Abstract
This paper presents the ArabicProcessors team’s system designed for sarcasm (subtask 1) and sentiment (subtask 2) detection shared task. We created a hybrid system by combining rule-based features and both static and dynamic embeddings using transformers and deep learning. The system’s architecture is an ensemble of Naive bayes, MarBERT and Mazajak embedding. This process scored an F1-score of 51% on sarcasm and 71% for sentiment detection.- Anthology ID:
- 2021.wanlp-1.45
- Volume:
- Proceedings of the Sixth Arabic Natural Language Processing Workshop
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv, Ukraine (Virtual)
- Editors:
- Nizar Habash, Houda Bouamor, Hazem Hajj, Walid Magdy, Wajdi Zaghouani, Fethi Bougares, Nadi Tomeh, Ibrahim Abu Farha, Samia Touileb
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 351–356
- Language:
- URL:
- https://aclanthology.org/2021.wanlp-1.45
- DOI:
- Cite (ACL):
- Kamel Gaanoun and Imade Benelallam. 2021. Sarcasm and Sentiment Detection in Arabic language A Hybrid Approach Combining Embeddings and Rule-based Features. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 351–356, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
- Cite (Informal):
- Sarcasm and Sentiment Detection in Arabic language A Hybrid Approach Combining Embeddings and Rule-based Features (Gaanoun & Benelallam, WANLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2021.wanlp-1.45.pdf