Focalization, the perspective through which narrative is presented, is encoded via a wide range of lexico-grammatical features and is subject to reader interpretation. Moreover, trained readers regularly disagree on interpretations, suggesting that this problem may be computationally intractable. In this paper, we provide experiments to test how well contemporary Large Language Models (LLMs) perform when annotating literary texts for focalization mode. Despite the challenging nature of the task, LLMs show comparable performance to trained human annotators in our experiments. We provide a case study working with the novels of Stephen King to demonstrate the usefulness of this approach for computational literary studies, illustrating how focalization can be studied at scale.

In this paper we describe a system for generating textual narrations of what happened in a simulation-based serious game, focusing on the use of focalization (telling the story from the perspective of one of the characters) and flashbacks to give the player insights into the internal state of non-player characters.

Processing the language of a narrative text, be it a novel, a extended flashbacks). These subsequent levels of blending film, or a play, is a crucial component of narrative of narrative spaces eventually yield the emergent space, comprehension. The research reported here shows how traditionally described as'the story'. The final product of processes driven by general linguistic and conceptual narrative comprehension is thus a mental construct, a patterns of meaning construction prompt the reader's or mega-blend, which emerges through multiple levels of viewer's response to the narrative artifact.