title: Large-scale latent semantic analysis.
abstract: Latent semantic analysis (LSA) is a statistical technique for representing word meaning that has been widely used for making semantic similarity judgments between words, sentences, and documents. In order to perform an LSA analysis, an LSA space is created in a two-stage procedure, involving the construction of a word frequency matrix and the dimensionality reduction of that matrix through singular value decomposition (SVD). This article presents LANSE, an SVD algorithm specifically designed for LSA, which allows extremely large matrices to be processed using off-the-shelf computer hardware.
authors.affiliation: Institute for Intelligent Systems, 365 Innovation Drive, Suite 303, Memphis, TN 38152, USA. aolney@memphis.edu
authors.forename: Andrew McGregor
authors.initials: AM
authors.lastname: Olney
mesh_terms: D000465:Algorithms; D032882:Comprehension; D019359:Knowledge; D012660:Semantics; D013223:Statistics as Topic