Information Management Tools for Updating an SVD-Encoded Indexing Scheme

Lexical-matching methods for information retrieval can be inaccurate when they are used to match a user's queries. Typically, information is retrieved by literally matching terms in documents with those of the query. The problem is that users want to retrieve on the basis of conceptual topic or meaning of a document. There are usually many ways to express a given concept (synonymy), so the literal terms in a user's query may not match those of a relevant document. In addition, most words have multiple meanings (polysemy), so terms in a user's query will literally match terms in irrelevant documents. The implicit high-order structure of associating terms with documents can be exploited by the singular value decomposition (SVD). Latent Semantic Indexing (LSI) is a conceptual indexing technique which uses the SVD to estimate the underlying latent semantic structure of the word to document association. By computing a lower-rank approximation to the original term-document matrix, LSI dampen..