Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs. Lenz, M., Sahitaj, P., Kallenberg, S., Coors, C., Dumani, L., Schenkel, R., & Bergmann, R. In Prakken, H., Bistarelli, S., Santini, F., & Taticchi, C., editors, Proceedings of the 8th International Conference on Computational Models of Argument, volume 326, of Frontiers in Artificial Intelligence and Applications, pages 263–270, Perugia, Italy, 2020. IOS Press.
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Paper doi abstract bibtex
Paper doi abstract bibtex This paper tackles the automated extraction of components of argumentative information and their relations from natural language text. Moreover, we address a current lack of systems to provide a complete argumentative structure from arbitrary natural language text for general usage. We present an argument mining pipeline as a universally applicable approach for transforming German and English language texts to graph-based argument representations. We also introduce new methods for evaluating the performance based on existing benchmark argument structures. Our results show that the generated argument graphs can be beneficial to detect new connections between different statements of an argumentative text.
@inproceedings{Lenz2020ArgumentMiningPipeline,
title = {Towards an {{Argument Mining Pipeline Transforming Texts}} to {{Argument Graphs}}},
booktitle = {Proceedings of the 8th {{International Conference}} on {{Computational Models}} of {{Argument}}},
author = {Lenz, Mirko and Sahitaj, Premtim and Kallenberg, Sean and Coors, Christopher and Dumani, Lorik and Schenkel, Ralf and Bergmann, Ralph},
editor = {Prakken, Henry and Bistarelli, Stefano and Santini, Francesco and Taticchi, Carlo},
year = {2020},
series = {Frontiers in {{Artificial Intelligence}} and {{Applications}}},
volume = {326},
eprint = {2006.04562},
pages = {263--270},
publisher = {IOS Press},
address = {Perugia, Italy},
doi = {10.3233/FAIA200510},
url = {http://www.wi2.uni-trier.de/shared/publications/Lenz2020ArgumentMiningPipeline.pdf},
abstract = {This paper tackles the automated extraction of components of argumentative information and their relations from natural language text. Moreover, we address a current lack of systems to provide a complete argumentative structure from arbitrary natural language text for general usage. We present an argument mining pipeline as a universally applicable approach for transforming German and English language texts to graph-based argument representations. We also introduce new methods for evaluating the performance based on existing benchmark argument structures. Our results show that the generated argument graphs can be beneficial to detect new connections between different statements of an argumentative text.},
}Downloads: 0
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