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.
Corazza, M., Menini, S., Arslan, P., Sprugnoli, R., Cabrio, E., Tonelli, S., Villata, S., InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks, in Proceedings of the GermEval 2018 Workshop, (Vienna, Austria, 21-21 December 2018), N/A, Vienna 2018: 80-84 [http://hdl.handle.net/10807/133007]
InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks
Sprugnoli, Rachele;
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.