The Graduate School at Arts, Faculty of Arts, Aarhus University, in collaboration with the Velux Foundation, invites applications for a fully-funded PhD scholarship/fellowship in Predicting reader assessment provided the necessary funding is available. This PhD scholarship is available as of July 1, 2021, for a period of up to four years.
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Scholarship Sponsor | Aarhus University |
Scholarships level | PhD Scholarship |
Award Amount | Not Specified |
Fellowship Period | Three years |
Opening date | February 10, 2021 |
Closing date | March 15, 2021 |
Fabula-NET is an interdisciplinary collaboration between literary research, linguistics, and informatics to improve literary scholarship, specifically academic assessment, through the state of the art “humanist-in-the-loop” machine learning. Literature’s multidimensional and complex texts constitute a particularly challenging material with the potential to develop new deep models for automated text classification.
The project combines fractal analysis, sentiment classification, and advanced language models with deep neural networks to describe the internal coherence of texts based on the hypothesis that a successful literary work exhibits a particular variation between predictability and unpredictability. This variation is mainly reflected in the dynamic properties of the narrative.
The overall model can classify texts as high/low quality and successful/unsuccessful, which is supported by preliminary studies of, for example, H. C. Andersen’s fairy tales and J. K. Rowling’s novels. The project is multilingual and works with a vast corpus in English, Danish, and Chinese in collaboration with several domain experts who help validate the automated analysis results.
The application possibilities for this technology are wide-ranging. It will be relevant for both libraries and publishers for searching through and evaluating texts and in research to understand and compare extensive collections of texts from world literature at a level higher than compiled single analyses. The application can also be developed for other types of texts and contribute to increasing the quality of, e.g., automated text generation.
The successful applicant will describe a PhD project that will focus on how intrinsic (e.g., narrative structure, stylistic features), extrinsic (e.g., reviews), and contextual (e.g., reader preferences) properties of literary works can be used to predict reader assessment in an unbiased and, to the extent it is the possible transparent way.
The deadline for applications is March 15, 2021
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