Discriminative Reranking for Semantic Parsing

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Discriminative Reranking for Semantic Parsing
Semantic parsing is the task of mapping natural language sentences to complete formal meaning representations. The performance of semantic parsing can be potentially improved by using discriminative reranking, which explores arbitrary global features. In this paper, we investigate discriminative reranking upon a baseline semantic parser, SCISSOR, where the composition of meaning representations is guided by syntax. We examine if features used for syntactic parsing can be adapted for semantic parsing by creating similar semantic features based on the mapping between syntax and semantics. We report experimental results on two real applications, an interpreter for coaching instructions in robotic soccer and a naturallanguage database interface. The results show that reranking can improve the performance on the coaching interpreter, but not on the database interface.
Ruifang Ge, Raymond J. Mooney
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ACL
Authors Ruifang Ge, Raymond J. Mooney
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