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<Paper uid="W04-2402">
  <Title>Semantic Lexicon Construction: Learning from Unlabeled Data via Spectral Analysis</Title>
  <Section position="8" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> We show that spectral analysis is useful for overcoming data sparseness on the task of classifying words into their entity classes. In a series of experiments, the proposed method compares favorably with a number of methods that employ techniques such as EM and co-training.</Paragraph>
    <Paragraph position="1"> We formalize the notion of harmful portions of the commonly used feature vectors for linear classifiers, and seek to factor out them via spectral analysis of unlabeled data. This process does not use any class information.</Paragraph>
    <Paragraph position="2"> By contrast, the process of bootstrapping is generally driven by class label prediction. As future work, we are interested in combining these somewhat orthogonal approaches. null</Paragraph>
  </Section>
class="xml-element"></Paper>