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<Paper uid="W03-0420">
  <Title>Maximum Entropy Models for Named Entity Recognition</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper, we describe a system that applies maximum entropy (ME) models to the task of named entity recognition (NER). Starting with an annotated corpus and a set of features which are easily obtainable for almost any language, we first build a baseline NE recognizer which is then used to extract the named entities and their context information from additional non-annotated data. In turn, these lists are incorporated into the final recognizer to further improve the recognition accuracy.</Paragraph>
  </Section>
class="xml-element"></Paper>