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<Paper uid="P05-1049">
  <Title>Word Sense Disambiguation Using Label Propagation Based Semi-Supervised Learning</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Shortage of manually sense-tagged data is an obstacle to supervised word sense disambiguation methods. In this paper we investigate a label propagation based semi-supervised learning algorithm for WSD, which combines labeled and unlabeled data in learning process to fully realize a global consistency assumption: similar examples should have similar labels.</Paragraph>
    <Paragraph position="1"> Our experimental results on benchmark corpora indicate that it consistently out-performs SVM when only very few labeled examples are available, and its performance is also better than monolingual bootstrapping, and comparable to bilingual bootstrapping.</Paragraph>
  </Section>
class="xml-element"></Paper>