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

Shany Barhom, Vered Shwartz, Alon Eirew, Michael Bugert, Nils Reimers, Ido Dagan


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 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.
Anthology ID:
P19-1409
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4179–4189
Language:
URL:
https://aclanthology.org/P19-1409
DOI:
10.18653/v1/P19-1409
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/P19-1409.pdf
Video:
 https://vimeo.com/385196879
Data
ECB+