(Ebook) Knowledge Needs and Information Extraction: Towards an Artificial Consciousness by Nicolas Turenne ISBN 9781848215153, 1848215150

(Ebook) Knowledge Needs and Information Extraction: Towards an Artificial Consciousness by Nicolas Turenne ISBN 9781848215153, 1848215150

This book presents a theory of consciousness which is unique and sustainable in nature, based on physiological and cognitive-linguistic principles controlled by a number of socio-psycho-economic factors. In order to anchor this theory, which draws upon various disciplines, the author presents a number of different theories, all of which have been abundantly studied by scientists from both a theoretical and experimental standpoint, including models of social organization, ego theories, theories of the motivational system in psychology, theories of the motivational system in neurosciences, language modeling and computational modeling of motivation.

The theory presented in this book is based on the hypothesis that an individual’s main activities are developed by self-motivation, managed as an informational need. This is described in chapters covering self-motivation on a day-to-day basis, the notion of need, the hypothesis and control of cognitive self-motivation and a model of self-motivation which associates language and physiology. The subject of knowledge extraction is also covered, including the impact of self-motivation on written information, non-transversal and transversal text-mining techniques and the fields of interest of text mining.

Contents:

1. Consciousness: an Ancient and Current Topic of Study
2. Self-motivation on a Daily Basis.
3. The Notion of Need
4. The Models of Social Organization
5. Self Theories
6. Theories of Motivation in Psychology
7. Theories of Motivation in Neurosciences
8. Language Modeling
9. Computational Modeling of Motivation
10. Hypothesis and Control of Cognitive Self-Motivation
11. A Model of Self-Motivation which Associates Language and Physiology
12. Impact of Self-Motivation on Written Information
13. Non-Transversal Text Mining Techniques
14. Transversal Text Mining Techniques
15. Fields of Interest for Text Mining