TY - GEN
T1 - Scaling semantic parsers with on-the-fly ontology matching
AU - Kwiatkowski, Tom
AU - Choi, Eunsol
AU - Artzi, Yoav
AU - Zettlemoyer, Luke
N1 - Publisher Copyright:
© 2013 Association for Computational Linguistics.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84926294305&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:84926294305
T3 - EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
SP - 1545
EP - 1556
BT - EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
PB - Association for Computational Linguistics (ACL)
T2 - 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013
Y2 - 18 October 2013 through 21 October 2013
ER -