Named entity normalization

Given a list of valid codes that includes all of ESCO and a selection of SNOMED-CT terms, participants were asked to automatically normalize the detected entity mentions to their corresponding concept identifier. 

Publication
Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias, Martin Krallinger (2021) NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts. Procesamiento del Lenguaje Natural, Revista nº 67, septiembre de 2021, pp. 243-256.
Competition
Recognition, classification and normalization of professions and occupations from medical texts
Language
Spanish
NLP topic
text classification
Abstract task
Classification
Dataset
MEDDOPROF
Year
2021
Publication link
http://journal.sepln.org/sepl…
Ranking metric
F1

Task results

System Precision Recall F1 RMSE
TALP 0.6750 0.5720 0.6190 0.0000
Fadi 0.6820 0.5410 0.6030 0.0000
Galiza 0.7200 0.4820 0.5770 0.0000
KaushikAcharya 0.7200 0.4670 0.5660 0.0000
SINAI 0.5930 0.5410 0.5660 0.0000

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.