ECO-CollecTF: A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscripts

• Main Authors: Elizabeth T. Hobbs, Stephen M. Goralski, Ashley Mitchell, Andrew Simpson, Dorjan Leka, Emmanuel Kotey, Matt Sekira, James B. Munro, Suvarna Nadendla, Rebecca Jackson, Aitor Gonzalez-Aguirre, Martin Krallinger, Michelle Giglio, Ivan Erill
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-07-01
• Series: Frontiers in Research Metrics and Analytics
• Subjects: evidence; annotation; corpus; text- and data mining; literature; biocuration
• Online Access: https://www.frontiersin.org/articles/10.3389/frma.2021.674205/full

Analysis of high-throughput experiments in the life sciences frequently relies upon standardized information about genes, gene products, and other biological entities. To provide this information, expert curators are increasingly relying on text mining tools to identify, extract and harmonize statements from biomedical journal articles that discuss findings of interest. For determining reliability of the statements, curators need the evidence used by the authors to support their assertions. It is important to annotate the evidence directly used by authors to qualify their findings rather than simply annotating mentions of experimental methods without the context of what findings they support. Text mining tools require tuning and adaptation to achieve accurate performance. Many annotated corpora exist to enable developing and tuning text mining tools; however, none currently provides annotations of evidence based on the extensive and widely used Evidence and Conclusion Ontology. We present the ECO-CollecTF corpus, a novel, freely available, biomedical corpus of 84 documents that captures high-quality, evidence-based statements annotated with the Evidence and Conclusion Ontology.