Update README.md

README.md

@@ -1,4 +1,7 @@
 ---
+language: da
+YAML tags:
+- copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
 dataset_info:
   features:
   - name: text
@@ -36,24 +39,363 @@ dataset_info:
   - name: dagw_source_full
     dtype: string
   splits:
-  - name: train
-    num_bytes: 4699956.536726911
-    num_examples: 11762
-  - name: test
-    num_bytes: 589804.9133333333
-    num_examples: 1462
   - name: dev
-    num_bytes: 585061.346
-    num_examples: 1461
-  download_size: 1413153
-  dataset_size: 5874822.796060244
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
-  - split: dev
-    path: data/dev-*
+    num_bytes: 600679
+    num_examples: 1500
+  - name: test
+    num_bytes: 605135
+    num_examples: 1500
+  - name: train
+    num_bytes: 4819833
+    num_examples: 12062
+  download_size: 1439625
+  dataset_size: 6025647
 ---
+
+
+## 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
+The paper is in progress.
+