def classify(self, nl_query):
#pos_tree = tagger.to_tree(nl_query)
tagged_yield = tagger.tagged_labeled_yield(nl_query)
pos_tree = []
for i in tagged_yield:
pos_tree.append(i['ValueAnnotation'])
pos_tree = " ".join(pos_tree)
_, labels, trees = Preprocessing.data()
text_clf = Pipeline([ ('vect', CountVectorizer(min_n=1, max_n=1)), ('tfidf', TfidfTransformer(use_idf=False)), ('clf', LinearSVC()) ])
_ = text_clf.fit(trees, labels)
predicted = text_clf.predict([pos_tree])[0]
predicted = Preprocessing.query(predicted)
return predicted
