This work proposes a novel distributed scheme based on a part-of-speech tagged lexicographic encoding to represent the context in which a particular word occurs in an evolutionary approach for word sense disambiguation. Tagged dataset for every sense of a polysemous word are considered as inputs to supervised classifiers, Artificial Neural Networks (ANNs), which are evolved by a joint optimization of their structures and weights, together with a similarity based recombination operator. The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words. Comparison with the best entries of the Semeval-2007 competition has shown that the proposed approach is competitive with state-of-the-art WSD approaches. © 2011 Springer-Verlag.
CITATION STYLE
Azzini, A., Dragoni, M., & Tettamanzi, A. G. B. (2011). A part-of-speech lexicographic encoding for an evolutionary word sense disambiguation approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6624 LNCS, pp. 244–253). https://doi.org/10.1007/978-3-642-20525-5_25
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