Learning to Parse Database Queries using Inductive Logic Programming (1996)
This paper presents recent work using the CHILL parser acquisition system to automate the construction of a natural-language interface for database queries. CHILL treats parser acquisition as the learning of search-control rules within a logic program representing a shift-reduce parser and uses techniques from Inductive Logic Programming to learn relational control knowledge. Starting with a general framework for constructing a suitable logical form, CHILL is able to train on a corpus comprising sentences paired with database queries and induce parsers that map subsequent sentences directly into executable queries. Experimental results with a complete database-query application for U.S. geography show that CHILL is able to learn parsers that outperform a pre-existing, hand-crafted counterpart. These results demonstrate the ability of a corpus-based system to produce more than purely syntactic representations. They also provide direct evidence of the utility of an empirical approach at the level of a complete natural language application.
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In AAAI/IAAI, pp. 1050-1055, Portland, OR, August 1996. AAAI Press/MIT Press.
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Raymond J. Mooney Faculty mooney [at] cs utexas edu
John M. Zelle Ph.D. Alumni john zelle [at] wartburg edu