@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://preview.aclanthology.org/ingest-emnlp/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."
}