Corpus based preposition selection - HOWTO

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Revision as of 16:58, 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 the source language preposition. tl_prep stands for the target language preposition, and that is the actual label used in classification