Data and Knowledge Organization for Natural Language Processing: Searching and Identifying Better Arrangements of Texts Based on Multimodal Information Architecture

Abstract

Processing texts of multiple knowledge areas is a hard task. This article presents an Information Science contribution to natural language processing based on artificial neural networks through data arrangement. An extended concept of Information architecture was used, aggregating a multimodal view of organizing data. The Multimodal Information Architecture definition served as foundations for a five-step procedure to design, analyze and transform data used for artificial neural networks training and learning methods, complementing gaps identified by authors focused on Computer Science implementations. The proposal was validated with three datasets formed by texts coming from 16 knowledge areas. Results obtained through the usage of pre-processed data and raw data where compared. In each of the three datasets, the method identified arrangements which led to better and worst results, separating which corpus samples are more susceptible for knowledge extraction.