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Related Questions
- What are some common pitfalls that annotators may encounter when labeling negative examples?
- How can annotators ensure consistency in labeling negative examples across different datasets?
- What techniques can annotators use to reduce the impact of personal biases when labeling negative examples?
- Can you describe some strategies for handling ambiguous or unclear negative examples?
- How can annotators verify the accuracy of their labels for negative examples?
- What role does active learning play in mitigating the impact of annotator variability in labeling negative examples?
- Can you explain how to use gold-standard datasets to evaluate and improve annotator performance on negative examples?
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