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IMT Mines Ales at HASOC 2019: Automatic Hate Speech Detection

Abstract : This paper presents the contribution of the LGI2P (Labo-ratoire de Génie Informatique et d'Ingénierie de Production) team from IMT Mines Alès to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) 2019 shared task. This challenge aims at automatically identifying hate speech content in social media through three sub-tasks, each available in three different languages (En-glish, German and Hindi). We are interested in sub-tasks A and B, requiring to (A) classify tweets as offensive or as non offensive, and (B) to further classify offensive tweets from sub-task A as hate speech, offensive speech or profane. We trained a fastText model for each proposed language and obtained promising results on the Hindi dataset for both sub-tasks A and B.
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Submitted on : Saturday, January 4, 2020 - 12:12:18 PM
Last modification on : Wednesday, November 3, 2021 - 5:10:30 AM
Long-term archiving on: : Monday, April 6, 2020 - 10:08:03 PM


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  • HAL Id : hal-02427843, version 1



Jean-Christophe Mensonides, Pierre-Antoine Jean, Andon Tchechmedjiev, Sébastien Harispe. IMT Mines Ales at HASOC 2019: Automatic Hate Speech Detection. FIRE 2019 - 11th Forum for Information Retrieval Evaluation, Dec 2019, Kolkata, India. p.279-284. ⟨hal-02427843⟩



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