8,317 research outputs found

    Argumentation Mining in User-Generated Web Discourse

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    The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

    Resilience, reliability, and coordination in autonomous multi-agent systems

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    Acknowledgements The research reported in this paper was funded and supported by various grants over the years: Robotics and AI in Nuclear (RAIN) Hub (EP/R026084/1); Future AI and Robotics for Space (FAIR-SPACE) Hub (EP/R026092/1); Offshore Robotics for Certification of Assets (ORCA) Hub (EP/R026173/1); the Royal Academy of Engineering under the Chair in Emerging Technologies scheme; Trustworthy Autonomous Systems “Verifiability Node” (EP/V026801); Scrutable Autonomous Systems (EP/J012084/1); Supporting Security Policy with Effective Digital Intervention (EP/P011829/1); The International Technology Alliance in Network and Information Sciences.Peer reviewedPostprin

    CHORUS Deliverable 4.5: Report of the 3rd CHORUS Conference

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    The third and last CHORUS conference on Multimedia Search Engines took place from the 26th to the 27th of May 2009 in Brussels, Belgium. About 100 participants from 15 European countries, the US, Japan and Australia learned about the latest developments in the domain. An exhibition of 13 stands presented 16 research projects currently ongoing around the world

    Intuitive and Reflective Inferences

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    Much evidence has accumulated in favor of such a dual view of reasoning (Evans, 2003, in press; for arguments against, see Osman, 2004). There is however some vagueness in the way the two systems are characterized. Instead of a principled distinction, we are presented with a bundle of contrasting features - slow/fast, automatic/controlled, explicit/implicit, associationist/rule based, modular/central - that, depending on the specific dual process theory, are attributed more or less exclusively to one of the two systems. As Evans states in a recent review, “it would then be helpful to have some clear basis for this distinction”; he also suggests that “we might be better off talking about type 1 and type 2 processes” rather than systems (Evans, in press). We share the intuitions that drove the development of dual system theories. Our goal here is to propose in the same spirit a principled distinction between two types of inferences: ‘intuitive inference’ and ‘reflective inference’ (or reasoning proper). We ground this distinction in a massively modular view of the human mind where metarepresentational modules play an important role in explaining the peculiarities of human psychological evolution. We defend the hypothesis that the main function of reflective inference is to produce and evaluate arguments occurring in interpersonal communication (rather than to help individual ratiocination). This function, we claim, helps explain important aspects of reasoning. We review some of the existing evidence and argue that it gives support to this approach
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