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Unsupervised Ranking Model for Entity Coreference Resolution
March 15, 2016 ยท Declared Dead ยท ๐ North American Chapter of the Association for Computational Linguistics
"No code URL or promise found in abstract"
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Authors
Xuezhe Ma, Zhengzhong Liu, Eduard Hovy
arXiv ID
1603.04553
Category
cs.CL: Computation & Language
Cross-listed
cs.LG
Citations
17
Venue
North American Chapter of the Association for Computational Linguistics
Last Checked
4 months ago
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%.
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