Difference between revisions of "Calculating coverage"
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* [[Wikipedia dumps]] |
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* [[Cleanstream]] |
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* [https://gist.github.com/2283105 apertium-cleanstream] |
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[[Category:Documentation]] |
[[Category:Documentation]] |
Revision as of 13:27, 30 January 2016
Contents
Simple bidix-trimmed coverage testing
First install apertium-cleanstream:
svn checkout https://svn.code.sf.net/p/apertium/svn/trunk/apertium-tools/apertium-cleanstream cd apertium-cleanstream make sudo cp apertium-cleanstream /usr/local/bin
Then save this as coverage.sh:
#!/bin/bash mode=$1 outfile=/tmp/$mode.clean apertium -d . $mode | apertium-cleanstream -n > $outfile total=$(grep -c '^\^' $outfile) unknown=$(grep -c '/\*' $outfile) bidix_unknown=$(grep -c '/@' $outfile) known_percent=$(calc -p "round( 100*($total-$unknown-$bidix_unknown)/$total, 3)") echo "$known_percent % known tokens ($unknown unknown, $bidix_unknown bidix-unknown of total $total tokens)" echo "Top unknown words:" grep '/[*@]' $outfile | sort | uniq -c | sort -nr | head
And run it like
cat asm.corpus | bash coverage.sh asm-eng-biltrans
(The bidix-unknown count should always be 0 if your pair uses automatic analyser trimming.)
TODO: paradigm-coverage (less naïve)
On an analysed corpus, we can sum frequencies into bins for each lemma+mainpos, so if the analysed corpus contains
musa/mus<n><f><sg><def>/muse<vblex><past> mus/mus<n><f><sg><ind>/mus<n><f><pl><ind>/muse<vblex><imp> musene/mus<n><f><pl><def>
then output has
3 mus<n><f> 2 muse<vblex>
and we can find paradigms that are likely to mess up disambiguation, or where we need to ensure that the bidix contains the highest-frequency paradigm (since the bidix is typically smaller than the monodix).
We could also weight these numbers by number of unique forms in the pardef; if the verb pardef has 6 unique forms and then noun only 3, then the above output should be even more skewed:
0.33 mus<n><f> 0.75 muse<vblex>
Faster coverage testing with frequency lists
If words appear several times in your corpus, why bother analysing them several times? We can make a frequency list first and add together the frequencies. This script does some very stupid tokenisation and creates a frequency list:
make-freqlist.sh:
#!/bin/bash if [[ -t 0 ]]; then echo "Expecting a corpus on stdin" exit 2 fi tr '[:space:][:punct:]' '\n' | grep . | sort | uniq -c | sort -nr
And this script runs your analyser, summing up the frequencies:
freqlist-coverage.sh:
#!/bin/bash set -e -u if [[ $# -eq 0 || -t 0 ]]; then echo "Expecting apertium arguments and a 'sort|uniq -c|sort -nr' style frequency list on stdin" echo "For example:" echo "\$ < spa.freqlist $0 -d . spa-morph" exit 2 fi sed 's%^ *%<apertium-notrans>%;s% %</apertium-notrans>%;s%$% .%' | apertium -f html-noent "$@" | awk -F'</?apertium-notrans>| *\\^\\./\\.<sent><clb>\\$' ' /[/][*@]/ { unknown+=$2 if(!printed) print "Top unknown tokens:" if(++printed<10) print $2,$3 next } { known+=$2 } END { total=known+unknown known_pct=100*known/total unk_pct=100*unknown/total print known_pct" % known of total "total" tokens" }'
Usage:
$ chmod +x make-freqlist.sh freqlist-coverage.sh $ bzcat ~/corpora/nno.txt.bz2 |./make-freqlist.sh > nno.freqlist $ <nno.freqlist ./freqlist-coverage.sh -d ~/apertium-svn/languages/apertium-nno/ nno-morph
coverage.py
https://svn.code.sf.net/p/apertium/svn/trunk/apertium-tools/coverage.py is a coverage script that wraps curl and bzcat (?)
More involved scripts
Often it's nice to clean up wikipedia fluff etc. for coverage testing.
Notes on calculating coverage from wikipedia dumps (based on Asturian#Calculating coverage).
(Mac OS X `sed' doesn't allow \n in replacements, so I just use an actual (escaped) newline...)
wikicat.sh:
#!/bin/sh # Clean up wikitext for running through apertium-destxt # awk prints full lines, make sure each html element has one bzcat "$@" | sed 's/>/>\ /g' | sed 's/</\ </g' |\ # want only stuff between <text...> and </text> awk ' /<text.*>/,/<\/text>/ { print $0 } ' |\ sed 's/\./ /g' |\ # Drop all transwiki links sed 's/\[\[\([a-z]\{2,3\}\|bat-smg\|be-x-old\|cbk-zam\|fiu-vro\|map-bms\|nds-nl\|roa-rup\|roa-tara\|simple\|zh-classical\|zh-min-nan\|zh-yue\):[^]]\+\]\]//g' |\ # wiki markup, retain bar and fie from [[foo|bar]] [[fie]] sed 's/\[\[[^]|]*|//g' | sed 's/\]\]//g' | sed 's/\[\[//g' |\ # wiki markup, retain `bar fie' from [http://foo bar fie] and remove [http://foo] sed 's/\[http[^ ]*\([^]]*\)\]/\1/g' |\ # remove entities sed 's/&[^;]*;/ /g' |\ # and put space instead of punctuation sed 's/[;:?,]/ /g' |\ # Keep only lines starting with a capital letter, removing tables with style info etc. grep '^[ ]*[A-ZÆØÅ]' # Your alphabet here
count-tokenized.sh:
#!/bin/sh # http://wiki.apertium.org/wiki/Asturian#Calculating_coverage # Calculate the number of tokenised words in the corpus: apertium-destxt | lt-proc $1 |apertium-retxt |\ # for some reason putting the newline in directly doesn't work, so two seds sed 's/\$[^^]*\^/$^/g' | sed 's/\$\^/$\ ^/g'
To find all tokens from a wiki dump:
$ ./wikicat.sh nnwiki-20090119-pages-articles.xml.bz2 > nnwiki.cleaned.txt cat nnwiki.cleaned.txt | ./count-tokenized.sh nn-nb.automorf.bin | wc -l
To find all tokens with at least one analysis (naïve coverage):
$ cat nnwiki.cleaned.txt | ./count-tokenized.sh nn-nb.automorf.bin | grep -v '\/\*' | wc -l
To find the top unknown tokens:
$ cat nnwiki.cleaned.txt | ./count-tokenized.sh nn-nb.automorf.bin | sed 's/[ ]*//g' |\ # tab or space grep '\/\*' | sort -f | uniq -c | sort -gr | head
Script ready to run
corpus-stat.sh
#!/bin/sh # http://wiki.apertium.org/wiki/Asturian#Calculating_coverage # Example use: # zcat corpa/en.crp.txt.gz | sh corpus-stat.sh #CMD="cat corpa/en.crp.txt" CMD="cat" F=/tmp/corpus-stat-res.txt # Calculate the number of tokenised words in the corpus: # for some reason putting the newline in directly doesn't work, so two seds $CMD | apertium-destxt | lt-proc en-eo.automorf.bin |apertium-retxt | sed 's/\$[^^]*\^/$^/g' | sed 's/\$\^/$\ ^/g' > $F NUMWORDS=`cat $F | wc -l` echo "Number of tokenised words in the corpus: $NUMWORDS" # Calculate the number of words that are not unknown NUMKNOWNWORDS=`cat $F | grep -v '\*' | wc -l` echo "Number of known words in the corpus: $NUMKNOWNWORDS" # Calculate the coverage COVERAGE=`calc "round($NUMKNOWNWORDS/$NUMWORDS*1000)/10"` echo "Coverage: $COVERAGE %" #If you don't have calc, change the above line to: #COVERAGE=$(perl -e 'print int($ARGV[0]/$ARGV[1]*1000)/10;' $NUMKNOWNWORDS $NUMWORDS) # Show the top-ten unknown words. echo "Top unknown words in the corpus:" cat $F | grep '\*' | sort -f | uniq -c | sort -gr | head -10
Sample output:
$ zcat corpa/en.crp.txt.gz | sh corpus-stat.sh Number of tokenised words in the corpus: 478187 Number of known words in the corpus: 450255 Coverage: 94.2 % Top unknown words in the corpus: 191 ^Apollo/*Apollo$ 104 ^Aramaic/*Aramaic$ 91 ^Alberta/*Alberta$ 81 ^de/*de$ 80 ^Abu/*Abu$ 63 ^Bakr/*Bakr$ 62 ^Agassi/*Agassi$ 59 ^Carnegie/*Carnegie$ 58 ^Agrippina/*Agrippina$ 58 ^Achilles/*Achilles$ 56 ^Adelaide/*Adelaide$