Wiley
Wiley.com
Print this page Share

Knowledge Needs and Information Extraction: Towards an Artificial Consciousness

ISBN: 978-1-84821-515-3
288 pages
March 2013, Wiley-ISTE
Knowledge Needs and Information Extraction: Towards an Artificial Consciousness (1848215150) cover image

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.

About the Authors

Nicolas Turenne is a researcher at INRA in the Science and Society team at the University of Paris-Est Marne la Vallée in France. He specializes in knowledge extraction from texts with theoretical research into relational and stochastic models. His research topics also concern the sociology of uses, food and environmental sciences, and bioinformatics.

See More

Introduction xi

Acknowledgements  xiii

Chapter 1. Consciousness: an Ancient and Current Topic of Study 1

1.1. Multidisciplinarity of the subject 1

1.2. Terminological outlook 2

1.3. Theological point of view 4

1.4. Notion of belief and autonomy 5

1.5. Scientific schools of thought 6

1.6. The question of experience 7

Chapter 2. Self-motivation on a Daily Basis 9

2.1. In news blogs 9

2.2. Marketing 9

2.3. Appearance 10

2.4. Mystical experiences 11

2.5. Infantheism 11

2.6. Addiction 11

Chapter 3. The Notion of Need 15

3.1. Hierarchy of needs 15

3.1.1. Level-1 needs 16

3.1.2. Level-3 needs 17

3.2. The satiation cycle 18

Chapter 4. The Models of Social Organization 21

4.1. The entrepreneurial model 21

4.2. Motivational and ethical states 23

Chapter 5. Self Theories 29

Chapter 6. Theories of Motivation in Psychology 33

6.1. Behavior and cognition 33

6.2. Theory of self-efficacy 34

6.3. Theory of self-determination 38

6.4. Theory of control 39

6.5. Attribution theory 39

6.6. Standards and self-regulation 42

6.7. Deviance and pathology 47

6.8. Temporal Motivation Theory 48

6.9. Effect of objectives 49

6.10. Context of distance learning 49

6.11. Maintenance model 49

6.12. Effect of narrative 49

6.13. Effect of eviction 50

6.14. Effect of the teacher–student relationship 50

6.15. Model of persistence and change 50

6.16. Effect of the man–machine relationship 51

Chapter 7. Theories of Motivation in Neurosciences  53

7.1. Academic literature on the subject 53

7.2. Psychology and Neurosciences 53

7.3. Neurophysiological theory 54

7.4. Relationship between the motivational system and the emotions 56

7.5. Relationship between the motivational system and language 58

7.6. Relationship between the motivational system and need 59

Chapter 8. Language Modeling  61

8.1. Issues surrounding language 61

8.2. Interaction and language 61

8.3. Development and language 62

8.4. Schools of thought in linguistic sciences 62

8.5. Semantics and combination 68

8.6. Functional grammar 68

8.7. Meaning-Text Theory 69

8.8. Generative lexicon 70

8.9. Theory of synergetic linguistics 70

8.10. Integrative approach to language processing 71

8.11. New spaces for date production 73

8.12. Notion of ontology 75

8.13. Knowledge representation 76

Chapter 9. Computational Modeling of Motivation 81

9.1. Notion of a computational model 81

9.2. Multi-agent systems 81

9.3. Artificial self-organization 85

9.4. Artificial neural networks 87

9.5. Free will theorem 88

9.6. The probabilistic utility model 89

9.7. The autoepistemic model 91

Chapter 10. Hypothesis and Control of Cognitive Self-Motivation 93

10.1. Social groups 93

10.2. Innate self-motivation 95

10.3. Mass communication 96

10.4. The Cost–Benefit ratio 97

10.5. Social representation 98

10.6. The relational environment 99

10.7. Perception 100

10.8. Identity 100

10.9. Social environment 101

10.10. Historical antecedence 102

10.11. Ethics 102

Chapter 11. A Model of Self-Motivation which Associates Language and Physiology 105

11.1. A new model 105

11.2. Architecture of a self-motivation subsystem 106

11.3. Level of certainty 108

11.4. Need for self-motivation 108

11.5. Notion of motive 109

11.6. Age and location 113

11.7. Uniqueness 113

11.8. Effect of spontaneity 114

11.9. Effect of dependence 114

11.10. Effect of emulation 115

11.11. Transition of belief 115

11.12. Effect of individualism 117

11.13. Modeling of the groups of beliefs 117

Chapter 12. Impact of Self-Motivation on Written Information 123

12.1. Platform for production and consultation of texts 123

12.2. Informational measure of the motives of self-motivation 124

12.2.1. Intra-phrastic extraction 125

12.2.2. Inter-phrastic extraction 126

12.2.3. Meta-phrastic extraction 128

12.3. The information market 129

12.4. Types of data 130

12.5. The outlines of text mining 133

12.6. Software economy 139

12.7. Standards and metadata 139

12.8. Open-ended questions and challenges for text-mining methods 140

12.9. Notion of lexical noise 141

12.10. Web mining 143

12.11. Mining approach 145

Chapter 13. Non-Transversal Text Mining Techniques 147

13.1. Constructivist activity 147

13.2. Typicality associated with the data 148

13.3. Specific character of text mining 148

13.4. Supervised, unsupervised and semi-supervised techniques 149

13.5. Quality of a model 149

13.6. The scenario 149

13.7. Representation of a datum 150

13.8. Standardization 151

13.9. Morphological preprocessing 152

13.10. Selection and weighting of terminological units 153

13.11. Statistical properties of textual units: lexical laws 154

13.12. Sub-lexical units 155

13.14. Shallow parsing or superficial syntactic analysis 157

13.15. Argumentation models 158

Chapter 14. Transversal Text Mining Techniques 159

14.1. Mixed and interdisciplinary text mining techniques 159

14.1.1. Supervised, unsupervised and semi-supervised techniques 159

14.2. Techniques for extraction of named entities 160

14.3. Inverse methods 163

14.4. Latent Semantic Analysis 164

14.5. Iterative construction of sub-corpora 165

14.6. Ordering approaches or ranking method 165

14.7. Use of ontology 166

14.8. Interdisciplinary techniques 167

14.9. Information visualization techniques 167

14.10. The k-means technique 168

14.11. Naive Bayes classifier technique 169

14.12. The k-nearest neighbors (KNN) technique 170

14.13. Hierarchical clustering technique 171

14.14. Density-based clustering techniques 172

14.15. Conditional fields 175

14.16. Nonlinear regression and artificial neural networks 176

14.17. Models of multi-agent systems (MASs) 177

14.18. Co-clustering models 178

14.19. Dependency models 179

14.20. Decision tree technique 179

14.21. The Support Vector Machine (SVM) technique 180

14.22. Set of frequent items 182

14.23. Genetic algorithms 184

14.24. Link analysis with a theoretical graph model 184

14.25. Link analysis without a graph model 185

14.26. Quality of a model 186

14.27. Model selection 189

Chapter 15. Fields of Interest for Text Mining 191

15.1. The avenues in text mining 191

15.1.1. Organization 191

15.1.2. Discovery 193

15.2. About decision support 194

15.3. Competitive intelligence (vigilance) 195

15.4. About strategy 197

15.5. About archive management 200

15.6. About sociology and the legal field 203

15.7. About biology 215

15.8. About other domains 219

Conclusion 221

Bibliography 225

Index 267

See More
Back to Top