Annotation Consensus Method
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A Annotation Consensus Method is a consensus building method that supports annotation consensus resolution tasks (disagreement resolution among multiple annotators to establish reliable labels).
- AKA: Annotator Agreement Method, Label Consensus Process, Multi-Annotator Resolution Method.
- Context:
- It can typically calculate Inter-Annotator Agreement using annotation agreement metrics.
- It can typically resolve Annotation Conflicts through annotation resolution rules.
- It can often weight Annotator Expertise in annotation consensus calculation.
- It can often identify Annotation Ambiguity for annotation guideline refinement.
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- It can range from being a Simple Annotation Consensus Method to being a Complex Annotation Consensus Method, depending on its annotation consensus algorithm.
- It can range from being a Majority-Vote Annotation Consensus Method to being a Weighted-Vote Annotation Consensus Method, depending on its annotation voting scheme.
- It can range from being a Binary Annotation Consensus Method to being a Probabilistic Annotation Consensus Method, depending on its annotation decision type.
- It can range from being a Domain-Agnostic Annotation Consensus Method to being a Domain-Specific Annotation Consensus Method, depending on its annotation domain knowledge.
- It can range from being a Real-Time Annotation Consensus Method to being a Batch Annotation Consensus Method, depending on its annotation processing timing.
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- It can support Dataset Creation through annotation quality assurance.
- It can enable Annotation Quality Metrics via annotation agreement measurement.
- It can integrate with Annotation Platforms for annotation workflow management.
- It can solve Annotation Consensus Resolution Tasks through annotation resolution algorithms.
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- Example(s):
- Legal Annotation Consensus Methods, such as:
- Medical Annotation Consensus Methods, such as:
- NLP Annotation Consensus Methods, such as:
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- Counter-Example(s):
- Single-Annotator Labeling, which lacks consensus requirements.
- Automated Labeling, which doesn't involve human agreement.
- Random Label Selection, which ignores annotator input.
- See: Consensus Building Method, Inter-Annotator Agreement, Annotation Quality Control, Label Adjudication Process, Crowdsourcing Quality Assurance, Cohen's Kappa.