Metadata-Version: 1.1
Name: wordfreq
Version: 1.0
Summary: Tools for working with word frequencies from various corpora.
Home-page: http://github.com/LuminosoInsight/wordfreq/
Author: Luminoso Technologies, Inc.
Author-email: info@luminoso.com
License: MIT
Description: Author: Rob 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
        
        ## Unicode data
        
        The tokenizers that split non-Japanese phrases utilize regexes built using the
        `unicodedata` module from Python 3.4, which supports Unicode version 6.3.0.  To
        update these regexes, run `scripts/gen_regex.py`.
        
        ## 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 only gives statistics about words that are very commonly
        used on Twitter; it does not display or republish any Twitter content.
        
Platform: any
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Classifier: Topic :: Text Processing :: Linguistic
