LSI offers a remedy to two of the most annoying deficiencies of the usual Boolean search technique.  These are the possibilities that a word has more than one meaning and several words having the same meanings.  These two problems are the usual reasons for documents or web pages appearing in the search results even if they are not relevant to the topic while certain web pages and documents that should have been included are absent. 

LSI is also useful for the automated specification of the categories for each document.  It utilizes sample documents to determine the conceptual foundations of every category.  The technique used is to compare the ideas that are found in the example documents for each category with those that can be extracted from the document to be classified and placing it in those categories where the concepts match. 

Another benefit offered by LSI is that it can be used for any language because it is purely dependent on mathematical formulas.  Thus, it can extract the semantic content from the documents written in any language without the need to consult any thesaurus or dictionary.  The query can also be made in one language while the documents are written in a different language. 

LSI can even be applied for those terms that are not words but are codes, such as the nucleotide sequences for various genes.  For example, LSI is capable of classifying genes based on the biological information that could be extracted from the abstracts and titles of biological databases.

It is also capable of automatically adjusting itself to changing terminology and it is hardly affected by unreadable characters, typographical mistakes, misspelled words, and other kinds of noise in documents.  Therefore, LSI is applicable for a body of text that is the result of speech-to-text conversion programs and those that have been extracted from images by optical character recognition software. Check out http://ArticlesOnTap.com for more on this