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What is latent semantic indexing (LSI)?
What is latent semantic indexing (LSI)? Latent semantic indexing (LSI) is a complex system used by search engines to analyze the relationships between terms and concepts on a page in an effort to provide the most accurate and relevant search results.
Despite there not being any proof in terms of patents and research papers that LSI/LSA are important ranking-related factors, Google is still associated with Latent Semantic Indexing. One reason for this is Google’s 2003 acquisition of a company called Applied Semantics.
The difference between latent semantic indexing and word vectors is that LSI is a count-based model – it simply counts how many times words occur in a certain context. But word vectors are a prediction-based model – they attempt to predict the meaning of a word, based on vector analysis.
The SEO myth that Google uses LSI Keywords quite possibly originated from the popularity of phrases like “Semantic Analysis,” “Semantic Indexing” and “Semantic Search” having become SEO buzzwords, given life by Ask Jeeves’ semantic search technology and Google’s purchase of semantic analysis company Applied Semantics.