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Related Questions
- What are some common biases that annotators may bring to labeling negative examples?
- How can annotators ensure that their labeling is consistent and reliable when dealing with negative examples?
- What are some common mistakes that annotators make when labeling negative examples in text classification tasks?
- How can annotators distinguish between genuine negative examples and those that are caused by noise or anomalies?
- What role does domain knowledge play in labeling negative examples, and how can annotators with limited domain expertise ensure accurate labeling?
- How can annotators balance the need for accurate labeling with the risk of introducing annotation bias?
- What are some strategies for detecting and correcting annotation errors that occur when labeling negative examples?
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