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;
2018-01-01

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.
2018
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).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2910339
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