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Related Questions
- What are some strategies for annotators to use when labeling negative examples?
- How can annotators avoid over-labeling negative examples in a dataset?
- What techniques can annotators use to ensure they are not under-labeling negative examples?
- What are some common pitfalls that annotators should avoid when labeling negative examples?
- How can annotators use context clues to determine whether an example is negative or not?
- What is the importance of balancing positive and negative examples in a dataset?
- Can annotators use active learning techniques to reduce the need for manual labeling of negative examples?
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