Statistical Decision-Tree Models for Parsing

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Statistical Decision-Tree Models for Parsing (1995)

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by David M. Magerman

Venue:	In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics
Citations:	367 - 1 self

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@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.

Keyphrases

statistical decision-tree model interesting domain decision-tree learning technique parseval measure highly-ambiguous large-vocabulary text computer manual statistical parser parsing-based approach grammar-based parser syntactic natural language parser n-gram modeling technique following premise accuracy rate wall street journal contextual information complete parse parsing model published result

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