A theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented to explain how people know as much as they do with the little information they get. LSA suggests that some domains of knowledge contain vast numbers of weak intercorrelations that amplify learning by inference. (SLD)

Offers an introduction to the theory and implementation of Latent Semantic Analysis (LSA), a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. Gives an overview of applications and modeling of human knowledge to which LSA has been applied. (SR)

Illustrates use of Latent Semantic Analysis (LSA) for predicting coherence through reanalyzing two studies that manipulated coherence of texts and assessed readers' comprehension. Finds that LSA predicts effects of text coherence on comprehension. Notes that LSA can be applied as an automated method that produces coherence predictions similar to…