Paper: An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars

ACL ID P98-1091
Title An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998
Authors

We present an empirical study of the applica- bility of Probabilistic Lexicalized Tree Inser- tion Grammars (PLTIG), a lexicalized counter- part to Probabilistic Context-Free Grammars (PCFG), to problems in stochastic natural- language processing. Comparing the perfor- mance of PLTIGs with non-hierarchical N-gram models and PCFGs, we show that PLTIG com- bines the best aspects of both, with language modeling capability comparable to N-grams, and improved parsing performance over its non- lexicalized counterpart. Furthermore, train- ing of PLTIGs displays faster convergence than PCFGs.