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Autor*innen: Stab, Christian; Gurevych, Iryna
Titel: Recognizing the absence of opposing arguments in persuasive essays
Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 3rd Workshop on Argument Mining held in conjunction with the 2016 Annual Meeting of the Association for Computational Linguistics (ACL 2016), Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 113-118
URL: http://aclweb.org/anthology/W/W16/W16-2813.pdf
Dokumenttyp: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Argumentation; Aufsatz; Computerlinguistik; Dokument; Gegensatz; Klassifikation; Modell
Abstract (english): 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. (DIPF/Orig.)
DIPF-Abteilung: Informationszentrum Bildung