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Related Questions
- What are the main purposes of contextualized embeddings and word embeddings in natural language processing?
- How do contextualized embeddings capture the nuances of language compared to word embeddings?
- What is the key difference in how contextualized embeddings and word embeddings handle polysemy and homographs?
- Can you provide examples of tasks where contextualized embeddings outperform word embeddings?
- How do contextualized embeddings and word embeddings differ in terms of their dimensionality and computational requirements?
- What are the implications of using contextualized embeddings versus word embeddings in downstream NLP tasks like sentiment analysis and text classification?
- Are there any specific applications or use cases where word embeddings are still preferred over contextualized embeddings?
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