---
title: "Generalizing Case Frames Using a Thesaurus and the MDL Principle — Li 1998"
description: "The paper addresses how to generalize the nouns observed in one case frame slot of a verb, such as the subject of… 77 references, cited by 53 papers here."
authors: ["Hang Li", "Naoki Abe"]
year: 1998
source: "https://aclanthology.org/J98-2002/"
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---
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# Generalizing Case Frames Using a Thesaurus and the MDL Principle

**Authors:** [Hang Li](https://md.research.elamur.ai/free/en/authors/hang-li/) and Naoki Abe · **Year:** 1998 · **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/J98-2002/) · [PDF](https://aclanthology.org/J98-2002.pdf)

**Citations:** [53](https://md.research.elamur.ai/free/en/papers/li-1998-generalizing-case-frames-thesaurus-mdl/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary
The paper addresses how to generalize the nouns observed in one case frame slot of a verb, such as the subject of *fly*, so that unseen but plausible fillers also receive sensible probability. Word-level estimates suffer badly from data sparseness. The authors treat slot generalization as estimating a conditional distribution over a partition of nouns into classes. To keep the search tractable, they allow only partitions that are "cuts" through an existing thesaurus tree. The task then becomes selecting a "tree cut model", and they choose it with the Minimum Description Length (MDL) principle, which weighs model complexity against fit to the data. They give an efficient algorithm that provably finds the MDL-optimal cut for a slot's frequency data. The learned case frame patterns were applied to PP-attachment disambiguation, where they outperformed or roughly matched existing methods. The work matters because it gives class-based selectional modelling a clear statistical-estimation foundation, which earlier heuristics such as Resnik's selectional association lacked.

## Key points
- Input data are (verb, slot_name, slot_value) triples extracted automatically from corpora. Their counts form co-occurrence data used to estimate P(n | v, r).
- Word-based models need O(N·V·R) parameters. Plain MLE leaves most of these at zero; in the toy *fly* example, swallow gets 0, eagle 0.2 and bird 0.4.
- A tree cut model pairs a cut through the thesaurus with one probability per class. Nouns inside a class share that class's probability uniformly. The small example thesaurus (ANIMAL, BIRD, INSECT) admits five cuts.
- Word-sense ambiguity is handled with a simple heuristic: each noun occurrence is split equally among its possible senses. The authors argue that MDL's robustness to noise limits the harm.
- The method belongs to the thesaurus-based family of approaches. It is compared against Resnik's selectional association and similarity-based smoothing. The reference list includes WordNet and the Penn Treebank.

*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: [AI & machine learning](https://md.research.elamur.ai/free/en/shelves/ai/)

## Cite

Cite as: Hang Li and Naoki Abe. 1998. Generalizing Case Frames Using a Thesaurus and the MDL Principle.

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

```bibtex
@misc{li1998generalizing,
  title = {{Generalizing Case Frames Using a Thesaurus and the MDL Principle}},
  author = {Hang Li and Naoki Abe},
  year = {1998},
  url = {https://aclanthology.org/J98-2002/}
}
```

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## References

77 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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