Addax at WojoodNER 2024: Attention-Based Dual-Channel Neural Network for Arabic Named Entity Recognition
Year: 2024
Type: conference-paper
Abstract: Named Entity Recognition (NER) is a fundamental task in Natural Language Processing 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, 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.For the WojoodNER 2024 Shared Task, 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.
Authors: Issam Yahia, Houdaifa Atou, Ismail Berrada
Institution: Université Mohammed VI Polytechnique
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4402671358