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
- Cite (ACL):
- Shany Barhom, Vered Shwartz, Alon Eirew, Michael Bugert, Nils Reimers, and Ido Dagan. 2019. Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4179–4189, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution (Barhom et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-1409.pdf
- Data
- ECB+