Formalisation of transformation-based learning

Research in automatic part of speech (POS) tagging has been dominated by Markov model (MM) taggers. E. Brill (1997) has recently described a transformation-based system with comparable accuracy, and simpler algorithms and representation than MM taggers. We present a set-based formal model of natural language ambiguity and semantic tagging that forms a basis for the generalisation of the transformation-based learning (TBL) and Brill's TBL tagger. We discuss empirical observations of the training algorithm that suggest a new evolutionary transformation learning strategy may dramatically improve learning time without loss of accuracy.

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