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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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