Distant Supervision

Distant supervision leverages readily available, albeit noisy, data sources to train machine learning models for tasks like relation extraction and named entity recognition, circumventing the need for extensive manual annotation. Current research focuses on mitigating the inherent noise in distantly supervised data through techniques such as curriculum learning, co-training, and weighted contrastive learning, often employing transformer-based models and incorporating additional supervision from other sources like knowledge graphs or social networks. This approach significantly reduces the cost and time associated with data annotation, enabling the development of more robust and scalable models for various natural language processing tasks and impacting fields like biomedical research and digital humanities.