Feature Generation for Robust Semantic Role Labeling
Table 2: Columns are the dataset used for feature selection and rows are the dataset used for training and testing.
['[EMPTY]', 'FN', 'PB']
but it is another question of whether this matters towards system performance. It could be that there are many different types of feature sets which lead to good performance on either task/dataset, and only one is needed (possibly created manually). The performance on the diagonal is considerably higher, indicating empirically that there likely isn’t one “SRL feature set”. If you weight both equally, the average increase in error due to domain shift is 7.9%. This is even the case for feature selection with FrameNet, where you might expect that selecting features on Propbank, a much larger resource, could yield gains because of much lower variance without much bias.