argumentmining

Argument mining is a field within natural language processing and artificial intelligence that focuses on the automatic identification, extraction, and analysis of argumentative structures in text. It seeks to detect argumentative discourse units such as claims and evidence and to reveal how these units support or oppose one another. Drawing on theories from argumentation and rhetoric, argument mining aims to produce machine-readable representations of argumentation for tasks such as reasoning, evaluation, and decision support.

Typical tasks include segmenting text into argumentative discourse units, classifying units into types (e.g., claim, premise, warrant), detecting relations of support or attack between units, and reconstructing overarching argument graphs that capture the structure of the argument. Some work also analyzes stance, credibility, or the strength of warrants. Methods range from supervised learning with handcrafted features to neural models and graph-based parsers; commonly, models leverage discourse markers, syntactic features, and, increasingly, transformer representations. The Toulmin model and Rhetorical Structure Theory are often used as theoretical frameworks to annotate and reason about argument structure.

Corpora for argument mining come from domains such as persuasive essays, online debates, legal texts, and political discourse. Annotating agreement on components and relations is challenging and inter-annotator agreement varies by task. Evaluation typically reports precision, recall, and F1 on component detection and relation extraction, as well as graph-based measures for the correctness of the reconstructed argument structure.

Applications include automated essay feedback, legal analysis, policy monitoring, media analysis, and assistive tools for debate preparation and decision making. The field intersects with related areas such as discourse parsing, stance detection, and claim detection.

Ongoing challenges include domain transfer, annotation consistency, multilingual support, interpretability, and handling rhetorical complexity. Ongoing research explores combining symbolic theories with neural models and developing standardized benchmarks and evaluation frameworks.