The SourceData-NLP dataset: integrating curation into scientific
  publishing for training large language models
Jorge Abreu-Vicente Hannah Sonntag Thomas Eidens Thomas Lemberger
Introduction: The scientific publishing landscape is expanding rapidly,
creating challenges for researchers to stay up-to-date with the evolution of
the literature. Natural Language Processing (NLP) has emerged as a potent
approach to automating knowledge extraction from this vast amount of
publications and preprints. Tasks such as Named-Entity Recognition (NER) and
Named-Entity Linking (NEL), in conjunction with context-dependent semantic
interpretation, offer promising and complementary approaches to extracting
structured information and revealing key concepts.
  Results: We present the SourceData-NLP dataset produced through the routine
curation of papers during the publication process. A unique feature of this
dataset is its emphasis on the annotation of bioentities in figure legends. We
annotate eight classes of biomedical entities (small molecules, gene products,
subcellular components, cell lines, cell types, tissues, organisms, and
diseases), their role in the experimental design, and the nature of the
experimental method as an additional class. SourceData-NLP contains more than
620,000 annotated biomedical entities, curated from 18,689 figures in 3,223
papers in molecular and cell biology. We illustrate the dataset's usefulness by
assessing BioLinkBERT and PubmedBERT, two transformers-based models, fine-tuned
on the SourceData-NLP dataset for NER. We also introduce a novel
context-dependent semantic task that infers whether an entity is the target of
a controlled intervention or the object of measurement.
  Conclusions: SourceData-NLP's scale highlights the value of integrating
curation into publishing. Models trained with SourceData-NLP will furthermore
enable the development of tools able to extract causal hypotheses from the
literature and assemble them into knowledge graphs.