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Dataset . 2019
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args.me corpus

Authors: Ajjour, Yamen; Wachsmuth, Henning; Kiesel, Johannes; Potthast, Martin; Hagen, Matthias; Stein, Benno;
Abstract

The args.me corpus comprises 387 740 arguments. They are crawled from the debate portals Debatewise (14 353 arguments), IDebate.org (13 522 arguments), Debatepedia (21 197 arguments), and Debate.org (338 620 arguments). Moreover, the corpus contains 48 arguments from Canadian Parliament discussions. The arguments are extracted using heuristics that are designed for each debate portal. These arguments are the ones currently provided through the args.me search engine. Note that the args API does not return the sourceText (which is indexed by args.me an included in this dataset) due to its size. Cite args.me as Henning Wachsmuth, Martin Potthast, Khalid Al-Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein. Building an Argument Search Engine for the Web. In 4th Workshop on Argument Mining (ArgMining 2017) at EMNLP, pages 49-59, September 2017. Association for Computational Linguistics. Cite this dataset as Yamen Ajjour, Henning Wachsmuth, Johannes Kiesel, Martin Potthast, Matthias Hagen, and Benno Stein. Data Acquisition for Argument Search: The args.me corpus. In 42nd German Conference on Artificial Intelligence (KI 2019), September 2019. Springer. and with the DOI of Zenodo. The development for args.me is hosted in our Gitlab. This collection is licensed with the Creative Commons Attribution 4.0 International. Individual rights to the content still apply.

{"references": ["Yamen Ajjour, Henning Wachsmuth, Johannes Kiesel, Martin Potthast, Matthias Hagen, and Benno Stein. Data Acquisition for Argument Search: The args.me corpus. In 42nd German Conference on Artificial Intelligence (KI 2019), September 2019. Springer.", "Henning Wachsmuth, Martin Potthast, Khalid Al-Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein. Building an Argument Search Engine for the Web. In 4th Workshop on Argument Mining (ArgMining 2017) at EMNLP, pages 49-59, September 2017. Association for Computational Linguistics."]}

Keywords

Computational Argumentation, Argument Search, Information Retrieval, Natural Language Processing

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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