Adapting coreference resolution for narrative processing

Abstract

Domain adaptation is a challenge for supervised NLP systems because of expensive and time-consuming manual annotated resources. We present a novel method to adapt a supervised coreference resolution system trained on newswire to short narrative stories without retraining the system. The idea is to perform inference via an Integer Linear Programming (ILP) formulation with the features of narratives adopted as soft constraints. When testing on the UMIREC1 and N22 corpora with the-stateof-the-art Berkeley coreference resolution system trained on OntoNotes3, our inference substantially outperforms the original inference on the CoNLL 2011 metric.