Object-Extraction and Question-Parsing using CCG

Stephen Clark, Mark Steedman, James R. Curran

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

Abstract / Description of output

Accurate dependency recovery has recently been reported for a number of wide-coverage statistical parsers using CombinatoryCategorialGrammar (CCG). However, overall figures give no indication of a parser’s performance on specific constructions, nor how suitable a parser is for specific applications. In this paper we givea detailed evaluation of a CCG parser on object extraction dependencies found in WSJ text.We also show how the parser can be used to parse questions for Question Answering. Theaccuracy of the original parser on questions is very poor, and we propose a novel technique forporting the parser to a new domain, by creatingn ew labelled data at the lexical category levelonly. Using a super tagger to assign categoriesto words, trained on the new data, leads to a dramatic increase in question parsing accuracy
Original languageEnglish
Title of host publicationProceedings of the 2004 Conference on Empirical Methods in Natural Language Processing , EMNLP 2004, A meeting of SIGDAT, a Special Interest Group of the ACL, held in conjunction with ACL 2004, 25-26 July 2004, Barcelona, Spain
PublisherAssociation for Computational Linguistics
Number of pages8
Publication statusPublished - 2004


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