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Effect of Semantic Parsing Depth on the Identification of Paraphrases in Russian Texts

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Artificial Intelligence and Natural Language (AINL 2017)

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

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Abstract

As a tool to solve the problem of identification of paraphrases in Russian texts, the paper proposes the semantic-syntactic parser SemSin and a semantic classifier. Several alternative methods for evaluating the similarity of sentence pairs—by words, by lemmas, by classes, by semantically related concepts, by predicate groups—have been analyzed. Advantages and drawbacks of the methods are discussed. The paraphrase identification quality has been shown to rise with increasing depth of using the semantic information. Yet, complementing the analysis with predicate groups, identified by the dependency tree, may even cause the identification to degrade due to the growing number of false positive decisions.

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Authors and Affiliations

  1. ITMO University, St. Petersburg, Russia

    Kirill Boyarsky & Eugeni Kanevsky

Authors
  1. Kirill Boyarsky
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  2. Eugeni Kanevsky
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Correspondence to Kirill Boyarsky .

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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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Boyarsky, K., Kanevsky, E. (2018). Effect of Semantic Parsing Depth on the Identification of Paraphrases in Russian Texts. 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_19

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  • DOI: https://doi.org/10.1007/978-3-319-71746-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71745-6

  • Online ISBN: 978-3-319-71746-3

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