Work

A Bayesian Mixed Effects Model of Literary Character

Year: 2014

Type: conference-paper

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.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.3115/v1/p14-1035

Authors: David Bamman, Ted Underwood, Noah A. Smith

Institutions: Carnegie Mellon University, University of Illinois Urbana-Champaign

Language: English

Topic: Authorship Attribution and Profiling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, National Endowment for the Humanities

OpenAlex ID: W2095881835