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