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A major barrier to successful retrieval from external sources (e.g., electronic databases) is the tremendous variability in the words that people use to describe objects of interest. The fact that different authors use different words to describe essentially the same idea means that relevant objects will be missed; conversely, the fact that the same word can be used to refer to many different things means that irrelevant objects will be retrieved. We describe a statistical method called latent semantic indexing, which models the implicit higher order structure in the association of words and objects and improves retrieval performance by up to 30%. Additional large performance improvements of 40% and 67% can be achieved through the use of differential term weighting and iterative retrieval methods.