Coupled semi-supervised learning for information extraction

  • Authors:

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

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Federal University of Sao Carlos, Sao Carlos, Brazil;Carnegie Mellon University, Pittsburgh, PA, USA

  • Venue:
  • Proceedings of the third ACM international conference on Web search and data mining
  • Year:
  • 2010

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Abstract

We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled examples is typically unreliable because the learning task is underconstrained. This paper pursues the thesis that much greater accuracy can be achieved by further constraining the learning task, by coupling the semi-supervised training of many extractors for different categories and relations. We characterize several ways in which the training of category and relation extractors can be coupled, and present experimental results demonstrating significantly improved accuracy as a result.