HAD-Tübingen at SemEval-2019 Task 6: Deep Learning Analysis of Offensive Language on Twitter: Identification and Categorization

Himanshu Bansal, Daniel Nagel, Anita Soloveva

Abstract
This paper describes the submissions of our team, HAD-Tübingen, 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.
Anthology ID:
S19-2111
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
622–627
Language:
URL:
https://aclanthology.org/S19-2111
DOI:
10.18653/v1/S19-2111
Bibkey:
Cite (ACL):
Himanshu Bansal, Daniel Nagel, and Anita Soloveva. 2019. HAD-Tübingen at SemEval-2019 Task 6: Deep Learning Analysis of Offensive Language on Twitter: Identification and Categorization. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 622–627, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
HAD-Tübingen at SemEval-2019 Task 6: Deep Learning Analysis of Offensive Language on Twitter: Identification and Categorization (Bansal et al., SemEval 2019)
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PDF:
https://preview.aclanthology.org/autopr/S19-2111.pdf
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