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Related Questions
- What are some common pitfalls of using non-contextual word embeddings in natural language processing tasks?
- How do non-contextual word embeddings represent polysemous words, and what are the implications for word sense disambiguation?
- Can you provide examples of linguistic nuances that non-contextual word embeddings struggle to capture, such as idioms and figurative language?
- What are the key differences between non-contextual and contextual word embeddings in terms of their ability to capture word meanings in context?
- How do contextual word embeddings, such as those generated by BERT and RoBERTa, address the limitations of non-contextual word embeddings?
- Can you discuss the impact of non-contextual word embeddings on downstream NLP tasks, such as sentiment analysis and machine translation?
- What are some alternative approaches to word embeddings that aim to capture more nuanced and context-dependent word meanings?
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