Feature Generation for Robust Semantic Role Labeling
Table 3: Columns how many features were used for argument identification and rows how many features were used for role classification. FrameNet (FN) is on top β=10, Propbank (PB) is below β=0.01.
['FN', '0', '320', '640', '1280']
Given that we can automatically generate feature sets, we can easily determine how adding or removing features from each stage will affect performance. This is useful for choosing a feature set which balances the cost of prediction time with performance, which is labor intensive and error prone when done manually. id features than role classification ones.