Emad at SemEval-2019 Task 6: Offensive Language Identification using Traditional Machine Learning and Deep Learning approaches

Emad Kebriaei,Azadeh Shakery,Samaneh Karimi,Nazanin Sabri

doi:10.18653/v1/s19-2107

Emad Kebriaei, Azadeh Shakery + Show 2 more

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https://doi.org/10.18653/v1/s19-2107

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Publication Date: Jan 1, 2019
Citations: 9	License type: cc-by

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In this paper, the used methods and the results obtained by our team, entitled Emad, on the OffensEval 2019 shared task organized at SemEval 2019 are presented. The OffensEval shared task includes three sub-tasks namely Offensive language identification, Automatic categorization of offense types and Offense target identification. We participated in sub-task A and tried various methods including traditional machine learning methods, deep learning methods and also a combination of the first two sets of methods. We also proposed a data augmentation method using word embedding to improve the performance of our methods. The results show that the augmentation approach outperforms other methods in terms of macro-f1.

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With the growth of social networking platforms, the need for automatic methods that manages the emerging issues or facilitate using them is rising
In addition to the methods mentioned above, we proposed an augmentation method in order to improve the performance of our methods
The results of three systems that we submitted to OffensEval 2019 is reported

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With the growth of social networking platforms, the need for automatic methods that manages the emerging issues or facilitate using them is rising. The need for effective automatic methods for identifying offensive language in textual data is important. The OffensEval shared task has been organized in order to give a boost to computational methods for identifying and categorizing offensive content on social media. Three sub-tasks defined in the offensEval shared task are identification of offensive language(sub-task A), categorization of offense types(sub-task B) and identification of the offense target(sub-task C) (Zampieri et al, 2019b). The main goal in sub-task A is to identify offensive tweets from non-offensive ones. A post is labeled as offensive if it contains any form of non-acceptable language (profanity) or a targeted offense, which can be veiled or direct

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