Latent Dirichlet Allocation (original) (raw)

1 Daniel Jurafsky and James Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 3rd edition, 2023, https://web.stanford.edu/~jurafsky/slp3/. Jay Alammar and Maarten Grootendorst, Hands-On Large Language Models, O’Reilly, 2024.

2 David Blei, “Probabilistic Topic Models,” Communications of the ACM, Vol. 55, No. 4, 2012, pp. 77-84. Zhiyuan Chen and Bing Liu, “Topic Models for NLP Applications,” Encyclopedia of Machine Learning and Data Mining, Springer, 2017.

3 Matthew Jockers, Text Analysis with R for Students of Literature, Springer, 2014.

4 Cole Howard, Hobson Lane, and Hannes Hapke, Natural Language Processing in Action, Manning Publications, 2019_. Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana, Practical Natural Language Processing, O’Reilly, 2020._

5 Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana, Practical Natural Language Processing, O’Reilly, 2020. David Blei, Andrew Ng, and Michael Jordan, “Lantent Dirichlet Allocation,” Journal of Machine Learning Research, Vol. 3, 2003, pp. 993-1022.

6 Zhiyuan Chen and Bing Liu, “Topic Models for NLP Applications,” Encyclopedia of Machine Learning and Data Mining, Springer, 2017.

7 David Blei, “Probabilistic Topic Models,” Communications of the ACM, Vol. 55, No. 4, 2012, pp. 77-84.

8 Chandler Camille May, “Topic Modeling in Theory and Practice,” Dissertation, John Hopkins University, 2022.

9 Matthew Gillings and Andrew Hardie, “The interpretation of topic models for scholarly analysis: An evaluation and critique of current practice,” Digital Scholarship in the Humanities, Vol. 38, No. 2, 2023, pp. 530-543, https://academic.oup.com/dsh/article-abstract/38/2/530/6957052

10 Chandler Camille May, “Topic Modeling in Theory and Practice,” Dissertation, John Hopkins University, 2022. https://aclanthology.org/D11-1024/

11 Zachary Lipton, “The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery,” Queue, Vol. 16, No. 3, pp. 31-57, https://dl.acm.org/doi/10.1145/3236386.3241340 . Caitlin Doogan and Wray Buntine, “Topic Model or Topic Twaddle? Re-evaluating Semantic Interpretability Measures,” Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, pp. 3824-3848, https://aclanthology.org/2021.naacl-main.300 .

12 Alexander Hoyle, Pranav Goel, Andrew Hian-Cheong, Denis Peskov, Jordan Boyd-Graber, Philip Resnik, “Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence,” Advances in Neural Information Processing Systems, 2021, pp. 2018-2033, https://proceedings.neurips.cc/paper_files/paper/2021/hash/0f83556a305d789b1d71815e8ea4f4b0-Abstract.html . Caitlin Doogan and Wray Buntine, “Topic Model or Topic Twaddle? Re-evaluating Semantic Interpretability Measures,” Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, pp. 3824-3848, https://aclanthology.org/2021.naacl-main.300 .

13 Dominik Stammbach, Vilém Zouhar, Alexander Hoyle, Mrinmaya Sachan, and Elliott Ash, “Revisiting Automated Topic Model Evaluation with Large Language Models,” Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023, pp. 9348-9357, https://aclanthology.org/2023.emnlp-main.581/ .