35 research outputs found

    Reflective writing analysis approach based on semantic concepts : an evaluation of WordNet affect efficiency

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    Automatic analysis of reflective writing involves identifying indicator strings and using string matching or rule matching processes, which flag sections of a text containing reflective material. The problem with the string-based approach is its inability to deal with knowledge inference from the text, such as the content, context, relevance, clarity, and interconnection, which can be identified by semantic analysis. The semantic analysis depends mainly on mapping the text into stored knowledge sources, such as WordNet, and analyzing the associations in the underlying knowledge source. In this paper, a semantic-based approach for reflective writing analysis is proposed, in which the input text, which is being analyzed, is mapped into semantic concepts. Moreover, a machine learning (ML) approach for reflective writing identification and analysis has been implemented to overcome the limitations of rule execution and keyword matching. The proposed approach addresses the efficiency of using several effective concepts, correlated with effective words that are identified in WordNet-Affect. The input text is classified into reflective or non-reflective categories, after which the input text is classified into various reflective classes, based on the type of the document. Moreover, the concepts in WordNet-Affect are evaluated and analyzed to demonstrate their effects on classification and labeling tasks

    Contested Collective Intelligence: rationale, technologies, and a human-machine annotation study

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    We propose the concept of Contested Collective Intelligence (CCI) as a distinctive subset of the broader Collective Intelligence design space. CCI is relevant to the many organizational contexts in which it is important to work with contested knowledge, for instance, due to different intellectual traditions, competing organizational objectives, information overload or ambiguous environmental signals. The CCI challenge is to design sociotechnical infrastructures to augment such organizational capability. Since documents are often the starting points for contested discourse, and discourse markers provide a powerful cue to the presence of claims, contrasting ideas and argumentation, discourse and rhetoric provide an annotation focus in our approach to CCI. Research in sensemaking, computer-supported discourse and rhetorical text analysis motivate a conceptual framework for the combined human and machine annotation of texts with this specific focus. This conception is explored through two tools: a social-semantic web application for human annotation and knowledge mapping (Cohere), plus the discourse analysis component in a textual analysis software tool (Xerox Incremental Parser: XIP). As a step towards an integrated platform, we report a case study in which a document corpus underwent independent human and machine analysis, providing quantitative and qualitative insight into their respective contributions. A promising finding is that significant contributions were signalled by authors via explicit rhetorical moves, which both human analysts and XIP could readily identify. Since working with contested knowledge is at the heart of CCI, the evidence that automatic detection of contrasting ideas in texts is possible through rhetorical discourse analysis is progress towards the effective use of automatic discourse analysis in the CCI framework

    Proceedings du Workshop international « ConMAd :Concrete with high content of Mineral Additions »

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