Paper: A Linear Observed Time Statistical Parser Based On Maximum Entropy Models

ACL ID W97-0301
Title A Linear Observed Time Statistical Parser Based On Maximum Entropy Models
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 1997
Authors

This paper presents a statistical parser for natural language that obtains a parsing accuracy--roughly 87% precision and 86% recall--which surpasses the best previously published results on the Wall St. Journal domain. The parser itself requires very lit- tle human intervention, since the informa- tion it uses to make parsing decisions is specified in a concise and simple manner, and is combined in a fully automatic way under the maximum entropy framework. The observed running time of the parser on a test sentence is linear with respect to the sentence length. Furthermore, the parser returns several scored parses for a sentence, and this paper shows that a scheme to pick the best parse from the 20 highest scoring parses could yield a dramatically higher ac- curacy of 93% precision and reca...