wordfreq/wordfreq_builder

wordfreq_builder

This package builds the data files for wordfreq.

It requires a fair amount of external input data (42 GB of it, as of this writing), which is unfortunately not version-controlled. We'd like to remedy this situation using some sort of framework, but this requires sorting things out with Tools.

How to build it

Set up your external hard disk, your networked file system, or whatever thing you have that's got a couple hundred GB of space free. Let's suppose the directory of it that you want to use is called /ext/data.

Copy the input data:

cp -rv /nfs/broadway/data/wordfreq_builder /ext/data/

Make a symbolic link so that data/ in this directory points to your copy of the input data:

ln -s /ext/data/wordfreq_builder data

Install the Ninja build system:

sudo apt-get install ninja-build

We need to build a Ninja build file using the Python code in wordfreq_builder/ninja.py. We could do this with Ninja, but... you see the chicken-and-egg problem, don't you. So this is the one thing the Makefile knows how to do.

make

Start the build, and find something else to do for a few hours:

ninja -v

You can copy the results into wordfreq with this command (supposing that $WORDFREQ points to your wordfreq repo):

cp data/generated/combined/*.msgpack.gz $WORDFREQ/wordfreq/data/

The dBpack data format

We pack the wordlists into a small amount of space using a format that I call "dBpack". This is the data that's found in the .msgpack.gz files that are output at the end. The format is as follows:

• The file on disk is a gzipped file in msgpack format, which decodes to a list of lists of words.

• Each inner list of words corresponds to a particular word frequency, rounded to the nearest decibel. 0 dB represents a word that occurs with probability 1, so it is the only word in the data (this of course doesn't happen). -20 dB represents a word that occurs once per 100 tokens, -30 dB represents a word that occurs once per 1000 tokens, and so on.

• The index of each list within the overall list is the negative of its frequency in decibels.

• Each inner list is sorted in alphabetical order.

As an example, consider a corpus consisting only of the words "red fish blue fish". The word "fish" occurs as 50% of tokens (-3 dB), while "red" and "blue" occur as 25% of tokens (-6 dB). The dBpack file of their word frequencies would decode to this list:

[[], [], [], ['fish'], [], [], ['blue', 'red']]

The Ninja build process

Ninja is a lot like Make, except with one big {drawback|advantage}: instead of writing bizarre expressions in an idiosyncratic language to let Make calculate which files depend on which other files...

...you just tell Ninja which files depend on which other files.

The Ninja documentation suggests using your favorite scripting language to create the dependency list, so that's what we've done in ninja.py.

Dependencies in Ninja refer to build rules. These do need to be written by hand in Ninja's own format, but the task is simpler. In this project, the build rules are defined in rules.ninja. They'll be concatenated with the Python-generated dependency definitions to form the complete build file, build.ninja, which is the default file that Ninja looks at when you run ninja.

So a lot of the interesting work in this package is done in rules.ninja. This file defines shorthand names for long commands. As a simple example, the rule named format_twitter applies the command

python -m wordfreq_builder.cli.format_twitter $in $out

to the dependency file $in and the output file $out.

The specific rules are described by the comments in rules.ninja.

Data sources

Leeds Internet Corpus

Also known as the "Web as Corpus" project, this is a University of Leeds project that collected wordlists in assorted languages by crawling the Web. The results are messy, but they're something. We've been using them for quite a while.

The original files are in data/source-lists/leeds, and they're processed by the convert_leeds rule in rules.ninja.

Twitter

The file data/raw-input/twitter/all-2014.txt contains about 72 million tweets collected by the ftfy.streamtester package in 2014.

It takes a lot of work -- and a lot of Rosette, in particular -- to convert these tweets into data that's usable for wordfreq. They have to be language-detected and then tokenized. So the result of language-detection and tokenization is stored in data/intermediate/twitter.

Google Books

We use English word frequencies from Google Books Syntactic Ngrams. We pretty much ignore the syntactic information, and only use this version because it's cleaner. The data comes in the form of 99 gzipped text files in data/raw-input/google-books.

OpenSubtitles

Some guy made word frequency lists out of the subtitle text on OpenSubtitles. This data was used to make Wiktionary word frequency lists at one point, but it's been updated significantly since the version Wiktionary got.

The wordlists are in data/source-lists/opensubtitles.

In order to fit into the wordfreq pipeline, we renamed lists with different variants of the same language code, to distinguish them fully according to BCP 47. Then we concatenated the different variants into a single list, as follows:

• zh_tw.txt was renamed to zh-Hant.txt
• zh_cn.txt was renamed to zh-Hans.txt
• zh.txt was renamed to zh-Hani.txt
• zh-Hant.txt, zh-Hans.txt, and zh-Hani.txt were concatenated into zh.txt
• pt.txt was renamed to pt-PT.txt
• pt_br.txt was renamed to pt-BR.txt
• pt-BR.txt and pt-PT.txt were concatenated into pt.txt

We also edited the English data to re-add "'t" to words that had obviously lost it, such as "didn" in the place of "didn't". We applied this to words that became much less common words in the process, which means this wordlist no longer represents the words 'don' and 'won', as we assume most of their frequency comes from "don't" and "won't". Words that turned into similarly common words, however, were left alone: this list doesn't represent "can't" because the word was left as "can".