Unsupervised Argumentation Mining in Student Essays

Isaac Persing, Vincent Ng


Abstract
State-of-the-art systems for argumentation mining are supervised, thus relying on training data containing manually annotated argument components and the relationships between them. To eliminate the reliance on annotated data, we present a novel approach to unsupervised argument mining. The key idea is to bootstrap from a small set of argument components automatically identified using simple heuristics in combination with reliable contextual cues. Results on a Stab and Gurevych’s corpus of 402 essays show that our unsupervised approach rivals two supervised baselines in performance and achieves 73.5-83.7% of the performance of a state-of-the-art neural approach.
Anthology ID:
2020.lrec-1.839
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6795–6803
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.839
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.839.pdf