Argument Mining

Argument mining studies how to locate argumentative components such as claims and premises, classify their roles or stance, and identify relations such as support and attack. Systems use supervised sequence tagging and transformer models, as well as generative language models, prompting, and graph-based or symbolic methods to build structured arguments. Evaluation spans essay benchmarks and settings such as political debate, online conversation, and legal or clinical text, where annotation schemes and context differ. A central challenge is recovering implicit, context-dependent relations and generalizing across domains; benchmark scores may also miss whether a system genuinely reasons about an argument or merely recognizes familiar patterns.