Robustness and Generalization of Role Sets: PropBank vs. VerbNet
Beñat Zapirain, Eneko Agirre, Lluı́s Màrquez · Meeting of the Association for Computational Linguistics · 2008
This paper presents an empirical study on the robustness and generalization of two alternative role sets for semantic role labeling: PropBank numbered roles and VerbNet thematic roles. By testing a state–of–the–art SRL system with the two alternative role annotations, we show that the PropBank role set is more robust to the lack of verb–specific semantic information and generalizes better to infrequent and unseen predicates. Keeping in mind that thematic roles are better for application needs, we also tested the best way to generate VerbNet annotation. We conclude that tagging first PropBank roles and mapping into VerbNet roles is as effective as training and tagging directly on VerbNet, and more robust for domain shifts.