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-specific systems that perform relatively deep processing. I first present a historical view of the shifting emphasis of research on various tasks in natural language processing and then briefly 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 offer at the level of producing semantic interpretations of complete sentences.

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In Workshop Notes for the Workshop on Learning Language in Logic, pp. 7-15, Bled, Slovenia 2000.

@InProceedings{mooney:lll99, title={Learning for Semantic Interpretation: Scaling Up Without Dumbing Down}, author={Raymond J. Mooney}, booktitle={Workshop Notes for the Workshop on Learning Language in Logic}, address={Bled, Slovenia}, pages={7-15}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:lll99", year={2000} }