---
title: "ISP: Learning Inferential Selectional Preferences (2007)"
description: "The authors present ISP, a set of methods that learn which argument types make a rule applicable… 20 references, cited by 50 papers here."
authors: ["Patrick Pantel", "Rahul Bhagat", "Bonaventura Coppola", "Timothy Chklovski", "Eduard Hovy"]
year: 2007
venue: "Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference"
source: "https://aclanthology.org/N07-1071/"
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/pantel-2007-isp-learning-inferential-selectional-preferences/"
---
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# ISP: Learning Inferential Selectional Preferences

**Authors:** [Patrick Pantel](https://md.research.elamur.ai/free/en/authors/patrick-pantel/), [Rahul Bhagat](https://md.research.elamur.ai/free/en/authors/rahul-bhagat/), [Bonaventura Coppola](https://md.research.elamur.ai/free/en/authors/bonaventura-coppola/), [Timothy Chklovski](https://md.research.elamur.ai/free/en/authors/timothy-chklovski/) and [Eduard Hovy](https://md.research.elamur.ai/free/en/authors/eduard-hovy/) · **Year:** 2007 · **Venue:** [Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference](https://md.research.elamur.ai/free/en/venues/naacl/) ([NAACL 2007](https://md.research.elamur.ai/free/en/venues/naacl/2007/)) · **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/N07-1071/) · [PDF](https://aclanthology.org/N07-1071.pdf) · [OpenAlex](https://openalex.org/W1521365985)

**Citations:** 91 citations (OpenAlex, 2026-10-14) · [50](https://md.research.elamur.ai/free/en/papers/pantel-2007-isp-learning-inferential-selectional-preferences/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Automatically harvested inference rules (e.g., from DIRT) are often applied in contexts where they do not hold. A rule linking "charged by" to "announced the arrest of" works for criminal suspects but fails when the argument is a bank account. The authors present ISP, a set of methods that learn which argument types make a rule applicable, called inferential selectional preferences. ISP uses these preferences to reject inferences that do not fit.

ISP gathers corpus statistics on how binary relations are instantiated and maps each argument to semantic classes from WordNet or CBC clusters. It scores these preferences in two ways:
- **Joint models (JRM/JIM)** treat the two arguments together and score class pairs with PMI.
- **Independent models (IRM/IIM)** treat each argument separately, which helps with sparse data.

A rule's preferences are formed by intersecting the preferences of its two sides. The authors evaluated ISP on DIRT rules. The most permissive filter clearly beat the baselines, and the strict variants gave high specificity. This makes noisy rule collections more usable for question answering and textual entailment.

## Key points
- **Variants and tuning:** The variants are ISP.JIM, ISP.IIM.∧ (both arguments must match) and ISP.IIM.∨ (one argument suffices). The authors tuned them across 180 parameter configurations on a development set, then tested on a held-out set.
- **Best accuracy:** ISP.IIM.∨ achieved the highest accuracy, about 0.62 at threshold τ=30%. It was significantly better than all three baselines, and its accuracy stayed within 0.05 of that peak across all τ values.
- **High-specificity option:** Selected for at least 90% specificity, ISP.JIM recovers roughly 17% of correct inferences with a false-positive rate of about 12%.
- **WordNet vs. CBC:** CBC classes outperformed the authors' simple WordNet-based classes, largely because CBC covers more words, especially proper names. Arguments that belong to no class cause the inference to be rejected.
- **Main remaining error:** Antonym-like DIRT rules (e.g., "rejected in" ⇒ "accepted in") slip through. Such opposite relations share the same argument types, so ISP learns preferences that accept them.

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

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## Cite

Cite as: Patrick Pantel, Rahul Bhagat, Bonaventura Coppola, Timothy Chklovski and Eduard Hovy. 2007. ISP: Learning Inferential Selectional Preferences. Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference.

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

```bibtex
@inproceedings{pantel2007isp,
  title = {{ISP: Learning Inferential Selectional Preferences}},
  author = {Patrick Pantel, Rahul Bhagat, Bonaventura Coppola, Timothy Chklovski and Eduard Hovy},
  booktitle = {Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference},
  year = {2007},
  url = {https://aclanthology.org/N07-1071/}
}
```

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## References

20 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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*Updated 2 November 2026*