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Related Questions
- What are the key differences between contextual and non-contextual word embeddings in terms of capturing linguistic nuances?
- How do non-contextual word embeddings, such as word2vec, handle polysemy and homophones?
- Can non-contextual word embeddings capture subtle differences in word meanings, such as connotation and implication?
- How do non-contextual word embeddings compare to contextual models like BERT in terms of capturing idiomatic expressions?
- What are the limitations of non-contextual word embeddings in capturing complex linguistic phenomena, such as metaphor and metonymy?
- Can non-contextual word embeddings be fine-tuned to improve their ability to capture nuances in language?
- How do non-contextual word embeddings affect the performance of downstream NLP tasks, such as named entity recognition and sentiment analysis?
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