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Discovering Verb Relations in Corpora: Distributional Versus Non-distributional Approaches

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 4031)

Abstract

Verbs represent a way in which ontological relationships between concepts and instances are expressed in natural language utterances. Moreover, an organized network of semantically related verbs can play a crucial role in applications. For example, if a Question-Answering system could exploit the direction of the entailment relation win → play, it may expand the question “Who played against Liverpool?” with “X won against Liverpool” and it may avoid the expansion of “Who won against Liverpool?” in “X played against Liverpool” that would be wrong. In this paper, we present a survey of the methods proposed to extract verb relations in corpora. These methods can be divided in two classes: those using the Harris distributional hypothesis and those based on point-wise assertions. These methods are analysed and compared.

Keywords

  • Natural Language Processing
  • Selectional Preference
  • Distributional Hypothesis
  • Agentive Nominalization
  • Question Answering System

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Pazienza, M.T., Pennacchiotti, M., Zanzotto, F.M. (2006). Discovering Verb Relations in Corpora: Distributional Versus Non-distributional Approaches. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_111

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  • DOI: https://doi.org/10.1007/11779568_111

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35453-6

  • Online ISBN: 978-3-540-35454-3

  • eBook Packages: Computer ScienceComputer Science (R0)