Towards automated identification and analysis of argumentation structures in the decision corpus of the German Federal Constitutional Court

Houy, Constantin; Niesen, Tim; Fettke, Peter; Loos, Peter

doi:10.1109/dest.2013.6611332

Cited by 13 publications

(5 citation statements)

References 13 publications

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“…Argumentation mining is an evolving research topic that deals with the automatic extraction of argument components from text. Most research focuses on English text, but there is also research for German (Houy et al, 2013;Habernal et al, 2014;Eckle-Kohler et al, 2015) and for the Greek language (Goudas et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

“…Previous research spans a variety of domains, such as the legal domain (Palau and Moens, 2009;Houy et al, 2013), eRulemaking (Park and Cardie, 2014), student essays (Stab and Gurevych, 2014b), news (Eckle-Kohler et al, 2015), and web content (Goudas et al, 2014;Habernal and Gurevych, 2015). Most of the papers share common tasks, such as separating text into argumentative and non-argumentative parts, classifying argumentative text into argument components and identifying relations between them.…”

Section: Related Workmentioning

confidence: 99%

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

Liebeck

Esau

Conrad

2016

Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

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This paper focuses on the automated extraction of argument components from user content in the German online participation project Tempelhofer Feld. We adapt existing argumentation models into a new model for decision-oriented online participation. Our model consists of three categories: major positions, claims, and premises. We create a new German corpus for argument mining by annotating our dataset with our model. Afterwards, we focus on the two classification tasks of identifying argumentative sentences and predicting argument components in sentences. We achieve macro-averaged F 1 measures of 69.77% and 68.5%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Liebeck

Esau

Conrad

2016

Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

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“…The corpus comprises 70,000 tokens and the inter-annotator agreement was 0.40 (Krippendorff's α U ). Houy et al (2013) targeted argumentation mining of German legal cases. Table 1 gives an overview of annotation studies with their respective argumentation model, domain, size, and agreement.…”

Section: Annotation Studiesmentioning

confidence: 99%

“…The corpus comprises 70,000 tokens and the inter-annotator agreement was 0.40 (Krippendorff's α U ). Houy et al (2013) targeted argumentation mining of German legal cases.…”

Section: Argumentation Miningmentioning

confidence: 99%

Argumentation Mining in User-Generated Web Discourse

Habernal

Gurevych

2017

Computational Linguistics

199

159

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The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

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“…One example is by Peldszus and Stede (2013), where artificially constructed short texts were used to determine inter-annotator aggreement on argument annotation. Kluge (2014) used web documents from the educational domain, and Houy et al (2013) used legal cases. All authors analysed the argumentative structure of their documents.…”

Section: Argument Miningmentioning

confidence: 99%

Language Technologies for the Challenges of the Digital Age

Rehm¹,

Declerck²

2018

Lecture Notes in Computer Science

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Towards automated identification and analysis of argumentation structures in the decision corpus of the German Federal Constitutional Court

Cited by 13 publications

References 13 publications

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

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

Argumentation Mining in User-Generated Web Discourse

Language Technologies for the Challenges of the Digital Age

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