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Semi-supervised clustering for word instances and its effect on word sense disambiguation (2009)
| Content Provider | CiteSeerX |
|---|---|
| Author | Sugiyama, Kazunari Okumura, Manabu |
| Description | 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. In CICLing |
| File Format | |
| Language | English |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Subject Keyword | Semi-supervised Clustering Supervised Word Sense Disambiguation Exclude Outlier Must-link Constraint Supervised Wsd Accuracy Word Sens Wsd Accuracy Frequency Distribution Word Sense Disambiguation Experimental Result Seed Instance Word Instance |
| Content Type | Text |
| Resource Type | Article |
