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11 mai 2021
- doi: 10.4000/books.aaccademia.7067
- eisbn: 9791280136329
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
Mots-clés
EVALITA COVID-19 Infodemic linguistica computazionale Automatic Misogyny Identification Misogyny on Twitter Posts AlBERTo Convolutional Neural Network BERT Model Hate Speech Detection Multimodal Meme Detection MEME management Language Game ``La Ghigliottina'' Language & Linguistics LAN000000 CBXSujets proches
Language (New words, slang, etc.) Field guides Keys (Identification guides) Identification guides Speech, Hate Group libel Group defamation Defamation against groups Racist speech Women-hating Forensic identification Pattern ModelCiter ce document
Elia Bisconti et al., « Montanti @ HaSpeeDe2 EVALITA 2020: Hate Speech Detection inOnline Contents », Accademia University Press, ID : 10.4000/books.aaccademia.7067
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Résumé
This report describes an approach to face a task regarding the identification of hate content and stereotypes within tweets. Two models will be shown, both presented to the HaSpeeDe competition proposed by EVALITA 2020. They are based on a Logistic Regression model that takes different types of embedding as input. The best system shows interesting results.