  Recognizing coreferring events and entities across multiple texts is crucial
for many NLP applications. Despite the task's importance, research focus was
given mostly to within-document entity coreference, with rather little
attention to the other variants. We propose a neural architecture for
cross-document coreference resolution. Inspired by Lee et al (2012), we jointly
model entity and event coreference. We represent an event (entity) mention
using its lexical span, surrounding context, and relation to entity (event)
mentions via predicate-arguments structures. Our model outperforms the previous
state-of-the-art event coreference model on ECB+, while providing the first
entity coreference results on this corpus. Our analysis confirms that all our
representation elements, including the mention span itself, its context, and
the relation to other mentions contribute to the model's success.
