Abstract
Exploring the cutting-edge field of artificial intelligence, this work delves into how this emerging technology can be applied to the field of automated storytelling. The objective is to identify a clear and well-defined methodology capable of analyzing a text, recognizing the narrative elements within the story and, based on this, structuring the narrative into sequences. All these processes are to be carried out in an automated manner. Subsequently, based on this methodology, an application will be implemented that introduces a new algorithm that integrates natural language processing and unsupervised machine learning techniques. Finally, through a series of experiments on historical texts and the use of metrics, the effectiveness and potential of the proposed model will be demonstrated.
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Acknowledgements
The work described in this paper has been supported by the research project RASTA: Realtà Aumentata e Story-Telling Automatizzato per la valorizzazione di Beni Culturali ed Itinerari; Italian MUR PON Proj. ARS01 00540.
Gennaro Junior Pezzullo is a PhD student enrolled in the National PhD in Artificial Intelligence, XXXVII cycle, course on Health and life sciences, organized by “Università Campus Bio-Medico di Roma”.
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Di Martino, B., Pezzullo, G.J., Grassia, E. (2024). Support for Automated Story Telling Using Natural Language Processing Techniques Aimed at Recognizing Narrative Elements. In: Barolli, L. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 193. Springer, Cham. https://doi.org/10.1007/978-3-031-53555-0_58
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