Markedannotated

Markedannotated is a data labeling approach used in data curation and machine learning that combines two labeling layers: marking data items with one or more markers (tags, labels, or attributes) and annotating them with descriptive notes or metadata. The term highlights the integration of coarse categorization and contextual information in a single label set.

In a markedannotated dataset, each item typically includes: an identifier; a markers field listing one or more tags; an annotation field containing a narrative or structured notes such as rationale, examples, or constraints; and optional metadata like annotator, timestamp, and confidence score.

Common formats include JSON and YAML, which can be stored in data repositories and integrated with labeling tools. Markedannotated data supports multi-label experiments, traceability, and nuanced evaluation.

Applications span natural language processing, where markers indicate topics or sentiment and annotations provide rationale or examples; computer vision, where markers describe regions or objects and annotations record attributes; and audio datasets, where markers tag speakers or conditions and annotations capture transcripts or context.

Workflow typically proceeds through vocabulary design for markers, data collection, marking, annotation writing, quality control, and storage in a versioned repository. Some implementations link markers to metadata schemas and enable automated checks for consistency and completeness.

Advantages include richer supervision signals for models, improved auditability, and flexible filtering; limitations involve higher labeling effort, potential inconsistency across annotators, and the need for clear guidelines and governance.

Related concepts are data labeling, annotation schemas, metadata tagging, and data governance. Markedannotated workflows can be combined with standard evaluation metrics for multiclass and multilabel tasks, and may support explainability by attaching justification to each labeled item.