An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion Grammars
<|reference_start|>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 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.<|reference_end|>
@article{hwa1998an,
    title={An Empirical Evaluation of Probabilistic Lexicalized Tree Insertion
  Grammars},
    author={Rebecca Hwa (Harvard University)},
    journal={Proceedings of COLING-ACL'98},
    year={1998},
    number={TR-06-98},
    archivePrefix={arXiv},
    eprint={cmp-lg/9808001},
    primaryClass={cmp-lg cs.CL}
}