@inproceedings{chen-etal-2020-contextualized,
title = "Contextualized End-to-End Neural Entity Linking",
author = "Chen, Haotian and
Li, Xi and
Zukov Gregoric, Andrej and
Wadhwa, Sahil",
editor = "Wong, Kam-Fai and
Knight, Kevin and
Wu, Hua",
booktitle = "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 = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-main.64/",
doi = "10.18653/v1/2020.aacl-main.64",
pages = "637--642",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Contextualized End-to-End Neural Entity Linking
%A Chen, Haotian
%A Li, Xi
%A Zukov Gregoric, Andrej
%A Wadhwa, Sahil
%Y Wong, Kam-Fai
%Y Knight, Kevin
%Y Wu, Hua
%S 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
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F chen-etal-2020-contextualized
%X 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.
%R 10.18653/v1/2020.aacl-main.64
%U https://aclanthology.org/2020.aacl-main.64/
%U https://doi.org/10.18653/v1/2020.aacl-main.64
%P 637-642
Markdown (Informal)
[Contextualized End-to-End Neural Entity Linking](https://aclanthology.org/2020.aacl-main.64/) (Chen et al., AACL 2020)
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.