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<Paper uid="W06-1659">
  <Title>Unsupervised Information Extraction Approach Using Graph Mutual Reinforcement</Title>
  <Section position="9" start_page="505" end_page="506" type="evalu">
    <SectionTitle>
6 Results and Discussion
</SectionTitle>
    <Paragraph position="0"> We compare our results to a state-of-the-art supervised system similar to the system described in (Kambhatla, 2004). Although it is unfair to make a comparison between a supervised system and a completely unsupervised system, we chose to make this comparison to test the performance of the proposed unsupervised approach on a real task with defined test set and state-of-the-art performance. The supervised system was trained on 145 K words which contain 2368 instances of the two relation types we are considering.</Paragraph>
    <Paragraph position="1"> The system performance is measured using precision, recall and F-Measure with various amounts of induced patterns. Table 1 presents the precision, recall and F-measure for the two relations using the presented approach with the utilization of different amount of highly weighted patterns. Table 2 presents the same results using semantic tuple matching and clustering, as described in section 4.3.</Paragraph>
    <Paragraph position="2">  system (Sup), the unsupervised system with syntactic tuple matching (Unsup-Syn), and with semantic tuple matching (Unsup-Sem) Best F-Measure is achieved using relatively small number of induced patterns (400 and 500 patterns) while using more patterns increases the recall but degrades the precision.</Paragraph>
    <Paragraph position="3"> Table 2 indicates that the semantic clustering of tuples did not provide significant improve- null ment; although better performance was achieved with less number of patterns (400 patterns). We think that the deployed similarity measure and it needs further investigation to figure out the reason for that.</Paragraph>
    <Paragraph position="4"> Figure 4 presents the comparison between the proposed unsupervised systems and the reference supervised system. The unsupervised systems achieves good results even in comparison to a state-of-the-art supervised system.</Paragraph>
    <Paragraph position="5"> Sample patterns and corresponding matching text are introduced in Table 3 and Table 4. Table</Paragraph>
  </Section>
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