Paper: Semantic Role Labeling Using LibSVM

ACL ID W05-0631
Title Semantic Role Labeling Using LibSVM
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2005
Authors

We describe a system for the CoNLL- 2005 shared task of Semantic Role Label- ing. The system implements a two-layer architecture to first identify the arguments and then to label them for each predicate. The components are implemented as SVM classifiers using libSVM. Features were adapted and tuned for the system, including a reduced set for the identifier classifier. Experiments were conducted to find kernel parameters for the Radial Ba- sis Function (RBF) kernel. An algorithm was defined to combine the results of the argument labeling classifier according to the constraints of the argument labeling problem.