Semiotic term expansion as the basis for thematic models in narrative systems
Authors: Hargood, C.
Editors: Millard, D. and Weal, M.
Narratives are a method of communicating information that comes naturally to people and is present in much of our digital and non-digital lives. While work has been undertaken investigating the nature of plot and content within narrative systems little has been done to model subtext or themes. In this thesis a machine understandable thematic model is presented for representing themes within narrative. Each instance of this model forms a definition of a theme and how it may be deconstructed into other thematic elements and their related features. The model is based on semiotic term expansion where terms may be shown to denote motifs which in turn connote themes. An authoring method has been developed to allow for instances of the model to be created. The effectiveness of this approach is demonstrated in four experiments presented within this thesis centred around the concept of creating thematic definitions and generating thematically relevant images. The first experiment explored a semiotic term expansion method for creating thematic definitions in terms of the model and a guide to support authors in doing so. This demonstrated that, though further support for authors is needed, creating valid definitions of themes was possible using the method. The following two experiments used a system called the Thematic Montage Builder; a prototype using definitions of the model to create themed photo montages. The first of these experiments compares the ability of this system to generate montages relevant to specific titles containing themes to Flickr keyword searches while the second compares this system to a term expansion system based on co-occurrence. In both cases the TMB generates montages that are judged by participants to better represent the theme in question. In the final experiment the effect of thematic emphasis on narrative cohesion is investigated. In this experiment a set of variables for measuring narrative cohesion are identified and the impact of using themed illustrations from the TMB on short stories is measured. The illustrations reduced the thematic noise of the short stories and further analysis shows a correlation between thematic cohesion and the perceived `logical sense' and `genre cohesion' of the narratives. This work shows that better machine understandable models of narrative can benefit from an understanding of themes, and that semiotic term expansion may be used to build successful thematic models.