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Related Questions
- What are the common techniques used to handle out-of-vocabulary words in named entity recognition models?
- How do NER models handle unknown words or phrases that are not present in their training data?
- What strategies can be employed to improve the performance of NER models on out-of-vocabulary words?
- Can you explain how subwording techniques, such as WordPiece or BPE, can help handle OOV words in NER?
- How do language models that use character-level embeddings, such as character n-grams, handle OOV words?
- What is the impact of OOV words on the overall performance of NER models, and how can it be mitigated?
- Are there any techniques that can be used to pre-train NER models to handle OOV words more effectively?
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