Latent Semantic Analysis
Latent Semantic Analysis (LSA) is a natural language processing technique that analyzes relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. It uses singular value decomposition (SVD) to reduce the dimensionality of a term-document matrix, capturing hidden semantic structures and identifying patterns of word usage across documents. LSA is widely used for tasks like document similarity, information retrieval, and text classification by uncovering latent semantic relationships.
Developers should learn LSA when working on text-based applications that require understanding semantic meaning beyond simple keyword matching, such as search engines, recommendation systems, or automated essay grading. It is particularly useful for handling synonymy (different words with similar meanings) and polysemy (words with multiple meanings) in large text corpora, improving the accuracy of document clustering and topic modeling. However, it has been largely superseded by more advanced techniques like word embeddings (e.g., Word2Vec) and transformer-based models (e.g., BERT) for many modern NLP tasks.