A Bayesian Mixed Effects Model of Literary Character

David Bamman, Ted Underwood, Noah A. Smith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 52nd Annual Meeting of the Association for Computational Linguistics
Place of PublicationBaltimore
PublisherAssociation for Computational Linguistics (ACL)
Pages370-379
Number of pages10
ISBN (Print)9781937284725
StatePublished - Jan 1 2014
Event52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States
Duration: Jun 22 2014Jun 27 2014

Publication series

Name52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference
Volume1

Other

Other52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014
CountryUnited States
CityBaltimore, MD
Period6/22/146/27/14

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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