Europe PMC annotated full-text corpus for gene/proteins, diseases and organisms.

Journal: Scientific data
Published Date: Oct 19, 2023

Abstract

Named entity recognition (NER) is a widely used text-mining and natural language processing (NLP) subtask. In recent years, deep learning methods have superseded traditional dictionary- and rule-based NER approaches. A high-quality dataset is essential to fully leverage recent deep learning advancements. While several gold-standard corpora for biomedical entities in abstracts exist, only a few are based on full-text research articles. The Europe PMC literature database routinely annotates Gene/Proteins, Diseases, and Organisms entities. To transition this pipeline from a dictionary-based to a machine learning-based approach, we have developed a human-annotated full-text corpus for these entities, comprising 300 full-text open-access research articles. Over 72,000 mentions of biomedical concepts have been identified within approximately 114,000 sentences. This article describes the corpus and details how to access and reuse this open community resource.

Authors

  • Xiao Yang
    Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Shyamasree Saha
    Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK.
  • Aravind Venkatesan
    Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France.
  • Santosh Tirunagari
    Department of Psychology, Middlesex University, London, United Kingdom. Correspondence to: Dr Santosh Tirunagari, Department of Psychology, Middlesex University, London, United Kingdom. s.tirunagari@mdx.ac.uk.
  • Vid Vartak
    Literature Services, EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, UK.
  • Johanna McEntyre
    European Molecular Biology Laboratory (EMBL-EBI), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.

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