Probabilistic Latent Semantic Indexing Probabilistic latent semantic indexing is an automated document that is based on a statistical latent model for factor analysis of data. It is an approach to automatic indexing and information retrieval, which overcomes problems by mapping documents and terms to a LSI space. Although LSI has been applied with much success in different domains, it has a number of deficits. These are due to its statistical foundation. One typical scenario of human and machine interaction in the information retrieval is by using natural language queries. A natural language query provides a number of key words and expects the system to pull up all relevant articles or pages that include the key words. But the systems are not infallible. Most search engines will come up with a big number of unrelated searches. This is usually due to a key word having two meanings or where an idea, or multiple uses of key words comes up with many words. These problems are called polysymy and synonymy. But many of the newer, better-derived latent semantic indexing programs have reduced much of this unneeded search results. Many retrieval methods are based on simple word matches. It is well known that literal term matching has severe drawbacks. But newer LSA's are more specific in their searching and do a much better job than what the old search queries would give for results. The standard procedure for maximum likelihood estimates a latent variable model as the expectation.
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