Generative models for statistical parsing with Combinatory Categorial Grammar

  • Authors:
  • Julia Hockenmaier;Mark Steedman

  • Affiliations:
  • University of Edinburgh, Edinburgh, United Kingdom;University of Edinburgh, Edinburgh, United Kingdom

  • Venue:
  • ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2002

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Abstract

This paper compares a number of generative probability models for a wide-coverage Combinatory Categorial Grammar (CCG) parser. These models are trained and tested on a corpus obtained by translating the Penn Treebank trees into CCG normal-form derivations. According to an evaluation of unlabeled word-word dependencies, our best model achieves a performance of 89.9%, comparable to the figures given by Collins (1999) for a linguistically less expressive grammar. In contrast to Gildea (2001), we find a significant improvement from modeling word-word dependencies.