Latent Semantic Indexing (LSI) is a mathematical technique used in natural language processing and information retrieval to identify patterns and relationships between words in a large corpus of text data. LSI goes beyond keyword matching by analyzing the contextual meaning of words.

Key points about LSI:

  • LSI uses singular value decomposition (SVD) to reduce the dimensions of the word-document matrix, revealing latent semantic structures.
  • It helps search engines understand the context and meaning of words, improving the relevance of search results.
  • LSI identifies synonyms and related terms, allowing search engines to return relevant pages even if they don’t contain the exact keywords used in the search query.
  • In the context of SEO, using LSI keywords (words semantically related to the main keyword) can help search engines better understand the content and context of a webpage.
  • LSI is not a specific algorithm used by search engines but rather a general concept in information retrieval that search engines may incorporate in their ranking algorithms.

By understanding the contextual relationships between words, LSI helps improve the accuracy and relevance of search results and enables more effective information retrieval.