---
title: "The Contextual Variability of English Nouns: The Impact of"
description: "Earlier distributional studies found that abstract nouns appear in more varied contexts than concrete nouns. 26 references."
authors: ["Giulia Rambelli", "Marianna Bolognesi"]
year: 2024
venue: "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)"
source: "https://aclanthology.org/2024.lrec-main.1377/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/rambelli-2024-contextual-variability-english-nouns-impact/"
---
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# The Contextual Variability of English Nouns: The Impact of Categorical Specificity beyond Conceptual Concreteness

**Authors:** [Giulia Rambelli](https://md.research.elamur.ai/free/en/authors/giulia-rambelli/) and Marianna Bolognesi · **Year:** 2024 · **Venue:** [Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)](https://md.research.elamur.ai/free/en/venues/lrec/) ([LREC 2024](https://md.research.elamur.ai/free/en/venues/lrec/2024/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.lrec-main.1377/) · [PDF](https://aclanthology.org/2024.lrec-main.1377.pdf) · [OpenAlex](https://openalex.org/W7131859870)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Earlier distributional studies found that abstract nouns appear in more varied contexts than concrete nouns. Those studies ignored categorical specificity, meaning how inclusive a word's category is (ragdoll vs. mammal). As a result, they may have compared specific concrete words with generic abstract ones. This paper checks whether specificity explains contextual variability on its own and alongside concreteness.

The authors took 676 English nouns. Each noun had human specificity ratings (collected for ANEW words) and Brysbaert et al. concreteness norms. They trained a word2vecf space on roughly 2 billion tokens of ukWaC plus Wikipedia 2018, preprocessed with CoreNLP. From this space they computed several variability measures:
- neighbour-based density: TN and NN;
- context-based richness: TC, CC and DCR;
- contextual entropy.

Each measure was then regressed on concreteness, on specificity, and on their interaction.

Both factors carry signal. Concrete and specific nouns sit in tighter, more coherent contexts. Generic nouns, whether concrete or abstract, are spread over loosely related contexts, and specificity is clearly linked to lower entropy. The work refines how we model abstraction and suggests that NLP features should separate the two dimensions.

## Key points
- **Data:** specificity ratings exist for 1,034 ANEW words. Of these, 676 nouns found in the semantic space were analysed, with concreteness taken from Brysbaert et al. (2014).
- **Semantic space:** word2vecf skip-gram with 300 dimensions and 15 negative samples. Training used a ±10-word window, a target frequency of at least 100 and pair frequency above 20, over ukWaC + Wikipedia 2018 parsed with CoreNLP.
- **Measures:**
  - TN, NN, TC and CC were each computed for k = 5, 10, 20 and 50.
  - Contexts were ranked by LMI for TC and CC, and by PPMI for DCR.
  - Entropy was computed as well.
  - All models were linear regressions in R with centred predictors, evaluated by adjusted R².
- **Concreteness:** it explains over 10% of the variance of NN and TC in English. The earlier Italian study found only 1.3–5%. NN and TC correlate at Spearman ρ = 0.38.
- **Specificity:** it explains about 9–13% of the variance for NN and TC, and entropy is especially well explained. More specific nouns have lower entropy. For example, pasta and food are equally concrete, but TC_10 is .65 for pasta and .49 for food.

## Interesting ideas
- Abstraction is treated as two separate axes: concreteness (how perceivable something is) and specificity (how narrow its category is). This prevents confounded comparisons such as strawberry vs. knowledge.
- Contextual entropy acts as a cheap gauge of how predictable a noun is in context: generic nouns are spread thin, specific ones are expected.
- Paradigmatic signals (neighbours in the space) are kept apart from syntagmatic ones (actual co-occurring contexts), because the two answer different questions.
- The pasta/food contrast shows that specific nouns attract same-domain contexts, while generic nouns attract events, loose associates and collocations (fast food).
- Each measure is scanned over several k values (5 to 50) instead of picking one, which shows how sensitive each measure is to its cutoff.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (world): Ontology nodes from WordNet could carry both a concreteness score and a specificity score (for example, derived from hypernym depth), so the world model can tell "a generic thing" apart from "an abstract thing".
- **I2** (frames): High-entropy generic nouns like food or thing could be flagged as weak anchors for frame selection, prompting the system to look for a more specific coreferent or a modifier before committing to a frame.
- **I3** (learn): When building lexical features for repair rules, "words like this" and "words seen with this" could be kept as separate evidence channels, so errors can be traced to one source.
- **I4** (answer): In tale or school-test QA, a generic noun in a question could trigger a search over a broader neighbourhood of events, while a specific noun could be matched within a narrow domain.
- **I5** (learn): Evaluation of any similarity-based heuristic could routinely report results across a sweep of cutoffs, which works as a negative control against cherry-picked parameters.

*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: Giulia Rambelli and Marianna Bolognesi. 2024. The Contextual Variability of English Nouns: The Impact of Categorical Specificity beyond Conceptual Concreteness. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).

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

```bibtex
@inproceedings{rambelli2024contextual,
  title = {{The Contextual Variability of English Nouns: The Impact of Categorical Specificity beyond Conceptual Concreteness}},
  author = {Giulia Rambelli and Marianna Bolognesi},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year = {2024},
  doi = {10.63317/3de8qyegi4wr},
  url = {https://doi.org/10.63317/3de8qyegi4wr}
}
```

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## References

26 references, 2 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*