Mention detection, normalization & classification of species, pathogens, humans and food in clinical documents: Overview of the LivingNER shared task and resources

Antonio Miranda-Escalada, Eulàlia Farré-Maduell, Salvador Lima-López, Darryl Estrada, Luis Gascó, Martin Krallinger


There is a pressing need to generate tools for finding mentions of species, pathogens, or food from medical texts. To promote the development of such tools we organized the LivingNER task. LivingNER relied on a large Gold Standard corpus of 2000 carefully selected clinical cases in Spanish covering diverse specialties. It was manually annotated with species mentions that were also carefully mapped to their corresponding NCBI Taxonomy identifiers. Besides, we have generated Silver Standard versions of LivingNER for 7 languages: English, Portuguese, Galician, Catalan, Italian, French, and Romanian. LivingNER had three subtasks: LivingNERSpecies NER (species mention detection sub-task), LivingNER-Species Norm (species mention detection and normalization to NCBI taxonomy Ids), and LivingNERClinical IMPACT (a document classification task related to the detection of pets, animalscausing injuries, food, and nosocomial entities). We received and evaluated 62 systems from 20 teams from 11 countries worldwide, obtaining highly competitive results. Successful approaches typically modified pre-trained transformer-like language models (BERT, BETO, RoBERTa, etc.) and employed embedding distance metrics for entity linking. LivingNER corpus:

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