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HAD-T ¨ubingen at SemEval-2019 Task 6: Deep Learning Analysis of Offensive Language on Twitter: Identification and Categorizationстатья
- Авторы: Anita Soloveva, Himanshu Bansal, Daniel Nagel
- Сборник: Proceedings of The 13th International Workshop onSemantic Evaluation
- Год издания: 2019
- Первая страница: 622
- Последняя страница: 627
- DOI: 10.18653/v1/S19-2111
- Аннотация: This paper describes the submissions of our team, HAD-T{\"u}bingen, for the SemEval 2019 - Task 6: "OffensEval: Identifying and Categorizing Offensive Language in Social Media". We participated in all the three sub-tasks: Sub-task A - "Offensive language identification", sub-task B - "Automatic categorization of offense types" and sub-task C - "Offense target identification". As a baseline model we used a Long short-term memory recurrent neural network (LSTM) to identify and categorize offensive tweets. For all the tasks we experimented with external databases in a postprocessing step to enhance the results made by our model. The best macro-average F1 scores obtained for the sub-tasks A, B and C are 0.73, 0.52, and 0.37, respectively.
- Добавил в систему: Соловьёва Анита Вячеславовна