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An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars

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

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Rebecca Hwa. 1998-08-04. An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars. https://arxiv.org/abs/cmp-lg/9808001

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