master.py

import viterbi_tagger, perceptron_tagger, cfg

# test values for developers
test_sentence = ["I", "saw", "the", "duck"]
cfg_test = ["NNP","VBD","NNP"]
from nltk.corpus import conll2000, brown

if __name__ == '__main__':
    # Build HMM
    print "Generating Hidden Markov Model..."
    viterbi_tagger = viterbi_tagger.PartOfSpeechTagger()
    # Build probability distributions for each of the corpora we want to use
    print "Building POS tag probability distributions based on..."
    print "Corpora 1: Conll2000,"
    viterbi_tagger.buildProbDist(conll2000)
    print "Corpora 2: Brown."
    viterbi_tagger.buildProbDist(brown)
    #
    #Train the AP Tagger weights
    print "Prepare Averaged Perceptron tagger based on tagged corpora"
    taggerAP = perceptron_tagger.AP_Tagger(False)
    #
    # Build CFG rule set based on treebank
    print "Generating Context Free Grammar based on Treebank..."
    cfg_checker = cfg.Grammar()
    tbank_grammar = cfg_checker.buildFromTreebank()
    #
    # Loop input to get and check sentences
    print "If an input word is not in the corpora, the averaged perceptron \
    tagger will be used instead of the Viterbi tagger.\n"
    while True:
         # Turn sentence into part-of-speech tags
         tag_sequence = viterbi_tagger.inputToPOS()
         print "TAG SEQUENCE:", tag_sequence

         # Pass tag sequence to CFG checker
         cfg_checker.verify(tbank_grammar, tag_sequence)