Automatic recognition of narrative drama units: A structured learning approach

Drama is a story told through the live actions of characters; dramatic writing is characterized by aspects that are central to identify, interpret, and relate the different elements of a story. The Drammar ontology has been proposed to represent core dramatic qualities of a dramatic text, namely Actions, Agents, Scenes and Conflicts, evoked by individual text units. The automatic identification of such elements in a drama is the first step in the recognition of their evolution, both at coarse and fine grain text level. In this paper, we address the issue of segmentation, that is, the partition of the drama into meaningful unit sequences We study the role of editorial as well as content–based text properties, without relying on deep ontological relations. We propose a generative inductive machine learning framework, combining Hidden Markov models and SVM and discuss the role of event information (thus involving agents and actions) at the lexical and grammatical level.