Latent Semantic Analysis (LSA) is applied by taking millions of web pages, where the search engines can learn which words are related and which noun concepts relate to one another. Searh Engines are considering related terms and recognizing which terms that frequently occur together, maybe on the same page, or in close enough proximity. So it is mainly used for language modeling or most other applications.
Part of this process involves looking at the copy content of a page, or included on the links, and looking through the ways on how they are related. Latent Semantic Analysis (LSA) is based on the well known Singular Value Decomposition Theorem from Matrix Algebra but applied to text. That is why some of the semantic analysis that is done at the page content level it may also be done on the linkage data.
LSA represents the meaning of words as a vector, thus calculating word similarity. Iit has been very efficient to that purpose, and is still used. Regarding text for this application, is considered linear. This makes LSA slow due to using a matrix method called Singular Value Decomposition to create the concept space. But it does only address semantic similarity...
To view and download this full PLR article, you must be logged in. Registration is completely free. Once you create your account, you will be able to browse, search & downlod from our PLR articles database of over "1,57,897+" on 1,000's of niches and 200+ categories without paying a penny. Click here to signup...
