The closest search engines have come to actual applications of this technology so far is know as “Associative Indexing” and it is put in effect under Stemming, or the indexing of words on the basis of their uninflected roots (plurals, advers, and adjectival forms are reduced to simplified noun and verb forms before indexing).
Latent Semantic Analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, invented in 1990  by Scott Deerwester, Susan Dumais, George Furnas, Thomas Landauer, and Richard Harshman. In the context of its application to information retrieval, it is sometimes called Latent Semantic Indexing (LSI).
Here are some quick facts about Latent Semantic Indexing:
1. LSI is 30% more effective than popular word matching methods.
2. LSI uses a fully automatic statistical method (Singular Value Decomposition)
3. It is very effective in cross-languages retrievals.
5. LSI can retrieve relevant information that does not contain query words.
6. It finds more relevant information than other methods.
Latent Semantic Indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines document collections as a whole, to see which others do contain some of those same words. LSI considers documents that have many words in common to be semantically close, and ones that have few words in common to be semantically distant. This method correlates surprisingly well with how a human being looking at content, classifies multiple documents.
Latent semantic indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines the document collection as a whole, to see which other documents contain some of those same words. LSI considers documents that have many words in common to be semantically close, and ones with few words in common to be semantically distant. This simple method correlates surprisingly well with how a human being, looking at content, might classify a document collection. Although the LSI algorithm doesn’t understand anything about what the words mean, the patterns it notices can make it seem astonishingly intelligent.
When you search an LSI-indexed database, the search engine looks at similarity values it has calculated for every content word, and returns the documents that it thinks best fit the query. Because two documents may be semantically very close even if they do not share a particular keyword, LSI does not require an exact match to return useful results. Where a plain keyword search will fail if there is no exact match, LSI will often return relevant documents that don’t contain the keyword at all.
Advantages & SEO Benefits of LSI Keywords
1. To make your article more keyword rich, you can add more long tail & LSI keywords in the post. LSI & long tail keywords make the article more natural & increases readability.
2. Keyword density optimization can be done with the help of LSI keywords. Also at the same time you can optimize the article for multiple search terms.
3. If the article is stuffed with the same keyword over and over again, there is a chance that your post may get penalized. To fix this problem you can use various LSI keywords in your article which also will help you to rank for multiple queries.
4. Search engines like Google are in the process to improve their algorithm. If someone types any search query, it looks for the most relevant content for that particular search term. It uses LSI keywords to find whether a post or a webpage is relevant or not. To say it in a simple manner, using the latent semantic keywords increases the ranking in different search engines.