Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques

Most recent work on semantic analysis of natural language has focused on “shallow” semantics such as word-sense disambiguation and semantic role labeling. Our work addresses a more ambitious task we call semantic parsing where natural language sentences are mapped to complete formal meaning representations. We present our system SCISSOR based on a statistical parser that generates a semanticallyaugmented parse tree (SAPT), in which each internal node has both a syntactic and semantic label. A compositional-semantics procedure is then used to map the augmented parse tree into a final meaning representation. Training the system requires sentences annotated with augmented parse trees. 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 on long sentences. In the future, we intend to pursue several directions in developing more accurate semantic parsing algorithms and automating the annotation process. This work will involve exploring alternative tree representations for better generalization in parsing. We also plan to apply discriminative reranking methods to semantic parsing, which allows exploring arbitrary, potentially correlated features not usable by the baseline learner. We also propose to design a method for automating the SAPT-generation process to alleviate the extra annotation work currently required for training SCISSOR. Finally, we will investigate the impact of different statistical syntactic parsers on semantic parsing using the automated SAPT-generation process.