Unsupervised Ranking Model for Entity Coreference Resolution
This addresses coreference resolution for natural language processing, offering an incremental improvement over existing methods.
The paper tackles entity coreference resolution by proposing an unsupervised ranking model with resolution mode variables, achieving a 58.44% F1 score on CoNLL-2012 data, which outperforms a prior system by 3.01%.
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%.