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Conference on Artificial Intelligence and Natural Language

AINL 2017: Artificial Intelligence and Natural Language pp 242–256Cite as

RuThes Thesaurus in Detecting Russian Paraphrases

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 789))

Abstract

In this paper we study the contribution of semantic features to the detection of Russian paraphrases. The features were calculated on the Russian Thesaurus RuThes. First, we applied RuThes synonyms in clustering news articles, many of which had been created with rewriting (that is paraphrasing) of source news, and found significant improvement. Second, we applied several semantic similarity measures proposed for English thesaurus WordNet to RuThes thesaurus and utilized them for detecting Russian paraphrased sentences.

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Notes

  1. 1.

    http://www.labinform.ru/ruthes/index.htm.

  2. 2.

    http://romip.ru/ru/collections/news-collection.html.

  3. 3.

    http://scikit-learn.org/stable/index.html.

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Acknowledgments

This work was partially supported by Russian National Foundation, grant N16-18-02074.

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Authors and Affiliations

  1. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch, Aleksandr Shevelev, Valerie Mozharova, Boris Dobrov & Andrey Pavlov

Authors
  1. Natalia Loukachevitch
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  2. Aleksandr Shevelev
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  3. Valerie Mozharova
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  4. Boris Dobrov
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  5. Andrey Pavlov
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Corresponding author

Correspondence to Natalia Loukachevitch .

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Editors and Affiliations

  1. ITMO University, St. Petersburg, Russia

    Andrey Filchenkov

  2. University of Helsinki, Helsinki, Finland

    Lidia Pivovarova

  3. Mendel University , Brno, Czech Republic

    Jan Žižka

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Loukachevitch, N., Shevelev, A., Mozharova, V., Dobrov, B., Pavlov, A. (2018). RuThes Thesaurus in Detecting Russian Paraphrases . In: Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2017. Communications in Computer and Information Science, vol 789. Springer, Cham. https://doi.org/10.1007/978-3-319-71746-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-71746-3_20

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  • Online ISBN: 978-3-319-71746-3

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