preannotated

Preannotated refers to data that has labels, metadata, or annotations assigned before its use in a specific task or study. Unlike post hoc annotation, where labeling occurs after data collection, preannotation embeds the expected annotations into the data preparation process. The term is used across fields such as machine learning, data science, and information retrieval to describe datasets that arrive with ready-made labels, segmentation masks, transcripts, or other structured annotations.

Preannotations can be produced by automated systems, human annotators following a predefined schema, or a combination of both. In many pipelines, an initial automated pass is followed by human review and correction. This approach can speed up development, improve consistency, and facilitate benchmarking by providing standardized labeling conventions.

Applications include computer vision datasets with bounding boxes or segmentations, natural language processing corpora with labeled entities or relations, speech or audio datasets with transcripts and timestamps, and medical datasets with coded diagnoses or annotated regions of interest. Preannotated data is commonly used to bootstrap labeling-heavy tasks and to enable researchers to compare methods on common baselines.

Considerations include the risk that preannotations reflect initial biases or errors that propagate into models if insufficient verification is performed. Quality assurance should document annotation guidelines, provenance, and versioning, and verification steps should be transparent. Privacy, consent, and data governance are also important when preannotations involve sensitive information. As a workflow strategy, preannotation often complements active learning and crowdsourcing rather than replacing them.