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Stance-based Argument Mining - Modeling Implicit Argumentation Using Stance

A major remaining challenge in argument mining is implicitness. We propose to model implicit argumentation using explicit stances and the overall stance of a debate. Our evaluation on a social media corpus shows that our model (i) can be reliably annotated even on noisy data and (ii) has the potential to improve the performance of automated argument mining.

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Conference:
13th Conference on Natural Language Processing (KONVENS 2016), Bochum, Germany, September 19-21, 2016
Date Issued:
09.2016
URN:
urn:nbn:de:hbz:464-20180627-121622-4
DOI:
10.17185/duepublico/46445
Language:
English
Type of Resource:
Text
Collection:
E-Publications
Dewey Decimal Classification:
000 Computer science, information & general works
Subject categories of the Deutsche Nationalbibliografie:
004 Data processing Computer sciences
URL:
https://www.linguistics.rub.de/bla/
Hosting institution:
Faculty of Engineering, Computer Science and Applied Cognitive Science
Hosting institution:
Scientific institutes, Institute for optional studies (IOS)
Info on primary publication:

Wojatzki, Michael; Zesch, Torsten: Stance-based Argument Mining – Modeling Implicit Argumentation Using Stance

Published in: Stefanie Dipper, Friedrich Neubarth and Heike Zinsmeister (Eds.). Proceedings of the 13th Conference on Natural Language Processing (KONVENS 2016), Bochum, Germany, September 19-21, 2016, (Bochumer Linguistische Arbeitsberichte ; 16), https://www.linguistics.rub.de/forschung/arbeitsberichte/16.pdf, pp. 313-322

Published: September 2016

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© Die Autoren

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