Abstract
We propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED). Our model applies task-specific heads on top of shared BERT contextualized embeddings. We achieve state-of-the-art results across a standard EL dataset using our model; we also study our model’s performance under the setting when hand-crafted entity candidate sets are not available and find that the model performs well under such a setting too.- Anthology ID:
- 2020.aacl-main.64
- Volume:
- Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Kam-Fai Wong, Kevin Knight, Hua Wu
- Venue:
- AACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 637–642
- Language:
- URL:
- https://aclanthology.org/2020.aacl-main.64
- DOI:
- Cite (ACL):
- Haotian Chen, Xi Li, Andrej Zukov Gregoric, and Sahil Wadhwa. 2020. Contextualized End-to-End Neural Entity Linking. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 637–642, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- Contextualized End-to-End Neural Entity Linking (Chen et al., AACL 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.aacl-main.64.pdf
- Data
- AIDA CoNLL-YAGO