@inproceedings{ajjour-etal-2018-visualization,
title = "Visualization of the Topic Space of Argument Search Results in args.me",
author = {Ajjour, Yamen and
Wachsmuth, Henning and
Kiesel, Dora and
Riehmann, Patrick and
Fan, Fan and
Castiglia, Giuliano and
Adejoh, Rosemary and
Fr{\"o}hlich, Bernd and
Stein, Benno},
editor = "Blanco, Eduardo and
Lu, Wei",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-2011",
doi = "10.18653/v1/D18-2011",
pages = "60--65",
abstract = "In times of fake news and alternative facts, pro and con arguments on controversial topics are of increasing importance. Recently, we presented args.me as the first search engine for arguments on the web. In its initial version, args.me ranked arguments solely by their relevance to a topic queried for, making it hard to learn about the diverse topical aspects covered by the search results. To tackle this shortcoming, we integrated a visualization interface for result exploration in args.me that provides an instant overview of the main aspects in a barycentric coordinate system. This topic space is generated ad-hoc from controversial issues on Wikipedia and argument-specific LDA models. In two case studies, we demonstrate how individual arguments can be found easily through interactions with the visualization, such as highlighting and filtering.",
}
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<abstract>In times of fake news and alternative facts, pro and con arguments on controversial topics are of increasing importance. Recently, we presented args.me as the first search engine for arguments on the web. In its initial version, args.me ranked arguments solely by their relevance to a topic queried for, making it hard to learn about the diverse topical aspects covered by the search results. To tackle this shortcoming, we integrated a visualization interface for result exploration in args.me that provides an instant overview of the main aspects in a barycentric coordinate system. This topic space is generated ad-hoc from controversial issues on Wikipedia and argument-specific LDA models. In two case studies, we demonstrate how individual arguments can be found easily through interactions with the visualization, such as highlighting and filtering.</abstract>
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%0 Conference Proceedings
%T Visualization of the Topic Space of Argument Search Results in args.me
%A Ajjour, Yamen
%A Wachsmuth, Henning
%A Kiesel, Dora
%A Riehmann, Patrick
%A Fan, Fan
%A Castiglia, Giuliano
%A Adejoh, Rosemary
%A Fröhlich, Bernd
%A Stein, Benno
%Y Blanco, Eduardo
%Y Lu, Wei
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F ajjour-etal-2018-visualization
%X In times of fake news and alternative facts, pro and con arguments on controversial topics are of increasing importance. Recently, we presented args.me as the first search engine for arguments on the web. In its initial version, args.me ranked arguments solely by their relevance to a topic queried for, making it hard to learn about the diverse topical aspects covered by the search results. To tackle this shortcoming, we integrated a visualization interface for result exploration in args.me that provides an instant overview of the main aspects in a barycentric coordinate system. This topic space is generated ad-hoc from controversial issues on Wikipedia and argument-specific LDA models. In two case studies, we demonstrate how individual arguments can be found easily through interactions with the visualization, such as highlighting and filtering.
%R 10.18653/v1/D18-2011
%U https://aclanthology.org/D18-2011
%U https://doi.org/10.18653/v1/D18-2011
%P 60-65
Markdown (Informal)
[Visualization of the Topic Space of Argument Search Results in args.me](https://aclanthology.org/D18-2011) (Ajjour et al., EMNLP 2018)
ACL
- Yamen Ajjour, Henning Wachsmuth, Dora Kiesel, Patrick Riehmann, Fan Fan, Giuliano Castiglia, Rosemary Adejoh, Bernd Fröhlich, and Benno Stein. 2018. Visualization of the Topic Space of Argument Search Results in args.me. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 60–65, Brussels, Belgium. Association for Computational Linguistics.