This leads to a highly imbalanced learning problem because our model needs to learn (the weight of the single indicator feature) to predict NEW as the best action for a large set of mentions, which will bias our model to predict large number of NEW actions during testing.
then for all learning problems, min Fscore E(F score , F score ) ≥ min (Fprune,Fscore) E(F prune , F score ).