---
title: "A Latent Dirichlet Allocation Method for Selectional Preferences"
description: "Selectional preferences describe which argument values a relation typically accepts… 0.88 accuracy. 35 references, cited by 76 papers here."
authors: ["Alan Ritter", "Mausam", "Oren Etzioni"]
year: 2010
venue: "Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P10-1044/"
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/en/papers/ritter-2010-latent-dirichlet-allocation-method-selectional/"
---
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# A Latent Dirichlet Allocation Method for Selectional Preferences

**Authors:** [Alan Ritter](https://md.research.elamur.ai/free/en/authors/alan-ritter/), [Mausam](https://md.research.elamur.ai/free/en/authors/mausam/) and [Oren Etzioni](https://md.research.elamur.ai/free/en/authors/oren-etzioni/) · **Year:** 2010 · **Venue:** [Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2010](https://md.research.elamur.ai/free/en/venues/acl/2010/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **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/P10-1044/) · [PDF](https://aclanthology.org/P10-1044.pdf) · [OpenAlex](https://openalex.org/W2168185617)

**Citations:** 149 citations (OpenAlex, 2026-10-14) · [76](https://md.research.elamur.ai/free/en/papers/ritter-2010-latent-dirichlet-allocation-method-selectional/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Selectional preferences describe which argument values a relation typically accepts, such as companies as subjects of "is headquartered in." Class-based methods built on WordNet give readable classes but suffer from gaps in coverage and from word ambiguity. Similarity-based and discriminative methods predict better but do not produce explicit classes. The authors propose LDA-SP, which treats each relation as a document whose words are its observed arguments. They compare three topic models: IndependentLDA, JointLDA and LinkLDA. LinkLDA performs best because it models both arguments together while keeping separate topic sets for each. Inference uses collapsed Gibbs sampling. The training data are binary relation tuples harvested from the Web by TextRunner. On pseudo-disambiguation, LDA-SP clearly beats strong baselines. It also filters faulty inference-rule applications better than Pantel et al.'s ISP systems. Because its latent topics act as interpretable classes, it offers both predictive strength and transparency. It also yields a reusable preference repository.

## Key points
- At precision 0.9 on pseudo-disambiguation, LDA-SP achieves about 85% higher recall than the mutual-information approach of Erk (2007).
- In inference-rule filtering, it surpasses ISP.IIM-∨ in both precision and recall, compared using CBC clusters (Pantel, 2003). It matches the high-precision point of ISP.JIM while offering much greater recall. KL divergence between relation topic distributions was also used to rank 26,000 rules.
- One author spent roughly 4 hours mapping the 600 topics (300 per argument) to WordNet classes semi-automatically. The resulting class preferences for 100 unseen relations reached about 0.88 accuracy, compared with roughly 0.55 for Pantel's repository. The authors describe these results as preliminary.
- A preference repository covering 10,000 relations was released at cs.washington.edu/research/ldasp.

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

## Institutions

- [University of Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/) — University, Seattle, United States · 353 works · 200,043 citations

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## Cite

Cite as: Alan Ritter, Mausam and Oren Etzioni. 2010. A Latent Dirichlet Allocation Method for Selectional Preferences. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.

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

```bibtex
@inproceedings{ritter2010latent,
  title = {{A Latent Dirichlet Allocation Method for Selectional Preferences}},
  author = {Alan Ritter, Mausam and Oren Etzioni},
  booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
  year = {2010},
  url = {https://aclanthology.org/P10-1044/}
}
```

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## References

35 references, 11 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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*Updated 29 October 2026*