Parsing Argumentation Structures in Persuasive Essays (1604.07370v2)

Abstract: 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.

• The paper presents a joint approach using CRF for component segmentation and ILP for identifying argumentative relationships in persuasive essays.
• The proposed model achieves macro F1 scores of 0.826 for component classification and 0.751 for relation identification, outperforming earlier baselines.
• The annotated corpus of 402 essays lays a solid foundation for advancing argument mining research and improving automated writing evaluation.

Parsing Argumentation Structures in Persuasive Essays: A Detailed Exploration

The paper "Parsing Argumentation Structures in Persuasive Essays" by Christian Stab and Iryna Gurevych offers an in-depth paper on computational methods for parsing argumentation structures, a pivotal aspect of computational linguistics aimed at translating natural language arguments into structured representations for analysis. In this paper, the authors propose a novel model that encompasses both component classification and relation identification, focusing on the educational domain of persuasive essays.

Model Overview and Methodology

The paper introduces a comprehensive end-to-end approach, addressing previous limitations in argumentation structure parsing by classifying argument components (major claims, claims, and premises) and identifying argumentative relationships between them. The research builds a corpus of 402 persuasive essays annotated with discourse-level argumentation structures, establishing a foundation for developing and testing their parsing model.

Key components of their approach include:

• Sequence Labeling for Component Identification: The authors employ a Conditional Random Field (CRF) model to segment essays into argumentative and non-argumentative components at the token level, effectively separating relevant discourse from non-essential text.
• Joint Modeling of Component Types and Relations Using Integer Linear Programming (ILP): The model optimizes the identification of argumentation structures by jointly considering argumentative components and relations between them, using ILP to generate a globally consistent argumentation structure across paragraphs.
• Stance Recognition: The model further distinguishes between support and attack relations, leveraging syntactic, lexical, and contextual features to accurately determine the stance of each argument component.

Results and Performance

The proposed model demonstrates significant improvements over heuristic baselines. Specifically, the joint ILP model outperforms base classifiers on both component classification and relation identification tasks, achieving a macro F1 score of 0.826 and 0.751 respectively. The authors also benchmark their model against existing resources like the microtext corpus, achieving notable accuracy improvements in component classification and stance recognition.

Furthermore, the introduction of a novel corpus with annotated argumentation structures enriches the resources available for computational argumentation research, facilitating reproducibility and encouraging future exploration.

Implications and Future Directions

The research delineates several implications for educational technology and computational linguistics:

• Educational Applications: The model's ability to parse essay structures has potential applications in automated writing evaluation systems, where it could provide feedback on argumentative writing, enhancing educational tools for essay composition and analysis.
• Advancement in Argument Mining: The model contributes to the broader field of argument mining, showcasing how machine learning and structured prediction can be effectively applied to understand argumentation.
• Corpus for Diverse Genres: While the current work focuses on persuasive essays, future research can explore the adaptability of this model to other genres, such as legal documents or online discourse, thus broadening its applicability.

In summary, Stab and Gurevych's work represents a significant step in parsing argumentation structures, setting a foundation for ongoing advancements in computational argumentation. The methodologies and resources developed in this paper continue to influence research and practical applications within AI-driven text analysis and educational technologies. Future studies could expand upon this by incorporating more complex discourse structures and evaluating the model's effectiveness across diverse linguistic datasets.