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# Corpus Statistics Meet the Noun Compound: Some Empirical Results

**Authors:** Mark Lauer · **Year:** 1995 · **Venue:** 33rd Annual Meeting of the Association for Computational Linguistics (ACL 1995) **Link:** [aclanthology.org/P95-1007](https://aclanthology.org/P95-1007/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**81** citations](https://openalex.org/works?filter=cites:W2135375068) openalex · 2026-10-18[**32** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-1995-lauer-corpus-statistics-meet-noun-compound/) of the 140673 papers whose bibliographies we read[33rd Annual Meeting of the Association for Computational Linguistics](https://aclanthology.org/volumes/P95-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P95-1007/)
- [PDF](https://aclanthology.org/P95-1007.pdf)
- [DOI 10.3115/981658.981665](https://doi.org/10.3115/981658.981665)
- [OpenAlex](https://openalex.org/W2135375068)

**Authors (1)**

1. Lauer, Mark

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper applies the Hindle and Rooth idea of corpus association to noun compounds: which bracketing of a three-noun compound, left or right, is correct. Two models are compared: an adjacency model, which uses neighbouring nouns, and a dependency model, which uses the head-modifier structure. Several training schemes are tried, with word and concept associations. The test set has 244 compounds that received a parse, taken from an 8-million-word encyclopedia corpus, and the training data had those test compounds removed. The dependency model reaches 77.5% against 68.9% for adjacency, a difference the paper reports as significant at the 5% level for the pattern training scheme. The best tuned technique reaches 81%, against a 67% baseline. Left-branching compounds make up 67% of the test set.

## Ideas

- I1. Bracketing decision for `en/src/parse.rs`: for EWT noun-noun runs with two nouns of a compound or `nmod` chain, score the left and right bracketing with head-modifier counts. Measure: accuracy on EWT test compound arcs against the 67% left-branching baseline, two identical runs.
- I2. Use the left-branching prior as the first baseline in `en explain`, and only then the count model. Measure: the gain of counts over the prior on EWT, reported by the explanation step.
- I3. Concept back-off through `onto` (WordNet hypernyms) for nouns not seen in the counts. Measure: accuracy on held-out compounds whose nouns are unseen, with and without the back-off.

## For Mova

The chapter is [18. Compounding, conversion and the minor processes](https://md.mova.elamur.ai/free/en/docs/languages/en/18-compounding-conversion-minor/), with [22. Phrases: noun, adjective, adverb and prepositional phrases](https://md.mova.elamur.ai/free/en/docs/languages/en/22-phrases/) for the bracketing. The code is `en/src/parse.rs` (the candidate choice) and `onto/src/wsd.rs` (concept back-off). The paper's data is an encyclopedia corpus, not Mova data; any figure must come from EWT test.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/981658.981665](https://doi.org/10.3115/981658.981665)

```
@inproceedings{lauer1995corpus,
  title = {{Corpus Statistics Meet the Noun Compound: Some Empirical Results}},
  author = {Lauer, Mark},
  booktitle = {33rd Annual Meeting of the Association for Computational Linguistics},
  year = {1995},
  doi = {10.3115/981658.981665},
  url = {https://doi.org/10.3115/981658.981665}
}
```

- [← previous Prepositional Phrase Attachment through a Backed-off Model](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-1995-collins-prepositional-phrase-attachment-through-backed/)
- [next → Robust, applied morphological generation](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2000-minnen-robust-applied-morphological-generation/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

25 references, 0 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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