An empirical evaluation of Probabilistic Lexicalized Tree Insertion Grammars

  • Authors:
  • Rebecca Hwa

  • Affiliations:
  • Harvard University, Cambridge, MA

  • Venue:
  • COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
  • Year:
  • 1998

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Abstract

We present an empirical study of the applicability of Probabilistic Lexicalized Tree Insertion Grammars (PLTIG), a lexicalized counterpart to Probabilistic Context-Free Grammars (PCFG), to problems in stochastic natural-language processing. Comparing the performance of PLTIGs, with non-hierarchical N-gram models and PCFGs, we show that PLTIG combines the best aspects of both, with language modeling capability comparable to N-gram models and PCFGs, we show that PLTIG combines the best aspects of both, with language modeling capability comparable to N-grams, and improved parsing performance over its nonlexicalized counterpart. Furthermore, training of PLTIGs displays faster convergence than PCFGs.