Investigating the cross-linguistic potential of VerbNet: style classification

  • Authors:
  • Lin Sun;Anna Korhonen;Thierry Poibeau;Cédric Messiant

  • Affiliations:
  • University of Cambridge;University of Cambridge;LaTTiCe, CNRS & ENS;LIPN, CNRS & U. Paris

  • Venue:
  • COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
  • Year:
  • 2010

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Abstract

Verb classes which integrate a wide range of linguistic properties (Levin, 1993) have proved useful for natural language processing (NLP) applications. However, the real-world use of these classes has been limited because for most languages, no resources similar to VerbNet (Kipper-Schuler, 2005) are available. We apply a verb clustering approach developed for English to French - a language for which no such experiment has been conducted yet. Our investigation shows that not only the general methodology but also the best performing features are transferable between the languages, making it possible to learn useful VerbNet style classes for French automatically without language-specific tuning.