Latent Semantic Indexing - What Is It?
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 information retrieval technique uses the natural language processing system known as latent semantic analysis or 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. 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 advantage of LSI is that it is applicable for all languages because it is entirely based on mathematical analyses. 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. 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
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