A model based on computational perceptions for the generation of linguistic descriptions of data

In this paper we present a general model for building linguistic descriptions of data (LDD) solutions, which is based on computational models of perception inspired in the computational theory of perceptions (CTP) and in fields of knowledge different from the computational intelligence area. The elements in the model aim to consider the richness and complexity that real LDD processes are endowed with and their actual role in data-to-text natural language generation (D2T-NLG) systems. As an example of application, the building of a LDD solution in the meteorology realm is presented.