An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars

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 naturallanguage 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-grams, and improved parsing performance over its nonlexicalized counterpart. Furthermore, training of PLTIGs displays faster convergence than PCFGs.

• Type: Conference
• Year: 1998
• Where: ACL
• Authors: Rebecca Hwa
• Keywords: ACL 1998, ACL 2007, Non-hierarchical N-gram Models, Probabilistic Context-Free Grammars, Probabilistic Lexicalized Tree