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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

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Лицензия на ресурс: Лицензия Creative Commons Creative Commons