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Related Questions
- Can you explain how word embeddings like Word2Vec and GloVe handle words with multiple meanings?
- How do word embeddings capture the nuances of polysemous words, such as 'bank'?
- What techniques are used to disambiguate words with multiple senses in word embeddings?
- Can you discuss the challenges of representing polysemous words in word embeddings and how they are addressed?
- How do word embeddings handle words that have different meanings in different contexts?
- Can you explain the concept of 'vector averaging' in word embeddings and how it relates to polysemous words?
- What is the impact of polysemy on the performance of natural language processing tasks that rely on word embeddings?
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