by disambiguating polysemous words and searching for synonyms of the query. However, searching in the high-dimensional continuous space is much slower than searching the standard trie data structure of search engines. Finding topics Multivariate analysis of the document-term matrix can reveal topics/themes of the corpus. Specifically, latent semantic analysis and data clustering can be used, and, more recently, probabilistic latent semantic analysis with its generalization Latent Dirichlet allocation, and non-negative matrix factorization, have been found to perform well for this task. See also Bag of words model Implementations Gensim: Open source Python framework for Vector Space modelling. Contains memory-efficient algorithms for constructing term-document matrices from text plus common transformations (tf-idf, LSA, LDA). References Natural language processing