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Related Questions
- How do contextualized embeddings capture the nuances of language and improve text classification accuracy?
- Can you explain how BERT and other contextualized embedding models can enhance sentiment analysis tasks?
- How do contextualized embeddings handle out-of-vocabulary words and improve question answering performance?
- What are some common applications of contextualized embeddings in natural language processing tasks?
- How do contextualized embeddings differ from traditional word embeddings in terms of their representation power?
- Can you provide examples of how contextualized embeddings can improve the performance of multi-class text classification tasks?
- How do contextualized embeddings handle tasks that require a deep understanding of context, such as idiomatic expressions and figurative language?
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