Bayesian learning of a tree substitution grammar

  • Authors:
  • Matt Post;Daniel Gildea

  • Affiliations:
  • University of Rochester, Rochester, NY;University of Rochester, Rochester, NY

  • Venue:
  • ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
  • Year:
  • 2009

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Abstract

Tree substitution grammars (TSGs) offer many advantages over context-free grammars (CFGs), but are hard to learn. Past approaches have resorted to heuristics. In this paper, we learn a TSG using Gibbs sampling with a nonparametric prior to control subtree size. The learned grammars perform significantly better than heuristically extracted ones on parsing accuracy.