Abstract
Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that focuses on extracting entities such as names of people, organizations, locations, and dates from text. Despite significant advancements due to deep learning and transformer architectures like BERT, NER still faces challenges, particularly in low-resource languages like Arabic. This paper presents a BERT-based NER system that utilizes a two-channel parallel hybrid neural network with an attention mechanism specifically designed for the NER Shared Task 2024. In the competition, our approach ranked second by scoring 90.13% in micro-F1 on the test set. The results demonstrate the effectiveness of combining advanced neural network architectures with contextualized word embeddings in improving NER performance for Arabic.- Anthology ID:
- 2024.arabicnlp-1.103
- Volume:
- Proceedings of The Second Arabic Natural Language Processing Conference
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Nizar Habash, Houda Bouamor, Ramy Eskander, Nadi Tomeh, Ibrahim Abu Farha, Ahmed Abdelali, Samia Touileb, Injy Hamed, Yaser Onaizan, Bashar Alhafni, Wissam Antoun, Salam Khalifa, Hatem Haddad, Imed Zitouni, Badr AlKhamissi, Rawan Almatham, Khalil Mrini
- Venues:
- ArabicNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 867–873
- Language:
- URL:
- https://aclanthology.org/2024.arabicnlp-1.103
- DOI:
- Cite (ACL):
- Issam Yahia, Houdaifa Atou, and Ismail Berrada. 2024. Addax at WojoodNER 2024: Attention-Based Dual-Channel Neural Network for Arabic Named Entity Recognition. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 867–873, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Addax at WojoodNER 2024: Attention-Based Dual-Channel Neural Network for Arabic Named Entity Recognition (Yahia et al., ArabicNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.arabicnlp-1.103.pdf