Latent Semantic Word Sense Disambiguation Using Global Co-Occurrence Information

摘要

In this paper, I propose a novel word sense disambiguation method based on the global cooccurrenceinformation using NMF. When I calculate the dependency relation matrix, theexisting 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 alarge number of co-occurrence relations, I propose to use co-occurrence frequencies ofdependency relations between word features in the whole training set. This enables us to solvedata sparseness problem and induce more effective latent features. To evaluate the efficiency ofthe method of word sense disambiguation, I make some experiments to compare with the resultof the two baseline methods. The results of the experiments show this method is effective forword sense disambiguation in comparison with the all baseline methods. Moreover, theproposed method is effective for obtaining a stable effect by analyzing the global co-occurrenceinformation.