Abstract
Despite the significant progress on entity coreference resolution observed in recent years, there is a general lack of understanding of what has been improved. We present an empirical analysis of state-of-the-art resolvers with the goal of providing the general NLP audience with a better understanding of the state of the art and coreference researchers with directions for future research.- Anthology ID:
- 2020.emnlp-main.536
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6620–6631
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.536
- DOI:
- 10.18653/v1/2020.emnlp-main.536
- Cite (ACL):
- Jing Lu and Vincent Ng. 2020. Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6620–6631, Online. Association for Computational Linguistics.
- Cite (Informal):
- Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art (Lu & Ng, EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.emnlp-main.536.pdf