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).

Collaborative Partitioning for Coreference Resolution / Uryupina, Olga; Moschitti, Alessandro. - ELETTRONICO. - (2017), pp. 47-57. (Intervento presentato al convegno 21st Conference on Computational Natural Language Learning (CoNLL 2017) tenutosi a Vancouver, Canada nel 3-4 August, 2017) [10.18653/v1/K17-1007].

Collaborative Partitioning for Coreference Resolution

Olga Uryupina;Alessandro Moschitti
2017-01-01

Abstract

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).
2017
Proceedings of the 21st Conference on Computational Natural LanguageLearning (CoNLL 2017), Vancouver, Canada, August 3-4, 2017
Stroudsburg, PA USA
Association for Computational Linguistics (ACL)
978-1-5108-4564-0
Uryupina, Olga; Moschitti, Alessandro
Collaborative Partitioning for Coreference Resolution / Uryupina, Olga; Moschitti, Alessandro. - ELETTRONICO. - (2017), pp. 47-57. (Intervento presentato al convegno 21st Conference on Computational Natural Language Learning (CoNLL 2017) tenutosi a Vancouver, Canada nel 3-4 August, 2017) [10.18653/v1/K17-1007].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/195341
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