Understanding narrative content has become an increasingly popular topic. Nonetheless, research on identifying common types of narrative characters, or personae, is impeded by the lack of automatic and broad-coverage evaluation methods. We argue that computationally modeling actors provides benefits, including novel evaluation mechanisms for personae. Specifically, we propose two actor-modeling tasks, cast prediction and versatility ranking, which can capture complementary aspects of the relation between actors and the characters they portray. For an actor model, we present a technique for embedding actors, movies, character roles, genres, and descriptive keywords as Gaussian distributions and translation vectors, where the Gaussian variance corresponds to actors' versatility. Empirical results indicate that (1) the technique considerably outperforms TransE ( 2013) and ablation baselines and (2) automatically identified persona topics (Bamman, O'Connor, and Smith 2013) yield statistically significant improvements in both tasks, whereas simplistic persona descriptors including age and gender perform inconsistently, validating prior research.
Computational techniques that model story characters are the most relevant to this work. Bamman ( Bamman2013,bamman2014 ) extract persona as a group of topics, which are multinomial distributions over action verbs, possessives, and modifiers respectively. The rationale is that similar characters would perform receive similar actions and have similar descriptions. For example, a hero often rescues, a sidekick would banter, and a villain tends to be angry. Bamman2013 adopt a logistic prior uses the actor's age, gender, and movie genre to aid the assignment of movie characters to personae. bamman2014 further incorporate the influence of the author and the background topic frequency into a log-linear topic model @cite_3 . Valls-Vargas2015 propose a system architecture with feedback loops for identifying personae from Russian folklore. Chaturvedi2017a employ hidden Markov models to learn how relationships evolve between characters.
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Generative models of text typically associate a multinomial with every class label or topic. Even in simple models this requires the estimation of thousands of parameters; in multi-faceted latent variable models, standard approaches require additional latent "switching" variables for every token, complicating inference. In this paper, we propose an alternative generative model for text. The central idea is that each class label or latent topic is endowed with a model of the deviation in log-frequency from a constant background distribution. This approach has two key advantages: we can enforce sparsity to prevent overfitting, and we can combine generative facets through simple addition in log space, avoiding the need for latent switching variables. We demonstrate the applicability of this idea to a range of scenarios: classification, topic modeling, and more complex multifaceted generative models.