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
Number of pages8
Publication statusPublished - 2002


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