We propose a supervised word sense disambiguation (WSD) system that uses features obtained from clustering results of word instances. Our approach is novel in that we employ semi-supervised clustering that controls the fluctuation of the centroid of a cluster, and we select seed instances by considering the frequency distribution of word senses and exclude outliers when we introduce "must-link" constraints between seed instances. In addition, we improve the supervised WSD accuracy by using features computed from word instances in clusters generated by the semi-supervised clustering. Experimental results show that these features are effective in improving WSD accuracy. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Sugiyama, K., & Okumura, M. (2009). Semi-supervised clustering forword instances and its effect on word sense disambiguation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5449 LNCS, pp. 266–279). https://doi.org/10.1007/978-3-642-00382-0_22
Mendeley helps you to discover research relevant for your work.