A Comparison of Two Methods Employing 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 grammar acquisition as the learning of search-control rules within a logic program. Unlike many current corpus-based approaches that use propositional or probabilistic learning algorithms, Chill uses techniques from inductive logic programming (ILP) to learn relational representations. 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 propositional methods and that the control-rule framework is fundamental to Chill's success.