Learning a Compositional Semantic Parser using an Existing Syntactic Parser

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 611–619, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP 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}@cs.utexas.edu Abstract We present a new approach to learning a semantic parser (a system that maps natu- ral language sentences into logical form). Unlike previous methods, it exploits an ex- isting syntactic parser to produce disam- biguated parse trees that drive the compo- sitional semantic interpretation. The re- sulting system produces improved results on standard corpora on natural language interfaces for database querying and sim- ulated robot control. 1 Introduction Semantic parsing is the task of mapping a natu- ral language (NL) sentence into a completely for- mal meaning representation (MR) or logical form. A meaning representation language (MRL) is a formal unambiguous language that supports au- tomated 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 pro- vides disambiguated parse trees.1 However, ac- curate 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 gram- mar template. (a) If our player 2 has the ball, then position our player 5 in the midfield. ((bowner (player our {2})) (do (player our {5}) (pos (midfield)))) (b) Which river is the longest? answer(x1,longest(x1,river(x1))) Figure 1: Sample NLs and their MRs in the ROBOCUP and GEOQUERY domains respectively. languages and could potentially be used to learn more effective semantic analyzers. This paper presents an approach to learning semantic parsers that uses parse trees from an existing syntactic analyzer to drive the interpretation process. The learned parser uses standard compositional seman- tics to construct alternative MRs for a sentence based on its syntax tree, and then chooses the best MR based on a trained statistical disambiguation model. The learning system first employs a word alignment method from statistical machine trans- lation (GIZA++ (Och and Ney, 2003)) to acquire a semantic lexicon that maps words to logical predicates. Then it induces rules for composing MRs and estimates the parameters of a maximum- entropy model for disambiguating semantic inter- pretations. After describing the details of our ap- proach, we present experimental results on stan- dard corpora demonstrating improved results on learning NL …

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