Lexical Generalization in CCG Grammar Induction for Semantic Parsing

Tom Kwiatkowski, Luke Zettlemoyer, Sharon Goldwater, Mark Steedman

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

Abstract / Description of output

We consider the problem of learning factored probabilistic CCG grammars for semantic parsing from data containing sentences paired with logical-form meaning representations. Traditional CCG lexicons list lexical items that pair words and phrases with syntactic and semantic content. Such lexicons can be inefficient when words appear repeatedly with closely related lexical content. In this paper, we introduce factored lexicons, which include both lexemes to model word meaning and templates to model systematic variation in word usage. We also present an algorithm for learning factored CCG lexicons, along with a probabilistic parse-selection model. Evaluations on benchmark datasets demonstrate that the approach learns highly accurate parsers, whose generalization performance benefits greatly from the lexical factoring.
Original languageEnglish
Title of host publicationProceedings of the Conference on Empirical Methods in Natural Language Processing
Number of pages12
ISBN (Print)978-1-937284-11-4
Publication statusPublished - 2011


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