Generalized inference with multiple semantic role labeling systems

  • Authors:
  • Peter Koomen;Vasin Punyakanok;Dan Roth;Wen-tau Yih

  • Affiliations:
  • University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL

  • Venue:
  • CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
  • Year:
  • 2005

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Abstract

We present an approach to semantic role labeling (SRL) that takes the output of multiple argument classifiers and combines them into a coherent predicate-argument output by solving an optimization problem. The optimization stage, which is solved via integer linear programming, takes into account both the recommendation of the classifiers and a set of problem specific constraints, and is thus used both to clean the classification results and to ensure structural integrity of the final role labeling. We illustrate a significant improvement in overall SRL performance through this inference.