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Related Questions
- What are the primary advantages of contextual word embeddings over non-contextual word embeddings in terms of capturing linguistic nuances?
- How do contextual word embeddings, such as ELMo and BERT, improve the representation of words in different contexts?
- Can you explain the difference in capturing idiomatic expressions, colloquialisms, and figurative language between contextual and non-contextual word embeddings?
- How do contextual word embeddings handle polysemy and homophones, and what are the implications for natural language processing tasks?
- What are the limitations of non-contextual word embeddings, such as Word2Vec, in capturing linguistic nuances, and how do they compare to contextual word embeddings?
- Can you discuss the impact of contextual word embeddings on tasks such as sentiment analysis, named entity recognition, and machine translation?
- How do contextual word embeddings, such as transformers, leverage attention mechanisms to capture contextual information and improve linguistic nuance?
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