Stance Classification using Dialogic Properties of Persuasion

By Marilyn A. Walker, Pranav Anand, Rob Abbott, R. Grant. 2012

194 citations recorded by the source corpus.

Subfields: Natural Language Processing, Sentiment & Opinion Analysis

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Citations in the corpus

Hypothesis Only Baselines in Natural Language Inference (2018, methodology): “Premise sentences were extracted from Annotated Gigaword (Napoles et al., 2012), image captions (Young et al., 2014), the Internet Argument Corpus (Walker et al., 2012), and fictional stories from the GutenTag dataset (Mac Kim and Cassidy, 2015).”
How would Stance Detection Techniques Evolve after the Launch of ChatGPT? (2022, background): “Early research works on stance detection from the 1950s mainly adopted rule-based techniques (Anand et al., 2011; Walker et al., 2012).”
Author’s Sentiment Prediction (2020, background): “Most of the researches in stance detection field are using debate posts in forums or political congress debates (Walker et al., 2012; Hasan and Ng, 2013; Augenstein et al., 2016).”
DEBAGREEMENT: A comment-reply dataset for (dis)agreement detection in online debates (2021, background): “(Dis)agreement detection falls under the field of stance detection [39] – the automatic classification of the position (or stance) of the producer of a piece of text, towards a target, into one of three classes: ∗Equal contribution (2)https://datareportal.”
Stance and Sentiment in Tweets (2016, methodology): “Existing datasets for stance detection were created from online debate forums like 4forums.com and createdebates.com [Somasundaran and Wiebe 2010; Walker et al. 2012b; Hasan and Ng 2013].”
Emergent: a novel data-set for stance classification (2016, background): “It also differs from work on target-specific stance prediction in debates (Walker et al., 2012; Hasan and Ng, 2013), since the targets considered there are topic labels such as abortion, instead of event claims as in this work.”
Argumentation Mining in User-Generated Web Discourse (2016, background): “Walker et al. (2012) incorporated features with some limited aspects of the argument structure, such as cue words signaling rhetorical relations between posts, POS generalized dependencies, and a representation of the parent post (context) to improve stance classification over 14 topics from…”
Stance Detection with Bidirectional Conditional Encoding (2016, methodology): “The two best-performing systems submitted to Task B, pkudblab (Wei et al., 2016) and LitisMind (Zarrella and Marsh, 2016) made use of this, thus changing the task to weakly supervised seen target stance detection, instead of an unseen target task.”