applied to the semantic h...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [142, 146, 1.0], [147, 155, 1.0]]}, "2020.ictir_conference-2020.29": {"snippet": "...n in Information Retrieval using PyTerrier\n\n\n The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed in expressive high-level languages such as Python, have allowed mo...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [63, 67, 1.0], [76, 84, 1.0]]}, "2019.wsdm_conference-2019.113": {"snippet": "...hine learning methods have been exploited to address the problem, which learn a matching function from labeled data, also referred to as \"learning to match\" . In recent years, deep learning has been s...", "weights": [[8, 16, 1.0], [141, 149, 1.0], [179, 183, 1.0], [184, 192, 1.0]]}, "2020.ijmir_journal-ir0volumeA9A2.0": {"snippet": "...e on deep learning in image and video retrieval", "weights": [[8, 12, 1.0], [13, 21, 1.0], [41, 50, 1.0]]}, "2018.sigirconf_conference-2018.222": {"snippet": "...s of deep learning  have made it applied to various fields including information retrieval (IR). In fact, during the recent three years deep learning has to-some-extent been revolutionizing the IR are...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [72, 83, 1.0], [84, 93, 1.0], [139, 143, 1.0], [144, 152, 1.0]]}, "W19-3216": {"snippet": "...hine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media\n\n\n Today social networks play an important role, where people can share information related to health...", "weights": [[8, 16, 1.0], [21, 25, 1.0], [26, 34, 1.0], [174, 185, 1.0]]}, "P19-1501": {"snippet": "...d on Deep Learning and Semantic Content Generalization\n\n\n This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along w...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [171, 175, 1.0], [176, 184, 1.0]]}, "C18-1201": {"snippet": "...odal deep learning in human spoken language understanding. We present a deep multimodal network with both feature attention and modality attention to classify utterance-level speech data. The proposed...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [75, 79, 1.0]]}, "D19-1567": {"snippet": "...tial Learning of Convolutional Features for Effective Text Classification\n\n\n Text classification has been one of the major problems in natural language processing. With the advent of deep learning, co...", "weights": [[8, 16, 1.0], [186, 190, 1.0], [191, 199, 1.0]]}, "2018.sigirconf_conference-2018.142": {"snippet": "...lem, deep learning techniques have not been explored in this domain. In this paper, we develop a deep learning framework, for the first time, to predict epidemiology profiles in the time-series perspe...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [100, 104, 1.0], [105, 113, 1.0]]}, "2018.ecir_conference-2018.62": {"snippet": "...ving Deep Learning for Multiple Choice Question Answering with Candidate Contexts\n\n\n Abstract. Deep learning solutions have been widely used lately for improving question answering systems, especially...", "weights": [[8, 12, 1.0], [13, 21, 1.0], [98, 102, 1.0], [103, 111, 1.0]]}, "2018.wsdm_conference-2018.102": {"snippet": "... for Information Retrieval\n\n\n AbstractMachine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given ri...", "weights": [[8, 19, 1.0], [20, 29, 1.0], [49, 57, 1.0], [113, 117, 1.0], [118, 126, 1.0]]}, "2017.sigirconf_conference-2017.10": {"snippet": "...y of deep neural networks, which is very suitable for text modeling. However, li le work has leveraged the recent progress in deep learning for text hashing.In this paper, we propose a series of novel...", "weights": [[8, 12, 1.0], [129, 133, 1.0], [134, 142, 1.0]]}}}
diff --git a/ui/src/example-documents.json b/ui/src/example-documents.json
index 58912bf..dc4913d 100644
--- a/ui/src/example-documents.json
+++ b/ui/src/example-documents.json
@@ -28,5 +28,35 @@
                 "url": "https://aclanthology.org/O02-2002"
             }
         }
+    },
+    "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test": {
+        "O02-2002": {
+            "docno": "O02-2002",
+            "text": "A Study on Word Similarity using Context Vector Models\n\n\n There is a need to measure word similarity when processing natural languages, especially when using generalization, classification, or example -based approaches. Usually, measures of similarity between two words are defined according to the distance between their semantic classes in a semantic taxonomy . The taxonomy approaches are more or less semantic -based that do not consider syntactic similarit ies. However, in real applications, both semantic and syntactic similarities are required and weighted differently. Word similarity based on context vectors is a mixture of syntactic and semantic similarit ies. In this paper, we propose using only syntactic related co-occurrences as context vectors and adopt information theoretic models to solve the problems of data sparseness and characteristic precision. The probabilistic distribution of co-occurrence context features is derived by parsing the contextual environment of each word , and all the context features are adjusted according to their IDF (inverse document frequency) values. The agglomerative clustering algorithm is applied to group similar words according to their similarity values. It turns out that words with similar syntactic categories and semantic classes are grouped together.",
+            "original_document": {
+                "title": "A Study on Word Similarity using Context Vector Models",
+                "abstract": "There is a need to measure word similarity when processing natural languages, especially when using generalization, classification, or example -based approaches. Usually, measures of similarity between two words are defined according to the distance between their semantic classes in a semantic taxonomy . The taxonomy approaches are more or less semantic -based that do not consider syntactic similarit ies. However, in real applications, both semantic and syntactic similarities are required and weighted differently. Word similarity based on context vectors is a mixture of syntactic and semantic similarit ies. In this paper, we propose using only syntactic related co-occurrences as context vectors and adopt information theoretic models to solve the problems of data sparseness and characteristic precision. The probabilistic distribution of co-occurrence context features is derived by parsing the contextual environment of each word , and all the context features are adjusted according to their IDF (inverse document frequency) values. The agglomerative clustering algorithm is applied to group similar words according to their similarity values. It turns out that words with similar syntactic categories and semantic classes are grouped together.",
+                "year": "2002",
+                "authors": "Chen, Keh-Jiann  and\nYou, Jia-Ming",
+                "anthology": "acl",
+                "anthology_id": "O02-2002",
+                "url": "https://aclanthology.org/O02-2002"
+            }
+        }
+    },
+    "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test": {
+        "O02-2002": {
+            "docno": "O02-2002",
+            "text": "A Study on Word Similarity using Context Vector Models\n\n\n There is a need to measure word similarity when processing natural languages, especially when using generalization, classification, or example -based approaches. Usually, measures of similarity between two words are defined according to the distance between their semantic classes in a semantic taxonomy . The taxonomy approaches are more or less semantic -based that do not consider syntactic similarit ies. However, in real applications, both semantic and syntactic similarities are required and weighted differently. Word similarity based on context vectors is a mixture of syntactic and semantic similarit ies. In this paper, we propose using only syntactic related co-occurrences as context vectors and adopt information theoretic models to solve the problems of data sparseness and characteristic precision. The probabilistic distribution of co-occurrence context features is derived by parsing the contextual environment of each word , and all the context features are adjusted according to their IDF (inverse document frequency) values. The agglomerative clustering algorithm is applied to group similar words according to their similarity values. It turns out that words with similar syntactic categories and semantic classes are grouped together.",
+            "original_document": {
+                "title": "A Study on Word Similarity using Context Vector Models",
+                "abstract": "There is a need to measure word similarity when processing natural languages, especially when using generalization, classification, or example -based approaches. Usually, measures of similarity between two words are defined according to the distance between their semantic classes in a semantic taxonomy . The taxonomy approaches are more or less semantic -based that do not consider syntactic similarit ies. However, in real applications, both semantic and syntactic similarities are required and weighted differently. Word similarity based on context vectors is a mixture of syntactic and semantic similarit ies. In this paper, we propose using only syntactic related co-occurrences as context vectors and adopt information theoretic models to solve the problems of data sparseness and characteristic precision. The probabilistic distribution of co-occurrence context features is derived by parsing the contextual environment of each word , and all the context features are adjusted according to their IDF (inverse document frequency) values. The agglomerative clustering algorithm is applied to group similar words according to their similarity values. It turns out that words with similar syntactic categories and semantic classes are grouped together.",
+                "year": "2002",
+                "authors": "Chen, Keh-Jiann  and\nYou, Jia-Ming",
+                "anthology": "acl",
+                "anthology_id": "O02-2002",
+                "url": "https://aclanthology.org/O02-2002"
+            }
+        }
     }
 }
\ No newline at end of file
diff --git a/ui/src/run_overview.json b/ui/src/run_overview.json
index afa4a5c..2623c35 100644
--- a/ui/src/run_overview.json
+++ b/ui/src/run_overview.json
@@ -1 +1 @@
-[{"dataset": "ir-lab-sose-2024/ir-acl-anthology-20240504-training", "team": "tira-ir-starter", "run": "BM25 (tira-ir-starter-pyterrier)", "tira_run": "ir-lab-sose-2024/tira-ir-starter/BM25 (tira-ir-starter-pyterrier)", "nDCG@10": 0.374, "R@100": 0.601, "P@10": 0.332, "Judged@10": 0.526}, {"dataset": "ir-lab-sose-2024/ir-acl-anthology-20240504-training", "team": "tira-ir-starter", "run": "DirichletLM (tira-ir-starter-pyterrier)", "tira_run": "ir-lab-sose-2024/tira-ir-starter/DirichletLM (tira-ir-starter-pyterrier)", "nDCG@10": 0.375, "R@100": 0.553, "P@10": 0.326, "Judged@10": 0.594}, {"dataset": "ir-lab-sose-2024/ir-acl-anthology-20240504-training", "team": "tira-ir-starter", "run": "PL2 (tira-ir-starter-pyterrier)", "tira_run": "ir-lab-sose-2024/tira-ir-starter/PL2 (tira-ir-starter-pyterrier)", "nDCG@10": 0.363, "R@100": 0.586, "P@10": 0.329, "Judged@10": 0.516}, {"dataset": "ir-lab-sose-2024/ir-acl-anthology-20240504-training", "team": "tira-ir-starter", "run": "LGD 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\ No newline at end of file
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\ No newline at end of file
diff --git a/ui/src/topics.json b/ui/src/topics.json
index 704dfe7..97cc52d 100644
--- a/ui/src/topics.json
+++ b/ui/src/topics.json
@@ -4,29 +4,29 @@
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@@ -112,29 +112,29 @@
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@@ -147,30 +147,30 @@
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         "default_text": "audio based animal recognition",
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+        "min_Judged@10": 0.4,
+        "max_Judged@10": 1.0,
+        "median_Judged@10": 1.0,
+        "var_nDCG@10": 0.011,
+        "var_P@10": 0.019,
+        "var_R@100": 0.002,
+        "var_Judged@10": 0.017,
+        "run_details": {
+            "start": 13084278,
+            "end": 13169950,
+            "path": "run-details.jsonl"
+        },
+        "qrel_details": {
+            "start": 1160907,
+            "end": 1170917,
+            "path": "qrel-details.jsonl"
+        },
+        "topic_details": {
+            "start": 77873,
+            "end": 78447,
+            "path": "qrel-details.jsonl"
+        }
+    },
+    {
+        "dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test",
+        "query_id": "37",
+        "default_text": "Entity Recognition in Web Documents",
+        "min_P@10": 0.0,
+        "max_P@10": 0.9,
+        "median_P@10": 0.8,
+        "min_R@100": 0.195,
+        "max_R@100": 0.732,
+        "median_R@100": 0.659,
+        "min_nDCG@10": 0.0,
+        "max_nDCG@10": 0.936,
+        "median_nDCG@10": 0.78,
+        "min_Judged@10": 0.2,
+        "max_Judged@10": 1.0,
+        "median_Judged@10": 1.0,
+        "var_nDCG@10": 0.042,
+        "var_P@10": 0.048,
+        "var_R@100": 0.013,
+        "var_Judged@10": 0.046,
+        "run_details": {
+            "start": 13169951,
+            "end": 13252986,
+            "path": "run-details.jsonl"
+        },
+        "qrel_details": {
+            "start": 1170918,
+            "end": 1180997,
+            "path": "qrel-details.jsonl"
+        },
+        "topic_details": {
+            "start": 78448,
+            "end": 79011,
+            "path": "qrel-details.jsonl"
+        }
+    },
+    {
+        "dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test",
+        "query_id": "38",
+        "default_text": "histroy of information retrieval",
+        "min_P@10": 0.0,
+        "max_P@10": 0.6,
+        "median_P@10": 0.3,
+        "min_R@100": 0.0,
+        "max_R@100": 0.714,
+        "median_R@100": 0.381,
+        "min_nDCG@10": 0.0,
+        "max_nDCG@10": 0.687,
+        "median_nDCG@10": 0.371,
+        "min_Judged@10": 0.0,
+        "max_Judged@10": 1.0,
+        "median_Judged@10": 0.7,
+        "var_nDCG@10": 0.026,
+        "var_P@10": 0.018,
+        "var_R@100": 0.018,
+        "var_Judged@10": 0.103,
+        "run_details": {
+            "start": 13252987,
+            "end": 13364536,
+            "path": "run-details.jsonl"
+        },
+        "qrel_details": {
+            "start": 1097613,
+            "end": 1109975,
+            "path": "qrel-details.jsonl"
+        },
+        "topic_details": {
+            "start": 79012,
+            "end": 79388,
+            "path": "qrel-details.jsonl"
+        }
+    },
+    {
+        "dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test",
+        "query_id": "39",
+        "default_text": "query length influence on retrieval",
+        "min_P@10": 0.3,
+        "max_P@10": 0.8,
+        "median_P@10": 0.5,
+        "min_R@100": 0.389,
+        "max_R@100": 0.944,
+        "median_R@100": 0.722,
+        "min_nDCG@10": 0.218,
+        "max_nDCG@10": 0.87,
+        "median_nDCG@10": 0.612,
+        "min_Judged@10": 0.5,
+        "max_Judged@10": 1.0,
+        "median_Judged@10": 1.0,
+        "var_nDCG@10": 0.016,
+        "var_P@10": 0.017,
+        "var_R@100": 0.016,
+        "var_Judged@10": 0.019,
+        "run_details": {
+            "start": 13364537,
+            "end": 13451730,
+            "path": "run-details.jsonl"
+        },
+        "qrel_details": {
+            "start": 1298138,
+            "end": 1307378,
+            "path": "qrel-details.jsonl"
+        },
+        "topic_details": {
+            "start": 79389,
+            "end": 79908,
+            "path": "qrel-details.jsonl"
+        }
+    },
+    {
+        "dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test",
+        "query_id": "40",
+        "default_text": "temporal document summarization in information retrieval systems",
+        "min_P@10": 0.1,
+        "max_P@10": 1.0,
+        "median_P@10": 0.8,
+        "min_R@100": 0.071,
+        "max_R@100": 0.964,
+        "median_R@100": 0.857,
+        "min_nDCG@10": 0.066,
+        "max_nDCG@10": 1.0,
+        "median_nDCG@10": 0.867,
+        "min_Judged@10": 0.4,
+        "max_Judged@10": 1.0,
+        "median_Judged@10": 1.0,
+        "var_nDCG@10": 0.045,
+        "var_P@10": 0.041,
+        "var_R@100": 0.023,
+        "var_Judged@10": 0.014,
+        "run_details": {
+            "start": 13451731,
+            "end": 13540855,
+            "path": "run-details.jsonl"
+        },
+        "qrel_details": {
+            "start": 1307379,
+            "end": 1317367,
+            "path": "qrel-details.jsonl"
+        },
+        "topic_details": {
+            "start": 79909,
+            "end": 80437,
+            "path": "qrel-details.jsonl"
+        }
+    },
+    {
+        "dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test",
+        "query_id": "42",
+        "default_text": "Modifications in Deep Learning Architectures",
+        "min_P@10": 0.0,
+        "max_P@10": 0.8,
+        "median_P@10": 0.4,
+        "min_R@100": 0.107,
+        "max_R@100": 0.786,
+        "median_R@100": 0.643,
+        "min_nDCG@10": 0.0,
+        "max_nDCG@10": 0.841,
+        "median_nDCG@10": 0.517,
+        "min_Judged@10": 0.2,
+        "max_Judged@10": 1.0,
+        "median_Judged@10": 0.9,
+        "var_nDCG@10": 0.033,
+        "var_P@10": 0.034,
+        "var_R@100": 0.023,
+        "var_Judged@10": 0.049,
+        "run_details": {
+            "start": 13540856,
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+            "path": "run-details.jsonl"
+        },
+        "qrel_details": {
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+            "path": "qrel-details.jsonl"
+        },
+        "topic_details": {
+            "start": 81022,
+            "end": 81898,
+            "path": "qrel-details.jsonl"
+        }
+    },
+    {
+        "dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test",
+        "query_id": "43",
+        "default_text": "knowledge graph construction",
+        "min_P@10": 0.0,
+        "max_P@10": 1.0,
+        "median_P@10": 0.6,
+        "min_R@100": 0.111,
+        "max_R@100": 0.815,
+        "median_R@100": 0.778,
+        "min_nDCG@10": 0.0,
+        "max_nDCG@10": 1.0,
+        "median_nDCG@10": 0.573,
+        "min_Judged@10": 0.1,
+        "max_Judged@10": 1.0,
+        "median_Judged@10": 1.0,
+        "var_nDCG@10": 0.038,
+        "var_P@10": 0.039,
+        "var_R@100": 0.029,
+        "var_Judged@10": 0.047,
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+            "path": "run-details.jsonl"
+        },
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+            "path": "qrel-details.jsonl"
+        },
+        "topic_details": {
+            "start": 81899,
+            "end": 82377,
+            "path": "qrel-details.jsonl"
+        }
+    },
+    {
+        "dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test",
+        "query_id": "47",
+        "default_text": "Deep learning approaches for Information Retrieval",
+        "min_P@10": 0.1,
+        "max_P@10": 1.0,
+        "median_P@10": 0.8,
+        "min_R@100": 0.432,
+        "max_R@100": 0.773,
+        "median_R@100": 0.659,
+        "min_nDCG@10": 0.078,
+        "max_nDCG@10": 1.0,
+        "median_nDCG@10": 0.87,
+        "min_Judged@10": 0.1,
+        "max_Judged@10": 1.0,
+        "median_Judged@10": 0.9,
+        "var_nDCG@10": 0.031,
+        "var_P@10": 0.035,
+        "var_R@100": 0.007,
+        "var_Judged@10": 0.034,
+        "run_details": {
+            "start": 13711349,
+            "end": 13804543,
+            "path": "run-details.jsonl"
+        },
+        "qrel_details": {
+            "start": 1109976,
+            "end": 1131708,
+            "path": "qrel-details.jsonl"
+        },
+        "topic_details": {
+            "start": 83847,
+            "end": 84430,
+            "path": "qrel-details.jsonl"
+        }
     }
 ]
\ No newline at end of file
diff --git a/ui/topic-details.jsonl b/ui/topic-details.jsonl
index 35ef3f9..85e2839 100644
--- a/ui/topic-details.jsonl
+++ b/ui/topic-details.jsonl
@@ -102,3 +102,63 @@
 {"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-koeln-20240614-in-progress-test", "qid": "63", "default_text": "the role of user feedback in enhancing search engine relevance", "description": "how is user feedback used to improve the relevance of search engine results and what is its impact on search perfomance", "narrative": "relevant documents should discuss the methods of collecting and utilizing user feedback both implicit and explicit to refine search algorithms this includes examining techniques like click through data dwell time ratings and comments the focus should be on evaluating the effectiveness of these feedback mechanisms in improving search precision recall and overall user satisfaction additionally challenge such as data sparsity noise and privacy concerns should be adressed"}
 {"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-koeln-20240614-in-progress-test", "qid": "65", "default_text": "multimedia in information retrieval", "description": "how do information retrieval systems handle multimedia content such as images videos and audio what are the key techniques and challenges", "narrative": "relevant documents should discuss approaches for indexing searching and retrieving multimedia content including content based retrieval and cross modal retrieval"}
 {"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-koeln-20240614-in-progress-test", "qid": "67", "default_text": "social media information retrieval", "description": "what challenges arise in dealing with informal and unstructured data in the context of information retrieval on social media platforms", "narrative": "how do the challenges of dealing with informal and noisy data affect the effectiveness of information retrieval systems on social media platforms"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "1", "default_text": "event extraction using convolutional neural networks", "description": "how can convolutional neural networks help extracting events", "narrative": "relevant papers include research on event extraction systems or tools based on convolutional neural networks this excludes any research on machine learning approach other than cnns or research that does not focus on extracting events"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "2", "default_text": "similarity metrics for event extraction from social media", "description": "what similarity metrics can be used when extracting events from social media", "narrative": "relevant papers include research on similarity metrics that can be used when extracting events from social media this excludes any research that doesn t focus on similarity scores or does not focus on event extraction from social media"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "3", "default_text": "event extraction from cab traces", "description": "how can events get extracted based on the traces of cabs", "narrative": "relevant papers include research on event extraction from the traces of cabs taxis this excludes any research on event extraction that does not focus on event extraction from cab taxi traces like event extraction from social media"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "4", "default_text": "multimodal zero shot learning", "description": "which papers focus on multimodal zero shot learning methods", "narrative": "relevant papers include research on multimodal zero shot learning methods particularly those that involve multiple types of data such as images text audio or other modalities studies that propose new algorithms improve existing methods or apply multimodal zero shot learning are relevant papers that focus solely on single modal zero shot learning or multimodal methods without a zero shot learning component are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "5", "default_text": "self supervised image retrieval methods", "description": "which papers focus on self supervised image retrieval methods", "narrative": "relevant papers include research on self supervised image retrieval methods or how to improve those methods papers that focus on supervised unsupervised or semi supervised methods are not relevant as well as papers that do not focus on image retrieval tasks"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "6", "default_text": "multimodal large language models", "description": "which papers focus on multimodal large language models", "narrative": "relevant papers include research on multimodal large language models that integrate multiple types of data such as text images audio or video studies that propose new architectures techniques or applications of multimodal large language models are relevant papers that focus solely on unimodal large language models or multimodal models without a significant language component are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "7", "default_text": "multi hop question answering using retrieval augmented generation", "description": "wich papers focus on improving retrieval augmented generation for the purpose of multi hop question answering", "narrative": "relevant papers focus on research on retrieval augmented generation for the purpose of multi hop question answering or how to improve this methods papers that do focus on multi hop question answering using a different methode are not relevant papers that focus on retrieval augmented generation for a different purpose than multi hop question answering are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "8", "default_text": "sequence to sequence models for table question answering", "description": "wich papers focus on improving semantic parsing for question decomposition", "narrative": "relevant papers focus on research on semantic parsing for question decomposition or how to improve this methods papers that focus on other methods for question decomposition are not relevant as well as papers that do not focus on question decomposition"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "9", "default_text": "weakly supervised table semantic parsing", "description": "which papers focus on introducing new or improving existing weakly supervised semantic parsing methods for table question answering", "narrative": "relevant papers include research on weakly supervised semantic parsing methods for table question answering or how to improve those methods papers that focus on other methods for table question answering and papers that focus on question answering for something other than tables are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "10", "default_text": "relevance aware diversity metrics", "description": "which papers focus on introducing new or evaluating existing metrics for search result diversity that take the relevance of items into account", "narrative": "relevant papers include research on new or evaluations of existing relevance aware diversity metrics papers that focus on relevance oblivious diversity metrics and papers that do not focus on search result diversity metrics are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "11", "default_text": "coordination between small and large language models", "description": "which papers explore methods for coordinating small and large language models to improve reasoning capabilities", "narrative": "relevant papers include research on techniques for fine tuning small language models to enhance the performance of large language models particularly in complex reasoning tasks papers that do not focus on coordination between different sized models or reasoning capabilities are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "12", "default_text": "cross lingual information retrieval", "description": "which papers focus on improving cross lingual information retrieval systems", "narrative": "relevant papers include research on methods and technologies that enhance the retrieval of information across different languages including machine translation integration and multilingual embeddings papers that do not address cross lingual retrieval or related challenges are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-augsburg-20240525_0-test", "qid": "13", "default_text": "knowledge graph construction and utilization in nlp", "description": "which papers focus on the construction and utilization of knowledge graphs for enhancing natural language processing tasks", "narrative": "relevant papers include research on methods for constructing knowledge graphs from text techniques for improving the efficiency and robustness of knowledge graphs and their application in various nlp tasks such as information retrieval entity recognition and semantic search papers that do not address the construction or practical use of knowledge graphs in nlp are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "1", "default_text": "pagerank algorithm construction", "description": "what papers focus on the construction of the pagerank algorithm", "narrative": "relevant papers include research on how the pagerank algorithm is constructed and how the pagerank algorithm works papers that focus on the construction of another algorithm or on another aspects of the pagerank algorithm are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "2", "default_text": "tokenization multilingual challenge", "description": "what papers focus on the challenges of multilingual tokenization", "narrative": "relevant papers include research on the challenges of multilingual tokenization and how the challenges are dealt with papers that focus on the challenges of other kinds of tokenization or on other aspects of multilingual tokenization are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "3", "default_text": "enhancing semantic search accuracy", "description": "which papers focus on improving the accuracy of semantic search", "narrative": "relevant papers include research aimed at enhancing the accuracy of semantic search and investigating factors contributing to its improvement papers that focus on improving something else or improving the effectiveness of a search that is not a semantic search are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "4", "default_text": "crawling techniques overview", "description": "a top level overview of different crawling techniques and ideally their respective advantages and disadvangtages", "narrative": "relevant papers include comparisons between different crawling techniques and to a lesser extend presentations of a single crawling technique that also mentions its advantages and disadvantages"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "5", "default_text": "detecting query ambiguity", "description": "what papers focus on automatic detection of ambiguous queries and resolving the ambiguous meanings", "narrative": "relevant papers include research on detecting ambiguities in queries and resolving those ambiguities in different ways papers that focus on identifying some other part of the query or improving queries in general are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "6", "default_text": "log information for analysing metadata of search engine user behaviour", "description": "which papers focus on the use of log information for analysing metadata of search engine user behaviour", "narrative": "relevant papers will include research on the collection and analysis of log data from search engines with a focus on the types of metadata collected such as query terms click through rates session durations and other user interactions the goal is to understand how this data can be used to infer user intent personalize search results improve information retrieval effectiveness and address privacy concerns papers that focus on other aspects of user behaviour or do not use log information for analysis are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "7", "default_text": "decrease search time retrieval system", "description": "what papers focus on decreasing the search time of a retrieval system", "narrative": "relevant papers include research on what makes a retrieval system faster while ideally not decreasing the quality of results papers that focus on improving something else or decreasing the search time of a system that is not a retrieval system are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "8", "default_text": "impact of query expansion techniques on search performance", "description": "what studies investigate the impact of query expansion techniques on search performance", "narrative": "relevant studies explore the effects of various query expansion techniques on the performance of search systems papers focusing solely on query formulation or unrelated performance metrics are considered irrelevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "9", "default_text": "retrieval system ranking", "description": "what papers focus on the ranking task of a retrieval system", "narrative": "relevant papers include research on how different ranking systems work in retrieval systems papers that focus on ranking systems of something else or ranking systems of a system that is not a retrieval system are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "10", "default_text": "most efficient retrieval models", "description": "what papers focus on discussing about the most efficient retrieval models", "narrative": "relevant papers discuss the advantages and disadvantages of the most important retrieval models focussing on efficiency they should compare different models considering different factors that lead to more efficiency papers that focus on just effectiveness or other most efficient models that are not retrieval models are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "11", "default_text": "personalized results in retrieval systems", "description": "what papers focus on the personalization of the results of an information retrieval system", "narrative": "relevant papers include research on what is important for personalizing the results of an information retrieval system maybe also evaluate the effectiveness of certain approaches and compare not relevant papers are such that don t concentrate on personalized results or on information retrieval systems"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "12", "default_text": "AI's impact on search engine effectiveness", "description": "does ai have a significant impact on the effectiveness of search engines and how does this impact the user", "narrative": "relevant papers explore the extent to which ai technologies impact the effectiveness of search engines this includes discussions on ai driven enhancements such as natural language processing semantic understanding and machine learning algorithms in search engine operations additionally relevant papers delve into the user experience improvements resulting from ai integration in search engines such as improved relevance precision and speed of search results papers not directly addressing ai s impact on search engine performance or user experience are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "13", "default_text": "information retrieval approaches", "description": "which papers focus on the ideas and concepts used for ir systems or introduce new approaches ideas", "narrative": "relevant papers include ones that focus on concepts used for modeling ir systems or on approaches used for implementing and evaluating an ir system as well as papers focused on introducing new approaches ideas for specific parts of an ir system e g evaluation only papers with a focus on ir are relevant introductory papers are not the focus"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "14", "default_text": "retrieval system speed", "description": "what papers focus on improving the speed of a retrieval system", "narrative": "relevant papers include research on what makes a retrieval system faster papers that focus on improving something else or improving the speed of a system that is not a retrieval system are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "15", "default_text": "retrieval systems indexing methods to optimize search precision", "description": "what papers discuss methods of indexing to enhance search precision in retrieval systems", "narrative": "relevant papers include research on indexing techniques specifically aimed at improving search precision and recalling in retrieval systems papers discussing indexing methods in other areas that aren t related to search precision enhancement in retrieval systems are not relevant papers that discuss search enhancing methods that aren t indexing aren t relevant as well as indexing methods that are used in systems that aren t retrieval systems"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "16", "default_text": "ranking functions", "description": "what papers mention ranking functions", "narrative": "relevant papers contain the types of ranking functions their advantages and disadvantages and their field of application papers that doesn t mentioned ranking function are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "17", "default_text": "AI-driven Web Crawling", "description": "research papers that explore the use of artificial intelligence in web crawling technologies", "narrative": "relevant studies should focus on how artificial intelligence can optimize the efficiency and accuracy of web crawling research that primarily discusses traditional web crawling technologies without integrating ai elements will be considered irrelevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "18", "default_text": "Content Relevance Detection on Webpages", "description": "research papers that detail methods for determining the relevance of webpage content to specific topics or queries", "narrative": "relevant studies will explore specific algorithms or methodologies used to assess content relevance on webpages general content management or broad seo strategies that do not focus on relevance assessment will be deemed irrelevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "19", "default_text": "Optimal Crawler for Genre Detection", "description": "research papers discussing the development and effectiveness of web crawlers designed for genre detection", "narrative": "relevant studies will focus on specialized web crawlers that are tailored to identify and categorize different genres on websites general web crawler technologies or those not specifically addressing genre detection will be considered irrelevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "20", "default_text": "Formulating Optimal Searches for Specific Topics in Scientific Papers", "description": "research papers on developing optimal search queries for retrieving specific topics in scientific literature", "narrative": "relevant studies should discuss the creation and effectiveness of search queries tailored to specific scientific topics papers that broadly address search engine optimization without a clear focus on academic search queries will be deemed irrelevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "21", "default_text": "Link Analysis in Information Retrieval", "description": "research papers that analyze the role of link analysis in improving the retrieval of information", "narrative": "relevant studies will focus on how link analysis can be leveraged to enhance the performance of information retrieval systems research not centered on link analysis or that broadly covers data mining techniques without a specific focus on links is deemed irrelevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "22", "default_text": "query expansion techniques effectiveness", "description": "what papers focus on the effectiveness of different query expansion techniques in retrieval systems", "narrative": "relevant papers include various query expansion techniques and evaluate their impact on information retrieval performance papers focusing on other aspects of query processing or unrelated techniques are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "23", "default_text": "Influence of social media on political opinions and elections", "description": "which academic studies investigate how social media influence political attitudes and voting decisions particularly with a focus on the dissemination of information opinion forming election campaign strategies and the effect of disinformation in social networks", "narrative": "the search results should include both qualitative and quantitative research analysing the role of social media in modern political communication of interest are articles that examine the mechanisms of influencing opinion through social media their impact on voter behaviour and the strategies of political actors to use these platforms particularly relevant are studies that deal with recent elections and analyse the impact of targeted campaigns and fake news less relevant are general studies on media use that are not directly related to political processes"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "24", "default_text": "Use of stemming and lemmatisation in retrieval systems", "description": "which articles describe the possibilities and applications of the stemming and lemmatisation method in retrieval systems", "narrative": "relevant articles highlight the possible uses of stemming and or lemmatisation in retrieval systems or their future potential in this area irrelevant articles are those that refer to other methods or are based on the general explanation of lemmatisation and stemming or their differences"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "25", "default_text": "Reliability of information from user-generated content", "description": "which documents describe how reliable information from social media posts etc is", "narrative": "interesting are documents that deal with how to find or filter out informative good content and how to avoid giving too much weight to bad content is given too much weight less interesting is the description of user behaviour with search engines for example"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "26", "default_text": "Use and application of AI", "description": "which articles describe the use and different areas of application of modern ais", "narrative": "the search results should display high quality articles or scientific papers that describe the various areas of application of ais explain how they work and their future development less relevant articles are those that only refer to the latest trends and use simple buzzwords such as like ai or describe a more general ai system without real world use cases"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "27", "default_text": "retrieval models bm25 disadvantages", "description": "which papers discuss the disadvantages of the bm25 retrieval model", "narrative": "relevant papers include research on the disadvantages of the okapti bm25 retrieval model and how to improve the model papers that focus on other retrieval models or the advantages of bm25 are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "28", "default_text": "query-by-example retrieval of music", "description": "what papers focus on building a query by example retrieval system for music files", "narrative": "relevant papers include research on how to build a content based retrieval system which uses any kind of query by audio to retrieve relevant audio files papers which focus on text or video based queries or non audio retrieval systems are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "29", "default_text": "cross language information retrieval", "description": "what papers contain information on cross language information retrieval", "narrative": "relevant papers only include research specifically on clir or topics closely related to it"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "30", "default_text": "advancements in user interaction", "description": "what papers focus on improving the ways a user interacts with an information retrieval system", "narrative": "relevant papers include research on how to make user interfaces for retrieval systems easier to use or more effective in creating a useful query papers that focus on improving something else or improving the effectiveness of user interaces for a system that is not a retrieval system are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "31", "default_text": "retrieval system multiple languages challenges", "description": "what papers focus on the challenges for a retrieval system when dealing with multiple languages", "narrative": "relevant papers include research on the specific challenges faced by retrieval systems when dealing with content in multiple languages this includes but is not limited to issues related to language detection translation accuracy search query understanding across languages and cultural context considerations papers that focus solely on monolingual retrieval systems or that do not address the unique challenges of multilingual environments or that focus on other challenges entirely are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "32", "default_text": "discerning LLM-written texts from human-written texts", "description": "what methods and technologies can identify texts written by large language models as opposed to those written by humans", "narrative": "relevant papers focus on differentiating texts created by language models from those written by human authors this includes research on linguistic for example syntax or word choice or stylistic differences as well as algorithms or tools specifically designed for this purpose papers discussing general text analysis without focus on distinguishing between llm and human authors are not relevant neither are papers that generally describe quality of llm written texts not mentioning comparison methods"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "33", "default_text": "Neuroscience and brain-computer interfaces", "description": "how do neuroscience and brain computer interfaces revolutionize our understanding of the brain s complexities and enable direct communication between the mind and technology", "narrative": "relevant papers encompass studies exploring advancements in neuroscience and brain computer interfaces bcis highlighting techniques methodologies and algorithms driving innovation in these fields this includes research demonstrating how neuroscience sheds light on the intricacies of brain function and how bcis facilitate direct communication between the mind and technology discussions on the transformative impact of bcis on individuals with disabilities as well as their broader implications for human machine interaction are pertinent papers not directly addressing neuroscience or bcis or those lacking insights into their advancements and applications fall outside the scope of relevance"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "34", "default_text": "effectiveness supervised and unsupervised learning in NLP", "description": "what papers focus on the effectiveness of supervised and unsupervised learning in nlp", "narrative": "relevant papers include research on what the effectiveness of supervised and unsupervised learning in nlp are and how they affect the performance of the system papers that focus on something other than the effectiveness or performance of supervised and unsupervised learning in nlp are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "35", "default_text": "bm25 algorithm effectiveness", "description": "what papers explore bm25 and its effectiveness", "narrative": "relevant papers include information on the bm25 function and present results that show the effectiveness of bm25 they additionally explain why bm25 is more or less effective compared to other ranking methods"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "36", "default_text": "Image and Video Retrieval", "description": "research papers on techniques for retrieving relevant images and videos from large collections", "narrative": "relevant studies should explore methods such as content based image retrieval visual feature extraction and video summarization to improve the effectiveness of image and video retrieval systems research not addressing image or video retrieval or focusing solely on textual information is considered irrelevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "37", "default_text": "Entity Recognition in Web Documents", "description": "research papers discussing techniques for identifying and extracting entities from web documents", "narrative": "relevant studies should detail methods for accurately recognizing entities such as people organizations and locations within web documents research not specifically focused on entity recognition or that covers generic text analysis without considering web documents is deemed irrelevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "38", "default_text": "histroy of information retrieval", "description": "what papers focus on the history of retrieval system", "narrative": "relevant papers include research on retrieval systems and how they evolved over time papers that focus on improvements from the past to the future"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "39", "default_text": "query length influence on retrieval", "description": "what papers focus on the effect that query length has on information retrieval results", "narrative": "relevant papers include research on the correlation between query length and relevance of derived results papers that deal with any other influences on result relevance or any other research regarding lengths that are not query lengths are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "40", "default_text": "temporal document summarization in information retrieval systems", "description": "what papers focus on temporal document summarization in information retrieval systems", "narrative": "relevant papers adress algorithms and their characteristics like effectiveness and limits and their usage in the context of an information retrieval system papers that focus on document summarization for other system are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "41", "default_text": "how to evaluate a user interface of a retrieval system", "description": "what papers focus on evaluating the user interface of a retrieval system", "narrative": "relevant papers include research on how to evaluate user interfaces of retrieval systems and which measurement methods you could use for evaluating the user interface also the standard for a language is relevant any other papers that are connected to the user interface but not for the evaluation are irrelevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "42", "default_text": "Modifications in Deep Learning Architectures", "description": "papers in this topic explore alterations and enhancements made to machine learning architectures to improve performance across diverse tasks these modifications encompass adjustments to network structure activation functions regularization methods optimization algorithms and integration of domain specific knowledge", "narrative": "this topic covers research that investigates the modifications applied to machine learning architectures aiming to enhance their effectiveness and adaptability papers within this domain discuss the rationale behind these modifications evaluate their impact on model performance and provide insights into the broader implications for advancing machine learning techniques"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "43", "default_text": "knowledge graph construction", "description": "what papers focus on constructing knowledge graphs", "narrative": "relevant papers include research on algorithms and methodologies for automatically constructing knowledge graphs from unstructured or semi structured data sources papers that discuss other graph related tasks or focus on unrelated topics are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "44", "default_text": "fake news detection techniques", "description": "what papers explore techniques for detecting fake news", "narrative": "relevant papers include research on methods and approaches for identifying misinformation disinformation or fake news in textual or multimedia content papers that focus on unrelated tasks or on non fake news detection applications are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "45", "default_text": "recommendation systems using collaborative filtering", "description": "what papers focus on recommendation systems utilizing collaborative filtering", "narrative": "relevant papers include research on algorithms and approaches for generating personalized recommendations by leveraging user item interactions and collaborative filtering techniques papers that focus on other recommendation methods or unrelated topics are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "46", "default_text": "text summarization techniques", "description": "what papers explore techniques for text summarization", "narrative": "relevant papers include research on algorithms and methods for generating concise summaries of textual documents papers that discuss other natural language processing tasks or focus on unrelated topics are not relevant"}
+{"dataset": "ir-lab-sose-2024/ir-acl-anthology-topics-leipzig-20240423-test", "qid": "47", "default_text": "Deep learning approaches for Information Retrieval", "description": "what research focuses on using deep learning methods for information retrieval", "narrative": "relevant papers include studies on the application of deep learning techniques such as neural networks convolutional neural networks recurrent neural networks and transformers for improving information retrieval tasks papers discussing traditional machine learning methods or unrelated topics are not relevant"}