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