Added KeyNMF reference to README

README.md

@@ -7,7 +7,7 @@
 ## Features
  - Novel transformer-based topic models:
    - Semantic Signal Separation - S³ 🧭
-   - KeyNMF 🔑 (paper in progress ⏳)
+   - KeyNMF 🔑 
    - GMM :gem: (paper soon)
  - Implementations of other transformer-based topic models
    - Clustering Topic Models: BERTopic and Top2Vec
@@ -171,5 +171,5 @@ Alternatively you can use the [Figures API](https://x-tabdeveloping.github.io/to
  - Grootendorst, M. (2022, March 11). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv.org. https://arxiv.org/abs/2203.05794
  - Angelov, D. (2020, August 19). Top2VEC: Distributed representations of topics. arXiv.org. https://arxiv.org/abs/2008.09470
  - Bianchi, F., Terragni, S., & Hovy, D. (2020, April 8). Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence. arXiv.org. https://arxiv.org/abs/2004.03974
- - Bianchi, F., Terragni, S., Hovy, D., Nozza, D., & Fersini, E. (2021). Cross-lingual Contextualized Topic Models with Zero-shot Learning. In Proceedings of the 16th Conference of the European 
- - Chapter of the Association for Computational Linguistics: Main Volume (pp. 1676–1683). Association for Computational Linguistics.
+ - Bianchi, F., Terragni, S., Hovy, D., Nozza, D., & Fersini, E. (2021). Cross-lingual Contextualized Topic Models with Zero-shot Learning. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (pp. 1676–1683). Association for Computational Linguistics.
+ - Kristensen-McLachlan, R. D., Hicke, R. M. M., Kardos, M., & Thunø, M. (2024, October 16). Context is Key(NMF): Modelling Topical Information Dynamics in Chinese Diaspora Media. arXiv.org. https://arxiv.org/abs/2410.12791