TAILIEUCHUNG - Báo cáo khoa học: "A Bayesian Model for Unsupervised Semantic Parsing"

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. | A Bayesian Model for Unsupervised Semantic Parsing Ivan Titov Saarland University Saarbruecken Germany titov@ Alexandre Klementiev Johns Hopkins University Baltimore MD USA aklement@ 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 Metropolis-Hastings 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. 1 Introduction Statistical approaches to semantic parsing have recently received considerable attention. While some methods focus on predicting a complete formal representation of meaning Zettlemoyer and Collins 2005 Ge and Mooney 2005 Mooney 2007 others consider more shallow forms of representation Carreras and Marquez 2005 Liang et al. 2009 . However most of this research has concentrated on supervised methods requiring large amounts of labeled data. Such annotated resources are scarce expensive to create and even the largest of them tend to have 1445 low coverage Palmer and Sporleder 2010 motivating 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 .

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• A Bayesian Model
• Unsupervised Semantic Parsing
• Ivan Titov Saarland
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