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Related Questions
- How do out-of-vocabulary (OOV) words affect the accuracy of named entity recognition (NER) models?
- What is the typical impact of OOV words on the F1 score of NER models?
- Can you explain how NER models handle OOV words and their effect on model performance?
- How do different NER architectures, such as BERT and LSTM, handle OOV words and their impact on accuracy?
- What are some strategies to mitigate the impact of OOV words on NER model performance?
- Can you provide examples of how OOV words can affect the accuracy of NER models in different domains, such as news articles and social media?
- How do OOV words impact the performance of NER models in low-resource languages?
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