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<Paper uid="W99-0613">
  <Title>Unsupervised Models for Named Entity Classification</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper discusses the use of unlabeled examples for the problem of named entity classification. A large number of rules is needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. However, we show that the use of unlabeled data can reduce the requirements for supervision to just 7 simple &amp;quot;seed&amp;quot; rules. The approach gains leverage from natural redundancy in the data: for many named-entity instances both the spelling of the name and the context inwhich it appears are sufficient to determine its type.</Paragraph>
    <Paragraph position="1"> We present two algorithms. The first method uses a similar algorithm to that of (Yarowsky 95), with modifications motivated by (Blum and Mitchell 98).</Paragraph>
    <Paragraph position="2"> The second algorithm extends ideas from boosting algorithms, designed for supervised learning tasks, to the framework suggested by (Blum and Mitchell 98).</Paragraph>
  </Section>
class="xml-element"></Paper>