Urdu Dataset

Research on Urdu datasets focuses on addressing the challenges of low-resource language processing for this widely spoken language. Current efforts concentrate on developing and improving datasets for various NLP tasks, including machine translation, dependency parsing, question answering, sentiment analysis, and fake news detection, often employing techniques like data augmentation and multilingual model adaptation. These advancements leverage models such as Support Vector Machines, various neural networks (including CNNs, HMMs, and transformers), and ensemble methods to improve performance across these tasks. The resulting datasets and improved models are crucial for advancing Urdu NLP and enabling broader access to information and technology for Urdu speakers.