  In this paper, I propose a novel word sense disambiguation method based on
the global co-occurrence 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.
