Contextualized End-to-End Neural Entity Linking

Haotian Chen, Xi Li, Andrej Zukov Gregoric, Sahil Wadhwa


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:
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.aacl-main.64.pdf
Data
AIDA CoNLL-YAGO