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 ---
language: da
size_categories:
- 10K<n<100K
task_categories:
- token-classification
pretty_name: DANSK
YAML tags:
- copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
dataset_info:
features:
- name: text
dtype: string
- name: ents
list:
- name: end
dtype: int64
- name: label
dtype: string
- name: start
dtype: int64
- name: sents
list:
- name: end
dtype: int64
- name: start
dtype: int64
- name: tokens
list:
- name: end
dtype: int64
- name: id
dtype: int64
- name: start
dtype: int64
- name: spans
struct:
- name: incorrect_spans
sequence: 'null'
- name: dagw_source
dtype: string
- name: dagw_domain
dtype: string
- name: dagw_source_full
dtype: string
splits:
- name: train
num_bytes: 4738527
num_examples: 11762
- name: dev
num_bytes: 590198
num_examples: 1461
- name: test
num_bytes: 594600
num_examples: 1462
download_size: 1413153
dataset_size: 5923325
tags:
- legal
- news
- spoken
- encyclopedia
- 'spoken '
- SoMe
- books
- ontonotes
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
## Dataset Description
- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
### Dataset Summary
DANSK: Danish Annotations for NLP Specific TasKs is a dataset consisting of texts from multiple domains, sampled from the Danish GigaWord Corpus (DAGW).
The dataset was created to fill in the gap of Danish NLP datasets from different domains, that are required for training models that generalize across domains. The Named-Entity annotations are moreover fine-grained and have a similar form to that of OntoNotes v5, which significantly broadens the use cases of the dataset.
The domains include Web, News, Wiki & Books, Legal, Dannet, Conversation and Social Media. For a more in-depth understanding of the domains, please refer to [DAGW](https://huggingface.co/datasets/DDSC/partial-danish-gigaword-no-twitter).
The distribution of texts and Named Entities within each domain can be seen in the table below:
### Update log
- 2024-03-12: Removed OpenSubtitles from DANSK due to potential copyright infringement
- 2023-05-26: Added individual annotations for each annotator to allow for analysis of inter-annotator agreement
### Supported Tasks
The DANSK dataset currently only supports Named-Entity Recognition, but additional version releases will contain data for more tasks.
### Languages
All texts in the dataset are in Danish.
Slang from various platforms or dialects may appear, consistent with the domains from which the texts originally have been sampled - e.g. Social Media.
## Dataset Structure
### Data Instances
The JSON-formatted data is in the form seen below:
```
{
"text": "Aborrer over 2 kg er en uhyre sj\u00e6lden fangst.",
"ents": [{"start": 13, "end": 17, "label": "QUANTITY"}],
"sents": [{"start": 0, "end": 45}],
"tokens": [
{"id": 0, "start": 0, "end": 7},
{"id": 1, "start": 8, "end": 12},
{"id": 2, "start": 13, "end": 14},
{"id": 3, "start": 15, "end": 17},
{"id": 4, "start": 18, "end": 20},
{"id": 5, "start": 21, "end": 23},
{"id": 6, "start": 24, "end": 29},
{"id": 7, "start": 30, "end": 37},
{"id": 8, "start": 38, "end": 44},
{"id": 9, "start": 44, "end": 45},
],
"spans": {"incorrect_spans": []},
"dagw_source": "wiki",
"dagw_domain": "Wiki & Books",
"dagw_source_full": "Wikipedia",
}
```
### Data Fields
- `text`: The text
- `ents`: The annotated entities
- `sents`: The sentences of the text
- `dagw_source`: Shorthand name of the source from which the text has been sampled in the Danish Gigaword Corpus
- `dagw_source_full`: Full name of the source from which the text has been sampled in the Danish Gigaword Corpus
- `dagw_domain`: Name of the domain to which the source adheres to
### Data Splits
The data was randomly split up into three distinct partitions; train, dev, as well as a test partition.
The splits come from the same pool, and there are thus no underlying differences between the sets.
To see the distribution of named entities, and domains of the different partitions,
please refer to the paper, or read the superficial statistics provided in the Dataset composition section of this markdown
## Descriptive Statistics
### Dataset Composition
Named entity annotation composition across partitions can be seen in the table below:
| | Full | Train | Validation | Test |
| :------------: | :---: | :------------: | :----------: | :-----------: |
| Texts | 15062 | 12062 (80%) | 1500 (10%) | 1500 (10%) |
| Named entities | 14462 | 11638 (80.47%) | 1327 (9.18%) | 1497 (10.25%) |
| CARDINAL | 2069 | 1702 (82.26%) | 168 (8.12%) | 226 (10.92%) |
| DATE | 1756 | 1411 (80.35%) | 182 (10.36%) | 163 (9.28%) |
| EVENT | 211 | 175 (82.94%) | 19 (9.00%) | 17 (8.06%) |
| FACILITY | 246 | 200 (81.30%) | 25 (10.16%) | 21 (8.54%) |
| GPE | 1604 | 1276 (79.55%) | 135 (8.42%) | 193 (12.03%) |
| LANGUAGE | 126 | 53 (42.06%) | 17 (13.49%) | 56 (44.44%) |
| LAW | 183 | 148 (80.87%) | 17 (9.29%) | 18 (9.84%) |
| LOCATION | 424 | 351 (82.78%) | 46 (10.85%) | 27 (6.37%) |
| MONEY | 714 | 566 (79.27%) | 72 (10.08%) | 76 (10.64%) |
| NORP | 495 | 405 (81.82%) | 41 (8.28%) | 49 (9.90%) |
| ORDINAL | 127 | 105 (82.68%) | 11 (8.66%) | 11 (8.66%) |
| ORGANIZATION | 2507 | 1960 (78.18%) | 249 (9.93%) | 298 (11.87%) |
| PERCENT | 148 | 123 (83.11%) | 13 (8.78%) | 12 (8.11%) |
| PERSON | 2133 | 1767 (82.84%) | 191 (8.95%) | 175 (8.20%) |
| PRODUCT | 763 | 634 (83.09%) | 57 (7.47%) | 72 (9.44%) |
| QUANTITY | 292 | 242 (82.88%) | 28 (9.59%) | 22 (7.53%) |
| TIME | 218 | 185 (84.86%) | 18 (8.26%) | 15 (6.88%) |
| WORK OF ART | 419 | 335 (79.95%) | 38 (9.07%) | 46 (10.98%) |
### Domain distribution
Domain and source distribution across partitions can be seen in the table below:
| Domain | Source | Full | Train | Dev | Test |
| :----------: | :----------------: | :---: | :---: | :---: | :---: |
| Conversation | Europa Parlamentet | 206 | 173 | 17 | 16 |
| Conversation | Folketinget | 23 | 21 | 1 | 1 |
| Conversation | NAAT | 554 | 431 | 50 | 73 |
| Conversation | OpenSubtitles* | 377 | 300 | 39 | 38 |
| Conversation | Spontaneous speech | 489 | 395 | 54 | 40 |
| Dannet | Dannet | 25 | 18 | 4 | 3 |
| Legal | Retsinformation.dk | 965 | 747 | 105 | 113 |
| Legal | Skat.dk | 471 | 364 | 53 | 54 |
| Legal | Retspraktis | 727 | 579 | 76 | 72 |
| News | DanAvis | 283 | 236 | 20 | 27 |
| News | TV2R | 138 | 110 | 16 | 12 |
| Social Media | hestenettet.dk | 554 | 439 | 51 | 64 |
| Web | Common Crawl | 8270 | 6661 | 826 | 783 |
| Wiki & Books | adl | 640 | 517 | 57 | 66 |
| Wiki & Books | Wikipedia | 279 | 208 | 30 | 41 |
| Wiki & Books | WikiBooks | 335 | 265 | 36 | 34 |
| Wiki & Books | WikiSource | 455 | 371 | 43 | 41 |
> **Note**: Due to OpenSubtitles potentially containing copyrighted data we have removed it from the dataset.
### Entity Distribution across
Domain and named entity distributions for the training set can be seen below:
| | All domains combined | Conversation | Dannet | Legal | News | Social Media | Web | Wiki and Books |
| :----------: | :------------------: | :----------: | :----: | :---: | :---: | :----------: | :---: | :------------: |
| DOCS | 12062 | 1320 | 18 | 1690 | 346 | 439 | 6661 | 1361 |
| ENTS | 11638 | 1060 | 15 | 1292 | 419 | 270 | 7502 | 883 |
| CARDINAL | 1702 | 346 | 6 | 95 | 35 | 17 | 1144 | 59 |
| DATE | 1411 | 113 | 5 | 257 | 40 | 29 | 831 | 126 |
| EVENT | 175 | 43 | 0 | 1 | 9 | 3 | 106 | 8 |
| FACILITY | 200 | 2 | 0 | 4 | 18 | 3 | 159 | 10 |
| GPE | 1276 | 130 | 2 | 60 | 68 | 31 | 846 | 128 |
| LANGUAGE | 53 | 3 | 0 | 0 | 0 | 0 | 34 | 16 |
| LAW | 148 | 10 | 0 | 100 | 1 | 0 | 22 | 13 |
| LOCATION | 351 | 18 | 0 | 1 | 7 | 7 | 288 | 29 |
| MONEY | 566 | 1 | 0 | 62 | 13 | 18 | 472 | 0 |
| NORP | 405 | 70 | 0 | 61 | 22 | 1 | 188 | 42 |
| ORDINAL | 105 | 11 | 0 | 17 | 9 | 2 | 43 | 22 |
| ORGANIZATION | 1960 | 87 | 0 | 400 | 61 | 39 | 1303 | 58 |
| PERCENT | 123 | 5 | 0 | 10 | 11 | 0 | 91 | 4 |
| PERSON | 1767 | 189 | 2 | 194 | 101 | 69 | 970 | 121 |
| PRODUCT | 634 | 3 | 0 | 10 | 2 | 33 | 581 | 3 |
| QUANTITY | 242 | 1 | 0 | 9 | 6 | 17 | 188 | 20 |
| TIME | 185 | 16 | 0 | 5 | 13 | 1 | 144 | 6 |
| WORK OF ART | 335 | 12 | 0 | 6 | 3 | 0 | 92 | 218 |
Domain and named entity distributions for the validation set can be seen below:
| | Sum | Conversation | Dannet | Legal | News | Social Media | Web | Wiki |
| :----------: | :---: | :----------: | :----: | :---: | :---: | :----------: | :---: | :---: |
| DOCS | 1500 | 161 | 4 | 234 | 36 | 51 | 826 | 166 |
| ENTS | 1497 | 110 | 4 | 171 | 43 | 30 | 983 | 143 |
| CARDINAL | 226 | 41 | 2 | 19 | 7 | 5 | 139 | 13 |
| DATE | 163 | 11 | 0 | 27 | 6 | 4 | 89 | 26 |
| EVENT | 17 | 2 | 0 | 0 | 1 | 0 | 13 | 1 |
| FACILITY | 21 | 1 | 0 | 0 | 0 | 0 | 16 | 4 |
| GPE | 193 | 17 | 1 | 8 | 7 | 2 | 131 | 25 |
| LANGUAGE | 56 | 0 | 0 | 0 | 0 | 0 | 50 | 6 |
| LAW | 18 | 2 | 0 | 8 | 0 | 0 | 8 | 0 |
| LOCATION | 27 | 2 | 0 | 1 | 0 | 0 | 21 | 3 |
| MONEY | 76 | 2 | 0 | 9 | 1 | 6 | 58 | 0 |
| NORP | 49 | 8 | 0 | 8 | 1 | 2 | 21 | 9 |
| ORDINAL | 11 | 2 | 0 | 2 | 0 | 1 | 3 | 3 |
| ORGANIZATION | 298 | 6 | 0 | 68 | 5 | 3 | 212 | 4 |
| PERCENT | 12 | 0 | 0 | 2 | 0 | 0 | 10 | 0 |
| PERSON | 175 | 16 | 1 | 16 | 11 | 4 | 96 | 20 |
| PRODUCT | 72 | 0 | 0 | 0 | 0 | 2 | 69 | 1 |
| QUANTITY | 22 | 0 | 0 | 1 | 2 | 1 | 17 | 1 |
| TIME | 15 | 0 | 0 | 0 | 2 | 0 | 13 | 0 |
| WORK OF ART | 46 | 0 | 0 | 2 | 0 | 0 | 17 | 27 |
Domain and named entity distributions for the testing set can be seen below:
| | Sum | Conversation | Dannet | Legal | News | Social Media | Web | Wiki |
| :----------: | :---: | :----------: | :----: | :---: | :---: | :----------: | :---: | :---: |
| DOCS | 1500 | 161 | 4 | 234 | 36 | 51 | 826 | 166 |
| ENTS | 1497 | 110 | 4 | 171 | 43 | 30 | 983 | 143 |
| CARDINAL | 226 | 41 | 2 | 19 | 7 | 5 | 139 | 13 |
| DATE | 163 | 11 | 0 | 27 | 6 | 4 | 89 | 26 |
| EVENT | 17 | 2 | 0 | 0 | 1 | 0 | 13 | 1 |
| FACILITY | 21 | 1 | 0 | 0 | 0 | 0 | 16 | 4 |
| GPE | 193 | 17 | 1 | 8 | 7 | 2 | 131 | 25 |
| LANGUAGE | 56 | 0 | 0 | 0 | 0 | 0 | 50 | 6 |
| LAW | 18 | 2 | 0 | 8 | 0 | 0 | 8 | 0 |
| LOCATION | 27 | 2 | 0 | 1 | 0 | 0 | 21 | 3 |
| MONEY | 76 | 2 | 0 | 9 | 1 | 6 | 58 | 0 |
| NORP | 49 | 8 | 0 | 8 | 1 | 2 | 21 | 9 |
| ORDINAL | 11 | 2 | 0 | 2 | 0 | 1 | 3 | 3 |
| ORGANIZATION | 298 | 6 | 0 | 68 | 5 | 3 | 212 | 4 |
| PERCENT | 12 | 0 | 0 | 2 | 0 | 0 | 10 | 0 |
| PERSON | 175 | 16 | 1 | 16 | 11 | 4 | 96 | 20 |
| PRODUCT | 72 | 0 | 0 | 0 | 0 | 2 | 69 | 1 |
| QUANTITY | 22 | 0 | 0 | 1 | 2 | 1 | 17 | 1 |
| TIME | 15 | 0 | 0 | 0 | 2 | 0 | 13 | 0 |
| WORK OF ART | 46 | 0 | 0 | 2 | 0 | 0 | 17 | 27 |
## Dataset Creation
### Curation Rationale
The dataset is meant to fill in the gap of Danish NLP that up until now
has been missing a dataset with 1) fine-grained named entity recognition
labels, and 2) high variance in domain origin of texts. As such, it is the
intention that DANSK should be employed in training by anyone who wishes
to create models for NER that are both generalizable across domains and
fine-grained in their predictions. It may also be utilized to assess across-domain evaluations, in order to unfold any potential domain biases. While
the dataset currently only entails annotations for named entities, it is the
intention that future versions of the dataset will feature dependency Parsing,
pos tagging, and possibly revised NER annotations.
### Source Data
The data collection, annotation, and normalization steps of the data were extensive.
As the description is too long for this readme, please refer to the associated paper upon its publication for a full description.
#### Initial Data Collection and Normalization
### Annotations
#### Annotation process
To afford high granularity, the DANSK dataset utilized the annotation standard of OntoNotes 5.0.
The standard features 18 different named entity types. The full description can be seen in the associated paper.
#### Who are the annotators?
10 English Linguistics Master’s program students from Aarhus University were employed.
They worked 10 hours/week for six weeks from October 11, 2021, to November 22, 2021.
Their annotation tasks included part-of-speech tagging, dependency parsing, and NER annotation.
Named entity annotations and dependency parsing was done from scratch, while the POS tagging consisted of corrections of silver-standard predictions by an NLP model.
### Annotator Compensation
10 English Linguistics Master’s program students from Aarhus University
were employed. They worked 10 hours/week for six weeks from October 11,
2021, to November 22, 2021. Their annotation tasks included
part-of-speech tagging, dependency parsing, and NER annotation. **Annotators were compensated at the standard rate for students, as determined by the collective agreement of the Danish Ministry of Finance and the Central Organization of Teachers and the
CO10 Central Organization of 2010 (the CO10 joint agreement), which is 140DKK/hour.** Named
entity annotations and dependency parsing was done from scratch, while
the POS tagging consisted of corrections of predictions by an NLP model.
### Automatic correction
During the manual correction of the annotation a series of consistent errors were found. These were corrected using the following Regex patterns (see also the Danish Addendum to the Ontonotes annotation guidelines):
<details><summary>Regex Patterns</summary>
<p>
For matching with TIME spans, e.g. [16:30 - 17:30] (TIME):
```
\d{1,2}:\d\d ?[-|\||\/] ?\d
dag: \d{1,2}
```
For matching with DATE spans, e.g. [1938 - 1992] (DATE):
```
\d{2,4} ?[-|–] ?\d{2,4}
```
For matching companies with A/S og ApS,
```
e.g. [Hansens Skomager A/S] (ORGANIZATION):
ApS
A\/S
```
For matching written numerals, e.g. "en":
```
to | to$|^to| To | To$|^To| TO | TO$|^TO|
tre | tre$|^tre| Tre | Tre$|^Tre| TRE | TRE$|^TRE|
fire | fire$|^fire| Fire | Fire$|^Fire| FIRE | FIRE$|^FIRE|
fem | fem$|^fem| Fem | Fem$|^Fem| FEM | FEM$|^FEM|
seks | seks$|^seks| Seks | Seks$|^Seks| SEKS | SEKS$|
^SYV|
otte | otte$|^otte| Otte | Otte$|^Otte| OTTE | OTTE$|^OTTE|
ni | ni$|^ni| Ni | Ni$|^Ni| NI | NI$|^NI|
ti | ti$|^ti| Ti | Ti$|^Ti| TI | TI$|^TI
```
For matching "Himlen" or "Himmelen" already annotated
as LOCATION, e.g. "HIMLEN":
```
[Hh][iI][mM][lL][Ee][Nn]|[Hh][iI][mM][mM][Ee][lL][Ee][Nn]
```
For matching "Gud" already tagged as PERSON, e.g. "GUD":
```
[Gg][Uu][Dd]
```
For matching telephone numbers wrongly already
tagged as CARDINAL, e.g. "20 40 44 30":
```
\d{2} \d{2} \d{2} \d{2}
\+\d{2} \d{2} ?\d{2} ?\d{2} ?\d{2}$
\+\d{2} \d{2} ?\d{2} ?\d{2} ?\d{2}$
\d{4} ?\d{4}$
^\d{4} ?\d{4}$
```
For matching websites already
wrongly tagged as ORGANIZATION:
```
.dk$|.com$
```
For matching Hotels and Resorts
already wrongly tagged as ORGANIZATION:
```
.*[h|H]otel.*|.*[R|r]esort.*
```
For matching numbers including /
or :, already wrongly tagged as CARDINAL:
```
\/
\/
-
```
For matching rights already
wrongly tagged as LAW:
```
[C|c]opyright
[®|©]
[f|F]ortrydelsesret
[o|O]phavsret$
enneskeret
```
</p>
</details>
### Licensing Information
Creative Commons Attribution-Share Alike 4.0 International license
### Citation Information
If you use this work please cite our [preprint](DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition)
```
@misc{enevoldsen2024dansk,
title={DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition},
author={Kenneth Enevoldsen and Emil Trenckner Jessen and Rebekah Baglini},
year={2024},
eprint={2402.18209},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```