Which method in NLP helps in reducing the dimensionality of word vectors while retaining most of the important information?
- Dimensionality Reduction
- Latent Semantic Analysis (LSA)
- Neural Networks
- Word Embedding
'Latent Semantic Analysis (LSA)' is a technique in NLP that reduces the dimensionality of word vectors while preserving important semantic information. It's a method used for semantic analysis and text retrieval.
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