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Latent semantic indexing or LSI is an advanced technique for information retrieval that uses a mathematical procedure to extract the idea or concept from a group of text.  This is an information retrieval method that utilizes the natural language processing method of latent semantic analysis (LSA).  LSA examines the interrelationships between various documents and the words that they contain and then creates a set of ideas for these documents.  With LSI, the documents that are presented in response to a particular query do not necessarily have the exact words or phrases that the searcher has keyed in.

LSI provides the solution to two main problems with the common Boolean search method.  One is that several words can have similar meanings and another is that a particular word can have several 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. 

Another application for LSI is the automation of the categorization of a document.  For this method, it uses sample documents as the foundation for understanding the concepts embodied by each 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.  To illustrate, LSI can be used to determine the categories for genes by looking at the biological information available in the titles and abstracts found in biological databases.

LSI can also easily adapt itself to any modifications in the terminology and it can still function in spite of the presence of misspelled words, unreadable characters, typographical errors, and other types of noise in documents.  Thus, LSI could be very helpful for text that have been obtained from images through optical character recognition and through speech-to-text conversion technologies. Check out http://ArticlesOnTap.com for more on this.

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