JAIST: Clustering and Classification Based Approaches for Japanese WSD

This paper reports about our three participating systems in SemEval-2 Japanese WSD task. The first one is a clustering based method, which chooses a sense for, not individual instances, but automatically constructed clusters of instances. The second one is a classification method, which is an ordinary SVM classifier with simple domain adaptation techniques. The last is an ensemble of these two systems. Results of the formal run shows the second system is the best. Its precision is 0.7476.