Kernel Methods for Relation Extraction

Dmitry Zelenko, Chinatsu Aone, Anthony Raymond Richardella

Research output: Contribution to journalArticle

494 Citations (Scopus)

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)
Pages (from-to)1083-1106
Number of pages24
JournalJournal of Machine Learning Research
Volume3
Issue number6
DOIs
StatePublished - Aug 15 2003

Fingerprint

Kernel Methods
Learning algorithms
kernel
Learning Algorithm
Support vector machines
Perceptron
Neural networks
Natural Language
Support Vector Machine
Person
Efficient Algorithms
Computing
Evaluate
Text

All Science Journal Classification (ASJC) codes

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

Cite this

Zelenko, Dmitry ; Aone, Chinatsu ; Richardella, Anthony Raymond. / Kernel Methods for Relation Extraction. In: Journal of Machine Learning Research. 2003 ; Vol. 3, No. 6. pp. 1083-1106.
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Kernel Methods for Relation Extraction. / Zelenko, Dmitry; Aone, Chinatsu; Richardella, Anthony Raymond.

In: Journal of Machine Learning Research, Vol. 3, No. 6, 15.08.2003, p. 1083-1106.

Research output: Contribution to journalArticle

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