Application Research On Textual Information Search Based On LSA

Currently, most approaches to retrieving textual materials from scientific databases depend on a lexical match between words in users'request and those in or assigned to documents in a database.Because of the tremendous diversity in the words people use to describe the same document, lexical methods are necessarily incomplete and imprecise. A new method for automatic indexing and retrieval, Latent Semantic Analysis (LSA), is described. And the particular technique used is singular-value decomposition (SVD). The approach is to take advantage of implicit higher-order structure in the association of terms with documents ("semantic structure") by determining the SVD of large sparse term by document matrices. LSA is a completely automatic yet intelligent indexing method, widely applicable, and a promising way to improve users' access to many kinds of textual materials, or to documents and services for which textual descriptionsors are available.The approach of textual information search based on LSA starts with a large-scale matrix in which terms are related to documents.It constructs a semantic space automatically.It makes the user discover the related information.Even if the keywords of users'access are not in the document,they are still tight near the document only if they are in accord with the document in conception.So the position of terms and documents in the semantic space can be a semantic point.The information searching is to use the keywords of the users'access to identify a point in the semantic space.The documents near the point are arranged by the cosine value of the term's vector and the document's vector.They are returned to the user by the correlativity of the terms and documents. The article is divided into five chapters. Chapter 1 and Chapter 2 are mainly about the background and general research situations, basic principle and way of thinking of the Latent Semantic Analysis (LSA). Chapter 3 is mainly about the LSA key technique singular-value decomposition (SVD). Chapter 4 is mainly to precede a Chinese text sample with Latent Semantic Analysis (LSA) and analysis the test results. The last chapter is to introduce the other special application of LSA.