Difference between revisions of "Corpus based preposition selection - HOWTO"
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# tag the tl-set
# tag the tl-set
cat setimes.en | head -n 150000 | apertium -d ~/Apertium/apertium-en-es en-es-tagger > training-patterns-en
Revision as of 17:40, 20 August 2012
The general algorithm for performing corpus based preposition selection is as follows:
- Download a parallel corpus
- Extract patterns which contain prepositions from the source-language corpus
- Align the patterns to their translations in the target-language corpus
- Extract the features and label (the correct preposition from the target-language corpus) for classification.
- Train a model
- Use the trained model in the pipeline
The general toolkit for performing these tasks can be found here.
The training phase is done in two steps:
- Extract patterns in the form of (n|vblex) (pr) (adj | det)* (n|vblex) from the source language corpus and translate the using apertium to the target language.
- Go through the source language file again, matching those same patterns and trying to find their translations in the target language. If a translation is found, extract the features and correct preposition as a training-set example. You could theoretically choose any combination of features, however, the tools provided so far support only 3 different combinations:
- 1-feature model -- extract an example in the following format: sl_nv1-sl_pr-sl_nv2<delimiter>tl_pr
- 2-feature model -- extract an example in the following format: sl_nv1-sl_pr<delimiter>-sl_nv2<delimiter>tl_pr
- 3-feature model -- extract an example in the following format: sl_nv1<delimiter>sl_pr<delimiter>sl_nv2<delimiter>tl_pr
sl_nv1, sl_nv1 and sl_pr stand for the first and second source language noun or verb, and for the source language preposition. tl_prep stands for the target language preposition, and that is the actual label used in classification
This is an example script that uses these two tools to create a training set:
cat setimes.mk | head -n 150000 | apertium -d ~/Apertium/apertium-mk-en mk-en-pretransfer > training-patterns-mk cat training-patterns-mk | ~/Apertium/fpetkovski/morph-parser/preposition-extraction \ | lt-proc -g ~/Apertium/apertium-mk-en/en-mk.autogen.bin \ | apertium -d ~/Apertium/apertium-mk-en/ mk-en-postchunk > extracted-patterns-train # In Macedonian, the definiteness of the noun is encoded in the noun itself, # while in English it is denoted by the article before the noun. # As a result, the extracted patterns after translation can have up to 5 tokens instead of the desired three. # That's why we want to remove the articles from the patterns. #remove articles cat extracted-patterns-train | sed 's/[ ]*\^[Tt]he<[^\$]*\$[ ]*//g' > extracted-patterns-nodef-train; # tag the tl-set cat setimes.en | head -n 150000 | apertium -d ~/Apertium/apertium-en-es en-es-tagger > training-patterns-en # alignment preposition-aligner -sl training-patterns-mk -tl training-patterns-en -tr extracted-patterns-nodef-train -n 1 > training-set