Argumentation mining in user-generated web discourse.

Autor/inn/en	Habernal, Ivan; Gurevych, Iryna
Titel	Argumentation mining in user-generated web discourse.
Quelle	In: Computational linguistics, 43 (2017) 1, S. 125-179 PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	1530-9312
DOI	10.1162/COLI_a_00276
Schlagwörter	Erziehungswissenschaft; Reliabilität; Soziale Software; Argumentation; Textanalyse; Computerlinguistik; Automatisierung; Diskurs; Modell; Information Retrieval; World Wide Web
Abstract	The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task. (DIPF/Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2023/1