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# #### Classifiers
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# 1. ``model1`` = your POS tagger model (english)
# 2. ``model2`` = pre-trained POS tagger model using NLTK (maxentropy english)
# 3. ``model3.x`` = rule-based classifiers (x = 1 to 5)
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# 3. ``model4`` = your POS tagger model (not english)
# 4. ``model5`` = pre-trained POS tagger model using RDRPOSTagger [1](http://rdrpostagger.sourceforge.net/) or TreeTagger [2](http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/) (not english)
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# note: for ``model1`` and ``model3`` you can try different classifiers: Hidden Markov Model, Logistic Regression, Maximum Entropy Markov Models, Decision Trees, Naive Bayes, etc..**choose one**!
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# #### Corpora
# 1. ``X1`` = nltk.corpus.treebank (english)
# 2. ``X2`` = nltk.corpus.brown (english)
# 3. ``X3`` = other language (not english)
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# **note**: data split for training/test = 0.8/0.2 (sequencial)
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# #### Task 1
# - get results for english (**plot a graph with all classifiers x results**)
#     - ``performance 1.1`` = ``model1`` in ``X1``
#     - ``performance 1.2`` = ``model2`` in ``X1``
#     - ``performance 1.3.x`` = ``model3.x`` in ``X1``
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#     - ``performance 1.4`` = ``model1`` in ``X2``
#     - ``performance 1.5`` = ``model2`` in ``X2``
#     - ``performance 1.6.x`` = ``model3.x`` in ``X2``
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# #### Task 2
# - train your model with standard features (**plot a graph with all classifiers x results**)
#     - ``performance 2.1`` = ``model4`` in ``X3``
#     - ``performance 2.2`` = ``model5`` in ``X3``
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