The p(e|m) is also transformed to a 10-d vector using RBF binning (Sil et al., 2018) to create prior feature f (p) ij ∈ R 10 .
for each mention x i we use ground truth entities for the other mentions in E i and update weights of C and w such that the true candidate of x i gets higher score than its false candidates.