Information architecture applied on natural language processing: a proposal Information Science contributions on data preprocessing for training and learning of artificial neural networks

Introduction: Natural Language Processing through artificial neural networks has gaps that can be addressed by Information Science through Information Architecture. Objective: To present Information Science contributions on Knowledge Organization applied to artificial neural networks training methods, positioning it as an active body of knowledge in artificial intelligence problems. Methodology: A three -leveled analysis path (metaphysical, scientific, and technological) is adopted to guide and ground the study. On metaphysical level, current development stage of natural language processing techniques is verified and analyzed. On scientific findings, a five-step procedure is proposed which aims to design, analyze, and prepare information spaces for artificial neural networks training and learning methods, fulfilling gaps identified by authors focused on Computer Science implementations. On technological implementation, the five-step procedure is applied to 3 datasets formed by texts from 16 scientific knowledge areas, as an evaluation basis. Results: Results obtained through pre-processed data and raw data where compared, showing great potential in developing a structured method of Multimodal Information Architecture that provide instruments able to organize data used as test and learning samples in artificial neural networks. Conclusion: This method could place Information Science as a producer of data pre-processing solutions, replacing its current role as consumer of prefabricated solutions made by Computer Science.