Proceedings of the Ninth Conference on Computational Natural Language Learning, Ann Arbor, MI, pp. 9--16, June 2005.
Paper ID: 171
Category: Advice-Taking Learners, Learning for Semantic Parsing, Natural Language Learning
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.
