Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking

Anton Alekseev, Zulfat Miftahutdinov, Elena Tutubalina, Artem Shelmanov, Vladimir Ivanov, Vladimir Kokh, Alexander Nesterov, Manvel Avetisian, Andrei Chertok, Sergey Nikolenko


Abstract
Medical data annotation requires highly qualified expertise. Despite the efforts devoted to medical entity linking in different languages, available data is very sparse in terms of both data volume and languages. In this work, we establish benchmarks for cross-lingual medical entity linking using clinical reports, clinical guidelines, and medical research papers. We present a test set filtering procedure designed to analyze the “hard cases” of entity linking approaching zero-shot cross-lingual transfer learning, evaluate state-of-the-art models, and draw several interesting conclusions based on our evaluation results.
Anthology ID:
2022.lrec-1.447
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4212–4220
Language:
URL:
https://aclanthology.org/2022.lrec-1.447
DOI:
Bibkey:
Cite (ACL):
Anton Alekseev, Zulfat Miftahutdinov, Elena Tutubalina, Artem Shelmanov, Vladimir Ivanov, Vladimir Kokh, Alexander Nesterov, Manvel Avetisian, Andrei Chertok, and Sergey Nikolenko. 2022. Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4212–4220, Marseille, France. European Language Resources Association.
Cite (Informal):
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking (Alekseev et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.447.pdf
Code
 airi-institute/medical_crossing