Generative Models for Statistical Parsing with Combinatory Categorial Grammar

Julia Hockenmaier, Mark Steedman

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

Abstract

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
Pages335-342
Number of pages8
DOIs
Publication statusPublished - 2002

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