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Learning Latent Personas of Film Characters

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journal contribution
posted on 01.08.2014 by David Bamman, Brendan O'Connor, Noah A. Smith

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.


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