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Argument Mining

Author Mathilde Janier , Patrick Saint-Dizier
Release Date: 2019/12/01
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18
Chapters
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Book Description


This book is an introduction to the linguistic concepts of argumentation relevant for argument mining, an important research and development activity which can be viewed as a highly complex form of information retrieval, requiring high-level natural language processing technology. While the first four chapters develop the linguistic and conceptual aspects of argument expression, the last four are devoted to their application to argument mining. These chapters investigate the facets of argument annotation, as well as argument mining system architectures and evaluation. How annotations may be used to develop linguistic data and how to train learning algorithms is outlined. A simple implementation is then proposed. The book ends with an analysis of non-verbal argumentative discourse. Argument Mining is an introductory book for engineers or students of linguistics, artificial intelligence and natural language processing. Most, if not all, the concepts of argumentation crucial for argument mining are carefully introduced and illustrated in a simple manner.

Table of Contents

  1. Cover
  2. Preface
  3. 1 Introduction and Challenges
    1. 1.1. What is argumentation?
    2. 1.2. Argumentation and argument mining
    3. 1.3. The origins of argumentation
    4. 1.4. The argumentative discourse
    5. 1.5. Contemporary trends
  4. 2 The Structure of Argumentation
    1. 2.1. The argument–conclusion pair
    2. 2.2. The elementary argumentative schema
    3. 2.3. Modeling agreement and disagreement
    4. 2.4. The structure of an argumentation: argumentation graphs
    5. 2.5. The role of argument schemes in argumentation
    6. 2.6. Relations between Toulmin’s model and argumentation schemes
  5. 3 The Linguistics of Argumentation
    1. 3.1. The structure of claims
    2. 3.2. The linguistics of justifications
    3. 3.3. Evaluating the strength of claims, justifications and arguments
    4. 3.4. Rhetoric and argumentation
  6. 4 Advanced Features of Argumentation for Argument Mining
    1. 4.1. Managing incoherent claims and justifications
    2. 4.2. Relating claims and justifications: the need for knowledge and reasoning
    3. 4.3. Argument synthesis in natural language
  7. 5 From Argumentation to Argument Mining
    1. 5.1. Some facets of argument mining
    2. 5.2. Designing annotation guidelines: some methodological elements
    3. 5.3. What results can be expected from an argument mining system?
    4. 5.4. Architecture of an argument mining system
    5. 5.5. The next chapters
  8. 6 Annotation Frameworks and Principles of Argument Analysis
    1. 6.1. Principles of argument analysis
    2. 6.2. Examples of argument analysis frameworks
    3. 6.3. Guidelines for argument analysis
    4. 6.4. Annotation tools
    5. 6.5. Argument corpora
    6. 6.6. Conclusion
  9. 7 Argument Mining Applications and Systems
    1. 7.1. Application domains for argument mining
    2. 7.2. Principles of argument mining systems
    3. 7.3. Some existing systems for argument mining
    4. 7.4. Efficiency and limitations of existing argument mining systems
    5. 7.5. Conclusion
  10. 8 A Computational Model and a Simple Grammar-Based Implementation
    1. 8.1. Identification of argumentative units
    2. 8.2. Mining for claims
    3. 8.3. Mining for supports and attacks
    4. 8.4. Evaluating strength
    5. 8.5. Epilogue
  11. 9 Non-Verbal Dimensions of Argumentation: a Challenge for Argument Mining
    1. 9.1. The text and its additions
    2. 9.2. Argumentation and visual aspects
    3. 9.3. Argumentation and sound aspects
    4. 9.4. Impact of non-verbal aspects on argument strength and on argument schemes
    5. 9.5. Ethical aspects
  12. Bibliography
  13. Index
  14. End User License Agreement