Unsupervised Coreference Resolution with HyperGraph Partitioning

Jun Lang, Bing Qin, Ting Liu, Sheng Li

Abstract


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.


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Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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