Latent Semantic Indexing Information

The latent semantic indexing information retrieval model builds the prior research of information retrieval. LSI uses the singular value decomposition, or SVD, to reduce the dimensions of the space and attempts to solve the problems that seem to plague the auto info retrieval system.

The LSI represents terms and documents in rich and high dimensional space. This allows the underlying semantic relationships that come between the terms and documents.

The latent semantic indexing model views the terms in a document as unreliable indicators of the information within the document. The variability of word choice obscures the semantic structure of the documents involved.

When the term-document space is reduced, the underlying semantic relationships are then revealed. Much of the noise is eliminated when the space is reduced.

Latent Semantic Indexing differs from other attempts at using reduced space models for info retrieval. LSI represents documents in a high dimensional space.

Both terms and documents are represented in the same space and no attempt is made to change the meaning of each dimension. Limits imposed by the demands of vector space are...