Latent Semantic Analysis (LSA) is a technique in natural language processing, particularly in information retrieval, that analyzes relationships between documents and terms to uncover latent semantic relationships. It works by creating a term-document matrix representing the frequency of terms in documents, then applies Singular Value Decomposition (SVD) to reduce the dimensionality and identify underlying patterns in the data. This allows for improved search results by finding documents that are semantically similar even if they don't share the exact same keywords.
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