Knowledge needs and information extraction : towards an artificial consciousness /

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Bibliographic Details
Call Number:Libro Electrónico
Main Author: Turenne, Nicolas
Format: Electronic eBook
Language:Inglés
Published: London : Hoboken, N.J. : ISTE ; Wiley, 2013.
Series:Computer engineering and IT series.
Subjects:
Consciousness.
Conscious automata.
Conscience.
Machines intelligentes.
Conscious automata
Consciousness
Online Access:Texto completo (Requiere registro previo con correo institucional)
Table of Contents:
  • 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.

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