Paper: Semantic Role Labeling Via Tree Kernel Joint Inference

ACL ID W06-2909
Title Semantic Role Labeling Via Tree Kernel Joint Inference
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2006
Authors

Recent work on Semantic Role Labeling (SRL) has shown that to achieve high accuracy a joint inference on the whole predicate argument structure should be ap- plied. In this paper, we used syntactic sub- trees that span potential argument struc- tures of the target predicate in tree ker- nel functions. This allows Support Vec- tor Machines to discern between correct and incorrect predicate structures and to re-rank them based on the joint probabil- ity of their arguments. Experiments on the PropBank data show that both classifica- tion and re-ranking based on tree kernels can improve SRL systems.