MEDDOPROF corpus: sample set

dc.contributor.authorSalvador Lima López
dc.contributor.authorEulàlia Farré-Maduell
dc.contributor.authorVicent Brivá-Iglesias
dc.contributor.authorAntonio Miranda-Escalada
dc.contributor.authorMartin Krallinger
dc.coverage.spatialBolivia
dc.date.accessioned2026-03-22T20:54:40Z
dc.date.available2026-03-22T20:54:40Z
dc.date.issued2021
dc.description.abstractThe <strong>MEDDOPROF corpus</strong> is a collection of 2000 clinical cases from over 20 different specialties annotated with professions, employment statuses and other work-related activities. It is used for the MEDDOPROF Shared Task on occupations and employment status detection and normalization in Spanish medical documents, which will be celebrated as part of IberLEF 2021. The sample set is composed of <strong>15 clinical cases</strong> extracted from the training set from four different specialties: radiology, oncology, psychiatry and occupational health. The files are distributed as follows: - For the <strong>subtask 1 (MEDDOPROF-NER)</strong>, annotations are distributed in Brat standoff format with PROFESION/SITUACION_LABORAL tags only. - For the<strong> subtask 2 (MEDDOPROF-CLASS)</strong>, annotations are distributed in Brat standoff format with PACIENTE/FAMILIAR/SANITARIO/OTROS tags only. - For the <strong>subtask 3 (MEDDOPROF-NORM)</strong>, annotations are distributed in a tab-separated file (TSV) with a code column that includes the mapping of entities to ESCO and SNOMED-CT. For further information, please visit https://temu.bsc.es/meddoprof/ or email us at encargo-pln-life@bsc.es
dc.identifier.doi10.5281/zenodo.4518733
dc.identifier.urihttps://doi.org/10.5281/zenodo.4518733
dc.identifier.urihttps://andeanlibrary.org/handle/123456789/84800
dc.language.isoen
dc.publisherEuropean Organization for Nuclear Research
dc.relation.ispartofZenodo (CERN European Organization for Nuclear Research)
dc.sourceBarcelona Supercomputing Center
dc.subjectSample (material)
dc.subjectSet (abstract data type)
dc.subjectNatural language processing
dc.subjectComputer science
dc.subjectArtificial intelligence
dc.titleMEDDOPROF corpus: sample set
dc.typedataset

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