Empirically, we drop the zero-count dimensions directly during training and assign conceptually low scores 3 when a non-zero count is seen during scoring.
we use normalized feature vectors e = e 1 , e 2 , ..., e B , where 1, 2, ..., B are dimensions in B whose original counts are non-zero, for training.