From 23c5c4adca720e2d848895eac3d9938f463cc04c Mon Sep 17 00:00:00 2001 From: Rob Speer Date: Fri, 22 Jan 2016 16:23:43 -0500 Subject: [PATCH] Add and document large wordlists Former-commit-id: d79ee37da958ea1d92cbf73ccf43c965c8933f48 --- README.md | 77 +++++++++++++++----- wordfreq/data/large_en.msgpack.gz | Bin 0 -> 2150732 bytes wordfreq/data/large_es.msgpack.gz | Bin 0 -> 2661185 bytes wordfreq/data/large_fr.msgpack.gz | Bin 0 -> 2141003 bytes wordfreq/data/large_pt.msgpack.gz | Bin 0 -> 2366731 bytes wordfreq_builder/wordfreq_builder/config.py | 2 +- 6 files changed, 58 insertions(+), 21 deletions(-) create mode 100644 wordfreq/data/large_en.msgpack.gz create mode 100644 wordfreq/data/large_es.msgpack.gz create mode 100644 wordfreq/data/large_fr.msgpack.gz create mode 100644 wordfreq/data/large_pt.msgpack.gz diff --git a/README.md b/README.md index ccd00c7..1d57786 100644 --- a/README.md +++ b/README.md @@ -39,11 +39,18 @@ For example: ## Usage wordfreq provides access to estimates of the frequency with which a word is -used, in 18 languages (see *Supported languages* below). It loads -efficiently-packed data structures that contain all words that appear at least -once per million words. +used, in 18 languages (see *Supported languages* below). -The most useful function is: +It provides three kinds of pre-built wordlists: + +- `'combined'` lists, containing words that appear at least once per + million words, averaged across all data sources. +- `'twitter'` lists, containing words that appear at least once per + million words on Twitter alone. +- `'large'` lists, containing words that appear at least once per 100 + million words, averaged across all data sources. + +The most straightforward function is: word_frequency(word, lang, wordlist='combined', minimum=0.0) @@ -64,7 +71,37 @@ frequencies by a million (1e6) to get more readable numbers: >>> word_frequency('café', 'fr') * 1e6 77.62471166286912 -The parameters are: + +`zipf_frequency` is a variation on `word_frequency` that aims to return the +word frequency on a human-friendly logarithmic scale. The Zipf scale was +proposed by Marc Brysbaert, who created the SUBTLEX lists. The Zipf frequency +of a word is the base-10 logarithm of the number of times it appears per +billion words. A word with Zipf value 6 appears once per thousand words, for +example, and a word with Zipf value 3 appears once per million words. + +Reasonable Zipf values are between 0 and 8, but because of the cutoffs +described above, the minimum Zipf value appearing in these lists is 1.0 for the +'large' wordlists and 3.0 for all others. We use 0 as the default Zipf value +for words that do not appear in the given wordlist, although it should mean +one occurrence per billion words. + + >>> zipf_frequency('the', 'en') + 7.59 + + >>> zipf_frequency('word', 'en') + 5.34 + + >>> zipf_frequency('frequency', 'en') + 4.44 + + >>> zipf_frequency('zipf', 'en') + 0.0 + + >>> zipf_frequency('zipf', 'en', 'large') + 1.42 + + +The parameters to `word_frequency` and `zipf_frequency` are: - `word`: a Unicode string containing the word to look up. Ideally the word is a single token according to our tokenizer, but if not, there is still @@ -73,21 +110,18 @@ The parameters are: - `lang`: the BCP 47 or ISO 639 code of the language to use, such as 'en'. - `wordlist`: which set of word frequencies to use. Current options are - 'combined', which combines up to five different sources, and - 'twitter', which returns frequencies observed on Twitter alone. + 'combined', 'twitter', and 'large'. - `minimum`: If the word is not in the list or has a frequency lower than - `minimum`, return `minimum` instead. In some applications, you'll want - to set `minimum=1e-6` to avoid a discontinuity where the list ends, because - a frequency of 1e-6 (1 per million) is the threshold for being included in - the list at all. + `minimum`, return `minimum` instead. You may want to set this to the minimum + value contained in the wordlist, to avoid a discontinuity where the wordlist + ends. Other functions: `tokenize(text, lang)` splits text in the given language into words, in the same way that the words in wordfreq's data were counted in the first place. See -*Tokenization*. Tokenizing Japanese requires the optional dependency `mecab-python3` -to be installed. +*Tokenization*. `top_n_list(lang, n, wordlist='combined')` returns the most common *n* words in the list, in descending frequency order. @@ -168,6 +202,8 @@ it, but we have too few data sources for it so far: ──────────────────┼─────────────────────────────────────── Korean ko │ - - - Yes Yes +The 'large' wordlists are available in English, Spanish, French, and Portuguese. + [1] We've counted the frequencies from tweets in German, such as they are, but you should be aware that German is not a frequently-used language on Twitter. Germans just don't tweet that much. @@ -179,7 +215,8 @@ 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][uax29]. +Annex #29, Text Segmentation][uax29], including the optional rule that +splits words between apostrophes and vowels. There are language-specific exceptions: @@ -199,10 +236,10 @@ Because tokenization in the real world is far from consistent, wordfreq will also try to deal gracefully when you query it with texts that actually break into multiple tokens: - >>> word_frequency('New York', 'en') - 0.0002315934248950231 - >>> word_frequency('北京地铁', 'zh') # "Beijing Subway" - 3.2187603965715087e-06 + >>> zipf_frequency('New York', 'en') + 5.31 + >>> zipf_frequency('北京地铁', 'zh') # "Beijing Subway" + 3.51 The word frequencies are combined with the half-harmonic-mean function in order to provide an estimate of what their combined frequency would be. In Chinese, @@ -216,8 +253,8 @@ 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: - >>> word_frequency('owl-flavored', 'en') - 1.3557098723512335e-06 + >>> zipf_frequency('owl-flavored', 'en') + 3.18 ## License diff --git a/wordfreq/data/large_en.msgpack.gz b/wordfreq/data/large_en.msgpack.gz new file mode 100644 index 0000000000000000000000000000000000000000..ebb70f16c8094a89143d8b5615e6256a2deb405a GIT binary patch literal 2150732 zcmV(^K-Iq=iwFpClA=}u|6^}$Vrgz=WG!rAa%W{LbAR$>oNU$$cB|qYnY&Kg0wIf!_x_FjssDiQPw*IHt;k(1@dLQJy=$L(?2K4zt~tk?bIh@x z{^dXTd(+95E-CVVY1|fNW~w&&n?L#g)BkeeBcZU+_nnttpLd2#N}`{JiVT;yL>9bTo+iqdwDOZ&0g8#kQyNwoin zhduXIf_Lcl`0k94{K@z2uE1Z3hjUS<@yDLeF&=GkO!2pzwS1@S`+ipxafd&{PtWl2 z8*6hsbCMsv>vDVkekhaOzUp0(S^U^E#k1qzCFRGy$WyIL{(ZMAs-fVE_GgphwR)4f zV%R37tG??uCOT;2@{fIlA2aNVB;S4C#Yx;Jc~x}Px%k*;=3sl+bn~%GzRLOn-~Mdc z*vQn*_9_0e&+2GDmKMw2;H~j8AKThtc^d9@gC*Tow){9md|NDL`F(FsSjq}NxT}f+ zA849BslIY;fdzA>;fpx?Qy5=^Wp!1bTO0SzB=Pt7#NyCp20Kxd2Ru^cy42!j(qwDD 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