Crowd Sourced Annotation

Crowd-sourced annotation leverages large numbers of individuals to label data, addressing the cost and time constraints of manual annotation in diverse fields like natural language processing and image analysis. Current research focuses on mitigating inherent noise and biases in crowd-sourced labels, employing techniques like confusion matrix correction and multi-view aggregation to improve model accuracy and robustness. This approach is crucial for scaling data annotation efforts, enabling advancements in areas such as commonsense knowledge base population, privacy policy analysis, and ecological monitoring, where large datasets are essential but manual labeling is impractical.