Generalized Inference with Multiple Semantic Role Labeling Systems

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.

KoomenPuRoYi-conll05.pdf
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In  Proc. of the Annual Conference on Computational Natural Language Learning (CoNLL)

Publisher  Association for Computational Linguistics
All copyrights reserved by ACL 2005

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TypeInproceedings
Pages181-184
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