arXiv ·
KAUST researchers introduced MOLE, a framework leveraging LLMs for automated metadata extraction from scientific papers. The system processes documents in multiple formats and validates outputs, targeting datasets beyond Arabic. A new benchmark dataset has been released to evaluate progress in metadata extraction.
arXiv ·
Researchers created Masader, the largest public catalog for Arabic NLP datasets, containing 200 datasets annotated with 25 attributes. They developed a metadata annotation strategy applicable to other languages. The paper highlights issues within current Arabic NLP datasets and suggests recommendations. Why it matters: This curated dataset directory helps lower the barrier to entry for Arabic NLP research and development.
arXiv ·
This paper introduces a hybrid deep learning and machine learning pipeline for classifying construction and demolition waste. A dataset of 1,800 images from UAE construction sites was created, and deep features were extracted using a pre-trained Xception network. The combination of Xception features with machine learning classifiers achieved up to 99.5% accuracy, demonstrating state-of-the-art performance for debris identification.