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

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.
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Dates and versions

hal-01906096 , version 1 (26-10-2018)

Identifiers

  • HAL Id : hal-01906096 , version 1

Cite

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. ⟨hal-01906096⟩
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