A Bayesian Model for Unsupervised Semantic Parsing

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1445–1455, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics A Bayesian Model for Unsupervised Semantic Parsing Ivan Titov Saarland University Saarbruecken, Germany titov@mmci.uni-saarland.de Alexandre Klementiev Johns Hopkins University Baltimore, MD, USA aklement@jhu.edu Abstract We propose a non-parametric Bayesian model for unsupervised semantic parsing. Follow- ing 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 semanti- cally equivalent syntactic structures, and (3) predict predicate-argument relations between the fragments. We use hierarchical Pitman- Yor processes to model statistical dependen- cies between meaning representations of pred- icates and those of their arguments, as well as the clusters of their syntactic realizations. We develop a modification of the Metropolis- Hastings split-merge sampler, resulting in an efficient inference algorithm for the model. The method is experimentally evaluated by us- ing the induced semantic representation for the question answering task in the biomedical domain. 1 Introduction Statistical approaches to semantic parsing have re- cently received considerable attention. While some methods focus on predicting a complete formal rep- resentation of meaning (Zettlemoyer and Collins, 2005; Ge and Mooney, 2005; Mooney, 2007), others consider more shallow forms of representation (Car- reras and M`arquez, 2005; Liang et al., 2009). How- ever, most of this research has concentrated on su- pervised methods requiring large amounts of labeled data. Such annotated resources are scarce, expensive to create and even the largest of them tend to have low coverage (Palmer and Sporleder, 2010), moti- vating the need for unsupervised or semi-supervised techniques. Conversely, research in the closely related task of relation extraction has focused on unsupervised or minimally supervised methods (see, for example, (Lin and Pantel, 2001; Yates and Etzioni, 2009)). These approaches cluster semantically equivalent verbalizations of relations, often relying on syn- tactic fragments as features for relation extraction and clustering (Lin and Pantel, 2001; Banko et al., 2007). The success of these methods suggests that semantic parsing can also be tackled as clustering of syntactic realizations of predicate-argument rela- tions. While a similar direction has been previously explored in (Swier and Stevenson, 2004; Abend et al., 2009; Lang and Lapata, 2010), the recent work of (Poon and Domingos, 2009) takes it one step further by not only predicting predicate-argument structure of a sentence but also assigning sentence fragments to clusters of semantically similar expres- sions. For example, for a pair of sentences on Fig- ure 1, in addition to inducing predicate-argument structure, they aim to assign expressions “Steelers” and “the Pittsburgh team” to the same semantic class Steelers, and group expressions “defeated” and “secured the victory over”. Such semantic rep- resentation can be useful for entailment or question answering tasks, as an entailment model can ab- stract away from specifics of syntactic and lexical realization relying instead on the induced semantic representation. For example, the two sentences in …

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