1. An information retrieval method comprising the steps of: generating a term-document matrix to represent electronic information files stored in a computer system, each element in said term-document matrix indicating a number of occurrences of a term within a respective one of said electronic information files, wherein said term-document matrix includes a first partition, said first partition including entries representing at least a first version and a second version of at least one reference document within said electronic information files, wherein said first version of said reference document is in a first natural language and said second version of said reference document is a translation of said first version of said reference document into a second natural language, and wherein said term-document matrix further includes a second partition, elements in said second partition representing at least one target document within said electronic information files, wherein said target document is in one of the set of natural languages consisting of said first natural language and said second natural language; generating, responsive to said term-document matrix, a term-spread matrix, wherein said term spread matrix is a weighted autocorrelation of said term-document matrix, said term-spread matrix indicating an amount of variation in term usage in the information files and, also, the extent to which terms are correlated; receiving a user query from a user, said user query consisting of at least one term; in response to said user query, generating a user query vector, wherein said user query vector has as many elements as the rows of the term-spread matrix; generating, responsive to said user query vector, an error-covariance matrix, wherein said error-covariance matrix reflects an expected degree of uncertainty in the initial choice of keywords of said user; formulating, responsive to said term-spread matrix, error-covariance matrix, and user query vector, a constrained optimization problem, wherein the choice of a lambda value equal to a LaGrange multiplier value in said constrained optimization problem determines the extent of a trade-off between a degree of fit and the stability of all solutions to said constrained optimization problem; generating, responsive to said constrained optimization problem, a solution vector including a plurality of document weights, each one of said plurality of document weights corresponding to one of each said target documents, wherein each of said document weights reflects a degree of correlation between said user query and the corresponding one of said target documents; and providing an information response to said user reflecting said document weights, wherein at least one of said document weights is positive and at least one of said document weights is negative, wherein said positive document weights represent the relevance of selected ones of said target documents in said first natural language to said user query, and wherein absolute values of said negative document weights represent the relevance of selected ones of said target documents in said second natural language to said user query.