Using Argumentative Structure to Interpret Debates in Online Deliberative Democracy and eRulemaking

Lawrence, John; Park, Joonsuk; Budzyńska, Katarzyna; Cardie, Claire; Konat, Barbara; Reed, Chris

doi:10.1145/3032989

Cited by 18 publications

(13 citation statements)

References 36 publications

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“…Users in social media platforms usually express emotions or quick messages with very little argumentation, however the introduction of argumentative features can enhance other NLP tasks [60,66]. Both micro [93] and macro [94] analysis have the attention of the research community, whereas they have been approaches that combine them [87,88]. Another research topic that has gained the interest of the research community is the reconstruction of implicit warrants, although the existing research papers [8,9,29] do not utilize social media as source.…”

Section: Relations Identificationmentioning

confidence: 99%

The evolution of argumentation mining: From models to social media and emerging tools

Lytos

Λάγκας²,

Sarigiannidis

et al. 2019

Information Processing & Management

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Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.

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Section: Relations Identificationmentioning

confidence: 99%

The evolution of argumentation mining: From models to social media and emerging tools

Lytos

Λάγκας²,

Sarigiannidis

et al. 2019

Information Processing & Management

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“…Computational regulatory comment analysis has received significant attention including identifying stakeholders [1], clustering duplicate comments [30], topic modeling [15], creating topic ontologies [29], verifiability of propositions [21], automatically classifying the dialogical type of relations [12], theoretical argumentation models [20], and a rule-dependent issue hierarchy [6].…”

Section: Related Workmentioning

confidence: 99%

“…Lawrence et al [12] annotate a corpus of comments from one rule with an argumentative and dialogical structure. They then build models to automatically classify the dialogical type of relations between propositions.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Argument Identification in Public Comments from eRulemaking

Eidelman¹,

Grom²

2019

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law

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Administrative agencies in the United States receive millions of comments each year concerning proposed agency actions during the eRulemaking process. These comments represent a diversity of arguments in support and opposition of the proposals. While agencies are required to identify and respond to substantive comments, they have struggled to keep pace with the volume of information.In this work we address the tasks of identifying argumentative text, classifying the type of argument claims employed, and determining the stance of the comment. First, we propose a taxonomy of argument claims based on an analysis of thousands of rules and millions of comments. Second, we collect and semi-automatically bootstrap annotations to create a dataset of millions of sentences with argument claim type annotation at the sentence level. Third, we build a system for automatically determining argumentative spans and claim type using our proposed taxonomy in a hierarchical classification model.

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“…This suitability of argumentation applies to problems with strict specifications, requiring expert (e.g., scientific) reasoning, as well as to problems of human-like AI, where the reasoning required is closer to that of common sense used by people when they tackle their everyday tasks. We, therefore, see applications of argumentation over a wide spectrum of problems, ranging from applications focusing on the analysis of debates (e.g., debates on on-line social interaction settings [16,29,52]) to problems where a more formal structure of argumentation needs to be captured computationally (e.g., in automating legislation [7,45]). There are also several systems of argumentation, such as CaSAPI [14], DeLP [15], TOAST [46] and Gorgias [21], that provide tool support for studying various aspects of computational argumentation and its application.…”

Section: Introductionmentioning

confidence: 99%

GORGIAS: Applying argumentation

Kakas

Moraïtis

Spanoudakis

2018

AAC

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This paper presents the past and present efforts of developing real-life applications of argumentation with the Gorgias preference-based structured argumentation framework of Logic Programming with Priorities. Since its free availability on the web in 2003, the Gorgias system has been used by different groups in a variety of real-life applications in areas such as medical support, network security, business computing, ambient intelligence and, recently, in the area of cognitive personal assistants. We briefly review the Gorgias framework and its past applications and present an emerging general methodological approach for developing "decision making" applications of argumentation. This approach allows the development of real world applications directly from their high-level requirements expressed in the language of the application without the need for familiarity with the underlying technical details of argumentation. A new tool, called Gorgias-B, supports this high-level development of applications and automatically generates the underlying argumentation theory in Gorgias code. The paper also reports on ongoing real-life applications in two domains: an eye-clinic assistant for first level support, and systems for patient data access and data sharing agreements according to the relevant legislation and contracts or policies of the several stakeholders involved. The proposed approach is quite general for this type of applications of argumentation and, thus, it can be used with any preference-based argumentation framework.

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Using Argumentative Structure to Interpret Debates in Online Deliberative Democracy and eRulemaking

Cited by 18 publications

References 36 publications

The evolution of argumentation mining: From models to social media and emerging tools

The evolution of argumentation mining: From models to social media and emerging tools

Argument Identification in Public Comments from eRulemaking

GORGIAS: Applying argumentation

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