Abstract

Coreference resolution is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community. In this paper, we propose a generative, unsupervised ranking model for entity coreference resolution by introducing resolution mode variables. Our unsupervised system achieves 58.44% F1 score of the CoNLL metric on the English data from the CoNLL-2012 shared task (Pradhan et al., 2012), outperforming the Stanford deterministic system (Lee et al., 2013) by 3.01%.

Date

2016

Authors

Xuezhe Ma, Zhengzhong Liu, Eduard Hovy

Conference

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2016)

Publisher

Association for Computational Linguistics