RuCCoN: Clinical Concept Normalization in Russian

Alexandr Nesterov, Galina Zubkova, Zulfat Miftahutdinov, Vladimir Kokh, Elena Tutubalina, Artem Shelmanov, Anton Alekseev, Manvel Avetisian, Andrey Chertok, Sergey Nikolenko


Abstract
We present RuCCoN, a new dataset for clinical concept normalization in Russian manually annotated by medical professionals. It contains over 16,028 entity mentions manually linked to over 2,409 unique concepts from the Russian language part of the UMLS ontology. We provide train/test splits for different settings (stratified, zero-shot, and CUI-less) and present strong baselines obtained with state-of-the-art models such as SapBERT. At present, Russian medical NLP is lacking in both datasets and trained models, and we view this work as an important step towards filling this gap. Our dataset and annotation guidelines are available at https://github.com/sberbank-ai-lab/RuCCoN.
Anthology ID:
2022.findings-acl.21
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
239–245
Language:
URL:
https://aclanthology.org/2022.findings-acl.21
DOI:
10.18653/v1/2022.findings-acl.21
Bibkey:
Cite (ACL):
Alexandr Nesterov, Galina Zubkova, Zulfat Miftahutdinov, Vladimir Kokh, Elena Tutubalina, Artem Shelmanov, Anton Alekseev, Manvel Avetisian, Andrey Chertok, and Sergey Nikolenko. 2022. RuCCoN: Clinical Concept Normalization in Russian. In Findings of the Association for Computational Linguistics: ACL 2022, pages 239–245, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
RuCCoN: Clinical Concept Normalization in Russian (Nesterov et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.21.pdf
Data
XL-BEL