Implementing the argument web

Bex, Floris; Lawrence, John; Snaith, Mark; Reed, Chris

doi:10.1145/2500891

Cited by 68 publications

(52 citation statements)

References 13 publications

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“…Further pioneer research was conducted by Rahwan et al (2007), who foresaw a world wide argument web with structured argument 1 The framework, index, and search engine can be accessed at: http://www.arguana.com/software.html ontologies and tools for creating and analyzing arguments -the semantic web approach to argumentation. Meanwhile, key parts of the approach have surfaced: the argument interchange format (AIF), a large collection of human-annotated corpora, and tool support, together called AIFdb (Bex et al, 2013). Part of AIFdb is a query interface to browse arguments in the corpora based on words they contain.…”

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

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%

Building an Argument Search Engine for the Web

Wachsmuth¹,

Potthast²,

Khatib³

et al. 2017

Proceedings of the 4th Workshop on Argument Mining

118

View full text Add to dashboard Cite

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“…RationalGRL also provides tool support for argumentation, i.e. Argument Web toolset, to which OVA belongs [4], and for goal modeling, i.e. jUCMNav [12].…”

Section: Related Workmentioning

confidence: 99%

“…This framework facilitates argument construction and analysis on the one hand and the rationalization and evaluation of goal models on the other hand. More specifically, RationalGRL framework combines an existing argument diagramming tool 5 [4] based on a formal theory of practical (i.e. goal-driven) argumentation [2,10,16,17] with a standardized goal modeling language and its tool support [1].…”

Section: Introductionmentioning

confidence: 99%

RationalGRL: A Framework for Rationalizing Goal Models Using Argument Diagrams

Zee

Marosin

Bex

et al. 2016

Conceptual Modeling

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Abstract. Goal modeling languages, such as i* and the Goal-oriented Requirements Language (GRL), capture and analyze high-level goals and their relationships with lower level goals and tasks. However, in such models, the rationalization behind these goals and tasks and the selection of alternatives are usually left implicit. To better integrate goal models and their rationalization, we develop the RationalGRL framework, in which argument diagrams can be mapped to goal models. Moreover, we integrate the result of the evaluation of arguments and their counterarguments with GRL initial satisfaction values. We develop an interface between the argument web tools OVA and TOAST and the Eclipse-based tool for GRL called jUCMNav. We demonstrate our methodology with a case study from the Schiphol Group.

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“…Although there are a number of argument analysis tools (such as Araucaria , Carneades (Gordon et al, 2007), Rationale and OVA (Bex et al, 2013)) which allow the analyst to identify the argumentation scheme related to a particular argumentative structure, the vast majority of analyses which are produced using these tools do not include this information. For example, less than 10% of the OVA analyses contained in AIFdb (Lawrence et al, 2012) include any scheme structure.…”

Section: Datasetmentioning

confidence: 99%

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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Implementing the argument web

Abstract: Improve online public discourse by connecting opinions across blogs, editorials, and social media.

Cited by 68 publications

References 13 publications

Building an Argument Search Engine for the Web

Building an Argument Search Engine for the Web

RationalGRL: A Framework for Rationalizing Goal Models Using Argument Diagrams

Proceedings of the 2nd Workshop on Argumentation Mining

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