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 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.
LSI is also useful for the automated specification of the categories for each 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 search can also be made in a particular language while the documents to be queried can be in another language.
LSI is also applicable for terms that are not exactly words, such as the DNA sequences of genes. Thus, biological and medical documents can easily be searched and categorized using LSI. 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.
