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Related Questions
- What are the key differences between t-SNE and UMAP in reducing dimensionality for word embeddings?
- How do t-SNE and UMAP preserve the local and global structure of high-dimensional word embeddings?
- Can t-SNE or UMAP be used to visualize the semantic relationships between words in a word embedding space?
- What is the effect of hyperparameter tuning on the performance of t-SNE and UMAP in dimensionality reduction?
- Can t-SNE or UMAP be used to reduce the dimensionality of contextualized word embeddings, such as those from BERT?
- How do t-SNE and UMAP compare to other dimensionality reduction techniques, such as PCA or LLE, in the context of word embeddings?
- Are there any limitations or challenges associated with using t-SNE or UMAP to improve interpretability of word embeddings?
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