An Online Annotation Assistant for Argument Schemes

Lawrence, John; Visser, Jacky; Reed, Chris

doi:10.18653/v1/w19-4012

Cited by 11 publications

(7 citation statements)

References 26 publications

(19 reference statements)

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“…Experiments on the annotation of Walton schemes by annotators with a strong background in linguistics but who were provided with only the description of the schemes given in Walton, Reed, and Macagno (2008) have shown that this is an exceptionally difficult task, with results differing in both numbers of arguments annotated and the distributions of units (Lindahl, Borin, and Rouces 2019). However, recent developments in annotation guidelines for these schemes, including the decision tree-based method described in Lawrence, Visser, and Reed (2019), suggest that this situation can be improved and offer hope for the construction of scheme annotated corpora.…”

Section: Conclusion: a Is True (False)mentioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

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269

186

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Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.

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Section: Conclusion: a Is True (False)mentioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

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269

186

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“…Some research adopted argumentation schemes as a framework, making comparisons with discourse relations (Cabrio et al, 2013) and collecting and leveraging data at varying degrees of granularity. At a coarse level, prior studies annotated the presence of particular argumentation schemes in text (Visser et al, 2020;Lawrence et al, 2019;Lindahl et al, 2019;Reed et al, 2008) and developed models to classify different schemes (Feng and Hirst, 2011). However, each scheme often accommodates both support and attack relations between statements, so classifying those relations requires semantically richer information within the scheme than just its presence.…”

Section: Related Workmentioning

confidence: 99%

Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes

Bang

Reed

et al. 2021

Transactions of the Association for Computational Linguistics

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While argument mining has achieved significant success in classifying argumentative relations between statements (support, attack, and neutral), we have a limited computational understanding of logical mechanisms that constitute those relations. Most recent studies rely on black-box models, which are not as linguistically insightful as desired. On the other hand, earlier studies use rather simple lexical features, missing logical relations between statements. To overcome these limitations, our work classifies argumentative relations based on four logical and theory-informed mechanisms between two statements, namely, (i) factual consistency, (ii) sentiment coherence, (iii) causal relation, and (iv) normative relation. We demonstrate that our operationalization of these logical mechanisms classifies argumentative relations without directly training on data labeled with the relations, significantly better than several unsupervised baselines. We further demonstrate that these mechanisms also improve supervised classifiers through representation learning.

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“…Among existing datasets, the AraucariaDB corpus is the largest as far as we know and includes approximately 660 manually annotated arguments (Feng and Hirst, 2011). Lawrence et al (2019) state that annotated corpora of argumentation schemes are scarce, small, and unrepresentative. They provide an annotation tool to address the issue.…”

Section: Datasetsmentioning

confidence: 99%

Bayesian Argumentation-Scheme Networks: A Probabilistic Model of Argument Validity Facilitated by Argumentation Schemes

Kondo¹,

Washio²,

Hayashi³

et al. 2021

Proceedings of the 8th Workshop on Argument Mining

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We propose a methodology for representing the reasoning structure of arguments using Bayesian networks and predicate logic facilitated by argumentation schemes. We express the meaning of text segments using predicate logic and map the boolean values of predicate logic expressions to nodes in a Bayesian network. The reasoning structure among text segments is described with a directed acyclic graph. While our formalism is highly expressive and capable of describing the informal logic of human arguments, it is too open-ended to actually build a network for an argument. It is not at all obvious which segment of argumentative text should be considered as a node in a Bayesian network, and how to decide the dependencies among nodes. To alleviate the difficulty, we provide abstract network fragments, called idioms, which represent typical argument justification patterns derived from argumentation schemes. The network construction process is decomposed into idiom selection, idiom instantiation, and idiom combination. We define 17 idioms in total by referring to argumentation schemes as well as analyzing actual arguments and fitting idioms to them. We also create a dataset consisting of pairs of an argumentative text and a corresponding Bayesian network. Our dataset contains about 2,400 pairs, which is large in the research area of argumentation schemes.

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An Online Annotation Assistant for Argument Schemes

Cited by 11 publications

References 26 publications

Argument Mining: A Survey

Argument Mining: A Survey

Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes

Bayesian Argumentation-Scheme Networks: A Probabilistic Model of Argument Validity Facilitated by Argumentation Schemes

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