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# Coarse Lexical Semantic Annotation with Supersenses: An Arabic Case Study

**Authors:** [Nathan Schneider](https://md.mova.elamur.ai/free/en/docs/papers/authors/nathan-schneider/), Behrang Mohit, Kemal Oflazer, [Noah A. Smith](https://md.mova.elamur.ai/free/en/docs/papers/authors/noah-a-smith/) · **Year:** 2012 · **Venue:** Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) **Link:** [aclanthology.org/P12-2050](https://aclanthology.org/P12-2050/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**24** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2012-schneider-coarse-lexical-semantic-annotation-supersenses/) of the 140673 papers whose bibliographies we read[Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)](https://aclanthology.org/volumes/P12-2/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P12-2050/)
- [PDF](https://aclanthology.org/P12-2050.pdf)

**Authors (4)**

1. Nathan Schneider
2. Behrang Mohit
3. Kemal Oflazer
4. Noah A. Smith

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

## What it is about

The authors reuse WordNet's supersense labels (the coarse lexicographer classes) as a light scheme for lexical semantics, and apply it to Arabic. They write guidelines for nominal expressions and annotate 2,219 sentences (65,452 tokens, 23,239 mentions) from Arabic Wikipedia articles in four domains, without needing a full semantic lexicon. Two annotators agreed on 87 sentences: mention-level F1 was 70%, token-level agreement 71% with Cohen's kappa 0.69, and soft F1 (any overlap counts) 79%. Eight of the ten most frequent labels reached 73 to 80% mention F1; QUANTITY (63%) and COGNITION (49%) were the weak ones. About half of all tokens are covered by a nominal supersense. Boundary decisions, such as a city and its country as one span, cause much of the disagreement.

## Ideas

- I1. `onto` lexfile layer: tag nouns of English running text with their WordNet supersense from the first-sense lexfile, with no disambiguation, and measure what share of EWT noun tokens is covered. The paper's Arabic figure is about half of all tokens; the English figure is the one to measure.
- I2. Agreement gate for `annot`: have a second person hand-tag about 80 sentences with the supersense scheme, then compute kappa, mention F1 and soft F1 against the `annot` silver layer. Use these three numbers as the release gate before the silver layer is used for training.
- I3. Span rule in `en/src/tok.rs` or `en/src/ptag.rs`: treat a place name followed by a country as one span (for example "Fez, Morocco") in the supersense layer, and count how often the soft match and the strict match differ on the hand-tagged sample.

## For Mova

The touch points are `onto` (WordNet lexfiles), `en/src/ptag.rs` and `en/src/tok.rs` (spans), and the `annot` teacher side for silver labels. The chapters are [11. Nouns](https://md.mova.elamur.ai/free/en/docs/languages/en/11-nouns/) and [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/). The paper's Arabic corpus is not used. Its scheme is the reusable part; any English figure must come from Mova's own text. Measure first the lexfile coverage of EWT nouns, with two identical runs.

## Cite

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

```
@inproceedings{schneider2012coarse,
  title = {{Coarse Lexical Semantic Annotation with Supersenses: An Arabic Case Study}},
  author = {Schneider, Nathan and Mohit, Behrang and Oflazer, Kemal and Smith, Noah A.},
  booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2012},
  url = {https://aclanthology.org/P12-2050/}
}
```

- [← previous Sentence Boundary Detection: A Long Solved Problem?](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2012-read-sentence-boundary-detection-long-solved/)
- [next → POS Tagging for Historical Texts with Sparse Training Data](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2013-bollmann-pos-tagging-historical-texts-sparse/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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