Human Annotator
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A Human Annotator is a human worker who performs annotation tasks to add annotation items to data items.
- AKA: Annotator, Human Labeler, Manual Annotator, Data Annotator.
- Context:
- Annotator Input: Data Items, Annotation Guidelines, Annotation Tools.
- Annotator Output: Annotated Data Items, Annotation Metadata.
- Annotator Performance Measure: Annotation Accuracy, Annotation Speed, Inter-Annotator Agreement Rate, Annotation Consistency Score.
- It can typically follow Annotation Guidelines for annotation standardization.
- It can typically apply Human Judgment to resolve ambiguities.
- It can typically maintain Annotation Consistency across data batches.
- It can typically provide Annotation Rationales for quality assurance.
- It can typically identify Edge Cases requiring special handling.
- It can typically collaborate with other human annotators for consensus building.
- It can typically adapt to Annotation Requirement Changes through feedback incorporation.
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- It can often participate in Annotator Training Programs for skill development.
- It can often contribute to Annotation Guideline Refinement based on practical experience.
- It can often perform Annotation Quality Reviews on peer work.
- It can often utilize Annotation Management Platforms for workflow coordination.
- It can often provide Annotation Feedback to improve annotation processes.
- It can often specialize in specific annotation domains through experience accumulation.
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- It can range from being a Novice Human Annotator to being an Expert Human Annotator, depending on its annotation experience level.
- It can range from being a General-Purpose Human Annotator to being a Domain-Specific Human Annotator, depending on its annotation specialization.
- It can range from being a Part-Time Human Annotator to being a Full-Time Human Annotator, depending on its annotation commitment level.
- It can range from being an Independent Human Annotator to being a Team-Based Human Annotator, depending on its annotation work arrangement.
- It can range from being a Local Human Annotator to being a Remote Human Annotator, depending on its annotation work location.
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- It can work within Annotation Projects managed by annotation project managers.
- It can utilize Annotation Tools for annotation efficiency.
- It can contribute to Machine Learning Training Datasets through high-quality annotations.
- It can participate in Crowdsourcing Platforms for distributed annotation.
- It can support AI Development through human-in-the-loop annotation.
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- Example(s):
- Data Type-Specific Human Annotators, such as:
- Human Text Annotators, such as:
- Part-of-Speech Annotator labeling categories in sentences.
- Named Entity Annotator identifying person names, organization names, and location names.
- Sentiment Annotator marking positive sentiments, negative sentiments, and neutral sentiments.
- Human Image Annotators, such as:
- Object Detection Annotator drawing bounding boxes around objects.
- Image Segmentation Annotator marking boundaries.
- Face Recognition Annotator labeling facial features and identities.
- Human Audio Annotators, such as:
- Speech Transcription Annotator converting spoken words to text.
- Music Annotation Specialist marking musical elements and tempo changes.
- Sound Event Annotator identifying environmental sounds and acoustic events.
- Human Video Annotators, such as:
- Action Recognition Annotator marking human actions in video frames.
- Video Event Annotator identifying temporal events and scene changes.
- Human Text Annotators, such as:
- Domain-Specific Human Annotators, such as:
- Medical Annotators marking tumor regions in medical images.
- Legal Text Annotators identifying entities in contracts.
- Financial Document Annotators tagging financial metrics in reports.
- Scientific Paper Annotators marking citations and methodology sections.
- Platform-Based Human Annotators, such as:
- Experience-Level Human Annotators, such as:
- Entry-Level Human Annotators performing simple labeling tasks.
- Senior Human Annotators handling complex annotation decisions.
- Lead Human Annotators managing annotation teams and quality control.
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- Data Type-Specific Human Annotators, such as:
- Counter-Example(s):
- Automated Annotation System, which performs annotation tasks using algorithms rather than human judgment.
- Semi-Automated Annotator, which combines human input with machine assistance.
- Data Creator, who generates original content rather than annotating existing data.
- Data Reviewer, who validates data quality without adding annotations.
- Subject Matter Expert, who provides domain consultation without performing annotation tasks.
- See: Annotation Task, Domain-Specific Annotator, Annotation Project, Annotation Quality Control, Inter-Annotator Agreement, Human-in-the-Loop System, Crowdsourcing Platform.