Paper: Towards Robust Semantic Role Labeling

ACL ID N07-1070
Title Towards Robust Semantic Role Labeling
Venue Human Language Technologies
Session Main Conference
Year 2007
Authors

Most research on semantic role labeling (SRL) has been focused on training and evaluating on the same corpus in order to develop the technology. This strategy, while appropriate for initiating research, can lead to over-training to the particular corpus. The work presented in this pa- per focuses on analyzing the robustness of an SRL system when trained on one genre of data and used to label a different genre. Our state-of-the-art semantic role labeling system, while performing well on WSJ test data, shows significant perfor- mance degradation when applied to data from the Brown corpus. We present a se- ries of experiments designed to investigate the source of this lack of portability. These experiments are based on comparisons of performance using PropBanked WSJ data and PropBanked Brown ...