2018-05-01 21:16:53 +00:00
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wordfreq is a Python library for looking up the frequencies of words in many languages, based on many sources of data.
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2013-10-28 23:26:44 +00:00
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Author: Rob Speer
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2014-06-02 20:37:32 +00:00
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2015-08-25 21:44:34 +00:00
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2015-05-28 18:02:12 +00:00
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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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2018-03-14 19:01:08 +00:00
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(msgpack, langcodes, and regex). 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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2018-02-28 21:14:29 +00:00
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See [Additional CJK installation](#additional-cjk-installation) for extra
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steps that are necessary to get Chinese, Japanese, and Korean word frequencies.
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2015-09-24 21:54:52 +00:00
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2015-07-08 22:48:33 +00:00
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2015-08-28 21:49:38 +00:00
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## Usage
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wordfreq provides access to estimates of the frequency with which a word is
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used, in 35 languages (see *Supported languages* below).
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It provides both 'small' and 'large' wordlists:
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- The 'small' lists take up very little memory and cover words that appear at
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least once per million words.
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- The 'large' lists cover words that appear at least once per 100 million
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words.
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The default list is 'best', which uses 'large' if it's available for the
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language, and 'small' otherwise.
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The most straightforward function for looking up frequencies is:
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word_frequency(word, lang, wordlist='best', minimum=0.0)
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This function looks up a word's frequency in the given language, returning its
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frequency as a decimal between 0 and 1. In these examples, we'll multiply the
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frequencies by a million (1e6) to get more readable numbers:
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>>> from wordfreq import word_frequency
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>>> word_frequency('cafe', 'en') * 1e6
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11.748975549395302
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>>> word_frequency('café', 'en') * 1e6
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3.890451449942805
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>>> word_frequency('cafe', 'fr') * 1e6
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1.4454397707459279
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>>> word_frequency('café', 'fr') * 1e6
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53.70317963702532
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`zipf_frequency` is a variation on `word_frequency` that aims to return the
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word frequency on a human-friendly logarithmic scale. The Zipf scale was
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proposed by Marc Brysbaert, who created the SUBTLEX lists. The Zipf frequency
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of a word is the base-10 logarithm of the number of times it appears per
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billion words. A word with Zipf value 6 appears once per thousand words, for
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example, and a word with Zipf value 3 appears once per million words.
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Reasonable Zipf values are between 0 and 8, but because of the cutoffs
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described above, the minimum Zipf value appearing in these lists is 1.0 for the
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'large' wordlists and 3.0 for 'small'. We use 0 as the default Zipf value
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for words that do not appear in the given wordlist, although it should mean
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one occurrence per billion words.
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>>> from wordfreq import zipf_frequency
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>>> zipf_frequency('the', 'en')
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7.77
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>>> zipf_frequency('word', 'en')
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5.32
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>>> zipf_frequency('frequency', 'en')
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4.38
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>>> zipf_frequency('zipf', 'en')
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1.32
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>>> zipf_frequency('zipf', 'en', wordlist='small')
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0.0
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The parameters to `word_frequency` and `zipf_frequency` are:
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- `word`: a Unicode string containing the word to look up. Ideally the word
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is a single token according to our tokenizer, but if not, there is still
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hope -- see *Tokenization* below.
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- `lang`: the BCP 47 or ISO 639 code of the language to use, such as 'en'.
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- `wordlist`: which set of word frequencies to use. Current options are
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'small', 'large', and 'best'.
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- `minimum`: If the word is not in the list or has a frequency lower than
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`minimum`, return `minimum` instead. You may want to set this to the minimum
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value contained in the wordlist, to avoid a discontinuity where the wordlist
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ends.
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Other functions:
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`tokenize(text, lang)` splits text in the given language into words, in the same
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way that the words in wordfreq's data were counted in the first place. See
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*Tokenization*.
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`top_n_list(lang, n, wordlist='best')` returns the most common *n* words in
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the list, in descending frequency order.
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>>> from wordfreq import top_n_list
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>>> top_n_list('en', 10)
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['the', 'of', 'to', 'and', 'a', 'in', 'i', 'is', 'that', 'for']
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>>> top_n_list('es', 10)
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['de', 'la', 'que', 'el', 'en', 'y', 'a', 'los', 'no', 'se']
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`iter_wordlist(lang, wordlist='best')` iterates through all the words in a
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wordlist, in descending frequency order.
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`get_frequency_dict(lang, wordlist='best')` returns all the frequencies in
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a wordlist as a dictionary, for cases where you'll want to look up a lot of
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words and don't need the wrapper that `word_frequency` provides.
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`supported_languages(wordlist='best')` returns a dictionary whose keys are
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language codes, and whose values are the data file that will be loaded to
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provide the requested wordlist in each language.
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`random_words(lang='en', wordlist='best', nwords=5, bits_per_word=12)`
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returns a selection of random words, separated by spaces. `bits_per_word=n`
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will select each random word from 2^n words.
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If you happen to want an easy way to get [a memorable, xkcd-style
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password][xkcd936] with 60 bits of entropy, this function will almost do the
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job. In this case, you should actually run the similar function
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`random_ascii_words`, limiting the selection to words that can be typed in
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ASCII. But maybe you should just use [xkpa][].
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[xkcd936]: https://xkcd.com/936/
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[xkpa]: https://github.com/beala/xkcd-password
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## Sources and supported languages
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2017-01-06 00:18:06 +00:00
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This data comes from a Luminoso project called [Exquisite Corpus][xc], whose
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goal is to download good, varied, multilingual corpus data, process it
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appropriately, and combine it into unified resources such as wordfreq.
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[xc]: https://github.com/LuminosoInsight/exquisite-corpus
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Exquisite Corpus compiles 8 different domains of text, some of which themselves
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come from multiple sources:
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- **Wikipedia**, representing encyclopedic text
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- **Subtitles**, from OPUS OpenSubtitles 2016 and SUBTLEX
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- **News**, from NewsCrawl 2014 and GlobalVoices
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- **Books**, from Google Books Ngrams 2012
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- **Web** text, from the Leeds Internet Corpus and the MOKK Hungarian Webcorpus
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- **Twitter**, representing short-form social media
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- **Reddit**, representing potentially longer Internet comments
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- **Miscellaneous** word frequencies: in Chinese, we import a free wordlist
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that comes with the Jieba word segmenter, whose provenance we don't really know
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The following languages are supported, with reasonable tokenization and at
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least 3 different sources of word frequencies:
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Language Code # Large? WP Subs News Books Web Twit. Redd. Misc.
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──────────────────────────────┼────────────────────────────────────────────────
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Arabic ar 5 Yes │ Yes Yes Yes - Yes Yes - -
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Bengali bn 3 - │ Yes - Yes - - Yes - -
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Bosnian bs [1] 3 - │ Yes Yes - - - Yes - -
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Bulgarian bg 3 - │ Yes Yes - - - Yes - -
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Catalan ca 4 - │ Yes Yes Yes - - Yes - -
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Chinese zh [3] 6 Yes │ Yes - Yes Yes Yes Yes - Jieba
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Croatian hr [1] 3 │ Yes Yes - - - Yes - -
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Czech cs 3 - │ Yes Yes - - - Yes - -
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Danish da 3 - │ Yes Yes - - - Yes - -
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Dutch nl 4 Yes │ Yes Yes Yes - - Yes - -
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English en 7 Yes │ Yes Yes Yes Yes Yes Yes Yes -
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Finnish fi 5 Yes │ Yes Yes Yes - - Yes Yes -
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French fr 7 Yes │ Yes Yes Yes Yes Yes Yes Yes -
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German de 7 Yes │ Yes Yes Yes Yes Yes Yes Yes -
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Greek el 3 - │ Yes Yes - - Yes - - -
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Hebrew he 4 - │ Yes Yes - Yes - Yes - -
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Hindi hi 3 - │ Yes - - - - Yes Yes -
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Hungarian hu 3 - │ Yes Yes - - Yes - - -
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Indonesian id 3 - │ Yes Yes - - - Yes - -
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Italian it 7 Yes │ Yes Yes Yes Yes Yes Yes Yes -
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Japanese ja 5 Yes │ Yes Yes - - Yes Yes Yes -
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Korean ko 4 - │ Yes Yes - - - Yes Yes -
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Macedonian mk 3 - │ Yes Yes Yes - - - - -
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Malay ms 3 - │ Yes Yes - - - Yes - -
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Norwegian nb [2] 4 - │ Yes Yes - - - Yes Yes -
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Persian fa 3 - │ Yes Yes - - - Yes - -
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Polish pl 5 Yes │ Yes Yes Yes - - Yes Yes -
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Portuguese pt 5 Yes │ Yes Yes Yes - Yes Yes - -
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Romanian ro 3 - │ Yes Yes - - - Yes - -
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Russian ru 6 Yes │ Yes Yes Yes Yes Yes Yes - -
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Serbian sr [1] 3 - │ Yes Yes - - - Yes - -
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Spanish es 7 Yes │ Yes Yes Yes Yes Yes Yes Yes -
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Swedish sv 4 - │ Yes Yes - - - Yes Yes -
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Turkish tr 3 - │ Yes Yes - - - Yes - -
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Ukrainian uk 4 - │ Yes Yes - - - Yes Yes -
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[1] Bosnian, Croatian, and Serbian use the same underlying word list, because
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they share most of their vocabulary and grammar, they were once considered the
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same language, and language detection cannot distinguish them. This word list
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can also be accessed with the language code `sh`.
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[2] The Norwegian text we have is specifically written in Norwegian Bokmål, so
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we give it the language code 'nb' instead of the vaguer code 'no'. We would use
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'nn' for Nynorsk, but there isn't enough data to include it in wordfreq.
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[3] This data represents text written in both Simplified and Traditional
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Chinese, with primarily Mandarin Chinese vocabulary. See "Multi-script
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languages" below.
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Some languages provide 'large' wordlists, including words with a Zipf frequency
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between 1.0 and 3.0. These are available in 13 languages that are covered by
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enough data sources.
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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], including the optional rule that
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splits words between apostrophes and vowels.
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There are language-specific exceptions:
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- In Arabic and Hebrew, it additionally normalizes ligatures and removes
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combining marks.
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- In Japanese and Korean, instead of using the regex library, it uses the
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external library `mecab-python3`. This is an optional dependency of wordfreq,
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and compiling it requires the `libmecab-dev` system package to be installed.
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- In Chinese, it uses the external Python library `jieba`, another optional
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dependency.
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[uax29]: http://unicode.org/reports/tr29/
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When wordfreq's frequency lists are built in the first place, the words are
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tokenized according to this function.
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Because tokenization in the real world is far from consistent, wordfreq will
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also try to deal gracefully when you query it with texts that actually break
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into multiple tokens:
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2016-01-22 21:23:43 +00:00
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>>> zipf_frequency('New York', 'en')
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5.35
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>>> zipf_frequency('北京地铁', 'zh') # "Beijing Subway"
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3.54
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The word frequencies are combined with the half-harmonic-mean function in order
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to provide an estimate of what their combined frequency would be. In Chinese,
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where the word breaks must be inferred from the frequency of the resulting
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words, there is also a penalty to the word frequency for each word break that
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must be inferred.
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|
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:
|
2015-08-28 21:45:50 +00:00
|
|
|
|
2016-01-22 21:23:43 +00:00
|
|
|
>>> zipf_frequency('owl-flavored', 'en')
|
2017-01-31 23:25:41 +00:00
|
|
|
3.18
|
2015-08-28 21:45:50 +00:00
|
|
|
|
2015-07-08 22:48:33 +00:00
|
|
|
|
2017-01-07 00:04:40 +00:00
|
|
|
## Multi-script languages
|
|
|
|
|
|
|
|
Two of the languages we support, Serbian and Chinese, are written in multiple
|
|
|
|
scripts. To avoid spurious differences in word frequencies, we automatically
|
|
|
|
transliterate the characters in these languages when looking up their words.
|
|
|
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|
|
Serbian text written in Cyrillic letters is automatically converted to Latin
|
|
|
|
letters, using standard Serbian transliteration, when the requested language is
|
|
|
|
`sr` or `sh`. If you request the word list as `hr` (Croatian) or `bs`
|
|
|
|
(Bosnian), no transliteration will occur.
|
|
|
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|
|
Chinese text is converted internally to a representation we call
|
|
|
|
"Oversimplified Chinese", where all Traditional Chinese characters are replaced
|
|
|
|
with their Simplified Chinese equivalent, *even if* they would not be written
|
|
|
|
that way in context. This representation lets us use a straightforward mapping
|
|
|
|
that matches both Traditional and Simplified words, unifying their frequencies
|
|
|
|
when appropriate, and does not appear to create clashes between unrelated words.
|
|
|
|
|
|
|
|
Enumerating the Chinese wordlist will produce some unfamiliar words, because
|
|
|
|
people don't actually write in Oversimplified Chinese, and because in
|
|
|
|
practice Traditional and Simplified Chinese also have different word usage.
|
|
|
|
|
|
|
|
|
|
|
|
## Similar, overlapping, and varying languages
|
|
|
|
|
|
|
|
As much as we would like to give each language its own distinct code and its
|
|
|
|
own distinct word list with distinct source data, there aren't actually sharp
|
|
|
|
boundaries between languages.
|
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|
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|
|
Sometimes, it's convenient to pretend that the boundaries between
|
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|
|
languages coincide with national borders, following the maxim that "a language
|
|
|
|
is a dialect with an army and a navy" (Max Weinreich). This gets complicated
|
|
|
|
when the linguistic situation and the political situation diverge.
|
|
|
|
Moreover, some of our data sources rely on language detection, which of course
|
|
|
|
has no idea which country the writer of the text belongs to.
|
|
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|
|
So we've had to make some arbitrary decisions about how to represent the
|
|
|
|
fuzzier language boundaries, such as those within Chinese, Malay, and
|
|
|
|
Croatian/Bosnian/Serbian. See [Language Log][] for some firsthand reports of
|
|
|
|
the mutual intelligibility or unintelligibility of languages.
|
|
|
|
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|
|
[Language Log]: http://languagelog.ldc.upenn.edu/nll/?p=12633
|
|
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|
|
|
|
Smoothing over our arbitrary decisions is the fact that we use the `langcodes`
|
|
|
|
module to find the best match for a language code. If you ask for word
|
|
|
|
frequencies in `cmn-Hans` (the fully specific language code for Mandarin in
|
|
|
|
Simplified Chinese), you will get the `zh` wordlist, for example.
|
|
|
|
|
|
|
|
|
2017-08-25 21:38:31 +00:00
|
|
|
## Additional CJK installation
|
|
|
|
|
|
|
|
Chinese, Japanese, and Korean have additional external dependencies so that
|
|
|
|
they can be tokenized correctly. Here we'll explain how to set them up,
|
|
|
|
in increasing order of difficulty.
|
|
|
|
|
|
|
|
|
|
|
|
### Chinese
|
|
|
|
|
|
|
|
To be able to look up word frequencies in Chinese, you need Jieba, a
|
|
|
|
pure-Python Chinese tokenizer:
|
|
|
|
|
|
|
|
pip3 install jieba
|
|
|
|
|
|
|
|
|
|
|
|
### Japanese
|
|
|
|
|
|
|
|
We use MeCab, by Taku Kudo, to tokenize Japanese. To use this in wordfreq, three
|
|
|
|
things need to be installed:
|
|
|
|
|
|
|
|
* The MeCab development library (called `libmecab-dev` on Ubuntu)
|
|
|
|
* The UTF-8 version of the `ipadic` Japanese dictionary
|
|
|
|
(called `mecab-ipadic-utf8` on Ubuntu)
|
|
|
|
* The `mecab-python3` Python interface
|
|
|
|
|
|
|
|
To install these three things on Ubuntu, you can run:
|
|
|
|
|
|
|
|
```sh
|
|
|
|
sudo apt-get install libmecab-dev mecab-ipadic-utf8
|
|
|
|
pip3 install mecab-python3
|
|
|
|
```
|
|
|
|
|
|
|
|
If you choose to install `ipadic` from somewhere else or from its source code,
|
|
|
|
be sure it's configured to use UTF-8. By default it will use EUC-JP, which will
|
|
|
|
give you nonsense results.
|
|
|
|
|
|
|
|
|
|
|
|
### Korean
|
|
|
|
|
|
|
|
Korean also uses MeCab, with a Korean dictionary package by Yongwoon Lee and
|
|
|
|
Yungho Yu. This dictionary is not available as an Ubuntu package.
|
|
|
|
|
|
|
|
Here's a process you can use to install the Korean dictionary and the other
|
|
|
|
MeCab dependencies:
|
|
|
|
|
|
|
|
```sh
|
|
|
|
sudo apt-get install libmecab-dev mecab-utils
|
|
|
|
pip3 install mecab-python3
|
|
|
|
wget https://bitbucket.org/eunjeon/mecab-ko-dic/downloads/mecab-ko-dic-2.0.1-20150920.tar.gz
|
|
|
|
tar xvf mecab-ko-dic-2.0.1-20150920.tar.gz
|
|
|
|
cd mecab-ko-dic-2.0.1-20150920
|
|
|
|
./autogen.sh
|
|
|
|
make
|
|
|
|
sudo make install
|
|
|
|
```
|
|
|
|
|
|
|
|
If wordfreq cannot find the Japanese or Korean data for MeCab when asked to
|
|
|
|
tokenize those languages, it will raise an error and show you the list of
|
|
|
|
paths it searched.
|
|
|
|
|
|
|
|
Sorry that this is difficult. We tried to just package the data files we need
|
|
|
|
with wordfreq, like we do for Chinese, but PyPI would reject the package for
|
|
|
|
being too large.
|
|
|
|
|
|
|
|
|
2014-06-02 20:37:32 +00:00
|
|
|
## License
|
|
|
|
|
2015-05-13 08:09:34 +00:00
|
|
|
`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/).
|
2014-06-02 20:37:32 +00:00
|
|
|
|
2015-05-13 08:09:34 +00:00
|
|
|
`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:
|
2014-06-02 20:37:32 +00:00
|
|
|
|
|
|
|
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.
|
|
|
|
|
2016-07-15 19:10:25 +00:00
|
|
|
`wordfreq` also contains data derived from the following Creative Commons-licensed
|
2015-05-13 08:09:34 +00:00
|
|
|
sources:
|
|
|
|
|
|
|
|
- The Leeds Internet Corpus, from the University of Leeds Centre for Translation
|
|
|
|
Studies (http://corpus.leeds.ac.uk/list.html)
|
|
|
|
|
2015-08-28 21:45:50 +00:00
|
|
|
- The OpenSubtitles Frequency Word Lists, compiled by Hermit Dave
|
2015-05-13 08:09:34 +00:00
|
|
|
(https://invokeit.wordpress.com/frequency-word-lists/)
|
|
|
|
|
|
|
|
- Wikipedia, the free encyclopedia (http://www.wikipedia.org)
|
|
|
|
|
2018-04-26 19:53:07 +00:00
|
|
|
It contains data from OPUS OpenSubtitles 2018
|
|
|
|
(http://opus.nlpl.eu/OpenSubtitles.php), whose data originates from the
|
|
|
|
OpenSubtitles project (http://www.opensubtitles.org/).
|
2017-01-07 00:04:40 +00:00
|
|
|
|
2015-09-08 21:43:16 +00:00
|
|
|
It contains data from various SUBTLEX word lists: SUBTLEX-US, SUBTLEX-UK,
|
2015-09-22 18:23:55 +00:00
|
|
|
SUBTLEX-CH, SUBTLEX-DE, and SUBTLEX-NL, created by Marc Brysbaert et al.
|
|
|
|
(see citations below) and available at
|
2015-09-09 17:10:18 +00:00
|
|
|
http://crr.ugent.be/programs-data/subtitle-frequencies.
|
2015-09-04 04:57:04 +00:00
|
|
|
|
2015-09-22 18:23:55 +00:00
|
|
|
I (Rob 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:
|
2015-09-03 22:56:56 +00:00
|
|
|
|
|
|
|
- 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.
|
|
|
|
|
2015-05-13 08:09:34 +00:00
|
|
|
Some additional data was collected by a custom application that watches the
|
|
|
|
streaming Twitter API, in accordance with Twitter's Developer Agreement &
|
2015-08-25 21:44:34 +00:00
|
|
|
Policy. This software gives statistics about words that are commonly used on
|
|
|
|
Twitter; it does not display or republish any Twitter content.
|
2015-09-04 04:57:04 +00:00
|
|
|
|
2016-09-12 22:24:55 +00:00
|
|
|
|
|
|
|
## Citing wordfreq
|
|
|
|
|
|
|
|
If you use wordfreq in your research, please cite it! We publish the code
|
|
|
|
through Zenodo so that it can be reliably cited using a DOI. The current
|
|
|
|
citation is:
|
|
|
|
|
2017-09-27 17:36:30 +00:00
|
|
|
> Robert Speer, Joshua Chin, Andrew Lin, Sara Jewett, & Lance Nathan.
|
|
|
|
> (2017, September 27). LuminosoInsight/wordfreq: v1.7. Zenodo.
|
|
|
|
> http://doi.org/10.5281/zenodo.998161
|
|
|
|
|
2016-09-12 22:24:55 +00:00
|
|
|
|
|
|
|
The same citation in BibTex format:
|
|
|
|
|
|
|
|
```
|
2017-09-27 17:36:30 +00:00
|
|
|
@misc{robert_speer_2017_998161,
|
2016-09-12 22:24:55 +00:00
|
|
|
author = {Robert Speer and
|
|
|
|
Joshua Chin and
|
|
|
|
Andrew Lin and
|
2017-09-27 17:36:30 +00:00
|
|
|
Sara Jewett and
|
|
|
|
Lance Nathan},
|
|
|
|
title = {LuminosoInsight/wordfreq: v1.7},
|
2016-09-12 22:24:55 +00:00
|
|
|
month = sep,
|
2017-09-27 17:36:30 +00:00
|
|
|
year = 2017,
|
|
|
|
doi = {10.5281/zenodo.998161},
|
|
|
|
url = {https://doi.org/10.5281/zenodo.998161}
|
2016-09-12 22:24:55 +00:00
|
|
|
}
|
|
|
|
```
|
|
|
|
|
|
|
|
|
2015-09-04 04:57:04 +00:00
|
|
|
## Citations to work that wordfreq is built on
|
|
|
|
|
2017-01-06 00:18:06 +00:00
|
|
|
- Bojar, O., Chatterjee, R., Federmann, C., Haddow, B., Huck, M., Hokamp, C.,
|
|
|
|
Koehn, P., Logacheva, V., Monz, C., Negri, M., Post, M., Scarton, C.,
|
|
|
|
Specia, L., & Turchi, M. (2015). Findings of the 2015 Workshop on Statistical
|
|
|
|
Machine Translation.
|
|
|
|
http://www.statmt.org/wmt15/results.html
|
|
|
|
|
2015-09-04 04:57:04 +00:00
|
|
|
- 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
|
|
|
|
|
2015-09-04 19:52:21 +00:00
|
|
|
- Brysbaert, M., Buchmeier, M., Conrad, M., Jacobs, A. M., Bölte, J., & Böhl, A.
|
|
|
|
(2015). The word frequency effect. Experimental Psychology.
|
|
|
|
http://econtent.hogrefe.com/doi/abs/10.1027/1618-3169/a000123?journalCode=zea
|
|
|
|
|
2015-09-08 21:43:16 +00:00
|
|
|
- Brysbaert, M., Buchmeier, M., Conrad, M., Jacobs, A.M., Bölte, J., & Böhl, A.
|
|
|
|
(2011). The word frequency effect: A review of recent developments and
|
|
|
|
implications for the choice of frequency estimates in German. Experimental
|
|
|
|
Psychology, 58, 412-424.
|
|
|
|
|
2015-09-04 04:57:04 +00:00
|
|
|
- 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
|
|
|
|
|
2015-09-04 19:57:40 +00:00
|
|
|
- Dave, H. (2011). Frequency word lists.
|
|
|
|
https://invokeit.wordpress.com/frequency-word-lists/
|
|
|
|
|
2015-09-04 04:57:04 +00:00
|
|
|
- Davis, M. (2012). Unicode text segmentation. Unicode Standard Annex, 29.
|
|
|
|
http://unicode.org/reports/tr29/
|
|
|
|
|
2017-01-06 00:18:06 +00:00
|
|
|
- Halácsy, P., Kornai, A., Németh, L., Rung, A., Szakadát, I., & Trón, V.
|
|
|
|
(2004). Creating open language resources for Hungarian. In Proceedings of the
|
|
|
|
4th international conference on Language Resources and Evaluation (LREC2004).
|
|
|
|
http://mokk.bme.hu/resources/webcorpus/
|
|
|
|
|
2015-09-04 19:57:40 +00:00
|
|
|
- Keuleers, E., Brysbaert, M. & New, B. (2010). SUBTLEX-NL: A new frequency
|
|
|
|
measure for Dutch words based on film subtitles. Behavior Research Methods,
|
|
|
|
42(3), 643-650.
|
|
|
|
http://crr.ugent.be/papers/SUBTLEX-NL_BRM.pdf
|
|
|
|
|
2015-09-04 04:57:04 +00:00
|
|
|
- Kudo, T. (2005). Mecab: Yet another part-of-speech and morphological
|
|
|
|
analyzer.
|
|
|
|
http://mecab.sourceforge.net/
|
|
|
|
|
2017-01-07 00:04:40 +00:00
|
|
|
- Lison, P. and Tiedemann, J. (2016). OpenSubtitles2016: Extracting Large
|
|
|
|
Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th
|
|
|
|
International Conference on Language Resources and Evaluation (LREC 2016).
|
|
|
|
http://stp.lingfil.uu.se/~joerg/paper/opensubs2016.pdf
|
|
|
|
|
2015-09-04 04:57:04 +00:00
|
|
|
- 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
|