Latent Semantic Indexing (LSI) is a technique used in information retrieval that is more effective than word matching methods. LSI uses statistical modeling to analyze relationships between documents and terms and represents words and documents as vectors in a multidimensional space to capture the contextual usage of words. Some benefits of LSI include its ability to retrieve relevant documents that do not contain the exact search terms and finding more relevant information than other indexing methods.

1. Latent Semantic Indexing and
Search Engines Optimimization
(SEO)

3. 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, adverbs, and adjectival forms
are reduced to simple noun and verb forms before
indexing).

4. Latent Semantic Analysis (LSA) is a technique in natural
language processing, in particular in vectorial semantics,
invented in 1990 [1] 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).

5. Here are some quick facts about Latent Semantic Indexing:

6. 1. LSI is 30% more effective than popular word matching
methods.

7. 2. LSI uses a fully automatic statistical method (Singular
Value Decomposition)

8. 3. It is very effective in cross-languages retrievals.

9. 5. LSI can retrieve relevant information that does not
contain query words.

10. 6. It finds more relevant information than other methods.

11. 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.

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