Also known as Latent Semantic Analysis, LSI is a natural language processing (NLP) technique developed in the 1980s to identify the contextual relationship between words.
In short , latent semantic indexing "finds the german whatsapp number hidden (latent) relationship between words (semantics) in order to improve the understanding of the information (indexing)."
Understanding the content of a page can help determine its topic, based on how language is used.
In theory, it makes sense that Google could use this technique to understand synonyms in particular, and that their use in a piece of content helps the search engine better understand the topic.
The controversy surrounding the use of the phrase “LSI keywords” is not so much about the importance of writing content that speaks to what you would expect to see on a page about a given topic, but rather that there is no evidence that Google uses latent semantic indexing for this purpose.
LSI is now an obsolete technology, developed for smaller document sets, not the entire Web.
That said, semantics is the key to understanding how search engines understand a content page. There is no denying that writing in depth about a topic should involve the use of semantically related words and synonyms.
Get semantically related keywords
But be careful not to decontextualize the matter. Using related keywords in your content is important: doing so allows you to launch a page that fully covers a topic, and this is the key to success.
Calling them LSI keywords is not necessarily correct, but it is the underlying idea that matters.
And while LSI keywords don’t really exist, understanding latent semantic indexing can definitely help you create better content that ranks well in SERPs.
What is Latent Semantic Indexing?
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