---
title: "A Benchmark of Rule-Based and Neural Coreference — Poot 2020"
description: "Dutch coreference resolution has lagged behind English, where end-to-end neural models now lead."
authors: ["Corbèn Poot", "Andreas van Cranenburgh"]
year: 2020
venue: "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference"
source: "https://aclanthology.org/2020.crac-1.9/"
arxiv: "2011.01615"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/poot-2020-benchmark-rule-based-neural-coreference/"
---
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# A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News

**Authors:** Corbèn Poot, [Andreas van Cranenburgh](https://md.research.elamur.ai/free/en/authors/andreas-van-cranenburgh/) · **Year:** 2020 · **Venue:** Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.crac-1.9/) · [PDF](https://aclanthology.org/2020.crac-1.9.pdf) · [arXiv 2011.01615](https://arxiv.org/abs/2011.01615) · [OpenAlex](https://openalex.org/W3095691493)

**Citations:** 10 citations (openalex-snapshot · 2026-10-14) · [11](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Dutch coreference resolution has lagged behind English, where end-to-end neural models now lead. Dutch tools have so far been rule-based or used classic machine learning. The authors compare two systems. The first is dutchcoref, a Dutch adaptation of the Stanford rule-based system (Lee et al., 2013). The second is e2e-Dutch, a variant of the Lee et al. (2018) end-to-end model that uses BERT token representations. Both are evaluated on SoNaR-1 (mostly news and Wikipedia, about one million words) and RiddleCoref (fragments of contemporary novels). For each corpus the authors created new splits that reserve 70% for training, making supervised learning possible.

The neural model performs best on news/Wikipedia, while the rule-based system does better on fiction. Follow-up analyses point to several confounding factors. The neural model is hurt by the small novel training set and by very long documents. The rule-based system is penalized by SoNaR-1's annotation conventions. The work shows how strongly domain, document length and annotation scheme affect such comparisons, and it argues for harmonizing Dutch coreference corpora.

## Key points
- The training data differ sharply in size: RiddleCoref has only 23 training documents, while SoNaR-1 has 581. Novel documents are much longer, averaging 295.78 sentences versus 64.28.
- The two corpora use different parse inputs. For SoNaR-1, the gold Lassy Small treebank trees were aligned to the coreference annotations and enriched with gold NER and Alpino features. RiddleCoref relies on automatically predicted Alpino parses.
- In a truncation experiment on the novels, e2e-Dutch scores fall as documents grow longer, under both CoNLL and LEA. dutchcoref shows no such trend.
- Excluding singletons or supplying gold mentions shifts scores considerably, but the ranking of the two systems stays the same.
- A manual inspection of SoNaR-1 found unlinked exact-match mentions, missing mentions and questionable markable boundaries. The code and models are released at github.com/andreasvc/crac2020.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

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

## Cited by

Cited by

11 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 7 not yet.

- [Lions: 1 and Tigers: 2 and Bears: 3, Oh My! Literary Coreference Annotation with LLMs](https://md.research.elamur.ai/free/en/papers/hicke-2024-lions-1-tigers-2-bears/)
- [Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective](https://md.research.elamur.ai/free/en/papers/porada-2024-challenges-evaluating-generalization-coreference-resolution/)
- [On Generalization in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/toshniwal-2021-generalization-coreference-resolution/)
- [Adapting Coreference Resolution Models through Active Learning](https://md.research.elamur.ai/free/en/papers/yuan-2022-adapting-coreference-resolution-models-through/)

## Cite

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

```bibtex
@inproceedings{poot2020benchmark,
  title = {{A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News}},
  author = {Poot, Corbèn and Cranenburgh, Andreas van},
  booktitle = {Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference},
  year = {2020},
  eprint = {2011.01615},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2011.01615}
}
```

- [← previous A Benchmark Arabic Dataset for Commonsense Explanation](https://md.research.elamur.ai/free/en/papers/al-2020-tawalbeh-benchmark-arabic-dataset-commonsense-explanation/)
- [next → A closer look at the performance of neural language models on reflexive anaphor licensing](https://md.research.elamur.ai/free/en/papers/hu-2020-closer-look-performance-neural-language/)
- [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/)

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