What is Latent Semantic Analysis and how relevant is it to search engine optimization?
I personally think that LSA may be a key technology to improving the ability of current search technology to “understand” and answer questions asked in natural language. Here is information on LSA from Wikipedia: 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). LSA uses a term-document matrix which describes the occurrences of terms in documents; it is a sparse matrix whose rows correspond to documents and whose columns correspond to terms, typically stemmed words that appear in the documents. A typical example of the weighting of the elements of the matrix is tf-idf: the element of the matrix proportional to the number of times the terms appear in each document, where rare terms are upweighted to r
