Abstract
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.
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Azzini, A., Dragoni, M., Tettamanzi, A.G.B. (2011). A Part-Of-Speech Lexicographic Encoding for an Evolutionary Word Sense Disambiguation Approach. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_25
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DOI: https://doi.org/10.1007/978-3-642-20525-5_25
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