Semi-supervised semantic pattern discovery with guidance from unsupervised pattern clusters

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

Abstract

We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply this approach to the task of relation extraction. The evaluation results demonstrate that our novel bootstrapping procedure significantly outperforms a standard bootstrapping. Most importantly, our algorithm can effectively prevent semantic drift and provide semi-supervised learning with a natural stopping criterion.

Original languageEnglish (US)
Title of host publicationColing 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference
Pages1194-1202
Number of pages9
Volume2
StatePublished - 2010
Event23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China
Duration: Aug 23 2010Aug 27 2010

Other

Other23rd International Conference on Computational Linguistics, Coling 2010
CountryChina
CityBeijing
Period8/23/108/27/10

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Linguistics and Language

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  • Cite this

    Sun, A., & Grishman, R. (2010). Semi-supervised semantic pattern discovery with guidance from unsupervised pattern clusters. In Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference (Vol. 2, pp. 1194-1202)