Generative Models for Statistical Parsing with Combinatory Categorial Grammar

Julia Hockenmaier, Mark Steedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper compares a number of generative probability models for a wide-coverage Combinatory Categorial Grammar (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 linguistically less expressive grammar. In contrast to Gildea (2001), we find a significant improvement from modeling word-word dependencies.
Original languageEnglish
Title of host publicationACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Place of PublicationStroudsburg, PA, USA
PublisherAssociation for Computational Linguistics
Number of pages8
Publication statusPublished - 2002

Cite this