This paper presents results from recent experiments with CHILL, a corpus-based parser acquisition system. CHILL treats language acquisition as the learning of search-control rules within a logic program. Unlike many current corpus-based approaches that use statistical learning algorithms, CHILL uses techniques from inductive logic programming (ILP) to learn relational representations. CHILL is a very flexible system and has been used to learn parsers that produce syntactic parse trees, case-role analyses, and executable database queries. The reported experiments compare CHILL's performance to that of a more naive application of ILP to parser acquisition. The results show that ILP techniques, as employed in CHILL, are a viable alternative to statistical methods and that the control-rule framework is fundamental to CHILL's success.

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In Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, Stefan Wermter and Ellen Riloff and Gabriela Scheler (Eds.), pp. 355-369, Berlin 1996. Springer.

@InCollection{zelle:comparative96, title={Comparative Results on Using Inductive Logic Programming for Corpus-based Parser Construction}, author={John M. Zelle and Raymond J. Mooney}, booktitle={Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing}, editor={Stefan Wermter and Ellen Riloff and Gabriela Scheler}, address={Berlin}, publisher={Springer}, pages={355-369}, url="http://www.cs.utexas.edu/users/ai-lab?zelle:comparative96", year={1996} }