2026-04-17T07:57:24Zhttps://recercat.cat/oai/request
oai:recercat.cat:2117/3498072026-01-21T04:37:55Zcom_2072_1033col_2072_452950
RECERCAT author Hobbs, Elizabeth T. author Goralski, Stephen M. author Mitchell, Ashley author Simpson, Andrew author Leka, Dorjan author Kotey, Emmanuel author Sekira, Matt author Munro, James B. author Nadendla, Suvarna author Jackson, Rebecca author Gonzalez Aguirre, Aitor author Krallinger, Martin author Giglio, Michelle author Erill, Ivan 2021 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.This work was supported by the National Science Foundation, Division of Biological Infrastructure (1458400) and the National Institutes of Health (R01GM089636, U41HG008735), and by a management commission from Plan TL (Plan de Impulso de las Tecnologías del Lenguaje) of the Spanish Ministerio de Asuntos Económicos y Transformación Digital to BSC-CNS.Peer ReviewedPostprint (published version) http://creativecommons.org/licenses/by/3.0/es/ https://creativecommons.org/licenses/by/4.0/ Open Access Attribution 3.0 Spain Attribution 4.0 International (CC BY 4.0) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural Text mining Life sciences Evidence Annotation Corpus Text- and data mining Literature Biocuration Mineria de dades ECO-CollecTF: A corpus of annotated evidence-based assertions in biomedical manuscripts Article