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Related Questions
- What strategies can annotators use to reduce ambiguity in labeling negative examples across different contexts?
- How can annotators ensure inter-annotator agreement when labeling negative examples in a large dataset?
- What are some best practices for annotators to follow when labeling negative examples in a multi-scenario setting?
- Can you provide examples of common pitfalls that annotators should avoid when labeling negative examples across different contexts?
- How can annotators use active learning to improve the consistency of negative example labeling?
- What role does annotation guidelines play in ensuring consistency in labeling negative examples across different contexts?
- Can you discuss the importance of validation and verification in maintaining consistency in negative example labeling across different scenarios?
- What techniques can annotators use to detect and correct inconsistencies in negative example labeling?
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