Paper: Learning Latent Personas of Film Characters

ACL ID P13-1035
Title Learning Latent Personas of Film Characters
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013

We present two latent variable models for learning character types, or personas, in film, in which a persona is defined as a set of mixtures over latent lexical classes. These lexical classes capture the stereo- typical actions of which a character is the agent and patient, as well as attributes by which they are described. As the first attempt to solve this problem explicitly, we also present a new dataset for the text-driven analysis of film, along with a benchmark testbed to help drive future work in this area.