Knowledge needs and information extraction : towards an artificial consciousness - Turenne, Nicolas (London : ISTE ; Hoboken, N.J. : Wiley, 2013)
Machine generated contents note: 1.1. Multidisciplinarity of the subject -- 1.2. Terminological outlook -- 1.3. Theological point of view -- 1.4. Notion of belief and autonomy -- 1.5. Scientific schools of thought -- 1.6. question of experience -- 2.1. In news blogs -- 2.2. Marketing -- 2.3. Appearance -- 2.4. Mystical experiences -- 2.5. Infantheism -- 2.6. Addiction -- 3.1. Hierarchy of needs -- 3.1.1. Level-1 needs -- 3.1.2. Level-3 needs -- 3.2. satiation cycle -- 4.1. entrepreneurial model -- 4.2. Motivational and ethical states -- 6.1. Behavior and cognition -- 6.2. Theory of self-efficacy -- 6.3. Theory of self-determination -- 6.4. Theory of control -- 6.5. Attribution theory -- 6.6. Standards and self-regulation -- 6.7. Deviance and pathology -- 6.8. Temporal Motivation Theory -- 6.9. Effect of objectives -- 6.10. Context of distance learning -- 6.11. Maintenance model -- 6.12. Effect of narrative -- 6.13. Effect of eviction -- 6.14. Effect of the teacher-student relationship -- 6.15. Model of persistence and change -- 6.16. Effect of the man-machine relationship -- 7.1. Academic literature on the subject -- 7.2. Psychology and Neurosciences -- 7.3. Neurophysiological theory -- 7.4. Relationship between the motivational system and the emotions -- 7.5. Relationship between the motivational system and language -- 7.6. Relationship between the motivational system and need -- 8.1. Issues surrounding language -- 8.2. Interaction and language -- 8.3. Development and language -- 8.4. Schools of thought in linguistic sciences -- 8.5. Semantics and combination -- 8.6. Functional grammar -- 8.7. Meaning-Text Theory -- 8.8. Generative lexicon -- 8.9. Theory of synergetic linguistics -- 8.10. Integrative approach to language processing -- 8.11. New spaces for date production -- 8.12. Notion of ontology -- 8.13. Knowledge representation -- 9.1. Notion of a computational model -- 9.2. Multi-agent systems -- 9.3. Artificial self-organization -- 9.4. Artificial neural networks -- 9.5. Free will theorem -- 9.6. probabilistic utility model -- 9.7. autoepistemic model -- 10.1. Social groups -- 10.2. Innate self-motivation -- 10.3. Mass communication -- 10.4. Cost-Benefit ratio -- 10.5. Social representation -- 10.6. relational environment -- 10.7. Perception -- 10.8. Identity -- 10.9. Social environment -- 10.10. Historical antecedence -- 10.11. Ethics -- 11.1. new model -- 11.2. Architecture of a self-motivation subsystem -- 11.3. Level of certainty -- 11.4. Need for self-motivation -- 11.5. Notion of motive -- 11.6. Age and location -- 11.7. Uniqueness -- 11.8. Effect of spontaneity -- 11.9. Effect of dependence -- 11.10. Effect of emulation -- 11.11. Transition of belief -- 11.12. Effect of individualism -- 11.13. Modeling of the groups of beliefs -- 12.1. Platform for production and consultation of texts -- 12.2. Informational measure of the motives of self-motivation -- 12.2.1. Intra-phrastic extraction -- 12.2.2. Inter-phrastic extraction -- 12.2.3. Meta-phrastic extraction -- 12.3. information market -- 12.4. Types of data -- 12.5. outlines of text mining -- 12.6. Software economy -- 12.7. Standards and metadata -- 12.8. Open-ended questions and challenges for text-mining methods -- 12.9. Notion of lexical noise -- 12.10. Web mining -- 12.11. Mining approach -- 13.1. Constructivist activity -- 13.2. Typicality associated with the data -- 13.3. Specific character of text mining -- 13.4. Supervised, unsupervised and semi-supervised techniques -- 13.5. Quality of a model -- 13.6. scenario -- 13.7. Representation of a datum -- 13.8. Standardization -- 13.9. Morphological preprocessing -- 13.10. Selection and weighting of terminological units -- 13.11. Statistical properties of textual units: lexical laws -- 13.12. Sub-lexical units -- 13.14. Shallow parsing or superficial syntactic analysis -- 13.15. Argumentation models -- 14.1. Mixed and interdisciplinary text mining techniques -- 14.1.1. Supervised, unsupervised and semi-supervised techniques -- 14.2. Techniques for extraction of named entities -- 14.3. Inverse methods -- 14.4. Latent Semantic Analysis -- 14.5. Iterative construction of sub-corpora -- 14.6. Ordering approaches or ranking method -- 14.7. Use of ontology -- 14.8. Interdisciplinary techniques -- 14.9. Information visualization techniques -- 14.10. k-means technique -- 14.11. Naive Bayes classifier technique -- 14.12. k-nearest neighbors (KNN) technique -- 14.13. Hierarchical clustering technique -- 14.14. Density-based clustering techniques -- 14.15. Conditional fields -- 14.16. Nonlinear regression and artificial neural networks -- 14.17. Models of multi-agent systems (MASs) -- 14.18. Co-clustering models -- 14.19. Dependency models -- 14.20. Decision tree technique -- 14.21. Support Vector Machine (SVM) technique -- 14.22. Set of frequent items -- 14.23. Genetic algorithms -- 14.24. Link analysis with a theoretical graph model -- 14.25. Link analysis without a graph model -- 14.26. Quality of a model -- 14.27. Model selection -- 15.1. avenues in text mining -- 15.1.1. Organization -- 15.1.2. Discovery -- 15.2. About decision support -- 15.3. Competitive intelligence (vigilance) -- 15.4. About strategy -- 15.5. About archive management -- 15.6. About sociology and the legal field -- 15.7. About biology -- 15.8. About other domains.
Includes bibliographical references (pages 225-265) and index.
John Wiley and Sons
Keywords: Consciousness; Conscious automata; Conscience; Machines intelligentes; Conscious automata; Consciousness