Pattern recognition applied to the acquisition of a grammatical classification system from unrestricted English text

Within computational linguistics, the use of statistical pattern matching is generally restricted to speech processing. We have attempted to apply statistical techniques to discover a grammatical classification system from a Corpus of 'raw' English text. A discovery procedure is simpler for a simpler language model; we assume a first-order Markov model, which (surprisingly) is shown elsewhere to be sufficient for practical applications. The extraction of the parameters of a standard Markov model is theoretically straightforward; however, the huge size of the standard model for a Natural Language renders it incomputable in reasonable time. We have explored various constrained models to reduce computation, which have yielded results of varying success.