#Evaluate Accuracy of tagger
print("Skipping Accuracy")
#print("Evaluating Accuracy")
#print("Accuracy: " + str(tagger.evaluate(test_sents)))
#Evaluate Accuracy/Precision/Recall of Chunker
print("Evaluating Accuracy/Precision/Recall of Chunker")
score = chunker.evaluate(conll_test)
print("Accuracy: " + str(score.accuracy()))
print("Precision: " + str(score.precision()))
print("Recall: " + str(score.recall()))
# assign pos
tagged_sents = tagger.tag_sents(word_list)
# assign chunks
tagged_sents = [chunker.parse(tagged_sent) for tagged_sent in tagged_sents]
#From www.nltk.org/_modules/nltk/grammar.html pcfg_demo()
# import nltk.grammar as gram
# productions = []
# for tree in tagged_sents:
# tree.collapse_unary(collapsePOS = False)
# tree.chomsky_normal_form(horzMarkov = 2)
# production += tree.productions()
# S = gram.Nonterminal('S')
# grammar = gram.induce_pcfg(S,productions)
# #This generates a grammar derived from the tagger and the trained chunker
