@inproceedings{schaller-etal-2024-darius-comprehensive,
title = "{DARIUS}: A Comprehensive Learner Corpus for Argument Mining in {G}erman-Language Essays",
author = {Schaller, Nils-Jonathan and
Horbach, Andrea and
H{\"o}ft, Lars Ingver and
Ding, Yuning and
Bahr, Jan Luca and
Meyer, Jennifer and
Jansen, Thorben},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.389",
pages = "4356--4367",
abstract = "In this paper, we present the DARIUS (Digital Argumentation Instruction for Science) corpus for argumentation quality on 4589 essays written by 1839 German secondary school students. The corpus is annotated according to a fine-grained annotation scheme, ranging from a broader perspective like content zones, to more granular features like argumentation coverage/reach and argumentative discourse units like claims and warrants. The features have inter-annotator agreements up to 0.83 Krippendorff{'}s α. The corpus and dataset are publicly available for further research in argument mining.",
}
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<abstract>In this paper, we present the DARIUS (Digital Argumentation Instruction for Science) corpus for argumentation quality on 4589 essays written by 1839 German secondary school students. The corpus is annotated according to a fine-grained annotation scheme, ranging from a broader perspective like content zones, to more granular features like argumentation coverage/reach and argumentative discourse units like claims and warrants. The features have inter-annotator agreements up to 0.83 Krippendorff’s α. The corpus and dataset are publicly available for further research in argument mining.</abstract>
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%0 Conference Proceedings
%T DARIUS: A Comprehensive Learner Corpus for Argument Mining in German-Language Essays
%A Schaller, Nils-Jonathan
%A Horbach, Andrea
%A Höft, Lars Ingver
%A Ding, Yuning
%A Bahr, Jan Luca
%A Meyer, Jennifer
%A Jansen, Thorben
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F schaller-etal-2024-darius-comprehensive
%X In this paper, we present the DARIUS (Digital Argumentation Instruction for Science) corpus for argumentation quality on 4589 essays written by 1839 German secondary school students. The corpus is annotated according to a fine-grained annotation scheme, ranging from a broader perspective like content zones, to more granular features like argumentation coverage/reach and argumentative discourse units like claims and warrants. The features have inter-annotator agreements up to 0.83 Krippendorff’s α. The corpus and dataset are publicly available for further research in argument mining.
%U https://aclanthology.org/2024.lrec-main.389
%P 4356-4367
Markdown (Informal)
[DARIUS: A Comprehensive Learner Corpus for Argument Mining in German-Language Essays](https://aclanthology.org/2024.lrec-main.389) (Schaller et al., LREC-COLING 2024)
ACL
- Nils-Jonathan Schaller, Andrea Horbach, Lars Ingver Höft, Yuning Ding, Jan Luca Bahr, Jennifer Meyer, and Thorben Jansen. 2024. DARIUS: A Comprehensive Learner Corpus for Argument Mining in German-Language Essays. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 4356–4367, Torino, Italia. ELRA and ICCL.