Argumentation Mining

Stede, Manfred; Schneider, Jodi

doi:10.2200/s00883ed1v01y201811hlt040

Cited by 36 publications

(29 citation statements)

References 134 publications

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“…For this reason, the 2008 system of classifying schemes is to be regarded as a provisional hypothesis that should be subject to improvement as further empirical and analytical work on schemes classification continues. In the ten year interval, the explosion of research on argument mining (Lawrence and Reed 2019 ; Stede and Schneider 2018 ) has raised many fine-grained questions about how particular groups of schemes should be fitted together into the larger picture of any general classification system.…”

Section: Annotation Guidelines For Argument Schemesmentioning

confidence: 99%

“…Argument mining (Stede and Schneider 2018 ; Lawrence and Reed 2019 ) is a rapidly growing field, with an increasing number of methods being developed to automatically process textual data and reconstruct argumentative content. However, most of these techniques are limited by the lack of consistently annotated argument data at sufficiently large scale.…”

Section: Applications Of Annotated Argument Scheme Corporamentioning

confidence: 99%

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Annotating Argument Schemes

et al. 2020

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Argument schemes are abstractions substantiating the inferential connection between premise(s) and conclusion in argumentative communication. Identifying such conventional patterns of reasoning is essential to the interpretation and evaluation of argumentation. Whether studying argumentation from a theory-driven or data-driven perspective, insight into the actual use of argumentation in communicative practice is essential. Large and reliably annotated corpora of argumentative discourse to quantitatively provide such insight are few and far between. This is all the more true for argument scheme corpora, which tend to suffer from a combination of limited size, poor validation, and the use of ad hoc restricted typologies. In the current paper, we describe the annotation of schemes on the basis of two distinct classifications: Walton's taxonomy of argument schemes, and Wagemans' Periodic Table of Arguments. We describe the annotation procedure for each, and the quantitative characteristics of the resulting annotated text corpora. In doing so, we extend the annotation of the preexisting US2016 corpus of televised election debates, resulting in, to the best of our knowledge, the two largest consistently annotated corpora of schemes in argumentative dialogue publicly available. Based on evaluation in terms of inter-annotator agreement, we propose further improvements to the guidelines for annotating schemes: the argument scheme key, and the Argument Type Identification Procedure.

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Section: Annotation Guidelines For Argument Schemesmentioning

confidence: 99%

Section: Applications Of Annotated Argument Scheme Corporamentioning

confidence: 99%

Annotating Argument Schemes

et al. 2020

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“…premises, claims), and relationships between them (e.g. support, attack), within free text (for reviews see [42,55]), the use of machine learning to predict the convincingness of arguments [23], and the use of textual entailment to select appropriate responses in an argumentation dialogue [59].…”

Section: Next Steps For Argumentative Chatbotsmentioning

confidence: 99%

Towards Computational Persuasion via Natural Language Argumentation Dialogues

Hunter

Chalaguine

Czernuszenko

et al. 2019

KI 2019: Advances in Artificial Intelligence

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Computational persuasion aims to capture the human ability to persuade through argumentation for applications such as behaviour change in healthcare (e.g. persuading people to take more exercise or eat more healthily). In this paper, we review research in computational persuasion that incorporates domain modelling (capturing arguments and counterarguments that can appear in a persuasion dialogues), user modelling (capturing the beliefs and concerns of the persuadee), and dialogue strategies (choosing the best moves for the persuader to maximize the chances that the persuadee is persuaded). We discuss evaluation of prototype systems that get the user's counterarguments by allowing them to select them from a menu. Then we consider how this work might be enhanced by incorporating a natural language interface in the form of an argumentative chatbot.

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“…Conforme Stede e Schneider [6], a mineração de argumentos é uma técnica do Processamento de Linguagem Natural (PLN) útil para o entendimento do discurso. Ela visa identificar a estrutura argumentativa dos textos.…”

Section: Introductionunclassified

“…As abordagens que propõem resolver os desafios da mineração de argumentos, de forma conjunta, são conhecidas como abordagens ponta a ponta. Normalmente, abordagens ponta a ponta são compostas por sete tarefas [6]: (i) identificar o texto argumentativo; (ii) segmentar o texto em unidades discursivas argumentativas; (iii) identificar a tese; (iv) identificar a função dos componentes argumentativos; (v) identificar as relações entre os componentes argumentativos; (vi) construir a representação estrutural geral; e (vii) identificar o tipo e a qualidade da argumentação.…”

Section: Introductionunclassified

Uma Abordagem para Mineração de Argumentos em Redações do Português Brasileiro

Nau¹,

Filho²,

Dazzi³

2020

Anais Do XI Computer on the Beach - COTB '20

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Argument mining consists of extracting the argumentative structure of a text. The challenge in finding the argumentative structure lies in: identifying the components of the argument and the relationships that occur between them. Approaches that propose to solve both challenges of argument mining together are known as end-to-end methods. In the literature, some papers were found that perform argument mining in essays, but no papers were found for Brazilian Portuguese. Therefore, in this paper, we propose an end-to-end approach for argument mining of Brazilian Portuguese essays in the ENEM model.

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Argumentation Mining

Cited by 36 publications

References 134 publications

Annotating Argument Schemes

Annotating Argument Schemes

Towards Computational Persuasion via Natural Language Argumentation Dialogues

Uma Abordagem para Mineração de Argumentos em Redações do Português Brasileiro

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