The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Identifying Argumentative Discourse Structures in Persuasive Essays
Open accessAbstract
In this paper, we present a novel approach for identifying argumentative discourse structures in persuasive essays. The structure of argumentation consists of several components (i.e. claims and premises) that are connected with argumentative relations. We consider this task in two consecutive steps. First, we identify the components of arguments using multiclass classification. Second, we classify a pair of argument components as either support or non-support for identifying the structure of argumentative discourse. For both tasks, we evaluate several classifiers and propose novel feature sets including structural, lexical, syntactic and contextual features. In our experiments, we obtain a macro F1-score of 0.726 for identifying argument components and 0.722 for argumentative relations.
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Classification and Clustering of Arguments with Contextualized Word Embeddings
Reimers, N., Schiller, B., Beck, T., Daxenberger, J., Stab, C., & Gurevych, I. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1054 · Cited by 157
Self-citationArgueTutor: An Adaptive Dialog-Based Learning System for Argumentation Skills
Wambsganss, T., Kueng, T., Soellner, M., & Leimeister, J. M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445781 · Cited by 138
AL: An Adaptive Learning Support System for Argumentation Skills
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Metadata
- Authors
- Stab, C., & Gurevych, I.
- Venue
- Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2014
- Pages
- 46-56
- OpenAlex
- W2250309026
- Cited by
- 363
- References
- 26
- FWCI
- 391.59
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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