tailieunhanh - Báo cáo khoa học: "Learning a Compositional Semantic Parser using an Existing Syntactic Parser"

We present a new approach to learning a semantic parser (a system that maps natural language sentences into logical form). Unlike previous methods, it exploits an existing syntactic parser to produce disambiguated parse trees that drive the compositional semantic interpretation. The resulting system produces improved results on standard corpora on natural language interfaces for database querying and simulated robot control. | Learning a Compositional Semantic Parser using an Existing Syntactic Parser Ruifang Ge Raymond J. Mooney Department of Computer Sciences University of Texas at Austin Austin TX 78712 grf mooney @ Abstract We present a new approach to learning a semantic parser a system that maps natural language sentences into logical form . Unlike previous methods it exploits an existing syntactic parser to produce disambiguated parse trees that drive the compositional semantic interpretation. The resulting system produces improved results on standard corpora on natural language interfaces for database querying and simulated robot control. 1 Introduction Semantic parsing is the task of mapping a natural language NL sentence into a completely formal meaning representation MR or logical form. A meaning representation language MRL is a formal unambiguous language that supports automated inference such as first-order predicate logic. This distinguishes it from related tasks such as semantic role labeling SRL Carreras and Marquez 2004 and other forms of shallow semantic analysis that do not produce completely formal representations. A number of systems for automatically learning semantic parsers have been proposed Ge and Mooney 2005 Zettlemoyer and Collins 2005 Wong and Mooney 2007 Lu et al. 2008 . Given a training corpus of NL sentences annotated with their correct MRs these systems induce an interpreter for mapping novel sentences into the given MRL. Previous methods for learning semantic parsers do not utilize an existing syntactic parser that provides disambiguated parse However accurate syntactic parsers are available for many 1Ge and Mooney 2005 use training examples with semantically annotated parse trees and Zettlemoyer and Collins 2005 learn a probabilistic semantic parsing model which initially requires a hand-built ambiguous CCG grammar template. a If our player 2 has the ball then position our player 5 in the midfield. bowner player our 2 do player our