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---
license: cc-by-sa-3.0
config_names:
- Abbreviation equality
- Adjective inflection analogy
- Clinical analogy
- Clinical similarity
- Noun inflection analogy
- UMNSRS relatedness
- UMNSRS similarity
- Verb inflection analogy
#dataset_info:
#- config_name: Abbreviation equality
# features:
# - name: train
# dtype: string
configs:
- config_name: Abbreviation equality
data_files:
- split: train
path: Abbreviation equality/train*
- config_name: Adjective inflection analogy
data_files:
- split: train
path: Adjective inflection analogy/train*
- config_name: Clinical analogy
data_files:
- split: train
path: Clinical analogy/train*
- config_name: Clinical similarity
data_files:
- split: train
path: Clinical similarity/train*
- config_name: Noun inflection analogy
data_files:
- split: train
path: Noun inflection analogy/train*
- config_name: UMNSRS relatedness
data_files:
- split: train
path: UMNSRS relatedness/train*
- config_name: UMNSRS similarity
data_files:
- split: train
path: UMNSRS similarity/train*
- config_name: Verb inflection analogy
data_files:
- split: train
path: Verb inflection analogy/train*
---
# Danish medical word embedding evaluation
The development of the dataset is described further in our [paper](https://aclanthology.org/2023.nejlt-1.4/).
### Citing
```
@inproceedings{laursen-etal-2023-benchmark,
title = "Benchmark for Evaluation of {D}anish Clinical Word Embeddings",
author = "Laursen, Martin Sundahl and
Pedersen, Jannik Skyttegaard and
Vinholt, Pernille Just and
Hansen, Rasmus S{\o}gaard and
Savarimuthu, Thiusius Rajeeth",
editor = "Derczynski, Leon",
booktitle = "Northern European Journal of Language Technology, Volume 9",
year = "2023",
address = {Link{\"o}ping, Sweden},
publisher = {Link{\"o}ping University Electronic Press},
url = "https://aclanthology.org/2023.nejlt-1.4",
doi = "https://doi.org/10.3384/nejlt.2000-1533.2023.4132",
abstract = "In natural language processing, benchmarks are used to track progress and identify useful models. Currently, no benchmark for Danish clinical word embeddings exists. This paper describes the development of a Danish benchmark for clinical word embeddings. The clinical benchmark consists of ten datasets: eight intrinsic and two extrinsic. Moreover, we evaluate word embeddings trained on text from the clinical domain, general practitioner domain and general domain on the established benchmark. All the intrinsic tasks of the benchmark are publicly available.",
}
```
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