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Article Writing and the Use of LSI - Latent Semantic Indexing
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LSI, or latent semantic indexing, is used by Google to assess the relevance of your web page content to specific search terms. By using that concept properly, you can significantly improve your search engine listing and gain more traffic to your website.

Let me state right now that there can be no such thing as LSI-compliant content: there is no such thing for the simple reason that LSI is not a tool but an analytical concept, and you can't use an analytical concept to design web page content. It is correctly referred to as LSA (Latent Semantic Analysis), though the reason that Google refers to is as LSI is because it is used to index your web pages in their index.

You can. however, utilise the concept when composing your web page content by making sure that you use language that enables the Google algorithm to match your content with the search term used by Google's customer looking for information. In providing a form of explanation of how this is achieved, therefore, I apologise to those that know more about the theory and real meaning of the term LSA who rightly state that a page cannot comply with requirements of LSI, or even that LSI can be used in any way on a website.

Here's an example of what I mean by contextual relevance. It is a simple example, and perhaps contrived, but it does explain the concept, and also how you can apply it to improve the listing position of any web page you apply it to. Let's take the example of somebody using Google to find out 'how to write'.

How to write what? If you use any popular keyword anaylsis tool you will find that the term' how to write' is commonly used and can be used as the primary keyword on a web page focused on the topic of writing. In fact, when you use a keyword on your website, you are doing no more than letting Google know what your page is about, and you are hoping that the word or phrase you chose matches that used by a search engine user.

The main question, however, is 'how to write' what? The visitor to your page might be looking for help on how to write content for their web pages, magazine articles, articles for newspapers or even how to write novels, and every one of these would need a different approach. What the concept of LSI does is to look at the text in close proximity to the keyword and use that to determine its meaning. Thus, 'newspaper', 'novel'. 'Submission', 'web page' for instance, can be used in the concept of LSI to index your page in the right category for its actual content, so that when your page is listed it offers the Google user the information they are looking for.

The real customer of a search engine is the person using it to find information - to carry out a search. You are not Google's customer, and neither are all those advertisers using AdWords, because without the person carrying out the search Google would not exist. Google's use of LSI makes sure the company is providing its customers with as good as service as it can.

My website offers different examples of contextual relevance, such as how the keyword 'the history of locks' can one of three different things that could not be distinguished by keyword-stuffing. This applies equally to the term German Shepherd, that is also used as an example. Are you looking for details of how Germans tend to their sheep, or on how you can train your dog? Does the word 'Alsatian' refer to a dog or a resident of Alsace?

Google started using this LSI concept with their AdSense program, where customers place advertising blocks on their web pages for Google to fill with adverts related to their page content. However, people began making thousands from AdSense by automatically generating pages of meaningless text into which any keyword could be inserted and make sense to the reader, thus:

"Information on KW is available internet-wide, and KW is used as a keyword in many searches on Google. There is a large number of websites providing information on KW, and an equally large number of people using KW as their keyword in their Google search."

That's just a brief example, but you could use any keyword you can think of as 'KW', and entire web pages would be generated by software designed simply to enter a keyword of your choice in place of KW. Many of these sites received top listings because the algorithms were predominantly keyword orientated, and endless repetition of a keyword would almost guarantee a high listing. I did it myself: I could generate 5,000 pages for AdSense from a list of 5,000 keywords just from one template into which every keyword could be inserted and still make grammatical sense. It doesn't take too many clicks per page to make a good living from that many pages.

Google stopped it all with LSI. They applied the concept of latent semantic indexing used in their AdSense program to their search engine algorithm, and overnight websites with no text related in context to the keyword were dropped. People's income was decimated and their businesses destroyed - and likely quite rightly, although it wasn't them that made the rules and they had simply used them as they stood at the time.

Hence, for a high listing for the term 'article writing', it would be necessary to make it obvious to the algorithm to what form of articles you are referring. In the case of the German shepherd, you would have to include words such as sheep or wool to impart the meaning of your content to Google. That is the way LSI works, and even though many are still not sure how latent semantic indexing really works, all you need to is to write naturally, without trying to use the keywords too often, and you will achieve good results.

For more detailed information on how to use the concept of LSI to improve your listing position on Google, check out Pete's web page Article Services - LSI where you will find details of exactly what can be done to get higher listings and more traffic by using latent semantic indexing to improve the relevance of your pages to your keyword.

Article Source: http://EzineArticles.com/?expert=Peter_Nisbet

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Article Submitted On: October 30, 2009



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