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<Paper uid="W06-1659">
  <Title>Unsupervised Information Extraction Approach Using Graph Mutual Reinforcement</Title>
  <Section position="10" start_page="506" end_page="506" type="concl">
    <SectionTitle>
7 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> In this work, a general framework for unsupervised information extraction based on mutual reinforcement in graphs has been introduced. We construct generalized extraction patterns and deploy graph based mutual reinforcement to automatically identify the most informative patterns.</Paragraph>
    <Paragraph position="1"> We provide motivation for our approach from a graph theory and graph link analysis perspective.</Paragraph>
    <Paragraph position="2"> Experimental results have been presented supporting the applicability of the proposed approach to ACE Relation Detection and Characterization (RDC) task, demonstrating its applicability to hard information extraction problems. The proposed approach achieves remarkable results comparable to a state-of-the-art supervised system, achieving 51.94 F-measure compared to 59.96 F-measure of the state-of-the-art supervised system which requires huge amount of human annotated data. The proposed approach represents a powerful unsupervised technique for information extraction in general and particularly for relations extraction that requires no seed patterns or examples and achieves significant performance. null In our future work, we plan to focus on generalizing the approach for targeting more NLP problems. null</Paragraph>
  </Section>
class="xml-element"></Paper>