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

Emad Kebriaei, Samaneh Karimi, Nazanin Sabri, Azadeh Shakery


Abstract
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.
Anthology ID:
S19-2107
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:
600–603
Language:
URL:
https://aclanthology.org/S19-2107
DOI:
10.18653/v1/S19-2107
Bibkey:
Cite (ACL):
Emad Kebriaei, Samaneh Karimi, Nazanin Sabri, and Azadeh Shakery. 2019. Emad at SemEval-2019 Task 6: Offensive Language Identification using Traditional Machine Learning and Deep Learning approaches. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 600–603, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Emad at SemEval-2019 Task 6: Offensive Language Identification using Traditional Machine Learning and Deep Learning approaches (Kebriaei et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2107.pdf