# A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News

arXiv: https://arxiv.org/abs/2011.01615 · HTML: https://mlsanity.com/paper/2011.01615/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [On Generalization in Coreference Resolution](https://mlsanity.com/paper/2109.09667/) (2021)
- [Moving on from OntoNotes: Coreference Resolution Model Transfer](https://mlsanity.com/paper/2104.08457/) (2021)
- [Adapting Coreference Resolution Models through Active Learning](https://mlsanity.com/paper/2104.07611/) (2021)
- [[Lions: 1] and [Tigers: 2] and [Bears: 3], Oh My! Literary Coreference Annotation with LLMs](https://mlsanity.com/paper/2401.17922/) (2024)
- [Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective](https://mlsanity.com/paper/2303.09092/) (2023)
- [The Elephant in the Coreference Room: Resolving Coreference in Full-Length French Fiction Works](https://mlsanity.com/paper/2510.15594/) (2025)
- [Transforming Dutch: Debiasing Dutch Coreference Resolution Systems for Non-binary Pronouns](https://mlsanity.com/paper/2405.00134/) (2024)
- [Ensemble Transfer Learning for Multilingual Coreference Resolution](https://mlsanity.com/paper/2301.09175/) (2023)

## Abstract (arXiv)

We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018) coreference system on Dutch datasets of two domains: literary novels and news/Wikipedia text. The results provide insight into the relative strengths of data-driven and knowledge-driven systems, as well as the influence of domain, document length, and annotation schemes. The neural system performs best on news/Wikipedia text, while the rule-based system performs best on literature. The neural system shows weaknesses with limited training data and long documents, while the rule-based system is affected by annotation differences. The code and models used in this paper are available at https://github.com/andreasvc/crac2020