Non-contextual binary classification of offensive comments MEX

The main purpose of MeOffendEs is to promote research on the detection of offensive language in Spanish variants. This task consists in classifying tweets as offensive or non-offensive in the Offend-MEX corpus, this is, a binary text classification problem. A text is considered as offensive when language is used to commit an explicit or implicitly directed offense that may include insults, threats, profanity or swearing.

 

Publication
Flor Miriam Plaza-del-Arco, Marco Casavantes, Hugo Jair Escalante, M. Teresa Martín-Valdivia, Arturo Montejo-Ráez, Manuel Montes-y-Gómez, Horacio Jarquín-Vásquez, Luis Villaseñor-Pineda (2021) Overview of MeOffendEs at IberLEF 2021: Offensive Language Detection in Spanish Variants.Procesamiento del Lenguaje Natural, Revista nº 67, septiembre de 2021, pp. 183-194.
NLP topic
Abstract task
Dataset
Year
2021

Task results

System Precision Recall F1 Sort ascending
CIMAT-MTY-GTO 0.7600 0.6533 0.7026
NLP-CIC 0.7550 0.6407 0.6932
DCCD-INFOTEC 0.6733 0.6966 0.6847
CIMAT-GTO 0.6633 0.6958 0.6792
UMUTeam 0.6650 0.6763 0.6706

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.