Latent Semantic Indexing And Search Engines Optimimization SEO

Latent Semantic Indexing And Search Engines Optimimization SEO

By Jose Nuñez

The closest search engines have come to​ actual applications of​ this technology so far is​ know as​ "Associative Indexing" and it​ is​ put in​ effect under Stemming,​ or​ the​ indexing of​ words on​ the​ basis of​ their uninflected roots (plurals,​ adverbs,​ and adjectival forms are reduced to​ simple noun and verb forms before indexing).

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).

Here are some quick facts about Latent Semantic Indexing:
1. LSI is​ 30% more effective than popular word matching methods.
2. LSI uses a​ fully automatic statistical method (Singular Value Decomposition)
3. it​ is​ very effective in​ cross-languages retrievals.
5. LSI can retrieve relevant information that does not contain query words.
6. it​ finds more relevant information than other methods.

Latent Semantic Indexing adds an​ important step to​ the​ document indexing process. in​ addition to​ recording which keywords a​ document contains,​ the​ method examines document collections as​ a​ whole,​ to​ see which others do contain some of​ those same words. LSI considers documents that have many words in​ common to​ be semantically close,​ and ones that have few words in​ common to​ be semantically distant. This method correlates surprisingly well with how a​ human being looking at​ content,​ classifies multiple documents.

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