Paper: A Transformation-Based Approach To Argument Labeling

ACL ID W04-2417
Title A Transformation-Based Approach To Argument Labeling
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2004

This paper presents the results of applying transformation-based learning (TBL) to the problem of semantic role labeling. The great advantage of the TBL paradigm is that it pro- vides a simple learning framework in which the parallel tasks of argument identification and ar- gument labeling can mutually influence one an- other. Semantic role labeling nevertheless dif- fers from other tasks in which TBL has been successfully applied, such as part-of-speech tagging and named-entity recognition, because of the large span of some arguments, the de- pendence of argument labels on global infor- mation, and the fact that core argument labels are largely arbitrary. Consequently, some care is needed in posing the task in a TBL frame- work.