@inproceedings{3a76d62f6f244c37ad50860b51120602,
title = "A Bayesian Mixed Effects Model of Literary Character",
abstract = "We consider the problem of automatically inferring latent character types in a collection of 15,099 English novels published between 1700 and 1899. Unlike prior work in which character types are assumed responsible for probabilistically generating all text associated with a character, we introduce a model that employs multiple effects to account for the influence of extra-linguistic information (such as author). In an empirical evaluation, we find that this method leads to improved agreement with the preregistered judgments of a literary scholar, complementing the results of alternative models.",
author = "David Bamman and Ted Underwood and Smith, {Noah A.}",
year = "2014",
doi = "10.3115/v1/p14-1035",
language = "English (US)",
isbn = "9781937284725",
series = "52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "370--379",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics",
note = "52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 ; Conference date: 22-06-2014 Through 27-06-2014",
}