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# Supersense Tagging of Unknown Nouns in WordNet

**Authors:** [Massimiliano Ciaramita](https://md.mova.elamur.ai/free/en/docs/papers/authors/massimiliano-ciaramita/), [Mark Johnson](https://md.mova.elamur.ai/free/en/docs/papers/authors/mark-johnson/) · **Year:** 2003 · **Venue:** Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (EMNLP 2003) **Link:** [aclanthology.org/W03-1022](https://aclanthology.org/W03-1022/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**114** citations](https://openalex.org/works?filter=cites:W2026185168) openalex-snapshot · 2026-10-14[**62** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2003-ciaramita-supersense-tagging-unknown-nouns-wordnet/) of the 140673 papers whose bibliographies we read[Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/W03-10/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/W03-1022/)
- [PDF](https://aclanthology.org/W03-1022.pdf)
- [DOI 10.3115/1119355.1119377](https://doi.org/10.3115/1119355.1119377)
- [OpenAlex](https://openalex.org/W2026185168)

**Authors (2)**

1. [Massimiliano Ciaramita](https://openalex.org/A5009724893) [Semantic Scholar](https://www.semanticscholar.org/author/2754495) · h 30 · 3574 citations · 102 works
2. [Mark Johnson](https://openalex.org/A5034461489) [ORCID](https://orcid.org/0000-0003-4809-8441) · [Semantic Scholar](https://www.semanticscholar.org/author/145177220) · h 60 · 25884 citations · 354 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 paper extends named-entity classification to common nouns. Each unknown noun is assigned one of 26 supersenses, the lexicographer classes that WordNet uses to organise its noun entries (person, location, event, quantity and so on). The classifier is an averaged multiclass perceptron over features from the context and the syntax, trained on about 787,000 noun instances from a 40-million-word parsed corpus. Nouns with more than one supersense were removed from training. Because the WordNet glosses and examples add about 66,800 training instances, the authors can test whether dictionary text helps. It does: the model trained on the glosses beats the same model without them on both test sets, and the paper also reports that a most-frequent-label baseline ("person") scores in the low twenties. The paper also argues for an evaluation on new nouns, not cross-validation.

## Ideas

- I1. `onto/src/wsd.rs` or the `onto` lookup: when a noun has no WordNet entry, give a supersense from a rule-plus-perceptron step over the head noun and its suffix, and print the rule in the explanation. Measure supersense accuracy on the unknown nouns of EWT against the "person" baseline.
- I2. Gloss instances for `en/src/ptag.rs`-style training: turn WordNet glosses into labelled noun instances and add them to the averaged-perceptron training of a noun-class model, and measure accuracy on a held-out split of the open treebanks, with two identical runs.
- I3. Ambiguity filter: count how many EWT noun tokens have more than one WordNet supersense (the paper's figure is 72% of tokens for its corpus) and train only on unambiguous nouns. Report the size of the training set before and after the filter.

## For Mova

The touch points are `onto` (WordNet lexicographer files), `en/src/ptag.rs` (averaged perceptron, the same learner family) and `en/src/tok.rs` for tokens. The chapter is [11. Nouns](https://md.mova.elamur.ai/free/en/docs/languages/en/11-nouns/). The paper's corpus and WordNet test sets are NC-tagged, so only the method and tests use them; the build can use WordNet itself (open) and EWT (CC BY-SA). Measure first the "person" baseline and the perceptron on the EWT unknown nouns.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1119355.1119377](https://doi.org/10.3115/1119355.1119377)

```
@inproceedings{ciaramita2003supersense,
  title = {{Supersense Tagging of Unknown Nouns in WordNet}},
  author = {Ciaramita, Massimiliano and Johnson, Mark},
  booktitle = {Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing},
  year = {2003},
  doi = {10.3115/1119355.1119377},
  url = {https://doi.org/10.3115/1119355.1119377}
}
```

- [← previous Combining Unsupervised and Supervised Methods for PP Attachment Disambiguation](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2002-volk-combining-unsupervised-supervised-methods-pp/)
- [next → Accurate Unlexicalized Parsing](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2003-klein-accurate-unlexicalized-parsing/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

15 references, 1 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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