Automated event annotation in literary texts

We approach the modeling of event structure of literary texts with narratological event concepts. A manually annotated corpus of 4 prose texts with event categories allows us to learn to automatically classify events using a transformer model, relying on a rule-based system in conjunction with a pre-trained parser to identify events. For the evaluation of both manual and automated annotation, we use narrativity graphs, which capture the change in narrativity over the course of the text. In an exploratory analysis, we apply the event classifier in conjunction with graph-based narrativity metrics to a large literary corpus. We find that text length does neither influence the length of eventful passages nor the number of eventful passages in the beginnings of texts.