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Related Questions
- Can you explain how word embeddings update their vectors when encountering new words or terms in a text?
- How do word embeddings handle out-of-vocabulary (OOV) words and their impact on the overall model performance?
- What techniques are used to fine-tune word embeddings for specific tasks or domains, such as sentiment analysis or named entity recognition?
- How do word embeddings learn to capture nuances of word meanings, such as context-dependent or figurative language?
- Can you discuss the role of word embeddings in handling polysemous words and their impact on downstream tasks?
- How do word embeddings adapt to changes in language usage or trends over time, such as new slang or emerging concepts?
- What are some common challenges in updating word embeddings for new words or concepts, and how can they be addressed?
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