2.8 KiB
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
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.
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 thelibmecab-devsystem package to be installed. - It does not yet attempt to tokenize Chinese ideograms.
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, by Invoke IT Limited (https://invokeit.wordpress.com/frequency-word-lists/)
-
Wikipedia, the free encyclopedia (http://www.wikipedia.org)
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.