Collaborative Partitioning for Coreference Resolution

This paper presents a collaborative par- titioning algorithm—a novel ensemble- based approach to coreference resolution. Starting from the all-singleton partition, we search for a solution close to the en- semble’s outputs in terms of a task-specific similarity measure. Our approach assumes a loose integration of individual compo- nents of the ensemble and can therefore combine arbitrary coreference resolvers, regardless of their models. Our experi- ments on the CoNLL dataset show that collaborative partitioning yields results su- perior to those attained by the individual components, for ensembles of both strong and weak systems. Moreover, by applying the collaborative partitioning algorithm on top of three state-of-the-art resolvers, we obtain the second-best coreference per- formance reported so far in the literature (MELA v08 score of 64.47).