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.
BibTeX
@inproceedings{
limkonchotiwat2023mrefined,
title={mReFin{ED}: An Efficient End-to-End Multilingual Entity Linking System},
author={Peerat Limkonchotiwat and Weiwei Cheng and Christos Christodoulopoulos and Amir Saffari and Jens Lehmann},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=3JP1Jsng4G}
}