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 information retrieval technique uses the natural language processing system known as latent semantic analysis or 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. 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. 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. Thus, it can extract the semantic content from the documents written in any language without the need to consult any thesaurus or dictionary. 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. 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.
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