Abstract
In this paper, we introduce a free software package SVDPACKC computing the singular value decomposition (SVD) of large sparse matrics. First we explain how to use it, and then solve word sense disambiguation problems by using it. In information retrieval domain, Latent Semantic Indexing (LSI) has actively been researched. LSI maps a high dimensional term vector to the low dimensional concept vectors to overcome synonymy and polysemy problems over information retrieval using vector space model. To build low dimensional concept vectors LSI computes the SVD of term-document matrics. SVDPACKC is a software tool to computes the SVD of large sparse matrics like term-document matrics. LSI compresses a high dimensional future vector to the low dimensional concept vectors, so has many applications besides information retrieval. In this paper, we attack word sense disambiguation problems of 50 verbs in Japanese dictionary task of SENSEVAL2. By using cross validation and LSI, we improved simple Nearest Neighbor method (NN). And we showed that the methods based on NN achieve better precision than the decision list method and Naive Bayes method for some words.
