Question answering systems retrieve information from documents in response to queries. Most of the questions arewho-andwhat-type questions that deal with named entities. A less common and more challenging question to deal with is thewhy-question. In this paper, we introduceLemaza(Arabic forwhy), a system for automatically answeringwhy-questions for Arabic texts. The system is composed of four main components that make use of the Rhetorical Structure Theory. To evaluateLemaza, we prepared a set ofwhy-question–answer pairs whose answer can be found in a corpus that we compiled out of Open Source Arabic Corpora.Lemazaperformed best when the stop-words were not removed. The performance measure was 72.7%, 79.2% and 78.7% for recall, precision andc@1, respectively.

This study harnesses the linguistic diversity of Arabic dialects to create two expansive corpora from X (formerly Twitter). The Gulf Arabic Corpus (GAC-6) includes around 1.7 million tweets from six Gulf countries—Saudi Arabia, UAE, Qatar, Oman, Kuwait, and Bahrain—capturing a wide range of linguistic variations. The Saudi Dialect Corpus (SDC-5) comprises 790,000 tweets, offering in-depth insights into five major regional dialects of Saudi Arabia: Hijazi, Najdi, Southern, Northern, and Eastern, reflecting the complex linguistic landscape of the region. Both corpora are thoroughly annotated with dialect-specific seed words and geolocation data, achieving high levels of accuracy, as indicated by Cohen’s Kappa scores of 0.78 for GAC-6 and 0.90 for SDC-5. The annotation process leverages AI-driven techniques, including machine learning algorithms for automated dialect recognition and feature extraction, to enhance the granularity and precision of the data. These resources significantly contribute to the field of Arabic dialectology and facilitate the development of AI algorithms for linguistic data analysis, enhancing AI system design and efficiency. The data provided by this research are crucial for advancing AI methodologies, supporting diverse applications in the realm of next-generation AI technologies.

Background: Early childhood caries (ECC) affects a substantial proportion of young children worldwide, and timely screening is essential for early intervention and referral. While deep learning has shown promise for automated dental diagnostics, many existing approaches rely on computationally heavy models that limit deployment in community and mobile settings. This study investigates whether compact convolutional neural networks can achieve clinically meaningful performance for screening dental caries from oral photographs. Methods: We curated a dataset of 435 intraoral images from children aged 3–14 years, annotated by licensed dentists, and performed patient-level stratified splitting to prevent data leakage. Three convolutional neural networks (ResNet-18, MobileNetV3-Small, and EfficientNet-B0) were fine-tuned using ImageNet-pretrained weights and comparatively evaluated for the detection of dental caries from oral photographs. Models were trained with class-weighted cross-entropy loss and evaluated on a held-out test set using sensitivity, specificity, balanced accuracy, ROC-AUC, and PR-AUC with bootstrap 95% confidence intervals. Results: ResNet-18 achieved the highest balanced accuracy (0.929), weighted F1-score (0.954), and perfect sensitivity (1.00), while EfficientNet-B0 achieved the strongest threshold-independent discrimination with the highest ROC-AUC (0.978) and PR-AUC (0.990). MobileNetV3-Small maintained competitive performance (ROC-AUC 0.952; PR-AUC 0.976) with substantially lower computational complexity. Conclusions: In addition to performance evaluation, we incorporated an interpretability analysis using Grad-CAM to examine model decision behavior. The resulting attribution maps predominantly highlighted clinically relevant tooth regions associated with caries, providing evidence that the models rely on meaningful dental features rather than background artifacts. These results demonstrate that compact, deployment-friendly architectures can achieve clinically meaningful performance for ECC detection, supporting their suitability for scalable, real-world screening applications.

Nouf AlShenaifi, Nourah Alangari, Hadeel Al-Negheimish. Proceedings of The Second Arabic Natural Language Processing Conference. 2024.

Nourah Alangari, Nouf AlShenaifi. Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks. 2025.

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 ($L_1$), and a fine-grained sub-category classification across 63 legal topics ($L_2$), 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 $L_2$ accuracy of 83.04% and a Macro-F1 of 76.07%, while the Single Multi-Task configuration achieves the highest $L_1$ accuracy of 99.57%. GPT-4o under 5-shot prompting achieves 99.13% $L_1$ accuracy and a competitive $L_2$ 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.