Interdisciplinary Argument Relatedness: Integrating Generative Lexicon Theory with Psychological Argumentation Mining

Most argumentation mining relies on surface-level semantic similarity or logical entailment, but negotiation and persuasion often hinge on subtler, context-driven ties—such as appeals to fairness or group identification. By combining Saint-Dizier’s generative lexicon techniques with psychological constructs (e.g., perceived fairness, group loyalty), we could develop richer models for identifying when two arguments are “related” in ways that matter for persuasion. This might involve training models on annotated negotiation datasets where not just logical but also psychological links are labeled. The innovation here is in synthesizing computational linguistics and psychology to capture the full complexity of real-world argumentation, opening new frontiers for both theory and practice—such as building negotiation support tools that can surface not only explicit but also latent, psychologically resonant connections between proposals.

References:

1. The Economics of Behavior. Essays on the Organizational Effects of Identification, Wage Expectations, and Fairness Concerns. Lea M. Petters (2019).
2. Negotiation as Practical Argumentation. D. Castro (2023). Argumentation: an international journal on reasoning.
3. Persuasion or Co-creation? Social Identity Threat and the Mechanisms of Deliberative Transformation. G. Wright (2022). Special Issue: Psychological Phenomena in Democratic Deliberation.
4. Emotions and personality traits in argumentation: An empirical evaluation. S. Villata, Elena Cabrio, I. Jraidi, Mohamed S. Benlamine, Maher Chaouachi, C. Frasson, Fabien L. Gandon (2017). Argument Comput..
5. Natural Language Argumentation: Mining, Processing, and Reasoning over Textual Arguments (Dagstuhl Seminar 16161). Elena Cabrio, Graeme Hirst, S. Villata, A. Wyner (2016). Dagstuhl Reports.
6. An Exploration of the Relatedness Problem between Arguments : Combining the Generative Lexicon with Lexical Inference. P. Saint-Dizier (2016).
7. A Review Article for Argumentation Mining of Text through Machine Learning Techniques and Strategies. Rudrendra Bahadur Singh, Shobhit Sinha, Ankita Singh (2025). Journal of Information Systems Engineering & Management.