1. Auflage November 2019
202 Seiten, Hardcover
Wiley & Sons Ltd
Preis: 109,00 €
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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.
1. Definitions and Aims, historical perspectives, terminology, etc.
2. Basic schemes: Standpoints and the basic argument-conclusion pair
3. Structure of argumentation: typologies, forms of disagreement, semantics, structure of an argumentation, language aspects,
4. Argumentative discourse evaluation, schemes and fallacies, linguistic issues,
5. Specific forms of argumentation (analogy, nature of things, etc.), rules of argumentation
6. Argumentation and rhetoric, structure of an argumentation, the different steps of rhetoric, language issues
7. Knowledge and reasoning in argumentation
Part 2: application of theoretical concepts
1. Desiderata and limitations of resources for argument mining, specially annotated corpora.
2. Methodology for the development of corpora. Differences between human annotators and reproducibility.
3. Revision of annotation efforts: annotation corpora and annotation guidelines.
4. Argument components. Discrimination, inventories of classes, sources of disagreement and compromises for more stable annotation.
5. Relations between argument components.
6. Identifying other aspects of argumentation: implicit components, argumentative schemes, emotion, and strength.
7. Usage of corpora to train automatic argument analyzers.
8. Implementation issues: brief description of existing systems or modules, pipeline architectures, evaluation by component.