End-to-end Argument Mining with Cross-corpora Multi-task Learning by Gaku Morio, Hiroaki Ozaki, Terufumi Morishita, Kohsuke Yanai

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Unveiling the Argumentative Nature of Meta-Analysis in Applied Linguistics: An Argument-Mining Approach by Arsalan Yaghoubi, Mohammad Amini Farsani, Behrouz MinaeiBidgoli, Ali Asghar Taghizadeh

“…As such, following research synthesis techniques and an argument-mining approach, we examined the academic argumentation genre of meta-analysis published in leading applied linguistics journals through argument-mining techniques in light of the modified Toulmin framework proposed by Qin and Karabacak (2010). …”
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Perception and argumentation in the LK-99 superconductivity controversy: a sentiment and argument mining analysis by Eunhye Kim, Wei Luo, Hunkoog Jho

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Covid-on-the-Web: Exploring the COVID-19 scientific literature through visualization of linked data from entity and argument mining by Aline Menin, Franck Michel, Fabien Gandon, Raphaël Gazzotti, Elena Cabrio, Olivier Corby, Alain Giboin, Santiago Marro, Tobias Mayer, Serena Villata, Marco Winckler

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Argument mapping: Towards an interdisciplinary descriptive model by Thierry Herman

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Neural Classification of Argument Elements and Styles in Arabic Competitive Debates by Al-Zawqari Ali, Mohamed Ahmed, Abdul Gabbar Al-Sharafi, Mohammad M. Khader, Ali Safa, Gerd Vandersteen

Subjects: “…Neural argument mining…”
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Cartographier le raisonnement : pour un modèle descriptif interdisciplinaire by Thierry Herman

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Langage, post-politique et automatisation : critique préventive de l’argumentation artificielle by Samuel COSSETTE

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PubMed captures more fine-grained bibliographic data on scientific commentary than Web of Science: a comparative analysis by Kai Zhang, Shuang Wang, Jian Du

“…We constructed comment networks for each database for network structural analysis and compared the characteristics of commentary materials and commented papers from various facets.Results For network comparison, PubMed surpasses WoS with more closed feedback loops, reaching a deeper six-level network compared with WoS’ four levels, making PubMed well-suited for evidence appraisal through argument mining. PubMed excels in identifying specialised comments, displaying significantly lower author count (mean, 3.59) and page count (mean, 1.86) than WoS (authors, 4.31, 95% CI of difference of two means = [0.66, 0.79], p<0.001; pages, 2.80, 95% CI of difference of two means = [0.87, 1.01], p<0.001), attributed to PubMed’s CICO comment identification algorithm. …”
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