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.

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