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Acquisition of Domain-Specific Senses and Its Extrinsic Evaluation Through Text Categorization

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Computational Linguistics and Intelligent Text Processing (CICLing 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13452))

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Abstract

This paper focuses on domain-specific senses and proposes a method for detecting predominant sense depending on each domain. We applied a simple Markov Random Walk (MRW) model to rank senses for each domain. It decides the importance of a vertex (senses) within a graph by using the similarity of senses. The similarity of senses is obtained by using distributed representations of words from gloss texts in the thesaurus. It captures large semantic context and thus does not require manual annotation of sense-tagged data. In order to evaluate the method, we applied the results of domain-specific senses to text categorization. The performance achieved in our test set WordNet3.1 and the Reuters corpus demonstrates applicability for the text categorization task.

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Author information

Authors and Affiliations

  1. Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences, University of Yamanashi, Kofu, 400-8511, Japan

    Attaporn Wangpoonsarp & Kazuya Shimura

  2. Interdisciplinary Graduate School, University of Yamanashi, Kofu, 400-8511, Japan

    Fumiyo Fukumoto

Authors
  1. Attaporn Wangpoonsarp
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  2. Kazuya Shimura
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  3. Fumiyo Fukumoto
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Corresponding authors

Correspondence to Attaporn Wangpoonsarp or Fumiyo Fukumoto .

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Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Wangpoonsarp, A., Shimura, K., Fukumoto, F. (2023). Acquisition of Domain-Specific Senses and Its Extrinsic Evaluation Through Text Categorization. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13452. Springer, Cham. https://doi.org/10.1007/978-3-031-24340-0_34

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  • DOI: https://doi.org/10.1007/978-3-031-24340-0_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24339-4

  • Online ISBN: 978-3-031-24340-0

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