Extracting Implicitly Asserted Propositions in Argumentation
- Submitting institution
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University of Dundee
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 56152862
- Type
- E - Conference contribution
- DOI
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10.18653/v1/2020.emnlp-main.2
- Title of conference / published proceedings
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- First page
- 24
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- November
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This collaboration with Carnegie Mellon was an outcome of a research visit by CMU’s Jo to Dundee in 2019. The paper crosses the divide between computing and philosophy of argument by addressing an open challenge reconstructing argumentative discourse. Enthymemes are one of the most persistent challenges for argumentation modelling, and the methods developed here for their reconstruction provide a crucial building block for automated argument processing and fallacy detection, with applications in fake news, hypothesis formation, and conversational agents.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -