An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars
cmp-lg
2007-05-23 v1 Computation and Language
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.
Keywords
Cite
@article{arxiv.cmp-lg/9808001,
title = {An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars},
author = {Rebecca Hwa},
journal= {arXiv preprint arXiv:cmp-lg/9808001},
year = {2007}
}
Comments
10 pages, 6 encapsulated postscript figures and 2 latex figures, uses colacl.sty