Paper: Statistical Decision-Tree Models For Parsing

ACL ID P95-1037
Title Statistical Decision-Tree Models For Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1995
Authors

Syntactic natural language parsers have shown themselves to be inadequate for pro- cessing 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 text- processing in general. In this paper, I de- scribe SPATTER, a statistical parser based on decision-tree learning techniques which constructs a complete parse for every sen- tence and achieves accuracy rates far bet- ter 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 do- mains; (2) parsing models must rely heav- ily on lexical and contextual information to analyze sentences accurately; and (3) existing n-gram...