@inproceedings{Lenz2023CaseBasedAdaptationArgument,
  title = {Case-{{Based Adaptation}} of~{{Argument Graphs}} with~{{WordNet}} and~{{Large Language Models}}},
  booktitle = {Case-{{Based Reasoning Research}} and {{Development}}},
  author = {Lenz, Mirko and Bergmann, Ralph},
  editor = {Massie, Stewart and Chakraborti, Sutanu},
  year = {2023},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {14141},
  pages = {263--278},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-40177-0_17},
  abstract = {Finding information online is hard, even more so once you get into the domain of argumentation. There have been developments around the specialized argumentation machines that incorporate structural features of arguments, but all current approaches share one pitfall: They operate on a corpora of limited sizes. Consequently, it may happen that a user searches for a rather general term like cost increases, but the machine is only able to serve arguments concerned with rent increases. We aim to bridge this gap by introducing approaches to generalize/specialize a found argument using a combination of WordNet and Large Language Models. The techniques are evaluated on a new benchmark dataset with diverse queries using our fully featured implementation. Both the dataset and the code are publicly available on GitHub.},
  isbn = {978-3-031-40177-0},
  langid = {english},
  note = {Best Student Paper Award at ICCBR 2023},
  url = {http://www.wi2.uni-trier.de/shared/publications/Lenz2023CaseBasedAdaptationArgument.pdf}
}

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