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<Paper uid="P03-1029">
  <Title>An Improved Extraction Pattern Representation Model for Automatic IE Pattern Acquisition</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Information Extraction (IE) is the process of identifying events or actions of interest and their participating entities from a text. As the field of IE has developed, the focus of study has moved towards automatic knowledge acquisition for information extraction, including domain-specific lexicons (Riloff, 1993; Riloff and Jones, 1999) and extraction patterns (Riloff, 1996; Yangarber et al., 2000; Sudo et al., 2001). In particular, methods have recently emerged for the acquisition of event extraction patterns without corpus annotation in view of the cost of manual labor for annotation. However, there has been little study of alternative representation models of extraction patterns for unsupervised acquisition.</Paragraph>
    <Paragraph position="1"> In the prior work on extraction pattern acquisition, the representation model of the patterns was based on a fixed set of pattern templates (Riloff, 1996), or predicate-argument relations, such as subject-verb, and object-verb (Yangarber et al., 2000). The model of our previous work (Sudo et al., 2001) was based on the paths from predicate nodes in dependency trees.</Paragraph>
    <Paragraph position="2"> In this paper, we discuss the limitations of prior extraction pattern representation models in relation to their ability to capture the participating entities in scenarios. We present an alternative model based on subtrees of dependency trees, so as to extract entities beyond direct predicate-argument relations. An evaluation on scenario-template tasks shows that the proposed Subtree model outperforms the previous models.</Paragraph>
    <Paragraph position="3"> Section 2 describes the Subtree model for extraction pattern representation. Section 3 shows the method for automatic acquisition. Section 4 gives the experimental results of the comparison to other methods and Section 5 presents an analysis of these results. Finally, Section 6 provides some concluding remarks and perspective on future research.</Paragraph>
  </Section>
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