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<Paper uid="W03-0429">
  <Title>Named Entity Recognition using Hundreds of Thousands of Features</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present an approach to named entity recognition that uses support vector machines to capture transition probabilities in a lattice. The support vector machines are trained with hundreds of thousands of features drawn from the CoNLL-2003 Shared Task training data. Margin outputs are converted to estimated probabilities using a simple static function. Performance is evaluated using the CoNLL-2003 Shared Task test set; Test B results were Fb=1 = 84.67 for English, and Fb=1 = 69.96 for German. null</Paragraph>
  </Section>
class="xml-element"></Paper>