# wordfreq\_builder This package builds the data files for [wordfreq](https://github.com/LuminosoInsight/wordfreq). It requires a fair amount of external input data (42 GB of it, as of this writing), which unfortunately we don't have a plan for how to distribute outside of Luminoso yet. The data can be publicly obtained in various ways, so here we'll at least document where it comes from. We hope to come up with a process that's more reproducible eventually. The good news is that you don't need to be able to run this process to use wordfreq. The built results are already in the `wordfreq/data` directory. ## 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`. Get the input data. At Luminoso, this is available in the directory `/nfs/broadway/data/wordfreq_builder`. The sections below explain where the data comes from. 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/dist/*.msgpack.gz ../wordfreq/data/ ## 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. These files can be downloaded from the [Leeds corpus page][leeds]. The original files are in `data/source-lists/leeds`, and they're processed by the `convert_leeds` rule in `rules.ninja`. [leeds]: http://corpus.leeds.ac.uk/list.html ### Twitter The file `data/raw-input/twitter/all-2014.txt` contains about 72 million tweets collected by the `ftfy.streamtester` package in 2014. It's not possible to distribute the text of tweets. However, this process could be reproduced by running `ftfy.streamtester`, part of the [ftfy][] package, for a couple of weeks. [ftfy]: https://github.com/LuminosoInsight/python-ftfy ### Google Books We use English word frequencies from [Google Books Syntactic Ngrams][gbsn]. 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`. [gbsn]: http://commondatastorage.googleapis.com/books/syntactic-ngrams/index.html ### Wikipedia Another source we use is the full text of Wikipedia in various languages. This text can be difficult to extract efficiently, and for this purpose we use a custom tool written in Nim 0.11, called [wiki2text][]. To build the Wikipedia data, you need to separately install Nim and wiki2text. The input data files are the XML dumps that can be found on the [Wikimedia backup index][wikidumps]. For example, to get the latest Spanish data, go to https://dumps.wikimedia.org/frwiki/latest and look for the filename of the form `*.pages-articles.xml.bz2`. If this file isn't there, look for an older dump where it is. You'll need to download such a file for each language that's configured for Wikipedia in `wordfreq_builder/config.py`. [wiki2text]: https://github.com/rspeer/wiki2text [wikidumps]: https://dumps.wikimedia.org/backup-index.html ### OpenSubtitles [Hermit Dave](https://invokeit.wordpress.com/frequency-word-lists/) 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".