arXiv ·
Researchers introduce ASAD, a new large-scale, high-quality Arabic Sentiment Analysis Dataset based on 95K tweets with positive, negative, and neutral labels. The dataset is launched with a competition sponsored by KAUST offering a total of 17000 USD in prizes. Baseline models are implemented and results reported to provide a reference for competition participants.
arXiv ·
KAUST organized an Arabic Sentiment Analysis Challenge where participants developed ML models to classify tweets as positive, negative, or neutral. The competition used the ASAD dataset with 55K tweets for training, 20K for validation, and 20K for final evaluation. The full dataset of 100K labeled tweets has been released for public use.
arXiv ·
The paper introduces ADAB (Arabic Politeness Dataset), a new annotated Arabic dataset for politeness detection collected from online platforms. The dataset covers Modern Standard Arabic and multiple dialects (Gulf, Egyptian, Levantine, and Maghrebi). It contains 10,000 samples across 16 politeness categories and achieves substantial inter-annotator agreement (kappa = 0.703). Why it matters: This dataset addresses the under-explored area of Arabic-language resources for politeness detection, which is crucial for culturally-aware NLP systems.
arXiv ·
The paper introduces Sadeed, a fine-tuned decoder-only language model based on the Kuwain 1.5B Hennara model, for improved Arabic text diacritization. Sadeed is fine-tuned on high-quality diacritized datasets and achieves competitive results compared to larger proprietary models. The authors also introduce SadeedDiac-25, a new benchmark for fairer evaluation of Arabic diacritization across diverse text genres. Why it matters: This work advances Arabic NLP by providing both a competitive diacritization model and a more robust evaluation benchmark, facilitating further research and development in the field.
KAUST ·
MEDAD, a KAUST spin-off, won the 2020 MEED Sustainability Medal for its "Innovative Hybrid Solar Desalination Cycle." The MEDAD hybrid cycle desalinates seawater using solar energy at 60-80 degrees Celsius, combining adsorption with multi-effect desalination. The cycle achieved performance levels of 20% of thermodynamic limits and a water production cost of $0.48/m3. Why it matters: This award recognizes the potential of KAUST-developed technology to address critical water scarcity challenges in the GCC region through sustainable and cost-effective desalination.