@inproceedings{yahia-etal-2024-addax,
    title = "Addax at {W}ojood{NER} 2024: Attention-Based Dual-Channel Neural Network for {A}rabic Named Entity Recognition",
    author = "Yahia, Issam  and
      Atou, Houdaifa  and
      Berrada, Ismail",
    editor = "Habash, Nizar  and
      Bouamor, Houda  and
      Eskander, Ramy  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Abdelali, Ahmed  and
      Touileb, Samia  and
      Hamed, Injy  and
      Onaizan, Yaser  and
      Alhafni, Bashar  and
      Antoun, Wissam  and
      Khalifa, Salam  and
      Haddad, Hatem  and
      Zitouni, Imed  and
      AlKhamissi, Badr  and
      Almatham, Rawan  and
      Mrini, Khalil",
    booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2024.arabicnlp-1.103/",
    doi = "10.18653/v1/2024.arabicnlp-1.103",
    pages = "867--873",
    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."
}