id
int32
raw_frequency
float64
relative_frequency
float64
cefr_level
string
source
string
marker
string
lemma
string
pos
string
examples
string
1
null
null
A1
manual
andra
numeral
2
null
null
A1
manual
arton
numeral
3
null
null
A1
manual
bakre
adjective
4
null
null
A1
manual
en
brunch
noun-en
5
null
null
A1
manual
en
decimeter
noun-en
6
null
null
A1
manual
elva
numeral
7
null
null
A1
manual
ett
numeral
8
416
null
A1
manual
Europa
proper name
9
null
null
A1
manual
fem
numeral
10
null
null
A1
manual
femte
numeral
11
null
null
A1
manual
femtio
numeral
12
null
null
A1
manual
femton
numeral
13
null
null
A1
manual
fjorton
numeral
14
null
null
A1
manual
fjärde
numeral
15
null
null
A1
manual
främre
adjective
16
null
null
A1
manual
fyra
numeral
17
null
null
A1
manual
fyrtio
numeral
18
null
null
A1
manual
första
numeral
19
null
null
A1
manual
Grekland
proper name
20
null
null
A1
manual
Göteborg
proper name
21
null
null
A1
manual
ett
hekto
noun-ett
22
null
null
A1
manual
en
hela
noun-en
23
null
null
A1
manual
hundra
numeral
24
null
null
A1
manual
hundratusen
numeral
25
null
null
A1
manual
hörsel
noun-en
26
null
null
A1
manual
en
inrikesminister
noun-en
27
null
null
A1
manual
inrikespolitik
noun-en
28
null
null
A1
manual
Italien
proper name
29
null
null
A1
manual
Kina
proper name
30
null
null
A1
manual
ett
kvällsmål
noun-ett
31
null
null
A1
manual
känsel
noun-en
32
null
null
A1
manual
ett
mellanmål
noun-ett
33
null
null
A1
manual
miljard
numeral
34
null
null
A1
manual
miljon
numeral
35
null
null
A1
manual
ett
milligram
noun-ett
36
null
null
A1
manual
en
millimeter
noun-en
37
null
null
A1
manual
en
morbror
noun-en
38
null
null
A1
manual
ett
månadsskifte
noun-ett
39
null
null
A1
manual
en
nia
noun-en
40
null
null
A1
manual
nio
numeral
41
null
null
A1
manual
nionde
numeral
42
null
null
A1
manual
nittio
numeral
43
null
null
A1
manual
nitton
numeral
44
null
null
A1
manual
nordost
noun
45
null
null
A1
manual
Norge
proper name
46
null
null
A1
manual
pingst
noun-en
47
null
null
A1
manual
Polen
proper name
48
null
null
A1
manual
Ryssland
proper name
49
null
null
A1
manual
sex
numeral
50
null
null
A1
manual
en
sexa
noun-en
51
null
null
A1
manual
sextio
numeral
52
null
null
A1
manual
sexton
numeral
53
null
null
A1
manual
sju
numeral
54
null
null
A1
manual
en
sjua
noun-en
55
null
null
A1
manual
sjunde
numeral
56
null
null
A1
manual
sjuttio
numeral
57
null
null
A1
manual
sjutton
numeral
58
null
null
A1
manual
sjätte
numeral
59
541
null
A1
SweWaC
Stockholm
proper name
60
null
null
A1
manual
en
storasyster
noun-en
61
null
null
A1
manual
Storbritannien
proper name
62
null
null
A1
manual
syd
noun
63
null
null
A1
manual
sydväst
noun
64
null
null
A1
manual
sydöst
noun
65
null
null
A1
manual
en
syssling
noun-en
66
null
null
A1
manual
en
tia
noun-en
67
null
null
A1
manual
tio
numeral
68
null
null
A1
manual
tionde
numeral
69
null
null
A1
manual
tiotusen
numeral
70
null
null
A1
manual
tjugo
numeral
71
null
null
A1
manual
tolv
numeral
72
null
null
A1
manual
tre
numeral
73
null
null
A1
manual
tredje
numeral
74
null
null
A1
manual
trettio
numeral
75
null
null
A1
manual
tretton
numeral
76
null
null
A1
manual
tusen
numeral
77
null
null
A1
manual
två
numeral
78
null
null
A1
manual
en
utbildningsminister
noun-en
79
null
null
A1
manual
en
veckodag
noun-en
80
null
null
A1
manual
en
åtta
noun-en
81
null
null
A1
manual
åtta
numeral
82
null
null
A1
manual
åttio
numeral
83
null
null
A1
manual
åttonde
numeral
84
null
null
A1
manual
en
änkeman
noun-en
85
null
null
A1
manual
en
änkling
noun-en
86
null
null
A1
manual
ett
ögonlock
noun-ett
87
2,966,316
26,019.68
A1
SweWaC
och (vardagl. å; förk. o.)
conj
88
2,624,032
23,017.26
A1
SweWaC
att
vara (vardagl. va)
verb
e.g. var så god!
89
2,197,163
19,272.89
A1
SweWaC
i
prep
90
1,822,142
15,983.31
A1
SweWaC
att
ha
verb
91
1,816,701
15,935.58
A1
SweWaC
dess
pronoun
92
1,662,413
14,582.21
A1
SweWaC
det
pronoun
93
1,654,320
14,511.22
A1
SweWaC
en
det
94
1,451,792
12,734.7
A1
SweWaC
som
pronoun
95
1,435,426
12,591.15
A1
SweWaC
prep
96
1,420,786
12,462.73
A1
SweWaC
å
prep
e.g. å ena sidan…å andra sidan
97
1,315,607
11,540.13
A1
SweWaC
av
prep
98
1,300,637
11,408.82
A1
SweWaC
för
prep
99
1,284,991
11,271.57
A1
SweWaC
att
subj
100
1,267,606
11,119.08
A1
SweWaC
att
kunna
verb

Dataset Card for Kelly

Keywords for Language Learning for Young and adults alike

Dataset Summary

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.

Languages

Swedish (sv-SE)

Dataset Structure

Data Instances

Here is a sample of the data:

{
    'id': 190,
    'raw_frequency': 117835.0,
    'relative_frequency': 1033.61,
    'cefr_level': 'A1',
    'source': 'SweWaC',
    'marker': 'en',
    'lemma': 'dag',
    'pos': 'noun-en',
    'examples': 'e.g. god dag'
}

This can be understood as:

The common noun "dag" ("day") has a rank of 190 in the list. It was used 117,835 times in SweWaC, meaning it occured 1033.61 times per million words. This word is among the most important vocabulary words for Swedish language learners and should be learned at the A1 CEFR level. An example usage of this word is the phrase "god dag" ("good day").

Data Fields

  • id: The row number for the data entry, starting at 1. Generally corresponds to the rank of the word.
  • raw_frequency: The raw frequency of the word.
  • relative_frequency: The relative frequency of the word measured in number of occurences per million words.
  • cefr_level: The CEFR level (A1, A2, B1, B2, C1, C2) of the word.
  • source: Whether the word came from SweWaC, translation lists (T2), or was manually added (manual).
  • marker: The grammatical marker of the word, if any, such as an article or infinitive marker.
  • lemma: The lemma of the word, sometimes provided with its spelling or stylistic variants.
  • pos: The word's part-of-speech.
  • examples: Usage examples and comments. Only available for some of the words.

Manual entries were prepended to the list, giving them a higher rank than they might otherwise have had. For example, the manual entry "Göteborg ("Gothenberg") has a rank of 20, while the first non-manual entry "och" ("and") has a rank of 87. However, a conjunction and common stopword is far more likely to occur than the name of a city.

Data Splits

There is a single split, train.

Dataset Creation

Please refer to the article Corpus-based approaches for the creation of a frequency based vocabulary list in the EU project KELLY – issues on reliability, validity and coverage for information about how the original dataset was created and considerations for using the data.

The following changes have been made to the original dataset:

  • Changed header names.
  • Normalized the large web-acquired corpus name to "SweWac" in the source field.
  • Set the relative frequency of manual entries to null rather than 1000000.

Additional Information

Licensing Information

CC BY 4.0

Citation Information

Please cite the authors if you use this dataset in your work:

@article{Kilgarriff2013,
  doi = {10.1007/s10579-013-9251-2},
  url = {https://doi.org/10.1007/s10579-013-9251-2},
  year = {2013},
  month = sep,
  publisher = {Springer Science and Business Media {LLC}},
  volume = {48},
  number = {1},
  pages = {121--163},
  author = {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},
  title = {Corpus-based vocabulary lists for language learners for nine languages},
  journal = {Language Resources and Evaluation}
}

Contributions

Thanks to @spraakbanken for creating this dataset and to @codesue for adding it.

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