This paper examines how a new class of nonparametric Bayesian models can be effectively applied to an open-domain event coreference task. Designed with the purpose of clustering complex linguistic objects, these models consider a potentially infinite number of features and categorical outcomes. The evaluation performed for solving both within-and cross-document event coreference shows significant improvements of the models when compared against two baselines for this task.
In REF an unsupervised Bayesian clustering model is proposed for within-and cross-document event coreference.
Unsupervised Event Coreference Resolution with Rich Linguistic Features
ACL
Computer Science