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<Paper uid="W06-1659">
  <Title>Unsupervised Information Extraction Approach Using Graph Mutual Reinforcement</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Information Extraction (IE) is the task of extracting knowledge from unstructured text. We present a novel unsupervised approach for information extraction based on graph mutual reinforcement.</Paragraph>
    <Paragraph position="1"> The proposed approach does not require any seed patterns or examples. Instead, it depends on redundancy in large data sets and graph based mutual reinforcement to induce generalized &amp;quot;extraction patterns&amp;quot;. The proposed approach has been used to acquire extraction patterns for the ACE</Paragraph>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
(Automatic Content Extraction) Relation
Detection and Characterization (RDC)
</SectionTitle>
      <Paragraph position="0"> task. ACE RDC is considered a hard task in information extraction due to the absence of large amounts of training data and inconsistencies in the available data.</Paragraph>
      <Paragraph position="1"> The proposed approach achieves superior performance which could be compared to supervised techniques with reasonable training data.</Paragraph>
    </Section>
  </Section>
class="xml-element"></Paper>