  Event coreference continues to be a challenging problem in information
extraction. With the absence of any external knowledge bases for events,
coreference becomes a clustering task that relies on effective representations
of the context in which event mentions appear. Recent advances in
contextualized language representations have proven successful in many tasks,
however, their use in event linking been limited. Here we present a three part
approach that (1) uses representations derived from a pretrained BERT model to
(2) train a neural classifier to (3) drive a simple clustering algorithm to
create coreference chains. We achieve state of the art results with this model
on two standard datasets for within-document event coreference task and
establish a new standard on a third newer dataset.
