Parsing Argumentation Structures in Persuasive Essays
This work addresses the need for automated analysis of argumentation in essays, which is incremental as it builds on existing methods with a new corpus and model.
The paper tackles the problem of parsing argumentation structures in persuasive essays by proposing a joint model that globally optimizes component types and relations using integer linear programming, resulting in significant performance improvements over baselines.
In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using integer linear programming. We show that our model considerably improves the performance of base classifiers and significantly outperforms challenging heuristic baselines. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement. This corpus and the annotation guidelines are freely available for ensuring reproducibility and to encourage future research in computational argumentation.