Recognizing Coherent Narrative Blog Content

Conference paper

DOI: 10.1007/978-3-319-48279-8_21

Volume 10045 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Ryan J., Swanson R. (2016) Recognizing Coherent Narrative Blog Content. In: Nack F., Gordon A. (eds) Interactive Storytelling. ICIDS 2016. Lecture Notes in Computer Science, vol 10045. Springer, Cham

Abstract

Interactive storytelling applications have at their disposal massive numbers of human-authored stories, in the form of narrative weblog posts, from which story content could be harvested and repurposed. Such repurposing is currently inhibited, however, in that many blog narratives are not sufficiently coherent for use in these applications. In a narrative that is not coherent, the order of the events in the narrative is not clear given the text of the story. We present the results of a study exploring automatic methods for estimating the coherence of narrative blog posts. In the end, our simplest model—one that only considers the degree to which story text is capitalized and punctuated—vastly outperformed a baseline model and, curiously, a series of more sophisticated models. Future work may use this simple model as a baseline, or may use it along with the classifier that it extends to automatically extract large numbers of narrative blog posts from the web for purposes such as interactive storytelling.

Keywords

Coherence Blogs Content harvesting Machine learning 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Expressive Intelligence StudioUniversity of California, Santa CruzSanta CruzUSA
  2. 2.Institute for Creative TechnologiesUniversity of Southern CaliforniaLos AngelesUSA