marketraise int
Joined: 12 Mar 2007
Posts: 41
|
Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms.LSA uses a term-document matrix which describes the occurrences of terms in documents; it is a sparse matrix whose rows correspond to terms and whose columns correspond to documents , typically stemmed words that appear in the documents.This matrix is also common to standard semantic models, though it is not necessarily explicitly expressed as a matrix, since the mathematical properties of matrix are not always used.LsA is basically used for the following purposes1)Compare the documents in the concept space 2)Find similar documents across languages, after analyzing a base set of translated documents 3)Find relations between terms 4)Given a query of terms, translate it into the concept space, and find matching documents.After the construction of the occurrence matrix, LSA finds a low-rank approximation to the term-document matrix. The consequence of the rank lowering is that some dimensions are combined and depend on more than one term.LSA has two drawbacks:a)The resulting dimensions might be difficult to interpret b)The probabilistic model of LSA does not match observed data.It is implemented by typically computed using large matrix methods but may also be computed incrementally and with greatly reduced resources via a neural network-like approach which does not require the large, full-rank matrix to be held in memory.
_________________
MarketRaise Corp.
www.marketraise.com
WEB-DEVELOPMENT,SEO MARKETING,IT OUTSOURCING
|
 
|
|
|
