Stanford tagger

This node assigns to each term of a document a part of speech (POS) tag. It is applicable for French, English and German texts. The underlying tagger models are models of the Stanford NLP group:
http://nlp.stanford.edu/software/tagger.shtml

For English texts the Penn Treebank tag set is used:
http://www.cis.upenn.edu/~treebank).
For German texts the STTS tag set is used:
http://www.ims.uni-stuttgart.de/projekte/CQPDemos/Bundestag/help-tagset.html.
For French texts the French Treebank tag set is used: http://www.llf.cnrs.fr/Gens/Abeille/French-Treebank-fr.php.

Note: the provided tagger models vary in memory consumption and processing speed. Especially the models English bidirectional, German hgc, and Germany dewac require a lot of memory. For the usage of these models it is recommended to run KNIME with at least 2GB of heap space. To increase the head space, change the -Xmx setting in the knime.ini file. If KNIME is running with less than 1.5GB heap space it is recommended to use English left3words, English left3words caseless, or German fast models for tagging of english or german texts.

Descriptions of the models (taken from the website of the Stanford NLP group):

• English bidirectional: Trained on WSJ sections 0-18 using a bidirectional architecture and including word shape and distributional similarity features.
• English left3words: Trained on WSJ sections 0-18 and extra parser training data using the left3words architecture and includes word shape and distributional similarity features.
• English left3words caseless: Trained on WSJ sections 0-18 and extra parser training data using the left3words architecture and includes word shape and distributional similarity features. Ignores case.
• German hgc: Trained on the first 80% of the Negra corpus, which uses the STTS tagset.
• German dewac: This model uses features from the distributional similarity clusters built from the deWac web corpus.
• German Fast: Lacks distributional similarity features, but is several times faster than the other alternatives.
• French: Trained on the French treebank.