Since the entity-mention model aims to classify whether an NP is coreferent with a preceding cluster, each of its training instances (1) corresponds to an NP, NP k , and a preceding cluster, C j , and (2) is labeled with either POSITIVE or NEGATIVE, depending on whether NP k should be assigned to C j .
we can represent each instance by a set of cluster-level features (i.e., features that are defined over an arbitrary subset of the NPs in C j ).