Local Estimation In local estimation, we treat each span-role assignment pair (s, r) ∈ S×R as an individual binary decision problem and maximize the corresponding log-likelihood of the training set.
5 Denote by z s,r ∈ {0, 1} the decision variable, such that z s,r = 1 iff span s is assigned role r. By passing the potential g NN (s, r; θ) through the logistic function, we obtain the log-likelihood the gold role for a given span according to the training data serves as the positive example, while all the other potential roles serve as negatives.