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# Supersense Tagging of Unknown Nouns using Semantic Similarity

**Authors:** James R. Curran · **Year:** 2005 · **Venue:** Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005) **Link:** [aclanthology.org/P05-1004](https://aclanthology.org/P05-1004/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**43** citations](https://openalex.org/works?filter=cites:W2132767156) openalex-snapshot · 2026-10-14[**21** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2005-curran-supersense-tagging-unknown-nouns-semantic/) of the 140673 papers whose bibliographies we read[Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05)](https://aclanthology.org/volumes/P05-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P05-1004/)
- [PDF](https://aclanthology.org/P05-1004.pdf)
- [DOI 10.3115/1219840.1219844](https://doi.org/10.3115/1219840.1219844)
- [OpenAlex](https://openalex.org/W2132767156)

**Authors (1)**

1. Curran, James [Semantic Scholar](https://www.semanticscholar.org/author/1733593)

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

## What it is about

Curran classifies unknown common nouns into the 26 WordNet lexicographer supersenses without any annotated examples. The method extracts about 50 distributionally similar nouns for the target from a shallow-parsed corpus of 2 billion words, and each of these synonyms votes with its WordNet 1.6 supersenses, weighted by similarity. The best configuration reaches 68% on the WordNet 1.6 test set and 63% on the harder WordNet 1.7.1 test set. The baseline is 21% and 28%, and the supervised perceptron of Ciaramita and Johnson reaches 53% on both sets, so the unsupervised method is significantly better. Proper names are not included, which makes locations hard, and multiword terms (23% of the 1.7.1 test set) use the synonyms of their last word. The fallback for nouns without any synonym is 37% accurate on the 1.7.1 set.

## Ideas

- I1. `onto/src/wsd.rs`: a synonym vote for unknown nouns. Take the nouns that co-occur with the target in a large open corpus (parsed with `en`), let each WordNet neighbour vote with its lexicographer file, and keep the top 50 as the explanation. Measure accuracy on the unknown nouns in an open treebank, and check the first two runs are the same.
- I2. Multiword fallback in `en/src/lexgen.rs`: when an unknown noun is a multiword term, look up the head (last) noun's supersense and count how often that is right on a list of multiword nouns from `onto`. The paper reports 23% of its test set are multiword.
- I3. Proper-name routing in `en/src/ptag.rs` and `prag`: send capitalised names to a separate place or person decision, so that the vote is not used for them. Measure the location errors that the paper reports, on EWT names.

## For Mova

The touch points are `onto/src/wsd.rs` (the vote and its explanation), `en/src/lexgen.rs` (multiword heads) and `en/src/ptag.rs` (names). The chapter is [11. Nouns](https://md.mova.elamur.ai/free/en/docs/languages/en/11-nouns/), with [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/) for multiword terms. The 2-billion-word corpus is not open and is not used in the build; the method can run on an open corpus instead, and the WordNet 1.7.1 test is for tests only. Measure first the head-noun fallback on multiword unknown nouns.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1219840.1219844](https://doi.org/10.3115/1219840.1219844)

```
@inproceedings{curran2005supersense,
  title = {{Supersense Tagging of Unknown Nouns using Semantic Similarity}},
  author = {Curran, James},
  booktitle = {Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05)},
  year = {2005},
  doi = {10.3115/1219840.1219844},
  url = {https://doi.org/10.3115/1219840.1219844}
}
```

- [← previous Differentiating Types of Verb Particle Constructions](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2004-patrick-differentiating-types-verb-particle-constructions/)
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## References

27 references, 3 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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