Abstract
In this paper we address the issue of creativity and style computation from a natural language processing perspective. We introduce a computational framework for creativity analysis with two approaches, one agnostic, based on clustering, and one knowlegde-based, that exploits supervised learning and feature selection. While the agnostic approach can reveal the uniqueness of authors in a meaningful context, the knowledge-based approach can be exploited to extract the culturally relevant features of works and to predict social acceptance. In both the approaches, it is required a great effort to define symbols to represent meaningful cues in creativity and style.
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Celli, F. (2016). Computational Approaches to the Analysis of Human Creativity. In: Degli Esposti, M., Altmann, E., Pachet, F. (eds) Creativity and Universality in Language. Lecture Notes in Morphogenesis. Springer, Cham. https://doi.org/10.1007/978-3-319-24403-7_12
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DOI: https://doi.org/10.1007/978-3-319-24403-7_12
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