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MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

Authors: López, Salvador Lima; Eulàlia Farré-Maduell; Briva-Iglesias, Vicent; Gasco, Luis; Krallinger, Martin;

MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

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 corpus' train and test sets in multiple formats, as well as the SNOMED gazetteer, cross-mapping between SNOMED and MeSH and the multilingual silver standard in 8 languages (Catalan, English, French, Italian, Dutch, Portuguese, Romanian and Swedish). For more information, please check the attached README file. 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. Please cite if you use this resource: Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias and Martin Krallinger. NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts. In Procesamiento del Lenguaje Natural, 67. 2021. @article{meddoplace, title={MEDDOPLACE Shared Task overview: recognition, normalization and classification of locations and patient movement in clinical texts}, author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Brivá-Iglesias, Vicent and Gasco-Sanchez, Luis and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, volume = {71}, year={2023}, issn = {1135-5948},DOI = {10.26342/2023-71-23}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561/3961}, pages = {301--311} } Related Links: - MEDDOPLACE website: https://temu.bsc.es/meddoplace - MEDDOPLACE overview paper: http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561 - Annotation Guidelines (Spanish): https://doi.org/10.5281/zenodo.7775234 - Annotation 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 ()- Martin Krallinger ()

Keywords

locations, gold standard, corpus, entity linking, toponym resolution, nlp, gis, clinical departments, normalization, NER, shared task, clinical nlp, information extraction, bionlp

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selected citations
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This is an alternative to the "Influence" indicator, which also 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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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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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