Contrastive Reasons Detection and Clustering from Online Polarized Debates
- Amine Trabelsi
- Osmar R. Zaiane, University of Alberta (Database)
This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints on polarized issues. It proposes a pipeline approach centered around the detection and clustering of phrases, assimilated to argument facets using a novel Phrase Author Interaction Topic-Viewpoint model. The evaluation is based on the informativeness, the relevance and the clustering accuracy of extracted reasons. The pipeline approach shows a significant improvement over state-of-the-art methods in contrastive summarization on online debate datasets.
Citation
A. Trabelsi, O. Zaiane. "Contrastive Reasons Detection and Clustering from Online Polarized Debates". International Conference on Intelligent Text Processing and Computational Linguistics (CICLing), La Rochelle, France, April 2019.| Keywords: | |
| Category: | In Conference |
| Web Links: | Webdocs |
BibTeX
@incollection{Trabelsi+Zaiane:CICLing19,
author = {Amine Trabelsi and Osmar R. Zaiane},
title = {Contrastive Reasons Detection and Clustering from Online Polarized
Debates},
booktitle = {International Conference on Intelligent Text Processing and
Computational Linguistics (CICLing)},
year = 2019,
}Last Updated: September 15, 2020Submitted by Sabina P
