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Bigram Anchor Words Topic Model

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

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.

Keywords

Topic model Anchor words Bigram 

Notes

Acknowledgments

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

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Moscow Institute of Physics and TechnologyDolgoprudnyRussia
  2. 2.Research Computing Center of Lomonosov Moscow State UniversityMoscowRussia

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