Latent Semantic Indexing (LSI) is a technique used in information retrieval (IR) to discover hidden relationships between words and documents. LSI uses singular value decomposition (SVD) to reduce the dimensionality of a term-document matrix, identifying patterns and latent semantic structures within the data.
In a traditional term-document matrix, words are represented by rows, and documents by columns. LSI finds associations between words and documents by analyzing co-occurrence patterns, helping to capture the underlying meaning of words, especially when synonyms or related terms are used. For instance, LSI can help link documents about "heart disease" and "cardiology" even if they don’t share exact keywords.
LSI enhances search results by improving the system's ability to handle synonymy and polysemy (multiple meanings of words). This allows IR systems to return more relevant results even if the exact terms used in the query aren’t present in the document, making the search process more efficient and accurate.