We introduce a learning semantic parser, SCISSOR, that maps natural-language sentences to a detailed, formal, meaningrepresentation 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.
Ge and Mooney REF learn a semantic parser that relates natural language descriptions to a formal representation language.
A Statistical Semantic Parser That Integrates Syntax And Semantics
Conference On Computational Natural Language Learning CoNLL
Computer Science