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E-grāmata: Argument Mining: Linguistic Foundations

(CNRS-IRIT, University Paul Sabatier, Toulouse, France), (CNRS-IRIT, University Paul Sabatier, Toulouse, France)
  • Formāts: EPUB+DRM
  • Izdošanas datums: 21-Oct-2019
  • Izdevniecība: ISTE Ltd and John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119671046
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Argument Mining: Linguistic FoundationsPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 21-Oct-2019
  • Izdevniecība: ISTE Ltd and John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119671046
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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.
Preface xi
Chapter 1 Introduction and Challenges 1(12)
1.1 What is argumentation?
1(3)
1.2 Argumentation and argument mining
4(3)
1.3 The origins of argumentation
7(1)
1.4 The argumentative discourse
8(2)
1.5 Contemporary trends
10(3)
Chapter 2 The Structure of Argumentation 13(26)
2.1 The argument-conclusion pair
13(1)
2.2 The elementary argumentative schema
14(6)
2.2.1 Toulmin's argumentative model
14(3)
2.2.2 Some elaborations and refinements of Toulmin's model
17(1)
2.2.3 The geometry of arguments
18(2)
2.3 Modeling agreement and disagreement
20(5)
2.3.1 Agreeing versus disagreeing
20(3)
2.3.2 The art of resolving divergences
23(2)
2.4 The structure of an argumentation: argumentation graphs
25(2)
2.5 The role of argument schemes in argumentation
27(4)
2.5.1 Argument schemes: main concepts
27(1)
2.5.2 A few simple illustrations
28(1)
2.5.3 Argument schemes based on analogy
29(1)
2.5.4 Argument schemes based on causality
30(1)
2.6 Relations between Toulmin's model and argumentation schemes
31(8)
2.6.1 Warrants as a popular opinion
32(2)
2.6.2 Argument schemes based on rules, explanations or hypothesis
34(1)
2.6.3 Argument schemes based on multiple supports or attacks
35(2)
2.6.4 Causality and warrants
37(2)
Chapter 3 The Linguistics of Argumentation 39(26)
3.1 The structure of claims
40(5)
3.2 The linguistics of justifications
45(2)
3.3 Evaluating the strength of claims, justifications and arguments
47(12)
3.3.1 Strength factors within a proposition
49(2)
3.3.2 Structuring expressions of strength by semantic category
51(1)
3.3.3 A simple representation of strength when combining several factors
52(1)
3.3.4 Pragmatic factors of strength expression
53(6)
3.4 Rhetoric and argumentation
59(6)
3.4.1 Rhetoric and communication
60(1)
3.4.2 Logos: the art of reasoning and of constructing demonstrations
61(1)
3.4.3 Ethos: the orator profile
62(1)
3.4.4 Pathos: how to persuade an audience
63(2)
Chapter 4 Advanced Features of Argumentation for Argument Mining 65(14)
4.1 Managing incoherent claims and justifications
65(2)
4.1.1 The case of justifications supporting opposite claims
66(1)
4.1.2 The case of opposite justifications justifying the same claim
67(1)
4.2 Relating claims and justifications: the need for knowledge and reasoning
67(7)
4.2.1 Investigating relatedness via corpus analysis
68(1)
4.2.2 A corpus analysis of the knowledge involved
69(3)
4.2.3 Observation synthesis
72(2)
4.3 Argument synthesis in natural language
74(5)
4.3.1 Features of a synthesis
75(1)
4.3.2 Structure of an argumentation synthesis
76(3)
Chapter 5 From Argumentation to Argument Mining 79(6)
5.1 Some facets of argument mining
79(2)
5.2 Designing annotation guidelines: some methodological elements
81(1)
5.3 What results can be expected from an argument mining system?
82(1)
5.4 Architecture of an argument mining system
83(1)
5.5 The next chapters
84(1)
Chapter 6 Annotation Frameworks and Principles of Argument Analysis 85(34)
6.1 Principles of argument analysis
86(11)
6.1.1 Argumentative discourse units
86(2)
6.1.2 Conclusions and premises
88(1)
6.1.3 Warrants and backings
89(1)
6.1.4 Qualifiers
89(1)
6.1.5 Argument schemes
90(1)
6.1.6 Attack relations: rebuttals, refutations, undercutters
90(2)
6.1.7 Illocutionary forces, speech acts
92(1)
6.1.8 Argument relations
93(2)
6.1.9 Implicit argument components and tailored annotation frameworks
95(2)
6.2 Examples of argument analysis frameworks
97(6)
6.2.1 Rhetorical Structure Theory
97(1)
6.2.2 Toulmin's model
98(1)
6.2.3 Inference Anchoring Theory
99(3)
6.2.4 Summary
102(1)
6.3 Guidelines for argument analysis
103(5)
6.3.1 Principles of annotation guidelines
103(1)
6.3.2 Inter-annotator agreements
104(1)
6.3.3 Interpretation of IAA measures
105(1)
6.3.4 Some examples of IAAs
106(1)
6.3.5 Summary
107(1)
6.4 Annotation tools
108(6)
6.4.1 Brat
108(1)
6.4.2 RST tool
109(1)
6.4.3 AGORA-net
110(1)
6.4.4 Araucaria
110(1)
6.4.5 Rationale
111(1)
6.4.6 OVA+
112(1)
6.4.7 Summary
113(1)
6.5 Argument corpora
114(4)
6.5.1 COMARG
115(1)
6.5.2 A news editorial corpus
115(1)
6.5.3 THF Airport ArgMining corpus
115(1)
6.5.4 A Wikipedia articles corpus
115(1)
6.5.5 AraucariaDB
115(1)
6.5.6 An annotated essays corpus
116(1)
6.5.7 A written dialogs corpus
116(1)
6.5.8 A web discourse corpus
116(1)
6.5.9 Argument Interchange Format Database
116(1)
6.5.10 Summary
117(1)
6.6 Conclusion
118(1)
Chapter 7 Argument Mining Applications and Systems 119(14)
7.1 Application domains for argument mining
119(3)
7.1.1 Opinion analysis augmented by argument mining
120(1)
7.1.2 Summarization
120(1)
7.1.3 Essays
120(1)
7.1.4 Dialogues
120(1)
7.1.5 Scientific and news articles
120(1)
7.1.6 The web
121(1)
7.1.7 Legal field
121(1)
7.1.8 Medical field
121(1)
7.1.9 Education
121(1)
7.2 Principles of argument mining systems
122(4)
7.2.1 Argumentative discourse units detection
123(1)
7.2.2 Units labeling
123(1)
7.2.3 Argument structure detection
124(1)
7.2.4 Argument completion
125(1)
7.2.5 Argument structure representation
125(1)
7.3 Some existing systems for argument mining
126(4)
7.3.1 Automatic detection of rhetorical relations
126(1)
7.3.2 Argument zoning
126(1)
7.3.3 Stance detection
127(1)
7.3.4 Argument mining for persuasive essays
127(1)
7.3.5 Argument mining for web discourse
127(1)
7.3.6 Argument mining for social media
128(1)
7.3.7 Argument scheme classification and enthymemes reconstruction
128(1)
7.3.8 Argument classes and argument strength classification
128(1)
7.3.9 Textcoop
129(1)
7.3.10 IBM debating technologies
129(1)
7.3.11 Argument mining for legal texts
129(1)
7.4 Efficiency and limitations of existing argument mining systems
130(1)
7.5 Conclusion
131(2)
Chapter 8 A Computational Model and a Simple Grammar-Based Implementation 133(22)
8.1 Identification of argumentative units
134(5)
8.1.1 Challenges raised by the identification of argumentative units
134(1)
8.1.2 Some linguistic techniques to identify ADUs
135(4)
8.2 Mining for claims
139(11)
8.2.1 The grammar formalisms
140(2)
8.2.2 Lexical issues
142(3)
8.2.3 Grammatical issues
145(3)
8.2.4 Templates for claim analysis
148(2)
8.3 Mining for supports and attacks
150(3)
8.3.1 Structures introduced by connectors
150(1)
8.3.2 Structures introduced by propositional attitudes
151(1)
8.3.3 Other linguistic forms to express supports or attacks
152(1)
8.4 Evaluating strength
153(1)
8.5 Epilogue
154(1)
Chapter 9 Non-Verbal Dimensions of Argumentation: a Challenge for Argument Mining 155(8)
9.1 The text and its additions
156(1)
9.1.1 Text, pictures and icons
156(1)
9.1.2 Transcriptions of oral debates
156(1)
9.2 Argumentation and visual aspects
157(1)
9.3 Argumentation and sound aspects
158(3)
9.3.1 Music and rationality
159(1)
9.3.2 Main features of musical structure: musical knowledge representation
160(1)
9.4 Impact of non-verbal aspects on argument strength and on argument schemes
161(1)
9.5 Ethical aspects
162(1)
Bibliography 163(12)
Index 175
Mathilde Janier has co-authored over 15 publications, principally dealing with the annotation and modeling of argumentative dynamics in debate and dispute mediation. Her work mainly focuses on argumentation in dialogical contexts. Patrick Saint-Dizier is Senior Researcher at CNRS – IRIT Toulouse, France. His work is based on logic, language, argumentation, natural language processing and logic programming. He is the author and co-author of 11 books on these topics.
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