This paper reviews our recent work on applying inductive logic programming to the construction of natural language processing systems. We have developed a system, CHILL, that learns a parser from a training corpus of parsed sentences by inducing heuristics that control an initial overly-general shift-reduce parser. CHILL learns syntactic parsers as well as ones that translate English database queries directly into executable logical form. The ATIS corpus of airline information queries was used to test the acquisition of syntactic parsers, and CHILL performed competitively with recent statistical methods. English queries to a small database on U.S. geography were used to test the acquisition of a complete natural language interface, and the parser that CHILL acquired was more accurate than an existing hand-coded system. The paper also includes a discussion of several issues this work has raised regarding the capabilities and testing of ILP systems as well as a summary of our current research directions.

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In Inductive Logic Programming: Selected papers from the 6th International Workshop, Stephen Muggleton (Eds.), pp. 3-22, Berlin 1996. Springer Verlag.

@InCollection{mooney:bkchapter97, title={Inductive Logic Programming for Natural Language Processing}, author={Raymond J. Mooney}, booktitle={Inductive Logic Programming: Selected papers from the 6th International Workshop}, editor={Stephen Muggleton}, address={Berlin}, publisher={Springer Verlag}, key={ILP, emnlp, CHILL}, pages={3-22}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:bkchapter97", year={1996} }