Joshua Daniel Eisenberg

School of Computing and Information Sciences

Lecture Information:
  • November 2, 2018
  • 11:00 AM
  • PG-6 105

Speaker Bio

Joshua Eisenberg received a B.A. in mathematics from Brandeis University in 2012. In 2014, he earned a B.S. in computer engineering from Florida International University. Joshua was a fellow of the National Science Foundation (NSF) Open Science Data Cloud (OSDC) Partnerships for International Research and Education (PIRE) in 2013, 2014, and 2016. Outside of research, Joshua is interested in improvisational music, music festivals, art, traveling, and food.


Automatic understanding of stories is a long-held goal of artificial intelligence and natural language processing research communities. Stories explain the human experience. Understanding stories promotes the understanding of both individuals and groups of people; cultures, societies, families, organizations, governments, and corporations, to name a few. People use stories to share information. Stories are told–by narrators–in linguistic bundles of words called narratives.

My work has given computers understanding of some aspects of narrative structure. Specifically, where are the boundaries of a narrative in a text. This is the task of determining where a narrative begins and ends, a nontrivial task, because people rarely tell one story at a time. People don’t specifically announce when we are starting or stopping our stories: We interrupt each other. We tell stories within stories. Before my work, computers had no awareness of narrative boundaries, essentially where stories begin and end. My programs can extract narrative boundaries from novels and short stories with an F1 of 0.65.

Before this I worked on teaching computers to identify which paragraphs of text have story content, with an F1 of 0.75 (which is state of the art). Additionally, I have taught computers to identify the narrative point of view (POV; how the narrator refers to theirself) and diegesis (how is the narrator involved in the story’s action) with F1 of over 0.90 for both narrative characteristics. For the narrative POV, diegesis, and narrative level extractors I ran annotation studies, with high agreement, that allowed me to teach computational models to identify structural elements of narrative through supervised machine learning.

My work has given computers the ability to find where stories begin and end in raw text. This will allow for further, automatic analysis, like extraction of plot, intent, event causality, and event coreference. These are difficult when there are multiple narratives in one text. There are two key contributions in my work: 1) my identification of features that accurately extract information about narrators, and narrative levels and 2) the goldstandard data generated from running annotation studies on identifying these same elements of narrative structure.