Rethinking Probabilistic Topic Modeling from the Point of View of Classical Non-Bayesian Regularization

Author:

Vorontsov Konstantin

Publisher

Springer Nature Switzerland

Reference65 articles.

1. Apishev, M.A., Vorontsov, K.V.: Learning topic models with arbitrary loss. In: Proceeding of the 26th Conference of FRUCT (Finnish-Russian University Cooperation in Telecommunications) Association, pp. 30–37 (2020)

2. Apishev, M., Koltcov, S. Koltsova, O., Nikolenko, S., Vorontsov, K.: Additive regularization for topic modeling in sociological studies of user-generated text content. In: MICAI 2016, 15th Mexican International Conference on Artificial Intelligence, Springer, Lecture Notes in Artificial Intelligence, vol. 10061, pp. 166–181 (2016)

3. Apishev, M., Koltcov, S., Koltsova, O., Nikolenko, S., Vorontsov, K.: Mining ethnic content online with additively regularized topic models. Comput. Sist. 20(3), 387–403 (2016)

4. Balikas, G., Amini, M., Clausel, M.: On a topic model for sentences. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 921–924. ACM, New York, NY, SIGIR ’16 (2016)

5. Belyy, A.V., Seleznova, M.S., Sholokhov, A.K., Vorontsov K.V.: Quality evaluation and improvement for hierarchical topic modeling. In: Computational Linguistics and Intellectual Technologies. Dialogue 2018, pp. 110–123 (2018)

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