Building Deep Dependency Structures with a Wide-coverage CCG Parser

Stephen Clark, Julia Hockenmaier, Mark Steedman

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

Abstract / Description of output

This paper describes a wide-coverage statistical parser that uses Combinatory Categorial Grammar (CCG) to derive dependency structures. The parser differs from most existing wide-coverage treebank parsers in capturing the long-range dependencies inherent in constructions such as coordination, extraction, raising and control, as well as the standard local predicate-argument dependencies. A set of dependency structures used for training and testing the parser is obtained from a treebank of CCG normal-form derivations, which have been derived (semi-) automatically from the Penn Treebank. The parser correctly recovers over 80% of labelled dependencies, and around 90% of unlabelled 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
Pages327-334
Number of pages8
DOIs
Publication statusPublished - 2002

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