Integrating natural language processing and pragmatic argumentation theories for argumentation support

Natural language processing (NLP) research and design that aims to model and detect opposition in text for the purpose of opinion classification, sentiment analysis, and meeting tracking, generally excludes the interactional, pragmatic aspects of online text. We propose that a promising direction for NLP is to incorporate the insights of pragmatic, dialectical theories of argumentation to more fully exploit the potential of NLP to offer sound, robust systems for various kinds of argumentation support

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Combining conceptual graphs and argumentation for aiding in the teleexpertise

Current medical information systems are too complex to be meaningfully exploited. Hence there is a need to develop new strategies for maximising the exploitation of medical data to the benefit of medical professionals. It is against this backdrop that we want to propose a tangible contribution by providing a tool which combines conceptual graphs and Dung׳s argumentation system in order to assist medical professionals in their decision making process. The proposed tool allows medical professionals to easily manipulate and visualise queries and answers for making decisions during the practice of teleexpertise. The knowledge modelling is made using an open application programming interface (API) called CoGui, which offers the means for building structured knowledge bases with the dedicated functionalities of graph-based reasoning via retrieved data from different institutions (hospitals, national security centre, and nursing homes). The tool that we have described in this study supports a formal traceable structure of the reasoning with acceptable arguments to elucidate some ethical problems that occur very often in the telemedicine domain

A framework for decision making on teleexpertise with traceability of the reasoning

This paper provides a methodological framework for decision making process to ensure its traceability generally in the context of telemedicine and particularly in the act of teleexpertise. This act permits to medical professionals and/or health professionals to collaborate in order to take suitable decisions for a patient diagnosis or treatment. The main problem dealing with teleexpertise is the following: How to ensure the traceability of the decisions making process? This problem is solved in this paper through a conceptualisation of a rigorous framework coupling semantic modelling and explicit reasoning which permits to fully support the analysis and rationale for decisions made. The logical semantic underlying this framework is the argumentative logicto provide adequate management of information with traceability of the reasoning including options and constraints. Thus our proposal will permit to formally ensure the traceability of reasoning in telemedicine and particularly in teleexpertise in order to favour the quality of telemedicine’s procedure checking. This traceability is to guarantee equitable access to the benefits of the collective knowledge and experience and to provide remote collaborative practices with a sufficient safety margin to guard against the legal requirements. An illustrative case study is provided by the modelling of a decision making process applied to teleexpertise for chronic diseases such as diabetes mellitus type2

Discourse-centric learning analytics: mapping the terrain

There is an increasing interest in developing learning analytic techniques for the analysis, and support of, high quality learning discourse. This paper maps the terrain of discourse-centric learning analytics (DCLA), outlining the distinctive contribution of DCLA and outlining a definition for the field moving forwards. It is our claim that DCLA provide the opportunity to explore the ways in which: discourse of various forms both resources and evidences learning; the ways in which small and large groups, and individuals make and share meaning together through their language use; and the particular types of language – from discipline specific, to argumentative and socio-emotional – associated with positive learning outcomes. DCLA is thus not merely a computational aid to help detect or evidence ‘good’ and ‘bad’ performance (the focus of many kinds of analytic), but a tool to help investigate questions of interest to researchers, practitioners, and ultimately learners. The paper ends with three core issues for DCLA researchers – the challenge of context in relation to DCLA; the various systems required for DCLA to be effective; and the means through which DCLA might be delivered for maximum impact at the micro (e.g. learner), meso (e.g. school), and macro (e.g. governmental) levels

Specification of knowledge acquisition and modeling of the process of the consensus

zhdanova2004aIn this deliverable, specification of knowledge acquisition and modeling of the process of consensus is provided