Coupling semi-supervised learning of categories and relations

  • Authors:

  • Andrew Carlson;Justin Betteridge;Estevam R. Hruschka, Jr.;Tom M. Mitchell

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA and Federal University of Sao Carlos, Sao Carlos, SP - Brazil;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
  • Year:
  • 2009

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Abstract

We consider semi-supervised learning of information extraction methods, especially for extracting instances of noun categories (e.g., 'athlete', 'team') and relations (e.g., 'playsForTeam(athlete, team)'). Semi-supervised approaches using a small number of labeled examples together with many un-labeled examples are often unreliable as they frequently produce an internally consistent, but nevertheless incorrect set of extractions. We propose that this problem can be overcome by simultaneously learning classifiers for many different categories and relations in the presence of an ontology defining constraints that couple the training of these classifiers. Experimental results show that simultaneously learning a coupled collection of classifiers for 30 categories and relations results in much more accurate extractions than training classifiers individually.