english-words

:memo: A text file containing 479k English words for all your dictionary/word-based projects e.g: auto-completion / autosuggestion

10,574

1,838

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google-10000-english

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This repo contains a list of the 10,000 most common English words in order of frequency, as determined by n-gram frequency analysis of the Google's Trillion Word Corpus.

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SecLists is the security tester's companion. It's a collection of multiple types of lists used during security assessments, collected in one place. List types include usernames, passwords, URLs, sensitive data patterns, fuzzing payloads, web shells, and many more.

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Quick Overview

The dwyl/english-words repository is a comprehensive collection of English words stored in text files. It aims to provide a reliable and extensive list of words for various applications, such as spell-checking, word games, or natural language processing tasks. The repository contains multiple word lists, including a large file with over 466,000 English words.

Pros

Extensive word collection with over 466,000 entries
Multiple word lists available for different use cases
Simple and easy-to-use plain text format
Regularly maintained and updated

Cons

Large file size may be challenging for some applications
No built-in search or filtering functionality
May include some archaic or uncommon words
Lacks additional linguistic information (e.g., part of speech, definitions)

Getting Started

To use the word lists from the dwyl/english-words repository:

Clone the repository or download the desired word list file:
```
git clone https://github.com/dwyl/english-words.git
```
Choose the appropriate word list file for your needs (e.g., words.txt for the complete list).

Read the file in your preferred programming language. For example, in Python:

with open('words.txt', 'r') as file:
    words = file.read().splitlines()

print(f"Total words: {len(words)}")
print(f"First 5 words: {words[:5]}")

This simple setup allows you to start working with the word list in your projects, whether for spell-checking, word games, or other text-based applications.

Competitor Comparisons

google-10000-english

3,903

This repo contains a list of the 10,000 most common English words in order of frequency, as determined by n-gram frequency analysis of the Google's Trillion Word Corpus.

Pros of google-10000-english

Smaller, more curated list of common English words
Includes word frequency data
Easier to use for applications requiring a concise vocabulary

Cons of google-10000-english

Limited vocabulary size may not be suitable for all applications
Less comprehensive representation of the English language
May not include domain-specific or technical terms

Code Comparison

english-words:

aback
abacus
abandon
abandoned
abandonment

google-10000-english:

the,29971563
of,17538671
and,12545825
to,10741073
a,10343885

Summary

english-words is a comprehensive list of English words, containing over 466,000 entries. It's suitable for a wide range of applications but may require additional processing for specific use cases.

google-10000-english provides a curated list of the 10,000 most common English words, along with their frequency data. This makes it ideal for applications focused on everyday language or those requiring word popularity information.

The choice between these repositories depends on the specific needs of your project. If you need a comprehensive word list, english-words is the better option. For applications focusing on common vocabulary or requiring frequency data, google-10000-english would be more suitable.

wordlists

1,118

📜 A collection of wordlists for many different usages

Pros of wordlists

Offers a wider variety of wordlists, including specialized categories like usernames, passwords, and domain names
Includes wordlists in multiple languages, not just English
Provides more frequent updates and contributions from the community

Cons of wordlists

Smaller overall word count compared to english-words
Less focus on comprehensive English vocabulary
May contain potentially sensitive or offensive content due to its diverse sources

Code comparison

wordlists:

with open('wordlist.txt', 'r') as file:
    words = file.read().splitlines()

english-words:

import json
with open('words_dictionary.json', 'r') as file:
    words = json.load(file)

The main difference in usage is that wordlists typically provides plain text files, while english-words offers a JSON format for easy integration into Python projects. wordlists may require additional processing depending on the specific list format, whereas english-words provides a ready-to-use dictionary structure.

Both repositories serve different purposes: wordlists is more suitable for security testing and diverse language applications, while english-words is better for projects requiring a comprehensive English vocabulary.

SecLists

57,590

Pros of SecLists

More comprehensive, including various types of lists beyond just words (e.g., passwords, usernames, URLs)
Regularly updated and maintained, with contributions from the security community
Organized into categories, making it easier to find specific types of lists

Cons of SecLists

Larger repository size, which may be unnecessary for simple word list needs
Some lists may contain potentially offensive or sensitive content
More complex structure, which could be overwhelming for basic use cases

Code Comparison

SecLists:

admin
password
123456
12345678
qwerty

english-words:

aardvark
abacus
abandon
abandoned
abandonment

Summary

SecLists is a more comprehensive and regularly updated repository, focusing on security-related lists. It offers a wide range of content beyond simple word lists, making it valuable for security professionals and penetration testers. However, its size and complexity may be excessive for basic word list needs.

english-words provides a straightforward list of English words, which is simpler and more focused. It's ideal for general language-related tasks but lacks the specialized security-oriented content found in SecLists.

Choose SecLists for security testing and comprehensive list needs, or english-words for simpler, language-focused applications.

dictionaries

1,186

Hunspell dictionaries in UTF-8

Pros of dictionaries

Offers multiple languages and dictionaries, not just English
Provides more structured data, including parts of speech and definitions
Regularly updated and maintained

Cons of dictionaries

Larger file sizes due to additional data and multiple languages
May require more processing to extract simple word lists
More complex structure might be overkill for basic word list needs

Code comparison

english-words:

aback
abacus
abandon
abandoned
abandonment

dictionaries:

{
  "word": "abandon",
  "phonetic": "əˈbandən",
  "partOfSpeech": "verb",
  "definition": "To give up completely (a practice or a course of action)"
}

Summary

english-words is a simple, straightforward list of English words, ideal for basic word-related tasks. dictionaries offers a more comprehensive, multi-language solution with richer linguistic data, suitable for more complex language processing applications. The choice between them depends on the specific requirements of your project, balancing simplicity against depth of information.

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README

List Of English Words

A text file containing over 466k English words.

While searching for a list of english words (for an auto-complete tutorial) I found: https://stackoverflow.com/questions/2213607/how-to-get-english-language-word-database which refers to https://www.infochimps.com/datasets/word-list-350000-simple-english-words-excel-readable (archived).

No idea why infochimps put the word list inside an excel (.xls) file.

I pulled out the words into a simple new-line-delimited text file. Which is more useful when building apps or importing into databases etc.

Files you may be interested in:

words.txt contains all words.
words_alpha.txt contains only [[:alpha:]] words (words that only have letters, no numbers or symbols). If you want a quick solution choose this.
words_dictionary.json contains all the words from words_alpha.txt as json format. If you are using Python, you can easily load this file and use it as a dictionary for faster performance. All the words are assigned with 1 in the dictionary.

See read_english_dictionary.py for example usage.

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