Latent Semantic Indexing
Latent Semantic Indexing (LSI) is a technique used in natural language processing and information retrieval to discover the relationships between terms and concepts in a body of text. It helps search engines understand the context of a document and deliver more relevant search results to users.
LSI works by analyzing the relationships between terms in a document and creating a mathematical representation of these relationships. This allows search engines to group related terms together and understand the underlying concepts in a document, even if they are not explicitly mentioned.
For example, if a document contains the terms „dog“ and „puppy“, LSI can infer that these terms are related because they both refer to young dogs. This allows the search engine to deliver results for both terms when a user searches for „dog“ or „puppy“.
LSI is particularly useful for improving the accuracy of search results and reducing the impact of keyword stuffing, where websites use irrelevant keywords to manipulate search rankings. By understanding the context of a document, search engines can deliver more relevant results to users and improve the overall search experience.
- LSI helps search engines understand the context of a document
- It groups related terms together based on their relationships
- LSI reduces the impact of keyword stuffing and improves search accuracy
Learn more about Latent Semantic Indexing on Wikipedia