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https://github.com/rspeer/wordfreq.git
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76 lines
2.8 KiB
Markdown
76 lines
2.8 KiB
Markdown
Tools for working with word frequencies from various corpora.
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Author: Robyn Speer
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## Installation
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wordfreq requires Python 3 and depends on a few other Python modules
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(msgpack-python, langcodes, and ftfy). You can install it and its dependencies
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in the usual way, either by getting it from pip:
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pip3 install wordfreq
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or by getting the repository and running its setup.py:
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python3 setup.py install
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To handle word frequency lookups in Japanese, you need to additionally install
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mecab-python3, which itself depends on libmecab-dev. These commands will
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install them on Ubuntu:
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sudo apt-get install mecab-ipadic-utf8 libmecab-dev
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pip3 install mecab-python3
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## Tokenization
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wordfreq uses the Python package `regex`, which is a more advanced
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implementation of regular expressions than the standard library, to
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separate text into tokens that can be counted consistently. `regex`
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produces tokens that follow the recommendations in [Unicode
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Annex #29, Text Segmentation][uax29].
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There are language-specific exceptions:
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- In Arabic, it additionally normalizes ligatures and removes combining marks.
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- In Japanese, instead of using the regex library, it uses the external library
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`mecab-python3`. This is an optional dependency of wordfreq, and compiling
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it requires the `libmecab-dev` system package to be installed.
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- It does not yet attempt to tokenize Chinese ideograms.
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[uax29]: http://unicode.org/reports/tr29/
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## License
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`wordfreq` is freely redistributable under the MIT license (see
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`MIT-LICENSE.txt`), and it includes data files that may be
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redistributed under a Creative Commons Attribution-ShareAlike 4.0
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license (https://creativecommons.org/licenses/by-sa/4.0/).
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`wordfreq` contains data extracted from Google Books Ngrams
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(http://books.google.com/ngrams) and Google Books Syntactic Ngrams
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(http://commondatastorage.googleapis.com/books/syntactic-ngrams/index.html).
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The terms of use of this data are:
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Ngram Viewer graphs and data may be freely used for any purpose, although
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acknowledgement of Google Books Ngram Viewer as the source, and inclusion
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of a link to http://books.google.com/ngrams, would be appreciated.
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It also contains data derived from the following Creative Commons-licensed
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sources:
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- The Leeds Internet Corpus, from the University of Leeds Centre for Translation
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Studies (http://corpus.leeds.ac.uk/list.html)
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- The OpenSubtitles Frequency Word Lists, by Invoke IT Limited
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(https://invokeit.wordpress.com/frequency-word-lists/)
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- Wikipedia, the free encyclopedia (http://www.wikipedia.org)
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Some additional data was collected by a custom application that watches the
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streaming Twitter API, in accordance with Twitter's Developer Agreement &
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Policy. This software gives statistics about words that are commonly used on
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Twitter; it does not display or republish any Twitter content.
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