Features of Latent Semantic Indexing
Latent semantic indexing (LSI) is an information retrieval strategy that applies a certain mathematical technique to determine the concept or idea that is found in a body 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 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 possibilities are the common reasons for the irritating appearance of documents for a particular query even if they are not relevant and the absence of documents that should have been included.
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. Therefore, it is able to determine the semantic content of documents in any language without requiring a dictionary or thesaurus. 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. 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.