Date of Original Version
Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL)
Copyright 2013 ACL
Abstract or Description
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 stereotypical 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.
Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 352-361.