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 incomputahle in reasonable time. We have explored various constrained models to reduce computation, which have yielded results of varying success. Pattern recognition and NLP In the area of language-related computational research, there is a perceived dichotomy between, on the one hand, "Natural Language" research dealing principally with syntactic and other analysis of typed text, and on the other hand, "Speech Processing" research dealing with synthesis, recognition, and understanding of speech signals. This distinction is nut based merely on a difference of input and/or output media, but seems also to correlate to noticeable differences in assumptions and techniques used in research. One example is in the use of statistical pattern recognition techniques: these are used in a wide variety of computerbased research areas, and many speech researchers take it for granted that such methods are part of their stock in trade. In contrast, statistical pattern recognition is hardly ever even considered as a technique to be used in "Natural Language" text analysis. One reason for this is that speech researchers deal with "real", "unrestricted" data (speech samples), whereas much NLP research deals with highly restricted language data, such as examples intuited by theoreticians, or simplified English as allowed by a dialogue system, sach as a Natural Language Database Query system. Chomsky (57) did much to discredit the use of representative text samples or Corpora in syntactic research; he dismissed both statistics and semantics as being of no use to syntacticians: "Despite the undeniable interest and importance of semantic and statistical studies of language, they appear to have no direct relevance to the problem of determining or characterizing the set of grammatical utterances" (Chomsky 57 p.17). Subsequent research in Computational Linguistics has shown that Semantics is far more relevant and important than Chomsky gave credit for. Phenomenal advances in computer power and capabilities mean that we can now try statistical pattern recognition techniques which would have been incomputable in Chomsky's early days. Therefore, we felt that the case for Corpus-based statistical Pattern Recognition techniques should be reopened. Specifically, we have investigated the possibility of using Pattern Recognition techniques for the acquisition of a grammatical classification system from Unrestricted English text.