This work presents a research that proposes a new model of pre-processing for text mining in portuguese using computational intelligence techniques based on existing concepts, such as neural networks, dinamic systems and multidimensional statistics. The object of this doctoral thesis is, therefore, innovation in the pre-processing phase of text-mining, proposing an automatic model for the enrichment of textual data. This approach is presented as an extension of the traditional bag-of-words model, that has a more statistical emphasis, and proposes a bag-of-lexemes model with greater usage of the texts' linguistic content in a more computational approach, providing more efficient results. The work is complemented by the development and implementation of a text pre-processing system that automates this phase of th text mining process as proposed. Despite the object of this thesis being the pre- processing stage, one feels apropriate to describe, in overview, every step of the text mining process in order to provide the basic theory necessary to understand the process as a whole. Beyond presenting the theory of every stage individually, one executes a complete process (with data collection, indexing, pre-processing, mining and postprocessing) using tried-and-true models in all the other stages, which were implemented during the development of this work. At last some functionalities and aplications are shown, such as: document classification, information extraction and natural language interface (NLI).