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# The semantic transparency of English compound nouns

**Authors:** Martin Schäfer · **Year:** 2018 · **Venue:** Morphological Investigations 3, Language Science Press (book) **Link:** [zenodo.org/records/1134595](https://zenodo.org/records/1134595) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

grammar-book[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://zenodo.org/records/1134595)
- [PDF](https://zenodo.org/records/1134595/files/main.pdf?download=1)

**Authors (1)**

1. Martin Schäfer

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The book asks how far the meaning of an English noun-noun compound can be read from its two constituents, a property called semantic transparency. The first part reviews psycholinguistic models of morphological processing, the ways transparency is measured, and approaches to compound semantics. The empirical part re-analyses earlier models built on the Reddy et al. transparency ratings, with semantic relations and meaning shifts as predictors. The author finds that these predictors largely fail once the statistics are made more careful (mixed-effects models, cleaned outliers), and that the coding of meaning shifts lacked a principled basis. He then proposes expectancy-based predictors: each compound's relation or sense is scored against the distribution of the same features across the compound families of its constituents. The annotated database covers families from 1 to 363 members. The resulting models show that the N1 and N2 family predictors behave differently.

## Ideas

- I1. `en/src/dict.rs`: store, for each noun-noun compound, the relation between the constituents, and compute a family-level count (how often that relation occurs with the same head or modifier). Use the count as an explicit, explainable feature for the sense chosen by the reader, and keep it in BTreeMap order.
- I2. Before any new predictor, repeat the paper's warning: check the coding. Have two annotators label a sample of compounds for meaning shift and report agreement; drop any predictor whose coding cannot be reproduced. This is a negative control on Mova's own labels.
- I3. Build a small compound-family index from the lexicon (constituent, count of members) and report how the family size of a head varies, so the school dictionary knows where a compound's meaning is predictable and where it must be listed.

## For Mova

The chapters are [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/) (compounds and multiword expressions) and [18. Compounding, conversion and the minor processes](https://md.mova.elamur.ai/free/en/docs/languages/en/18-compounding-conversion-minor/). The code is `en/src/dict.rs` and the pupil dictionary `world/src/dictionary.rs`. The statistical models are not for Mova; the family-expectancy feature and the reliability check are. Measure: the share of compounds in a school text whose sense is predicted by the family feature, and the agreement of two codings on a sample.

## Cite

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

```
@book{schafer2018semantic,
  title = {{The semantic transparency of English compound nouns}},
  author = {Schäfer, Martin},
  year = {2018},
  url = {https://zenodo.org/records/1134595}
}
```

- [← previous CoBra: A Compound Branching Resource for Nominal Triconstituent Compounds in English and German](https://md.mova.elamur.ai/free/en/docs/papers/schacht-2026-cobra-compound-branching-resource-nominal/)
- [next → SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference](https://md.mova.elamur.ai/free/en/docs/papers/schmitt-2019-sherliic-typed-event-focused-lexical/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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