Extracting relations with integrated information using kernel methods

Shubin Zhao, Ralph Grishman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Entity relation detection is a form of information extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues from different levels of syntactic processing using kernel methods. Information from three different levels of processing is considered: tokenization, sentence parsing and deep dependency analysis. Each source of information is represented by kernel functions. Then composite kernels are developed to integrate and extend individual kernels so that processing errors occurring at one level can be overcome by information from other levels. We present an evaluation of these methods on the 2004 ACE relation detection task, using Support Vector Machines, and show that each level of syntactic processing contributes useful information for this task. When evaluated on the official test data, our approach produced very competitive ACE value scores. We also compare the SVM with KNN on different kernels.

Original languageEnglish (US)
Title of host publicationACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages419-426
Number of pages8
ISBN (Print)1932432515, 9781932432510
DOIs
StatePublished - 2005
Event43rd Annual Meeting of the Association for Computational Linguistics, ACL-05 - Ann Arbor, MI, United States
Duration: Jun 25 2005Jun 30 2005

Publication series

NameACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Other

Other43rd Annual Meeting of the Association for Computational Linguistics, ACL-05
CountryUnited States
CityAnn Arbor, MI
Period6/25/056/30/05

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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