A statistical semantic parser that integrates syntax and semantics

Authors:
Ruifang Ge;Raymond J. Mooney
Affiliations:
University of Texas, Austin, TX;University of Texas, Austin, TX
Venue:
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Year:
2005

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Abstract

We introduce a learning semantic parser, Scissor, that maps natural-language sentences to a detailed, formal, meaning-representation language. It first uses an integrated statistical parser to produce 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 produces more accurate semantic representations than several previous approaches.