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

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...