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Related Questions
- How do word embeddings like Word2Vec and GloVe represent words with multiple meanings?
- Can you explain the challenges of capturing homophones and homographs in word embeddings?
- How do different word embedding algorithms handle words with multiple senses?
- What techniques are used to disambiguate homophones and homographs in word embeddings?
- Can you discuss the impact of homophones and homographs on the performance of natural language processing tasks?
- How do word embeddings handle words with multiple related meanings, such as 'bank' (financial institution) and 'bank' (riverbank)?
- What are some common methods for improving the handling of homophones and homographs in word embeddings?
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