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