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This paper presents the approach implemented as part of a conversational bot named WIH (Word Intelligent Handler). It has a complex architecture with several components. Some of them are the ER-memory, EP-memory and other minor modules that provide the prototype of good modeling of Spanish sentences. They constitute the knowledge-representation mechanism that is used by WIH to build automatic answers during dialogs with humans. In this paper a small description of these components and some of its interaction is given, along with test cases and a statistical analysis of the results obtained. It is shown here that WIH prototype can adapt its behavior and learning rate of its internal working memories according to dialogs contents.