Annotator
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An Annotator is a cognitive entity that performs annotation tasks to add annotation items to data items.
- AKA: Annotation Agent, Labeler, Tagger, Data Annotator.
- Context:
- Annotator Input: Raw Data Items, Annotation Instructions.
- Annotator Output: Annotated Data Items, Annotation Logs.
- Annotator Performance Measure: Annotation Accuracy, Annotation Throughput, Annotation Consistency, Annotation Coverage.
- It can typically identify Annotation Targets within data items.
- It can typically apply Annotation Labels according to annotation schemas.
- It can typically maintain Annotation Consistency across data collections.
- It can typically follow Annotation Protocols for standardization.
- It can typically produce Annotation Outputs for downstream tasks.
- It can typically handle Annotation Complexity at various levels.
- It can typically generate Annotation Metadata for quality tracking.
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- It can often adapt to Annotation Requirement Changes over time.
- It can often process Modalities including text, image, and audio.
- It can often collaborate with other annotators for consensus achievement.
- It can often provide Annotation Confidence Scores for uncertainty quantification.
- It can often learn from Annotation Feedback for performance improvement.
- It can often scale Annotation Operations based on workload demands.
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- It can range from being a Human Annotator to being an AI Annotator, depending on its cognitive architecture.
- It can range from being a General-Purpose Annotator to being a Domain-Specific Annotator, depending on its annotation specialization.
- It can range from being a Simple Annotator to being a Complex Annotator, depending on its annotation capability.
- It can range from being a Single-Task Annotator to being a Multi-Task Annotator, depending on its annotation versatility.
- It can range from being a Standalone Annotator to being a Collaborative Annotator, depending on its annotation workflow integration.
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- It can utilize Annotation Tools for task execution.
- It can contribute to Training Dataset Creation for machine learning applications.
- It can support Data Pipelines through annotation services.
- It can enable Human-AI Collaboration in hybrid annotation systems.
- It can participate in Active Learning Loops for annotation optimization.
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- Example(s):
- Cognitive Type-Based Annotators, such as:
- Human Annotators, such as:
- AI Annotators, such as:
- Machine Learning Annotators using trained models.
- Rule-Based Annotators following predefined rules.
- Hybrid AI Annotators combining multiple approaches.
- Task-Specific Annotators, such as:
- Domain-Focused Annotators, such as:
- Workflow-Based Annotators, such as:
- Quality Control Annotators verifying annotation accuracy.
- Training Data Annotators creating ML datasets.
- Production Annotators in operational systems.
- ...
- Cognitive Type-Based Annotators, such as:
- Counter-Example(s):
- Data Generator, which creates new data rather than annotating existing data.
- Data Validator, which checks data quality without adding annotations.
- Data Consumer, which uses annotated data rather than creating it.
- Annotation Reviewer, which evaluates annotation quality without performing annotations.
- Subject Matter Expert, which provides domain knowledge without executing annotation tasks.
- See: Human Annotator, AI Annotator, Annotation Task, Annotation System, Cognitive Entity, Data Processing Agent, Human-AI Collaboration.