Probabilistic topic modeling of text collections is a powerful tool for statistical text analysis. Determining the optimal number of topics remains a challenging problem in topic modeling. We propose a simple entropy regularization for topic selection in terms of Additive Regularization of Topic Models (ARTM), a multicriteria approach for combining regularizers. The entropy regularization gradually eliminates insignificant and linearly dependent topics. This process converges to the correct value on semi-real data. On real text collections it can be combined with sparsing, smoothing and decorrelation regularizers to produce a sequence of models with different numbers of well interpretable topics.
CITATION STYLE
Vorontsov, K., Potapenko, A., & Plavin, A. (2015). Additive regularization of topic models for topic selection and sparse factorization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9047, pp. 193–202). Springer Verlag. https://doi.org/10.1007/978-3-319-17091-6_14
Mendeley helps you to discover research relevant for your work.