Paper: Unsupervised Models for Coreference Resolution

ACL ID D08-1067
Title Unsupervised Models for Coreference Resolution
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
  • Vincent Ng (University of Texas at Dallas, Richardson TX)

We present a generative model for unsuper- vised coreference resolution that views coref- erence as an EM clustering process. For comparison purposes, we revisit Haghighi and Klein’s (2007) fully-generative Bayesian model for unsupervised coreference resolu- tion, discuss its potential weaknesses and con- sequently propose three modifications to their model. Experimental results on the ACE data sets show that our model outperforms their original model by a large margin and com- pares favorably to the modified model.