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

Michele Corazza 1 Stefano Menini 2 Pinar Arslan 1 Rachele Sprugnoli 2 Elena Cabrio 1 Sara Tonelli 2 Serena Villata 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
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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https://hal.archives-ouvertes.fr/hal-01906096
Contributor : Michele Corazza <>
Submitted on : Friday, October 26, 2018 - 2:13:52 PM
Last modification on : Tuesday, May 26, 2020 - 6:50:41 PM
Document(s) archivé(s) le : Sunday, January 27, 2019 - 2:04:42 PM

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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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