Comparing Extant Story Classifiers: Results & New Directions

Having access to a large set of stories is a necessary first step for robust and wide-ranging computational narrative modeling; happily, language data - including stories - are increasingly available in electronic form. Unhappily, the process of automatically separating stories from other forms of written discourse is not straightforward, and has resulted in a data collection bottleneck. Therefore researchers have sought to develop reliable, robust automatic algorithms for identifying story text mixed with other non-story text. In this paper we report on the reimplementation and experimental comparison of the two approaches to this task: Gordon\u27s unigram classifier, and Corman\u27s semantic triplet classifier. We cross-analyze their performance on both Gordon\u27s and Corman\u27s corpora, and discuss similarities, differences, and gaps in the performance of these classifiers, and point the way forward to improving their approaches