First-Order Probabilistic Models for Coreference Resolution

Traditional noun phrase coreference res- olution systems represent features only of pairs of noun phrases. In this paper, we propose a machine learning method that enables features over sets of noun phrases, resulting in a first-order proba- bilistic model for coreference. We out- line a set of approximations that make this approach practical, and apply our method to the ACE coreference dataset, achiev- ing a 45% error reduction over a com- parable method that only considers fea- tures of pairs of noun phrases. This result demonstrates an example of how a first- order logic representation can be incorpo- rated into a probabilistic model and scaled efficiently.