Similarity Measures for Case-Based Retrieval of Natural Language Argument Graphs in Argumentation Machines. Bergmann, R., Lenz, M., Ollinger, S., & Pfister, M. In Barták, R. & Brawner, K. W., editors, Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conference, pages 329–334, Sarasota, Florida, USA, 2019-05-19/2019-05-22. AAAI Press. ![Similarity Measures for Case-Based Retrieval of Natural Language Argument Graphs in Argumentation Machines [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex In the field of argumentation, the vision of robust argumentation machines is investigated. They explore natural language arguments from information sources on the web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular user query. We aim at combining methods from case-based reasoning (CBR), information retrieval, and computational argumentation to contribute to the foundations of argumentation machines. In this paper, we focus on the retrieval phase of a CBR approach for an argumentation machine and propose similarity measures for arguments represented as argument graphs. We evaluate the similarity measures on a corpus of annotated micro texts and demonstrate the benefit of semantic similarity measures and the relevance of structural aspects.
@inproceedings{Bergmann2019SimilarityMeasuresCaseBased,
title = {Similarity {{Measures}} for {{Case-Based Retrieval}} of {{Natural Language Argument Graphs}} in {{Argumentation Machines}}},
booktitle = {Proceedings of the {{Thirty-Second International Florida Artificial Intelligence Research Society Conference}}},
author = {Bergmann, Ralph and Lenz, Mirko and Ollinger, Stefan and Pfister, Maximilian},
editor = {Bart{\'a}k, Roman and Brawner, Keith W.},
year = {2019-05-19/2019-05-22},
pages = {329--334},
publisher = {AAAI Press},
address = {Sarasota, Florida, USA},
url = {http://www.wi2.uni-trier.de/shared/publications/Bergmann2019SimilarityMeasuresCaseBased.pdf},
abstract = {In the field of argumentation, the vision of robust argumentation machines is investigated. They explore natural language arguments from information sources on the web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular user query. We aim at combining methods from case-based reasoning (CBR), information retrieval, and computational argumentation to contribute to the foundations of argumentation machines. In this paper, we focus on the retrieval phase of a CBR approach for an argumentation machine and propose similarity measures for arguments represented as argument graphs. We evaluate the similarity measures on a corpus of annotated micro texts and demonstrate the benefit of semantic similarity measures and the relevance of structural aspects.},
}
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