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# WordNet under Scrutiny: Dictionary Examples in the Era of Large Language Models

**Authors:** Fatemah Almeman, Steven Schockaert, Luis Espinosa-Anke · **Year:** 2024 · **Venue:** Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) **Link:** [aclanthology.org/2024.lrec-main.1538](https://aclanthology.org/2024.lrec-main.1538/) · **Licence:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (LREC proceedings) — summary only

[**1** citations](https://openalex.org/works?filter=cites:W7131834166) openalex · 2026-10-18[**1** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lex-2024-almeman-wordnet-examples-llm/) of the 140673 papers whose bibliographies we read[LREC-COLING 2024 (Torino)](https://openalex.org/S7407088301)[licence](https://creativecommons.org/licenses/by-nc/4.0/)

- [Publisher's page](https://doi.org/10.63317/4skerayorcxh)
- [PDF](https://aclanthology.org/2024.lrec-main.1538.pdf)
- [DOI 10.63317/4skerayorcxh](https://doi.org/10.63317/4skerayorcxh)
- [OpenAlex](https://openalex.org/W7131834166)

**Authors (3)**

1. [Fatemah Yousef Almeman](https://openalex.org/A5092616523) [Semantic Scholar](https://www.semanticscholar.org/author/6439177) · h 1 · 2 citations · 5 works
2. [Steven Schockaert](https://openalex.org/A5052196540) [ORCID](https://orcid.org/0000-0002-9256-2881) · [Semantic Scholar](https://www.semanticscholar.org/author/2265382) · h 29 · 3955 citations · 383 works
3. [Luis Espinosa Anke](https://openalex.org/A5002661723) [ORCID](https://orcid.org/0000-0001-6830-9176) · [Semantic Scholar](https://www.semanticscholar.org/author/1405384395) · h 22 · 2708 citations · 156 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper asks how good the usage examples in WordNet are, compared with examples from other dictionaries and with examples generated by ChatGPT and GPT-3. Two annotators rated 87 words, giving 522 examples and 3,132 ratings on the GDEX criteria (naturalness, informativeness and similar). WordNet examples are rated highly natural but weak on informativeness, especially for the 45 words whose definitions were judged unclear. ChatGPT examples, including those filtered by GDEX-style criteria, were the most informative. On a word-similarity test with contextual embeddings built from examples, ChatGPT examples gave the best results on all datasets. Automatic GDEX-style scores were also computed for a larger set of examples.

## Ideas

- I1. `onto/src/wsd.rs`: the extended Lesk step compares a sense's gloss with the context. Add the WordNet examples to the gloss bag, and measure WSD accuracy on a held-out English WSD test set with and without the examples. This tests whether WordNet examples carry signal for the deterministic system.
- I2. `onto` or `annot`: for synsets with unclear glosses or no examples, generate candidate examples offline with a large model, then keep only those that pass Rust GDEX-style penalties (common-word share, pronoun share, length window, ambiguity). Measure the share of kept examples on a 87-word style sample rated by hand.
- I3. Test of the penalties: rebuild the paper's penalties as a small Rust function and check whether its score ranks the annotators' informativeness ratings in the same direction (Spearman correlation on the released ratings, if the license allows use).

## For Mova

The touch points are `onto` (WordNet data and `onto/src/wsd.rs`) and the `annot` teacher side for offline generation only. The paper's ratings and examples are CC BY-NC, so use them for tests and measurements, never in what is built. Measure first the effect of adding WordNet examples to the Lesk gloss bag on the deterministic WSD, with two identical runs.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.63317/4skerayorcxh](https://doi.org/10.63317/4skerayorcxh)

```
@inproceedings{almeman2024wordnet,
  title = {{WordNet under Scrutiny: Dictionary Examples in the Era of Large Language Models}},
  author = {Almeman, Fatemah Yousef and Schockaert, Steven and Anke, Luis Espinosa},
  booktitle = {LREC-COLING 2024 (Torino)},
  year = {2024},
  doi = {10.63317/4skerayorcxh},
  url = {https://doi.org/10.63317/4skerayorcxh}
}
```

- [← previous Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model](https://md.mova.elamur.ai/free/en/docs/papers/lex-2023-weissweiler-chatgpt-wugs-morphology/)
- [next → EtymoLink: A Structured English Etymology Dataset](https://md.mova.elamur.ai/free/en/docs/papers/lex-2024-gao-etymolink-english/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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