Corpus based preposition selection - HOWTO

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Revision as of 17:45, 20 August 2012 by Fpetkovski (talk | contribs)
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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.

Training phase

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 | 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 2 > training-set

And the output:

head -n 10 training-set


where the '$' character here serves as a delimiter.