Paper: Semantic Role Labeling Using Dependency Trees

ACL ID C04-1186
Title Semantic Role Labeling Using Dependency Trees
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004

In this paper, a novel semantic role labeler based on dependency trees is developed. This is ac- complished by formulating the semantic role la- beling as a classification problem of dependency relations into one of several semantic roles. A dependency tree is created from a constituency parse of an input sentence. The dependency tree is then linearized into a sequence of dependency relations. A number of features are extracted for each dependency relation using a predefined lin- guistic context. Finally, the features are input to a set of one-versus-all support vector machine (SVM) classifiers to determine the corresponding semantic role label. We report results on CoNLL2004 shared task data using the represen- tation and scoring scheme adopted for that task.