Mining Arguments From 19th Century Philosophical Texts Using Topic Based Modelling

Lawrence, John; Reed, Chris; Allen, Colin; McAlister, Simon; Ravenscroft, Andrew

doi:10.3115/v1/w14-2111

Cited by 36 publications

(26 citation statements)

References 15 publications

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“…As Lawrence et al [26] suggested, the presence of topic-relevant words and phrases in a sentence is a good indicator of the sentence being a candidate.…”

Section: Semi-supervised Argument Component Detectionmentioning

confidence: 99%

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Changing views: Persuasion modeling and argument extraction from online discussions

Dutta

Das

Chakraborty

2020

Information Processing & Management

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Persuasion and argumentation are possibly among the most complex examples of the interplay between multiple human subjects. With the advent of the Internet, online forums provide wide platforms for people to share their opinions and reasonings around various diverse topics. In this work, we attempt to model persuasive interaction between users on Reddit, a popular online discussion forum. We propose a deep LSTM model to classify whether a conversation leads to a successful persuasion or not, and use this model to predict whether a certain chain of arguments can lead to persuasion. While learning persuasion dynamics, our model tends to identify argument facets implicitly, using an attention mechanism. We also propose a semi-supervised approach to extract argumentative components from discussion threads. Both these models provide useful insight into how people engage in argumentation on online discussion forums.

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“…As Lawrence et al [26] suggested, the presence of topic-relevant words and phrases in a sentence is a good indicator of the sentence being a candidate.…”

Section: Semi-supervised Argument Component Detectionmentioning

confidence: 99%

“…The annotators were experts in computational linguistics and are of age between[25][26][27][28][29][30][31][32][33][34][35] years.…”

mentioning

confidence: 99%

Changing views: Persuasion modeling and argument extraction from online discussions

Dutta

Das

Chakraborty

2020

Information Processing & Management

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“…Previous work employs rule-based identification (Persing and Ng, 2016), featurebased classification (Lawrence et al, 2014), conditional random fields (Sardianos et al, 2015;Stab, 2017), or deep neural networks (Eger et al, 2017). Especially the most recent approaches by Stab and Eger et al rely on sophisticated structural, syntactical, and lexical features.…”

Section: Related Workmentioning

confidence: 99%

Unit Segmentation of Argumentative Texts

Ajjour

Chen

Kiesel

et al. 2017

Proceedings of the 4th Workshop on Argument Mining

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The segmentation of an argumentative text into argument units and their nonargumentative counterparts is the first step in identifying the argumentative structure of the text. Despite its importance for argument mining, unit segmentation has been approached only sporadically so far. This paper studies the major parameters of unit segmentation systematically. We explore the effectiveness of various features, when capturing words separately, along with their neighbors, or even along with the entire text. Each such context is reflected by one machine learning model that we evaluate within and across three domains of texts. Among the models, our new deep learning approach capturing the entire text turns out best within all domains, with an F-score of up to 88.54. While structural features generalize best across domains, the domain transfer remains hard, which points to major challenges of unit segmentation.

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“…In Lawrence et al (2014), LDA is used to decide whether a proposition can be attached to its previous proposition in order to identify non directional relations among propositions detected through classifiers based on words and part-ofspeech tags. LDA has been also used to mine lexicons of argument (words that are topic independent) and domain words (Nguyen and Litman, 2015), by post-processing document topics generated by LDA.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Detection of Argumentative Units though Topic Modeling Techniques

Ferrara

Montanelli

Petasis

2017

Proceedings of the 4th Workshop on Argument Mining

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In this paper we present a new unsupervised approach, "Attraction to Topics" -A2T , for the detection of argumentative units, a sub-task of argument mining. Motivated by the importance of topic identification in manual annotation, we examine whether topic modeling can be used for performing unsupervised detection of argumentative sentences, and to what extend topic modeling can be used to classify sentences as claims and premises. Preliminary evaluation results suggest that topic information can be successfully used for the detection of argumentative sentences, at least for corpora used in the evaluation. Our approach has been evaluated on two English corpora, the first of which contains 90 persuasive essays, while the second is a collection of 340 documents from user generated content.

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Mining Arguments From 19th Century Philosophical Texts Using Topic Based Modelling

Cited by 36 publications

References 15 publications

Changing views: Persuasion modeling and argument extraction from online discussions

Changing views: Persuasion modeling and argument extraction from online discussions

Unit Segmentation of Argumentative Texts

Unsupervised Detection of Argumentative Units though Topic Modeling Techniques

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