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Related Questions
- What are the common techniques used by NER models to handle out-of-vocabulary (OOV) words?
- How do subword models like WordPiece and BPE handle OOV words in NER tasks?
- What is the impact of OOV words on the performance of NER models in terms of accuracy and F1 score?
- Can you explain the concept of subword embeddings and their role in handling OOV words in NER models?
- How do pre-trained language models like BERT and RoBERTa handle OOV words in NER tasks?
- What are the trade-offs between using subword models and traditional word-based models in handling OOV words?
- Can you discuss the impact of OOV words on the interpretability of NER models and their predictions?
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