Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification

• URL: http://arxiv.org/abs/2009.10792v1
• Date: Tue, 22 Sep 2020 20:13:48 GMT
• Title: Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification
• Authors: Ehsan Doostmohammadi, Hossein Sameti, Ali Saffar
• Abstract summary: OffensEval addresses the problem of identifying and categorizing offensive language in social media. The proposed approach includes character-level Convolutional Neural Network, word-level Recurrent Neural Network, and some preprocessing. The performance achieved by the proposed model for subtask A is 77.93% macro-averaged F1-score.
• Score: 1.192436948211501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the models submitted by Ghmerti team for subtasks A and B of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem of identifying and categorizing offensive language in social media in three subtasks; whether or not a content is offensive (subtask A), whether it is targeted (subtask B) towards an individual, a group, or other entities (subtask C). The proposed approach includes character-level Convolutional Neural Network, word-level Recurrent Neural Network, and some preprocessing. The performance achieved by the proposed model for subtask A is 77.93% macro-averaged F1-score.

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