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Related Questions
- How does active learning improve the performance of named entity recognition models when fine-tuning on out-of-domain data?
- Can you discuss the effect of active learning on the transferability of NER models across languages with different linguistic and cultural characteristics?
- What are the key factors that influence the effectiveness of active learning for improving the generalizability of NER models to new domains and languages?
- How does active learning address the class imbalance problem in named entity recognition, and what are its implications for model generalizability?
- What are the trade-offs between active learning and other strategies for improving model generalizability, such as transfer learning and domain adaptation?
- Can you elaborate on the role of human-in-the-loop active learning in improving the generalizability of NER models to low-resource languages?
- What are the challenges and limitations of using active learning to improve the generalizability of NER models, and how can they be addressed?
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