Difference between revisions of "Using GIZA++"
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if it is a large corpus you may get a lot of warnings... |
if it is a large corpus you may get a lot of warnings... |
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After you've done this, you should have a couple of <code>.snt</code> files and a couple of <code>.vcb</code> files. |
After you've done this, you should have a couple of <code>.snt</code> files and a couple of <code>.vcb</code> files. |
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Next you need to generate word classes, using <code>mkcls</code>: |
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<pre> |
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$ mkcls -m2 -psv-text.txt -c50 -Vsv-text.vcb.classes opt >& mkcls1.log |
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$ mkcls -m2 -pda-text.txt -c50 -Vda-text.vcb.classes opt >& mkcls1.log |
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</pre> |
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Now use GIZA++ to build your dictionary (<code>-S</code> is the source language, <code>-T</code> is the target language, <code>-C</code> is the generated aligned text file, and <code>-o</code> is the output file prefix): |
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<pre> |
<pre> |
Revision as of 12:51, 7 October 2007
If you have parallel corpora you can use GIZA++ to make bilingual dictionaries.
Download your corpora, and convert into one sentence per line.
Download and compile GIZA++.
Use plain2snt.out
to convert your corpus into GIZA++ format:
$ plain2snt.out sv-text.txt da-text.txt w1:sv-text w2:da-text sv-text -> sv-text da-text -> da-text
You may get some warnings about empty sentences like these:
WARNING: filtered out empty sentence (source: sv-text.txt 23 target: da-text.txt 0). WARNING: filtered out empty sentence (source: sv-text.txt 34 target: da-text.txt 0).
if it is a large corpus you may get a lot of warnings...
After you've done this, you should have a couple of .snt
files and a couple of .vcb
files.
Next you need to generate word classes, using mkcls
:
$ mkcls -m2 -psv-text.txt -c50 -Vsv-text.vcb.classes opt >& mkcls1.log $ mkcls -m2 -pda-text.txt -c50 -Vda-text.vcb.classes opt >& mkcls1.log
Now use GIZA++ to build your dictionary (-S
is the source language, -T
is the target language, -C
is the generated aligned text file, and -o
is the output file prefix):
$ GIZA++ -S sv-text.vcb -T da-text.vcb -C sv-text_da-text.snt -p0 0.98 -o dictionary >& dictionary.log
and wait... You can watch the log in dictionary.log
... but the training is likely to take upwards of 10 hours, so have something else planned.