Argumentom

Argumentom is a term used in argumentation theory and computational linguistics to denote a discrete unit of argument. It aims to encapsulate a proposition along with the supporting data and the reasoning that links the data to the claim, enabling modular analysis of discourse.

A typical argumentom comprises a claim (the proposition under consideration), evidence or data supporting the claim, and reasoning linking the evidence to the claim. Many models also include counterarguments and potential rebuttals, as well as metadata such as source, confidence, and scope. This structure helps to make explicit the components that contribute to persuasive force.

In formal representations, an argumentom may be encoded as a structured object with fields for claim, evidence, rationale, objections, and provenance. This modularization facilitates analysis of overall argumentative strength by combining multiple argumentoms and evaluating coherence across a discourse.

Applications include argument mining in text analytics, educational tools for teaching critical thinking, and automated debating systems that assemble argumentoms to build persuasive positions while exposing counterpoints. The concept supports the development of explainable AI systems that can present the justification behind a position in a transparent, stepwise fashion.

The concept is closely related to traditional argument theory, syllogistic structures, and the notion of argument maps or argument diagrams. It differs from a simple claim by emphasizing the explicit linkage between evidence and rationale and by enabling modular composition of complex arguments.

Critiques note that the usefulness of argumentoms depends on transparent, high-quality data and clear definitions of what constitutes acceptable evidence; real-world discourse often intermingles claims and evidence, complicating modularization.

See also: argumentation theory, argument mapping, argument mining, discourse analysis.