Knowledge Needs and Information Extraction
Copyright © 2013 by John Wiley & Sons, Inc.
Editor(s): Nicolas Turenne, Jean-Charles Pomerol
Published Online: 20 FEB 2013 02:44AM EST
Print ISBN: 9781848215153
Online ISBN: 9781118574560
About this Book
About The Product
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.
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.