Datasets:
Suzen Fylke
commited on
Commit
•
2137d4b
1
Parent(s):
d1b828f
Allow null frequency values and normalize SweWac source name
Browse files- README.md +22 -14
- dataset_infos.json +1 -0
- kelly.py +8 -8
- sv.csv +89 -89
README.md
CHANGED
@@ -36,6 +36,7 @@ Keywords for Language Learning for Young and adults alike
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Additional Information](#additional-information)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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The Swedish Kelly list is a freely available frequency-based vocabulary list
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that comprises general-purpose language of modern Swedish. The list was
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generated from a large web-acquired corpus (
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dating from the 2010s. It is adapted to the needs of language learners and
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-
contains 8,425 most frequent lemmas that cover 80% of
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-
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Please refer to the article [Corpus-based approaches for the creation of a frequency
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based vocabulary list in the EU project KELLY – issues on reliability, validity and
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coverage](https://gup.ub.gu.se/publication/148533?lang=en) for information about how
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the dataset was created.
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### Languages
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```python
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{
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-
'id':
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'raw_frequency': 117835.0,
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'relative_frequency': 1033.61,
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'cefr_level': 'A1',
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- `id`: The row number for the data entry, starting at 1. Generally corresponds
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to the rank of the word.
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-
- `raw_frequency`: The raw frequency of the word
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- `relative_frequency`: The relative frequency of the word measured in
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number of occurences per million words
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- `cefr_level`: The CEFR level (A1, A2, B1, B2, C1, C2) of the word.
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-
- `source`: Whether the word came from
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was manually added (manual).
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- `marker`: The grammatical marker of the word, if any, such as an article or
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infinitive marker.
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87. However, a conjunction and common stopword is far more likely to occur than
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the name of a city.
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Additionally, manually entries hav a recoreded relative frequency of `1000000`.
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### Data Splits
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There is a single split, `train`.
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## Additional Information
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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+
- [Dataset Creation](#dataset-creation)
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- [Additional Information](#additional-information)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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The Swedish Kelly list is a freely available frequency-based vocabulary list
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that comprises general-purpose language of modern Swedish. The list was
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+
generated from a large web-acquired corpus (SweWaC) of 114 million words
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dating from the 2010s. It is adapted to the needs of language learners and
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contains 8,425 most frequent lemmas that cover 80% of SweWaC.
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### Languages
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```python
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{
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'id': 190,
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'raw_frequency': 117835.0,
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'relative_frequency': 1033.61,
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'cefr_level': 'A1',
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- `id`: The row number for the data entry, starting at 1. Generally corresponds
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to the rank of the word.
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+
- `raw_frequency`: The raw frequency of the word.
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- `relative_frequency`: The relative frequency of the word measured in
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+
number of occurences per million words.
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- `cefr_level`: The CEFR level (A1, A2, B1, B2, C1, C2) of the word.
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- `source`: Whether the word came from SweWaC, translation lists (T2), or
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was manually added (manual).
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- `marker`: The grammatical marker of the word, if any, such as an article or
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infinitive marker.
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87. However, a conjunction and common stopword is far more likely to occur than
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the name of a city.
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### Data Splits
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There is a single split, `train`.
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## Dataset Creation
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+
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Please refer to the article [Corpus-based approaches for the creation of a frequency
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+
based vocabulary list in the EU project KELLY – issues on reliability, validity and
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+
coverage](https://gup.ub.gu.se/publication/148533?lang=en) for information about how
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the original dataset was created and considerations for using the data.
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**The following changes have been made to the original dataset**:
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- Changed header names.
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- Normalized the large web-acquired corpus name to "SweWac" in the `source` field.
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- Set the relative frequency of manual entries to null rather than 1000000.
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## Additional Information
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dataset_infos.json
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{"default": {"description": "The Swedish Kelly list is a freely available frequency-based vocabulary list that comprises general-purpose language of modern Swedish. The list was generated from a large web-acquired corpus (SweWaC) of 114 million words dating from the 2010s. It is adapted to the needs of language learners and contains 8,425 most frequent lemmas that cover 80% of SweWaC.", "citation": "@article{Kilgarriff2013,\ndoi = {10.1007/s10579-013-9251-2},\nurl = {https://doi.org/10.1007/s10579-013-9251-2},\nyear = {2013},\nmonth = sep,\npublisher = {Springer Science and Business Media {LLC}},\nvolume = {48},\nnumber = {1},\npages = {121--163},\nauthor = {Adam Kilgarriff and Frieda Charalabopoulou and Maria Gavrilidou and Janne Bondi Johannessen and Saussan Khalil and Sofie Johansson Kokkinakis and Robert Lew and Serge Sharoff and Ravikiran Vadlapudi and Elena Volodina},\ntitle = {Corpus-based vocabulary lists for language learners for nine languages},\njournal = {Language Resources and Evaluation}\n}\n", "homepage": "https://spraakbanken.gu.se/en/resources/kelly", "license": "CC BY 4.0", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "raw_frequency": {"dtype": "float64", "id": null, "_type": "Value"}, "relative_frequency": {"dtype": "float64", "id": null, "_type": "Value"}, "cefr_level": {"dtype": "string", "id": null, "_type": "Value"}, "source": {"dtype": "string", "id": null, "_type": "Value"}, "marker": {"dtype": "string", "id": null, "_type": "Value"}, "lemma": {"dtype": "string", "id": null, "_type": "Value"}, "pos": {"dtype": "string", "id": null, "_type": "Value"}, "examples": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "kelly", "config_name": "default", "version": {"version_str": "1.0.1", "description": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 568998, "num_examples": 8425, "dataset_name": "kelly"}}, "download_checksums": {"sv.csv": {"num_bytes": 377409, "checksum": "92a8b97affa5b36031c0854729ccbbeda3065d994d3f0e058c4b343bf2c6611c"}}, "download_size": 377409, "post_processing_size": null, "dataset_size": 568998, "size_in_bytes": 946407}}
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kelly.py
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@@ -25,9 +25,9 @@ journal = {Language Resources and Evaluation}
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_DESCRIPTION = """\
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The Swedish Kelly list is a freely available frequency-based vocabulary list \
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that comprises general-purpose language of modern Swedish. The list was \
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-
generated from a large web-acquired corpus (
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dating from the 2010s. It is adapted to the needs of language learners \
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-
and contains 8,425 most frequent lemmas that cover 80% of
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"""
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_HOMEPAGE = "https://spraakbanken.gu.se/en/resources/kelly"
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class Kelly(datasets.GeneratorBasedBuilder):
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"""Kelly: Keywords for Language Learning for Young and adults alike"""
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VERSION = datasets.Version("1.0.
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def _info(self):
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features = datasets.Features(
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{
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"id": datasets.Value("
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"raw_frequency": datasets.Value("float64"),
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"relative_frequency": datasets.Value("float64"),
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"cefr_level": datasets.Value("string"),
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"source": datasets.Value("string"),
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"marker": datasets.Value("string"),
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"lemma": datasets.Value("string"),
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-
"
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"examples": datasets.Value("string"),
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}
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)
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a, b, c, d, e, f, g, h, i = row
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yield key, {
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"id": a,
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"raw_frequency": b or "
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"relative_frequency": c or "
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"cefr_level": d,
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"source": e,
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"marker": f,
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"lemma": g,
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"
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"examples": i,
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}
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_DESCRIPTION = """\
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The Swedish Kelly list is a freely available frequency-based vocabulary list \
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that comprises general-purpose language of modern Swedish. The list was \
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+
generated from a large web-acquired corpus (SweWaC) of 114 million words \
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dating from the 2010s. It is adapted to the needs of language learners \
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+
and contains 8,425 most frequent lemmas that cover 80% of SweWaC.\
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"""
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_HOMEPAGE = "https://spraakbanken.gu.se/en/resources/kelly"
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class Kelly(datasets.GeneratorBasedBuilder):
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"""Kelly: Keywords for Language Learning for Young and adults alike"""
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VERSION = datasets.Version("1.0.1")
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def _info(self):
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features = datasets.Features(
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{
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"id": datasets.Value("int32"),
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"raw_frequency": datasets.Value("float64"),
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"relative_frequency": datasets.Value("float64"),
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"cefr_level": datasets.Value("string"),
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"source": datasets.Value("string"),
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"marker": datasets.Value("string"),
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"lemma": datasets.Value("string"),
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"pos": datasets.Value("string"),
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"examples": datasets.Value("string"),
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}
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)
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a, b, c, d, e, f, g, h, i = row
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yield key, {
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"id": a,
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"raw_frequency": b or "NaN",
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"relative_frequency": c or "NaN",
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"cefr_level": d,
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"source": e,
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"marker": f,
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"lemma": g,
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"pos": h,
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"examples": i,
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}
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sv.csv
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87,2966316,26019.68,A1,SweWaC,,och (vardagl. å; förk. o.),conj,
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88,2624032,23017.26,A1,SweWaC,att,vara (vardagl. va),verb,e.g. var så god!
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89,2197163,19272.89,A1,SweWaC,,i,prep,
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2224,3775,33.11,A2,SweWaC,en,finansiering,noun-en,
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2225,3775,33.11,A2,SweWaC,ett,löfte,noun-ett,
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2226,3775,33.11,A2,SweWaC,att,mörda,verb,
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2227,,33.10,A2,
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2228,3772,33.09,A2,SweWaC,en,amerikan,noun-en,
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2229,3772,33.09,A2,SweWaC,en,rit,noun-en,
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2230,3771,33.08,A2,SweWaC,,status,noun-en,
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6018,722,6.33,C1,SweWaC,,invandringspolitik,noun-en,
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6019,722,6.33,C1,SweWaC,att,utbrista,verb,
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6020,,6.33,C1,T2,,aids,noun,
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6021,,6.33,C1,
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6022,721,6.32,C1,SweWaC,en,utövare,noun-en,
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6023,,6.32,C1,T2,att,angå,verb,
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6024,720,6.32,C1,SweWaC,en,förlossning,noun-en,
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ID,Raw Frequency,Relative Frequency,CEFR Level,Source,Marker,Lemma,POS,Examples
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1,,,A1,manual,,andra,numeral,
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2,,,A1,manual,,arton,numeral,
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3,,,A1,manual,,bakre,adjective,
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4,,,A1,manual,en,brunch,noun-en,
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5,,,A1,manual,en,decimeter,noun-en,
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6,,,A1,manual,,elva,numeral,
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7,,,A1,manual,,ett,numeral,
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8,416,,A1,manual,,Europa,proper name,
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9,,,A1,manual,,fem,numeral,
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10,,,A1,manual,,femte,numeral,
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11,,,A1,manual,,femtio,numeral,
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12,,,A1,manual,,femton,numeral,
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13,,,A1,manual,,fjorton,numeral,
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14,,,A1,manual,,fjärde,numeral,
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15,,,A1,manual,,främre,adjective,
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16,,,A1,manual,,fyra,numeral,
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17,,,A1,manual,,fyrtio,numeral,
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18,,,A1,manual,,första,numeral,
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19,,,A1,manual,,Grekland,proper name,
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20,,,A1,manual,,Göteborg,proper name,
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21,,,A1,manual,ett,hekto,noun-ett,
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22,,,A1,manual,en,hela,noun-en,
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23,,,A1,manual,,hundra,numeral,
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24,,,A1,manual,,hundratusen,numeral,
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25,,,A1,manual,,hörsel,noun-en,
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26,,,A1,manual,en,inrikesminister,noun-en,
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27,,,A1,manual,,inrikespolitik,noun-en,
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28,,,A1,manual,,Italien,proper name,
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29,,,A1,manual,,Kina,proper name,
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30,,,A1,manual,ett,kvällsmål,noun-ett,
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31,,,A1,manual,,känsel,noun-en,
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32,,,A1,manual,ett,mellanmål,noun-ett,
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33,,,A1,manual,,miljard,numeral,
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34,,,A1,manual,,miljon,numeral,
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35,,,A1,manual,ett,milligram,noun-ett,
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36,,,A1,manual,en,millimeter,noun-en,
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37,,,A1,manual,en,morbror,noun-en,
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38,,,A1,manual,ett,månadsskifte,noun-ett,
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39,,,A1,manual,en,nia,noun-en,
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40,,,A1,manual,,nio,numeral,
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41,,,A1,manual,,nionde,numeral,
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42,,,A1,manual,,nittio,numeral,
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43,,,A1,manual,,nitton,numeral,
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44,,,A1,manual,,nordost,noun,
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45,,,A1,manual,,Norge,proper name,
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46,,,A1,manual,,pingst,noun-en,
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47,,,A1,manual,,Polen,proper name,
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48,,,A1,manual,,Ryssland,proper name,
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49,,,A1,manual,,sex,numeral,
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50,,,A1,manual,en,sexa,noun-en,
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51,,,A1,manual,,sextio,numeral,
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52,,,A1,manual,,sexton,numeral,
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53,,,A1,manual,,sju,numeral,
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54,,,A1,manual,en,sjua,noun-en,
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55,,,A1,manual,,sjunde,numeral,
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56,,,A1,manual,,sjuttio,numeral,
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57,,,A1,manual,,sjutton,numeral,
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59 |
+
58,,,A1,manual,,sjätte,numeral,
|
60 |
+
59,541,,A1,SweWaC,,Stockholm,proper name,
|
61 |
+
60,,,A1,manual,en,storasyster,noun-en,
|
62 |
+
61,,,A1,manual,,Storbritannien,proper name,
|
63 |
+
62,,,A1,manual,,syd,noun,
|
64 |
+
63,,,A1,manual,,sydväst,noun,
|
65 |
+
64,,,A1,manual,,sydöst,noun,
|
66 |
+
65,,,A1,manual,en,syssling,noun-en,
|
67 |
+
66,,,A1,manual,en,tia,noun-en,
|
68 |
+
67,,,A1,manual,,tio,numeral,
|
69 |
+
68,,,A1,manual,,tionde,numeral,
|
70 |
+
69,,,A1,manual,,tiotusen,numeral,
|
71 |
+
70,,,A1,manual,,tjugo,numeral,
|
72 |
+
71,,,A1,manual,,tolv,numeral,
|
73 |
+
72,,,A1,manual,,tre,numeral,
|
74 |
+
73,,,A1,manual,,tredje,numeral,
|
75 |
+
74,,,A1,manual,,trettio,numeral,
|
76 |
+
75,,,A1,manual,,tretton,numeral,
|
77 |
+
76,,,A1,manual,,tusen,numeral,
|
78 |
+
77,,,A1,manual,,två,numeral,
|
79 |
+
78,,,A1,manual,en,utbildningsminister,noun-en,
|
80 |
+
79,,,A1,manual,en,veckodag,noun-en,
|
81 |
+
80,,,A1,manual,en,åtta,noun-en,
|
82 |
+
81,,,A1,manual,,åtta,numeral,
|
83 |
+
82,,,A1,manual,,åttio,numeral,
|
84 |
+
83,,,A1,manual,,åttonde,numeral,
|
85 |
+
84,,,A1,manual,en,änkeman,noun-en,
|
86 |
+
85,,,A1,manual,en,änkling,noun-en,
|
87 |
+
86,,,A1,manual,ett,ögonlock,noun-ett,
|
88 |
87,2966316,26019.68,A1,SweWaC,,och (vardagl. å; förk. o.),conj,
|
89 |
88,2624032,23017.26,A1,SweWaC,att,vara (vardagl. va),verb,e.g. var så god!
|
90 |
89,2197163,19272.89,A1,SweWaC,,i,prep,
|
|
|
2225 |
2224,3775,33.11,A2,SweWaC,en,finansiering,noun-en,
|
2226 |
2225,3775,33.11,A2,SweWaC,ett,löfte,noun-ett,
|
2227 |
2226,3775,33.11,A2,SweWaC,att,mörda,verb,
|
2228 |
+
2227,,33.10,A2,SweWaC,,anställd,adjective,
|
2229 |
2228,3772,33.09,A2,SweWaC,en,amerikan,noun-en,
|
2230 |
2229,3772,33.09,A2,SweWaC,en,rit,noun-en,
|
2231 |
2230,3771,33.08,A2,SweWaC,,status,noun-en,
|
|
|
6019 |
6018,722,6.33,C1,SweWaC,,invandringspolitik,noun-en,
|
6020 |
6019,722,6.33,C1,SweWaC,att,utbrista,verb,
|
6021 |
6020,,6.33,C1,T2,,aids,noun,
|
6022 |
+
6021,,6.33,C1,SweWaC,att,bekanta,verb,e.g. bekanta sig
|
6023 |
6022,721,6.32,C1,SweWaC,en,utövare,noun-en,
|
6024 |
6023,,6.32,C1,T2,att,angå,verb,
|
6025 |
6024,720,6.32,C1,SweWaC,en,förlossning,noun-en,
|