Semi-supervised Clustering for Word Instances and Its Effect on Word Sense Disambiguation

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.

• Type: Conference
• Year: 2009
• Where: CICLING
• Authors: Kazunari Sugiyama, Manabu Okumura
• Keywords: CICLING 2009, Natural Language Processing, Seed Instances, Supervised Wsd Accuracy, Word Instances