Definition: Latent semantic indexing (LSI) is a mathematical/statistical technique for extracting and representing the similarity of meaning of words and passages by analysis of large bodies of text. As explained by Michael Duz in What is Latent Semantic Indexing:
[blockquote shade=Y]LSI considers pages that have many words in common to be close in meaning (semantically close) and pages with a few words in common to be semantically distant. The result is an LSI indexed database with similarity values it has calculated for every content word and phrase.
In response to a query an LSI indexed database will return the pages it thinks will best fit the search terms. The LSI algorithm doesn’t understand anything about what the words mean and does not require an exact match to return useful results.
[blockquote shade=Y]LSI considers pages that have many words in common to be close in meaning (semantically close) and pages with a few words in common to be semantically distant. The result is an LSI indexed database with similarity values it has calculated for every content word and phrase.
In response to a query an LSI indexed database will return the pages it thinks will best fit the search terms. The LSI algorithm doesn’t understand anything about what the words mean and does not require an exact match to return useful results.
Pronunciation: lay-tint sum-ant-ic in-dex-ing
Also Known As: Latent semantic analysis (LSA)
Examples:
Latent semantic indexing (LSI) is a means of comparing documents for common words in order to determine if they deal with the same subject.
