# DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition

arXiv: https://arxiv.org/abs/2402.18209 · HTML: https://mlsanity.com/paper/2402.18209/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://mlsanity.com/paper/2211.02570/) (2022)
- [T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition](https://mlsanity.com/paper/2209.12616/) (2022)
- [DaN+: Danish Nested Named Entities and Lexical Normalization](https://mlsanity.com/paper/2105.11301/) (2021)
- [NorNE: Annotating Named Entities for Norwegian](https://mlsanity.com/paper/1911.12146/) (2019)
- [The Danish Gigaword Project](https://mlsanity.com/paper/2005.03521/) (2020)
- [ScandEval: A Benchmark for Scandinavian Natural Language Processing](https://mlsanity.com/paper/2304.00906/) (2023)
- [Transfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese](https://mlsanity.com/paper/2304.08823/) (2023)
- [DaCy: A Unified Framework for Danish NLP](https://mlsanity.com/paper/2107.05295/) (2021)

## Abstract (arXiv)

Named entity recognition is one of the cornerstones of Danish NLP, essential for language technology applications within both industry and research. However, Danish NER is inhibited by a lack of available datasets. As a consequence, no current models are capable of fine-grained named entity recognition, nor have they been evaluated for potential generalizability issues across datasets and domains. To alleviate these limitations, this paper introduces: 1) DANSK: a named entity dataset providing for high-granularity tagging as well as within-domain evaluation of models across a diverse set of domains; 2) DaCy 2.6.0 that includes three generalizable models with fine-grained annotation; and 3) an evaluation of current state-of-the-art models' ability to generalize across domains. The evaluation of existing and new models revealed notable performance discrepancies across domains, which should be addressed within the field. Shortcomings of the annotation quality of the dataset and its impact on model training and evaluation are also discussed. Despite these limitations, we advocate for the use of the new dataset DANSK alongside further work on the generalizability within Danish NER.