The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.
Argumentation Mining: State of the Art and Emerging Trends
Open accessAbstract
Argumentation mining aims at automatically extracting structured arguments from unstructured textual documents. It has recently become a hot topic also due to its potential in processing information originating from the Web, and in particular from social media, in innovative ways. Recent advances in machine learning methods promise to enable breakthrough applications to social and economic sciences, policy making, and information technology: something that only a few years ago was unthinkable. In this survey article, we introduce argumentation models and methods, review existing systems and applications, and discuss challenges and perspectives of this exciting new research area.
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Cited by
Probing Neural Network Comprehension of Natural Language Arguments
Niven, T., & Kao, H.-Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1459 · Cited by 395
Toward Artificial Argumentation
Atkinson, K., Baroni, P., Giacomin, M., Hunter, A., Prakken, H., Reed, C., Simari, G., Thimm, M., & Villata, S. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2704 · Cited by 142
Bauer, E., Greisel, M., Kuznetsov, I., Berndt, M., Kollar, I., Dresel, M., Fischer, M. R., & Fischer, F. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13336 · Cited by 130
Human Judgment in algorithmic loops: Individual justice and automated decision‐making
Binns, R. (2022) · Regulation & Governance · doi:10.1111/rego.12358 · Cited by 134
AL: An Adaptive Learning Support System for Argumentation Skills
Wambsganss, T., Niklaus, C., Cetto, M., Söllner, M., Handschuh, S., & Leimeister, J. M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376732 · Cited by 101
Metadata
- Authors
- Lippi, M., & Torroni, P.
- Venue
- ACM Transactions on Internet Technology
- Published
- 2016-03-30
- Volume
- 16
- Issue
- 2
- Pages
- 1-25
- ISSN
- 1533-5399 (ISSN-L)
- 1557-6051
- OpenAlex
- W2327805699
- Cited by
- 363
- References
- 87
- FWCI
- 46.75
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
