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Biomedical Concept Normalization over Nested Entities with Partial UMLS Terminology in Russianстатья
- Авторы: Loukachevitch Natalia, Sakhovskiy Andrey, Tutubalina Elena
- Сборник: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Год издания: 2024
- Место издания: ELRA and ICCL Torino, Italia
- Первая страница: 2383
- Последняя страница: 2389
- Аннотация: We present a new manually annotated dataset of PubMed abstracts for concept normalization in Russian. It contains over 23,641 entity mentions in 756 documents linked to 4,544 unique concepts from the UMLS ontology. Compared to existing corpora, we explore two novel annotation characteristics: the nestedness of named entities and the incompleteness of the Russian medical terminology in UMLS. 4,424 entity mentions are linked to 1,535 unique English concepts absent in the Russian part of the UMLS ontology. We present several baselines for normalization over nested named entities obtained with state-of-the-art models such as SapBERT. Our experimental results show that models pre-trained on graph structural data from UMLS achieve superior performance in a zero-shot setting on bilingual terminology.
- Добавил в систему: Лукашевич Наталья Валентиновна