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• W2964261749 abstract "In this paper, I propose a novel word sense disambiguation method based on the global cooccurrence information using NMF. When I calculate the dependency relation matrix, the existing method tends to produce very sparse co-occurrence matrix from a small training set. Therefore, the NMF algorithm sometimes does not converge to desired solutions. To obtain a large number of co-occurrence relations, I propose to use co-occurrence frequencies of dependency relations between word features in the whole training set. This enables us to solve data sparseness problem and induce more effective latent features. To evaluate the efficiency of the method of word sense disambiguation, I make some experiments to compare with the result of the two baseline methods. The results of the experiments show this method is effective for word sense disambiguation in comparison with the all baseline methods. Moreover, the proposed method is effective for obtaining a stable effect by analyzing the global co-occurrence information." @default.
• W2964261749 created "2019-07-30" @default.
• W2964261749 creator A5091161390 @default.
• W2964261749 date "2014-02-21" @default.
• W2964261749 modified "2023-09-24" @default.
• W2964261749 title "Latent Semantic Word Sense Disambiguation Using Global Co-Occurrence Information" @default.
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• W2964261749 doi "https://doi.org/10.5121/csit.2014.4240" @default.
• W2964261749 hasPublicationYear "2014" @default.
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