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Related Questions
- Can keyword-based methods effectively capture nuanced and context-dependent negative examples in high-stakes applications?
- How might relying on keyword-based methods for negative example collection lead to biased or incomplete training datasets?
- What are the potential consequences of using keyword-based methods in situations where context and intent are crucial, such as in natural language processing and machine learning?
- Can keyword-based methods be used to capture rare or novel negative examples that may not be accounted for in traditional training datasets?
- How do keyword-based methods compare to more advanced techniques, such as entity recognition or contextual embedding, in terms of effectiveness for negative example collection?
- What are the potential risks of over-reliance on keyword-based methods for negative example collection in high-stakes applications, such as medical diagnosis or financial forecasting?
- Can keyword-based methods be adapted or combined with other techniques to improve their effectiveness in collecting negative examples in high-stakes applications?
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