Paper: A Bayesian Mixed Effects Model of Literary Character

ACL ID P14-1035
Title A Bayesian Mixed Effects Model of Literary Character
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We consider the problem of automatically inferring latent character types in a collec- tion of 15,099 English novels published between 1700 and 1899. Unlike prior work in which character types are assumed responsible for probabilistically generat- ing all text associated with a character, we introduce a model that employs mul- tiple effects to account for the influence of extra-linguistic information (such as au- thor). In an empirical evaluation, we find that this method leads to improved agree- ment with the preregistered judgments of a literary scholar, complementing the results of alternative models.