Abstract
This paper presents SI2M & AIOX Labs work among the propaganda detection in Arabic text shared task. The objective of this challenge is to identify the propaganda techniques used in specific propaganda fragments. We use a combination of data augmentation, Name Entity Recognition, rule-based repetition detection, and ARBERT prediction to develop our system. The model we provide scored 0.585 micro F1-Score and ranked 6th out of 12 teams.- Anthology ID:
- 2022.wanlp-1.58
- Volume:
- Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 506–510
- Language:
- URL:
- https://aclanthology.org/2022.wanlp-1.58
- DOI:
- Cite (ACL):
- Kamel Gaanoun and Imade Benelallam. 2022. SI2M & AIOX Labs at WANLP 2022 Shared Task: Propaganda Detection in Arabic, A Data Augmentation and Name Entity Recognition Approach. In Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP), pages 506–510, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- SI2M & AIOX Labs at WANLP 2022 Shared Task: Propaganda Detection in Arabic, A Data Augmentation and Name Entity Recognition Approach (Gaanoun & Benelallam, WANLP 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.wanlp-1.58.pdf