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<Paper uid="P04-1054">
  <Title>Dependency Tree Kernels for Relation Extraction</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
AT NEAR PART ROLE SOCIAL
</SectionTitle>
    <Paragraph position="0"> (e.g. part of speech) We choose this representation because we hypothesize that instances containing similar relations will share similar substructures in their dependency trees. The task of the kernel function is to find these similarities.</Paragraph>
    <Paragraph position="1"> We define a tree kernel over dependency trees and incorporate this kernel within an SVM to extract relations from newswire documents. The tree kernel approach consistently outperforms the bag-of-words kernel, suggesting that this highly-structured representation of sentences is more informative for detecting and distinguishing relations.</Paragraph>
  </Section>
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