RuCCoN: Clinical Concept Normalization in Russian
We present RuCCoN, a new dataset for clinical concept normalization in Russian manually annotated by medical professionals. It contains over 12;800 entity mentions manually linked to over 2100 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.