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Related Questions
- What are the key factors that influence the quality of data annotation in machine learning models?
- How does the type of data annotation (e.g. active learning, weak supervision) impact the model's ability to recognize nuanced language?
- Can you explain the relationship between data annotation and the model's understanding of context in language?
- What are some common pitfalls in data annotation that can lead to biased or inaccurate models?
- How does the level of annotation detail (e.g. fine-grained vs. coarse-grained) affect the model's ability to recognize nuanced language?
- Can you discuss the role of human annotators in data annotation and how their biases can impact the model's performance?
- What are some strategies for improving the quality of data annotation to enhance the model's ability to recognize nuanced language and context?
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