Argumentation Mining in User-Generated Web Discourse

Ivan Habernal, Iryna Gurevych


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.
Anthology ID:
J17-1004
Volume:
Computational Linguistics, Volume 43, Issue 1 - April 2017
Month:
April
Year:
2017
Address:
Cambridge, MA
Venue:
CL
SIG:
Publisher:
MIT Press
Note:
Pages:
125–179
Language:
URL:
https://aclanthology.org/J17-1004
DOI:
10.1162/COLI_a_00276
Bibkey:
Cite (ACL):
Ivan Habernal and Iryna Gurevych. 2017. Argumentation Mining in User-Generated Web Discourse. Computational Linguistics, 43(1):125–179.
Cite (Informal):
Argumentation Mining in User-Generated Web Discourse (Habernal & Gurevych, CL 2017)
Copy Citation:
PDF:
https://aclanthology.org/J17-1004.pdf
Data
UKP