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Auflistung nach Autor:in "Wiedemann, Gregor"

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  • Zeitschriftenartikel
    Content Analysis between Quality and Quantity
    (Datenbank-Spektrum: Vol. 15, No. 1, 2015) Lemke, Matthias; Niekler, Andreas; Schaal, Gary S.; Wiedemann, Gregor
    Social science research using Text Mining tools requires—due to the lack of a canonical heuristics in the digital humanities—a blended reading approach. Integrating quantitative and qualitative analyses of complex textual data progressively, blended reading brings up various requirements for the implementation of Text Mining infrastructures. The article presents the Leipzig Corpus Miner (LCM), developed in the joint research project ePol—Post-Democracy and Neoliberalism and responding to social science research requirements. The functionalities offered by the LCM may serve as best practice of processing data in accordance with blended reading.
  • Zeitschriftenartikel
    The Road Map to FAME: A Framework for Mining and Formal Evaluation of Arguments
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Baumann, Ringo; Wiedemann, Gregor; Heinrich, Maximilian; Hakimi, Ahmad Dawar; Heyer, Gerhard
    Two different perspectives on argumentation have been pursued in computer science research, namely approaches of argument mining in natural language processing on the one hand, and formal argument evaluation on the other hand. So far these research areas are largely independent and unrelated. This article introduces the agenda of our recently started project “FAME – A framework for argument mining and evaluation”. The main project idea is to link the two perspectives on argumentation and their respective research agendas by employing controlled natural language as a convenient form of intermediate knowledge representation. Our goal is to develop a framework which integrates argument mining and formal argument evaluation to study patterns of empirical argumentation usage. If successful, this combination will allow for new types of queries to be answered by argumentation retrieval systems and large-scale content analysis. Moreover, feeding evaluation results as additional knowledge input to argument mining processes could be utilized to further improve their results.