What to Do with an Airport? Mining Arguments in the German Online
            Participation Project Tempelhofer Feld

Liebeck, Matthias; Esau, Katharina; Conrad, Stefan

doi:10.18653/v1/w16-2817

Cited by 32 publications

(23 citation statements)

References 14 publications

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“…This work deals with controversial topics and the corresponding stances, but not on how relevant the topics are to propose public policies. Another similar corpus is the one regarding suggestions of the future use of a former German airport (Liebeck et al, 2016). This corpus is similar to ours in the sense of having informal arguments about public policies, but differs considerably in size (about 1% of our dataset).…”

Section: Related Workmentioning

confidence: 74%

200K+ Crowdsourced Political Arguments for a New Chilean Constitution

Fierro

Fuentes

Pérez

et al. 2017

Proceedings of the 4th Workshop on Argument Mining

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In this paper we present the dataset of 200,000+ political arguments produced in the local phase of the 2016 Chilean constitutional process. We describe the human processing of this data by the government officials, and the manual tagging of arguments performed by members of our research group. Afterwards we focus on classification tasks that mimic the human processes, comparing linear methods with neural network architectures. The experiments show that some of the manual tasks are suitable for automatization. In particular, the best methods achieve a 90% top-5 accuracy in a multiclass classification of arguments, and 65% macro-averaged F1-score for tagging arguments according to a three-part argumentation model.

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Section: Related Workmentioning

confidence: 74%

200K+ Crowdsourced Political Arguments for a New Chilean Constitution

Fierro

Fuentes

Pérez

et al. 2017

Proceedings of the 4th Workshop on Argument Mining

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Section: Prior Workmentioning

confidence: 99%

“…Stab and Gurevych (2014) classified clauses as major claim, claim, premise or non-argumentative, with directed argumentative relations possibly running from a premise to a major claim, a claim, or another premise. Liebeck et al (2016) adapted this approach to mining suggestions or claims on options for actions or decisions. Boltužic and Šnajder (2016) developed a typology for premises organized along three dimensions: premise type (fact, value, or policy), complexity (atomic, implication, or complex), and acceptance (universal or claim-specific).…”

Section: Prior Workmentioning

confidence: 99%

Evidence Types, Credibility Factors, and Patterns or Soft Rules for Weighing Conflicting Evidence: Argument Mining in the Context of Legal Rules Governing Evidence Assessment

Walker

Foerster²,

Ponce³

et al. 2018

Proceedings of the 5th Workshop on Argument Mining

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This paper reports on the results of an empirical study of adjudicatory decisions about veterans' claims for disability benefits in the United States. It develops a typology of kinds of relevant evidence (argument premises) employed in cases, and it identifies factors that the tribunal considers when assessing the credibility or trustworthiness of individual items of evidence. It also reports on patterns or "soft rules" that the tribunal uses to comparatively weigh the probative value of conflicting evidence. These evidence types, credibility factors, and comparison patterns are developed to be inter-operable with legal rules governing the evidence assessment process in the U.S. This approach should be transferable to other legal and non-legal domains.

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“…The recent advances in Machine Learning (ML) in combination with the emergence of social web can enable impressive progress in different scientific fields with great impact on commercial applications. An AM system has the capacity to mine and analyse a great volume of text data through a variety of sources, providing tools for policymaking and socio-political sciences [2,3,4], software engineering [5], while it opens new horizons for the broader area of business, economics and finance, with the digital marketing being the most promising field [6,7,8,9].…”

Section: Introductionmentioning

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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What to Do with an Airport? Mining Arguments in the German Online Participation Project Tempelhofer Feld

Cited by 32 publications

References 14 publications

200K+ Crowdsourced Political Arguments for a New Chilean Constitution

200K+ Crowdsourced Political Arguments for a New Chilean Constitution

Evidence Types, Credibility Factors, and Patterns or Soft Rules for Weighing Conflicting Evidence: Argument Mining in the Context of Legal Rules Governing Evidence Assessment

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

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