Abstract
Research on argumentation in Artificial Intelligence recently investigates new methods that contribute to the vision of developing robust argumentation machines. One line of research explores ways of reasoning with natural language arguments coming from information sources on the web as a foundation for the deliberation and synthesis of arguments in specific domains. This paper builds upon arguments represented as argument graphs in the standardized Argument Interchange Format. While previous work was focused on the development of semantic similarity measures used for the case-based retrieval of argument graphs, this paper addresses the problem of clustering argument graphs to explore structures that facilitate argumentation interpretation. We propose a k-medoid and an agglomerative clustering approach based on semantic similarity measures. We compare the clustering results based on a graph-based semantic measure that takes the structure of the argument into account with a semantic word2vec measure on the pure textual argument representation. Experiments based on the Microtext corpus show that the graph-based similarity is best on internal evaluation measures, while the pure textual measure performs very well for identifying topic-specific clusters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Atkinson, K., et al.: Towards artificial argumentation. AI Mag. 38(3), 25–36 (2017)
Bergmann, R., Gil, Y.: Similarity assessment and efficient retrieval of semantic workflows. Inf. Syst. 40, 115–127 (2014). https://doi.org/10.1016/j.is.2012.07.005
Bergmann, R., Lenz, M., Ollinger, S., Pfister, M.: Similarity measures for case-based retrieval of natural language argument graphs in argumentation machines. In: Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019, Sarasota, Florida, USA. AAAI-Press (2019)
Bergmann, R., Schenkel, R., Dumani, L., Ollinger, S.: ReCAP - information retrieval and case-based reasoning for robust deliberation and synthesis of arguments in the political discourse. In: Proceedings of the Conference “Lernen, Wissen, Daten, Analysen”, LWDA 2018, 22–24 August 2018, Mannheim, Germany, CEUR Workshop Proceedings, vol. 2191. CEUR-WS.org (2018). http://ceur-ws.org/Vol-2191/paper6.pdf
Bex, F., Reed, C.: Schemes of inference, conflict and preference in a computational model of argument. Stud. Logic Grammar Rhetoric 23(36), 39–58 (2011)
Boltužić, F., Šnajder, J.: Identifying prominent arguments in online debates using semantic textual similarity. In: Proceedings of the 2nd Workshop on Argumentation Mining, pp. 110–115. Association for Computational Linguistics, June 2015
Chesnevar, C., et al.: Towards an argument interchange format. Knowl. Eng. Rev. 21(4), 293–316 (2006)
Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–358 (1995)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973). https://doi.org/10.1080/01969727308546046
Habernal, I., Gurevych, I.: Exploiting debate portals for semi-supervised argumentation mining in user-generated web discourse. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2127–2137 (2015)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data - An Introduction to Cluster Analysis. Wiley, New York (1990)
Lenz, M., Ollinger, S., Sahitaj, P., Bergmann, R.: Semantic textual similarity measures for case-based retrieval of argument graphs. In: Case-Based Reasoning Research and Development: 27th International Conference, ICCBR 2019, 8–12 September 2019, Otzenhausen, Germany, Proceedings. Springer, Heidelberg (2019, accepted for publication)
Lippi, M., Torroni, P.: Argument mining from speech: detecting claims in political debates. In: Schuurmans, D., Wellman, M.P. (eds.) Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016), pp. 2979–2985. AAAI Press (2016). http://argumentationmining.disi.unibo.it/publications/AAAI2016.pdf
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781 [cs], January 2013
Peldszus, A., Stede, M.: From argument diagrams to argumentation mining in texts. Int. J. Cogn. Inform. Nat. Intell. 7(1), 1–31 (2013). https://doi.org/10.4018/jcini.2013010101
Peldszus, A., Stede, M.: An annotated corpus of argumentative microtexts. In: First European Conference on Argumentation: Argumentation and Reasoned Action, Portugal, Lisbon, June 2015. http://www.ling.uni-potsdam.de/~peldszus/eca2015-preprint.pdf
Rand, W.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)
Richter, M.M., Weber, R.O.: Case-Based Reasoning - A Textbook. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40167-1
Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20(1), 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7
Walton, D., Reed, C., Macagno, F.: Argumentation Schemes. Cambridge University Press, Cambridge (2008)
Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963). http://www.jstor.org/stable/2282967
Acknowledgments
This work was funded by the German Research Foundation (DFG), project 375342983.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Block, K., Trumm, S., Sahitaj, P., Ollinger, S., Bergmann, R. (2019). Clustering of Argument Graphs Using Semantic Similarity Measures. In: Benzmüller, C., Stuckenschmidt, H. (eds) KI 2019: Advances in Artificial Intelligence. KI 2019. Lecture Notes in Computer Science(), vol 11793. Springer, Cham. https://doi.org/10.1007/978-3-030-30179-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-30179-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30178-1
Online ISBN: 978-3-030-30179-8
eBook Packages: Computer ScienceComputer Science (R0)