What is Latent Semantic Indexing?
Latent Semantic Indexing (LSI) is a mathematical method used by search engines to better understand the relationships between terms and concepts in a given body of text. It is used to supplement regular keyword-based searches so that users can find more relevant results based on the context of what they are searching for.
How Does LSI Work?
LSI uses a technique called singular value decomposition (SVD) to analyze the relationships between terms in a document. SVD looks at the context of words and phrases to determine how they are related to each other. This helps search engines better understand the intent behind a user’s query so that they can return more relevant results.
Examples of Latent Semantic Indexing
- A user searches for “apple pies” and the search engine returns results about recipes for apple pies.
- A user searches for “How to play the guitar” and the search engine returns results with tutorials and guides about how to play the guitar.
- A user searches for “Children’s books” and the search engine returns results about books for children.
LSI helps search engines understand the intent behind a user’s query so that they can return results that are more relevant to the user. By using LSI, search engines can better understand the relationships between terms and concepts in a document and determine which results are the most relevant to the user’s query.
Conclusion
Latent Semantic Indexing (LSI) is a mathematical method used by search engines to better understand the relationships between terms and concepts in a given body of text. It helps search engines better understand the intent behind a user’s query so that they can return more relevant results based on the context of what they are searching for.
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