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Argumentation Mining in User-Generated Web Discourse

Habernal, Ivan and Gurevych, Iryna :
Argumentation Mining in User-Generated Web Discourse.
[Online-Edition: http://dx.doi.org/10.1162/COLI_a_00276]
In: Computational Linguistics, 43 (1) pp. 125-179.
[Article] , (2017)

Official URL: http://dx.doi.org/10.1162/COLI_a_00276

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.

Item Type: Article
Erschienen: 2017
Creators: Habernal, Ivan and Gurevych, Iryna
Title: Argumentation Mining in User-Generated Web Discourse
Language: English
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.

Journal or Publication Title: Computational Linguistics
Volume: 43
Number: 1
Uncontrolled Keywords: UKP_a_ArMin
Divisions: Department of Computer Science
Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Date Deposited: 31 Dec 2016 14:29
DOI: 10.1162/COLI_a_00276
Official URL: http://dx.doi.org/10.1162/COLI_a_00276
Identification Number: TUD-CS-2016-0013
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