Aug 2026· Bulletin of Electrical Engineering and Informatics· 0 citations· 24 references
TL;DR
A two-stage deep learning pipeline that combines Arabic text stemming and morphological classification within a single deep learning architecture is proposed that demonstrates a 38% improvement over the best traditional stemmer, P-Stemmer.
Abstract
Arabic language, due to its complex morphology and richness of grammar features poses significant challenges in natural language processing (NLP). In this paper, we propose a two-stage deep learning pipeline that combines Arabic text stemming and morphological classification within a single deep learning architecture. The relationship between morphological reduction and grammatical categorization is exploited by combining character-level sequence processing with transformer-based classification. A bidirectional long short-term memory (Bi-LSTM) model is employed for Arabic stem extraction to build a sequence-to-sequence (seq2seq) stemming model named Char Stemmer. To evaluate the proposed model, a gold standard dataset consisting of 260,000 traditional Arabic words extracted from Quranic words and classical Arabic books is utilized. This dataset contains a wide range of challenging word structures suitable for robust evaluation. The Char Stemmer achieved an accuracy of 93.88% on the stemming task. The proposed model obtained 93.88% accuracy, demonstrating a 38% improvement over the best traditional stemmer, P-Stemmer. Beyond stemming, the impact of stemmers on subsequent tasks is evaluated, particularly Arabic word classification. Words are categorized into three morphological classes: noun, verb, and particle. Experimental results show that the proposed system achieved macro average precision, recall, and F1-score of 0.91, 0.89, and 0.90, respectively, with an overall classification accuracy of approximately 99%.
This study integrates parallel multi-kernel word-level convolutional features into conventional and hybrid deep learning models for Arabic text analysis tasks, providing a systematic within-study assessment of model sensitivity to architecture, preprocessing, and learning-rate selection.
Ahmed I.Taloba, George Samy Rady, Khaled F. Hussain· International Journal of Adv...· 0 citations
A hybrid architecture that combines a Transformer-based DistilBERT model with a Graph Convolutional Network (GCN) that enhances detection by modeling structural relationships within text data is proposed.
A bidirectional Mamba encoder pretrained via masked language modeling on a corpus combining Arabic Wikipedia and CulturaX text is introduced, trained end-to-end on four consumer-grade NVIDIA RTX 2080Ti GPUs (11GB) over approximately ten days.
Ahmed Amine Aliane, H. Aliane, N. Semmar· 0 citations
The findings underscore the potential of advanced NLP techniques to overcome language-specific challenges, providing a foundation for future research in multilingual plagiarism detection and enhancing the development of tools for other languages facing similar challenges.
Hanan Fawzy, Ahmad Salah, Heba El-Fiqi et al.· Informatica· 0 citations
Automated classification of Arabic legal texts presents unique challenges stemming from the formal register of judicial language, domain-specific Sharī‘a terminology, and the severe class imbalance inherent in hierarchical legal taxonomies. This paper addresses these challenges through a systematic investigation of hierarchical multi-class classification applied to a dataset of 1146 Arabic judicial cases sourced from the Saudi Ministry of Justice open data portal. Cases are annotated at two hierarchical levels: a binary main-class distinction between criminal and civil cases (L1), and a fine-grained sub-category classification across 63 legal topics (L2), exhibiting a class imbalance ratio of 43:1. We evaluate three methodological families under four classification architectures. For encoder-only models, we fine-tune AraBERT, CAMeL-BERT, MARBERTv2, and XLM-RoBERTa under Flat (Bottom-Up), Hierarchical Multi-Task, Two-Stage, and Hierarchy-Aware configurations. For encoder–decoder models, we adapt AraT5v2 under the same four architectures. Finally, we conduct a prompt-based evaluation of GPT-4o under zero-shot and targeted few-shot settings. The experimental results demonstrate that the Hierarchy-Aware architecture consistently achieves the strongest fine-grained classification performance across both model families. AraBERTv2 with Hierarchy-Aware training achieves the best overall L2 accuracy of 83.04% and a Macro-F1 of 76.07%, while the Single Multi-Task configuration achieves the highest L1 accuracy of 99.57%. GPT-4o under 5-shot prompting achieves 99.13% L1 accuracy and a competitive L2 Macro-F1 of 74.30% without task-specific fine-tuning, though supervised models maintain stronger overall fine-grained performance. These findings highlight the importance of explicit hierarchical supervision and domain-adapted pre-training for Arabic legal text classification, and establish strong baselines for future research in this underexplored domain.
Nourah Alangari, Nouf Alshenaifi, H. Almuzaini· Electronics· 0 citations
