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Specification for annotator > tagger > metagger


Metagger (Maximum Entropy Tagger) is a simplistic part-of-speech tagger that can be easily custom-trained. For the tagger to work, it is necessary to include any morphological analyzer in the pipeline before the tagger is used.

Currently no pretrained part-of-speech models are available which renders the tagger unusable unless you provide your own models.


A typical pipeline for disambiguating potentially ambiguous output of the morfologik morphological analyzer is as follows:

read-text ! tokenize --lang pl ! morfologik ! metagger


Most options concern custom-trained models and are not neccessary if te default Polish POS-model is used. A detailed description of the training procedure will be provided in a separate tutorial which is currently under construction.


Allowed options:
  --lang arg (=guess)                 language
  --force-language                    force using specified language even if a 
                                      text was resognised otherwise
  --model arg (=%ITSDATA%/%LANG%.blm) model file
  --iterations arg (=50)              number of iterations
  --unknown-pos arg (=ign)            unknown part of speech label
  --cardinal-number-pos arg (=card)   cardinal number part of speech label
  --proper-noun-pos arg (=name)       proper noun part of speech label
  --open-class-labels arg             open class labels
  --train                             training mode
  --save-text-model-files             saves text model files in training model

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