Entity classification

This task focuses on the recognition of named entities NER and the classification of these entities according to their types. In particular, it is focused on the identification and classification of sensitive information (e.g., patient names, telephones, addresses, etc.) in medical documents.

The MEDDOCAN annotation guidelines defined a total of 29 entity types, that area available at: http://ceur-ws.org/Vol-2421/MEDDOCAN_overview.pdf

Publication
Montserrat Marimon, Aitor Gonzalez-Agirre, Ander Intxaurrondo, Heidy Rodríguez, Jose Lopez Martin, Marta Villegas, Martin Krallinger (2019) “Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results.” In: IberLEF@ SEPLN. 2019, pp. 618–638.
Competition
MEDDOCAN: Medical Document Anonymization Track
Language
Spanish
NLP topic
(named) entity recognition
Abstract task
Classification
Dataset
MEDDOCAN
Year
2019
Publication link
http://ceur-ws.org/Vol-2421/M…
Ranking metric
F1

Task results

If you have published a result better than those on the list, send a message to odesia-comunicacion@lsi.uned.es indicating the result and the DOI of the article, along with a copy of it if it is not published openly.