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Related Questions
- What are some common pitfalls to avoid when designing data annotation tasks for nuanced human language?
- How can annotators' biases influence the quality of annotated data, and what strategies can be employed to mitigate this?
- What is the difference between data annotation and data labeling, and how do these tasks impact the accuracy of machine learning models?
- How can data annotation tasks be designed to capture subtle variations in language and behavior, such as sarcasm or idioms?
- What are the challenges of annotating data for tasks that require common sense or world knowledge, and how can these challenges be overcome?
- How can data annotation tasks be made more efficient without sacrificing quality, and what tools or techniques can be used to achieve this?
- What role does active learning play in data annotation, and how can it be used to select the most informative samples for annotation?
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