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MEDDOPLACE Guidelines (Spanish): Annotation, Normalization and Classification of Locations and Related-Information in Medical Documents

Authors: López, Salvador Lima; Eulàlia Farré Maduell; Iglesias, Vicent Brivá; Sánchez, Luis Gascó; Krallinger, Martin;

MEDDOPLACE Guidelines (Spanish): Annotation, Normalization and Classification of Locations and Related-Information in Medical Documents

Abstract

MEDDOPLACE stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish. This repository includes the Annotation Guidelines in Spanish, a 60+ pages-long document that describes how to annotate, normalize and characterize locations and related-information in medical documents. MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/meddoplace. Related Links: - MEDDOPLACE website: https://temu.bsc.es/meddoplace - Gold Standard data: https://doi.org/10.5281/zenodo.7707566 - Guidelines (English): https://doi.org/10.5281/zenodo.7928145 License This work is licensed under a Creative Commons Attribution 4.0 International License. Contact If you have any questions or suggestions, please contact us at: - Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>) - Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)

Keywords

locations, classification, ner, clinical nlp, entity linking, toponym resolution, spanish, nlp, english, geoparsing

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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