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 |
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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.
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