mirror of
https://github.com/rspeer/wordfreq.git
synced 2024-12-24 01:41:39 +00:00
37e510345d
Former-commit-id: 81bbe663fb
271 lines
11 KiB
Markdown
271 lines
11 KiB
Markdown
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
|
|
|
|
To handle word frequency lookups in Japanese, you need to additionally install
|
|
mecab-python3, which itself depends on libmecab-dev. These commands will
|
|
install them on Ubuntu:
|
|
|
|
sudo apt-get install mecab-ipadic-utf8 libmecab-dev
|
|
pip3 install mecab-python3
|
|
|
|
|
|
## Usage
|
|
|
|
wordfreq provides access to estimates of the frequency with which a word is
|
|
used, in 15 languages (see *Supported languages* below). It loads
|
|
efficiently-packed data structures that contain all words that appear at least
|
|
once per million words.
|
|
|
|
The most useful 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
|
|
14.45439770745928
|
|
|
|
>>> word_frequency('café', 'en') * 1e6
|
|
4.7863009232263805
|
|
|
|
>>> word_frequency('cafe', 'fr') * 1e6
|
|
2.0417379446695274
|
|
|
|
>>> word_frequency('café', 'fr') * 1e6
|
|
77.62471166286912
|
|
|
|
The parameters 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', which combines up to five different sources, and
|
|
'twitter', which returns frequencies observed on Twitter alone.
|
|
|
|
- `minimum`: If the word is not in the list or has a frequency lower than
|
|
`minimum`, return `minimum` instead. In some applications, you'll want
|
|
to set `minimum=1e-6` to avoid a discontinuity where the list ends, because
|
|
a frequency of 1e-6 (1 per million) is the threshold for being included in
|
|
the list at all.
|
|
|
|
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*. Tokenizing Japanese requires the optional dependency `mecab-python3`
|
|
to be installed.
|
|
|
|
`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', 'of', 'to', 'in', 'and', 'a', 'i', 'you', 'is', 'it']
|
|
|
|
>>> top_n_list('es', 10)
|
|
['de', 'la', 'que', 'el', 'en', 'y', 'a', '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][xkcd936] 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.
|
|
|
|
[xkcd936]: https://xkcd.com/936/
|
|
|
|
|
|
## Sources and supported languages
|
|
|
|
We compiled word frequencies from five 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:
|
|
|
|
- **GBooks**: Google Books Ngrams 2013
|
|
- **LeedsIC**: The Leeds Internet Corpus
|
|
- **OpenSub**: OpenSubtitles
|
|
- **SUBTLEX**: The SUBTLEX word frequency lists
|
|
- **Twitter**: Messages sampled from Twitter's public stream
|
|
- **Wikipedia**: The full text of Wikipedia in 2015
|
|
|
|
The following 12 languages are well-supported, with reasonable tokenization and
|
|
at least 3 different sources of word frequencies:
|
|
|
|
Language Code GBooks SUBTLEX LeedsIC OpenSub Twitter Wikipedia
|
|
──────────────────┼──────────────────────────────────────────────────
|
|
Arabic ar │ - - Yes Yes Yes Yes
|
|
German de │ - Yes Yes Yes Yes[1] Yes
|
|
Greek el │ - Yes Yes Yes Yes Yes
|
|
English en │ Yes Yes Yes Yes Yes Yes
|
|
Spanish es │ - - Yes Yes Yes Yes
|
|
French fr │ - - Yes Yes Yes Yes
|
|
Indonesian id │ - - - Yes Yes Yes
|
|
Italian it │ - - Yes Yes Yes Yes
|
|
Japanese ja │ - - Yes - Yes Yes
|
|
Malay ms │ - - - Yes Yes Yes
|
|
Dutch nl │ - Yes - Yes Yes Yes
|
|
Portuguese pt │ - - Yes Yes Yes Yes
|
|
Russian ru │ - - Yes Yes Yes Yes
|
|
Turkish tr │ - - - Yes Yes Yes
|
|
|
|
These languages are only marginally supported so far. We have too few data
|
|
sources so far in Korean (feel free to suggest some), and we are lacking
|
|
tokenization support for Chinese.
|
|
|
|
Language Code GBooks SUBTLEX LeedsIC OpenSub Twitter Wikipedia
|
|
──────────────────┼──────────────────────────────────────────────────
|
|
Korean ko │ - - - - Yes Yes
|
|
Chinese zh │ - Yes Yes Yes - -
|
|
|
|
[1] We've counted the frequencies from tweets in German, such as they are, but
|
|
you should be aware that German is not a frequently-used language on Twitter.
|
|
Germans just don't tweet that much.
|
|
|
|
|
|
## 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][uax29].
|
|
|
|
There are language-specific exceptions:
|
|
|
|
- In Arabic, it additionally normalizes ligatures and removes combining marks.
|
|
- In Japanese, 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.
|
|
- It does not yet attempt to tokenize Chinese ideograms.
|
|
|
|
[uax29]: http://unicode.org/reports/tr29/
|
|
|
|
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:
|
|
|
|
>>> word_frequency('New York', 'en')
|
|
0.0002632772081925718
|
|
|
|
The word frequencies are combined with the half-harmonic-mean function in order
|
|
to provide an estimate of what their combined frequency would be.
|
|
|
|
This 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:
|
|
|
|
>>> word_frequency('owl-flavored', 'en')
|
|
1.3557098723512335e-06
|
|
|
|
|
|
## 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.
|
|
|
|
It also contains data derived from the following Creative Commons-licensed
|
|
sources:
|
|
|
|
- The Leeds Internet Corpus, from the University of Leeds Centre for Translation
|
|
Studies (http://corpus.leeds.ac.uk/list.html)
|
|
|
|
- The OpenSubtitles Frequency Word Lists, compiled by Hermit Dave
|
|
(https://invokeit.wordpress.com/frequency-word-lists/)
|
|
|
|
- Wikipedia, the free encyclopedia (http://www.wikipedia.org)
|
|
|
|
It contains data from various SUBTLEX word lists: SUBTLEX-US, SUBTLEX-UK, and
|
|
SUBTLEX-CH, created by Marc Brysbaert et al. and available at
|
|
http://crr.ugent.be/programs-data/subtitle-frequencies. SUBTLEX was first
|
|
published in this paper:
|
|
|
|
|
|
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
|
|
|
|
- Brysbaert, M. & New, B. (2009). Moving beyond Kucera and Francis: A Critical
|
|
Evaluation of Current Word Frequency Norms and the Introduction of a New and
|
|
Improved Word Frequency Measure for American English. Behavior Research
|
|
Methods, 41 (4), 977-990.
|
|
http://sites.google.com/site/borisnew/pub/BrysbaertNew2009.pdf
|
|
|
|
- Cai, Q., & Brysbaert, M. (2010). SUBTLEX-CH: Chinese word and character
|
|
frequencies based on film subtitles. PLoS One, 5(6), e10729.
|
|
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0010729
|
|
|
|
- Davis, M. (2012). Unicode text segmentation. Unicode Standard Annex, 29.
|
|
http://unicode.org/reports/tr29/
|
|
|
|
- Kudo, T. (2005). Mecab: Yet another part-of-speech and morphological
|
|
analyzer.
|
|
http://mecab.sourceforge.net/
|
|
|
|
- van Heuven, W. J., Mandera, P., Keuleers, E., & Brysbaert, M. (2014).
|
|
SUBTLEX-UK: A new and improved word frequency database for British English.
|
|
The Quarterly Journal of Experimental Psychology, 67(6), 1176-1190.
|
|
http://www.tandfonline.com/doi/pdf/10.1080/17470218.2013.850521
|
|
|