Analyzing Argumentative Discourse Units in Online Interactions

Ghosh, Debanjan; Muresan, Smaranda; Wacholder, Nina; Aakhus, Mark; Mitsui, Matthew

doi:10.3115/v1/w14-2106

Cited by 84 publications

(66 citation statements)

References 28 publications

(24 reference statements)

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“…In (Anand et al, 2011), several features were deployed in their rule-based classifier, such as unigrams, bigrams, punctuation marks, syntactic dependencies and the dialogic structure of the posts. The dialogic relations of agreement and disagreements between posts were exploited in (Walker et al, 2012b), (Ghosh et al, 2014), likewise; while in this paper our aim is to investigate stance without considering the conversational structure which is not always available.…”

Section: Related Workmentioning

confidence: 99%

“…use multi-class Support Vector Machines (SVM) (Crammer and Singer, 2002) to identify different classes of argumentative propositions in online user comments. (Ghosh et al, 2014) use SVM to analyse multilogue, instead, classifying relations between user comments. (Boltuzic andŠnajder, 2014) use Textual Entailment to identify support relations between posts in discussion fora.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Proceedings of the 2nd Workshop on Argumentation Mining

Cardie¹,

Green²,

Gurevych³

et al. 2015

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ii BackgroundThe goal of this workshop is to provide a follow-on forum to last year's very successful Argumentation Mining workshop at ACL, the first research forum devoted to argumentation mining in all domains of discourse.Argumentation mining is a relatively new challenge in corpus-based discourse analysis that involves automatically identifying argumentative structures within a document, e.g., the premises, conclusion, and argumentation scheme of each argument, as well as argument-subargument and argumentcounterargument relationships between pairs of arguments in the document. To date, researchers have investigated methods for argumentation mining of legal documents (Mochales and Moens 2011;Bach et al. 2013;Ashley and Walker 2013;Wyner et al. 2010), on-line debates (Cabrio and Villata 2012), product reviews (Villalba and Saint-Dizier 2012;, user comments on proposed regulations (Park and Cardie 2014), newspaper articles and court cases . A related older strand of research (that uses the term 'argumentative structure' in a related but different sense than ours) has investigated automatically classifying the sentences of a scientific article's abstract or full text in terms of their contribution of new knowledge to a field (e.g., Liakata et al. 2012, Teufel 2010, Mizuta et al. 2005). In addition, argumentation mining has ties to sentiment analysis (e.g., Somasundaran and Wiebe 2010). To date there are few corpora with annotations for argumentation mining research ) although corpora with annotations for argument sub-components have recently become available (e.g., Park and Cardie 2014).Proposed applications of argumentation mining include improving information retrieval and information extraction as well as end-user visualization and summarization of arguments. Textual sources of interest include not only the formal writing of legal text, but also a variety of informal genres such as microtext, spoken meeting transcripts, product reviews and user comments. In instructional contexts where argumentation is a pedagogically important tool for conveying and assessing students' command of course material, the written and diagrammed arguments of students (and the mappings between them) are educational data that can be mined for purposes of assessment and instruction (see e.g., Ong, Litman and Brusilovsky 2014). This is especially important given the wide-spread adoption of computer-supported peer review, computerized essay grading, and large-scale online courses and MOOCs.As one might expect, success in argumentation mining will require interdisciplinary approaches informed by natural language processing technology, theories of semantics, pragmatics and discourse, knowledge of discourse of domains such as law and science, artificial intelligence, argumentation theory, and computational models of argumentation. In addition, it will require the creation and annotation of high-quality corpora of argumentation from different types of sources in different domains.We are looking forward to a full day workshop to exchange i...

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Proceedings of the 2nd Workshop on Argumentation Mining

Cardie¹,

Green²,

Gurevych³

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Ghosh et al (2014), Swanson et al (2015), Carstens and Toni (2015). These models/schemes specify a set of argumentative elements and relations between them and, as noted by Peldszus and Stede (2013), approaches to argument mining typically address the subtasks of identifying, classifying and relating argumentative discourse units (ADUs) according to the types of ADU and argumentative relation specified in whatever model/scheme has been adopted.…”

Section: Related Workmentioning

confidence: 99%

“…Ghosh et al (2014), Habernal et al (2014), Swanson et al (2015)). If such elements and relations could be automatically extracted then they could potentially serve as a basis for generating a summary that better reflects the argumentative content of reader comment.…”

Section: Introductionmentioning

confidence: 99%

Summarizing Multi-Party Argumentative Conversations in Reader Comment on News

Barker

Gaizauskas

2016

Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

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Existing approaches to summarizing multi-party argumentative conversations in reader comment are extractive and fail to capture the argumentative nature of these conversations. Work on argument mining proposes schemes for identifying argument elements and relations in text but has not yet addressed how summaries might be generated from a global analysis of a conversation based on these schemes. In this paper we: (1) propose an issue-centred scheme for analysing and graphically representing argument in reader comment discussion in on-line news, and (2) show how summaries capturing the argumentative nature of reader comment can be generated from our graphical representation.

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“…Recently, the focus has also shifted to argumentation mining from social media texts, such as online debates (Cabrio and Villata, 2012;Habernal et al, 2014;Boltužić andŠnajder, 2014), discussions on regulations (Park and Cardie, 2014), product reviews (Ghosh et al, 2014), blogs (Goudas et al, 2014), and tweets (Llewellyn et al, 2014;Bosc et al, 2016). Mining arguments from social media can uncover valuable insights into peoples' opinions; in this context, it can be thought of as a sophisticated opinion mining technique -one that seeks to uncover the reasons for opinions and patterns of reasoning.…”

Section: Introductionmentioning

confidence: 99%

Fill the Gap! Analyzing Implicit Premises between Claims from Online Debates

Boltużić¹,

Šnajder²

2016

Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

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Identifying the main claims occurring across texts is important for large-scale argumentation mining from social media. However, the claims that users make are often unclear and build on implicit knowledge, effectively introducing a gap between the claims. In this work, we study the problem of matching user claims to predefined main claims, using implicit premises to fill the gap. We build a dataset with implicit premises and analyze how human annotators fill the gaps. We then experiment with computational claim matching models that utilize these premises. We show that using manually-compiled premises improves similarity-based claim matching and that premises generalize to unseen user claims.

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Analyzing Argumentative Discourse Units in Online Interactions

Cited by 84 publications

References 28 publications

Proceedings of the 2nd Workshop on Argumentation Mining

Proceedings of the 2nd Workshop on Argumentation Mining

Summarizing Multi-Party Argumentative Conversations in Reader Comment on News

Fill the Gap! Analyzing Implicit Premises between Claims from Online Debates

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