A Statistical Semantic Parser that Integrates Syntax and Semantics

Proceedings of the 9th Conference on Computational Natural Language Learning (CoNLL), pages 9–16, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics A Statistical Semantic Parser that Integrates Syntax and Semantics Ruifang Ge Raymond J. Mooney Department of Computer Sciences University of Texas, Austin TX 78712, USA fgrf,mooney g@cs.utexas.edu Abstract We introduce a learning semantic parser, SCISSOR, that maps natural-language sen- tences to a detailed, formal, meaning- representation language. It first uses an integrated statistical parser to pro- duce a semantically augmented parse tree, in which each non-terminal node has both a syntactic and a semantic label. A compositional-semantics procedure is then used to map the augmented parse tree into a final meaning representation. We evaluate the system in two domains, a natural-language database interface and an interpreter for coaching instructions in robotic soccer. We present experimental results demonstrating that SCISSOR pro- duces more accurate semantic representa- tions than several previous approaches. 1 Introduction Most recent work in learning for semantic parsing has focused on “shallow” analysis such as seman- tic role labeling (Gildea and Jurafsky, 2002). In this paper, we address the more ambitious task of learn- ing to map sentences to a complete formal meaning- representation language (MRL). We consider two MRL’s that can be directly used to perform useful, complex tasks. The first is a Prolog-based language used in a previously-developed corpus of queries to a database on U.S. geography (Zelle and Mooney, 1996). The second MRL is a coaching language for robotic soccer developed for the RoboCup Coach Competition, in which AI researchers compete to provide effective instructions to a coachable team of agents in a simulated soccer domain (et al., 2003). We present an approach based on a statisti- cal parser that generates a semantically augmented parse tree (SAPT), in which each internal node in- cludes both a syntactic and semantic label. We aug- ment Collins’ head-driven model 2 (Collins, 1997) to incorporate a semantic label on each internal node. By integrating syntactic and semantic inter- pretation into a single statistical model and finding the globally most likely parse, an accurate combined syntactic/semantic analysis can be obtained. Once a SAPT is generated, an additional step is required to translate it into a final formal meaning representa- tion (MR). Our approach is implemented in a system called SCISSOR (Semantic Composition that Integrates Syntax and Semantics to get Optimal Representa- tions). Training the system requires sentences an- notated with both gold-standard SAPT’s and MR’s. We present experimental results on corpora for both geography-database querying and Robocup coach- ing demonstrating that SCISSOR produces more ac- curate semantic representations than several previ- ous approaches based on symbolic learning (Tang and Mooney, 2001; Kate et al., 2005). 2 Target MRL’s We used two MRLs in our experiments: CLANG and GEOQUERY. They capture the meaning of linguistic utterances in their domain in a formal language. 9 2.1 CLANG: the RoboCup Coach Language RoboCup (www.robocup.org) is an interna- tional AI research initiative using robotic soccer as its primary domain. In the Coach …

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