You are previewing Knowledge Needs and Information Extraction: Towards an Artificial Consciousness.
O'Reilly logo
Knowledge Needs and Information Extraction: Towards an Artificial Consciousness

Book 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 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.

Table of Contents

  1. Cover
  2. Contents
  3. Dedication
  4. Title page
  5. Copyright page
  6. Introduction
  7. Acknowledgements
  8. Chapter 1: Consciousness: an Ancient and Current Topic of Study
    1. 1.1. Multidisciplinarity of the subject
    2. 1.2. Terminological outlook
    3. 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
  9. Chapter 2: Self-motivation on a Daily Basis
    1. 2.1. In news blogs
    2. 2.2. Marketing
    3. 2.3. Appearance
    4. 2.4. Mystical experiences
    5. 2.5. Infantheism
    6. 2.6. Addiction
  10. Chapter 3: The Notion of Need
    1. 3.1. Hierarchy of needs
    2. 3.2. The satiation cycle
  11. Chapter 4: The Models of Social Organization
    1. 4.1. The entrepreneurial model
    2. 4.2. Motivational and ethical states
  12. Chapter 5: Self Theories
  13. Chapter 6: Theories of Motivation in Psychology
    1. 6.1. Behavior and cognition
    2. 6.2. Theory of self-efficacy
    3. 6.3. Theory of self-determination
    4. 6.4. Theory of control
    5. 6.5. Attribution theory
    6. 6.6. Standards and self-regulation
    7. 6.7. Deviance and pathology
    8. 6.8. Temporal Motivation Theory
    9. 6.9. Effect of objectives
    10. 6.10. Context of distance learning
    11. 6.11. Maintenance model
    12. 6.12. Effect of narrative
    13. 6.13. Effect of eviction
    14. 6.14. Effect of the teacher–student relationship
    15. 6.15. Model of persistence and change
    16. 6.16. Effect of the man–machine relationship
  14. Chapter 7: Theories of Motivation in Neurosciences
    1. 7.1. Academic literature on the subject
    2. 7.2. Psychology and Neurosciences
    3. 7.3. Neurophysiological theory
    4. 7.4. Relationship between the motivational system and the emotions
    5. 7.5. Relationship between the motivational system and language
    6. 7.6. Relationship between the motivational system and need
  15. Chapter 8: Language Modeling
    1. 8.1. Issues surrounding language
    2. 8.2. Interaction and language
    3. 8.3. Development and language
    4. 8.4. Schools of thought in linguistic sciences
    5. 8.5. Semantics and combination
    6. 8.6. Functional grammar
    7. 8.7. Meaning-Text Theory
    8. 8.8. Generative lexicon
    9. 8.9. Theory of synergetic linguistics
    10. 8.10. Integrative approach to language processing
    11. 8.11. New spaces for date production
    12. 8.12. Notion of ontology
    13. 8.13. Knowledge representation
  16. Chapter 9: Computational Modeling of Motivation
    1. 9.1. Notion of a computational model
    2. 9.2. Multi-agent systems
    3. 9.3. Artificial self-organization
    4. 9.4. Artificial neural networks
    5. 9.5. Free will theorem
    6. 9.6. The probabilistic utility model
    7. 9.7. The autoepistemic model
  17. Chapter 10: Hypothesis and Control of Cognitive Self-Motivation
    1. 10.1. Social groups
    2. 10.2. Innate self-motivation
    3. 10.3. Mass communication
    4. 10.4. The Cost–Benefit ratio
    5. 10.5. Social representation
    6. 10.6. The relational environment
    7. 10.7. Perception
    8. 10.8. Identity
    9. 10.9. Social environment
    10. 10.10. Historical antecedence
    11. 10.11. Ethics
  18. Chapter 11: A Model of Self-Motivation which Associates Language and Physiology
    1. 11.1. A new model
    2. 11.2. Architecture of a self-motivation subsystem
    3. 11.3. Level of certainty
    4. 11.4. Need for self-motivation
    5. 11.5. Notion of motive
    6. 11.6. Age and location
    7. 11.7. Uniqueness
    8. 11.8. Effect of spontaneity
    9. 11.9. Effect of dependence
    10. 11.10. Effect of emulation
    11. 11.11. Transition of belief
    12. 11.12. Effect of individualism
    13. 11.13. Modeling of the groups of beliefs
  19. Chapter 12: Impact of Self-Motivation on Written Information
    1. 12.1. Platform for production and consultation of texts
    2. 12.2. Informational measure of the motives of self-motivation
    3. 12.3. The information market
    4. 12.4. Types of data
    5. 12.5. The outlines of text mining
    6. 12.6. Software economy
    7. 12.7. Standards and metadata
    8. 12.8. Open-ended questions and challenges for text-mining methods
    9. 12.9. Notion of lexical noise
    10. 12.10. Web mining
    11. 12.11. Mining approach
  20. Chapter 13: Non-Transversal Text Mining Techniques
    1. 13.1. Constructivist activity
    2. 13.2. Typicality associated with the data
    3. 13.3. Specific character of text mining
    4. 13.4.Supervised, unsupervised and semi-supervised techniques
    5. 13.5.Quality of a model
    6. 13.6. The scenario
    7. 13.7. Representation of a datum
    8. 13.8. Standardization
    9. 13.9. Morphological preprocessing
    10. 13.10. Selection and weighting of terminological units
    11. 13.11. Statistical properties of textual units: lexical laws
    12. 13.12. Sub-lexical units
    13. 13.14. Shallow parsing or superficial syntactic analysis
    14. 13.15. Argumentation models
  21. Chapter 14: Transversal Text Mining Techniques
    1. 14.1. Mixed and interdisciplinary text mining techniques
    2. 14.2. Techniques for extraction of named entities
    3. 14.3. Inverse methods
    4. 14.4. Latent Semantic Analysis
    5. 14.5. Iterative construction of sub-corpora
    6. 14.6. Ordering approaches or ranking method
    7. 14.7. Use of ontology
    8. 14.8. Interdisciplinary techniques
    9. 14.9. Information visualization techniques
    10. 14.10. The k-means technique
    11. 14.11. Naive Bayes classifier technique
    12. 14.12. The k-nearest neighbors (KNN) technique
    13. 14.13. Hierarchical clustering technique
    14. 14.14. Density-based clustering techniques
    15. 14.15. Conditional fields
    16. 14.16. Nonlinear regression and artificial neural networks
    17. 14.17. Models of multi-agent systems (MASs)
    18. 14.18. Co-clustering models
    19. 14.19. Dependency models
    20. 14.20. Decision tree technique
    21. 14.21. The Support Vector Machine (SVM) technique
    22. 14.22. Set of frequent items
    23. 14.23. Genetic algorithms
    24. 14.24. Link analysis with a theoretical graph model
    25. 14.25. Link analysis without a graph model
    26. 14.26. Quality of a model
    27. 14.27. Model selection
  22. Chapter 15: Fields of Interest for Text Mining
    1. 15.1. The avenues in text mining
    2. 15.2. About decision support
    3. 15.3. Competitive intelligence (vigilance)
    4. 15.4. About strategy
    5. 15.5. About archive management
    6. 15.6. About sociology and the legal field
    7. 15.7. About biology
    8. 15.8. About other domains
  23. Conclusion
  24. Bibliography
  25. Index