Semiotics and modeling computer classification of text with genetic algorithm : analysis and first results
Jean-Guy Meunier, Vincent Rialle, Sofiane Oussedik, Georges Nault · Archipel (Université du Québec à Montréal) · 1997
Computer engineering proposes the construction of complex systems by dynamic prototyping (Buddle and Bacon, 1992).But this prototyping cannot be inductive and purely considered as a trial an error process.To be successful, one must possess an underlying hypothetical model (Marr, 1982) of what are the functions of the system.If these functions relates to physical tasks, such as sensing temperature, manipulatiing objects, etc., the desired behavior can be observed, and a model can be built.Conversely, if the functions of the system are to be applied to semio-informational tasks, such as language translation, information retrieval, hypertext navigation, text generation, etc., the interpretative behavior is not readily observable.Now, as any other computer systems, these systems are symbol manipulation machines (Newell ,1980).They must also manipulate input and outputs, but, in themselves, these data are semiotic objects, and not physical ones.These systems manipulate objects that have to be interpreted by some cognitive agent.In other words, systems that manipulate physical objects require a model of the physical word, while systems that manipulate informational objects require a semiotic model.In this paper, we illustrate how a semiotic model can help in the conception, the modeling, and the experimentation of a semiotic behavior such as Computer Assisted Reading and Analysis of Text (CARAT), and how this model has called upon the Genetic Algorithm (GA) theory to realize some of its aspects.