Computational recognition of narratives

Computational recognition of narratives, if successful, would find innumerable applications with large digitized datasets. Systematic identification of narratives in the text flow could significantly contribute to such pivotal questions as where, when, and how narratives are employed. This paper discusses an approach to extract narratives from two datasets, Finnish parliamentary records (1980–2021) and oral history interviews with former Finnish MPs (1988–2018). Our study was based on an iterative approach, proceeding from original expert readings to a rule-based, computational approach that was elaborated with the help of annotated samples and annotation scheme. Annotated samples and computationally found extracts were compared, and a good correspondence was found. In this paper, we exhibit and compare the results from annotation and rule-based approach, and discuss examples of correctly and incorrectly found narrative sections. We consider that all attempts at recognizing and extracting narratives are definition dependent, and feed back to narrative theory.