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@article{hotelling_1935,
author = {Harold Hotelling},
title = {Canonical Correlation Analysis (cca)},
journal = {Journal of Educational Psychology},
year = {1935},
}
@article{harris_1954,
author = {Zellig S. Harris},
title = {Distributional Structure},
journal = {WORD},
volume = {10},
number = {2-3},
pages = {146-162},
year = {1954},
doi = {10.1080/00437956.1954.11659520},
URL = {http://dx.doi.org/10.1080/00437956.1954.11659520}
}
@book{firth_1957,
title={Papers in linguistics, 1934-1951},
author={Firth, J.R.},
lccn={57059616},
url={https://books.google.com.br/books?id=yxZZAAAAMAAJ},
year={1957},
publisher={Oxford University Press}
}
@ARTICLE{luhn_1957,
author={H. P. Luhn},
journal={IBM Journal of Research and Development},
title={A Statistical Approach to Mechanized Encoding and Searching of Literary Information},
year={1957},
volume={1},
number={4},
pages={309-317},
doi={10.1147/rd.14.0309},
ISSN={0018-8646},
month={Oct},}
@article{salton_1975,
author = {Salton, G. and Wong, A. and Yang, C. S.},
title = {A Vector Space Model for Automatic Indexing},
journal = {Commun. ACM},
issue_date = {Nov. 1975},
volume = {18},
number = {11},
month = nov,
year = {1975},
issn = {0001-0782},
pages = {613--620},
numpages = {8},
url = {http://doi.acm.org/10.1145/361219.361220},
doi = {10.1145/361219.361220},
acmid = {361220},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {automatic indexing, automatic information retrieval, content analysis, document space},
}
@ARTICLE{bahl_et_al_1983,
author={L. R. Bahl and F. Jelinek and R. L. Mercer},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={A Maximum Likelihood Approach to Continuous Speech Recognition},
year={1983},
volume={PAMI-5},
number={2},
pages={179-190},
keywords={Acoustic waves;Automatic speech recognition;Loudspeakers;Maximum likelihood decoding;Maximum likelihood estimation;Natural languages;Speech processing;Speech recognition;Statistical analysis;Vocabulary;Markov models;maximum likelihood;parameter estimation;speech recognition;statistical models},
doi={10.1109/TPAMI.1983.4767370},
ISSN={0162-8828},
month={March},}
@article {gentner_1983,
author = {Gentner, Dedre},
title = {Structure-Mapping: A Theoretical Framework for Analogy*},
journal = {Cognitive Science},
volume = {7},
number = {2},
publisher = {Lawrence Erlbaum Associates, Inc.},
issn = {1551-6709},
url = {http://dx.doi.org/10.1207/s15516709cog0702_3},
doi = {10.1207/s15516709cog0702_3},
pages = {155--170},
year = {1983},
}
@book{salton_and_mcgill_1983,
author = {Salton, Gerard and McGill, Michael J.},
title = {Introduction to Modern Information Retrieval},
year = {1986},
isbn = {0070544840},
publisher = {McGraw-Hill, Inc.},
address = {New York, NY, USA},
}
@incollection{hinton_et_al_1986,
author = {Hinton, G. E. and McClelland, J. L. and Rumelhart, D. E.},
chapter = {Distributed Representations},
title = {Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1},
editor = {Rumelhart, David E. and McClelland, James L. and PDP Research Group, CORPORATE},
year = {1986},
isbn = {0-262-68053-X},
pages = {77--109},
numpages = {33},
url = {http://dl.acm.org/citation.cfm?id=104279.104287},
acmid = {104287},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
}
@incollection{rumelhart_et_al_1986,
author = {Rumelhart, D. E. and Hinton, G. E. and Williams, R. J.},
chapter = {Learning Internal Representations by Error Propagation},
title = {Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1},
editor = {Rumelhart, David E. and McClelland, James L. and PDP Research Group, CORPORATE},
year = {1986},
isbn = {0-262-68053-X},
pages = {318--362},
numpages = {45},
url = {http://dl.acm.org/citation.cfm?id=104279.104293},
acmid = {104293},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
}
@INPROCEEDINGS{katz_1987,
author = {Slava M. Katz},
title = {Estimation of probabilities from sparse data for the language model component of a speech recognizer},
booktitle = {IEEE Transactions on Acoustics, Speech and Signal Processing},
year = {1987},
pages = {400--401}
}
@ARTICLE{deerwester_et_al_1990,
author = {Scott Deerwester and Susan T. Dumais and George W. Furnas and Thomas K. Landauer and Richard Harshman},
title = {Indexing by latent semantic analysis},
journal = {JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE},
year = {1990},
volume = {41},
number = {6},
pages = {391--407}
}
@article{mikkulainen_and_dyer_1991,
author = {Miikkulainen, Risto and Dyer, Michael G.},
year = {1991},
title = {Natural Language Processing With Modular Pdp Networks and Distributed Lexicon},
journal = {Cognitive Science},
publisher = {Wiley Online Library},
issn = {1551-6709},
doi = {10.1207/s15516709cog1503_2},
url = {http:https://dx.doi.org/10.1207/s15516709cog1503_2},
volume = {15},
month = {7},
pages = {343--399},
number = {3},
abstract = {An approach to connectionist natural language processing is proposed, which is based on hierarchically organized modular parallel distributed processing (PDP) networks and a central lexicon of distributed input/output representations. The modules communicate using these representations, which are global and publicly available in the system. The representations are developed automatically by all networks while they are learning their processing tasks. The resulting representations reflect the regularities in the subtasks, which facilitates robust processing in the face of noise and damage, supports improved generalization, and provides expectations about possible contexts. The lexicon can be extended by cloning new instances of the items, that is, by generating a number of items with known processing properties and distinct identities. This technique combinatorially increases the processing power of the system. The recurrent FGREP module, together with a central lexicon, is used as a basic building block in modeling higher level natural language tasks. A single module is used to form case-role representations of sentences from word-by-word sequential natural language input. A hierarchical organization of four recurrent FGREP modules (the DISPAR system) is trained to produce fully expanded paraphrases of script-based stories, where unmentioned events and role fillers are inferred.}
}
@article{miller_and_charles_1991,
abstract = {The relationship between semantic and contextual similarity is investigated for pairs of nouns that vary from high to low semantic similarity. Semantic similarity is estimated by subjective ratings; contextual similarity is estimated by the method of sorting sentential contexts. The results show an inverse linear relationship between similarity of meaning and the discriminability of contexts. This relation, is obtained for two separate corpora of sentence contexts. It is concluded that, on average, for words in the same language drawn from the same syntactic and semantic categories, the more often two words can be substituted into the same contexts the more similar in meaning they are judged to be.},
author = {Miller, George A. and Charles, Walter G.},
doi = {10.1080/01690969108406936},
issn = {01690965},
journal = {Language \& Cognitive Processes},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
number = {1},
pages = {1--28},
publisher = {Psychology Press},
title = {{Contextual Correlates of Semantic Similarity}},
url = {http://eric.ed.gov/ERICWebPortal/recordDetail?accno=EJ431389},
volume = {6},
year = {1991}
}
@ARTICLE{brown_et_al_1992,
author = {Peter F. Brown and Peter V. deSouza and Robert L. Mercer and Vincent J. Della Pietra and Jenifer C. Lai},
title = {Class-Based n-gram Models of Natural Language},
journal = {Computational Linguistics},
year = {1992},
volume = {18},
pages = {467--479}
}
@article{berger_et_al_1996,
author = {Berger, Adam L. and Pietra, Vincent J. Della and Pietra, Stephen A. Della},
title = {A Maximum Entropy Approach to Natural Language Processing},
journal = {Comput. Linguist.},
issue_date = {March 1996},
volume = {22},
number = {1},
month = mar,
year = {1996},
issn = {0891-2017},
pages = {39--71},
numpages = {33},
url = {http://dl.acm.org/citation.cfm?id=234285.234289},
acmid = {234289},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
}
@Article{lund_and_burgess_1996,
author="Lund, Kevin
and Burgess, Curt",
title="Producing high-dimensional semantic spaces from lexical co-occurrence",
journal="Behavior Research Methods, Instruments, {\&} Computers",
year="1996",
volume="28",
number="2",
pages="203--208",
abstract="A procedure that processes a corpus of text and produces numeric vectors containing information about its meanings for each word is presented. This procedure is applied to a large corpus of natural language text taken from Usenet, and the resulting vectors are examined to determine what information is contained within them. These vectors provide the coordinates in a high-dimensional space in which word relationships can be analyzed. Analyses of both vector similarity and multidimensional scaling demonstrate that there is significant semantic information carried in the vectors. A comparison of vector similarity with human reaction times in a single-word priming experiment is presented. These vectors provide the basis for a representational model of semantic memory, hyperspace analogue to language (HAL).",
issn="1532-5970",
doi="10.3758/BF03204766",
url="http://dx.doi.org/10.3758/BF03204766"
}
@book{jelinek_1997,
author = {Jelinek, Frederick},
title = {Statistical Methods for Speech Recognition},
year = {1997},
isbn = {0-262-10066-5},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
}
@inproceedings{bengio_et_al_2000,
added-at = {2016-05-29T11:37:13.000+0200},
author = {Bengio, Yoshua and Ducharme, Réjean and Vincent, Pascal},
biburl = {https://www.bibsonomy.org/bibtex/2d2793994ec10cb8139a9c7fda13bc019/albinzehe},
booktitle = {NIPS},
crossref = {conf/nips/2000},
editor = {Leen, Todd K. and Dietterich, Thomas G. and Tresp, Volker},
interhash = {d1d9b7d2f7b10da2d671f8d5fb92b2b0},
intrahash = {d2793994ec10cb8139a9c7fda13bc019},
keywords = {languagemodeling neuralnet},
pages = {932-938},
publisher = {MIT Press},
timestamp = {2016-05-29T11:37:13.000+0200},
title = {A Neural Probabilistic Language Model},
year = 2000
}
@Book{doucet_et_al_2001,
author = {Doucet, Arnaud},
title = {Sequential Monte Carlo methods in practice},
publisher = {Springer},
year = {2001},
address = {New York},
isbn = {9780387951461}
}
@article{goodman_2001,
author = {Joshua Goodman},
title = {Classes for Fast Maximum Entropy Training},
journal = {CoRR},
volume = {cs.CL/0108006},
year = {2001},
url = {http://arxiv.org/abs/cs.CL/0108006},
timestamp = {Mon, 05 Dec 2011 18:05:22 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/cs-CL-0108006},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@TECHREPORT{bengio_et_al_2002,
AUTHOR = {Bengio, Y. Ducharme, R. and Vincent, P.},
TITLE = {A neural probabilistic language model. Technical Report},
NUMBER = {1178},
INSTITUTION = {Dept. IRO, Université de Montréal},
YEAR = {2002},
}
@Article{legendre_and_gallagher_2001,
author="Legendre, Pierre
and Gallagher, Eugene D.",
title="Ecologically meaningful transformations for ordination of species data",
journal="Oecologia",
year="2001",
volume="129",
number="2",
pages="271--280",
abstract="This paper examines how to obtain species biplots in unconstrained or constrained ordination without resorting to the Euclidean distance [used in principal-component analysis (PCA) and redundancy analysis (RDA)] or the chi-square distance [preserved in correspondence analysis (CA) and canonical correspondence analysis (CCA)] which are not always appropriate for the analysis of community composition data. To achieve this goal, transformations are proposed for species data tables. They allow ecologists to use ordination methods such as PCA and RDA, which are Euclidean-based, for the analysis of community data, while circumventing the problems associated with the Euclidean distance, and avoiding CA and CCA which present problems of their own in some cases. This allows the use of the original (transformed) species data in RDA carried out to test for relationships with explanatory variables (i.e. environmental variables, or factors of a multifactorial analysis-of-variance model); ecologists can then draw biplots displaying the relationships of the species to the explanatory variables. Another application allows the use of species data in other methods of multivariate data analysis which optimize a least-squares loss function; an example is K-means partitioning.",
issn="1432-1939",
doi="10.1007/s004420100716",
url="http://dx.doi.org/10.1007/s004420100716"
}
@article{hinton_2002,
author = {Hinton, Geoffrey E.},
title = {Training Products of Experts by Minimizing Contrastive Divergence},
journal = {Neural Comput.},
issue_date = {August 2002},
volume = {14},
number = {8},
month = aug,
year = {2002},
issn = {0899-7667},
pages = {1771--1800},
numpages = {30},
url = {http://dx.doi.org/10.1162/089976602760128018},
doi = {10.1162/089976602760128018},
acmid = {639730},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
}
@article{bengio_et_al_2003,
author = {Bengio, Yoshua and Ducharme, Jean and Vincent, Pascal and Janvin, Christian},
title = {A Neural Probabilistic Language Model},
journal = {J. Mach. Learn. Res.},
issue_date = {3/1/2003},
volume = {3},
month = mar,
year = {2003},
issn = {1532-4435},
pages = {1137--1155},
numpages = {19},
url = {http://dl.acm.org/citation.cfm?id=944919.944966},
acmid = {944966},
publisher = {JMLR.org}
}
@MISC{bengio_and_senecal_2003,
author = {Yoshua Bengio and Jean-Sébastien Senécal},
title = {Quick Training of Probabilistic Neural Nets by Importance Sampling},
year = {2003}
}
@article{blei_et_al_2003,
author = {Blei, David M. and Ng, Andrew Y. and Jordan, Michael I.},
title = {Latent Dirichlet Allocation},
journal = {J. Mach. Learn. Res.},
issue_date = {3/1/2003},
volume = {3},
month = mar,
year = {2003},
issn = {1532-4435},
pages = {993--1022},
numpages = {30},
url = {http://dl.acm.org/citation.cfm?id=944919.944937},
acmid = {944937},
publisher = {JMLR.org},
}
@inproceedings{tellex_et_al_2003,
author = {Tellex, Stefanie and Katz, Boris and Lin, Jimmy and Fernandes, Aaron and Marton, Gregory},
title = {Quantitative Evaluation of Passage Retrieval Algorithms for Question Answering},
booktitle = {Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval},
series = {SIGIR '03},
year = {2003},
isbn = {1-58113-646-3},
location = {Toronto, Canada},
pages = {41--47},
numpages = {7},
url = {http://doi.acm.org/10.1145/860435.860445},
doi = {10.1145/860435.860445},
acmid = {860445},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {passage retrieval, question answering},
}
@article{dubin_2004,
added-at = {2016-09-20T10:33:10.000+0200},
author = {Dubin, David},
biburl = {https://www.bibsonomy.org/bibtex/2032630257147978169db9f9fe22fef59/thoni},
ee = {http://www.ideals.illinois.edu/bitstream/handle/2142/1697/Dubin748764.pdf},
interhash = {a1462ca87fcd4415e6ae2c5776074659},
intrahash = {032630257147978169db9f9fe22fef59},
journal = {Library Trends},
keywords = {funny model salton space vector},
number = 4,
pages = {748-764},
timestamp = {2016-11-02T06:50:19.000+0100},
title = {The Most Influential Paper Gerard Salton Never Wrote.},
url = {http://dblp.uni-trier.de/db/journals/libt/libt52.html#Dubin04},
volume = 52,
year = 2004
}
@INPROCEEDINGS{morin_and_bengio_2005,
author = {Morin, Frederic and Bengio, Yoshua},
editor = {Cowell, Robert G. and Ghahramani, Zoubin},
title = {Hierarchical Probabilistic Neural Network Language Model},
booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics},
year = {2005},
pages = {246--252},
publisher = {Society for Artificial Intelligence and Statistics},
url = {http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf},
abstract = {In recent years, variants of a neural network architecture for statistical language modeling have been proposed and successfully applied, e.g. in the language modeling component of speech recognizers. The main advantage of these architectures is that they learn an embedding for words (or other symbols) in a continuous space that helps to smooth the language model and provide good generalization even when the number of training examples is insufficient. However, these models are extremely slow in comparison to the more commonly used n-gram models, both for training and recognition. As an alternative to an importance sampling method proposed to speed-up training, we introduce a hierarchical decomposition of the conditional probabilities that yields a speed-up of about 200 both during training and recognition. The hierarchical decomposition is a binary hierarchical clustering constrained by the prior knowledge extracted from the WordNet semantic hierarchy.},
topics={Language},cat={C},
}
@ARTICLE{rohde_et_al_2006,
author = {Douglas L. T. Rohde and Laura M. Gonnerman and David C. Plaut},
title = {An improved model of semantic similarity based on lexical co-occurence },
journal = {COMMUNICATIONS OF THE ACM},
year = {2006},
volume = {8},
pages = {627--633}
}
@ARTICLE{bullinaria_and_levy_2007,
author = {John A. Bullinaria and Joseph P. Levy},
title = {Extracting semantic representations from word co-occurrence statistics: A computational study},
journal = {Behavior Research Methods},
year = {2007},
pages = {510--526}
}
@inproceedings{mnih_and_hinton_2007,
address = {New York, NY, USA},
author = {Mnih, Andriy and Hinton, Geoffrey},
booktitle = {ICML '07: Proceedings of the 24th international conference on Machine learning},
citeulike-article-id = {3575113},
keywords = {graphical-models, language-modeling},
location = {Corvalis, Oregon},
pages = {641--648},
posted-at = {2008-11-19 00:02:36},
priority = {2},
publisher = {ACM},
title = {Three new graphical models for statistical language modelling},
year = {2007}
}
@article{schwenk_2007,
author = {Schwenk, Holger},
title = {Continuous Space Language Models},
journal = {Comput. Speech Lang.},
issue_date = {July, 2007},
volume = {21},
number = {3},
month = jul,
year = {2007},
issn = {0885-2308},
pages = {492--518},
numpages = {27},
url = {http://dx.doi.org/10.1016/j.csl.2006.09.003},
doi = {10.1016/j.csl.2006.09.003},
acmid = {1230409},
publisher = {Academic Press Ltd.},
address = {London, UK, UK},
}
@inproceedings{collobert_and_weston_2008,
title = {A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning},
author = {R. Collobert and J. Weston},
booktitle = {International Conference on Machine Learning, {ICML}},
year = {2008}
}
@inproceedings{bengio_et_al_2009,
author = {Bengio, Yoshua and Louradour, J{\'e}r\^{o}me and Collobert, Ronan and Weston, Jason},
title = {Curriculum Learning},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
series = {ICML '09},
year = {2009},
isbn = {978-1-60558-516-1},
location = {Montreal, Quebec, Canada},
pages = {41--48},
numpages = {8},
url = {http://doi.acm.org/10.1145/1553374.1553380},
doi = {10.1145/1553374.1553380},
acmid = {1553380},
publisher = {ACM},
address = {New York, NY, USA},
}
@INPROCEEDINGS{mnih_and_hinton_2008,
author = {Andriy Mnih and Geoffrey Hinton},
title = {A scalable hierarchical distributed language model},
booktitle = {In NIPS},
year = {2008}
}
@inproceedings{mikolov_et_al_2009,
author = {Mikolov, Tomas and Kopecky, Jiri and Burget, Lukas and Glembek, Ondrej and Cernocky, Jan},
title = {Neural Network Based Language Models for Highly Inflective Languages},
booktitle = {Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing},
series = {ICASSP '09},
year = {2009},
isbn = {978-1-4244-2353-8},
pages = {4725--4728},
numpages = {4},
url = {http://dx.doi.org/10.1109/ICASSP.2009.4960686},
doi = {10.1109/ICASSP.2009.4960686},
acmid = {1583150},
publisher = {IEEE Computer Society},
address = {Washington, DC, USA},
}
@MISC{gutmann_and_hyvarinen_2010,
author = {Michael Gutmann and Aapo Hyvärinen},
title = {Noise-contrastive estimation: A new estimation principle for unnormalized statistical models},
year = {2010}
}
@inproceedings{mikolov_et_al_2010,
author = {Tomas Mikolov and
Martin Karafi{\'{a}}t and
Luk{\'{a}}s Burget and
Jan Cernock{\'{y}} and
Sanjeev Khudanpur},
title = {Recurrent neural network based language model},
booktitle = {{INTERSPEECH} 2010, 11th Annual Conference of the International Speech
Communication Association, Makuhari, Chiba, Japan, September 26-30,
2010},
pages = {1045--1048},
year = {2010},
crossref = {DBLP:conf/interspeech/2010},
url = {http://www.isca-speech.org/archive/interspeech_2010/i10_1045.html},
timestamp = {Thu, 09 Apr 2015 14:27:21 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/conf/interspeech/MikolovKBCK10},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@article{mitchell_and_lapata_2010,
author = {Mitchell, Jeff and Lapata, Mirella},
journal = {Cognitive Science},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
number = {8},
pages = {1388--1429},
publisher = {Wiley Online Library},
title = {{Composition in distributional models of semantics}},
volume = {34},
year = {2010}
}
@inproceedings{turian_et_al_2010,
author = {Turian, Joseph and Ratinov, Lev and Bengio, Yoshua},
title = {Word Representations: A Simple and General Method for Semi-supervised Learning},
booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
series = {ACL '10},
year = {2010},
location = {Uppsala, Sweden},
pages = {384--394},
numpages = {11},
url = {http://dl.acm.org/citation.cfm?id=1858681.1858721},
acmid = {1858721},
publisher = {Association for Computational Linguistics},
address = {Stroudsburg, PA, USA},
}
@article{turney_and_pantel_2010,
author = {Peter D. Turney and
Patrick Pantel},
title = {From Frequency to Meaning: Vector Space Models of Semantics},
journal = {CoRR},
volume = {abs/1003.1141},
year = {2010},
url = {http://arxiv.org/abs/1003.1141},
timestamp = {Mon, 05 Dec 2011 18:05:00 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1003-1141},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@article{collobert_et_al_2011,
title = {Natural Language Processing (Almost) from Scratch},
author = {R. Collobert and J. Weston and L. Bottou and M. Karlen and K. Kavukcuoglu and P. Kuksa},
journal = {Journal of Machine Learning Research},
volume = {12},
pages = {2493--2537},
year = {2011}
}
@incollection{dhillon_et_al_2011,
title = {Multi-View Learning of Word Embeddings via CCA},
author = {Dhillon, Paramveer and Foster, Dean P and Lyle H. Ungar},
booktitle = {Advances in Neural Information Processing Systems 24},
editor = {J. Shawe-Taylor and R. S. Zemel and P. L. Bartlett and F. Pereira and K. Q. Weinberger},
pages = {199--207},
year = {2011},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/4193-multi-view-learning-of-word-embeddings-via-cca.pdf}
}
@inproceedings{le_et_al_2011,
author = {Le, Q. V. and Zou, W. Y. and Yeung, S. Y. and Ng, A. Y.},
title = {Learning Hierarchical Invariant Spatio-temporal Features for Action Recognition with Independent Subspace Analysis},
booktitle = {Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition},
series = {CVPR '11},
year = {2011},
isbn = {978-1-4577-0394-2},
pages = {3361--3368},
numpages = {8},
url = {http://dx.doi.org/10.1109/CVPR.2011.5995496},
doi = {10.1109/CVPR.2011.5995496},
acmid = {2192108},
publisher = {IEEE Computer Society},
address = {Washington, DC, USA},
keywords = {YouTube action recognition dataset, hierarchical invariant spatio-temporal feature learning technique, action recognition, hand-designed local feature, SIFT, HOG, static image, video domain, unsupervised feature learning, video data, independent subspace analysis algorithm, hierarchical representation, UCF, KTH},
}
@inproceedings{maas_et_al_2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1},
series = {HLT '11},
year = {2011},
isbn = {978-1-932432-87-9},
location = {Portland, Oregon},
pages = {142--150},
numpages = {9},
url = {http://dl.acm.org/citation.cfm?id=2002472.2002491},
acmid = {2002491},
publisher = {Association for Computational Linguistics},
address = {Stroudsburg, PA, USA},
}
@article{silla_and_freitas_2011,
author = {Silla,Jr., Carlos N. and Freitas, Alex A.},
title = {A Survey of Hierarchical Classification Across Different Application Domains},
journal = {Data Min. Knowl. Discov.},
issue_date = {January 2011},
volume = {22},
number = {1-2},
month = jan,
year = {2011},
issn = {1384-5810},
pages = {31--72},
numpages = {42},
url = {http://dx.doi.org/10.1007/s10618-010-0175-9},
doi = {10.1007/s10618-010-0175-9},
acmid = {1937884},
publisher = {Kluwer Academic Publishers},
address = {Hingham, MA, USA},
keywords = {DAG-structured class hierarchies, Hierarchical classification, Tree-structured class hierarchies},
}
@inproceedings{socher_et_al_2011,
author = {Socher, Richard and Pennington, Jeffrey and Huang, Eric H. and Ng, Andrew Y. and Manning, Christopher D.},
title = {Semi-supervised Recursive Autoencoders for Predicting Sentiment Distributions},
booktitle = {Proceedings of the Conference on Empirical Methods in Natural Language Processing},
series = {EMNLP '11},
year = {2011},
isbn = {978-1-937284-11-4},
location = {Edinburgh, United Kingdom},
pages = {151--161},
numpages = {11},
url = {http://dl.acm.org/citation.cfm?id=2145432.2145450},
acmid = {2145450},
publisher = {Association for Computational Linguistics},
address = {Stroudsburg, PA, USA},
}
@InProceedings{blacoe_and_lapata_2012,
author = {Blacoe, William and Lapata, Mirella},
title = {A Comparison of Vector-based Representations for Semantic Composition},
booktitle = {Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
month = {July},
year = {2012},
address = {Jeju Island, Korea},
publisher = {Association for Computational Linguistics},
pages = {546--556},
url = {http://www.aclweb.org/anthology/D12-1050}
}
@article{blei_2012,
author = {Blei, David M.},
title = {Probabilistic Topic Models},
journal = {Commun. ACM},
issue_date = {April 2012},
volume = {55},
number = {4},
month = apr,
year = {2012},
issn = {0001-0782},
pages = {77--84},
numpages = {8},
url = {http://doi.acm.org/10.1145/2133806.2133826},
doi = {10.1145/2133806.2133826},
acmid = {2133826},
publisher = {ACM},
address = {New York, NY, USA},
}
@Article{bullinaria_and_levy_2012,
author="Bullinaria, John A.
and Levy, Joseph P.",
title="Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD",
journal="Behavior Research Methods",
year="2012",
volume="44",
number="3",
pages="890--907",
abstract="In a previous article, we presented a systematic computational study of the extraction of semantic representations from the word--word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of pointwise mutual information values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This article extends that study by investigating the use of three further factors---namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)---that have been used to provide improved performance elsewhere. It also introduces an additional semantic task and explores the advantages of using a much larger corpus. This leads to the discovery and analysis of improved SVD-based methods for generating semantic representations (that provide new state-of-the-art performance on a standard TOEFL task) and the identification and discussion of problems and misleading results that can arise without a full systematic study.",
issn="1554-3528",
doi="10.3758/s13428-011-0183-8",
url="http://dx.doi.org/10.3758/s13428-011-0183-8"
}
@inproceedings{huang_et_al_2012,
author = {Huang, Eric H. and Socher, Richard and Manning, Christopher D. and Ng, Andrew Y.},
title = {Improving Word Representations via Global Context and Multiple Word Prototypes},
booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1},
series = {ACL '12},
year = {2012},
location = {Jeju Island, Korea},
pages = {873--882},
numpages = {10},
url = {http://dl.acm.org/citation.cfm?id=2390524.2390645},
acmid = {2390645},
publisher = {Association for Computational Linguistics},
address = {Stroudsburg, PA, USA},
}
@INPROCEEDINGS{mnih_and_teh_2012,
author = {Andriy Mnih and Yee Whye Teh},
title = {A fast and simple algorithm for training neural probabilistic language models},
booktitle = {In Proceedings of the International Conference on Machine Learning},
year = {2012}
}
@incollection{frome_et_al_2013,
title = {DeViSE: A Deep Visual-Semantic Embedding Model},
author = {Frome, Andrea and Corrado, Greg S and Shlens, Jon and Bengio, Samy and Dean, Jeff and Ranzato, Marc\textquotesingle Aurelio and Mikolov, Tomas},
booktitle = {Advances in Neural Information Processing Systems 26},
editor = {C. J. C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K. Q. Weinberger},
pages = {2121--2129},
year = {2013},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/5204-devise-a-deep-visual-semantic-embedding-model.pdf}
}
@techreport{gao_et_al_2013,
author = {Gao, Jianfeng and He, Xiaodong and Yih, Scott Wen-tau and Deng, Li},
title = {Learning Semantic Representations for the Phrase Translation Model},
booktitle = {},
year = {2013},
month = {September},
abstract = {
This paper presents a novel semantic-based phrase translation model. A pair of source and target phrases are projected into continuous-valued vector representations in a low-dimensional latent semantic space, where their translation score is computed by the distance between the pair in this new space. The projection is performed by a multi-layer neural net-work whose weights are learned on parallel training data. The learning is aimed to directly optimize the quality of end-to-end machine translation results. Experimental evaluation has been performed on two Europarl translation tasks, English-French and German-English. The results show that the new semantic-based phrase translation model significantly improves the performance of a state-of-the-art phrase-based statistical machine translation system, leading to a gain of 0.7-1.0 BLEU points.
},
publisher = {},
url = {https://www.microsoft.com/en-us/research/publication/learning-semantic-representations-for-the-phrase-translation-model/},
address = {},
pages = {},
journal = {},
volume = {},
chapter = {},
isbn = {},
}
@MISC{labutov_and_lipson_2013,
author = {Igor Labutov and Hod Lipson},
title = {Re-embedding Words},
year = {2013}
}
@article{lebret_and_collobert_2013,
author = {R{\'{e}}mi Lebret and
Ronan Collobert},
title = {Word Emdeddings through Hellinger {PCA}},
journal = {CoRR},
volume = {abs/1312.5542},
year = {2013},
url = {http://arxiv.org/abs/1312.5542},
timestamp = {Mon, 06 Jan 2014 15:10:41 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/LebretL13},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@incollection{mnih_and_kavukcuoglu_2013,
title = {Learning word embeddings efficiently with noise-contrastive estimation},
author = {Mnih, Andriy and Kavukcuoglu, Koray},
booktitle = {Advances in Neural Information Processing Systems 26},
editor = {C. J. C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K. Q. Weinberger},
pages = {2265--2273},
year = {2013},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/5165-learning-word-embeddings-efficiently-with-noise-contrastive-estimation.pdf}
}
@InProceedings{baroni_et_al_2014,
author = {Baroni, Marco and Dinu, Georgiana and Kruszewski, Germ\'{a}n},
title = {Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors},
booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month = {June},
year = {2014},
address = {Baltimore, Maryland},
publisher = {Association for Computational Linguistics},
pages = {238--247},
url = {http://www.aclweb.org/anthology/P14-1023}
}
@article{le_and_mikolov_2014,
author = {Quoc V. Le and
Tomas Mikolov},
title = {Distributed Representations of Sentences and Documents},
journal = {CoRR},
volume = {abs/1405.4053},
year = {2014},
url = {http://arxiv.org/abs/1405.4053},
timestamp = {Mon, 02 Jun 2014 08:30:36 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/LeM14},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@inproceedings{levy_and_goldberg_2014a,
author = {Omer Levy and
Yoav Goldberg},
title = {Dependency-Based Word Embeddings},
booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational
Linguistics, {ACL} 2014, June 22-27, 2014, Baltimore, MD, USA, Volume
2: Short Papers},
pages = {302--308},
year = {2014},
crossref = {DBLP:conf/acl/2014-2},
url = {http://aclweb.org/anthology/P/P14/P14-2050.pdf},
timestamp = {Mon, 28 Jul 2014 19:50:44 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/conf/acl/LevyG14},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@inproceedings{levy_and_goldberg_2014b,
author = {Omer Levy and
Yoav Goldberg},
title = {Neural Word Embedding as Implicit Matrix Factorization},
booktitle = {Advances in Neural Information Processing Systems 27: Annual Conference
on Neural Information Processing Systems 2014, December 8-13 2014,
Montreal, Quebec, Canada},
pages = {2177--2185},
year = {2014},
crossref = {DBLP:conf/nips/2014},
url = {http://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization},
timestamp = {Wed, 10 Dec 2014 21:34:12 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/conf/nips/LevyG14},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@InProceedings{pennington_et_al_2014,
author = {Pennington, Jeffrey and Socher, Richard and Manning, Christopher},
title = {Glove: Global Vectors for Word Representation},
booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
month = {October},
year = {2014},
address = {Doha, Qatar},
publisher = {Association for Computational Linguistics},
pages = {1532--1543},
url = {http://www.aclweb.org/anthology/D14-1162}
}
@article{goldberg_2015,
author = {Yoav Goldberg},
title = {A Primer on Neural Network Models for Natural Language Processing},
journal = {CoRR},
volume = {abs/1510.00726},
year = {2015},
url = {http://arxiv.org/abs/1510.00726},
timestamp = {Sun, 01 Nov 2015 17:30:45 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/Goldberg15c},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@article{kiros_et_al_2015,
author = {Ryan Kiros and
Yukun Zhu and
Ruslan Salakhutdinov and
Richard S. Zemel and
Antonio Torralba and
Raquel Urtasun and
Sanja Fidler},
title = {Skip-Thought Vectors},
journal = {CoRR},
volume = {abs/1506.06726},
year = {2015},
url = {http://arxiv.org/abs/1506.06726},
timestamp = {Wed, 01 Jul 2015 15:10:24 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/KirosZSZTUF15},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@article{li_et_al_2015a,
author = {Shaohua Li and
Jun Zhu and
Chunyan Miao},
title = {A Generative Word Embedding Model and its Low Rank Positive Semidefinite
Solution},
journal = {CoRR},
volume = {abs/1508.03826},
year = {2015},
url = {http://arxiv.org/abs/1508.03826},
timestamp = {Tue, 01 Sep 2015 14:42:40 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/LiZM15},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@InProceedings{schnabel_et_al_2015,
author = "Schnabel, Tobias and Labutov, Igor and Mimno, David and Joachims, Thorsten",
title = "Evaluation methods for unsupervised word embeddings",
booktitle = "Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)",
pages = "298-307",
year = "2015"
}
@article{bojanowski_et_al_2016,
author = {Piotr Bojanowski and
Edouard Grave and
Armand Joulin and
Tomas Mikolov},
title = {Enriching Word Vectors with Subword Information},
journal = {CoRR},
volume = {abs/1607.04606},
year = {2016},
url = {http://arxiv.org/abs/1607.04606},
timestamp = {Tue, 02 Aug 2016 12:59:27 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/BojanowskiGJM16},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@article{joulin_et_al_2016,
author = {Armand Joulin and
Edouard Grave and
Piotr Bojanowski and
Tomas Mikolov},
title = {Bag of Tricks for Efficient Text Classification},
journal = {CoRR},
volume = {abs/1607.01759},
year = {2016},
url = {http://arxiv.org/abs/1607.01759},
timestamp = {Tue, 02 Aug 2016 12:59:27 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/JoulinGBM16},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@article{wieting_et_al_2016,
author = {John Wieting and
Mohit Bansal and
Kevin Gimpel and
Karen Livescu},
title = {Towards Universal Paraphrastic Sentence Embeddings},
journal = {CoRR},
volume = {abs/1511.08198},
year = {2015},
url = {http://arxiv.org/abs/1511.08198},
timestamp = {Tue, 01 Dec 2015 19:22:34 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/WietingBGL15a},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@misc{collins_log_linear_tutorial,
author = {Michael Collins},
title = {Log-linear models},
url = {http://www.cs.columbia.edu/~mcollins/loglinear.pdf},
urldate = {2017-04-07}
}
@inproceedings{rnnbasedmodel,
author = {Tomas Mikolov and
Martin Karafi{\'{a}}t and
Luk{\'{a}}s Burget and
Jan Cernock{\'{y}} and
Sanjeev Khudanpur},
title = {Recurrent neural network based language model},
booktitle = {{INTERSPEECH} 2010, 11th Annual Conference of the International Speech
Communication Association, Makuhari, Chiba, Japan, September 26-30,
2010},
pages = {1045--1048},
year = {2010},
crossref = {DBLP:conf/interspeech/2010},
url = {http://www.isca-speech.org/archive/interspeech_2010/i10_1045.html},
timestamp = {Thu, 09 Apr 2015 14:27:21 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/conf/interspeech/MikolovKBCK10},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@inproceedings{strategieslargemodels,
author = {Tomas Mikolov and
Anoop Deoras and
Daniel Povey and
Luk{\'{a}}s Burget and
Jan Cernock{\'{y}}},
title = {Strategies for training large scale neural network language models},
booktitle = {2011 {IEEE} Workshop on Automatic Speech Recognition {\&} Understanding,
{ASRU} 2011, Waikoloa, HI, USA, December 11-15, 2011},
pages = {196--201},
year = {2011},
crossref = {DBLP:conf/asru/2011},
url = {http://dx.doi.org/10.1109/ASRU.2011.6163930},
doi = {10.1109/ASRU.2011.6163930},
timestamp = {Thu, 09 Apr 2015 14:27:22 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/conf/asru/MikolovDPBC11},
bibsource = {dblp computer science bibliography, http://dblp.org}
}