Sildistamine

Sildistamine, also called märgistamine or annotatsioon, is the process of assigning descriptive labels to data items to enable machine learning and data analysis. In practice, labels may describe objects, categories, attributes, or events within a dataset. The goal is to produce labeled data suitable for supervised learning, evaluation, and data retrieval.

Domains and tasks: In images, common tasks include classification, object localization with bounding boxes, and semantic or instance segmentation. In text, labeling covers named entity recognition, sentiment annotation, topic tagging, and linguistic features. In audio, transcripts, event labels, or speaker identification are typical. Multimodal data combines labels across modalities.

Methods: Sildistamine can be performed manually by human annotators, semi-automatically with model-assisted labeling, or through crowdsourcing. Active learning strategies prioritize uncertain items to maximize efficiency. Quality control relies on clear guidelines, training, double annotation, adjudication, and gold standard datasets. Inter-annotator agreement metrics, such as Cohen’s kappa, help assess consistency.

Data governance and ethics: Good practice includes documenting labeling protocols, maintaining provenance, versioning annotations, and ensuring data privacy and consent where applicable. Awareness of potential biases in labeling and clear documentation of edge cases support fairer models.

Applications: Labeled data underpin supervised machine learning, model evaluation, and data curation across computer vision, natural language processing, and audio analysis. Sildistamine is an ongoing process that evolves with new domains, annotation standards, and tooling.