We propose a global entity disambiguation (ED) model based on BERT (Devlin et al., 2019). To capture global contextual information for ED, our model treats not only words but also entities as input tokens, and solves the task by sequentially resolving mentions to their referent entities and using resolved entities as inputs at each step. We train the model using a large entity-annotated corpus obtained from Wikipedia. We achieve new state-of-the-art results on five standard ED datasets: AIDA-CoNLL, MSNBC, AQUAINT, ACE2004, and WNED-WIKI. The source code and model checkpoint are available at https://github.com/studio-ousia/luke.
CITATION STYLE
Yamada, I., Washio, K., Shindo, H., & Matsumoto, Y. (2022). Global Entity Disambiguation with BERT. In NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 3264–3271). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.naacl-main.238
Mendeley helps you to discover research relevant for your work.