Generalized inference with multiple semantic role labeling systems

Published 1 January 2005Open access

P. Koomen, Vasin Punyakanok, Dan Roth, Wen-tau Yih

Citations130

TL;DR

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 is presented.

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.

Keywords

Computer Science

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