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Related Questions
- What are the key differences between traditional bag-of-words and word embeddings in topic modeling?
- How do word embeddings capture semantic relationships between words, and what impact does this have on topic modeling?
- Can you explain how word embeddings can help with topic modeling by reducing the dimensionality of the data?
- How do word embeddings influence the quality of topics discovered in topic modeling?
- What role do word embeddings play in handling out-of-vocabulary words in topic modeling?
- Can you discuss the challenges of using word embeddings in topic modeling, such as computational complexity and interpretability?
- How do word embeddings contribute to the robustness of topic modeling against noise and sparsity in the data?
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