This paper reports on the systems the InriaFBK Team submitted to the EVALITA 2018 - Shared Task on Hate Speech Detection in Italian Twitter and Facebook posts (HaSpeeDe). Our submissions were based on three separate classes of models: a model using a recurrent layer, an ngram-based neural network and a LinearSVC. For the Facebook task and the two cross-domain tasks we used the recurrent model and obtained promising results, especially in the cross-domain setting. For Twitter, we used an ngram-based neural network and the LinearSVC-based model
Comparing Different Supervised Approaches to Hate Speech Detection / Corazza, Michele; Menini, Stefano; Arslan, Pinar; Sprugnoli, Rachele; Cabrio, Elena; Tonelli, Sara; Villata, Serena. - (2018). (Intervento presentato al convegno Sixth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (Evalita 2018) tenutosi a Turin, Italy nel December 12-13, 2018).
Comparing Different Supervised Approaches to Hate Speech Detection
Rachele Sprugnoli;
2018-01-01
Abstract
This paper reports on the systems the InriaFBK Team submitted to the EVALITA 2018 - Shared Task on Hate Speech Detection in Italian Twitter and Facebook posts (HaSpeeDe). Our submissions were based on three separate classes of models: a model using a recurrent layer, an ngram-based neural network and a LinearSVC. For the Facebook task and the two cross-domain tasks we used the recurrent model and obtained promising results, especially in the cross-domain setting. For Twitter, we used an ngram-based neural network and the LinearSVC-based modelI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
