Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на этот ресурс:
http://elar.urfu.ru/handle/10995/130432
Название: | NEREL-BIO: A dataset of biomedical abstracts annotated with nested named entities |
Авторы: | Loukachevitch, N. Manandhar, S. Baral, E. Rozhkov, I. Braslavski, P. Ivanov, V. Batura, T. Tutubalina, E. |
Дата публикации: | 2023 |
Издатель: | Oxford University Press |
Библиографическое описание: | Loukachevitch, N, Manandhar, S, Baral, E, Rozhkov, I, Braslavski, P, Batura, T, Ivanov, V & Tutubalina, E 2023, 'NEREL-BIO: a dataset of biomedical abstracts annotated with nested named entities', Bioinformatics, Том. 39, № 4, btad161. https://doi.org/10.1093/bioinformatics/btad161 Loukachevitch, N., Manandhar, S., Baral, E., Rozhkov, I., Braslavski, P., Batura, T., Ivanov, V., & Tutubalina, E. (2023). NEREL-BIO: a dataset of biomedical abstracts annotated with nested named entities. Bioinformatics, 39(4), [btad161]. https://doi.org/10.1093/bioinformatics/btad161 |
Аннотация: | Motivation: This article describes NEREL-BIO-an annotation scheme and corpus of PubMed abstracts in Russian and smaller number of abstracts in English. NEREL-BIO extends the general domain dataset NEREL by introducing domain-specific entity types. NEREL-BIO annotation scheme covers both general and biomedical domains making it suitable for domain transfer experiments. NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL. Nested named entities may cross entity boundaries to connect to shorter entities nested within longer entities, making them harder to detect. Results: NEREL-BIO contains annotations for 700+ Russian and 100+ English abstracts. All English PubMed annotations have corresponding Russian counterparts. Thus, NEREL-BIO comprises the following specific features: Annotation of nested named entities, it can be used as a benchmark for cross-domain (NEREL → NEREL-BIO) and cross-language (English → Russian) transfer. We experiment with both transformer-based sequence models and machine reading comprehension models and report their results. © 2023 The Author(s). Published by Oxford University Press. |
Ключевые слова: | ARTICLE HUMAN HUMAN EXPERIMENT LANGUAGE MEDLINE READING NATURAL LANGUAGE PROCESSING SEMANTICS LANGUAGE NATURAL LANGUAGE PROCESSING PUBMED SEMANTICS |
URI: | http://elar.urfu.ru/handle/10995/130432 |
Условия доступа: | info:eu-repo/semantics/openAccess cc-by |
Текст лицензии: | https://creativecommons.org/licenses/by/4.0/ |
Идентификатор SCOPUS: | 85153975102 |
Идентификатор WOS: | 000978997300001 |
Идентификатор PURE: | 38476272 |
ISSN: | 1367-4803 |
DOI: | 10.1093/bioinformatics/btad161 |
Сведения о поддержке: | Russian Science Foundation, RSF: 20-11-20166 This work was supported by the Russian Science Foundation [20-11-20166]. |
Карточка проекта РНФ: | 20-11-20166 |
Располагается в коллекциях: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
Файлы этого ресурса:
Файл | Описание | Размер | Формат | |
---|---|---|---|---|
2-s2.0-85153975102.pdf | 952,84 kB | Adobe PDF | Просмотреть/Открыть |
Лицензия на ресурс: Лицензия Creative Commons