Learning for Semantic Interpretation : Scaling Up Without

Most recent research in learning approaches to natural language have studied fairly "low-level" tasks such as morphology, part-of-speech tagging, and syntactic parsing. However, I believe that logical approaches may have the most relevance and impact at the level of semantic interpretation, where a logical representation of sentence meaning is important and useful. We have explored the use of inductive logic programming for learning parsers that map natural-language database queries into executable logical form. This work goes against the growing trend in computational linguistics of focusing on shallow but broad-coverage natural language tasks ("scaling up by dumbing down") and instead concerns using logic-based learning to develop narrower, domain-speciic systems that perform relatively deep processing. I rst present a historical view of the shifting emphasis of research on various tasks in natural language processing and then brieey review our own work on learning for semantic interpretation. I will then attempt to encourage others to study such problems and explain why I believe logical approaches have the most to ooer at the level of producing semantic interpretations of complete sentences.