Chaos-Based Meaning Modeling

A central problem in language processing is meaning understanding. In a previous work we introduced the concept that meaning is structured information, and we devised a model, called differentiated-cognition, in order to describe in a compressed manner the meaning content of sentences. The purpose of this work is to develop this result and explore the possibility of modeling meaning in a dynamics-based approach by enfolding the differentiated-cognition description in chaotic attractors. The results of simulations revealed the potential offered by chaotic attractors to encode meaning of words and sentences in a series of complex enfoldments. This new approach may form the basis of an integrated language generation-understanding model.