Welcome to the Digital Humanities / Artificial Intelligence Seminar!
Fostered by the creation of new algorithms, computation power and the development of deep learning techniques, Artificial Intelligence needs constantly to confront new issues and data sets in order to deepen its methodologies and increase its range of scientific applications. Digital humanities, developing digital science methodologies in the study of humanities and using the critical approaches of humanities in the analysis of the contemporary “digital revolutions”, are constantly in search of new tools to explore more and more complex and diversified data sets.
The coupling AI/DH is globally emerging as one key interface for both domains and will probably prove to be a deep transformative trend in tomorrow intellectual world.
The ambition of this seminar is to be one of the places where this coupling is shaped, fostered and analyzed. It intends to offer a forum where both communities, understood in a very inclusive way, exchange around emerging issues, ongoing projects, and past experiences in order to build a common language, a shared space, and to encourage innovative cooperation on the long run.
You can subscribe to the mailing list from here, in order to receive the announcements of the seminars.
You can access here the list of past seminars.
June 8, 2021, 12:00-14:00, room online (link GotoMeeting).
Pablo Gervas (Universidad Complutense de Madrid)
Title: Embedded Stories and Narrative Levels: a challenge for Computational Narrative
Abstract: Stories told by a character within a story are known as embedded stories. They occur frequently in narrative and they constitute an important challenge to models of narrative interpretation. Computational procedures for interpreting a story need to account for these embedded stories in terms of how to represent them and how to process them in the context of the story acting as frame for them. The talk will explain the concept of embedded stories over some real examples of narrative, describe the challenges that their computational treatment poses, and describe a simplified computational model for the task. The proposed model is capable of representing discourses for embedded stories and interpret them onto a representation that captures their recursive structure. Some examples of application of the model to examples of stories from different domains will be presented, and some preliminary conclusions will be outlined on what embedding implies in terms of computational interpretation of narrative, and the challenges it poses for the ever-growing research efforts for the automated processing of narrative.