Generalized Inference with Multiple Semantic Role Labeling Systems.
CoNLL(2005)
摘要
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.
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关键词
significant improvement,overall srl performance,classification result,integer linear programming,optimization problem,multiple semantic role,multiple argument classifier,problem specific constraint,generalized inference,final role,coherent predicate-argument output,optimization stage,semantic role labeling
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