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Related Questions
- What are the limitations of one-hot encoding in text representation?
- Can you explain the concept of word embeddings and their significance in natural language processing?
- How do word embeddings like Word2Vec and GloVe differ from one-hot encoding in terms of dimensionality and representation?
- What are the advantages of using word embeddings over one-hot encoding in text classification tasks?
- Can you provide examples of real-world applications where word embeddings have been successfully used?
- How do the choice of word embedding algorithm and hyperparameters impact the performance of a text classification model?
- What are some common techniques for fine-tuning word embeddings for a specific NLP task, and how do they improve performance?
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