Semantic Parsing: The Task, the State of the Art and the Future

Semantic parsing is the task of mapping natural language sentences into complete formal meaning representations which a computer can execute for some domain-specific application. This is a challenging task and is critical for developing computing systems that can understand and process natural language input, for example, a computing system that answers natural language queries about a database, or a robot that takes commands in natural language. While the importance of semantic parsing was realized a long time ago, it is only in the past few years that the state-of-the-art in semantic parsing has been significantly advanced with more accurate and robust semantic parser learners that use a variety of statistical learning methods. Semantic parsers have also been extended to work beyond a single sentence, for example, to use discourse contexts and to learn domain-specific language from perceptual contexts. Some of the future research directions of semantic parsing with potentially large impacts include mapping entire natural language documents into machine processable form to enable automated reasoning about them and to convert natural language web pages into machine processable representations for the Semantic Web to support automated high-end web applications. This tutorial will introduce the semantic parsing task and will bring the audience up-to-date with the current research and state-of-the-art in semantic parsing. It will also provide insights about semantic parsing and how it relates to and differs from other natural language processing tasks. It will point out research challenges and some promising future directions for semantic parsing.