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# Anatomy of OntoGUM—Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms

**Authors:** [Yilun Zhu](https://md.mova.elamur.ai/free/en/docs/papers/authors/yilun-zhu/), [Sameer Pradhan](https://md.mova.elamur.ai/free/en/docs/papers/authors/sameer-pradhan/), [Amir Zeldes](https://md.mova.elamur.ai/free/en/docs/papers/authors/amir-zeldes/) · **Year:** 2021 · **Venue:** CRAC 2021 (Workshop on Computational Models of Reference, Anaphora and Coreference) **Link:** [aclanthology.org/2021.crac-1.15](https://aclanthology.org/2021.crac-1.15/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (paper); GUM and OntoGUM are CC BY-NC-SA — evaluation only

[**1** citations](https://openalex.org/works?filter=cites:W3205648957) openalex-snapshot · 2026-10-14[**5** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zhu-2021-anatomy-ontogum-adapting-gum-ontonotes/) of the 140673 papers whose bibliographies we read[Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference](https://aclanthology.org/volumes/2021.crac-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2021.crac-1.15/)
- [PDF](https://aclanthology.org/2021.crac-1.15.pdf)
- [DOI 10.18653/v1/2021.crac-1.15](https://doi.org/10.18653/v1/2021.crac-1.15)
- [arXiv 2110.05727](https://arxiv.org/abs/2110.05727)
- [OpenAlex](https://openalex.org/W3205648957)

**Authors (3)**

1. Yilun Zhu
2. [Sameer Pradhan](https://openalex.org/A5110598639) [ORCID](https://orcid.org/0000-0002-5537-2181) · h 31 · 5247 citations · 85 works
3. [Amir Zeldes](https://openalex.org/A5089212858) [ORCID](https://orcid.org/0000-0001-8016-6753) · h 16 · 1524 citations · 238 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

GUM annotates more coreference than OntoNotes, so the authors convert it automatically to the OntoNotes scheme, using GUM's gold UD trees, in a fixed order of steps: remove bridging and cataphora (an expletive or cataphoric pronoun becomes a singleton), contract verbal mentions to the verb, merge appositions into one mention, remove common-noun compound modifiers, remove copular predicates (detected by a `cop` on the second span's head), remove nested i-within-i mentions, split chains at indefinite mentions (definite = pronoun, possessed head, proper noun, or definite determiner), and finally drop singletons. The result, OntoGUM, has 168 documents in 12 genres (~150K tokens, 19,378 mentions, 4,471 chains). A SpanBERT end-to-end model trained on OntoNotes loses about 15 points (19%) on OntoGUM and the rule-based Stanford dcoref over 18 points (30%); both are worst on textbook text and genres with few pronouns and no speaker information, and best on vlogs with gold speakers.

## Ideas

- I1. Write the conversion as a Rust output mode of `coref resolve` (GUM-style chains → OntoNotes-style chains by the same ordered steps, all from the UD tree) and score Mova's sieve on OntoGUM test. That gives a direct comparison with the published rule-based dcoref and SpanBERT figures per genre, which the CRAC comparison in [coref — SLM coreference: a sieve of rules with a reason for every link](https://md.mova.elamur.ai/free/en/docs/coref/) lacks («the settings differ»). Evaluation only (NC data).
- I2. Report `coref` per GUM genre and look hardest at *textbook* and *academic*: those are the genres closest to Mova's school lessons and the worst for both systems in the paper. If they lag, collect the errors there first (it/its to sections and events is the likely leader).
- I3. Reuse the paper's definiteness rule (pronoun, possessed head, proper noun, or `det` with a definite lemma) as the single definition in `coref/src/mention.rs` and in the frame reader, so both crates agree on what «definite» means; check where they differ today.

## For Mova

[35. Reference and coreference](https://md.mova.elamur.ai/free/en/docs/languages/en/35-reference-and-coreference/) §7 (annotation schemes), §9.3 (results). Code: `coref/src/corefud.rs` and `coref/src/eval.rs` (OntoNotes-style output and scoring), `coref/src/mention.rs`. Measures: CoNLL F1 on OntoGUM test per genre against dcoref and SpanBERT; per-genre CoNLL F1 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/v1/2021.crac-1.15](https://doi.org/10.18653/v1/2021.crac-1.15) · [arXiv 2110.05727](https://arxiv.org/abs/2110.05727)

```
@inproceedings{zhu2021anatomy,
  title = {{Anatomy of OntoGUM—Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms}},
  author = {Zhu, Yilun and Pradhan, Sameer and Zeldes, Amir},
  booktitle = {Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference},
  year = {2021},
  doi = {10.18653/v1/2021.crac-1.15},
  eprint = {2110.05727},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.crac-1.15}
}
```

- [← previous GRICE: A Grammar-based Dataset for Recovering Implicature and Conversational rEasoning](https://md.mova.elamur.ai/free/en/docs/papers/zheng-2021-grice-grammar-based-dataset-recovering/)
- [next → VALUE: Understanding Dialect Disparity in NLU](https://md.mova.elamur.ai/free/en/docs/papers/ziems-2022-value-understanding-dialect-disparity-nlu/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

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