Abstract
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 classifiers 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) classification problem, we achieve better results than other semi-supervised methods. We also obtain performance comparable to the best results reported so far on this task and outperform systems with equivalent feature sets.
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