• Natalia Loukachevitch
  • Suresh Manandhar
  • Elina Baral
  • Igor Rozhkov
  • Pavel Braslavski
  • Tatiana Batura
  • Vladimir Ivanov
  • Elena Tutubalina
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. AVAILABILITY AND IMPLEMENTATION: The dataset and annotation guidelines are freely available at https://github.com/nerel-ds/NEREL-BIO. © The Author(s) 2023. Published by Oxford University Press.
Original languageEnglish
Article numberbtad161
JournalBioinformatics
Volume39
Issue number4
DOIs
Publication statusPublished - 2023

    WoS ResearchAreas Categories

  • Biochemical Research Methods
  • Biotechnology & Applied Microbiology
  • Computer Science, Interdisciplinary Applications
  • Mathematical & Computational Biology
  • Statistics & Probability

    ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Biochemistry
  • Molecular Biology

ID: 38476272