Linguistic spatial classifications of event domains in narratives of crime

Abstract

Structurally, formal definitions of the linguistic narrative minimally require two temporally linked past-time events. The role of space in this definition, based on spatial language indicating where events occur, is considered optional and non-structural. However, based on narratives with a high frequency of spatial language, recent research has questioned this perspective, suggesting that space is more critical than may be readily apparent. Through an analysis of spatially rich serial criminal narratives, it will be demonstrated that spatial information qualitatively varies relative to narrative events. In particular, statistical classifiers in a supervised machine learning task achieve a 90% accuracy in predicting Pre-Crime, Crime, and Post-Crime events based on spatial (and temporal) information. Overall, these results suggest a deeper spatial organization of discourse, which not only provides practical event resolution possibilities, but also challenges traditional formal linguistic definitions of narrative.