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.
we will generate many singleton clusters, which will hurt the recall of the mention detection after post-processing.