Paper: Generative Models For Statistical Parsing With Combinatory Categorial Grammar

ACL ID P02-1043
Title Generative Models For Statistical Parsing With Combinatory Categorial Grammar
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2002
Authors

This paper compares a number of gen- erative probability models for a wide- coverage Combinatory Categorial Gram- mar (CCG) parser. These models are trained and tested on a corpus obtained by translating the Penn Treebank trees into CCG normal-form derivations. According to an evaluation of unlabeled word-word dependencies, our best model achieves a performance of 89.9%, comparable to the figures given by Collins (1999) for a lin- guistically less expressive grammar. In contrast to Gildea (2001), we find a signif- icant improvement from modeling word- word dependencies.