An Algorithm for Pythonizing Rhetorical Structures
An implementation and test software of the algorithm described in this paper:
Potter, A. (2023). An algorithm for Pythonizing rhetorical structures. In S. Carvalho, A. F. Khan, A. O. Anić, Blerina Spahiu, J. Gracia, J. P. McCrae, D. Gromann, Barbara Heinisch, & A. Salgado (Eds.), Language, data and knowledge 2023 (LDK 2023): Proceedings of the 4th Conference on Language, Data and Knowledge (pp. 491-504). Lisbon: NOVA FCSH - CLUNL.
The paper is available here: https://www.researchgate.net/publication/373923815_An_algorithm_for_Pythonizing_rhetorical_structures
Abstract:
Diagrams produced using Rhetorical Structure Theory can be both informative and engaging, providing insight into the properties of discourse structures and other coherence phenomena. This paper presents a deep dive into these diagrams and shows how an RST analysis can be reconceived as an emergent process. The paper describes an algorithm for transforming RST diagrams into Pythonic relational propositions and applies it to a set of RST analyses. The resulting expressions are isomorphic with RST diagrams as well as machine processable. As executable specifications of discourse structure, they support scalable applications in applied and theoretical studies. Several sample applications are presented. The transformation process itself suggests an alternative to the traditional view of rhetorical structures as recursive trees. The construction of coherence is shown to be a bottom-up synthesis, wherein discourse units combine to form relational propositions which in turn rendezvous with other relational propositions to create increasingly complex expressions until a comprehensive analysis is produced. This progressive bottom-up development of coherence is observable in the performance of the algorithm.
Any questions? Feel free to contact me at apotter1@una.edu