Annotators

Annotators are individuals or automated systems that assign labels, tags, or metadata to raw data. The resulting annotated data is used to train, validate, and evaluate supervised machine learning models, as well as to support data analysis, information extraction, and quality assessment across fields such as natural language processing, computer vision, and biology.

Most commonly, annotators are humans performing tasks such as named entity recognition, sentiment labeling, image object labeling, segmentation, transcription, or medical coding. In some settings, automated or semi-automated annotators generate initial labels that are later corrected by human annotators. Many projects rely on crowdsourcing to scale labeling, while domain experts are employed for specialized tasks.

A typical workflow includes creating annotation guidelines, selecting or building an annotation tool, providing training examples, and assigning samples. Quality control uses measures like inter-annotator agreement, calibration tasks, gold-standard checks, and adjudication of disagreements.

Challenges include subjectivity, label ambiguity, bias, privacy concerns, inconsistent guidelines, and cost. Proper governance, transparent guidelines, and auditing help mitigate these issues. Annotated datasets enable model training, benchmarking, and reproducible research, and are central to supervised learning pipelines.