Published September 26, 2023 | Version 2
Dataset Open

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

  • 1. Barcelona Supercomputing Center
  • 2. Dublin University


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. 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:


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.

    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},
issn = {1135-5948},
DOI = {10.26342/2023-71-23}, url = {}, pages = {301--311} }

Related Links:

- MEDDOPLACE website:

- MEDDOPLACE overview paper:

- Annotation Guidelines (Spanish):

- Annotation Guidelines (English):


This work is licensed under a Creative Commons Attribution 4.0 International License.


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>)


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