JAIST: Clustering and Classification based Approaches for Japanese WSD
Kiyoaki Shirai, Makoto Nakamura · 2016
This paper reports about our three par-ticipating 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 sec-ond 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. 1