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Related Questions
- How do large language models handle unknown or unseen words during training?
- What techniques are used to handle out-of-vocabulary (OOV) words in named entity recognition (NER) models?
- Can you explain how NER models adapt to new entities or words that are not present in their training data?
- How do NER models handle entities that are misspelled or have variations in their names?
- What is the impact of out-of-vocabulary words on the performance of NER models?
- Can you describe the trade-offs between training a NER model on a large dataset versus a smaller dataset with more diverse entities?
- How do NER models handle entities that are context-dependent or have multiple possible interpretations?
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