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dc.contributor.authorHobbs, Elizabeth T
dc.contributor.authorGoralski, Stephen M
dc.contributor.authorMitchell, Ashley
dc.contributor.authorSimpson, Andrew
dc.contributor.authorLeka, Dorjan
dc.contributor.authorKotey, Emmanuel
dc.contributor.authorSekira, Matt
dc.contributor.authorMunro, James B
dc.contributor.authorNadendla, Suvarna
dc.contributor.authorJackson, Rebecca
dc.contributor.authorGonzalez-Aguirre, Aitor
dc.contributor.authorKrallinger, Martin
dc.contributor.authorGiglio, Michelle
dc.contributor.authorErill, Ivan
dc.date.accessioned2021-08-03T15:57:12Z
dc.date.available2021-08-03T15:57:12Z
dc.date.issued2021-07-13
dc.identifier.urihttp://hdl.handle.net/10713/16294
dc.description.abstractAnalysis 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.en_US
dc.description.urihttps://doi.org/10.3389/frma.2021.674205en_US
dc.language.isoenen_US
dc.publisherFrontiers Media S.A.en_US
dc.relation.ispartofFrontiers in Research Metrics and Analyticsen_US
dc.rightsCopyright © 2021 Hobbs, Goralski, Mitchell, Simpson, Leka, Kotey, Sekira, Munro, Nadendla, Jackson, Gonzalez-Aguirre, Krallinger, Giglio and Erill.en_US
dc.subjectannotationen_US
dc.subjectbiocurationen_US
dc.subjectcorpusen_US
dc.subjectevidenceen_US
dc.subjectliteratureen_US
dc.subjecttext- and data miningen_US
dc.titleECO-CollecTF: A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscriptsen_US
dc.typeArticleen_US
dc.identifier.doi10.3389/frma.2021.674205
dc.identifier.pmid34327299
dc.source.volume6
dc.source.beginpage674205
dc.source.endpage
dc.source.countrySwitzerland


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