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.
Corazza, M., Menini, S., Arslan, P., Sprugnoli, R., Cabrio, E., Tonelli, S., Villata, S., Comparing Different Supervised Approaches to Hate Speech Detection, in Proceedings of the Sixth Evaluation Campaign of Natural Language processing and Speech Tools for Italian (EVALITA 2018), (Torino, Italy, 12-13 December 2018), CEUR-WS, Torino 2018:<<CEUR WORKSHOP PROCEEDINGS>>,2263 230-234. [10.4000/books.aaccademia.4772] [http://hdl.handle.net/10807/133051]
Comparing Different Supervised Approaches to Hate Speech Detection
Sprugnoli, Rachele;
2018
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 model.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
