Structural, transitive and latent models for biographic fact extraction

  • Authors:
  • Nikesh Garera;David Yarowsky

  • Affiliations:
  • Johns Hopkins University, Baltimore MD;Johns Hopkins University, Baltimore MD

  • Venue:
  • EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
  • Year:
  • 2009

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Abstract

This paper presents six novel approaches to biographic fact extraction that model structural, transitive and latent properties of biographical data. The ensemble of these proposed models substantially outperforms standard pattern-based biographic fact extraction methods and performance is further improved by modeling inter-attribute correlations and distributions over functions of attributes, achieving an average extraction accuracy of 80% over seven types of biographic attributes.