Datasets:

Maximax67
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English-Valid-Words

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English

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100K<n<1M

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Subset (3)

sorted_alphabetically · 173k rows

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train · 173k rows

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

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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

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.
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.
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:

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

Size of the auto-converted Parquet files:

10.5 MB

Number of rows:

518,345