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Somia Khedimi
University of Naama

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Language models and deep neural networks for Arabic named entity recognition Somia Khedimi; Abdelghani Bouziane
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp142-148

Abstract

Token type identification lies at the core of named entity recognition, allowing models to distinguish named entities from non-entity tokens and thereby better capture sentence meaning. This paper presents a deep learning approach for the Arabic named entity recognition task, leveraging deep neural networks and pretrained language models. The proposed model is a combination of the AraELECTRA language model with the bidirectional long short-term memory (BiLSTM) neural network. We utilize the WojoodNER dataset, which provides fine-grained annotations of Arabic text across 21 entity types. The results of this approach are encouraging, with an accuracy of 98.29% and an F1-score of 87%.
Co-Authors Abdelghani Bouziane