Parsing Argumentation Structures in Persuasive Essays

Christian Stab, Iryna Gurevych


Abstract
In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using Integer Linear Programming. We show that our model significantly outperforms challenging heuristic baselines on two different types of discourse. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement.
Anthology ID:
J17-3005
Volume:
Computational Linguistics, Volume 43, Issue 3 - September 2017
Month:
September
Year:
2017
Address:
Venue:
CL
SIG:
Publisher:
Note:
Pages:
619–659
Language:
URL:
https://www.aclweb.org/anthology/J17-3005
DOI:
10.1162/COLI_a_00295
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PDF:
http://aclanthology.lst.uni-saarland.de/J17-3005.pdf