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Bigram Anchor Words Topic Model

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Analysis of Images, Social Networks and Texts (AIST 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 661))

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Abstract

A probabilistic topic model is a modern statistical tool for document collection analysis that allows extracting a number of topics in the collection and describes each document as a discrete probability distribution over topics. Classical approaches to statistical topic modeling can be quite effective in various tasks, but the generated topics may be too similar to each other or poorly interpretable. We supposed that it is possible to improve the interpretability and differentiation of topics by using linguistic information such as collocations while building the topic model. In this paper we offer an approach to accounting bigrams (two-word phrases) for the construction of Anchor Words Topic Model.

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Acknowledgments

This work was supported by grant RFFI 14-07-00383A Research of methods of integration of linguistic knowledge into statistical topic models.

Author information

Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Dolgoprudny, Russia

    Arseniy Ashuha

  2. Research Computing Center of Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

Authors
  1. Arseniy Ashuha
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  2. Natalia Loukachevitch
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Corresponding author

Correspondence to Natalia Loukachevitch .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  2. Krasovsky Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Mikhail Yu. Khachay

  3. Ural Federal University, Yekaterinbug, Russia

    Valeri G. Labunets

  4. Research Computing Center, Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Sergey I. Nikolenko

  6. Technische Universität Darmstadt, Darmstadt, Germany

    Alexander Panchenko

  7. Laboratory of Algorithms and Technologies for Networks Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  8. Dorodnicyn Computing Centre of Russian Academy of Sciences, Moscow, Russia

    Konstantin Vorontsov

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Ashuha, A., Loukachevitch, N. (2017). Bigram Anchor Words Topic Model. In: Ignatov, D., et al. Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-52920-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-52920-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52919-6

  • Online ISBN: 978-3-319-52920-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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