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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.

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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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