@InProceedings{uryupina-moschitti:2017:CoNLL,
  author    = {Uryupina, Olga  and  Moschitti, Alessandro},
  title     = {Collaborative Partitioning for Coreference Resolution},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {47--57},
  abstract  = {This paper presents a collaborative partitioning algorithm---a novel
	ensemble-based approach to coreference resolution. Starting from the
	all-singleton partition, we search for a solution close to the ensemble's
	outputs in terms of a task-specific similarity measure. Our approach assumes a
	loose integration of individual components of the ensemble and can therefore
	combine arbitrary coreference resolvers, regardless of their models. 
	Our experiments on the CoNLL dataset show that collaborative partitioning
	yields results superior 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 best coreference performance reported so far in the
	literature (MELA v08 score of 64.47).},
  url       = {http://aclweb.org/anthology/K17-1007}
}