More for Less: Learning a Wide Covering Grammar From a Small Training Set

This paper describes a grammar learning system which combines model-based and data-driven learning within a single framework. Results from learning grammars with the Spoken English Corpus (SEC) suggest that a combined model-based and data-driven learner can acquire a wide coverage grammar from only a small training corpus. Keywords: Corpus-based NLP, Statistical NLP, Deductive NLP, Hybrid approaches. 1: Introduction In this paper, we present some results of our grammar learning system. We show that using unification-based grammars, with a hybrid learning system allows a rapid rate of convergence upon a test corpus with only a modest amount of training material. In contrast to other researchers (for example (BMMS92; GLS87; Bak79; LY90; VB87)), we try to learn competence grammars and not performance grammars. We also try to learn grammars that assign linguistically plausible parses to sentences. Learning competence grammars that assign plausible parses is achieved by combining model-b..