We propose a supervised approach to word sense disambiguation based on neural networks combined with evolutionary algorithms. Large tagged datasets for every sense of a polysemous word are considered, and used to evolve an optimized neural network that correctly disambiguates the sense of the given word considering the context in which it occurs. A new distributed scheme based on a lexicographic encoding to represent the context in which a particular word occurs is proposed. The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words. © Springer-Verlag 2009.
CITATION STYLE
Azzini, A., Da Costa Pereira, C., Dragoni, M., & Tettamanzi, A. G. B. (2009). A lexicographic encoding for word sense disambiguation with evolutionary neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5883 LNAI, pp. 192–201). https://doi.org/10.1007/978-3-642-10291-2_20
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