Arabic Offensive Language Detection with Attention-based Deep Neural Networks
Bushr Haddad, Zoher Orabe, Anas Al-Abood, Nada Ghneim
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
- Venues:
- LREC | OSACT | WS
- SIG:
- Publisher:
- European Language Resource Association
- Note:
- Pages:
- 76–81
- URL:
- https://www.aclweb.org/anthology/2020.osact-1.12
- DOI:
- PDF:
- https://www.aclweb.org/anthology/2020.osact-1.12.pdf
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