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A Continuum-Based Model of Lexical Acquisition

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9623))

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Abstract

The automatic acquisition of verbal constructions is an important issue for natural language processing. In this paper, we have a closer look at two fundamental aspects of the description of the verb: the notion of lexical item and the distinction between arguments and adjuncts. Following up on studies in natural language processing and linguistics, we embrace the double hypothesis (i) of a continuum between ambiguity and vagueness, and (ii) of a continuum between arguments and adjuncts. We provide a complete approach to lexical knowledge acquisition of verbal constructions from an untagged news corpus. The approach is evaluated through the analysis of a sample of the 7,000 Japanese verbs automatically described by the system.

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Notes

  1. 1.

    http://nlp.ist.i.kyoto-u.ac.jp/index.php?KNP.

  2. 2.

    http://taku910.github.io/cabocha/.

  3. 3.

    http://plata.ar.media.kyoto-u.ac.jp/tool/EDA/home_en.html.

  4. 4.

    http://taku910.github.io/mecab/.

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Acknowledgement

Pierre Marchal’s research has been partially supported by a national “contrat doctoral” from the ministry of research.

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Authors and Affiliations

  1. ER-TIM, INaLCO, 2 rue de Lille, 75007, Paris, France

    Pierre Marchal

  2. LaTTiCe (CNRS, ENS and University Paris 3), PSL Research University and Univ. Sorbonne Paris Cité, 45 rue d’Ulm, 75005, Paris, France

    Thierry Poibeau

Authors
  1. Pierre Marchal
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  2. Thierry Poibeau
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Correspondence to Pierre Marchal .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Marchal, P., Poibeau, T. (2018). A Continuum-Based Model of Lexical Acquisition. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-75477-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75476-5

  • Online ISBN: 978-3-319-75477-2

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