Annotating Argument Components and Relations in Persuasive Essays

By Christian Stab, Iryna Gurevych. International Conference on Computational Linguistics, 2014

378 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8bd64a79f3c64592e8798ef52886656b2bea87d1

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Citations in the corpus

Identifying Argumentative Discourse Structures in Persuasive Essays (2014, methodology): “For instance, the argumentation scheme proposed by Peldszus and Stede (2013) includes three argumentative relations (support, attack and counter-attack), whereas Stab and Gurevych (2014) propose a scheme including only two relations (support and attack).”
Parsing Argumentation Structures in Persuasive Essays (2016, methodology): “The remainder of this article is structured as follows: In Section 2, we review related work in computational argumentation and discuss the difference to traditional discourse analysis.”
A large-scale comparison of human-written versus ChatGPT-generated essays (2023, unknown): “While Stab and Gurevych 41 randomly sampled the essays from the writing feedback section of an essay forum, it is unclear whether the essays posted there are representative of the general population of essay topics.”
TIARA: A Tool for Annotating Discourse Relations and Sentence Reordering (2020, background): “The discourse units are represented as nodes, and discourse relations hold between nodes (Wolf and Gibson, 2005; Kirschner et al., 2015; Stab and Gurevych, 2014) or between nodes and edges (for example, when challenging the acceptability of the inference between nodes–Peldszus and Stede (2016)).”
Argument Mining: A Survey (2020, methodology): “The Argument Annotated Essays Corpus (AAEC), presented in Stab and Gurevych (2014a) and updated in Stab and Gurevych (2017), consists of argument-annotated persuasive essays, and features topic and stance identification, annotation of argument components, and argumentative relations.”
Argumentation Mining on the Web from Information Seeking Perspective (2014, unknown): “, 2012) or COLING (Stab and Gurevych, 2014; Levy et al., 2014; Wachsmuth et al., 2014a).”
Returning the N to NLP: Towards Contextually Personalized Classification Models (2020, methodology): “Our systems typically use labels obtained as an average or majority vote provided by a number of impersonated annotators, even for tasks where they highly disagree (Waseem, 2016; Stab and Gurevych, 2014).”
DART: a Dataset of Arguments and their Relations on Twitter (2016, background): “…annotated corpora are needed, like for instance those proposed in (Reed and Rowe, 2004; Palau and Moens, 2011; Levy et al., 2014; Aharoni et al., 2014; Stab and Gurevych, 2014; Cabrio and Villata, 2014; Habernal et al., 2014), to be used as a training set for any kind of aforementioned prediction.”