Enhancing unrestricted cross-document event coreference with graph reconstruction networks
- Publication type
- C1
- Publication status
- Published
- Authors
- De Langhe, L., De Clercq, O., & Hoste, V.
- Editor
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti and Nianwen Xue
- Series
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Pagination
- 6122-6133
- Publisher
- ELRA
- Conference
- 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (Turin, Italy)
- Download
- (.pdf)
- View in Biblio
- (externe link)
Abstract
Event Coreference Resolution remains a challenging discourse-oriented task within the domain of Natural Language Processing. In this paper we propose a methodology where we combine traditional mention-pair coreference models with a lightweight and modular graph reconstruction algorithm. We show that building graph models on top of existing mention-pair models leads to improved performance for both a wide range of baseline mention-pair algorithms as well as a recently developed state-of-the-art model and this at virtually no added computational cost. Moreover, additional experiments seem to indicate that our method is highly robust in low-data settings and that its performance scales with increases in performance for the underlying mention-pair models.