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Unsupervised Ranking Model for Entity Coreference Resolution

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%.

preprint2016arXivOpen access
Xuezhe MaZhengzhong LiuEduard Hovy
Computation and LanguageMachine Learning
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Open access3 authors2 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
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Xuezhe MaZhengzhong LiuEduard Hovy

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