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Four Keys to Topic Interpretability in Topic Modeling

Part of the Communications in Computer and Information Science book series (CCIS,volume 930)


Interpretability of topics built by topic modeling is an important issue for researchers applying this technique. We suggest a new interpretability score, which we select from an interpretability score parametric space defined by four components: a splitting method, a probability estimation method, a confirmation measure and an aggregation function. We designed a regularizer for topic modeling representing this score. The resulting topic modeling method shows significant superiority to all analogs in reflecting human assessments of topic interpretability.


  • Topic modeling
  • Additive regularization for topic modeling
  • Topic interpretability
  • Human assessment

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  • DOI: 10.1007/978-3-030-01204-5_12
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Authors would like to thank Anton Belyy and Konstantin Vorontsov for useful conversation. Andrey Mavrin and Andrey Filchenkov were supported by the Government of the Russian Federation (Grant 08-08). Sergei Koltsov was supported by the Basic Research Program at the National Research University Higher School of Economics (HSE).

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Correspondence to Andrey Filchenkov .

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Mavrin, A., Filchenkov, A., Koltcov, S. (2018). Four Keys to Topic Interpretability in Topic Modeling. In: Ustalov, D., Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2018. Communications in Computer and Information Science, vol 930. Springer, Cham.

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