A Bayesian Model for Unsupervised Semantic Parsing

Ivan Titov, Alexandre Klementiev

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

Abstract / Description of output

We propose a non-parametric Bayesian model for unsupervised semantic parsing. Following Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) decompose the syntactic dependency tree of a sentence into fragments, (2) assign each of these fragments to a cluster of semantically equivalent syntactic structures, and (3) predict predicate-argument relations between the fragments. We use hierarchical PitmanYor processes to model statistical dependencies between meaning representations of predicates
and those of their arguments, as well as the clusters of their syntactic realizations. We develop a modification of the MetropolisHastings split-merge sampler, resulting in an efficient inference algorithm for the model. The method is experimentally evaluated by using the induced semantic representation for the question answering task in the biomedical domain.
Original languageEnglish
Title of host publicationThe 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 19-24 June, 2011, Portland, Oregon, USA
PublisherAssociation for Computational Linguistics
Number of pages11
ISBN (Print)978-1-932432-87-9
Publication statusPublished - 2011


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