Latent semantic analysis (LSA) is an information processing technique used in the fields of natural language processing (NLP) and information retrieval (IR). It is a mathematical algorithm that attempts to decipher the relationships between words used in text documents by relying on vector space models. LSA attempts to uncover the ‘latent’ or hidden relationships between various words in different documents, in order to produce a more accurate understanding of semantic relationships.
It was first proposed by University of California Berkeley Professor Peter Landauer and his colleague John Daugman in 1987. The basic technique involves analyzing the occurrence of terms in documents using a matrix of values. It employs matrix algebra and singular value decomposition to reduce the dimensionality of the original matrix, creating a vector space model of the document. This vector map then becomes the basis by which semantic relationships can be identified.
LSA has been used in applications such as auto-complete functions in web browsers, Q&A systems, and document classification. It is also a useful tool for finding articles and documents related to certain topics. One particular application is in search engine optimization: by utilizing LSA, search engine algorithms can better identify the relevance of query results to the particular query being performed.
In the academic field, LSA is used to identify disciplinary knowledge in educational systems, identify hidden trends in text corpora, and to detect plagiarism. LSA has also seen use in psychological studies to help identify significant aspects of human cognition and language comprehension.
Due to its ability to uncover latent semantic relationships, LSA has become an increasingly popular tool for natural language processing and information retrieval tasks. Its applications range from search engine optimization to academic research, making it an invaluable tool for a wide variety of users.
