| Summary: | {In this paper we present an evolutionary approach to the part-of-speech tagging problem. The goal of part-of-speech tagging is to assign to each word of a text its part-of-speech. The task is not straightforward, because a large percentage of words has more than one possible part-of-speech, and the right choice is determined by the surrounding word's part-of-speeches. This means that to solve this problem we need a method to disambiguate a word's possible tags set. Traditionally there are two groups of methods used to tackle this task. The first group is based on statistical data concerning the different context's possibilities for a word, while the second group is based on rules that capture the language properties. Normally these rules are designed by human experts. In this work we present a solution that tries to incorporate both these approaches. The proposed system is divided in two components. First, we use an evolutionary algorithm that for each part-of-speech tag of the training corpus, evolves a set of disambiguation rules. We then use a second evolutionary algorithm to solve the tagging problem. In this component the evolution is guided by the rules found earlier.
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