Details

Argumentation Mining in User-Generated Web Discourse

Author Ivan Habernal, Iryna Gurevych
Date April 2017
Kind Article
JournalComputational Linguistics
Number1
Pages125-179
DOI10.1162/COLI_a_00276
KeyTUD-CS-2016-0013
Research Areas Ubiquitous Knowledge Processing, UKP_a_ArMin
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.
Website http://dx.doi.org/10.1162/COLI_a_00276
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