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Modern Computational Models of Semantic Discovery in Natural Language

Book Description

Language—that is, oral or written content that references abstract concepts in subtle ways—is what sets us apart as a species, and in an age defined by such content, language has become both the fuel and the currency of our modern information society. This has posed a vexing new challenge for linguists and engineers working in the field of language-processing: how do we parse and process not just language itself, but language in vast, overwhelming quantities? Modern Computational Models of Semantic Discovery in Natural Language compiles and reviews the most prominent linguistic theories into a single source that serves as an essential reference for future solutions to one of the most important challenges of our age. This comprehensive publication benefits an audience of students and professionals, researchers, and practitioners of linguistics and language discovery. This book includes a comprehensive range of topics and chapters covering digital media, social interaction in online environments, text and data mining, language processing and translation, and contextual documentation, among others.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
  6. Preface
    1. THE BOOK INTENTION
    2. ORGANIZATION OF THE BOOK
    3. SUMMARY
    4. REFERENCES
  7. Acknowledgment
  8. Chapter 1: Sentiment Classification
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. METHODOLOGY
    5. EXPERIMENTS AND DISCUSSION
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  9. Chapter 2: Model of the Empirical Distribution Law for Syntactic and Link Words in “Perfect” Texts
    1. ABSTRACT
    2. INTRODUCTION
    3. GOLDEN SECTION IN THE RATIO OF CONTENT AND SYNTACTIC WORDS IN “PERFECT” TEXTS
    4. MODELS OF RANK DISTRIBUTIONS OF SYNTACTIC WORDS IN AN INTEGRATED TEXT
    5. TEXT DOCUMENT AS A SYSTEM OF OBJECTS AND CONNECTIONS BETWEEN THEM
    6. FORMATION AND ANALYSIS OF THE FREQUENCY LIST OF SYNTACTIC AND LINK WORDS (SLW)
    7. FORMATION AND ANALYSIS OF THE CONNECTION STRENGTH DISTRIBUTION
    8. LIMITATIONS FOR THE APPLICATION OF MODELS FOR VERY SHORT TEXTS
    9. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
    10. REFERENCES
    11. ADDITIONAL READING
    12. KEY TERMS AND DEFINITIONS
  10. Chapter 3: Extracting Definitional Contexts in Spanish Through the Identification of Hyponymy-Hyperonymy Relations
    1. ABSTRACT
    2. INTRODUCTION
    3. CONCEPTS AND CATEGORIES
    4. CONCEPTUAL INFORMATION
    5. LEXICAL RELATIONS
    6. LINGUISTIC STRUCTURE OF TERMS
    7. DESCRIPTION OF THE METHODOLOGY
    8. RESULTS
    9. CONCLUSIONS
    10. ACKNOWLEDGMENT
    11. REFERENCES
    12. ADDITIONAL READING
    13. KEY TERMS AND DEFINITIONS
  11. Chapter 4: Revealing Groups of Semantically Close Textual Documents by Clustering
    1. ABSTRACT
    2. INTRODUCTION
    3. CLUSTERING
    4. HOTEL SERVICE MULTILINGUAL REVIEWS: A CASE STUDY
    5. FUTURE RESEARCH DIRECTIONS
    6. CONCLUSION
    7. ACKNOWLEDGMENT
    8. REFERENCES
    9. ADDITIONAL READING
    10. KEY TERMS AND DEFINITIONS
  12. Chapter 5: Semantics-Based Document Categorization Employing Semi-Supervised Learning
    1. ABSTRACT
    2. INTRODUCTION
    3. TEXT MINING USING MACHINE LEARNING APPROACH
    4. A CASE STUDY: SEMI-SUPERVISED LEARNING FROM SERVICE REVIEWS
    5. DESCRIPTION OF THE REVIEW DATA SET
    6. THE SEMI-SUPERVISED PROCEDURE PARAMETERIZATION
    7. COMPARING UNSUPERVISED, SEMI-SUPERVISED, AND SUPERVISED LEARNING
    8. CONCLUSION
    9. ACKNOWLEDGMENT
    10. REFERENCES
    11. ADDITIONAL READING
    12. KEY TERMS AND DEFINITIONS
  13. Chapter 6: Natural Language Processing as Feature Extraction Method for Building Better Predictive Models
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. BUILDING BETTER PREDICTIVE MODELS BY INFORMATION RETRIVAL FROM CALL CENTER DATA
    5. SENTIMENT ANALYSIS CASE STUDY
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. REFERENCES
    9. ADDITIONAL READING
    10. KEY TERMS AND DEFINITIONS
  14. Chapter 7: Departing the Ontology Layer Cake
    1. ABSTRACT
    2. INTRODUCTION
    3. ONTOLOGY AND ONTOLOGY LEARNING: A REVIEW
    4. ONTOLOGY LEARNING FROM TEXT – THE OBJECTIVE, THE INPUT
    5. ANALYSIS OF THE ONTOLOGY LEARNING LAYER CAKE MODEL
    6. ENGLISH STRUCTURE FROM AN ONTOLOGY LEARNING OPTIC
    7. ALTERNATIVE MODELS FOR ONTOLOGY LEARNING
    8. CONCLUSION AND FURTHER RESEARCH
    9. CONCLUSION
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
  15. Chapter 8: Semantics of Techno-Social Spaces
    1. ABSTRACT
    2. INTRODUCTION
    3. ON THE NATURE OF MEANING IN PHYLOSOPHY
    4. MULTIDIMENSIONAL NETWORKS – AN EXAMPLE
    5. MULTIDIMENSIONAL NETWORKS AS ONTOLOGIES
    6. MODELLING USING MULTIDIMENSIONAL NETWORKS
    7. APPLICATIONS TO NATURAL LANGUAGE PROCESSING
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  16. Chapter 9: Translational Mismatches Involving Clitics (Illustrated from Serbian ~ Catalan Language Pair)
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. RESOLUTION OF TRANSLATIONAL MISMATHCES INVOLVING CLITICS IN A MEANING-TEXT LINGUISTIC MODEL: ILLUSTRATIVE EXAMPLES
    5. CONCLUSION
    6. ACKNOWLEDGMENT
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
    9. ENDNOTES
  17. Chapter 10: Machine Translation within Commercial Companies
    1. ABSTRACT
    2. INTRODUCTION
    3. CHALLENGES AND FUTURE DIRECTIONS
    4. REFERENCES
    5. KEY TERMS AND DEFINITIONS
  18. Chapter 11: A Corpus-Stylistic Approach of the Treatises of Great Athanasius About Idolatry
    1. ABSTRACT
    2. PURPOSE OF RESEARCH AND METHODOLOGY
    3. CONTRA GENTES
    4. DE INCARNATIONE VERBI
    5. CONCLUSION
    6. REFERENCES
    7. ADDITIONAL READING
    8. KEY TERMS AND DEFINITIONS
  19. Compilation of References
  20. About the Contributors