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Related Questions
- How do contextual word embeddings like ELMo and BERT handle polysemy, and what are the implications for natural language understanding?
- Can you explain the relationship between polysemy, homophones, and the limitations of word embeddings in handling them?
- How do contextualized word embeddings like RoBERTa and XLNet address the issue of polysemy and homophony in natural language processing?
- What are the implications of polysemy and homophones for machine translation, and how do contextual word embeddings help to mitigate these issues?
- Can you provide examples of how polysemy and homophones affect the performance of natural language processing tasks, and how contextual word embeddings can improve them?
- How do contextual word embeddings like Word2Vec and GloVe handle polysemy, and what are the limitations of these models in capturing nuanced meanings?
- What are the key differences between polysemy and homophony, and how do contextual word embeddings address these differences in natural language processing?
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