Empirical methods for building natural language systems has become an important area of research in recent years. Most current approaches are based on propositional learning algorithms and have been applied to the problem of acquiring broad-coverage parsers for relatively shallow (syntactic) representations. This paper outlines an alternative empirical approach based on techniques from a subfield of machine learning known as Inductive Logic Programming (ILP). ILP algorithms, which learn relational (first-order) rules, are used in a parser acquisition system called CHILL that learns rules to control the behavior of a traditional shift-reduce parser. Using this approach, CHILL is able to learn parsers for a variety of different types of analyses, from traditional syntax trees to more meaning-oriented case-role and database query forms. Experimental evidence shows that CHILL performs comparably to propositional learning systems on similar tasks, and is able to go beyond the broad-but-shallow paradigm and learn mappings directly from sentences into useful semantic representations. In a complete database-query application, parsers learned by CHILL outperform an existing hand-crafted system, demonstrating the promise of empricial techniques for automating the construction certain NLP systems.

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unpublished. Unpublished Technical Note.

@unpublished{Zelle:unpublished97, title={An Inductive Logic Programming Method for Corpus-based Parser Construction}, author={John M. Zelle and Raymond J. Mooney}, month={January}, note={Unpublished Technical Note}, url="http://www.cs.utexas.edu/users/ai-lab?Zelle:unpublished97", year={1997} }