---
title: "A Bayesian Mixed Effects Model of Literary — Bamman 2014"
description: "The paper addresses unsupervised discovery of character types (\"personas\") in fiction."
authors: ["David Bamman", "Ted Underwood", "Noah A. Smith"]
year: 2014
venue: "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P14-1035/"
doi: "10.3115/v1/p14-1035"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/bamman-2014-bayesian-mixed-effects-model-literary/"
---
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# A Bayesian Mixed Effects Model of Literary Character

**Authors:** [David Bamman](https://md.research.elamur.ai/free/authors/david-bamman/), Ted Underwood, [Noah A. Smith](https://md.research.elamur.ai/free/authors/noah-a-smith/) · **Year:** 2014 · **Venue:** Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/P14-1035/) · [PDF](https://aclanthology.org/P14-1035.pdf) · [DOI 10.3115/v1/p14-1035](https://doi.org/10.3115/v1/p14-1035) · [OpenAlex](https://openalex.org/W2095881835)

**Citations:** 185 citations (openalex-snapshot · 2026-10-14) · [99](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses unsupervised discovery of character types ("personas") in fiction. Earlier models treated a persona as the sole source of all words linked to a character, so learned types tended to group characters by author or period instead of by role. Bamman, Underwood and Smith propose a hierarchical Bayesian mixed effects model in which a character's descriptive vocabulary is jointly explained by its latent persona and by extra-linguistic factors, here the author.

They apply it to 15,099 English novels (1700–1899) from the HathiTrust Digital Library. A scalable NLP pipeline extracts the actions, possessions and attributes tied to each character. The model was compared against preregistered similarity judgments by a literary scholar. The Author/Persona model was best at telling apart different character types within one author's work, while a Persona Regression alternative did better at grouping characters by author.

This matters because it gives humanists a flexible way to separate character from authorial style. The same approach could later factor out genre, period or narrative point of view.

## Key points
- **Corpus:** 469,200 English volumes were filtered to 32,209 prose-fiction volumes. After removing duplicates and merging multi-volume works, 15,099 narratives remained, totaling about 1.8 billion tokens.
- **Persona definition:** A persona is a distribution over four dependency-based role types (agent, patient, possessive, predicative), extracted using Stanford typed dependencies.
- **Pipeline:** The pipeline uses the Stanford POS tagger, the linear-time MaltParser and the Stanford NER, plus greedy name-variant clustering.
  - About 74% of character references are pronouns.
  - A pronominal coreference classifier was trained on 832 annotated links from 3 novels. It reached 82.7% accuracy in 10-fold cross-validation.
- **Vocabulary reduction:** 100-dimensional skip-gram embeddings were learned for 512,344 word types. These were clustered via K-means into 1,000 groups with hierarchical bitstring codes.
- **Findings:** Learned personas (e.g., with P = 50) are clearly gendered and show coherent trends over time, even though gender was never given to the model. Comparisons involving first-person narrators were a major source of error for all models.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

99 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 93 not yet.

- [An Annotated Dataset of Coreference in English Literature](https://md.research.elamur.ai/free/papers/bamman-2020-annotated-dataset-coreference-english-literature/)
- [Neural End-to-end Coreference Resolution for German in Different Domains](https://md.research.elamur.ai/free/papers/coref--schroder-neural-end-end-coreference-resolution/)
- [The NarrativeQA Reading Comprehension Challenge](https://md.research.elamur.ai/free/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/)
- [Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities](https://md.research.elamur.ai/free/papers/novak-2026-findings-fifth-shared-task-multilingual/)
- [GerDraCor-Coref: A Coreference Corpus for Dramatic Texts in German](https://md.research.elamur.ai/free/papers/pagel-2020-gerdracor-coref-coreference-corpus-dramatic/)
- [Incremental Neural Coreference Resolution in Constant Memory](https://md.research.elamur.ai/free/papers/xia-2020-incremental-neural-coreference-resolution-constant/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{bamman2014bayesian,
  title = {{A Bayesian Mixed Effects Model of Literary Character}},
  author = {Bamman, David and Underwood, Ted and Smith, Noah A.},
  booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2014},
  doi = {10.3115/v1/p14-1035},
  url = {https://doi.org/10.3115/v1/p14-1035}
}
```

- [next → A Comparison of the Events and Relations Across ACE, ERE, TAC-KBP, and FrameNet Annotation Standards](https://md.research.elamur.ai/free/papers/aguilar-2014-comparison-events-relations-across-ace/)
- [↑ back to papers of 2014](https://md.research.elamur.ai/free/papers/2014/)

## References

30 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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