Latentti semanttinen indeksointi

Latent semantic indexing (LSI)

Latent semantic indexing is a technique used in natural language processing and information retrieval to analyze the relationships between a set of documents and the terms they contain. The goal of LSI is to uncover the latent (hidden) semantic structure within the documents, allowing for more accurate and efficient information retrieval.

One of the key components of LSI is the creation of a term-document matrix, where each row represents a term and each column represents a document. By applying singular value decomposition (SVD) to this matrix, LSI can identify patterns of term co-occurrence and similarity in the documents.

For example, consider a set of documents about cats and dogs. LSI might discover that the terms ”feline” and ”canine” are closely related, even though they do not appear together in any single document. This allows for more nuanced and context-aware search results.

  • LSI can improve search engine performance by accounting for synonymy and polysemy in queries.
  • LSI has applications in document clustering, text summarization, and machine translation.
  • LSI is a foundational technique in the field of computational linguistics and information retrieval.

For more information about latent semantic indexing, you can visit the Wikipedia page.