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<Paper uid="P04-1054">
  <Title>Dependency Tree Kernels for Relation Extraction</Title>
  <Section position="4" start_page="0" end_page="0" type="relat">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> Kernel methods (Vapnik, 1998; Cristianini and Shawe-Taylor, 2000) have become increasingly popular because of their ability to map arbitrary objects to a Euclidian feature space. Haussler (1999) describes a framework for calculating kernels over discrete structures such as strings and trees. String kernels for text classification are explored in Lodhi et al. (2000), and tree kernel variants are described in (Zelenko et al., 2003; Collins and Duffy, 2002; Cumby and Roth, 2003). Our algorithm is similar to that described by Zelenko et al. (2003). Our contributions are a richer sentence representation, a more general framework to allow feature weighting, as well as the use of composite kernels to reduce kernel sparsity.</Paragraph>
    <Paragraph position="1"> Brin (1998) and Agichtein and Gravano (2000) apply pattern matching and wrapper techniques for relation extraction, but these approaches do not scale well to fastly evolving corpora. Miller et al.</Paragraph>
    <Paragraph position="2"> (2000) propose an integrated statistical parsing technique that augments parse trees with semantic labels denoting entity and relation types. Whereas Miller et al. (2000) use a generative model to produce parse information as well as relation information, we hypothesize that a technique discriminatively trained to classify relations will achieve better performance. Also, Roth and Yih (2002) learn a Bayesian network to tag entities and their relations simultaneously. We experiment with a more challenging set of relation types and a larger corpus.</Paragraph>
  </Section>
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