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Related Questions
- Can you explain the concept of semantic meaning in the context of word embeddings?
- How do word embeddings capture nuances of language, such as connotations and associations?
- What is the significance of word vectors in machine learning models, and how do they contribute to accurate predictions?
- How do word embeddings facilitate the understanding of word relationships, such as synonyms and antonyms?
- Can you discuss the importance of context in word embeddings, and how it influences the meaning of words?
- How do word embeddings handle polysemous words, which have multiple related meanings?
- What are the limitations of word embeddings, and how can they be improved to better capture the complexities of human language?
- Can you explain the difference between pre-trained word embeddings and custom-trained word embeddings, and when to use each?
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