Data Labeling, Annotation, and Tagging

The collective process of enhancing data with additional information to facilitate machine learning and automated analysis. Data labeling involves assigning labels, typically text or numerical values, to data, making it suitable for supervised learning where labeled data trains models to make predictions on new, unlabeled data. Annotation is a broader term encompassing any additional information added to data, including bounding boxes, image captions, or metadata, aiding in model training. Tagging is a specific form of annotation involving attaching descriptive keywords, or “tags,” to organize and classify data, commonly used in social media and content organization applications.