Statistical Decision-Tree Models for Parsing (1995)
| Venue: | In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics |
| Citations: | 287 - 1 self |
BibTeX
@INPROCEEDINGS{Magerman95statisticaldecision-tree,
author = {David M. Magerman},
title = {Statistical Decision-Tree Models for Parsing},
booktitle = {In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics},
year = {1995},
pages = {276--283}
}
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Abstract
Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text, as is evidenced by their poor per- formance on domains like the Wall Street Journal, and by the movement away from parsing-based approaches to textprocessing in general. In this paper, I describe SPATTER, a statistical parser based on decision-tree learning techniques which constructs a complete parse for every sentence and achieves accuracy rates far better than any published result. This work is based on the following premises: (1) grammars are too complex and detailed to develop manually for most interesting domains; (2) parsing models must rely heavily on lexical and contextual information to analyze sentences accurately; and (3) existing n-gram modeling techniques are inadequate for parsing models. In experiments comparing SPATTER with IBM's computer manuals parser, SPATTER significantly outperforms the grammar-based parser. Evaluating SPATTER against the Penn Treebank Wall Street Journal corpus using the PARSEVAL measures, SPATTER achieves 86% precision, 86% recall, and 1.3 crossing brackets per sentence for sentences of 40 words or less, and 91% precision, 90% recall, and 0.5 crossing brackets for sentences between 10 and 20 words in length.
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