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<Paper uid="N06-2014">
  <Title>Agreement/Disagreement Classi cation: Exploiting Unlabeled Data using Contrast Classi ers</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> 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 methods. We also obtain performance comparable to the best results reported so far on this task and outperform systems with equivalent feature sets.</Paragraph>
  </Section>
class="xml-element"></Paper>