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<Paper uid="W99-0613">
  <Title>Unsupervised Models for Named Entity Classification</Title>
  <Section position="9" start_page="109" end_page="109" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> Unlabeled examples in the named-entity classification problem can reduce the need for supervision to a handful of seed rules. In addition to a heuristic based on decision list learning, we also presented a boosting-like framework that builds on ideas from (Blum and Mitchell 98). The method uses a &amp;quot;soft&amp;quot; measure of the agreement between two classifiers as an objective function; we described an algorithm which directly optimizes this function. We are currently exploring other methods that employ similar ideas and their formal properties. Future work should also extend the approach to build a complete named entity extractor -- a method that pulls proper names from text and then classifies them. The contextual rules are restricted and may not be applicable to every example, but the spelling rules are generally applicable and should have good coverage. The problem of &amp;quot;noise&amp;quot; items that do not fall into any of the three categories also needs to be addressed.</Paragraph>
  </Section>
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