Knowledge Needs and Information Extraction

Content: 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