• Korean Journal of Cognitive Science
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• v.27 no.1
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• pp.43-60
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• 2016

Recently, Unsupervised Word Sense Disambiguation research has focused on Graph based disambiguation. Graph-based disambiguation has built a semantic graph based on words collocated in context or sentence. However, building such a graph over all ambiguous word lead to unnecessary addition of edges and nodes (and hence increasing the error). In contrast, our work uses Word2Vec to consider the most similar words to an ambiguous word in the context or sentences, to rebuild a graph of the matched words. As a result, we show a higher F1-Measure value than the previous methods by using Word2Vec.