Abstract
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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Silva, A.P., Silva, A., Rodrigues, I. (2013). PSO-Tagger: A New Biologically Inspired Approach to the Part-of-Speech Tagging Problem. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_10
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DOI: https://doi.org/10.1007/978-3-642-37213-1_10
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