Unsupervised Coreference Resolution with HyperGraph Partitioning

  •  Jun Lang    
  •  Bing Qin    
  •  Ting Liu    
  •  Sheng Li    


Unsupervised-learning based coreference resolution obviates the need for annotation of training data. However, unsupervised approaches have traditionally been relying on the use of mention-pair models, which only consider information pertaining to a pair of mentions at a time. In this paper, it is proposed the use of hypergraph partitioning to overcome this limitation. The mentions are modeled as vertices. By allowing a hyperedge to cover multiple mentions that share a common property, the additional information beyond a mention pair can be captured. This paper introduces a hypergraph partitioning algorithm that divides mentions directly into equivalence classes representing individual entities. Evaluation on the ACE dataset shows that our unsupervised hypergraph based approach outperforms previous unsupervised methods.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-8989
  • ISSN(Online): 1913-8997
  • Started: 2008
  • Frequency: quarterly

Journal Metrics

Google-based Impact Factor (2019): 0.93

h-index (December 2019): 32

i10-index (December 2019): 127

h5-index (December 2019): N/A

h5-median(December 2019): N/A

( The data was calculated based on Google Scholar Citations. Click Here to Learn More. )