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Results for "metadata"

JobArabi: An Arabic Corpus and Analysis of Job Announcements from Social Media

arXiv · May 20

Researchers have introduced JobArabi, a new large-scale corpus consisting of 20,528 Arabic job announcements collected from X between January 2024 and October 2025. The dataset was compiled using a linguistically informed query framework covering various Arabic recruitment expressions, offering metadata like timestamps and geolocation for detailed analysis. Quantitative analysis of JobArabi reveals sociolinguistic patterns, including persistent gendered hiring language, regional occupational demand variations, and emotional framing in recruitment messages. Why it matters: This corpus provides a valuable resource for research in Arabic NLP, computational social science, and digital labor studies, offering unique insights into labor market communication and linguistic change in the Arab world.

MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs

arXiv · May 26

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.

Masader: Metadata Sourcing for Arabic Text and Speech Data Resources

arXiv · Oct 13

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.

Deep-Learning-based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images

arXiv · May 11

Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.