Paper: Iterative Constrained Clustering for Subjectivity Word Sense Disambiguation

ACL ID E14-1029
Title Iterative Constrained Clustering for Subjectivity Word Sense Disambiguation
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Subjectivity word sense disambiguation (SWSD) is a supervised and application- specific word sense disambiguation task disambiguating between subjective and objective senses of a word. Not sur- prisingly, SWSD suffers from the knowl- edge acquisition bottleneck. In this work, we use a ?cluster and label? strategy to generate labeled data for SWSD semi- automatically. We define a new algo- rithm called Iterative Constrained Cluster- ing (ICC) to improve the clustering purity and, as a result, the quality of the gener- ated data. Our experiments show that the SWSD classifiers trained on the ICC gen- erated data by requiring only 59% of the labels can achieve the same performance as the classifiers trained on the full dataset.