"PageRank" for Argument Relevance

Wachsmuth, Henning; Stein, Benno; Ajjour, Yamen

doi:10.18653/v1/e17-1105

Cited by 38 publications

(36 citation statements)

References 30 publications

(32 reference statements)

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“…As surveyed by Wachsmuth et al (2017a), several quality dimensions can be considered for arguments, from their logical cogency via their rhetorical effectiveness, to their dialectical reasonableness. So far, our prototype search engine makes use of a standard ranking scheme only (Robertson and Zaragoza, 2009), but recent research hints at future extensions: In (Wachsmuth et al, 2017b), we adapt the PageRank method (Page et al, 1999) to derive an objective relevance score for arguments from their relations, ranking arguments on this basis. Boltužić and Šnajder (2015) cluster arguments to find the most prominent ones, and Braunstain et al (2016) model argumentative properties of texts to better rank posts in community question answering.…”

Section: Related Workmentioning

confidence: 99%

“…Also, it plays an important role in others, such as automated decision making (Bench-Capon et al, 2009) and opinion summarization (Wang and Ling, 2016). Bex et al (2013) presented a first search interface for a collection of argument resources, while recent work has tackled subtasks of argument search, such as mining arguments from web text (Habernal and Gurevych, 2015) and assessing their relevance (Wachsmuth et al, 2017b). Still, the actual search for arguments on the web remains largely unexplored (Section 2 summarizes the related work).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Building an Argument Search Engine for the Web

Wachsmuth¹,

Potthast²,

Khatib³

et al. 2017

Proceedings of the 4th Workshop on Argument Mining

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118

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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%

Section: Introductionmentioning

confidence: 99%

Building an Argument Search Engine for the Web

Wachsmuth¹,

Potthast²,

Khatib³

et al. 2017

Proceedings of the 4th Workshop on Argument Mining

Self Cite

118

View full text Add to dashboard Cite

show abstract

“…Several approaches to this task exist as well as to subtasks thereof, such as argument unit segmentation (Ajjour et al, 2017). These approaches require different text analyses as preprocessing.…”

Section: The Indexing Processmentioning

confidence: 99%

“…Similarly, Wachsmuth et al (2017) propose a model for determining the relevance of arguments using PageRank (Brin and Page, 1998). In this approch, the relevance of an argument's conclusion is decided by what other arguments reuse it as a premise.…”

Section: Related Workmentioning

confidence: 99%

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Proceedings of the 4th Workshop on Argument Mining

2017

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ii IntroductionThe goal of this workshop is to provide a follow-on forum to the last three years' Argumentation Mining workshops at ACL and NAACL, the first research forum devoted to argumentation mining in all domains of discourse.Argument mining (also, "argumentation mining", referred to as "computational argumentation" in some recent works) is a relatively new challenge in corpus-based discourse analysis that involves automatically identifying argumentative structures within discourse, 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 argument mining in areas such as legal documents, on-line debates, product reviews, academic literature, user comments on proposed regulations, newspaper articles and court cases, as well as in dialogical domains. To date there are few corpora with annotations for argumentation mining research although corpora with annotations for argument sub-components have recently become available.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, scientific writing and parliamentary records, 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. This is especially important given the wide-spread adoption of computer-supported peer review, computerized essay grading, and large-scale online courses and MOOCs. Success in argument 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 ideas and present ongoing research on all of the above -see you all in Copenhagen, Denmark at EMNLP 2017! AbstractIn this paper we present the dataset of 200,000+ political arguments produced in the local phase of the 2016 Chilean constitutional process. We describe the human processing of this data by the government officials, and the manual tagging of arguments performed by members of our research group. Afterwards we focus on classification tasks that mimic the human processes, comparing linear methods ...

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Deploying Machine Learning Classifiers for Argumentative Relations “in the Wild”

Cocarascu

Toni

2019

Argumentation Library

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"PageRank" for Argument Relevance

Cited by 38 publications

References 30 publications

Building an Argument Search Engine for the Web

Building an Argument Search Engine for the Web

Proceedings of the 4th Workshop on Argument Mining

Deploying Machine Learning Classifiers for Argumentative Relations “in the Wild”

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