ET3 Digital Humanities: Challenges and Chances


The availability of Language Technology Resources and Tools generates a considerable methodological potential in the Digital Humanities: aspects of research questions from the Humanities and Social Sciences can be addressed on text collections in ways which were unavailable to traditional approaches. I start this talk by sketching some sample scenarios of Digital Humanities projects which involve various Humanities and Social Science disciplines, noting that the potential for a meaningful contribution to higher-level questions is highest when the employed language technological models are carefully tailored both (a) to characteristics of the given target corpus, and (b) to relevant analytical subtasks feeding the discipline-specific research questions.

Keeping up a multidisciplinary perspective, I then point out a recurrent dilemma in Digital Humanities projects that follow the conventional set-up of collaboration: to build high-quality computational models for the data, fixed analytical targets should be specified as early as possible — but to be able to respond to Humanities questions as they evolve over the course of analysis, the analytical machinery should be kept maximally flexible. To reach both, I argue for a novel collaborative culture that rests on a more interleaved, continuous dialogue. (Re-)Specification of analytical targets should be an ongoing process in which the Humanities Scholars and Social Scientists play a role that is as important as the Computational Scientists' role. A promising approach lies in the identification of re-occurring types of analytical subtasks, beyond linguistic standard tasks, which can form building blocks for text analysis across disciplines, and for which corpus-based characterizations (viz. reference annotations) can be collected, compared and revised. On such grounds, computational modeling is more directly tied to the evolving research questions, and hence the seemingly opposing needs of reliable target specifications vs. "malleable" frameworks of analysis can be reconciled. Experimental work following this approach is under way in the Center for Reflected Text Analytics (CRETA) in Stuttgart.





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Instructor's name

Jonas Kuhn


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Jonas Kuhn is Professor of Computational Linguistics in the Division of Computer Science at the University of Stuttgart, Germany. He did undergraduate and graduate studies in Computational Linguistics and Cognitive Science in Stuttgart and Edinburgh and completed his doctorate in Stuttgart in 2001 with a dissertation on “Formal and Computational Aspects of Optimality-theoretic Syntax”. He spent a post-doctoral stay at Stanford University (2001/02), held an assistant professorship at the University of Texas at Austin (2003-05), and subsequently led an Emmy-Noether junior research group at Saarland University, Germany, before taking up a full professorship at the Linguistics Department at the University of Potsdam, Germany, in 2006. In 2010 he moved on to his current position in Stuttgart. His research interests range broadly from linguistically informed data-driven models in Natural Language Processing to the development of cross-disciplinary methods for text analysis in the humanities and social sciences. Since 2010, Kuhn has also been leading the Stuttgart center in the CLARIN network of research infrastructure centers for language tools and resources. He is director of the collaborative research center SFB 732 “Incremental Specification in Context” (since October 2015) and the Digital Humanities Center for Reflected Text Analytics CRETA (since January 2016).