Paper: Agreement/Disagreement Classification: Exploiting Unlabeled Data Using Contrast Classifiers

ACL ID N06-2014
Title Agreement/Disagreement Classification: Exploiting Unlabeled Data Using Contrast Classifiers
Venue Human Language Technologies
Session Short Paper
Year 2006
Authors

Several semi-supervised learning methods have been proposed to leverage unlabeled data, but imbalanced class distributions in the data set can hurt the performance of most algorithms. In this paper, we adapt the new approach of contrast classi ers for semi-supervised learning. This enables us to exploit large amounts of unlabeled data with a skewed distribution. In experiments on a speech act (agreement/disagreement) classi cation problem, we achieve better results than other semi-supervised meth- ods. We also obtain performance com- parable to the best results reported so far on this task and outperform systems with equivalent feature sets.