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Recognizing coreferring events and entities across multiple texts is crucialfor many NLP applications. Despite the task's importance, research focus wasgiven mostly to within-document entity coreference, with rather littleattention to the other variants. We propose a neural architecture forcross-document coreference resolution. Inspired by Lee et al (2012), we jointlymodel entity and event coreference. We represent an event (entity) mentionusing its lexical span, surrounding context, and relation to entity (event)mentions via predicate-arguments structures. Our model outperforms the previousstate-of-the-art event coreference model on ECB+, while providing the firstentity coreference results on this corpus. Our analysis confirms that all ourrepresentation elements, including the mention span itself, its context, andthe relation to other mentions contribute to the model's success.

Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution