Unsupervised Detection of Argumentative Units though Topic Modeling Techniques

Alfio Ferrara, Stefano Montanelli, Georgios Petasis


Abstract
In this paper we present a new unsupervised approach, “Attraction to Topics” – A2T , for the detection of argumentative units, a sub-task of argument mining. Motivated by the importance of topic identification in manual annotation, we examine whether topic modeling can be used for performing unsupervised detection of argumentative sentences, and to what extend topic modeling can be used to classify sentences as claims and premises. Preliminary evaluation results suggest that topic information can be successfully used for the detection of argumentative sentences, at least for corpora used for evaluation. Our approach has been evaluated on two English corpora, the first of which contains 90 persuasive essays, while the second is a collection of 340 documents from user generated content.
Anthology ID:
W17-5113
Volume:
Proceedings of the 4th Workshop on Argument Mining
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
ArgMining | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
97–107
Language:
URL:
https://www.aclweb.org/anthology/W17-5113
DOI:
10.18653/v1/W17-5113
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-5113.pdf