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Building deep dependency structures with a wide-coverage CCG parser

Published: 06 July 2002 Publication History

Abstract

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.

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  1. Building deep dependency structures with a wide-coverage CCG parser

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      cover image DL Hosted proceedings
      ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
      July 2002
      543 pages

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      Association for Computational Linguistics

      United States

      Publication History

      Published: 06 July 2002

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