Kernel Methods for Relation Extraction

Dmitry Zelenko, Chinatsu Aone, Anthony Richardella

Research output: Contribution to journalArticlepeer-review

870 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1083-1106
Number of pages24
JournalJournal of Machine Learning Research
Issue number6
StatePublished - Aug 15 2003

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


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