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SEO, Latent Semantic Indexing and Natural Language Processing
By Michael Marshall


There has been much discussion lately about content relevancy and the relationship between Latent Semantic Indexing (LSI) and search engine optimization.  Some say LSI has nothing to do with how Google scores pages.  Others hail LSI as a powerful method of optimizing or theming your content and leads to marked improvement in natural search results.

Before proceeding, it is important to point out that there is much confusion and misconception in the SEO industry with regard to LSI.  Within the industry among the typical SEO professional, the term LSI is used quite pervasively but also quite loosely.  Almost any technology that a search engine might use to aid in the process of determining the meaning of content gets labeled as LSI.  The way most in the world of SEO use LSI has really become a catch all term for any technology used for the purpose of semantic analysis.

LSI is really a specific implementation of a whole family of approaches to semantic analysis, all of which might be said properly to fall under the area of Natural Language Processing (NLP).  Even the description which some critics of LSI provide, while accurate as far as they go, are only a description of one way of going about LSI which itself is only one method in the world of NLP.  Google could be using any of these other forms of LSI or even different approaches under NLP, any number of which still focuses on find meaning in a hidden or latent fashion.

One weakness of the form of LSI some critics describe is its scalability.  They are correct in that and for that reason a search engine like Google would most likely not be using that particular form of LSI.  However, they could be using some other form of LSI and most definitely do use one or more of the other many different approaches to semantic analysis within NLP.

Many offer experiments with search results to show that Google is not using LSI (as popularly described).  Off the top of my head, there are a few substantive reasons why such examples would not be a good test to determine whether LSI (as popularly described) was being used by Google.

  • Comparing results between singular vs. plural and different verb tenses has much more to do with linguistic stemming than it does with LSI.

  • Some analyses of the examples totally ignore how much other factors play a role in Google's algorithm and as such would obscure the influence of LSI if you look merely at the number of results returned and the ordering of those results.

  •   Not seeing the differences one expects in the number of results and similarity in the top 5 results only counts against the idea of Google using LSI if one presupposes that LSI is or must be the dominant factor in Google's algorithm.  Not even the most ardent proponents of LSI would hold to that.

Notwithstanding such the problems, I agree with some critics that Google probably does not use the form of LSI popularly described primarily because that particular form does have a serious scalability issue.  However, as I have said, that is not the only form of LSI and LSI is not the only approach to NLP that a search engine like Google would use.  

Google most definitely uses NLP technology in determining the meaning of content and therefore NLP has a significant relationship and importance to SEO.  Some awareness of and tools for NLP can greatly help in the optimization of web page content for relevance and can have a significant impact on performance in natural search results.

At the Marketing Pilgrim blog, an interview with Joe Hall and Marie-Claire Jenkins (C.J.), who is completing a PhD in Natural Language Processing (NLP) and Artificial Intelligence at the University of East Anglia, highlights the important relationship between SEO and NLP.  

Joe: Is there a relationship between SEO and NLP? If so, what is it?

CJ: There is indeed a relationship.  The search engines use words to assess what a web page is about, using NLP amongst other techniques. The content on a web page will help determine what the topic of the page is. 

Understanding the techniques used in NLP allows us to provide the best format and patterns for the search engine.


In fact I think that the entire site is affected because analysing a whole site, each page, helps to determine exactly what a site is about.  Seeing as NLP seeks to mimic human language understanding, using common sense is a good idea.  This is why search engines always recommend writing good relevant content.


It should be pointed out that SEM S.C.O.U.T., a tool promoted by Search Engine Workshops, does not use LSI
it uses another very powerful (well-tested) method among the many approaches to NLP.

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About Michael Marshall:

Michael has over 19 years experience in information technology covering a wide range of specialties including: web design, software engineering, e-commerce solutions, artificial intelligence, and Internet marketing. He is a member of the World Association of Internet Marketers and of SEO Professionals. He has degrees in Linguistics, Philosophy and Theology.

Michael is a contributing author to, the premier website for SEM professionals, and a contributor to “Building Your Business With Google for Dummies” by Brad Hill (Wiley Publishing). He is a frequent presenter at Ultra Advanced SEO Symposiums, a meeting of select masters of the search engine marketing industry, and has been invited many times to speak at Search Engine Workshops.  Michael is a licensed instructor at the North Carolina Search Engine Academy. He has also recently become an instructor on search engine technologies at the U.S. Patent Office.

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