Iterative constrained clustering for subjectivity word sense disambiguation

Abstract

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 surprisingly, SWSD suffers from the knowledge acquisition bottleneck. In this work, we use a "cluster and label" strategy to generate labeled data for SWSD semi-Automatically. We define a new algorithm called Iterative Constrained Clustering (ICC) to improve the clustering purity and, as a result, the quality of the generated data. Our experiments show that the SWSD classifiers trained on the ICC generated data by requiring only 59% of the labels can achieve the same performance as the classifiers trained on the full dataset. © 2014 Association for Computational Linguistics.