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Quantitative Semantics and Soft Computing Methods for the Web

Book Description

Quantitative Semantics and Soft Computing Methods for the Web: Perspectives and Applications will provide relevant theoretical frameworks and the latest empirical research findings related to quantitative, soft-computing and approximate methods for dealing with Internet semantics. The target audience of this book is composed of professionals and researchers working in the fields of information and knowledge related technologies (e.g. Information sciences and technology, computer science, Web science, and artificial intelligence).

Table of Contents

  1. Title Page
  2. Copyright Page
  3. Preface
    1. Approximate Methods In Semantics For The Internet: An Introductory Map To Quantitative Semantics
    2. Abstract
    3. Introduction
    4. Semantic Similarity
  4. Chapter 1: Clustering Via Centroids a Bag of Qualitative values and Measuring its Inconsistency
    1. Abstract
    2. Introduction
    3. 1. Previous Work And Problem Statement
    4. 2. When A Bag Can Have Several Centroids
    5. 3. Conclusions And Discussion
    6. Appendix
  5. Chapter 2: Probabilistic Topic Discovery and Automatic Document Tagging
    1. Abstract
    2. 1. Introduction
    3. 2. Background
    4. 3. The Information Processing Pipeline
    5. 4. Numerical Experiments
    6. 5. Future Research Directions
    7. 6. Conclusion
  6. Chapter 3: Smoothing Text Representation Models Based on Rough Set
    1. Abstract
    2. Introduction
    3. Background
    4. Preliminaries
    5. Smoothing Method Based On Tolerance Rough Set Theory
    6. Future Research Directions
    7. Conclusion
  7. Chapter 4: Query Based Topic Modeling
    1. Abstract
    2. Introduction
    3. Background: Identifying And Modeling Topics In Collections
    4. Problem Statement
    5. The Query-Based Topic Modeling Framework
    6. Probabilistic Metrics For Topic Model Evaluation
    7. Experimental Results
  8. Chapter 5: Combining Diverse Knowledge Based Features for Semantic Relatedness Measures
    1. Abstract
    2. Introduction
    3. Background
    4. Motivation Behind This Work
    5. A Novel Semantic Relatedness Measure
    6. Experiments
    7. Conclusions And Future Research Directions
  9. Chapter 6: Web Search Results Discovery by Multi-granular Graphs
    1. Abstract
    2. Introduction
    3. Background
    4. Multi-Granular Summarization Of Web Search Results1
    5. Example Of Multi-Granular Graph Of A Web Search Process
    6. Conclusion
  10. Chapter 7: Learning Full-Sentence Co-Related Verb Argument Preferences from Web Corpora
    1. Abstract
    2. 1. Introduction
    3. 2. Approaches For Learning Verb Argument Preferences
    4. 3. A Word Space Model
    5. 4. The Dependency Language Model
    6. 5. Interpolated Plsi
    7. 6. The Need For Full Co-Occurrence
    8. 7. Conclusion And Future Work
  11. Chapter 8: Evaluating and Enhancing Contextual Search with Semantic Similarity Data
    1. Abstract
    2. Introduction
    3. Background
    4. A New Semantic Framework For Evaluating Topical Search Methods
    5. Query Refinement And Topical Search
    6. Evaluation
    7. Future Research Directions
    8. Conclusion
  12. Chapter 9: Document Search Images in Text Collections for Restricted Domains on Websites
    1. Abstract
    2. 1. Introduction: Retrieval Problems For Digital Library Of Non-Commercial Website
    3. 2. Text Document Search Image
    4. 3. Toolkit For Digital Library Development
    5. 4. Retrieval Using Tdsi In Digital Libraries
    6. 5. Analysis Of Dialogs Using Ddl System
    7. 6. Restrictions And Benefits Of The Ddl System
    8. 7. Possibility To Apply Ontology Prototyping
    9. 8. Conclusion
  13. Chapter 10: Maximal Sequential Patterns
    1. Abstract
    2. Introduction
    3. Background
    4. Algorithms For Finding Sequential Patterns On Text
    5. Maximal Sequential Pattern Mining In Text Documents
    6. Msps As A Quantitative Semantic Tool For Text Analysis
    7. Future Research Directions
    8. Conclusion
  14. Chapter 11: Topic Discovery in Web Collections via Graph Local Clustering
    1. Abstract
    2. 1. Introduction
    3. 2. Background And State Of Art
    4. 3. Method
    5. 4. Experiments And Results
    6. 5. Related Work
    7. 6. Conclusion
  15. Compilation of References
  16. About the Contributors
  17. Index