This paper describes a grammar learning system that combines modelbased and data-driven learning within a single framework. Our results from learning grammars using the Spoken English Corpus (SEC) suggest that combined model-based and data-driven learning can produce a more plausible grammar than is the case when using either learning style in isolation. 1

September 1994‘It is high time we turned to Grammar now, ’ said Doctor Cornelius in a loud voice. ‘Will your Royal Highness be pleased to open Pulverulentus Siccus at the fourth page of his Grammatical Garden or the Arbour of Accidence pleasantlie open’d to Tender Wits?’ After that it was all nouns and verbs till lunchtime, but I don’t think Caspian learned much. C. S. Lewis, Prince Caspian. Practical text processing systems need wide covering grammars. When parsing unrestricted language, such grammars often fail to generate all of the sentences that humans would judge to be grammatical. This problem undermines successful parsing of the text and is known as undergeneration. There are two main ways of dealing with undergeneration: either by sentence correction, or by grammar correction. This thesis concentrates upon automatic grammar correction (or machine learning of grammar) as a solution to the problem of undergeneration. Broadly

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...

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