Datasets:

StephanAkkerman
/

imageability-corpus

word stringlengths 1 14	visual float64 0 1	phonetic float64 0 1	textual float64 0 1
a	0.193237	0.693046	0.302083
abandoned	0.652174	0.422062	0.682292
abbey	0.415459	0.736211	0.757813
abide	0.415459	0.28777	0.169271
about	0.227053	0	0.789063
abstract	0.664268	0.722682	0.43359
academy	0.376812	0.422062	0.572917
across	0.497585	0.2494	0.492188
action	0.318841	0.510791	0.309896
activism	0.425121	0.616307	0.476563
activities	0.362319	0.570743	0.481771
activity	0.396135	0.628297	0.231771
actually	0.091787	0.597122	0.15625
ad	0.449275	0.784173	0.572917
adam	0.449275	0.803357	0.71875
add	0.507246	0.784173	0.247396
added	0.251208	0.565947	0.416667
address	0.603865	0.736211	0.664063
adobe	0.516908	0.422062	0.825521
adventure	0.589372	0.467626	0.674479
advertising	0.47343	0.563549	0.627604
aerial	0.903745	0.135831	0.627401
affairs	0.661836	0.386091	0.552083
africa	0.57971	0.724221	0.588542
african	0.338164	0.640288	0.46875
after	0.26087	0.179856	0.434896
ago	0.101449	0.273381	0.265625
agriculture	0.492754	0.604317	0.648438
air	0.352657	0.592326	0.559896
aircraft	0.768116	0.635492	0.682292
airplane	0.811594	0.82494	0.940104
airplanes	0.772947	0.724221	0.893229
airport	0.483092	0.983213	0.570313
al	0.371981	0.800959	0.617188
alan	0.381643	0.755396	0.630208
albert	0.386473	0.609113	0.820313
album	0.555556	0.726619	0.658854
alex	0.386473	0.741007	0.614583
alexander	0.449275	0.577938	0.75
alice	0.444444	0.760192	0.570313
all	0.231884	0.827338	0.526042
allowed	0.36715	0.122302	0.148438
along	0.753623	0.136691	0.653646
alpha	0.587115	0.518718	0.46684
already	0.227053	0.395683	0.085938
also	0.231884	0.139089	0.309896
alumni	0.613527	0.577938	0.692708
always	0.458937	0.206235	0.830729
am	0.391304	0.556355	0.632813
amazing	0.231884	0.239808	0.502604
ambassador	0.657005	0.808153	0.627604
america	0.381643	0.417266	0.601563
american	0.541063	0.458034	0.5625
among	0.285024	0.18705	0.388021
amsterdam	0.454106	0.556355	0.757813
amy	0.415459	0.505995	0.721354
an	0.202899	0.498801	0.908854
ana	0.429952	0.760192	0.552083
analogue	0.429952	0.820144	0.369792
and	0.391304	0.776978	0.815104
andrea	0.42029	0.489209	0.726563
andrew	0.36715	0.733813	0.710938
andy	0.357488	0.53717	0.84375
angel	0.246377	0.729017	0.796875
angeles	0.400966	0.642686	0.763021
angels	0.700483	0.757794	0.903646
animals	0.676329	0.800959	0.919271
animation	0.478261	0.321343	0.692708
ann	0.2657	0.781775	0.786458
anna	0.405797	0.760192	0.570313
anne	0.429952	0.781775	0.403646
anniversary	0.342995	0.53717	0.476563
annual	0.458937	0.695444	0.403646
another	0.183575	0.227818	0.567708
antonio	0.333333	0.606715	0.760417
anyone	0.328502	0.386091	0.197917
anything	0.217391	0.479616	0.132813
apartment	0.357272	0.557809	0.945779
apple	0.449275	0.940048	0.760417
approximately	0.36715	0.431655	0.578125
april	0.31401	0.803357	0.604167
arch	0.594203	0.757794	0.708333
archaeology	0.724638	0.757794	0.721354
architect	0.541063	0.676259	0.6875
architectural	0.536232	0.70024	0.585938
architecture	0.666667	0.71223	0.580729
are	0.454106	0.59952	0.638021
area	0.376812	0.378897	0.479167
areas	0.468599	0.714628	0.528646
arena	0.550725	0.436451	0.8125
argentina	0.425121	0.70024	0.601563
arizona	0.521739	0.417266	0.867188
army	0.439614	0.892086	0.375
around	0.347826	0.321343	0.802083
arrangements	0.444444	0.453237	0.53125
arrived	0.202899	0.29976	0.809896
art	0.371981	0.678657	0.315104
artistic	0.497585	0.776978	0.546875
artists	0.439614	0.657074	0.606771
arts	0.347826	0.705036	0.385417

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English Words Imageability

This dataset is a collection of two datasets provided by Marc A. Kastner on GitHub. I merged the datasets and kept only the word, visual, phonetic, and textual columns. The data is scaled using a MinMaxScaler so that the whole dataset can be used as one.

Usage

This dataset is ideal for training and evaluating machine learning models for word imageability.

Acknowledgments

We extend our heartfelt gratitude to all the authors of the original datasets.

License

This dataset is made available under the MIT license.

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