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.

InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks / Corazza, Michele; Menini, Stefano; Arslan, Pinar; Sprugnoli, Rachele; Cabrio, Elena; Tonelli, Sara; Villata, Serena. - ELETTRONICO. - (2018), pp. 80-84. ((Intervento presentato al convegno GermEval 2018 tenutosi a Vienna nel September 21, 2018.

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

Rachele Sprugnoli;
2018

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.
InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks / Corazza, Michele; Menini, Stefano; Arslan, Pinar; Sprugnoli, Rachele; Cabrio, Elena; Tonelli, Sara; Villata, Serena. - ELETTRONICO. - (2018), pp. 80-84. ((Intervento presentato al convegno GermEval 2018 tenutosi a Vienna nel September 21, 2018.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2910339
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact