def __init__(self,
llwl='Brown',
llNL=2,
percen=80,
NE=True,
Col=True,
Gram=True,
Chu=True):
'''
@param llwl:LogLikleyHood Corpa name ('Brown','AmE06','BE06')
@param llNL:LogLikleyHood
@param percen: Presision of output default = 20, 20% returned
@param NE: Uses NE default True
@param Col: Uses Collocation default True
@param Gram: Uses N-Grams default True
@param Chu: Uses Chunking default True
'''
self.NEs = NE
self.Col = Col
self.Gram = Gram
self.Chu = Chu
self.p = percen
print 'Starting to build ', llwl
self.LL = LogLikelihood(wordlist=llwl, NLength=llNL)
print 'LL Loaded'
self.POS = POS()
print 'POS Loaded'
self.GD = GetData()
print 'GD Loaded'
self.Cu = Chunker(self.POS)
print 'Cu Loaded'
self.FL = Filter()
print 'FL Loaded'
self.CC = Collocation(self.POS)
print 'CC Loaded'
self.Ng = NGram()
print 'Ng Loaded'
self.S = Select(percentil=self.p)
print 'S Loaded'
self.To = Tokenize(self.FL)
print 'To Loaded'
# Convert the grammar trees in the corpus into a CFG (Context-Free Grammar).
grammar = funcs.InduceNonTerminal(grammarTrain)
# Save the grammar file.
pickle.dump(grammar, open("grammar.txt", "wb"))
print("Grammar induction finished.")
'''========= Part IV: Chunking ========'''
# In this part, we chunk sentences into different phrases using the IOB (Inside-Outside-Beginning) tags. There are 3 ki-
# nds of phrases: noun phrases (NP), verb phrases (VP) and preposition phrases (PP).
# Load the train and test dataset for chunking.
chunkTrain = nltk.corpus.conll2000.chunked_sents("train.txt")
chunkTest = nltk.corpus.conll2000.chunked_sents("test.txt")
# Initiate a Chunker object. Use the training corpus to train the chunker.
chunker = Chunker(chunkTrain)
# Evaluate the chunker's performance on the test corpus.
print(chunker.evaluate(chunkTest))
# Use the trained chunker to chunk our own texts.
chunkedSents = funcs.ChunkSents(tokens, chunker)
# Save the chunked texts.
pickle.dump(chunkedSents, open("chunked_sents.txt", "wb"))
print("Chunking finished.")
'''======== Part V: Deep parsing ========'''
# In this part, we used the grammar induced in previous step to parse our texts. Basically we used a shift-reduce parsi-
# ng algorithm to parse the texts and find out if there are larger phrases built on small phrases.
# Initiate a parser object. Load it with grammar.
