DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition
This addresses a data scarcity problem for Danish NLP researchers and practitioners, though it is incremental as it builds on existing NER methods.
The paper tackles the lack of datasets for Danish named entity recognition by introducing DANSK, a dataset for fine-grained tagging and cross-domain evaluation, and DaCy 2.6.0 with generalizable models, revealing notable performance discrepancies across domains.
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.