Publikation: Guided Linguistic Annotation of Argumentation through Visual Analytics
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2020
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COMMA Workshop on Argument Visualization (ArgVis). 2020
Zusammenfassung
We present a mixed-initiative approach to interactive annotation of argumentation, a typically time-consuming manual task. Our system facilitates the process by suggesting which fragments of text to annotate next. Suggestions are sourced from pre-annotations and user-preferences that are learned over time. Unused suggestions decay over time, reducing the amount of necessary interactions, while providing additional training data to the system. We show the effectiveness of the system for argument annotation according to Inference Anchoring Theory. The duality of suggestion sources and novel approach to suggestion decay are broadly applicable in linguistic annotation.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
visual analytics, argumentation annotation, guidance, interface
Konferenz
ArgVis Workshop at the 8th International Conference on Computational Models of Argument (online), 8. Sept. 2020
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SPERRLE, Fabian, Mark-Matthias ZYMLA, Mennatallah EL-ASSADY, Miriam BUTT, Daniel A. KEIM, 2020. Guided Linguistic Annotation of Argumentation through Visual Analytics. ArgVis Workshop at the 8th International Conference on Computational Models of Argument (online), 8. Sept. 2020. In: COMMA Workshop on Argument Visualization (ArgVis). 2020BibTex
@inproceedings{Sperrle2020Guide-66537,
year={2020},
title={Guided Linguistic Annotation of Argumentation through Visual Analytics},
url={https://argvis-workshop.lingvis.io/pdfs/ArgVis2020_paper_4.pdf},
booktitle={COMMA Workshop on Argument Visualization (ArgVis)},
author={Sperrle, Fabian and Zymla, Mark-Matthias and El-Assady, Mennatallah and Butt, Miriam and Keim, Daniel A.}
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URL der Originalveröffentl.
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2023-03-28
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