Datasets:
word
stringlengths 1
2.4k
⌀ | top_level_domain
stringclasses 8
values | freq
float64 0.43
13.5M
| log_prob
float64 -21.75
-4.49
| log_prob_smoothed
float64 -20.56
-4.5
|
|---|---|---|---|---|
Social Media | 1,721,348.74734 | -6.550506 | -6.561969 |
|
Social Media | 1,643.462516 | -13.504564 | -13.515419 |
|
Social Media | 1.14807 | -20.771043 | -20.156018 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 21.239299 | -17.853272 | -17.818727 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 1.14807 | -20.771043 | -20.156018 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 12,748.745726 | -11.455937 | -11.467322 |
|
Social Media | 4,856.337018 | -12.421085 | -12.432343 |
|
Social Media | 1.14807 | -20.771043 | -20.156018 |
|
Social Media | 52.81123 | -16.942401 | -16.935106 |
|
Social Media | 127.435794 | -16.061512 | -16.065159 |
|
Social Media | 4.018246 | -19.51828 | -19.307508 |
|
Social Media | 2.29614 | -20.077895 | -19.727836 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 2.29614 | -20.077895 | -19.727836 |
|
Social Media | 20.665264 | -17.880671 | -17.844878 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 6.888421 | -18.979283 | -18.855192 |
|
Social Media | 2.870176 | -19.854752 | -19.567288 |
|
Social Media | 1.14807 | -20.771043 | -20.156018 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 2.29614 | -20.077895 | -19.727836 |
|
Social Media | 1.14807 | -20.771043 | -20.156018 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 6.314386 | -19.066294 | -18.930745 |
|
Social Media | 2.870176 | -19.854752 | -19.567288 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 1.14807 | -20.771043 | -20.156018 |
|
Social Media | 1.14807 | -20.771043 | -20.156018 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 70.032283 | -16.660169 | -16.657454 |
|
Social Media | 30.997896 | -17.475206 | -17.454918 |
|
Social Media | 18.369123 | -17.998454 | -17.956908 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 1.14807 | -20.771043 | -20.156018 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 7.462456 | -18.89924 | -18.784949 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 2.29614 | -20.077895 | -19.727836 |
|
Social Media | 5.740351 | -19.161605 | -19.012476 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 5.740351 | -19.161605 | -19.012476 |
|
Social Media | 1.14807 | -20.771043 | -20.156018 |
|
Social Media | 2.870176 | -19.854752 | -19.567288 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 1.722105 | -20.365577 | -19.919183 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 2.29614 | -20.077895 | -19.727836 |
|
Social Media | 1.722105 | -20.365577 | -19.919183 |
|
Social Media | 2.29614 | -20.077895 | -19.727836 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 1.14807 | -20.771043 | -20.156018 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 0.574035 | -21.46419 | -20.466946 |
|
Social Media | 5,776.515297 | -12.247569 | -12.258859 |
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Dataset Card for DAGW Word Frequencies (normalized)
- Paper: Derczynski, L., Ciosici, M. R., Baglini, R., Christiansen, M. H., Dalsgaard, J. A., Fusaroli, R., ... & Varab, D. (2021). The Danish Gigaword Corpus. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa) (pp. 413-421).
- Point of Contact: Kenneth Enevoldsen (Kennethcenevoldsen (at) gmail (dot) com )
This is a list of word frequencies derived from the Danish Gigaword (collected before 2022-22-01).
These word frequencies are derived from tokens from the Danish Gigaword Corpus, which have been tokenized using
the spacy pipeline for Danish "da_core_news_lg" using spacy>=3.0.0,<3.4.0.
See the notebook "convert_to_hf_dataset.ipynb" and wordfreq.py for more information about how it was created.
Dataset formats
This dataset have been created in four formats:
chcaa/dagw-word-frequencies: Danish word frequencies from Danish Gigaword.chcaa/dagw-word-frequencies-by-domain: word frequencies pr. domain.chcaa/dagw-word-frequencies-by-domain-with-pos-tags: word frequencies pr. domain with their part-of-speech tags derived from the spacy pipeline for Danish"da_core_news_lg".chcaa/dagw-word-frequencies-normalized-by-domain: word frequencies pr. domain normalized by the top-level domain.
Dataset Creation
Curation Rationale
Word frequencies of large domains har often used to calculate metrics such as text entropy or word surprise. Word frequencies can also be used to to create stopword lists and similar.
Source Data
The frequencies are derived from the Danish Gigaword. To read more about the Danish Gigaword and its content please check out the entry on Danish language resources, which also links to latest publications.
Discussion of Biases
This dataset contains notably different distributions of the original domains; for instance, the legal domain is highly overrepresented within this corpus. Please see the normalized version of this dataset if you wish to see word frequencies which normalized across domains.
Other Known Limitations
The news data within this corpus ("danavis") have altered the text, this is described further in the original version (v1) of paper.
Licensing Information
Below follows a brief overview of the sources in the corpus along with their individual license.
| Source | License |
|---|---|
| adl | Creative Commons Legal Code 1.0 Universal |
| botxt | Creative Commons Legal Code 1.0 Universal |
| cc | Creative Commons Legal Code 1.0 Universal |
| danavis | Creative Commons Legal Code 1.0 Universal |
| dannet | dannet license |
| depbank | Attribution-ShareAlike 4.0 International |
| ep | Creative Commons Legal Code 1.0 Universal |
| ft | Creative Commons Legal Code 1.0 Universal |
| gutenberg | gutenberg license |
| hest | Creative Commons Legal Code 1.0 Universal |
| jvj | Attribution-ShareAlike 4.0 International |
| naat | Creative Commons Legal Code 1.0 Universal |
| opensub | The data set comes with the same license as the original sources. Please, check the information about the source that is given on http://opus.nlpl.eu/OpenSubtitles-v2018.php |
| relig | Creative Commons Legal Code 1.0 Universal |
| retsinformationdk | Danish Copyright law at https://www.retsinformation.dk/forms/r0710.aspx?id=164796 states "§ 9. Love, administrative forskrifter, retsafgørelser og lignende offentlige aktstykker er ikke genstand for ophavsret. Stk. 2. Bestemmelsen i stk. 1 gælder ikke for værker, der fremtræder som selvstændige bidrag i de i stk. 1 nævnte aktstykker. Sådanne værker må dog gengives i forbindelse med aktstykket. Retten til videre udnyttelse afhænger af de i øvrigt gældende regler." |
| retspraksis | Creative Commons Legal Code 1.0 Universal |
| skat | Creative Commons Legal Code 1.0 Universal |
| spont | Creative Commons Legal Code 1.0 Universal |
| synne | Creative Commons Legal Code 1.0 Universal |
| tv2r | The owner of this content is TV2 Regionerne, Denmark. Creative Commons Attribution 4.0 International |
| wiki | Creative Commons Legal Code 1.0 Universal |
| wikibooks | Creative Commons Legal Code 1.0 Universal |
| wikisource | Creative Commons Legal Code 1.0 Universal |
Contributions
Thanks to @KennethEnevoldsen for adding this dataset.
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