Paper: Two-Phase Semantic Role Labeling Based On Support Vector Machines

ACL ID W04-2420
Title Two-Phase Semantic Role Labeling Based On Support Vector Machines
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2004
Authors

In this study, we try to apply SVMs to the se- mantic role labeling task, which is one of the multiclass problems. As a result, we propose a two-phase semantic role labeling model which consists of the identification phase and the clas- sification phase. We first identify semantic ar- guments, and then assign semantic roles to the identified semantic arguments. By taking the two-phase approach, we can alleviate the un- balanced class distribution problem, and select the features appropriate for each task.