References
Rajkumar Arun, V. Suresh, C. E. Veni Madhavan, and M. N. Narasimha Murthy. 2010. On finding the natural number of topics with latent dirichlet allocation: Some observations. In Advances in knowledge discovery and data mining, Mohammed J. Zaki, Jeffrey Xu Yu, Balaraman Ravindran and Vikram Pudi (eds.). Springer Berlin Heidelberg, 391–402. <http://doi.org/10.1007/978-3-642-13657-3_43>.
Bouma, G. (2009). Normalized (pointwise) mutual information in collocation extraction. Proceedings of GSCL, 30, 31-40.
Cao Juan, Xia Tian, Li Jintao, Zhang Yongdong, and Tang Sheng. 2009. A density-based method for adaptive LDA model selection. Neurocomputing — 16th European Symposium on Artificial Neural Networks 2008 72, 7–9: 1775–1781. <http://doi.org/10.1016/j.neucom.2008.06.011>.
J. Chuang, C. Manning, J. Heer. 2012. Termite: Visualization Techniques for Assessing Textual Topic Models
Thomas L. Griffiths and Mark Steyvers. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences 101, suppl 1: 5228–5235. <http://doi.org/10.1073/pnas.0307752101>.
Jurafsky, D. and Martin, J. H., 2023. Speech and Language Processing. 3rd ed. draft. Online: <https://web.stanford.edu/~jurafsky/slp3/>.
D. Mimno, H. Wallach, E. Talley, M. Leenders, A. McCullum 2011: Optimizing semantic coherence in topic models
Role, François & Nadif, Mohamed. (2011). Handling the Impact of Low Frequency Events on Co-occurrence based Measures of Word Similarity - A Case Study of Pointwise Mutual Information.
Sievert, C., & Shirley, K. (2014, June). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the workshop on interactive language learning, visualization, and interfaces (pp. 63-70).
Wallach, H.M., Murray, I., Salakhutdinov, R. and Mimno, D., 2009. Evaluation methods for topic models.