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Autor:
Stab, Christian; Gurevych, Iryna:

Titel:
Recognizing the absence of opposing arguments in persuasive essays

Quelle:
In: 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) , 113-118

URL des Volltextes:
http://aclweb.org/anthology/W/W16/W16-2813.pdf

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Argumentation, Aufsatz, Computerlinguistik, Dokument, Gegensatz, Klassifikation, Modell


Abstract(englisch):
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:
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last modified Nov 11, 2016