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Supervised Approach to Finding Most Frequent Senses in Russian

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Analysis of Images, Social Networks and Texts (AIST 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 542))

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Abstract

The paper describes a supervised approach for the detection of the most frequent sense on the basis of RuThes thesaurus, which is a large linguistic ontology for Russian. Due to the large number of monosemous multiword expressions and the set of RuThes relations it is possible to calculate several context features for ambiguous words and to study their contribution in a supervised model for detecting frequent senses.

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Author information

Authors and Affiliations

  1. Research Computing Center of Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  2. Lomonosov Moscow State University, Moscow, Russia

    Ilia Chetviorkin

Authors
  1. Natalia Loukachevitch
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  2. Ilia Chetviorkin
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Corresponding author

Correspondence to Natalia Loukachevitch .

Editor information

Editors and Affiliations

  1. Krasovsky Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Mikhail Yu. Khachay

  2. Wolverhampton, United Kingdom

    Natalia Konstantinova

  3. Technische Universität Darmstadt, Darmstadt, Germany

    Alexander Panchenko

  4. National Research University Higher School of Economics, Moscow, Russia

    Dmitry Ignatov

  5. Ural Federal University, Yekaterinbug, Russia

    Valeri G. Labunets

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© 2015 Springer International Publishing Switzerland

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Loukachevitch, N., Chetviorkin, I. (2015). Supervised Approach to Finding Most Frequent Senses in Russian. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_33

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  • DOI: https://doi.org/10.1007/978-3-319-26123-2_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26122-5

  • Online ISBN: 978-3-319-26123-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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