Argumentation Mining. This item is unavailable.Argumentation Mining

Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others.

The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity.

Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches.

Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements.

Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text.

The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a--necessarily subjective--outlook for the field.

Manfred Stede is a professor of Applied Computational Linguistics at the University of Potsdam, Germany. He obtained his Ph.D. in Computer Science from the University of Toronto in 1996 with a thesis on language generation; in those years he studied discourse structure primarily for its role in text generation. After working for five years in a machine translation project at TU Berlin, he moved to Potsdam in 2001, where his interests shifted to text analysis. He conducted research projects on applications like information extraction and text summarization, and on more theoretical matters like the semantics and pragmatics of connectives. In conjunction with research on discourse parsing, he began to work on argumentation in the 2000s, focusing first on newspaper editorials. Following the design of an annotation scheme, he proceeded to work on approaches to deriving argumentation structure trees from short texts, and on various other aspects of argumentation mining.Jodi Schneider is an assistant professor in the University of Illinois at Urbana-Champaigns School of Information Sciences. She has held research positions across the United States as well as in Ireland, England, France, and Chile. She earned her Ph.D. in informatics (National University of Ireland, Galway), two Masters degrees in library & information science (UIUC) and in mathematics (UT-Austin), and a Bachelors degree (Great Books, St. Johns College, Annapolis, MD). She has authored over 30 research publications on topics in argumentation, artificial intelligence, biomedical informatics, and computer-supported collaborative work. Her research uses arguments, evidence, and persuasion as a lens to study scholarly communication and social media. She also develops and evaluates tools to manage scientific evidence from the biomedical literature with an NIH-funded project, Text Mining Pipeline to Accelerate Systematic Reviews in Evidence-Based Medicine.