N
int64
1
173k
Word
stringlengths
1
25
Frequency count
int64
10k
93.4B
Stem
stringlengths
3
21
Stem valid probability
float64
0
1
1
a
26,251,832,128
null
null
2
aa
14,251,871
null
null
3
aaa
3,175,263
null
null
4
aaaa
298,521
aaa
0.9141
5
aaas
634,068
aaa
0.8335
6
aaberg
23,475
null
null
7
aachen
1,039,285
null
null
8
aae
175,428
null
null
9
aaee
20,871
null
null
10
aaf
656,925
null
null
11
aag
353,325
null
null
12
aah
102,571
null
null
13
aahed
24,387
aah
0.8079
14
aahing
13,064
aah
0.887
15
aahs
35,739
aah
0.7416
16
aaii
17,184
null
null
17
aal
420,782
null
null
18
aalborg
273,298
null
null
19
aalesund
19,475
null
null
20
aals
117,651
aal
0.7815
21
aalst
79,297
null
null
22
aalto
266,606
null
null
23
aam
208,058
null
null
24
aandahl
65,687
null
null
25
aao
102,649
null
null
26
aap
477,677
null
null
27
aapss
10,387
null
null
28
aar
884,017
null
null
29
aarau
68,436
null
null
30
aarc
62,668
null
null
31
aardvark
94,884
null
null
32
aardvarks
17,256
aardvark
0.8461
33
aargau
66,565
null
null
34
aarhus
714,496
null
null
35
aaron
10,557,724
null
null
36
aaronic
84,390
aaron
0.9921
37
aaronson
229,107
null
null
38
aarp
533,976
null
null
39
aas
1,433,051
null
null
40
aau
353,828
null
null
41
aaup
309,824
null
null
42
aauw
225,711
null
null
43
aavso
27,471
null
null
44
ab
22,346,663
null
null
45
aba
4,779,659
null
null
46
ababa
1,712,113
null
null
47
abac
52,739
null
null
48
abaca
375,303
abac
0.1232
49
abaci
55,541
abac
0.4871
50
aback
1,708,114
null
null
51
abaco
124,561
null
null
52
abactinal
101,406
null
null
53
abacus
669,786
abac
0.073
54
abacuses
11,209
abacus
0.9835
55
abad
322,552
null
null
56
abada
16,398
abad
0.9516
57
abadan
208,489
abad
0.6074
58
abaddon
82,013
null
null
59
abadia
30,650
abad
0.9132
60
abaft
284,971
null
null
61
abagail
36,006
null
null
62
abailard
31,298
null
null
63
abakan
24,504
null
null
64
abakumov
31,556
null
null
65
abalone
627,974
null
null
66
abalones
68,655
null
null
67
abama
13,111
null
null
68
abana
45,949
null
null
69
aband
27,681
null
null
70
abandon
15,065,283
null
null
71
abandoned
38,029,233
abandon
0.2837
72
abandoning
4,698,131
abandon
0.7623
73
abandonment
11,707,018
abandon
0.5627
74
abandonments
251,195
abandon
0.9836
75
abandons
1,315,660
abandon
0.9197
76
abaris
26,280
null
null
77
abas
79,840
aba
0.9836
78
abase
114,735
abas
0.4103
79
abased
145,682
abas
0.354
80
abasement
322,659
abas
0.1984
81
abases
15,199
abas
0.8401
82
abash
37,005
null
null
83
abashed
529,913
abash
0.0653
84
abashment
11,187
abash
0.7679
85
abasia
14,051
abas
0.8503
86
abasic
21,503
abas
0.7878
87
abasing
37,577
abas
0.68
88
abassi
22,635
null
null
89
abatable
11,245
null
null
90
abate
2,459,564
null
null
91
abated
2,123,919
null
null
92
abatement
7,476,228
null
null
93
abatements
323,718
null
null
94
abates
201,925
null
null
95
abating
545,668
null
null
96
abatis
70,461
null
null
97
abattis
22,217
null
null
98
abattoir
628,682
null
null
99
abattoirs
470,642
abattoir
0.5719
100
abaxial
290,132
null
null

English Valid Words

This repository contains CSV files with valid English words along with their frequency, stem, and stem valid probability.

Dataset Github link: https://github.com/Maximax67/English-Valid-Words

Files included

  1. valid_words_sorted_alphabetically.csv:

    • N: Counter for each word entry.
    • Word: The English word itself.
    • Frequency count: The number of occurrences of the word in the 1-grams dataset.
    • Stem: The stem of the word.
    • Stem valid probability: Probability indicating the validity of the stem within the English language.
  2. valid_words_sorted_by_frequency.csv:

    • Rank: The ranking of the word based on its frequency count.
    • Word: The English word.
    • Frequency count: The count of occurrences of the word in the 1-grams dataset.
    • Stem: The stem of the word.
    • Stem valid probability: Probability indicating the validity of the stem within the English language.
  3. valid_words.txt: Txt file which contains valid words. Each word appears on a new line for convenient readability and usage.

Data Collection Process

In order to curate a comprehensive dataset of valid English words, the following steps were undertaken:

  1. Initial Dataset: I was searching a list of valid english words for my personal project and I found this github repo. However, to refine the dataset to meet my project specifications, a filtering process was necessary.

  2. Words Filtering: I wrote the Words-filter.ipynb notebook to remove of words with non-alphabetical characters and words exceeding 25 characters.

  3. Frequency Data Collection: To enrich the dataset with frequency information, the 1-grams dataset provided by Google was employed. Words with a frequency count less than 10,000 were removed.

  4. Stemming and Probability Calculation: I used NLTK's Porter, Lancaster, and Snowball stemmers, along with a custom prefix stemmer to get stems with the highest frequency among all stemmers, which also existed in the dataset. Additionally, the probability of stem validity was calculated based on the frequencies of the original word and its stem. For further insights into the data curation process, please refer to the Valid-Word-List-Maker.ipynb file.

License

This repository is released under the Unlicensed license. You are free to use, modify, and distribute the contents of this repository for any purpose without any restrictions.

Acknowledgments

I would like to acknowledge the contributions of the following resources:

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