Back up your Stance: Recognizing Arguments in Online Discussions

Boltużić, Filip; Šnajder, Jan

doi:10.3115/v1/w14-2107

Cited by 129 publications

(85 citation statements)

References 23 publications

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“…A few argument mining approaches deal with online resources. Among these, Boltužić and Šnajder (2014) as well as Park and Cardie (2014) search for supporting information in online discussions, and Swanson et al (2015) mine arguments on specific issues from such discussions. Habernal and Gurevych (2015) study how well mining works across genres of argumentative web text, and Al-Khatib et al (2016) use distant supervision to derive training data for mining from a debate portal.…”

Section: Related Workmentioning

confidence: 99%

Building an Argument Search Engine for the Web

Wachsmuth¹,

Potthast²,

Khatib³

et al. 2017

Proceedings of the 4th Workshop on Argument Mining

119

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Computational argumentation is expected to play a critical role in the future of web search. To make this happen, many searchrelated questions must be revisited, such as how people query for arguments, how to mine arguments from the web, or how to rank them. In this paper, we develop an argument search framework for studying these and further questions. The framework allows for the composition of approaches to acquiring, mining, assessing, indexing, querying, retrieving, ranking, and presenting arguments while relying on standard infrastructure and interfaces. Based on the framework, we build a prototype search engine, called args, that relies on an initial, freely accessible index of nearly 300k arguments crawled from reliable web resources. The framework and the argument search engine are intended as an environment for collaborative research on computational argumentation and its practical evaluation.

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Section: Related Workmentioning

confidence: 99%

Building an Argument Search Engine for the Web

Wachsmuth¹,

Potthast²,

Khatib³

et al. 2017

Proceedings of the 4th Workshop on Argument Mining

119

View full text Add to dashboard Cite

show abstract

“…Recall increases from 22.8% to 40.9% for FACT, and from 8.0% to 18.8% for FEEL. 3 The precision for the FACTUAL class is reasonably good, but the precision for the FEELING class is only moderate. However, although precision typically decreases during boostrapping due to the addition of imperfectly labeled data, the precision drop during bootstrapping is relatively small.…”

Section: Discussionmentioning

confidence: 96%

“…In terms of determining stance, previous work has utilized attack or support claims in user comments as a method for determining stance [3]. Inspired by Hashimoto et al [6]'s excitatory and inhibitory templates, in this work, we similarly compose a manual list of PROMOTE(X,Y) and SUPPRESS(X,Y) relations and rely on these relations, coupled with positive and negative sentiment values, as a means to signify stance.…”

Section: Related Workmentioning

confidence: 99%

“…Structure of the example "Even though Eve says that [Alice is nasty] (1) , I think [she is nice] (2) . [She helps me a lot] (3) . [She even taught me how to cook] (4) !"…”

Section: Detailed Model Descriptionmentioning

confidence: 99%

“…The annotation process is conducted with three workers from the online platform oDesk. 3 We first annotate ten articles in a pilot study and annotate the remaining ones (or even more) after we inspected the results. Annotation will be carried out in three steps: the identification of (1) the topics, (2) the argumentative units, and (3) the argumentative relations.…”

Section: Corpusmentioning

confidence: 99%

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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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Argument Mining

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Back up your Stance: Recognizing Arguments in Online Discussions

Cited by 129 publications

References 23 publications

Building an Argument Search Engine for the Web

Building an Argument Search Engine for the Web

Proceedings of the 2nd Workshop on Argumentation Mining

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