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<Paper uid="P03-1028">
  <Title>Closing the Gap: Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper, we present a learning approach to the scenario template task of information extraction, where information filling one template could come from multiple sentences. When tested on the MUC-4 task, our learning approach achieves accuracy competitive to the best of the MUC-4 systems, which were all built with manually engineered rules. Our analysis reveals that our use of full parsing and state-of-the-art learning algorithms have contributed to the good performance.</Paragraph>
    <Paragraph position="1"> To our knowledge, this is the first research to have demonstrated that a learning approach to the full-scale information extraction task could achieve performance rivaling that of the knowledge-engineering approach.</Paragraph>
  </Section>
class="xml-element"></Paper>