Scaling semantic parsers with on-the-fly ontology matching

Tom Kwiatkowski, Eunsol Choi, Yoav Artzi, Luke Zettlemoyer

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

Abstract

We consider the challenge of learning semantic parsers that scale to large, open-domain problems, such as question answering with Freebase. In such settings, the sentences cover a wide variety of topics and include many phrases whose meaning is difficult to represent in a fixed target ontology. For example, even simple phrases such as 'daughter' and 'number of people living in' cannot be directly represented in Freebase, whose ontology instead encodes facts about gender, parenthood, and population. In this paper, we introduce a new semantic parsing approach that learns to resolve such ontologi-cal mismatches. The parser is learned from question-answer pairs, uses a probabilistic CCG to build linguistically motivated logical-form meaning representations, and includes an ontology matching model that adapts the output logical forms for each target ontology. Experiments demonstrate state-of-the-art performance on two benchmark semantic parsing datasets, including a nine point accuracy improvement on a recent Freebase QA corpus.

Original languageEnglish (US)
Title of host publicationEMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages1545-1556
Number of pages12
ISBN (Electronic)9781937284978
StatePublished - 2013
Event2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013 - Seattle, United States
Duration: Oct 18 2013Oct 21 2013

Publication series

NameEMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Other

Other2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013
Country/TerritoryUnited States
CitySeattle
Period10/18/1310/21/13

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Information Systems
  • Computer Vision and Pattern Recognition

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