mReFinED: An Efficient End-to-End Multilingual Entity Linking System
Peerat Limkonchotiwat, Weiwei Cheng, Christos Christodoulopoulos, Amir Saffari, Jens Lehmann
Abstract
End-to-end multilingual entity linking (MEL) is concerned with identifying multilingual entity mentions and their corresponding entity IDs in a knowledge base. Existing works assumed that entity mentions were given and skipped the entity mention detection step due to a lack of high-quality multilingual training corpora. To overcome this limitation, we propose mReFinED, the first end-to-end multilingual entity linking. Additionally, we propose a bootstrapping mention detection framework that enhances the quality of training corpora. Our experimental results demonstrated that mReFinED outperformed the best existing work in the end-to-end MEL task while being 44 times faster.- Anthology ID:
- 2023.findings-emnlp.1007
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 15080–15089
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.1007
- DOI:
- 10.18653/v1/2023.findings-emnlp.1007
- Cite (ACL):
- Peerat Limkonchotiwat, Weiwei Cheng, Christos Christodoulopoulos, Amir Saffari, and Jens Lehmann. 2023. mReFinED: An Efficient End-to-End Multilingual Entity Linking System. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15080–15089, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- mReFinED: An Efficient End-to-End Multilingual Entity Linking System (Limkonchotiwat et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-emnlp.1007.pdf