Word Sense Disambiguation Based On Global Co-Occurrence Information Using Non-Negative Matrix Factorization

In this paper, I propose a novel word sense disambiguation method based on global co-occurrence information using Non-negative Matrix Factorization (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 cooccurrence 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. Keywords: Word sense disambiguation; Global co-occurrence information; Dependency relations; Non-negative matrix factorization