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<Paper uid="P04-1039">
  <Title>Relieving The Data Acquisition Bottleneck In Word Sense Disambiguation</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In this paper, we present an unsupervised bootstrapping approach for WSD which exploits huge amounts of automatically generated noisy data for training within a supervised learning framework. The method is evaluated using the 29 nouns in the English Lexical Sample task of SENSEVAL2. Our algorithm does as well as supervised algorithms on 31% of this test set, which is an improvement of 11% (absolute) over state-of-the-art bootstrapping WSD algorithms. We identify seven different factors that impact the performance of our system.</Paragraph>
  </Section>
class="xml-element"></Paper>