Kernel methods for relation extraction

  • Authors:
  • Dmitry Zelenko;Chinatsu Aone;Anthony Richardella

  • Affiliations:
  • SRA International, 4300 Fair Lakes Ct., Fairfax VA;SRA International, 4300 Fair Lakes Ct., Fairfax VA;SRA International, 4300 Fair Lakes Ct., Fairfax VA

  • Venue:
  • The Journal of Machine Learning Research
  • Year:
  • 2003

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Abstract

We present an application of kernel methods to extracting relations from unstructured natural language sources. We introduce kernels defined over shallow parse representations of text, and design efficient algorithms for computing the kernels. We use the devised kernels in conjunction with Support Vector Machine and Voted Perceptron learning algorithms for the task of extracting person-affiliation and organization-location relations from text. We experimentally evaluate the proposed methods and compare them with feature-based learning algorithms, with promising results.