Abstract

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 speaking, grammar correction approaches can be classified as being either datadriven, or model-based. Data-driven learners use data-intensive methods to acquire grammar. They typically use grammar formalisms unsuited to the needs of practical text processing and cannot guarantee that the resulting grammar is adequate for subsequent semantic interpretation. That is, data-driven learners acquire grammars that generate strings that humans would jud...

Citations