PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Bayesian Model for Unsupervised Semantic Parsing
Ivan Titov and Alexandre Klementiev
he 49th Annual Meeting of the Association for Computational Linguistics (ACL-2011). 2011.

Abstract

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 Pitman-Yor 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8747
Deposited By:Ivan Titov
Deposited On:21 February 2012