Narratives are a ubiquitous language phenomenon found in every society and culture, and are used by nearly every person in the world every single day. Perhaps the single most important feature that distinguishes narrative from other forms of discourse is their structure: the structure of a narrative communicates its meaning and purpose and gives rise to numerous cognitive benefits that improve comprehension, retention, understanding, and use of information given in narrative form. I present results from the Cognac Lab at FIU on automatically extracting narrative structure using approaches drawn from machine learning and computational linguistics. First, I present experiments that show that a classic theory of narrative structure (Vladimir Propp's morphology of the folktale) can be reliably reproduced by people. Second, I demonstrate a specially-designed learning algorithm can learn Propp’s theory from raw data. Finally, I outline the most recent results from student work in the lab, including algorithms for story detection, character classification, and motif extraction, which point the way forward to systems for fully automatic narrative structure extraction and a major advance in machine intelligence.
Dr. Mark Finlayson is Assistant Professor of Computer Science in the School of Computing and Information Sciences at Florida International University. He received his Ph.D. from MIT in 2011, and from 2012-2014 was a Research Scientist in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on representing, extracting, and using higher-order semantic patterns in natural language, especially focusing on narrative. His work intersects artificial intelligence, computational linguistics, and cognitive science. He is general chair of the Computational Models of Narrative Workshop Series.