@inproceedings{persing-ng-2020-unsupervised,
title = "Unsupervised Argumentation Mining in Student Essays",
author = "Persing, Isaac and
Ng, Vincent",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.839",
pages = "6795--6803",
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.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Unsupervised Argumentation Mining in Student Essays
%A Persing, Isaac
%A Ng, Vincent
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F persing-ng-2020-unsupervised
%X 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.
%U https://aclanthology.org/2020.lrec-1.839
%P 6795-6803
Markdown (Informal)
[Unsupervised Argumentation Mining in Student Essays](https://aclanthology.org/2020.lrec-1.839) (Persing & Ng, LREC 2020)
ACL