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# The lexical pragmatics of count-mass polysemy

**Authors:** Ingrid L. Falkum · **Year:** 2017 · **Venue:** Semantics and Pragmatics, 10(20) **Link:** [doi.org/10.3765/sp.10.20](https://doi.org/10.3765/sp.10.20) · **Licence:** [CC BY 3.0](https://creativecommons.org/licenses/by/3.0/)

[**13** citations](https://openalex.org/works?filter=cites:W2778732916) openalex · 2026-10-19[Semantics and Pragmatics](https://openalex.org/S33105140)[licence](https://creativecommons.org/licenses/by/3.0/)

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**Authors (1)**

1. [Falkum, Ingrid L.](https://openalex.org/A5050931834) [ORCID](https://orcid.org/0000-0002-1203-8036) · [Semantic Scholar](https://www.semanticscholar.org/author/15515709) · h 13 · 1088 citations · 63 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

The paper studies a regular kind of polysemy in English nouns that depends on count and mass use: *shoot a rabbit* (the animal), *eat rabbit* (the meat), and *wear rabbit* (the fur). Computational semantics has usually treated such alternations as produced by a fixed set of lexical rules. The paper argues against this. Count-mass syntax, on this view, is only a procedural constraint on the referent of the noun phrase, and it leaves a semantic output that is less specific than rule-based accounts assume. The author proposes a single relevance-guided lexical pragmatic mechanism, ad hoc concept construction, which handles the regular cases that the rules cover, as well as the cases that rule-based accounts can only solve by appealing to pragmatics. The paper is theoretical and uses worked examples; it reports no experiments or numbers.

## Ideas

- I1. `en/src/morph.rs` and the lexicon in `en/src/dict.rs`: mark nouns that have count and mass uses, and let the determiner or the bare form choose the sense (*a rabbit* against *rabbit* as food). Test the choice on the lesson sentences that use these nouns.
- I2. Keep one lexical sense with an "underspecified" flag and let the context step (`prag/src/infer.rs`) pick the specific reading. Log which cue chose it, so that every choice can be explained.
- I3. Negative control: proper nouns and mass-only nouns must not receive the alternation. A rule that gives them a count reading is wrong, and the check should show it.

## For Mova

The chapters are [11. Nouns](https://md.mova.elamur.ai/free/en/docs/languages/en/11-nouns/) (count and mass nouns) and [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/) (word senses). The code is `en/src/morph.rs` for the count-mass features, `world/src/dictionary.rs` for senses, and `prag/src/infer.rs` for the context choice. The measures: sense accuracy on the lesson sentences with count-mass nouns, and the negative-control error rate. The paper gives no numbers to match, so the test set has to be built by hand from its examples.

Topics: [Word sense disambiguation](https://md.mova.elamur.ai/free/en/docs/papers/topics/word-sense-disambiguation/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3765/sp.10.20](https://doi.org/10.3765/sp.10.20)

```
@article{falkum2017lexical,
  title = {{The lexical pragmatics of count-mass polysemy}},
  author = {Falkum, Ingrid L.},
  journal = {Semantics and Pragmatics},
  year = {2017},
  doi = {10.3765/sp.10.20},
  url = {https://doi.org/10.3765/sp.10.20}
}
```

- [← previous VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling](https://md.mova.elamur.ai/free/en/docs/papers/fabio-2019-verbatlas-novel-large-scale-verbal/)
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