Comparative results on using inductive logic programming for corpus-based parser construction

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 exible 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.