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Related Questions
- What are the common techniques used to handle out-of-vocabulary (OOV) words in NLP pipelines?
- How does Infermatic.ai's NLP pipeline handle OOV words, and what are the potential consequences if not addressed?
- What are some strategies for mitigating the impact of OOV words on text classification performance?
- Can you explain the concept of subwording and its role in handling OOV words in NLP pipelines?
- How does the use of word embeddings, such as Word2Vec or GloVe, affect the handling of OOV words in NLP pipelines?
- What is the role of language models, such as BERT or RoBERTa, in mitigating the impact of OOV words on text classification performance?
- Are there any other techniques or approaches that can be used to handle OOV words in NLP pipelines, and what are their trade-offs?
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