Abstract. Although effective for antecedent determination, the traditional twin-candidate model can not prevent the invalid resolution of non-anaphors without additional measures. In this paper we propose a modified learning framework for the twin-candidate model. In the new framework, we make use of non-anaphors to create a special class of training instances, which leads to a classifier capable of identifying the cases of non-anaphors during resolution. In this way, the twin-candidate model itself could avoid the resolution of non-anaphors, and thus could be directly deployed to coreference resolution. The evaluation done on newswire domain shows that the twin-candidate based system with our modified framework achieves better and more reliable performance than those with other solutions.
REF made use of nonanaphors to create a special class of training instances in the twin-candidate model (Yang et al 2003) and thus equipped it with the nonanaphoricity determination capability.
A Twin-Candidate Model of Coreference Resolution with Non-Anaphor Identification Capability
Second International Joint Conference on Natural Language Processing: Full Papers
Computer Science