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# PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution

**Authors:** Hong Chen, Zhenhua Fan, Hao Lu, Alan Yuille, Shu Rong · **Year:** 2018 · **Venue:** EMNLP 2018 **Link:** [aclanthology.org/D18-1016](https://aclanthology.org/D18-1016/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (paper); the texts are school English tests (about two thirds from RACE) — data licence to be checked, tests only unless it is clearly commercial-friendly

[**42** citations](https://openalex.org/works?filter=cites:W2964027319) openalex · 2026-10-19[**43** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/chen-2018-preco-large-scale-dataset-preschool/) of the 140673 papers whose bibliographies we read[Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/D18-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/D18-1016/)
- [PDF](https://aclanthology.org/D18-1016.pdf)
- [DOI 10.18653/v1/d18-1016](https://doi.org/10.18653/v1/d18-1016)
- [arXiv 1810.09807](https://arxiv.org/abs/1810.09807)
- [OpenAlex](https://openalex.org/W2964027319)

**Authors (5)**

1. [Hong Chen](https://openalex.org/A5100420451) [ORCID](https://orcid.org/0000-0002-9084-5482) · [Semantic Scholar](https://www.semanticscholar.org/author/2118061616) · h 24 · 2367 citations · 185 works
2. Zhenhua Fan [Semantic Scholar](https://www.semanticscholar.org/author/2113847617)
3. [Hao Lu](https://openalex.org/A5033681666) [ORCID](https://orcid.org/0000-0003-3854-8664) · [Semantic Scholar](https://www.semanticscholar.org/author/2115607504) · h 38 · 5867 citations · 151 works
4. [Alan Yuille](https://openalex.org/A5086706224) [ORCID](https://orcid.org/0000-0001-5207-9249) · [Semantic Scholar](https://www.semanticscholar.org/author/145081362) · h 134 · 106835 citations · 1179 works
5. Shu Rong [Semantic Scholar](https://www.semanticscholar.org/author/2068628178)

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

PreCo is 38K documents and 12.5M words of English reading-comprehension passages from Chinese middle- and high-school tests, chosen because their vocabulary (about 3,000 common words) resembles a preschooler's and because a small vocabulary raises the overlap between training and test, so that errors reflect coreference itself rather than unseen words. Annotation follows OntoNotes with changes: singletons are marked, generic mentions may corefer directly, non-proper noun modifiers are markable, and an event verb can corefer with a later noun («Sales [grew]… [The growth]…»). The development and test sets were annotated by two annotators and merged by a third; merging mainly raises recall, because single annotators miss distant links out of inattention. Inter-annotator CoNLL agreement is 85.3. A strong neural model reaches 81.5 F1 against an estimated human 87.9; with oracle mentions a model trained on 2.5K documents goes from 77.3 to 81.6, so most of the remaining gap is clustering.

## Ideas

- I1. School-register evaluation for Mova's coreference: PreCo's dev/test passages are exactly the register of Mova's courses (simple vocabulary, reading-test stories). If the licence allows evaluation, run `coref eval` (SLM trees) and the frame reader's resolver on them and report per-pronoun precision; if not, use the paper's finding (clustering, not vocabulary, is the hard part in simple texts) to prioritise pronoun linking over mention detection for lessons.
- I2. Event-to-noun coreference, one of the «What next» items of `coref` (event mentions): link a definite deverbal noun to an earlier verb of the same root («grew… the growth», «decided… the decision», «arrived… her arrival»). The root pairs come from Mova's own derivation rules (`en` morphology, the suffix chapter), not from NOMLEX (research-only). Count candidate pairs on GUM dev and check them against GUM's verbal mentions before turning the rule on.
- I3. Copy the two-pass-and-merge procedure for teacher silver on coreference: two independent teacher passes over a text, merged by rule (union of links, conflicts to a third look), since single passes lose recall on distant links. Measure the recall of each pass against the merge on a few documents.

## For Mova

[35. Reference and coreference](https://md.mova.elamur.ai/free/en/docs/languages/en/35-reference-and-coreference/) §3.4 (abstract and event anaphora), §9; `17-derivation-suffixes.md` (deverbal nouns). Code: `coref/src/mention.rs` and `coref/src/sieve.rs` (event-noun link), the `en` derivation tables. Measures: per-pronoun precision on school-register texts; precision of event-noun links on GUM dev.

Topics: [Coreference resolution](https://md.mova.elamur.ai/free/en/docs/papers/topics/coreference-resolution/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/d18-1016](https://doi.org/10.18653/v1/d18-1016) · [arXiv 1810.09807](https://arxiv.org/abs/1810.09807)

```
@inproceedings{chen2018preco,
  title = {{PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution}},
  author = {Chen, Hong and Fan, Zhenhua and Lu, Hao and Yuille, Alan and Rong, Shu},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1016},
  eprint = {1810.09807},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/d18-1016}
}
```

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## References

10 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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5. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250. [↗](https://arxiv.org/abs/1606.05250)
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