Knowledge Needs and Information Extraction: Towards an Artificial Consciousness


Knowledge Needs and Information Extraction: Towards an Artificial Consciousness (2013) .. by Nicolas Turenne


Table of Contents

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

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