Characters are central to narrative theory but remain under-specified in computational work, where they are often reduced to clusters of words or vectors. We propose an operationalizable ontology of characterization that bridges narratological theory and NLP. From BERT-based clustering of character descriptions, we derive 17 classes of attributes (actions, emotions, traits, relations, possessions, etc.), validated through manual annotation ($k = 0.77$) and automatic classification (64% accuracy vs. 12% baseline). Applied to character similarity tasks for French fiction, our framework outperforms existing models. By aligning narratological insights with computational methods, we move toward a representation of fictional characters as structured, comparable entities for large-scale literary analysis.

This article argues that a new stylometry of Hermann’s Bridge identifies unexpected metrical trends in authors, eras and speakers. Using computational and statistical means, it provides the first comprehensive survey of breaks and quasi-breaks of Hermann’s Bridge and analyses them according to corpus, formulaic constructions and the narratology of character speech. This approach both complements previous studies of the Bridge and enables future research into its potential literary effects.