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. 1 Introduction There are many advantages to expressing a grammar in a lexicalized form, where an observable word of the language is encoded in each grammar rule. First, the lexical words help to clarify ambiguities that cannot be resolved by the sentence structures alone. For example, to correctly attach a prepositional phrase, it is often necessary to consider the lexical relationships between the head word of the p..