How using latent semantic indexing transforms search results | Algolia

The English language has evolved with different groups of people using unique words and phrases, leading to a wealth of synonyms. This ambiguity can be problematic in computer science, particularly when it comes to search functionality on websites and apps. Natural language processing (NLP) helps software understand the nuances of natural language and utilize them intelligently. Latent semantic indexing (LSI), also known as latent semantic analysis (LSA), is a technical method for NLP that uses singular value decomposition (SVD) to analyze relationships between concepts and words in unstructured data. LSI helps search engines understand the relationships between concepts and words, improving search functionality on sites and apps. Semantic search technology, such as vector search, can anticipate which version of a word or phrase is being queried and respond with accurate search engine results pages (SERPs). AI models powered by vector engines can quickly retrieve relevant information and improve user experience in search queries.