| Summary: | In this paper we present an approach to the part-of-speech tagging problem based on particle swarm optimization. The part-of-speech tagging is a key input feature for several other natural language processing tasks, like phrase chunking and named entity recognition. A tagger is a system that should receive a text, made of sentences, and, as output, should return the same text, but with each of its words associated with the correct part-of-speech tag. The task is not straightforward, since a large percentage of words have more than one possible part-of-speech tag, and the right choice is determined by the part-of- speech tags of the surrounding words, which can also have more than one possible tag. In this work we investigate the possibility of using a particle swarm optimization algorithm to solve the part-of-speech tagging problem supported by a set of disambiguation rules. The results we obtained on two different corpora are amongst the best ones published for those corpora.
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