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Related Questions
- How do word embeddings, such as Word2Vec or GloVe, capture the nuances of word meaning through context?
- Can you explain how word embeddings account for the influence of surrounding words on a word's meaning?
- In what ways do word embeddings, like Skip-Gram or CBOW, incorporate contextual information to represent word meanings?
- How do word embeddings handle polysemy and homophony, where a word has multiple meanings or sounds similar to another word?
- Can you describe the role of context in word embeddings, such as the impact of word order, syntax, and semantics on word meaning?
- How do word embeddings, like FastText, handle out-of-vocabulary words and rare words in the context of a sentence or document?
- Can you explain how word embeddings can capture idiomatic expressions and figurative language, where word meanings are influenced by context?
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