NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities

Bioinformatics. 2023. Vol. 39. No. 4. Article btad161.

Loukachevitch N., Manandhar S., Baral E., Rozhkov I., Braslavski P., Ivanov V., Batura T., Tutubalina E.

Motivation

This paper 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 (Loukachevitch et al., 2021) 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 (MRC) models and report their results.

Availability

The dataset and annotation guidelines are freely available at https://github.com/nerel-ds/NEREL-BIO.

Keywords: named entity recognition

Publication based on the results of: Models and method for analysis of unstructured data, data mining and recommender systems (2023)