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