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Related Questions
- Do word embeddings like Word2Vec and GloVe capture the nuances of polysemy in a language model?
- How do word embeddings deal with homographs that have different meanings in various contexts?
- Can you explain how word embeddings handle the multiple meanings of a single word in a sentence?
- How do techniques like polysemous vector space models improve the capture of semantic relationships?
- What are the implications of word embeddings' limitations in handling polysemy and homographs on downstream NLP tasks?
- Can you provide examples of words that are polysemous and how word embeddings represent them?
- How do recent advancements in word embeddings, such as contextualized embeddings, address the challenges of polysemy and homographs?
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