Abstract
In this paper, we tackle the problem of offensive language and hate speech detection. We proposed our methods for data preprocessing and balancing, and then we presented our Convolutional Neural Network (CNN) and bidirectional Gated Recurrent Unit (GRU) models used. After that, we augmented these models with attention layer. The best results achieved was using the Bidirectional Gated Recurrent Unit augmented with attention layer (Bi-GRU_ATT). Keywords: Abusive Language, Text Mining, Arabic Language, Social Media Mining, Deep Learning, Convolutional Neural Network, Gated Recurrent Unit, Attention Mechanism, Machine Learning.- Anthology ID:
- 2020.osact-1.12
- Volume:
- Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Hend Al-Khalifa, Walid Magdy, Kareem Darwish, Tamer Elsayed, Hamdy Mubarak
- Venue:
- OSACT
- SIG:
- Publisher:
- European Language Resource Association
- Note:
- Pages:
- 76–81
- Language:
- English
- URL:
- https://aclanthology.org/2020.osact-1.12
- DOI:
- Cite (ACL):
- Bushr Haddad, Zoher Orabe, Anas Al-Abood, and Nada Ghneim. 2020. Arabic Offensive Language Detection with Attention-based Deep Neural Networks. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, pages 76–81, Marseille, France. European Language Resource Association.
- Cite (Informal):
- Arabic Offensive Language Detection with Attention-based Deep Neural Networks (Haddad et al., OSACT 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.osact-1.12.pdf