mirror of
https://github.com/rspeer/wordfreq.git
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81 lines
3.2 KiB
Plaintext
81 lines
3.2 KiB
Plaintext
# This defines the rules on how to build parts of the wordfreq lists, using the
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# Ninja build system:
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#
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# http://martine.github.io/ninja/manual.html
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#
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# Ninja is available in the 'ninja-build' Ubuntu package. It's like make with
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# better parallelism and the ability for build steps to produce multiple
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# outputs. The tradeoff is that its rule syntax isn't full of magic for
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# expanding wildcards and finding dependencies, so in general you have to
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# write the dependencies using a script.
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#
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# This file will become the header of the larger build.ninja file, which also
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# contains the programatically-defined dependency graph.
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# Variables
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DATA = ./data
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# How to build the build.ninja file itself. (Use the Makefile to get it the
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# first time.)
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rule build_deps
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command = python -m wordfreq_builder.cli.build_deps $in > $out
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# Splits the single file $in into $slices parts, whose names will be
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# $prefix plus a two-digit numeric suffix.
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rule split
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command = mkdir -p $$(dirname $prefix) && split -d -n r/$slices $in $prefix
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# wiki2text is a tool I wrote using Nim 0.11, which extracts plain text from
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# Wikipedia dumps obtained from dumps.wikimedia.org. The code is at
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# https://github.com/rspeer/wiki2text.
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rule wiki2text
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command = bunzip2 -c $in | wiki2text > $out
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# To tokenize Japanese, we run it through Mecab and take the first column.
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# We don't have a plan for tokenizing Chinese yet.
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rule tokenize_japanese
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command = mecab -b 1048576 < $in | cut -f 1 | grep -v "EOS" > $out
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# Tokenizing text from Twitter requires us to language-detect and tokenize
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# in the same step.
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rule tokenize_twitter
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command = mkdir -p $$(dirname $prefix) && python -m wordfreq_builder.cli.tokenize_twitter $in $prefix
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# To convert the Leeds corpus, look for space-separated lines that start with
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# an integer and a decimal. The integer is the rank, which we discard. The
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# decimal is the frequency, and the remaining text is the term. Use sed -n
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# with /p to output only lines where the match was successful.
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#
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# Grep out the term "EOS", an indication that Leeds used MeCab and didn't
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# strip out the EOS lines.
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rule convert_leeds
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command = sed -rn 's/([0-9]+) ([0-9.]+) (.*)/\3,\2/p' < $in | grep -v 'EOS,' > $out
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# To convert the OpenSubtitles frequency data, simply replace spaces with
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# commas.
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rule convert_opensubtitles
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command = tr ' ' ',' < $in > $out
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# Convert and clean up the Google Books Syntactic N-grams data. Concatenate all
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# the input files, keep only the single words and their counts, and only keep
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# lines with counts of 100 or more.
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#
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# (These will still be repeated as the word appears in different grammatical
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# roles, information that the source data provides that we're discarding. The
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# source data was already filtered to only show words in roles with at least
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# two-digit counts of occurences.)
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rule convert_google_syntactic_ngrams
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command = zcat $in | cut -f 1,3 | grep -v '[,"]' | sed -rn 's/(.*)\s(...+)/\1,\2/p' > $out
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rule count
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command = python -m wordfreq_builder.cli.count_tokens $in $out
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rule merge
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command = python -m wordfreq_builder.cli.combine_lists -o $out $in
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rule freqs2cB
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command = python -m wordfreq_builder.cli.freqs_to_cB $lang $in $out
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rule cat
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command = cat $in > $out
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