Knowledge Needs and Information Extraction: Towards an Artificial Consciousness

Knowledge Needs and Information Extraction: Towards an Artificial Consciousness

By: Nicolas Turenne (author)Hardback

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Description

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 Vallee 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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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

Product Details

  • publication date: 18/01/2013
  • ISBN13: 9781848215153
  • Format: Hardback
  • Number Of Pages: 284
  • ID: 9781848215153
  • weight: 570
  • ISBN10: 1848215150

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