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Published in:
2015 | OriginalPaper | Chapter
Additive Regularization of Topic Models for Topic Selection and Sparse Factorization
Authors : Konstantin Vorontsov, Anna Potapenko, Alexander Plavin
Published in: Statistical Learning and Data Sciences
Publisher: Springer International Publishing
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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.