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We present a novel hierarchical distance-dependent Bayesian model for eventcoreference resolution. While existing generative models for event coreferenceresolution are completely unsupervised, our model allows for the incorporationof pairwise distances between event mentions -- information that is widely usedin supervised coreference models to guide the generative clustering processingfor better event clustering both within and across documents. We model thedistances between event mentions using a feature-rich learnable distancefunction and encode them as Bayesian priors for nonparametric clustering.Experiments on the ECB+ corpus show that our model outperforms state-of-the-artmethods for both within- and cross-document event coreference resolution.

A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution