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Classification by compression: Application of information-theory methods for the identification of themes of scientific texts

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Abstract

A method for automatic classification of scientific texts based on data compression is proposed. The method is implemented and investigated based on the data from an archive of scientific texts (arXiv.org) and in the CyberLeninka scientific electronic library (CyberLeninka.ru). Experiments showed that the method correctly identified the themes of scientific texts with a probability of 75–95%; its accuracy depends on the quality of the original data.

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Authors and Affiliations

  1. The State Public Scientific Technological Library, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 123298, Russia

    I. V. Selivanova

  2. Novosibirsk State University, Novosibirsk, 630090, Russia

    B. Ya. Ryabko & A. E. Guskov

  3. Institute of Computational Technologies, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630090, Russia

    B. Ya. Ryabko & A. E. Guskov

Authors
  1. I. V. Selivanova
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  2. B. Ya. Ryabko
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  3. A. E. Guskov
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Corresponding author

Correspondence to I. V. Selivanova.

Additional information

Original Russian Text © I.V. Selivanova, B.Ya. Ryabko, A.E. Guskov, 2017, published in Nauchno-Tekhnicheskaya Informatsiya, Seriya 2: Informatsionnye Protsessy i Sistemy, 2017, No. 6, pp. 8–15.

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Selivanova, I.V., Ryabko, B.Y. & Guskov, A.E. Classification by compression: Application of information-theory methods for the identification of themes of scientific texts. Autom. Doc. Math. Linguist. 51, 120–126 (2017). https://doi.org/10.3103/S0005105517030116

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  • DOI: https://doi.org/10.3103/S0005105517030116

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