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Related Questions
- What are the key differences between word2vec and GloVe in terms of word embedding techniques?
- How do word embeddings address the issue of word polysemy and homograph in NLP tasks?
- What are the advantages of using pre-trained word embeddings in NLP tasks compared to training from scratch?
- Can you explain the concept of context-dependent word embeddings and how they can improve NLP tasks?
- How do word embeddings handle out-of-vocabulary words and unseen words in NLP tasks?
- What are the implications of word embeddings on the performance of NLP tasks such as sentiment analysis and named entity recognition?
- How can word embeddings be fine-tuned for specific NLP tasks such as text classification and question answering?
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