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# To Exhibit is not to Loiter: A Multilingual, Sense-Disambiguated Wiktionary for Measuring Verb Similarity

**Authors:** Christian M. Meyer, [Iryna Gurevych](https://md.mova.elamur.ai/free/en/docs/papers/authors/iryna-gurevych/) · **Year:** 2012 · **Venue:** Proceedings of COLING 2012: Technical Papers **Link:** [aclanthology.org/C12-1108](https://aclanthology.org/C12-1108/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**9** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lex-2012-meyer-sense-disambiguated-wiktionary/) of the 140673 papers whose bibliographies we readCOLING 2012 (Mumbai)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/C12-1108/)
- [PDF](https://aclanthology.org/C12-1108.pdf)

**Authors (2)**

1. Christian M. Meyer
2. Iryna Gurevych

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The paper turns Wiktionary's semantic relations and translations into a sense-disambiguated resource. Each relation or translation is linked to one sense of its target word by a method that combines features such as a monolingual-only candidate, a source-overlap score, a lexical-expansion score, a relation score and a most-frequent-sense rule. The method beats an earlier text-similarity method by a large margin, with a statistically significant gain. Four annotated sets are built for English, German and English–German. Raters agree on about 90% of cases, with Cohen's kappa above .80 for the monolingual sets. The resource is then used for verb similarity, where it gives better cross-lingual results than Wikipedia and the expert-built wordnets. Each feature is reported with its precision and coverage.

## Ideas

- I1. `en/src/lexgen.rs`: apply a feature list to the English relations that the lexicon already holds, and report the precision and coverage of each feature on a hand-checked sample, following the paper's per-feature table. Keep the features that are precise on the sample.
- I2. Verb similarity probe: score verb pairs from `onto` by the shortest path between their synsets, and report the Spearman rank correlation with human ratings on a set that we write and rate ourselves. Use a separate held-out set for the final number.
- I3. Agreement gate: two raters label 100 new relations, and a relation is accepted into `en` only when the kappa is above .80, as in the paper. A shuffled-label run is the negative control and should give a kappa near zero.

## For Mova

The code is `en/src/lexgen.rs` for the relations and `onto` for the verb pairs, with `en lexicon` as the check. The chapter is [13. Verbs](https://md.mova.elamur.ai/free/en/docs/languages/en/13-verbs/) for verb sense and `19-lexicon-mwe-register.md` for relations. The paper's data and Wiktionary text are CC BY-SA and NC-licensed sets, so use them as method references and for tests only, never in what is built. Measure first: the precision of each feature on the English sample, with two identical runs.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work.

```
@inproceedings{meyer2012exhibit,
  title = {{To Exhibit is not to Loiter: A Multilingual, Sense-Disambiguated Wiktionary for Measuring Verb Similarity}},
  author = {Meyer, Christian M. and Gurevych, Iryna},
  booktitle = {COLING 2012 (Mumbai)},
  year = {2012},
  url = {https://aclanthology.org/C12-1108/}
}
```

- [← previous A Lexicographer-Friendly Association Score](https://md.mova.elamur.ai/free/en/docs/papers/lex-2008-rychly-logdice/)
- [next → Supervised Learning of Complete Morphological Paradigms](https://md.mova.elamur.ai/free/en/docs/papers/lex-2013-durrett-morphological-paradigms/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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