Name
Abstract | ||
|---|---|---|
Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text, as is evidenced by their poor performance on domains like the Wall Street Journal, and by the movement away from parsing-based approaches to text-processing 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. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.3115/981658.981695 | meeting of the association for computational linguistics |
Keywords | DocType | Volume |
wall street journal,grammar-based parser,computer manuals parser,accuracy rate,parsing model,complete parse,parseval measure,journal corpus,statistical decision-tree model,penn treebank wall street,statistical parser,decision tree,decision tree learning,natural language | Conference | cmp-lg/9504030 |
ISSN | Citations | PageRank |
Proceedings of the 33rd Annual Meeting of the ACL | 279 | 108.09 |
References | Authors | |
4 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David M. Magerman | 1 | 726 | 512.15 |