Kernel Methods for Relation Extraction

Dmitry Zelenko, Chinatsu Aone, Anthony Richardella

Research output: Contribution to conferencePaperpeer-review

94 Scopus citations

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.

Original languageEnglish (US)
Pages71-78
Number of pages8
StatePublished - 2002
Event7th Conference on Empirical Methods in Natural Language Processing, EMNLP 2002 - Philadelphia, United States
Duration: Jul 6 2002Jul 7 2002

Conference

Conference7th Conference on Empirical Methods in Natural Language Processing, EMNLP 2002
Country/TerritoryUnited States
CityPhiladelphia
Period7/6/027/7/02

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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