Formal Identification of Argumentation Patterns in Scientific Texts

In this paper we present the methods for the automatic identification of argumentation patterns and the results of their application. These methods have been employed to analyze argumentation annotations of 25 scientific texts from two thematic areas. Under study were the expert annotations constructed manually with the help of web tools for visualizing argumentative statements and argumentation schemes, as well as for modelling the argumentation structure of a text as an oriented graph. Such graphs contain two node types: information nodes denoting statements and their connecting reasoning models (schemas) from Walton's compendium. The regularly employed reasoning models and their structural combinations (argumentation subgraphs) form argumentation patterns. Patterns containing more than one scheme are unmarked in initial graphs. As a result of processing argumentation annotations from the collection, we have constructed a joint spectrum of argumentation patterns with their absolute and text frequencies. The methods of frequent subgraph mining have been used to identify argumentation patterns containing three or more schemes. The subgraph matching has been performed through the use of the NetworkX package which implements the VF2 algorithm for subgraph isomorphism testing. We have analyzed the calculated frequencies to identify both the general principles behind the use of argumentation patterns typical of scientific texts, as well as the specific tendencies of their functioning within distinct thematic areas (linguistics and computer science). These general principles regulate the use of both separate schemes and their structural combinations. The latter appear in two configuration types: either as sequencies of argumentation schemes (up to 5 elements) or as tree structures (which contain up to 8 nodes). Specifically, we demonstrate that branching within a tree-form pattern typically presupposes the parallel use of identical argumentation schemes. Additionally, branching of argumentation patterns occurs more often in proximity to the main thesis of a text, than near the initial premises. Finally, the thematic area can condition not only the use of separate schemes, but also their structural combination within complex patterns. The results obtained are applicable to the evaluation of text similarity based on argumentation patterns employed in them, which, in its turn, can improve the clustering and classification of texts, evaluation of their persuasiveness, as well as the formal synthesis of argumentation.

1. Al-Khatib, K., Wachsmuth, H., Hagen, M., Stein, B. Patterns of Argumentation Strategies across Topics. In: Proc. of the 2017 Conference on Empirical Methods in Natural Language. Copenhagen, Denmark, 2017, pp. 1351–1357.

2. Anand, P. et al. Believe Me – We Can Do This! Annotating Persuasive Acts in Blog Text. In: Computational Models of Natural Argument. San Francisco, CA, USA, 2011, August 7.

3. Cordella, L. P., Foggia, P., Sansone, C., Vento, M. A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, vol. 26, no. 10, pp. 1367–1372.

4. El Baff, R., Wachsmuth, H., Al Khatib, Kh., Stede, M., Stein, B. Computational Argumentation Synthesis as a Language Modeling Task. In: Proc. of the 12th International Conference on Natural Language Generation. Tokyo, Japan, 2019, pp. 54–64. Hagberg, A. A., Schult, D. A., Swart, P. J. Exploring network structure, dynamics, and function using NetworkX. In: Proc. of the 7th Python in Science Conference (SciPy2008). Pasadena, CA, USA, 2008, pp. 11–15.

5. Fishcheva, I., Kotelnikov, E. Cross-Lingual Argumentation Mining for Russian Texts. In: Proc. of the 8th International Conference “Analysis of Images, Social Networks and Texts”. Kazan, 2019, pp. 134–144.

6. Inkova, O., Manzotti, E. Text coherence: mereological logico-semantic relations. Moscow, LRC Publ., 2019, 376 p. (in Russ.) Jiang, C., Coenen, F., Zito, M. A Survey of Frequent Subgraph Mining Algorithms. The Knowledge Engineering Review, 2004, no. 000(1), pp. 1–31.

7. Lawrence, J., Reed, C. Argument Mining: A Survey. Computational Linguistics, 2019, vol. 45, no. 4, pp. 765‒818.

8. Lippi, M., Torrony, P. Argumentation Mining: State of the Art and Emerging Trends. ACM Transactions on Internet Technology, 2016, vol. 16, article 10.

9. Lukashevich, N. V. Thesauri in Information Retrieval Problems. Moscow, Moscow State Uni. Press, 2011, 512 p. (in Russ.)

10. Madnani, N., Heilman, M., Tetreault, J., Chodorow, M. Identifying high-level organizational elements in argumentative discourse. In: Proc. of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Montréal, Canada, NAACL, Association for Computational Linguistics, 2012, pp. 20–28. Pimenov, I. S. On argumentation annotation of scientific and popular science texts. In: Corpus Linguistics – 2021. Proc. of the International Conference. St. Petersburg, Skifia Print, 2021. (in Russ.) ISBN 978-5-98620-557-1

11. Rahwan, I., Reed, C. The argument interchange format. In: Rahwan, I., Simari, G. (eds.). Argu-mentation in artificial intelligence. Springer, 2009, pp. 383–402.

12. Salomatina, N. V., Pimenov, I. S. The N-gram analysis of argumentation annotations of texts. In: Knowledge ‒ Ontology ‒ Theory. Proc. of the VIII International Conference, 2021. (in Russ.) URL: https://https://drive.google.com/file/d/1vLlHJk_cjYaSK9lwpw1i2HPNr8Qtmy1t/view

13. Sidorova, Е. А., Akhmadeeva, I. R., Zagorulko, Yu. A., Sery, A. S., Shestakov, V. K. Research platform for the study of argumentation in popular science discourse. Ontology Design, 2020, vol. 10, no. 4 (38), pp. 489–502. (in Russ.)

14. Taboada, M., Mann, W. Rhetorical Structure Theory: Looking back and moving ahead. Discourse Stud., 2006, no. 8, pp. 423–459.

15. Ullmann, J. R. An Algorithm for Subgraph Isomorphism. Journal of the ACM, 1976, vol. 23 (1), pp. 31–42.

16. Wachsmuth, H., Stede, M., El Baff, R., Al-Khatib, K., Skeppstedt, M., Stein, B. Argumentation Synthesis following Rhetorical Strategies. In: Proc. of the 27th International Conference on Computational Linguistics. Santa Fe, New Mexico, USA, 2018, pp. 3753–3765.

17. Walton, D., Reed, C., Macagno, F. Argumentation schemes Fundamentals of critical argumentation. New York, Cambridge Uni. Press, 2008, 443 p.

18. Xinyu Hua, Lu Wang. Understanding and Detecting Supporting Arguments of Diverse Types. In: Proc. of the 55th Annual Meeting of the Association for Computational Linguistics. Vancouver, Canada, ACL Association for Computational Linguistics, 2017, vol. 2, pp. 203–208.