Derp_NLTK_Derp

import nltk, csv, time
import nltk.data, nltk.tag

from nltk.tag.perceptron import PerceptronTagger
from nltk import word_tokenize
tagger = PerceptronTagger()
tagset = None
def pos_tag2 (tokens):
    tagged_tokens = tagger.tag(tokens)

    return tagged_tokens

models = {'select': 'VB'}
tagger2 = nltk.tag.UnigramTagger(model=models,backoff=nltk.DefaultTagger('derp'))

#tagger2 = nltk.tag.UnigramTagger(model=models,backoff = pos_tag2)
#I want it to do what's in the line above.
#I need it to first identify all of the words, just select for now, in the models list
#Then if it doesn't ID a word via models, to check via Perceptron

p = ['I', 'just', 'drank', 'some', 'select', 'coffee', '.']

q = tagger2.tag(p)

print q

# Right now it outputs:
# [('I', 'derp'), ('just', 'derp'), ('drank', 'derp'), ('some', 'derp'), ('select', 'VB'), ('coffee', 'derp'), ('.', 'derp')]