Identifying Argumentative Discourse Structures in Persuasive Essays

By Christian Stab, Iryna Gurevych. Conference on Empirical Methods in Natural Language Processing, 2014

434 citations recorded by the source corpus.

In this paper, we present a novel approach for identifying argumentative discourse structures in persuasive essays. The structure of argumentation consists of several components (i.e. claims and premises) that are connected with argumentative relations. We consider this task in two consecutive steps. First, we identify the components of arguments using multiclass classification. Second, we classify a pair of argument components as either support or non-support for identifying the structure of argumentative discourse. For both tasks, we evaluate several classifiers and propose novel feature sets including structural, lexical, syntactic and contextual features. In our experiments, we obtain a macro F1-score of 0.726 for identifying argument components and 0.722 for argumentative relations.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/50733b55f50514fe738cf40a56b1124d2448c9cc

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In Artificial Intelligence

Citations in the corpus

Argumentative Relation Classification as Plausibility Ranking (2019, background): “By E SSAY -”
Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms (2019, background): “For example, Stab and Gurevych (2014) proposed structural, lexical, syntactic and contextual features to identify convincing components of Web arguments including claim, major claim, and premise.”
Cross-topic Argument Mining from Heterogeneous Sources (2018, background): “…Goudas et al., 2014), classifying the function of argumentative discourse units (for example, as claims or premises ) (Mochales-Palau and Moens, 2009; Stab and Gurevych, 2014), and recognizing argumentative discourse relations (Eger et al., 2017; Stab and Gurevych, 2017; Nguyen and Litman, 2016).”
The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “…in-6 stance, text that aims to convince others often uses various persuasion strategies (Yang et al., 2019a; Chen and Yang, 2021), argumentation techniques (Stab and Gurevych, 2014), rhetorical structures (Rapp, 2011), and the exchange of social support (Wang and Jurgens, 2018; Yang et al., 2019b).”
An automated essay scoring systems: a systematic literature review (2021, background): “Argument Annotated Essays (AAE) Stab and Gurevych (2014) developed a corpus that contains 102 essays with 101 prompts taken from the essayforum2 site.”
Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions (2016, background): “Studies have also worked on understanding persuasive essays [18, 41, 51] and opinion analysis in terms of agreement and ideology [53, 49, 22, 50, 48].”
Argumentation Mining in User-Generated Web Discourse (2016, background): “Somasundaran and Wiebe (2009) built a computational model for recognizing stances in dual-topic debates about named entities in the electronic products domain by combining preferences learned from the Web data and discourse markers from PDTB (Prasad et al. 2008).”
Parsing Argumentation Structures in Persuasive Essays (2016, methodology): “…(Moens et al. 2007; Florou et al. 2013), classifying argument components into claims and premises (Mochales-Palau and Moens 2011; Rooney, Wang, and Browne 2012; Stab and Gurevych 2014b), and identifying argumentative relations (Mochales-Palau and Moens 2009; Peldszus 2014; Stab and Gurevych 2014b).”
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.”