Hybrid Lexical–Contextual Learning for Arabic News Classification: Integrating TF–IDF with AraBERT Embeddings

An intelligent deep learning for Arabic stemming and morphological classification

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%.

Multilingual AI-Generated Text Detection in Arabic, English, and Turkish Using a Hybrid Transformer–Graph Convolutional Network

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.

Sentiment Analysis of Moroccan Darija Arabizi: A Comparison of Pretrained Transformer Encoders and a Classical Baseline

Arabic News Text Classification Using Deep Learning Models with Dynamic N-grams

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.

DA-ICL: Distribution-Aware In-Context Learning for Arabic Automatic Speech Recognition Error Correction

Arabic Speech Recognition (ASR) faces compounded challenges due to rich dialectal variation, morphological complexity, and data scarcity. While self-supervised speech models such as HuBERT excel in acoustic representation, they lack the deep linguistic reasoning needed to resolve ambiguities unique to Arabic. Large Language Models (LLMs) offer complementary grammatical and semantic knowledge, yet their role in systematic, real-time Arabic ASR error correction remains underexplored. In this work, we propose a novel two-stage framework for accurate and efficient Arabic ASR enhancement. First, we fine-tune a HuBERT model on the Common Voice Arabic corpus, establishing a baseline word error rate (WER) of 19.3%. Second, we introduce Distribution-Aware In-Context Learning (DA-ICL), a prompting strategy that supplies the Arabic LLM Aya-23-8B with a curated set of few-shot examples derived from a systematic taxonomy of ASR error types, including phonetic confusions and morpho-orthographic errors. DA-ICL enables precise, structurally faithful corrections, reducing WER to 9.6% without undesirable sentence rephrasing. To address domain shift and catastrophic forgetting, we further apply Low-Rank Adaptation (LoRA) to adapt a pre-trained HuBERT model to new domains parameter-efficiently. This approach reduces out-of-domain WER from 67% to 24% while preserving in-domain performance, demonstrating improved generalization without full fine-tuning. Our results confirm that combining HuBERT-based acoustic modeling with LLM-based DA-ICL correction and LoRA-efficient adaptation yields a robust, accurate, and scalable solution for Arabic ASR, effectively bridging the gap between acoustic signal and linguistic knowledge. Our framework achieves a WER of 9.6% on Common Voice Arabic, significantly outperforming Whisper-large (47.49% zero-shot, 37.89% with LoRA fine-tuning) and demonstrating the effectiveness of our linguistically-aware approach for Arabic speech recognition.

Data-Centric Evaluation of Arabic Abstractive Summarization Using a Large-scale Curated News Corpus

This paper introduces MAAD, a high-quality, carefully constructed and curated by the authors large-scale Arabic dataset for abstractive news summarisation. The authors selected a high-quality subset of 50,000 articles from the dataset Original, which contains 602,792 articles. To maintain the quality, diversity, and training suitability of the subset, the subset underwent a multi-stage preprocessing pipeline involving noise removal, duplicate filtering, linguistic normalisation, and expert validation. The experimental evaluation was executed in two phases. In the first phase, three transformer-based models (ArabicT5, AraBART, and mT5) were evaluated on a controlled subset of 1,110 articles to establish fair baseline comparisons among models, where ArabicT5 achieved the best performance (ROUGE-1: 23.64, ROUGE-2: 11.82, ROUGE-L: 22.10). In the second phase, ArabicT5-base was trained on all 50,000 articles to evaluate scalability, achieving substantially improved results of 68.4, 52.3, and 64.1, respectively, with a BLEU score of 58.7. The findings emphasise the significance of scale, effective preprocessing, and the benefits of Arabic-specific pretraining on the quality of summarisation. Moreover, a human evaluation on 500 randomly sampled instances verified fluency and adequacy scores of 4.86 and 4.35, respectively, with a strong inter-annotator agreement (Cohen's Kappa: 0.78 and 0.74). Overall, the findings indicate that MAAD is a reliable and scalable dataset with strong potential to serve as a benchmark for Arabic abstractive summarisation and to support the development of robust transformer-based models.