---
title: "Learning Latent Personas of Film Characters — Bamman 2013"
description: "The authors propose viewing narrative through its characters rather than only as a sequence of events."
authors: ["David Bamman", "Brendan O'Connor", "Noah A. Smith"]
year: 2013
venue: "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P13-1035/"
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-2013-learning-latent-personas-film-characters/"
---
[Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/bamman-2013-learning-latent-personas-film-characters/)

[home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2013](https://md.research.elamur.ai/free/papers/2013/) / Learning Latent Personas of Film Characters

# Learning Latent Personas of Film Characters

**Authors:** [David Bamman](https://md.research.elamur.ai/free/authors/david-bamman/), [Brendan O'Connor](https://md.research.elamur.ai/free/authors/brendan-o-connor/), [Noah A. Smith](https://md.research.elamur.ai/free/authors/noah-a-smith/) · **Year:** 2013 · **Venue:** Proceedings of the 51st 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/P13-1035/) · [PDF](https://aclanthology.org/P13-1035.pdf)

**Citations:** [101](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary

The authors propose viewing narrative through its characters rather than only as a sequence of events. Their goal is to automatically discover the typical roles (personas) of film characters, that is, archetypes and stereotypes such as the villain or the protagonist's love interest. To this end, they assembled a new corpus of 42,306 movie plot summaries from the English-language Wikipedia and enriched it with metadata from Freebase: genres and the gender and age of the actors. The texts were processed with Stanford CoreNLP. For each character, they extracted a set of words of three types: actions the character performs, actions performed on the character, and attributes used to describe the character. Two generative latent-variable models are proposed. The Dirichlet Persona Model works with the text alone. The persona regression model additionally takes into account movie and actor metadata. Both models simultaneously group words into topics, topics into personas, and characters into personas. Inference is performed using collapsed Gibbs sampling. The authors regard this work as the first attempt to explicitly learn character personas. They openly release the data, the code, and a test set for evaluation, and the approach itself can be applied to literary fiction or to the analysis of media framing.

## Key points

- **Corpus:** 42,306 movie plots from a Wikipedia dump (November 2012). The median summary length is about 176 words, and about 715 for the 1,000 highest-grossing films. The metadata were taken from a Freebase dump, including 365 genre categories, which may overlap.
- **Features:** each character is represented as a bag of (r, w) pairs. Here r is the role of the word (agent verb, patient verb or attribute), determined from Stanford CC-processed dependencies, and w is the lemma.
- **Filtering:** the sample was restricted to the 1,000 most frequent events and to characters with at least 3 events. This yielded 120,345 characters. Of these, 33,559 could be linked to an actor of known gender, and 29,802 to an actor of known date of birth.
- **Actor demographics:** among actors of known gender, about two thirds (66.7%) are men. The mean age at the time of the film's release is 37.9 years. Actresses are on average younger than actors: 33.0 versus 40.5 years.
- **Result:** the model learned 100 latent personas. For example, the "action hero" cluster groups together Jason Bourne, Ethan Hunt and Peter Parker. The data and software are published at ark.cs.cmu.edu/personas.

*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

101 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 95 not yet.

- [A Bayesian Mixed Effects Model of Literary Character](https://md.research.elamur.ai/free/papers/bamman-2014-bayesian-mixed-effects-model-literary/)
- [Character Coreference Resolution in Movie Screenplays](https://md.research.elamur.ai/free/papers/baruah-2023-character-coreference-resolution-movie-screenplays/)
- [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://md.research.elamur.ai/free/papers/cheng-2023-marked-personas-natural-language-prompts/)
- [Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences](https://md.research.elamur.ai/free/papers/khashabi-2018-looking-beyond-surface-challenge-set/)
- [The NarrativeQA Reading Comprehension Challenge](https://md.research.elamur.ai/free/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/)
- [Can Large Language Models Transform Computational Social Science?](https://md.research.elamur.ai/free/papers/ziems-2023-large-language-models-transform-computational/)

## Cite

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

```bibtex
@inproceedings{bamman2013learning,
  title = {{Learning Latent Personas of Film Characters}},
  author = {Bamman, David and O'Connor, Brendan and Smith, Noah A.},
  booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2013},
  url = {https://aclanthology.org/P13-1035/}
}
```

- [← previous Latent Anaphora Resolution for Cross-Lingual Pronoun Prediction](https://md.research.elamur.ai/free/papers/hardmeier-2013-latent-anaphora-resolution-cross-lingual/)
- [next → Learning Whom to Trust with MACE](https://md.research.elamur.ai/free/papers/hovy-2013-learning-whom-trust-mace/)
- [↑ back to papers of 2013](https://md.research.elamur.ai/free/papers/2013/)

## References

29 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Aristotle. 335 BCE. Poetics, translated by Samuel H. Butcher (1902). Macmillan, London. [find](https://scholar.google.com/scholar?q=Aristotle%2E+335+BCE%2E+Poetics%2C+translated+by+Samuel+H%2E+Butcher+%281902%29%2E+Macmillan%2C+London%2E)
2. David M. Blei, Andrew Ng, and Michael Jordan. 2003. Latent dirichlet allocation. JMLR, 3:993–1022. [find](https://scholar.google.com/scholar?q=David+M%2E+Blei%2C+Andrew+Ng%2C+and+Michael+Jordan%2E+2003%2E+Latent+dirichlet+allocation%2E+JMLR%2C+3%3A993%E2%80%931022%2E) [our card](https://md.research.elamur.ai/free/papers/blei-2003-latent-dirichlet-allocation/)
3. Joseph Campbell. 1949. The Hero with a Thousand Faces. Pantheon Books. [find](https://scholar.google.com/scholar?q=Joseph+Campbell%2E+1949%2E+The+Hero+with+a+Thousand+Faces%2E+Pantheon+Books%2E)
4. Nathanael Chambers and Dan Jurafsky. 2008. Unsupervised learning of narrative event chains. In Proceedings of ACL-08: HLT. [find](https://scholar.google.com/scholar?q=Nathanael+Chambers+and+Dan+Jurafsky%2E+2008%2E+Unsupervised+learning+of+narrative+event+chains%2E+In+Proceedings+of+ACL%2D08%3A+HLT%2E)
5. Nathanael Chambers and Dan Jurafsky. 2009. Unsupervised learning of narrative schemas and their participants. In Proceedings of the 47th Annual Meeting of the ACL. [find](https://scholar.google.com/scholar?q=Nathanael+Chambers+and+Dan+Jurafsky%2E+2009%2E+Unsupervised+learning+of+narrative+schemas+and+their+participants%2E+In+Proceedings+of+the+47th+Annual+Meeting+of+the+ACL%2E)
6. Nathanael Chambers. 2011. Inducing Event Schemas and their Participants from Unlabeled Text. Ph.D. thesis, Stanford University. [find](https://scholar.google.com/scholar?q=Nathanael+Chambers%2E+2011%2E+Inducing+Event+Schemas+and+their+Participants+from+Unlabeled+Text%2E+Ph%2ED%2E+thesis%2C+Stanford+University%2E)
7. Marie-Catherine de Marneffe and Christopher D. Manning. 2008. Stanford typed dependencies manual. Technical report, Stanford University. [find](https://scholar.google.com/scholar?q=Marie%2DCatherine+de+Marneffe+and+Christopher+D%2E+Manning%2E+2008%2E+Stanford+typed+dependencies+manual%2E+Technical+report%2C+Stanford+University%2E)
8. Lajos Egri. 1946. The Art of Dramatic Writing. Simon and Schuster, New York. [find](https://scholar.google.com/scholar?q=Lajos+Egri%2E+1946%2E+The+Art+of+Dramatic+Writing%2E+Simon+and+Schuster%2C+New+York%2E)
9. Khalid El-Arini, Ulrich Paquet, Ralf Herbrich, Jurgen Van Gael, and Blaise Agüera y Arcas. 2012. Transparent user models for personalization. In Proceedings of the 18th ACM SIGKDD. [find](https://scholar.google.com/scholar?q=Khalid+El%2DArini%2C+Ulrich+Paquet%2C+Ralf+Herbrich%2C+Jurgen+Van+Gael%2C+and+Blaise+Agu%CC%88era+y+Arcas%2E+2012%2E+Transparent+user+models+for+personalization%2E+In+Proceedings+of+the+18th+ACM+SIGKDD%2E)
10. Micha Elsner. 2012. Character-based kernels for novelistic plot structure. In Proceedings of the 13th Conference of the EACL. [find](https://scholar.google.com/scholar?q=Micha+Elsner%2E+2012%2E+Character%2Dbased+kernels+for+novelistic+plot+structure%2E+In+Proceedings+of+the+13th+Conference+of+the+EACL%2E)
11. Mark Alan Finlayson. 2011. Learning Narrative Structure from Annotated Folktales. Ph.D. thesis, MIT. [find](https://scholar.google.com/scholar?q=Mark+Alan+Finlayson%2E+2011%2E+Learning+Narrative+Structure+from+Annotated+Folktales%2E+Ph%2ED%2E+thesis%2C+MIT%2E)
12. R. A. Fisher. 1935. The Design of Experiments. Oliver and Boyde, Edinburgh and London. [find](https://scholar.google.com/scholar?q=R%2E+A%2E+Fisher%2E+1935%2E+The+Design+of+Experiments%2E+Oliver+and+Boyde%2C+Edinburgh+and+London%2E)
13. Erving Goffman. 1959. The Presentation of the Self in Everyday Life. Anchor. [find](https://scholar.google.com/scholar?q=Erving+Goffman%2E+1959%2E+The+Presentation+of+the+Self+in+Everyday+Life%2E+Anchor%2E)
14. Amit Goyal, Ellen Riloff, and Hal Daumé, III. 2010. Automatically producing plot unit representations for narrative text. In Proceedings of the 2010 Conference on EMNLP. [find](https://scholar.google.com/scholar?q=Amit+Goyal%2C+Ellen+Riloff%2C+and+Hal+Daume%CC%81%2C+III%2E+2010%2E+Automatically+producing+plot+unit+representations+for+narrative+text%2E+In+Proceedings+of+the+2010+Conference+on+EMNLP%2E)
15. Trond Grenager and Christopher D. Manning. 2006. Unsupervised discovery of a statistical verb lexicon. In Proceedings of the 2006 Conference on EMNLP. [find](https://scholar.google.com/scholar?q=Trond+Grenager+and+Christopher+D%2E+Manning%2E+2006%2E+Unsupervised+discovery+of+a+statistical+verb+lexicon%2E+In+Proceedings+of+the+2006+Conference+on+EMNLP%2E)
16. Thomas L. Griffiths and Mark Steyvers. 2004. Finding scientific topics. PNAS, 101(suppl. 1):5228–5235. [find](https://scholar.google.com/scholar?q=Thomas+L%2E+Griffiths+and+Mark+Steyvers%2E+2004%2E+Finding+scientific+topics%2E+PNAS%2C+101%28suppl%2E+1%29%3A5228%E2%80%935235%2E)
17. Carl Jung. 1981. The Archetypes and The Collective Unconscious, volume 9 of Collected Works. Bollin-gen, Princeton, NJ, 2nd edition. [find](https://scholar.google.com/scholar?q=Carl+Jung%2E+1981%2E+The+Archetypes+and+The+Collective+Unconscious%2C+volume+9+of+Collected+Works%2E+Bollin%2Dgen%2C+Princeton%2C+NJ%2C+2nd+edition%2E)
18. Neil McIntyre and Mirella Lapata. 2010. Plot induction and evolutionary search for story generation. In Proceedings of the 48th Annual Meeting of the ACL. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Neil+McIntyre+and+Mirella+Lapata%2E+2010%2E+Plot+induction+and+evolutionary+search+for+story+generation%2E+In+Proceedings+of+the+48th+Annual+Meeting+of+the+ACL%2E+Association+for+Computational+Linguistics%2E)
19. Robert McKee. 1997. Story: Substance, Structure, Style and the Principles of Screenwriting. Harper-Colllins. [find](https://scholar.google.com/scholar?q=Robert+McKee%2E+1997%2E+Story%3A+Substance%2C+Structure%2C+Style+and+the+Principles+of+Screenwriting%2E+Harper%2DColllins%2E)
20. Marina Meilă. 2007. Comparing clusterings—an information based distance. Journal of Multivariate Analysis, 98(5):873–895. [find](https://scholar.google.com/scholar?q=Marina+Meila%CC%86%2E+2007%2E+Comparing+clusterings%E2%80%94an+information+based+distance%2E+Journal+of+Multivariate+Analysis%2C+98%285%29%3A873%E2%80%93895%2E)
21. David Mimno and Andrew McCallum. 2008. Topic models conditioned on arbitrary features with dirichlet-multinomial regression. In Proceedings of UAI. [find](https://scholar.google.com/scholar?q=David+Mimno+and+Andrew+McCallum%2E+2008%2E+Topic+models+conditioned+on+arbitrary+features+with+dirichlet%2Dmultinomial+regression%2E+In+Proceedings+of+UAI%2E)
22. Fernando Pereira, Naftali Tishby, and Lillian Lee. 1993. Distributional clustering of english words. In Proceedings of the 31st Annual Meeting of the ACL. [find](https://scholar.google.com/scholar?q=Fernando+Pereira%2C+Naftali+Tishby%2C+and+Lillian+Lee%2E+1993%2E+Distributional+clustering+of+english+words%2E+In+Proceedings+of+the+31st+Annual+Meeting+of+the+ACL%2E)
23. E. J. G. Pitman. 1937. Significance tests which may be applied to samples from any population. Supplement to the Journal of the Royal Statistical Society, 4(1):119–130. [find](https://scholar.google.com/scholar?q=E%2E+J%2E+G%2E+Pitman%2E+1937%2E+Significance+tests+which+may+be+applied+to+samples+from+any+population%2E+Supplement+to+the+Journal+of+the+Royal+Statistical+Society%2C+4%281%29%3A119%E2%80%93130%2E)
24. Michaela Regneri, Alexander Koller, and Manfred Pinkal. 2010. Learning script knowledge with web experiments. In Proceedings of the 48th Annual Meeting of the ACL. [find](https://scholar.google.com/scholar?q=Michaela+Regneri%2C+Alexander+Koller%2C+and+Manfred+Pinkal%2E+2010%2E+Learning+script+knowledge+with+web+experiments%2E+In+Proceedings+of+the+48th+Annual+Meeting+of+the+ACL%2E)
25. Michaela Regneri, Alexander Koller, Josef Ruppenhofer, and Manfred Pinkal. 2011. Learning script participants from unlabeled data. In Proceedings of the Conference on Recent Advances in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Michaela+Regneri%2C+Alexander+Koller%2C+Josef+Ruppenhofer%2C+and+Manfred+Pinkal%2E+2011%2E+Learning+script+participants+from+unlabeled+data%2E+In+Proceedings+of+the+Conference+on+Recent+Advances+in+Natural+Langua)
26. Roger C. Schank and Robert P. Abelson. 1977. Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Lawrence Erlbaum, Hillsdale, NJ. [find](https://scholar.google.com/scholar?q=Roger+C%2E+Schank+and+Robert+P%2E+Abelson%2E+1977%2E+Scripts%2C+plans%2C+goals%2C+and+understanding%3A+An+inquiry+into+human+knowledge+structures%2E+Lawrence+Erlbaum%2C+Hillsdale%2C+NJ%2E)
27. Ivan Titov and Alexandre Klementiev. 2012. A bayesian approach to unsupervised semantic role induction. In Proceedings of the 13th Conference of EACL. [find](https://scholar.google.com/scholar?q=Ivan+Titov+and+Alexandre+Klementiev%2E+2012%2E+A+bayesian+approach+to+unsupervised+semantic+role+induction%2E+In+Proceedings+of+the+13th+Conference+of+EACL%2E)
28. Greg C. G. Wei and Martin A. Tanner. 1990. A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms. Journal of the American Statistical Association, 85:699–704. [find](https://scholar.google.com/scholar?q=Greg+C%2E+G%2E+Wei+and+Martin+A%2E+Tanner%2E+1990%2E+A+Monte+Carlo+implementation+of+the+EM+algorithm+and+the+poor+man%E2%80%99s+data+augmentation+algorithms%2E+Journal+of+the+American+Statistical+Association%2C+85%3A699%E2%80%93704)
29. Limin Yao, Aria Haghighi, Sebastian Riedel, and Andrew McCallum. 2011. Structured relation discovery using generative models. In Proceedings of the Conference on EMNLP. [find](https://scholar.google.com/scholar?q=Limin+Yao%2C+Aria+Haghighi%2C+Sebastian+Riedel%2C+and+Andrew+McCallum%2E+2011%2E+Structured+relation+discovery+using+generative+models%2E+In+Proceedings+of+the+Conference+on+EMNLP%2E)