The tagging problem in natural language processing is to find a way to label every word in a text as a particular part of speech, e.g., proper noun. An effective way of solving this problem with high accuracy is the transformation-based or "Brill" tagger. In Brill's system, a number of transformation templates are specified a priori that are instantiated and ranked during a greedy search-based algorithm. This paper describes a variant of Brill's implementation that instead uses a genetic algorithm to generate the instantiated rules and provide an adaptive ranking. Based on tagging accuracy, the new system provides a better hybrid evolutionary computation solution to the part-of-speech (POS) problem than the previous attempt. Although not able to make up for the use of a priori knowledge utilized by Brill, the method appears to point the way for an improved solution to the tagging problem.
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