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Additive Regularization of Topic Models for Topic Selection and Sparse Factorization

  • Konstantin VorontsovEmail author
  • Anna Potapenko
  • Alexander Plavin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9047)

Abstract

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.

Keywords

Probabilistic topic modeling Regularization Probabilistic latent sematic analysis Topic selection EM-algorithm 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Konstantin Vorontsov
    • 1
    Email author
  • Anna Potapenko
    • 2
  • Alexander Plavin
    • 3
  1. 1.Moscow Institute of Physics and Technology, Dorodnicyn Computing Centre of RASNational Research University Higher School of EconomicsMoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia
  3. 3.Moscow Institute of Physics and TechnologyMoscowRussia

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