Latent Semantic Indexing Explained

If you plan on having a web page which you want many
people to visit, or if you are interested in knowing
just how your keyword searches turn up the results
that they do, then you will want to know a little more
about latent semantic indexing and just how it works.

Latent semantic indexing is a technique that projects
queries and documents into space with latent semantic
dimensions. In the latent semantic space, a query and
a document are similar even if they don’t share any of
the same terms if their terms are semantically
similar.

LSI is similarly metric to word overlap measures. LSI
has fewer dimensions than the original space and is a
method for dimensionality reduction.

There are several different mappings for latent
semantic indexing from high dimensional to low
dimensional spaces. LSI chooses the optimal mapping in
a sense that minimizes the distance.

Choosing the number of dimensions is a unique problem.
A reduction can remove much of the noise while keeping
too few dimensions may lose important information.

LSI performance is improved considerably after ten to
twenty dimensions and peaks at seventy to one hundred
dimensions. Then it slowly begins to diminish again.
There is a pattern of performance that is observed
with other datasets as well.

Latent semantic indexing is a creation gives us a
better gauge of the content of a web page to discover
the overall theme.

It is a more sophisticated measure of what sites and
their pages are all about. Webmasters don’t
necessarily need to redo all of their web pages
keywords, but it does optimize efforts and it does
mean depth needs to be a greater consideration.