Table Of Contents
-> Latent Semantic Indexing and Latent Semantic Analysis:
Differences & Similarities
-> Latent Semantic Analysis Functionality
-> The Importance of Latent Semantic Analysis
-> Latent Semantic Analysis’ continued importance
-> The Future of Keyword-Driven Web Pages
-> Latent Semantic Indexing and SEO
-> Latent Semantic Indexing and the Future of Your Website
-> Latent Semantic Indexing and Search Engine Results
-> Latent Semantic Indexing Websites
-> Latent Semantic Indexing and Your Business
-> Build Your Website using Latent Semantic Analysis
-> Tips for Effective Keyword Research using LSI
-> Inbound and Outbound Links for SEO
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I. Latent Semantic Indexing, Latent Semantic Analysis and what sets them apart from one another. What are the differences?
LSI which stands for Latent Semantic Indexing and LSA which stands for Latent Semantic Analysis are two terms which are essentially used interchangeably, although there is a marked difference in what each term actually means and how the processes involved with it are actually applied to websites and search engine results.
Latent Semantic Analysis or LSA is a technique that is used in natural language processing in order to analyze the relationships that exist between a specific set of documents and the terms that are contained within those documents by producing a concept set that directly relates to both the terms and the documents. When Latent Semantic Analysis or LSA is used in the context for information retrieval applications, Latent Semantic Indexing or LSI is the term that is normally used.
There are a wide variety of different applications which can involve Latent Semantic Indexing or Latent Semantic Analysis, including the comparison of documents within a concept space for document classification and data clustering and locating documents which are similar across different languages after analyzing translated documents through cross language retrieval. Latent Semantic Analysis can also be used in order to find relationships that exist between certain terms through synonymy and polysemy, and can translate a query of terms into a concept space, locating matching documents accordingly through information retrieval. In natural language processing, synonymy and polysemy have become fundamental problems.
Synonymy is a phenomenon that exists when several different words are used in order to describe the same idea. Therefore, a specific search engine query may actually fail at retrieving relevant documents if they do not contain the words that are contained within the query. Polysemy is a phenomenon that exists when the same word has several different meanings. Therefore, a specific search engine query may retrieve many documents that are irrelevant while still containing the desired words, because they are appearing using the wrong meaning. For example, there are many different types of people who would do a search for the word ‘tree’, including botanists, genealogists and computer scientists. But obviously the desired results are very different for each individual search.Other Details
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