Two-Phase Semantic Role Labeling based on Support Vector Machines

In this study, we try to apply SVMs to the semantic 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 classification phase. We first identify semantic arguments, and then assign semantic roles to the identified semantic arguments. By taking the two-phase approach, we can alleviate the unbalanced class distribution problem, and select the features appropriate for each task.