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5 juin 2019
- doi: 10.4000/books.aaccademia.4772
- isbn: 9788831978422
- eisbn: 9788831978699
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
Mots-clés
elaborazione del linguaggio naturale strumenti del linguaggio traitement du langage naturel outils du langage Natural Language Processing Speech Tools Language & Linguistics LAN009000 CFSujets proches
Speech, Hate Group libel Group defamation Defamation against groups Racist speech Pattern ModelCiter ce document
Michele Corazza et al., « Comparing Different Supervised Approaches to Hate Speech Detection », Accademia University Press, ID : 10.4000/books.aaccademia.4772
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Résumé
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.