Google seems eerily good at understanding language these days, but it is not because the search engine is growing a brain. Semantic searches have become ever-more sophisticated, and Google is “learning” how language is put together. Understanding how semantics affects Google’s search capacities is something that will benefit you as you construct search query terms and decide on keywords.
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How Do Phrase Lists Work?
Language is extremely dynamic, so it is difficult to build an exhaustive lexicon for search engines. It is actually difficult to do it with a dictionary, as well. Because language is constantly changing, manually building a phrase list is probably not feasible.
Search engines instead rely on algorithmic processes for maintaining data on relationships between words and phrases. It is not simple or perfect, but Google gets it right most of the time.
How Are Phrase Lists Built?
In some ways, search engines “learn” language the same way humans do. As they crawl millions of websites, they pick up on patterns that repeat themselves often. Humans also learn language this way–by absorbing repetitive groups of sounds that come to have discrete meaning.
How does the search engine know if these are “real” words, however? After all, just because the phrase “upside-down grape hairlessness” appears three times does not make it a sensible phrase. Search engines look at likelihood based on repetition in a variety of contexts. Humans can scale the probabilities for a group of words and give the search engine a parameter within which to work.
The Semantics Issue
So far, all the search engines are doing is mathematical. However, semantics brings a very interesting twist to the process. Understanding phrase relationships is ultimately much more important than identifying potential phrases.
The search engine algorithms are obviously becoming more complex in this regard. For example, the relationship between “fall season” and “pumpkin pie” is obvious to most humans, but now a search engine may be advanced enough to note the relationship, as well. This is often accomplished through the use of a co-occurrence matrix that demonstrates relative relationships between repetitive sets.
What Does This Mean for SEO?
Obviously if search engines can now recognize phrase relationships, this changes the whole context of building search terms. Instead of focusing on every possible permutation of a keyword phrase, SEO managers can now concentrate on more important issues, such as content relevancy.
There are two major ideas to take away from phrase-index-drive SEO strategy. First, content relevancy becomes supremely important. If terms have no relevance to the content, it is much easier for a sophisticated search engine to identify spam material. Second, the density of keywords becomes far less important than their relationship to the words around them.
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