Natural language processing and the conceptual model self-organizing map

Abstract

Self-organizing map can be an effective tool for the textual data classification. In this paper, we represent the methodology of an integration of the information system modeling and the development of the information system natural language interface. The main idea of the paper is to build the set of self-organising maps from information system documentation and then reuse it in human-machine communication as a semantic parsing component. The IBM's Information Framework (IFW) Financial Services Data Model has been used in an experiment where we tested how appropriate is presented methodology and what is classification accuracy of the received self-organizing maps. We compare classification accuracy with the IBM's WebSphere Voice Server NLU solution and demonstrate that self-organising maps can be a competitive components in the information systems natural language interfaces. © Springer-Verlag Berlin Heidelberg 2007.