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Generalized Inference with Multiple Semantic Role Labeling Systems

P. Koomen, V. Punyakanok, D. Roth, and W. Yih

Abstract

We present an approach to semantic role labeling (SRL) that takes the output of multiple argument classifiers and combines them into a coherent predicateargument 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.

Details

Publication typeInproceedings
Published inProc. of the Annual Conference on Computational Natural Language Learning (CoNLL)
Pages181-184
PublisherAssociation for Computational Linguistics
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