Learning Latent Personas of Film Characters

40274

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nan

Abstract

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. 1

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nan

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Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 352–361, Sofia, Bulgaria, August 4-9 2013. c⃝2013 Association for Computational Linguistics Learning Latent Personas of Film Characters David Bamman Brendan O’Connor School of Computer ScienceNoah A. Smith Carnegie Mellon University Pittsburgh, PA 15213, USA {dbamman,brenocon,nasmith}@cs.cmu.edu Abstract 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. 1 Introduction Philosophers and dramatists have long argued whether the most important element of narrative is plot or character. Under a classical Aristotelian perspective, plot is supreme;1 modern theoretical dramatists and screenwriters disagree.2 Without addressing this debate directly, much computational work on narrative has focused on learning the sequence of events by which a story is defined; in this tradition we might situate sem- inal work on learning procedural scripts (Schank and Abelson, 1977; Regneri et al., 2010), narrative chains (Chambers and Jurafsky, 2008), and plot structure (Finlayson, 2011; Elsner, 2012; McIn- tyre and Lapata, 2010; Goyal et al., 2010). We present a complementary perspective that addresses the importance of character in defining 1“Dramatic action ...is not with a view to the representa- tion of character: character comes in as subsidiary to the ac- tions . . . The Plot, then, is the first principle, and, as it were, the soul of a tragedy: Character holds the second place.” Po- etics I.VI (Aristotle, 335 BCE). 2“Aristotle was mistaken in his time, and our scholars are mistaken today when they accept his rulings concerning char- acter. Character was a great factor in Aristotle’s time, and no fine play ever was or ever will be written without it” (Egri, 1946, p. 94); “What the reader wants is fascinating, complex characters” (McKee, 1997, 100). a story. Our testbed is film. Under this perspec- tive, a character’s latent internal nature drives the action we observe. Articulating narrative in this way leads to a natural generative story: we first de- cide that we’re going to make a particular kind of movie (e.g., a romantic comedy), then decide on a set of character types, or personas, we want to see involved (the PROTAGONIST, the LOVE INTER- EST, the BEST FRIEND). After picking this set, we fill out each of these roles with specific attributes (female, 28 years old, klutzy); with this cast of characters, we then sketch out the set of events by which they interact with the world and with each other (runs but just misses the train, spills coffee on her boss) – through which they reveal to the viewer those inherent qualities about themselves. This …

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Labeled Text

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. 1

Publication Details

Paper ID:
40274
Year:
2013
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan