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# Finding Predominant Word Senses in Untagged Text

**Authors:** [Diana McCarthy](https://md.mova.elamur.ai/free/en/docs/papers/authors/diana-mccarthy/), [Rob Koeling](https://md.mova.elamur.ai/free/en/docs/papers/authors/rob-koeling/), [Julie Weeds](https://md.mova.elamur.ai/free/en/docs/papers/authors/julie-weeds/), [John Carroll](https://md.mova.elamur.ai/free/en/docs/papers/authors/john-carroll/) · **Year:** 2004 · **Venue:** Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04) **Link:** [aclanthology.org/P04-1036](https://aclanthology.org/P04-1036/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**309** citations](https://openalex.org/works?filter=cites:W2121147707) openalex-snapshot · 2026-10-14[**134** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/onto-2004-mccarthy-finding-predominant-word-senses-untagged/) of the 140673 papers whose bibliographies we read[Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)](https://aclanthology.org/volumes/P04-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P04-1036/)
- [PDF](https://aclanthology.org/P04-1036.pdf)
- [DOI 10.3115/1218955.1218991](https://doi.org/10.3115/1218955.1218991)
- [OpenAlex](https://openalex.org/W2121147707)

**Authors (4)**

1. [Diana McCarthy](https://openalex.org/A5063848163) [Semantic Scholar](https://www.semanticscholar.org/author/145586618) · h 33 · 3614 citations · 127 works
2. [Rob Koeling](https://openalex.org/A5003705762) [ORCID](https://orcid.org/0000-0002-8073-2409) · [Semantic Scholar](https://www.semanticscholar.org/author/2299876) · h 15 · 1198 citations · 62 works
3. [Julie Weeds](https://openalex.org/A5016270907) [ORCID](https://orcid.org/0000-0002-3831-4019) · [Semantic Scholar](https://www.semanticscholar.org/author/2500077) · h 15 · 1665 citations · 86 works
4. [John Carroll](https://openalex.org/A5101474712) [ORCID](https://orcid.org/0000-0003-3744-4919) · [Semantic Scholar](https://www.semanticscholar.org/author/144708726) · h 23 · 2418 citations · 118 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

The most-frequent (predominant) sense is a strong baseline for word sense disambiguation, but it normally needs a hand-tagged corpus such as SemCor, which is small (about 250,000 words) and fixed in genre. The paper asks whether the predominant sense of a noun can be ranked from raw text alone, and whether the ranking changes with domain. The method builds a thesaurus of distributional nearest neighbours (Lin's measure) from 90 million words of the British National Corpus, using grammatical relations (verb objects and subjects, noun-noun and adjective-noun modifiers, with a frequency floor of 10). Each neighbour votes for the WordNet senses it is semantically close to, weighted by its distributional similarity and by a WordNet similarity measure (lesk or jcn). On the polysemous nouns of SemCor the ranking is right about 54% of the time with either measure, against 32% for random choice; on the SENSEVAL-2 all-words nouns the automatic sense reaches 64% precision, against 69% for the SemCor sense and 92% for the test answer key. On two Reuters domain corpora (SPORTS and FINANCE) the top sense shifts toward the domain for several words, checked by hand on 10 words and on a 38-word set against domain labels.

## Ideas

- I1. Test the paper's weighting in `onto/src/wsd.rs`, step 2. The current prior counts the neighbour votes; the paper also weights each vote by the distributional similarity. Compare the two on the same held-out polysemous nouns with gold senses (tests only), by two-fold `cv`, and run both twice. Negative control: shuffle the neighbour lists within each noun; the prior must fall toward the random baseline.
- I2. Print the neighbours behind each decision. For the chosen sense and the runner-up, the explanation lists the top neighbours that voted for each, with their similarity. The paper's own example is *pipe*: neighbours such as tube, cable and wire support the non-tobacco sense. Check 50 random decisions by hand and record whether the listed neighbours really fit the sense.
- I3. A domain prior as a list of flips. Run the same ranking over two corpora Mova has, the tale texts from `tools/gutenberg` and a general text corpus, and list the nouns whose predominant sense changes. The paper shows the effect only qualitatively; Mova should count the flips and check 20 of them by hand. The tale reader in `world` can then use the tale-domain sense for the nouns on that list.
- I4. A backoff threshold. The paper uses the predominant sense as a backoff when the context evidence is weak, and `wsd.rs` sums its terms instead. Test a rule: if the margin of the context score is below a threshold, the prior alone decides. Choose the threshold by `cv` on training nouns and report once on the test set. Do not tune it on the frozen 60 sentences.

## For Mova

Step 2 of `onto/src/wsd.rs` already implements this paper's method, so the ideas refine it rather than add it. The grammatical relations for the thesaurus come from the parser output in `en/src/parse.rs` and `en/src/ud.rs`. The chapter is [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/), and the WSD v1 entry is in [Smart annotator — layers, context, memory](https://md.mova.elamur.ai/free/en/docs/docs/context/). Measures: the random and most-frequent-sense baselines on the same nouns, two identical runs before any gain is claimed, and the shuffled-neighbour control in I1. Licence: the paper is CC BY-NC-SA, so this card is a summary only. SemCor, the SENSEVAL-2 data and the Reuters corpus are not in the repo, and their licences are not given in the paper. Check each in [Data and model licences](https://md.mova.elamur.ai/free/en/docs/licence/DATA/); until then they are for tests only, and nothing from the paper's data goes into a release.

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 10.3115/1218955.1218991](https://doi.org/10.3115/1218955.1218991)

```
@inproceedings{mccarthy2004finding,
  title = {{Finding Predominant Word Senses in Untagged Text}},
  author = {McCarthy, Diana and Koeling, Rob and Weeds, Julie and Carroll, John},
  booktitle = {Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)},
  year = {2004},
  doi = {10.3115/1218955.1218991},
  url = {https://doi.org/10.3115/1218955.1218991}
}
```

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## References

26 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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