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Information-Theoretic method for classification of texts

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Abstract

We consider a method for automatic (i.e., unmanned) text classification based on methods of universal source coding (or “data compression”). We show that under certain restrictions the proposed method is consistent, i.e., the classification error tends to zero with increasing text lengths. As an example of practical use of the method we consider the classification problem for scientific texts (research papers, books, etc.). The proposed method is experimentally shown to be highly efficient.

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Authors and Affiliations

  1. Institute of Computational Technologies, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia

    B. Ya. Ryabko & A. E. Gus’kov

  2. Novosibirsk State University, Novosibirsk, Russia

    B. Ya. Ryabko & I. V. Selivanova

  3. Russian National Public Library for Science and Technnology, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia

    A. E. Gus’kov & I. V. Selivanova

Authors
  1. B. Ya. Ryabko
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  2. A. E. Gus’kov
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  3. I. V. Selivanova
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Corresponding author

Correspondence to B. Ya. Ryabko.

Additional information

Original Russian Text © B.Ya. Ryabko, A.E. Gus’kov, I.V. Selivanova, 2017, published in Problemy Peredachi Informatsii, 2017, Vol. 53, No. 3, pp. 100–111.

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Ryabko, B.Y., Gus’kov, A.E. & Selivanova, I.V. Information-Theoretic method for classification of texts. Probl Inf Transm 53, 294–304 (2017). https://doi.org/10.1134/S0032946017030115

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  • DOI: https://doi.org/10.1134/S0032946017030115

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