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Recognizing the Absence of Opposing Arguments in Persuasive Essays

Stab, Christian ; Gurevych, Iryna (2016):
Recognizing the Absence of Opposing Arguments in Persuasive Essays.
In: Proceedings of the 3rd Workshop on Argument Mining held in conjunction with the 2016 Annual Meeting of the Association for Computational Linguistics (ACL 2016), pp. 113-118,
Association for Computational Linguistics, Berlin, Germany, [Conference or Workshop Item]

Abstract

In this paper, we introduce an approach for recognizing the absence of opposing arguments in persuasive essays. We model this task as a binary document classification and show that adversative transitions in combination with unigrams and syntactic production rules significantly outperform a challenging heuristic baseline. Our approach yields an accuracy of 75.6% and 84% of human performance in a persuasive essay corpus with various topics.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Stab, Christian ; Gurevych, Iryna
Title: Recognizing the Absence of Opposing Arguments in Persuasive Essays
Language: English
Abstract:

In this paper, we introduce an approach for recognizing the absence of opposing arguments in persuasive essays. We model this task as a binary document classification and show that adversative transitions in combination with unigrams and syntactic production rules significantly outperform a challenging heuristic baseline. Our approach yields an accuracy of 75.6% and 84% of human performance in a persuasive essay corpus with various topics.

Title of Book: Proceedings of the 3rd Workshop on Argument Mining held in conjunction with the 2016 Annual Meeting of the Association for Computational Linguistics (ACL 2016)
Publisher: Association for Computational Linguistics
Uncontrolled Keywords: UKP_a_ArMin;UKP_reviewed
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Event Location: Berlin, Germany
Date Deposited: 31 Dec 2016 14:29
Official URL: http://www.aclweb.org/anthology/W16-2813
Identification Number: TUD-CS-2016-0137
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