Datasets:
Word
stringlengths 1
22
⌀ | Bigram
int64 0
1
| Conc.M
float64 1.04
5
| Conc.SD
float64 0
1.89
| Unknown
int64 0
620
| Total
int64 21
6.07k
| Percent_known
float64 0.85
1
| SUBTLEX
int64 0
2.13M
| Dom_Pos
stringclasses 18
values |
|---|---|---|---|---|---|---|---|---|
roadsweeper
| 0
| 4.85
| 0.37
| 1
| 27
| 0.96
| 0
|
0
|
traindriver
| 0
| 4.54
| 0.71
| 3
| 29
| 0.9
| 0
|
0
|
tush
| 0
| 4.45
| 1.01
| 3
| 25
| 0.88
| 66
|
0
|
hairdress
| 0
| 3.93
| 1.28
| 0
| 29
| 1
| 1
|
0
|
pharmaceutics
| 0
| 3.77
| 1.41
| 4
| 26
| 0.85
| 0
|
0
|
hoover
| 0
| 3.76
| 1.23
| 4
| 29
| 0.86
| 162
|
0
|
shopkeeping
| 0
| 3.18
| 1.19
| 1
| 29
| 0.97
| 0
|
0
|
pushiness
| 0
| 2.48
| 1.24
| 1
| 30
| 0.97
| 0
|
0
|
underdevelop
| 0
| 2.37
| 1.4
| 0
| 30
| 1
| 0
|
0
|
tirelessness
| 0
| 2.28
| 1.28
| 1
| 30
| 0.97
| 0
|
0
|
oldfashioned
| 0
| 2.26
| 1.02
| 0
| 27
| 1
| 0
|
0
|
wellmannered
| 0
| 2.25
| 1.14
| 2
| 30
| 0.93
| 0
|
0
|
dismissiveness
| 0
| 1.83
| 1
| 1
| 30
| 0.97
| 0
|
0
|
spitefulness
| 0
| 1.8
| 0.76
| 0
| 25
| 1
| 0
|
0
|
untruthfulness
| 0
| 1.73
| 0.92
| 4
| 30
| 0.87
| 0
|
0
|
dispiritedness
| 0
| 1.56
| 0.71
| 3
| 28
| 0.89
| 0
|
0
|
sled
| 0
| 5
| 0
| 0
| 28
| 1
| 149
|
Adjective
|
plunger
| 0
| 4.96
| 0.2
| 0
| 26
| 1
| 48
|
Adjective
|
human
| 0
| 4.93
| 0.26
| 0
| 28
| 1
| 6,363
|
Adjective
|
waterbed
| 0
| 4.93
| 0.27
| 0
| 27
| 1
| 27
|
Adjective
|
cymbal
| 0
| 4.92
| 0.28
| 1
| 25
| 0.96
| 13
|
Adjective
|
ginger
| 0
| 4.92
| 0.27
| 0
| 26
| 1
| 327
|
Adjective
|
bobsled
| 0
| 4.9
| 0.41
| 0
| 29
| 1
| 48
|
Adjective
|
cardboard
| 0
| 4.9
| 0.41
| 0
| 29
| 1
| 138
|
Adjective
|
olive
| 0
| 4.9
| 0.31
| 0
| 30
| 1
| 375
|
Adjective
|
dogsled
| 0
| 4.89
| 0.32
| 0
| 27
| 1
| 2
|
Adjective
|
rubber
| 0
| 4.86
| 0.74
| 0
| 29
| 1
| 714
|
Adjective
|
soybean
| 0
| 4.82
| 0.77
| 0
| 28
| 1
| 25
|
Adjective
|
tangerine
| 0
| 4.81
| 0.62
| 0
| 27
| 1
| 38
|
Adjective
|
headrest
| 0
| 4.8
| 0.76
| 0
| 30
| 1
| 6
|
Adjective
|
eucalyptus
| 0
| 4.77
| 0.57
| 0
| 30
| 1
| 25
|
Adjective
|
saltwater
| 0
| 4.77
| 0.82
| 0
| 30
| 1
| 31
|
Adjective
|
armrest
| 0
| 4.76
| 0.83
| 0
| 29
| 1
| 8
|
Adjective
|
paramedic
| 0
| 4.74
| 0.45
| 1
| 24
| 0.96
| 107
|
Adjective
|
liquid
| 0
| 4.72
| 0.54
| 0
| 25
| 1
| 395
|
Adjective
|
billfold
| 0
| 4.71
| 1
| 2
| 26
| 0.92
| 12
|
Adjective
|
canine
| 0
| 4.71
| 0.6
| 0
| 28
| 1
| 86
|
Adjective
|
flowerbed
| 0
| 4.71
| 0.6
| 0
| 28
| 1
| 6
|
Adjective
|
soy
| 0
| 4.7
| 0.6
| 0
| 30
| 1
| 118
|
Adjective
|
bald
| 0
| 4.69
| 0.47
| 0
| 26
| 1
| 496
|
Adjective
|
lilac
| 0
| 4.69
| 0.97
| 0
| 29
| 1
| 39
|
Adjective
|
hemorrhoid
| 0
| 4.68
| 0.72
| 1
| 29
| 0.97
| 18
|
Adjective
|
orange
| 0
| 4.66
| 0.9
| 0
| 29
| 1
| 1,138
|
Adjective
|
arachnid
| 0
| 4.65
| 0.69
| 3
| 29
| 0.9
| 13
|
Adjective
|
underarm
| 0
| 4.63
| 0.72
| 0
| 30
| 1
| 4
|
Adjective
|
barefoot
| 0
| 4.62
| 0.68
| 0
| 29
| 1
| 121
|
Adjective
|
bearded
| 0
| 4.62
| 0.7
| 1
| 27
| 0.96
| 65
|
Adjective
|
thyroid
| 0
| 4.61
| 0.74
| 0
| 28
| 1
| 28
|
Adjective
|
wooden
| 0
| 4.61
| 0.63
| 0
| 28
| 1
| 367
|
Adjective
|
sleeveless
| 0
| 4.6
| 0.5
| 0
| 25
| 1
| 5
|
Adjective
|
concrete
| 0
| 4.59
| 1.05
| 0
| 27
| 1
| 379
|
Adjective
|
panty
| 0
| 4.59
| 0.91
| 0
| 29
| 1
| 53
|
Adjective
|
pregnant
| 0
| 4.59
| 0.89
| 0
| 27
| 1
| 2,653
|
Adjective
|
helmeted
| 0
| 4.58
| 0.7
| 1
| 27
| 0.96
| 1
|
Adjective
|
hula
| 0
| 4.58
| 0.64
| 3
| 29
| 0.9
| 100
|
Adjective
|
female
| 0
| 4.57
| 0.88
| 0
| 28
| 1
| 1,612
|
Adjective
|
crematory
| 0
| 4.56
| 0.58
| 1
| 28
| 0.96
| 14
|
Adjective
|
pigtailed
| 0
| 4.55
| 0.78
| 0
| 29
| 1
| 2
|
Adjective
|
sphincter
| 0
| 4.54
| 0.99
| 1
| 27
| 0.96
| 57
|
Adjective
|
backrest
| 0
| 4.52
| 0.92
| 1
| 26
| 0.96
| 1
|
Adjective
|
blonde
| 0
| 4.52
| 1.09
| 0
| 29
| 1
| 710
|
Adjective
|
farmhand
| 0
| 4.52
| 1.01
| 0
| 27
| 1
| 3
|
Adjective
|
fat
| 0
| 4.52
| 0.85
| 0
| 27
| 1
| 4,051
|
Adjective
|
hairless
| 0
| 4.52
| 0.94
| 0
| 27
| 1
| 30
|
Adjective
|
afghan
| 0
| 4.5
| 0.92
| 0
| 28
| 1
| 29
|
Adjective
|
binocular
| 0
| 4.5
| 1.03
| 0
| 26
| 1
| 1
|
Adjective
|
naked
| 0
| 4.5
| 1
| 0
| 28
| 1
| 2,002
|
Adjective
|
hairy
| 0
| 4.48
| 0.98
| 0
| 27
| 1
| 322
|
Adjective
|
saline
| 0
| 4.48
| 0.94
| 0
| 27
| 1
| 175
|
Adjective
|
sudsy
| 0
| 4.48
| 1
| 3
| 28
| 0.89
| 3
|
Adjective
|
wet
| 0
| 4.46
| 0.58
| 0
| 28
| 1
| 2,000
|
Adjective
|
wooded
| 0
| 4.46
| 0.84
| 0
| 28
| 1
| 17
|
Adjective
|
male
| 0
| 4.45
| 1.02
| 0
| 29
| 1
| 1,731
|
Adjective
|
rusted
| 0
| 4.44
| 0.85
| 0
| 27
| 1
| 36
|
Adjective
|
aquamarine
| 0
| 4.43
| 0.92
| 0
| 28
| 1
| 23
|
Adjective
|
shirtless
| 0
| 4.43
| 0.97
| 0
| 30
| 1
| 14
|
Adjective
|
headless
| 0
| 4.42
| 0.95
| 0
| 26
| 1
| 67
|
Adjective
|
solid
| 0
| 4.42
| 0.81
| 0
| 26
| 1
| 998
|
Adjective
|
beaded
| 0
| 4.41
| 1.08
| 0
| 27
| 1
| 11
|
Adjective
|
blond
| 0
| 4.41
| 1.12
| 2
| 29
| 0.93
| 533
|
Adjective
|
soapy
| 0
| 4.41
| 0.93
| 0
| 27
| 1
| 25
|
Adjective
|
woodcutting
| 0
| 4.41
| 0.68
| 0
| 29
| 1
| 1
|
Adjective
|
prosthetic
| 0
| 4.4
| 1.04
| 0
| 25
| 1
| 34
|
Adjective
|
roast
| 0
| 4.4
| 0.97
| 0
| 30
| 1
| 499
|
Adjective
|
tartar
| 0
| 4.4
| 1.13
| 0
| 30
| 1
| 76
|
Adjective
|
newborn
| 0
| 4.39
| 1.03
| 0
| 28
| 1
| 128
|
Adjective
|
feline
| 0
| 4.38
| 0.98
| 1
| 30
| 0.97
| 46
|
Adjective
|
undercooked
| 0
| 4.38
| 0.71
| 0
| 24
| 1
| 9
|
Adjective
|
vertebrate
| 0
| 4.38
| 0.9
| 1
| 27
| 0.96
| 7
|
Adjective
|
bloodstained
| 0
| 4.37
| 1.18
| 0
| 27
| 1
| 16
|
Adjective
|
flatbed
| 0
| 4.37
| 0.97
| 0
| 27
| 1
| 21
|
Adjective
|
joint
| 0
| 4.37
| 1.08
| 0
| 27
| 1
| 1,405
|
Adjective
|
preschool
| 0
| 4.37
| 1.04
| 0
| 27
| 1
| 61
|
Adjective
|
beardless
| 0
| 4.36
| 0.91
| 0
| 28
| 1
| 8
|
Adjective
|
indigo
| 0
| 4.36
| 0.99
| 0
| 28
| 1
| 16
|
Adjective
|
frozen
| 0
| 4.34
| 1.2
| 0
| 29
| 1
| 782
|
Adjective
|
barefooted
| 0
| 4.33
| 1.3
| 0
| 27
| 1
| 9
|
Adjective
|
hashish
| 0
| 4.33
| 1.33
| 2
| 29
| 0.93
| 23
|
Adjective
|
jeweled
| 0
| 4.33
| 0.78
| 0
| 27
| 1
| 15
|
Adjective
|
mummified
| 0
| 4.33
| 0.62
| 0
| 27
| 1
| 14
|
Adjective
|
End of preview. Expand
in Data Studio
English Words Concreteness Rating
This dataset is provided by the research "Concreteness ratings for 40 thousand generally known English word lemmas" of Brysbaert et al. (2014). The original dataset can be found here.
Usage
This dataset is ideal for training and evaluating machine learning models for English word concreteness.
Acknowledgments
We extend our heartfelt gratitude to all the authors of the original dataset.
License
This dataset is made available under the MIT license.
- Downloads last month
- 58