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BioRED: a rich biomedical relation extraction dataset.

Ling Luo1, Po-Ting Lai1, Chih-Hsuan Wei1

  • 1National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

Briefings in Bioinformatics
|July 18, 2022
PubMed
Summary
This summary is machine-generated.

A new BioRED dataset aids biomedical text mining by enabling relation extraction across multiple entity types and document levels. It distinguishes novel findings from background knowledge, improving automated information discovery.

Keywords:
biomedical datasetbiomedical natural language processingnamed entity recognitionrelation extraction

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Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • Text Mining

Background:

  • Automated relation extraction (RE) is vital for biomedical text mining.
  • Existing datasets often lack multi-type relations and document-level context.
  • This limits the development of advanced RE systems.

Purpose of the Study:

  • Introduce BioRED, a novel biomedical relation extraction dataset.
  • Include multiple entity types (gene, disease, chemical) and relations.
  • Annotate relations as novel or background knowledge.

Main Methods:

  • Reviewed existing named entity recognition (NER) and RE datasets.
  • Developed BioRED using 600 PubMed abstracts.
  • Benchmarked state-of-the-art models (e.g., BERT) on NER and RE tasks.

Main Results:

  • NER performance reached high F-score (89.3%).
  • RE performance, especially for novel relations, showed room for improvement (F-score 47.7%).
  • BioRED facilitates development of more accurate RE systems.

Conclusions:

  • BioRED addresses limitations of existing RE datasets.
  • The dataset enables differentiation between novel and background information.
  • BioRED is crucial for advancing biomedical RE capabilities.