A connectionist approach to word sense disambiguation
A connectionist approach to word sense disambiguation
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An empirical study of the domain dependence of supervised word sense disambiguation systems
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Evolving Neural Networks for Word Sense Disambiguation
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PNNL: a supervised maximum entropy approach to word sense disambiguation
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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.