Exploring New Horizons in Word Sense Disambiguation and Topic Modeling: Potential of Deep Learning Based Transformers Models

Author:

Süerdem Ahmet K.

Publisher

Springer Nature Switzerland

Reference22 articles.

1. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993–1022.

2. Xie, P., Yang, D., & Xing, E.P. (2015). Incorporating Word Correlation Knowledge into Topic Modeling. North American Chapter of the Association for Computational Linguistics.

3. Boyd-Graber JL, Blei DM, Zhu X (2007) A topic model for word sense disambiguation. In: EMNLP-CoNLL, pp 1024–1033.

4. Guo W, Diab M (2011) Semantic topic models: combining word distributional statistics and dictionary definitions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Stroudsburg, PA, USA, EMNLP ’11, pp 552–561. http://dl.acm.org/citation.cfm?id=2145432.2145496.

5. Dimo Angelov 2020 Top2vec: Distributed representations of topics arXiv preprint arXiv:2008.09470.

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