Patterns of Argumentation Strategies across Topics

Khalid Al-Khatib, Henning Wachsmuth, Matthias Hagen, Benno Stein


Abstract
This paper presents an analysis of argumentation strategies in news editorials within and across topics. Given nearly 29,000 argumentative editorials from the New York Times, we develop two machine learning models, one for determining an editorial’s topic, and one for identifying evidence types in the editorial. Based on the distribution and structure of the identified types, we analyze the usage patterns of argumentation strategies among 12 different topics. We detect several common patterns that provide insights into the manifestation of argumentation strategies. Also, our experiments reveal clear correlations between the topics and the detected patterns.
Anthology ID:
D17-1141
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1351–1357
Language:
URL:
https://www.aclweb.org/anthology/D17-1141
DOI:
10.18653/v1/D17-1141
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1141.pdf