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This paper presents the models submitted by Ghmerti team for subtasks A and Bof the OffensEval shared task at SemEval 2019. OffensEval addresses the problemof identifying and categorizing offensive language in social media in threesubtasks; whether or not a content is offensive (subtask A), whether it istargeted (subtask B) towards an individual, a group, or other entities (subtaskC). The proposed approach includes character-level Convolutional NeuralNetwork, word-level Recurrent Neural Network, and some preprocessing. Theperformance achieved by the proposed model for subtask A is 77.93%macro-averaged F1-score.

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