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 looks at the various relationships between a number of documents and the body of text found in them and establishes a group of concepts for these documents. Therefore, LSI allows the inclusion of various documents as the results of a certain query even if they do not contain the exact words or phrases that have been typed in by the searcher.
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.
Another application for LSI is the automation of the categorization of a document. It utilizes sample documents to determine the conceptual foundations of every category. It then compares the concepts found in the documents to those that are present in the example documents and assigns a category for a document when there are similarities in its concepts with those of the example documents for that category.
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.
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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