Annotatorer

Annotatorer are individuals who perform annotation, attaching labels or metadata to data to enable analysis or training of machine learning models. They work across domains such as natural language processing, computer vision, and audio analysis, producing labeled datasets for algorithms. Tasks include linguistic labeling (part-of-speech tagging, named-entity recognition), image and video labeling (bounding boxes, segmentation), and audio transcription or speaker tagging.

Workflows usually start with a guideline that defines tags and meanings. Annotatorer receive training and may participate in pilot rounds to align labeling. Quality control is central and includes inter-annotator agreement, adjudication, and gold-standard checks. Data provenance tracks changes for reproducibility. Annotatorer can be part of dedicated teams under project managers or operate via crowdsourcing platforms that distribute microtasks. Outputs are labeled datasets, annotation schemas, and confidence measures per label.

Ethical considerations include privacy and consent, fair compensation, and reducing bias through clear guidelines and regular reviews. Annotation quality influences machine learning performance, model evaluation, and downstream applications. Reliability is assessed with agreement scores, accuracy, and task-specific metrics. Annotation work is foundational for creating corpora, datasets, and benchmarks used in research and industry.