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.
Cite
CITATION STYLE
Stab, C., & Gurevych, I. (2016). Recognizing the Absence of Opposing Arguments in Persuasive Essays. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 113–118). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-2813
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