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Robyn Speer 2a41d4dc5e Add Common Crawl data and more languages (#39) 
This changes the version from 1.4.2 to 1.5.  Things done in this update include:

* include Common Crawl; support 11 more languages

* new frequency-merging strategy

* New sources: Chinese from Wikipedia (mostly Trad.), Dutch big list

* Remove kinda bad sources, i.e. Greek Twitter (too often kaomoji are detected as Greek) and Ukrainian Common Crawl. This results in dropping Ukrainian as an available language, and causing Greek to not be a 'large' language after all.

* Add Korean tokenization, and include MeCab files in data

* Remove marks from more languages

* Deal with commas and cedillas in Turkish and Romanian



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Tools for working with word frequencies from various corpora.

Author: Robyn Speer

Installation

wordfreq requires Python 3 and depends on a few other Python modules (msgpack-python, langcodes, and ftfy). You can install it and its dependencies in the usual way, either by getting it from pip:

pip3 install wordfreq

or by getting the repository and running its setup.py:

python3 setup.py install

Japanese and Chinese have additional external dependencies so that they can be tokenized correctly.

To be able to look up word frequencies in Japanese, you need to additionally install mecab-python3, which itself depends on libmecab-dev and its dictionary. These commands will install them on Ubuntu:

sudo apt-get install mecab-ipadic-utf8 libmecab-dev
pip3 install mecab-python3

To be able to look up word frequencies in Chinese, you need Jieba, a pure-Python Chinese tokenizer:

pip3 install jieba

These dependencies can also be requested as options when installing wordfreq. For example:

pip3 install wordfreq[mecab,jieba]

Usage

wordfreq provides access to estimates of the frequency with which a word is used, in 18 languages (see Supported languages below).

It provides three kinds of pre-built wordlists:

  • 'combined' lists, containing words that appear at least once per million words, averaged across all data sources.
  • 'twitter' lists, containing words that appear at least once per million words on Twitter alone.
  • 'large' lists, containing words that appear at least once per 100 million words, averaged across all data sources.

The most straightforward function is:

word_frequency(word, lang, wordlist='combined', minimum=0.0)

This function looks up a word's frequency in the given language, returning its frequency as a decimal between 0 and 1. In these examples, we'll multiply the frequencies by a million (1e6) to get more readable numbers:

>>> from wordfreq import word_frequency
>>> word_frequency('cafe', 'en') * 1e6
12.88249551693135

>>> word_frequency('café', 'en') * 1e6
3.3884415613920273

>>> word_frequency('cafe', 'fr') * 1e6
2.6302679918953817

>>> word_frequency('café', 'fr') * 1e6
87.09635899560814

zipf_frequency is a variation on word_frequency that aims to return the word frequency on a human-friendly logarithmic scale. The Zipf scale was proposed by Marc Brysbaert, who created the SUBTLEX lists. The Zipf frequency of a word is the base-10 logarithm of the number of times it appears per billion words. A word with Zipf value 6 appears once per thousand words, for example, and a word with Zipf value 3 appears once per million words.

Reasonable Zipf values are between 0 and 8, but because of the cutoffs described above, the minimum Zipf value appearing in these lists is 1.0 for the 'large' wordlists and 3.0 for all others. We use 0 as the default Zipf value for words that do not appear in the given wordlist, although it should mean one occurrence per billion words.

>>> from wordfreq import zipf_frequency
>>> zipf_frequency('the', 'en')
7.67

>>> zipf_frequency('word', 'en')
5.39

>>> zipf_frequency('frequency', 'en')
4.19

>>> zipf_frequency('zipf', 'en')
0.0

>>> zipf_frequency('zipf', 'en', wordlist='large')
1.65

The parameters to word_frequency and zipf_frequency are:

  • word: a Unicode string containing the word to look up. Ideally the word is a single token according to our tokenizer, but if not, there is still hope -- see Tokenization below.

  • lang: the BCP 47 or ISO 639 code of the language to use, such as 'en'.

  • wordlist: which set of word frequencies to use. Current options are 'combined', 'twitter', and 'large'.

  • minimum: If the word is not in the list or has a frequency lower than minimum, return minimum instead. You may want to set this to the minimum value contained in the wordlist, to avoid a discontinuity where the wordlist ends.

Other functions:

tokenize(text, lang) splits text in the given language into words, in the same way that the words in wordfreq's data were counted in the first place. See Tokenization.

top_n_list(lang, n, wordlist='combined') returns the most common n words in the list, in descending frequency order.

>>> from wordfreq import top_n_list
>>> top_n_list('en', 10)
['the', 'i', 'to', 'a', 'and', 'of', 'you', 'in', 'that', 'is']

>>> top_n_list('es', 10)
['de', 'que', 'la', 'y', 'a', 'en', 'el', 'no', 'los', 'es']

iter_wordlist(lang, wordlist='combined') iterates through all the words in a wordlist, in descending frequency order.

get_frequency_dict(lang, wordlist='combined') returns all the frequencies in a wordlist as a dictionary, for cases where you'll want to look up a lot of words and don't need the wrapper that word_frequency provides.

supported_languages(wordlist='combined') returns a dictionary whose keys are language codes, and whose values are the data file that will be loaded to provide the requested wordlist in each language.

random_words(lang='en', wordlist='combined', nwords=5, bits_per_word=12) returns a selection of random words, separated by spaces. bits_per_word=n will select each random word from 2^n words.

If you happen to want an easy way to get a memorable, xkcd-style password with 60 bits of entropy, this function will almost do the job. In this case, you should actually run the similar function random_ascii_words, limiting the selection to words that can be typed in ASCII.

Sources and supported languages

We compiled word frequencies from seven different sources, providing us examples of word usage on different topics at different levels of formality. The sources (and the abbreviations we'll use for them) are:

  • LeedsIC: The Leeds Internet Corpus
  • SUBTLEX: The SUBTLEX word frequency lists
  • OpenSub: Data derived from OpenSubtitles but not from SUBTLEX
  • Twitter: Messages sampled from Twitter's public stream
  • Wpedia: The full text of Wikipedia in 2015
  • Reddit: The corpus of Reddit comments through May 2015
  • CCrawl: Text extracted from the Common Crawl and language-detected with cld2
  • Other: We get additional English frequencies from Google Books Syntactic Ngrams 2013, and Chinese frequencies from the frequency dictionary that comes with the Jieba tokenizer.

The following 27 languages are supported, with reasonable tokenization and at least 3 different sources of word frequencies:

Language    Code    Sources Large?   SUBTLEX OpenSub LeedsIC Twitter Wpedia  CCrawl  Reddit  Other
───────────────────────────────────┼──────────────────────────────────────────────────────────────
Arabic      ar      5       Yes    │ -       Yes     Yes     Yes     Yes     Yes     -       -
Bulgarian   bg      3       -      │ -       Yes     -       -       Yes     Yes     -       -
Catalan     ca      3       -      │ -       Yes     -       Yes     Yes     -       -       -
Danish      da      3       -      │ -       Yes     -       -       Yes     Yes     -       -
German      de      5       Yes    │ Yes     -       Yes     Yes     Yes     Yes     -       -
Greek       el      4       -      │ -       Yes     Yes     -       Yes     Yes     -       -
English     en      7       Yes    │ Yes     Yes     Yes     Yes     Yes     -       Yes     Google Books
Spanish     es      6       Yes    │ -       Yes     Yes     Yes     Yes     Yes     Yes     -
Finnish     fi      3       -      │ -       Yes     -       -       Yes     Yes     -       -
French      fr      5       Yes    │ -       Yes     Yes     Yes     Yes     Yes     -       -
Hebrew      he      4       -      │ -       Yes     -       Yes     Yes     Yes     -       -
Hindi       hi      3       -      │ -       -       -       Yes     Yes     Yes     -       -
Hungarian   hu      3       -      │ -       Yes     -       -       Yes     Yes     -       -
Indonesian  id      4       -      │ -       Yes     -       Yes     Yes     Yes     -       -
Italian     it      5       Yes    │ -       Yes     Yes     Yes     Yes     Yes     -       -
Japanese    ja      4       -      │ -       -       Yes     Yes     Yes     Yes     -       -
Korean      ko      3       -      │ -       -       -       Yes     Yes     Yes     -       -
Malay       ms      4       -      │ -       Yes     -       Yes     Yes     Yes     -       -
Norwegian   nb[1]   3       -      │ -       Yes     -       -       Yes     Yes     -       -
Dutch       nl      5       Yes    │ Yes     Yes     -       Yes     Yes     Yes     -       -
Polish      pl      4       -      │ -       Yes     -       Yes     Yes     Yes     -       -
Portuguese  pt      5       Yes    │ -       Yes     Yes     Yes     Yes     Yes     -       -
Romanian    ro      3       -      │ -       Yes     -       -       Yes     Yes     -       -
Russian     ru      5       Yes    │ -       Yes     Yes     Yes     Yes     Yes     -       -
Swedish     sv      4       -      │ -       Yes     -       Yes     Yes     Yes     -       -
Turkish     tr      4       -      │ -       Yes     -       Yes     Yes     Yes     -       -
Chinese     zh[2]   5       -      │ Yes     -       Yes     -       Yes     Yes     -       Jieba

[1] The Norwegian text we have is specifically written in Norwegian Bokmål, so we give it the language code 'nb'. We would use 'nn' for Nynorsk, but there isn't enough data to include it in wordfreq.

[2] This data represents text written in both Simplified and Traditional Chinese. (SUBTLEX is mostly Simplified, while Wikipedia is mostly Traditional.) The characters are mapped to one another so they can use the same word frequency list.

Some languages provide 'large' wordlists, including words with a Zipf frequency between 1.0 and 3.0. These are available in 9 languages that are covered by enough data sources.

Tokenization

wordfreq uses the Python package regex, which is a more advanced implementation of regular expressions than the standard library, to separate text into tokens that can be counted consistently. regex produces tokens that follow the recommendations in Unicode Annex #29, Text Segmentation, including the optional rule that splits words between apostrophes and vowels.

There are language-specific exceptions:

  • In Arabic and Hebrew, it additionally normalizes ligatures and removes combining marks.

  • In Japanese and Korean, instead of using the regex library, it uses the external library mecab-python3. This is an optional dependency of wordfreq, and compiling it requires the libmecab-dev system package to be installed.

  • In Chinese, it uses the external Python library jieba, another optional dependency.

When wordfreq's frequency lists are built in the first place, the words are tokenized according to this function.

Because tokenization in the real world is far from consistent, wordfreq will also try to deal gracefully when you query it with texts that actually break into multiple tokens:

>>> zipf_frequency('New York', 'en')
5.07
>>> zipf_frequency('北京地铁', 'zh')  # "Beijing Subway"
3.58

The word frequencies are combined with the half-harmonic-mean function in order to provide an estimate of what their combined frequency would be. In Chinese, where the word breaks must be inferred from the frequency of the resulting words, there is also a penalty to the word frequency for each word break that must be inferred.

This method of combining word frequencies implicitly assumes that you're asking about words that frequently appear together. It's not multiplying the frequencies, because that would assume they are statistically unrelated. So if you give it an uncommon combination of tokens, it will hugely over-estimate their frequency:

>>> zipf_frequency('owl-flavored', 'en')
3.19

License

wordfreq is freely redistributable under the MIT license (see MIT-LICENSE.txt), and it includes data files that may be redistributed under a Creative Commons Attribution-ShareAlike 4.0 license (https://creativecommons.org/licenses/by-sa/4.0/).

wordfreq contains data extracted from Google Books Ngrams (http://books.google.com/ngrams) and Google Books Syntactic Ngrams (http://commondatastorage.googleapis.com/books/syntactic-ngrams/index.html). The terms of use of this data are:

Ngram Viewer graphs and data may be freely used for any purpose, although
acknowledgement of Google Books Ngram Viewer as the source, and inclusion
of a link to http://books.google.com/ngrams, would be appreciated.

wordfreq uses MeCab, by Taku Kudo, plus Korean data files by Yongwoon Lee and Yungho Yu. The Korean data is under an Apache 2 license, a copy of which appears in wordfreq/data/mecab-ko-dic/COPYING.

wordfreq also contains data derived from the following Creative Commons-licensed sources:

It contains data from various SUBTLEX word lists: SUBTLEX-US, SUBTLEX-UK, SUBTLEX-CH, SUBTLEX-DE, and SUBTLEX-NL, created by Marc Brysbaert et al. (see citations below) and available at http://crr.ugent.be/programs-data/subtitle-frequencies.

I (Robyn Speer) have obtained permission by e-mail from Marc Brysbaert to distribute these wordlists in wordfreq, to be used for any purpose, not just for academic use, under these conditions:

  • Wordfreq and code derived from it must credit the SUBTLEX authors.
  • It must remain clear that SUBTLEX is freely available data.

These terms are similar to the Creative Commons Attribution-ShareAlike license.

Some additional data was collected by a custom application that watches the streaming Twitter API, in accordance with Twitter's Developer Agreement & Policy. This software gives statistics about words that are commonly used on Twitter; it does not display or republish any Twitter content.

Citations to work that wordfreq is built on