Because of the way the classifier is built, predictor features for class 2 need to have higher loadings to overcome the lower prior probability of the class.
we actually rank the features in Table 6 according to αP(c) k (which we denote as α c,k ).P(c) represents the empirical prior probability of a class c, and k is simply a constant (.25 in this case) chosen to try to get a mix of features for different classes at the top of the list.