Argument Mining

2. Auflage 2026
ISBN: 978-3-032-32290-6
Verlag: Springer

Buch, Englisch, 437 Seiten, Format (B × H): 168 mm x 240 mm

Reihe: Synthesis Lectures on Human Language Technologies

This Open Access book provides an essential overview of the topic of argumentation mining, an active research area of Natural Language Processing (NLP). Argument mining can automatically find and collect arguments published in newspapers, social media, or elsewhere. Argument mining can be also used to give a precise account of the argumentative structure of a single text or dialogue such as a legal or scientific document, a student essay, or an online conversation. Increasingly, computational approaches provide synopses of arguments and compare the relative strength of arguments. In this book, the authors start with basic argumentation concepts and describe the NLP methods for mining arguments automatically from natural language text. The authors then show how NLP is used for assessing the quality of arguments and generating new arguments. This presents an updated survey of the state of the art of argument mining, providing increased depth and insights into many more NLP tasks than the prior edition. This new edition discusses the disruptive changes that have come with large language models and highlights potential future developments. The authors also update the treatment of the common steps in argument mining, argument generation tasks and methods, and computational argumentation applications. Completely new chapters cover perspective assessment, argument quality assessment, and argument reasoning. 

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Stede, Manfred

Schneider, Jodi

Wachsmuth, Henning

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Geisteswissenschaften Sprachwissenschaft Computerlinguistik, Korpuslinguistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

Introduction.- Argumentative Language.- Modeling Arguments.- Corpus Annotation.- Finding Claims.- Finding Supporting and Objecting Statements.- Deriving the Structure of Argumentation.- Assessing Argumentation.- Generating Argumentative Text.- Summary and Perspectives.

Über Autor(innen)

Jodi Schneider, Ph.D., is an Associate Professor of Information at the University of Wisconsin-Madison Information School, where she directs the Information Quality Lab. Dr. Schneider completed her Ph.D. in Informatics at the National University of Ireland, Galway. She studies the science of science through the lens of arguments, evidence, and persuasion. Her long-term goal is to ensure that public policy decisions can apply the best available scientific evidence. She is investigating knowledge representation approaches for bridging formal and informal argumentation, and her recent work analyzes the argumentative features of chatbots.

Manfred Stede, Ph.D., is a Professor of Applied Computational Linguistics at the University of Potsdam, Germany. He completed his Ph.D. in Computer Science at the University of Toronto. He also studied Computer Science and Linguistics at TU Berlin and completed an M.Sc. in Computer Science at Purdue University. His research and teaching activities revolve around issues in discourse structure and automatic text understanding, and in the past 10 years has had a focus on discourse parsing and argument mining.

Henning Wachsmuth, Ph.D., is a Professor of Natural Language Processing at the Institute of Artificial Intelligence of Leibniz University Hannover, Germany. He received his Ph.D. in Computer Science from Paderborn University. His group studies how intentions and views of people are reflected in language and how machines can understand and imitate this with large language models. Dr. Wachsmuth’s main research interests include the computational assessment and generation of arguments, the mitigation of social bias and media bias, and the construction of human-like explanations for educational and explainable natural language processing.