InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks

Michele Corazza 1 Stefano Menini 2 Pinar Arslan 1 Rachele Sprugnoli 2 Elena Cabrio 1 Sara Tonelli 2 Serena Villata 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In this paper, we describe two systems for predicting message-level offensive language in German tweets: one discriminates between offensive and not offensive messages, and the second performs a fine-grained classification by recognizing also classes of offense. Both systems are based on the same approach, which builds upon Recurrent Neural Networks used with the following features: word embeddings, emoji embeddings and social-network specific features. The model is able to combine word-level information and tweet-level information in order to perform the classification tasks.
Type de document :
Communication dans un congrès
GermEval 2018 Workshop, Sep 2018, Vienna, Austria. Proceedings of the GermEval 2018 Workshop, 〈https://projects.fzai.h-da.de/iggsa/〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01906096
Contributeur : Michele Corazza <>
Soumis le : vendredi 26 octobre 2018 - 14:13:52
Dernière modification le : lundi 5 novembre 2018 - 15:52:10

Fichier

germeval_2018_latex.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01906096, version 1

Collections

Citation

Michele Corazza, Stefano Menini, Pinar Arslan, Rachele Sprugnoli, Elena Cabrio, et al.. InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks. GermEval 2018 Workshop, Sep 2018, Vienna, Austria. Proceedings of the GermEval 2018 Workshop, 〈https://projects.fzai.h-da.de/iggsa/〉. 〈hal-01906096〉

Partager

Métriques

Consultations de la notice

33

Téléchargements de fichiers

13