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Related Questions
- How do NLP pipelines typically handle out-of-vocabulary words in text classification tasks?
- What are some common techniques used to mitigate the impact of typos in text classification models?
- Can you explain the concept of subwording and its application in handling unknown words in NLP pipelines?
- How do machine learning models handle unseen words or typos during inference, and what are the potential consequences?
- What role does the choice of tokenizer play in handling unknown words and typos in NLP pipelines?
- Are there any techniques or tools available to automatically correct typos in text data before feeding it into an NLP pipeline?
- How does the performance of NLP models degrade when encountering unknown words or typos, and what can be done to improve robustness?
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