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Related Questions
- What are some common pitfalls annotators might encounter when labeling negative examples with multiple possible interpretations?
- How can annotators use active learning to improve the accuracy of labeled data when dealing with uncertain negative examples?
- What are some strategies for annotators to employ when faced with ambiguous or unclear negative examples in machine learning datasets?
- Can you provide examples of how annotators can use context to disambiguate negative examples with multiple possible interpretations?
- What are some best practices for annotators to follow when labeling negative examples with multiple possible interpretations in machine learning tasks?
- How can annotators use human evaluation to resolve uncertainty when labeling negative examples with multiple possible interpretations?
- What are some common tools or techniques that annotators can use to help mitigate the uncertainty associated with labeling negative examples with multiple possible interpretations?
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