Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques

摘要

Natural language understanding is a sub-field of natural language processing which builds automated systems to understand natural language. It is such an ambitious task that it sometimes is referred to as an AI-complete problem, implying that its difficulty is equivalent to solving the central artificial intelligence problem-making computers as intelligent as people. Despite its complexity, natural language understanding continues to be a fundamental problem in natural language processing in terms of its theoretical and empirical importance. In recent years, startling progress has been made at different levels of natural language processing tasks, which provides great opportunity for deeper natural language understanding. In this thesis, we focus on the task of semantic parsing which maps a natural language sentence into a complete, formal meaning representation in a meaning representation language. We present two novel state-of-the-art learned syntax-based semantic parsers using statistical syntactic parsing techniques motivated by the following two reasons. First, the syntax-based semantic parsing is theoretically well-founded in computational semantics. Second, adopting a syntaxbased approach allows us to directly leverage the enormous progress made in statistical syntactic parsing. The first semantic parser, SCISSOR, adopts an integrated syntactic- semantic parsing approach, in which a statistical syntactic parser is augmented with semantic parameters to produce a semantically-augmented parse tree (SAPT). This integrated approach allows both syntactic and semantic information to be available during parsing time to obtain an accurate combined syntactic-semantic analysis. The performance of SCISSOR is further improved by using discriminative reranking for incorporating non-local features. The second semantic parser, SYNSEM exploits an existing syntactic parser to produce disambiguated parse trees that drive the compositional semantic interpretation.