| Resumen |
Violent and several other related problems, such as aggressive speech, offensive language, or bullying, are experiencing a growing online presence in the context of contemporary social media platforms. The research efforts towards detecting, isolating, and stopping these disturbing behaviors have intensified, in tight relation to the increasing performance of deep learning techniques applied in various Natural Language Processing (NLP) tasks. This paper present the Instituto Politécnico Nacional, Centro de Investigación en Computación (CIC) team's system description paper for shared task @IberLEF2022. This study explores the applicability of language-specific pre-trained language model for tackling the problem of detection of aggressive and violent incidents from social media in Spanish for DA-VINCIS:@IberLEF2022 shared task. The proposed model on the DA-VINCIS dataset achieves F1 score of 0.7455 for violent event identification task (Task 1) and F1-score 0.4903 for violent event category recognition (Task 2). © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). |
