- Supports Python 3.8 through 3.12.
- Stopped using the deprecated
pkg_resourcesand implicitly depending on setuptools. We now use thelocatepackage instead.
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wordfreq has changed from the MIT license to the Apache License 2.0.
The Apache license is equally permissive, but clarifies requirements about attributing the author (Robyn Speer), making this attribution readable, and maintaining credit for the included data resources.
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Minor packaging changes to make sure that tox can test the library on Python 3.7 through 3.11.
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Updated the range of allowable versions of
regex. Versions before 2021.7.6 don't have theregex.Matchclass. -
Added the
extrasdependencies as optional dependencies in pyproject.toml.
mypy was accidentally listed as a full dependency; moved it to dev dependencies.
This is the "handle numbers better" release.
Previously, wordfreq would group all digit sequences of the same 'shape', with length 2 or more, into a single token and return the frequency of that token, which would be a vast overestimate.
Now it distributes the frequency over all numbers of that shape, with an estimated distribution that allows for Benford's law (lower numbers are more frequent) and a special frequency distribution for 4-digit numbers that look like years (2010 is more frequent than 1020).
More changes related to digits:
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Functions such as
iter_wordlistandtop_n_listno longer return multi-digit numbers (they used to return them in their "smashed" form, such as "0000"). -
lossy_tokenizeno longer replaces digit sequences with 0s. That happens instead in a place that's internal to theword_frequencyfunction, so we can look at the values of the digits before they're replaced.
Other changes:
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wordfreq is now developed using
poetryas its package manager, and withpyproject.tomlas the source of configuration instead ofsetup.py. -
The minimum version of Python supported is 3.7.
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Type information is exported using
py.typed.
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Import ftfy and use its
uncurl_quotesmethod to turn curly quotes into straight ones, providing consistency with multiple forms of apostrophes. -
Set minimum version requirements on
regex,jieba, andlangcodesso that tokenization will give consistent results. -
Workaround an inconsistency in the
msgpackAPI aroundstrict_map_key=False.
- Incorporate data from the OSCAR corpus.
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When tokenizing Japanese or Korean, MeCab's dictionaries no longer have to be installed separately as system packages. They can now be found via the Python packages
ipadicandmecab-ko-dic. -
When the tokenizer had to infer word boundaries in languages without spaces, inputs that were too long (such as the letter 'l' repeated 800 times) were causing overflow errors. We changed the sequence of operations so that it no longer overflows, and such inputs simply get a frequency of 0.
- Changed a log message to not try to call a language by name, to remove the dependency on a database of language names.
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The Exquisite Corpus data has been updated to include Google Books Ngrams 2019, Reddit data through 2019, Wikipedia data from 2020, and Twitter-sampled data from 2020, and somewhat more reliable language detection.
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Updated dependencies to require recent versions of
regexandjieba, to get tokenization that's consistent with the word lists.regexnow requires a version after 2020.04.04.
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Relaxing the dependency on regex had an unintended consequence in 2.3.1: it could no longer get the frequency of French phrases such as "l'écran" because their tokenization behavior changed.
2.3.2 fixes this with a more complex tokenization rule that should handle apostrophes the same across these various versions of regex.
- State the dependency on msgpack >= 1.0 in setup.py.
- Relax the dependency on regex to allow versions after 2018.02.08.
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Python 3.5 is the oldest maintained version of Python, and we have stopped claiming support for earlier versions.
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Updated to langcodes 2.0.
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Deprecated the
match_cutoffparameter, which was intended for situations where we need to approximately match a language code, but was not usefully configurable in those situations.
Library change:
- Fixed an incompatibility with newly-released
msgpack 1.0.
Library changes:
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Relaxed the version requirement on the 'regex' dependency, allowing compatibility with spaCy.
The range of regex versions that wordfreq now allows is from 2017.07.11 to 2018.02.21. No changes to word boundary matching were made between these versions.
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Fixed calling
msgpack.loadwith a deprecated parameter.
Library change:
- While the @ sign is usually considered a symbol and not part of a word, there is a case where it acts like a letter. It's used in one way of writing gender-neutral words in Spanish and Portuguese, such as "l@s niñ@s". The tokenizer in wordfreq will now allow words to end with "@" or "@s", so it can recognize these words.
Data changes:
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Updated the data from Exquisite Corpus to filter the ParaCrawl web crawl better. ParaCrawl provides two metrics (Zipporah and Bicleaner) for the goodness of its data, and we now filter it to only use texts that get positive scores on both metrics.
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The input data includes the change to tokenization described above, giving us word frequencies for words such as "l@s".
Data changes:
- Updated to the data from the latest Exquisite Corpus, which adds the ParaCrawl web crawl and updates to OpenSubtitles 2018
- Added small word list for Latvian
- Added large word list for Czech
- The Dutch large word list once again has 5 data sources
Library changes:
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The output of
word_frequencyis rounded to three significant digits. This provides friendlier output, and better reflects the precision of the underlying data anyway. -
The MeCab interface can now look for Korean and Japanese dictionaries in
/usr/lib/x86_64-linux-gnu/mecab, which is where Ubuntu 18.04 puts them when they are installed from source.
Fixed edge cases that inserted spurious token boundaries when Japanese text is
run through simple_tokenize, because of a few characters that don't match any
of our "spaceless scripts".
It is not a typical situation for Japanese text to be passed through
simple_tokenize, because Japanese text should instead use the
Japanese-specific tokenization in wordfreq.mecab.
However, some downstream uses of wordfreq have justifiable reasons to pass all
terms through simple_tokenize, even terms that may be in Japanese, and in
those cases we want to detect only the most obvious token boundaries.
In this situation, we no longer try to detect script changes, such as between kanji and katakana, as token boundaries. This particularly allows us to keep together Japanese words where ヶ appears between kanji, as well as words that use the iteration mark 々.
This change does not affect any word frequencies. (The Japanese word list uses
wordfreq.mecab for tokenization, not simple_tokenize.)
The big change in this version is that text preprocessing, tokenization, and postprocessing to look up words in a list are separate steps.
If all you need is preprocessing to make text more consistent, use
wordfreq.preprocess.preprocess_text(text, lang). If you need preprocessing
and tokenization, use wordfreq.tokenize(text, lang) as before. If you need
all three steps, use the new function wordfreq.lossy_tokenize(text, lang).
As a breaking change, this means that the tokenize function no longer has
the combine_numbers option, because that's a postprocessing step. For
the same behavior, use lossy_tokenize, which always combines numbers.
Similarly, tokenize will no longer replace Chinese characters with their
Simplified Chinese version, while lossy_tokenize will.
Other changes:
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There's a new default wordlist for each language, called "best". This chooses the "large" wordlist for that language, or if that list doesn't exist, it falls back on "small".
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The wordlist formerly named "combined" (this name made sense long ago) is now named "small". "combined" remains as a deprecated alias.
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The "twitter" wordlist has been removed. If you need to compare word frequencies from individual sources, you can work with the separate files in exquisite-corpus.
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Tokenizing Chinese will preserve the original characters, no matter whether they are Simplified or Traditional, instead of replacing them all with Simplified characters.
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Different languages require different processing steps, and the decisions about what these steps are now appear in the
wordfreq.language_infomodule, replacing a bunch of scattered and inconsistentifstatements. -
Tokenizing CJK languages while preserving punctuation now has a less confusing implementation.
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The preprocessing step can transliterate Azerbaijani, although we don't yet have wordlists in this language. This is similar to how the tokenizer supports many more languages than the ones with wordlists, making future wordlists possible.
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Speaking of that, the tokenizer will log a warning (once) if you ask to tokenize text written in a script we can't tokenize (such as Thai).
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New source data from exquisite-corpus includes OPUS OpenSubtitles 2018.
Nitty gritty dependency changes:
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Updated the regex dependency to 2018.02.21. (We would love suggestions on how to coexist with other libraries that use other versions of
regex, without a>=requirement that could introduce unexpected data-altering changes.) -
We now depend on
msgpack, the new name formsgpack-python.
- Tokenization will always keep Unicode graphemes together, including complex emoji introduced in Unicode 10
- Update the Wikipedia source data to April 2017
- Remove some non-words, such as the Unicode replacement character and the pilcrow sign, from frequency lists
- Support Bengali and Macedonian, which passed the threshold of having enough source data to be included
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Depend on langcodes 1.4, with a new language-matching system that does not depend on SQLite.
This prevents silly conflicts where langcodes' SQLite connection was preventing langcodes from being used in threads.
- Support Czech, Persian, Ukrainian, and Croatian/Bosnian/Serbian
- Add large lists in Chinese, Finnish, Japanese, and Polish
- Data is now collected and built using Exquisite Corpus (https://github.com/LuminosoInsight/exquisite-corpus)
- Add word frequencies from OPUS OpenSubtitles 2016
- Add word frequencies from the MOKK Hungarian Webcorpus
- Expand Google Books Ngrams data to cover 8 languages
- Expand language detection on Reddit to cover 13 languages with large enough Reddit communities
- Drop the Common Crawl; we have enough good sources now that we don't have to deal with all that spam
- Add automatic transliteration of Serbian text
- Adjust tokenization of apostrophes next to vowel sounds: the French word "l'heure" is now tokenized similarly to "l'arc"
- Multi-digit numbers of each length are smashed into the same word frequency, to remove meaningless differences and increase compatibility with word2vec. (Internally, their digits are replaced by zeroes.)
- Another new frequency-merging strategy (drop the highest and lowest, average the rest)
- Bug fix: Made it possible to load the Japanese or Korean dictionary when the other one is not available
- Include word frequencies learned from the Common Crawl
- Support Bulgarian, Catalan, Danish, Finnish, Hebrew, Hindi, Hungarian, Norwegian Bokmål, and Romanian
- Improve Korean with MeCab tokenization
- New frequency-merging strategy (weighted median)
- Include Wikipedia as a Chinese source (mostly Traditional)
- Include Reddit as a Spanish source
- Remove Greek Twitter because its data is poorly language-detected
- Add large lists in Arabic, Dutch, Italian
- Remove marks from more languages
- Deal with commas and cedillas in Turkish and Romanian
- Fix tokenization of Southeast and South Asian scripts
- Clean up Git history by removing unused large files
- Add large lists in English, German, Spanish, French, and Portuguese
- Add
zipf_frequencyfunction
- Add Reddit comments as an English source
- Add SUBTLEX data
- Better support for Chinese, using Jieba for tokenization, and mapping Traditional Chinese characters to Simplified
- Improve Greek
- Add Polish, Swedish, and Turkish
- Tokenizer can optionally preserve punctuation
- Detect when sources stripped "'t" off of English words, and repair their frequencies
- Use the 'regex' package to implement Unicode tokenization that's mostly consistent across languages
- Use NFKC normalization in Japanese and Arabic
- Create compact word frequency lists in English, Arabic, German, Spanish, French, Indonesian, Japanese, Malay, Dutch, Portuguese, and Russian
- Marginal support for Greek, Korean, Chinese
- Fresh start, dropping compatibility with wordfreq 0.x and its unreasonably large downloads