Annotation guidelines are standardized instructions and rules that direct human annotators or automated tools on how to systematically label, categorize, and structure raw data for machine learning and computational tasks. These documents define the target label taxonomy, provide explicit definitions and examples for each category, and establish criteria for handling edge cases and ambiguous instances. By outlining clear decision-making procedures and boundaries, annotation guidelines minimize individual subjectivity, maximize inter-annotator agreement, and ensure high levels of consistency, quality, and reproducibility across the resulting dataset.
