School of Computing and Information Sciences
Labiba Jahan is a fourth-year Ph.D. candidate at the School of Computing and Information Sciences, Florida International University under the supervision of Professor Mark A. Finlayson. Her research interests are in Natural Language Processing, with a focus on Story Understanding. She holds a B.Sc degree in Computer Science and Engineering from the Shahjalal University of Science and Technology, Bangladesh in 2014. From 2014 to 2016, before joining FIU, she worked as a Lecturer at Metropolitan University, Sylhet, Bangladesh. In 2019 she served as an intern at the Product Simulation and Modelling Group at Siemens Corporate Technology in Princeton, NJ. She won 3rd place at 2019 Florida International University Graduate Student Appreciation Week. She has published several workshop and conference papers.
If we are to understand stories, we must understand characters: characters are central to every narrative and drive the action forward. Critically, many stories (especially cultural ones) employ stereotypical character roles to efficiently communicate bundles of default characteristics and associations, and to ease understanding of the role of those characters in the overall narrative. These roles include hero, villain, or victim, as well as culturally specific roles such as, for example, the donor (in Russian tales) or the trickster (in Native American tales). The goal of my thesis is to learn these roles automatically, inducing them from data using a novel clustering technique. I first begin with animacy detection by developing a hybrid system that can detect animate coreference chains. Once the animacy of chains is determined, I then do character detection from a narratological point of view. Finally, I extract stereotypical roles by implementing the Infinite Relational Model, non-parametric Bayesian clustering with plot information.