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Human language is recognized as a very complex domain since decades. Nocomputer system has been able to reach human levels of performance so far. Theonly known computational system capable of proper language processing is thehuman brain. While we gather more and more data about the brain, itsfundamental computational processes still remain obscure. The lack of a soundcomputational brain theory also prevents the fundamental understanding ofNatural Language Processing. As always when science lacks a theoreticalfoundation, statistical modeling is applied to accommodate as many sampledreal-world data as possible. An unsolved fundamental issue is the actualrepresentation of language (data) within the brain, denoted as theRepresentational Problem. Starting with Jeff Hawkins' Hierarchical TemporalMemory (HTM) theory, a consistent computational theory of the human cortex, wehave developed a corresponding theory of language data representation: TheSemantic Folding Theory. The process of encoding words, by using a topographicsemantic space as distributional reference frame into a sparse binaryrepresentational vector is called Semantic Folding and is the central topic ofthis document. Semantic Folding describes a method of converting language fromits symbolic representation (text) into an explicit, semantically groundedrepresentation that can be generically processed by Hawkins' HTM networks. Asit turned out, this change in representation, by itself, can solve many complexNLP problems by applying Boolean operators and a generic similarity functionlike the Euclidian Distance. Many practical problems of statistical NLPsystems, like the high cost of computation, the fundamental incongruity ofprecision and recall , the complex tuning procedures etc., can be elegantlyovercome by applying Semantic Folding.

Semantic Folding Theory And its Application in Semantic Fingerprinting