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Rich Annotation Guided Learning
International Journal on Advances in Intelligent Systems
Supervised learning methods rely heavily on the quantity and quality of annotations provided by humans. As more natural language processing systems utilize human labeled data, it becomes beneficial to discover some hidden privileged knowledge from human annotators. In a traditional framework, a human annotator and a system are treated as isolated black-boxes. We propose better utilization of the valuable knowledge possessed by human annotators in the system development. This can be achieved byfatcat:jr2qxiogrrgvxmobv6huqtmthe