def tagBigboss(unsegSentences):
enLearner = learner.Learner()
enTrainData = 'C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\WSJ\\train.uni.txt'
enTrainSents = Reader.readTaggsedSentences(enTrainData)
enLearner.train(enTrainSents)
hiLearner = learner.Learner()
hiTrainData = 'C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\Hindi\\train.uni.txt'
hiTrainSents = Reader.readTaggsedSentences(hiTrainData)
hiLearner.train(hiTrainSents)
SS = SentenceSplitter()
SS.trainLMsDefault()
SS.loadSentences(unsegSentences)
SS.scoreSentences()
SS.splitSentences()
testSents = SS.splittedSentences
enTestSents = [(testSents[index][0],testSents[index][2],testSents[index][3]) for index in range(len(testSents)) if testSents[index][1] == 'E']
enTagger = tagger.Tagger(enLearner)
enTagger.loadTestSentences(map(lambda x:x[0], enTestSents))
enTagger.tag()
hiTestSents = [(testSents[index][0],testSents[index][2],testSents[index][3]) for index in range(len(testSents)) if testSents[index][1] == 'H']
hiTagger = tagger.Tagger(hiLearner)
hiTagger.loadTestSentences(map(lambda x:x[0],hiTestSents))
hiTagger.tag()
print "English Accuracy:", enTagger.accuracy()
print "Hindi Accuracy:", hiTagger.accuracy()
enCorrect, enTotal = enTagger.getAccuCounts()
hiCorrect, hiTotal = hiTagger.getAccuCounts()
print "EN Total:", enTotal, "EN Correct:", enCorrect
print "HI Total:", hiTotal, "HI Correct:", hiCorrect
print "Total Accuracy:", (enCorrect+hiCorrect)*100.0/(enTotal+hiTotal)
taggedSentneces = dd(list)
assert len(enTestSents) == len(enTagger.outputs)
for sentIndex in range(len(enTagger.outputs)):
output = enTagger.outputs[sentIndex]
taggedSentneces[enTestSents[sentIndex][1]].append((output, enTestSents[sentIndex][2]))
assert len(hiTestSents) == len(hiTagger.outputs)
for sentIndex in range(len(hiTagger.outputs)):
output = hiTagger.outputs[sentIndex]
taggedSentneces[hiTestSents[sentIndex][1]].append((output, hiTestSents[sentIndex][2]))
for sentId in taggedSentneces:
sent = []
taggedChunks = sorted(taggedSentneces[sentId], cmp=lambda x, y:int(x[1]) - int(y[1]))
for chunk in taggedChunks:
sent.extend(chunk[0])
taggedSentneces[sentId] = sent
taggedFB = 'C:\Users\\t-phgad\Documents\Project\Data\\Bigboss\\longPosAutoTags.csv'
writer = UnicodeWriter(open(taggedFB, 'w'), lineterminator='\n')
for sent in taggedSentneces.itervalues():
for line in sent:
writer.writerow(line)
writer.writerow(["", ""])
def tagPure(unsegSentences):
unsegSentences = normalizeManually(unsegSentences)
testSents = purelySplitSentences(unsegSentences)
enLearner = learner.Learner()
enTrainData = 'C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\WSJ\\train.uni.txt'
enTrainSents = Reader.readTaggsedSentences(enTrainData)
enLearner.train(enTrainSents)
hiLearner = learner.Learner()
hiTrainData = 'C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\Hindi\\train.uni.txt'
hiTrainSents = Reader.readTaggsedSentences(hiTrainData)
hiLearner.train(hiTrainSents)
enTestSents = [(testSents[index][0], testSents[index][2], testSents[index][3]) for index in range(len(testSents)) if testSents[index][1] == 'E']
enTagger = tagger.Tagger(enLearner)
enTagger.loadTestSentences(map(lambda x:x[0], enTestSents))
# enTagger.tag()
enTagger.tagTopK(5)
hiTestSents = [(testSents[index][0], testSents[index][2], testSents[index][3]) for index in range(len(testSents)) if testSents[index][1] == 'H']
hiTagger = tagger.Tagger(hiLearner)
hiTagger.loadTestSentences(map(lambda x:x[0], hiTestSents))
# hiTagger.tag()
hiTagger.tagTopK(5)
print "English Accuracy:", enTagger.accuracy()
print "Hindi Accuracy:", hiTagger.accuracy()
enCorrect, enTotal = enTagger.getAccuCounts()
hiCorrect, hiTotal = hiTagger.getAccuCounts()
print "EN Total:", enTotal, "EN Correct:", enCorrect
print "HI Total:", hiTotal, "HI Correct:", hiCorrect
print "Total Accuracy:", (enCorrect + hiCorrect) * 100.0 / (enTotal + hiTotal)
taggedSentneces = dd(list)
assert len(enTestSents) == len(enTagger.outputs)
for sentIndex in range(len(enTagger.outputs)):
output = enTagger.outputs[sentIndex]
taggedSentneces[enTestSents[sentIndex][1]].append((output, enTestSents[sentIndex][2]))
assert len(hiTestSents) == len(hiTagger.outputs)
for sentIndex in range(len(hiTagger.outputs)):
output = hiTagger.outputs[sentIndex]
taggedSentneces[hiTestSents[sentIndex][1]].append((output, hiTestSents[sentIndex][2]))
for sentId in taggedSentneces:
sent = []
taggedChunks = sorted(taggedSentneces[sentId], cmp=lambda x, y:int(x[1]) - int(y[1]))
for chunk in taggedChunks:
sent.extend(chunk[0])
taggedSentneces[sentId] = sent
return taggedSentneces
def tagWithBigbossTransitions(unsegSentences):
taggedSentences = tagPure(unsegSentences)
# # Context Viterbi Decoding ##
bigbossLearner = learner.Learner()
bigbossTrainData = 'C:\\Users\\t-phgad\\Documents\\Project\\Data\\Bigboss\\FromDOcs\\bigbossDev.uni.txt'
bigbossSents = Reader.readTaggsedSentences(bigbossTrainData)
bigbossLearner.train(bigbossSents)
bigbossLearner.laplaceSmoothTransitions()
vitDecoder = tagger.ViteriDecoder(bigbossLearner)
outputs = []
for _, topKOutput in taggedSentences.iteritems():
sentence = map(lambda x:x[0], topKOutput)
topKTags = map(lambda x:x[1], topKOutput)
tags = vitDecoder.decodeTopK(topKTags, sentence)
output = zip(sentence, tags)
outputs.append(output)
print "Context Decoding Accuracy:", accuracy(unsegSentences, outputs)
return
# # Writing
taggedFB = 'C:\Users\\t-phgad\Documents\Project\Data\\FB\\FBContextTagged.csv'
writer = UnicodeWriter(open(taggedFB, 'w'), lineterminator='\n')
# for sent in taggedSentneces.itervalues():
for sent in outputs:
for line in sent:
writer.writerow(line)
writer.writerow(["", ""])
def tagTaggedSentences(testSents, taggerTags):
enLearner = learner.Learner()
enTrainData = "C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\WSJ\\train.uni.txt"
enTrainSents = Reader.readTaggsedSentences(enTrainData)
enLearner.train(enTrainSents)
hiLearner = learner.Learner()
hiTrainData = "C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\Hindi\\train.uni.txt"
hiTrainSents = Reader.readTaggsedSentences(hiTrainData)
hiLearner.train(hiTrainSents)
enTestSents = [(testSents[index], index) for index in range(len(testSents)) if taggerTags[index] == "E"]
enTagger = tagger.Tagger(enLearner)
enTagger.loadTestSentences(map(lambda x: x[0], enTestSents))
enTagger.tag()
hiTestSents = [(testSents[index], index) for index in range(len(testSents)) if taggerTags[index] == "H"]
hiTagger = tagger.Tagger(hiLearner)
hiTagger.loadTestSentences(map(lambda x: x[0], hiTestSents))
hiTagger.tag()
print "English Accuracy:", enTagger.accuracy()
print "Hindi Accuracy:", hiTagger.accuracy()
enCorrect, enTotal = enTagger.getAccuCounts()
hiCorrect, hiTotal = hiTagger.getAccuCounts()
print "EN Total:", enTotal, "EN Correct:", enCorrect
print "HI Total:", hiTotal, "HI Correct:", hiCorrect
print "Total Accuracy:", (enCorrect + hiCorrect) * 100.0 / (enTotal + hiTotal)
taggedSentences = {}
assert len(enTagger.outputs) == len(enTestSents)
for sentIndex in range(len(enTagger.outputs)):
output = enTagger.outputs[sentIndex]
testSent = enTestSents[sentIndex]
testSentIndex = testSent[1]
taggedSentences[testSentIndex] = output
assert len(hiTagger.outputs) == len(hiTestSents)
for sentIndex in range(len(hiTagger.outputs)):
output = hiTagger.outputs[sentIndex]
testSent = hiTestSents[sentIndex]
testSentIndex = testSent[1]
taggedSentences[testSentIndex] = output
for index in range(len(testSents)):
print "\n".join(map(lambda x: "\t".join(x), taggedSentences[index])) + "\n"
def tagBigboss(): unsegBigBoss = 'C:\Users\\t-phgad\Documents\Project\Data\Bigboss\FromDocs\CSSentsWithIDs.csv' unsegSentences = Reader.readTaggsedSentencesCSV(unsegBigBoss, 2, 5) unsegSentences = map2UniWithLtag(unsegSentences) #for sent in unsegSentences: # print '\n'.join(map(lambda x:x[1], sent))+'\n' #sys.exit() tagTaggedSentences(unsegSentences)
def tagUntaggedSentences(unsegSentences):
testSents = purelySplitUntaggedSentences(unsegSentences)
enLearner = learner.Learner()
enTrainData = 'C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\WSJ\\train.uni.txt'
enTrainSents = Reader.readTaggsedSentences(enTrainData)
enLearner.train(enTrainSents)
hiLearner = learner.Learner()
hiTrainData = 'C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\Hindi\\train.uni.txt'
hiTrainSents = Reader.readTaggsedSentences(hiTrainData)
hiLearner.train(hiTrainSents)
enTestSents = [(testSents[index][0], testSents[index][2], testSents[index][3]) for index in range(len(testSents)) if testSents[index][1] == 'E']
enTagger = tagger.Tagger(enLearner)
enTagger.loadTestSentences(map(lambda x:x[0], enTestSents))
enTagger.tag()
hiTestSents = [(testSents[index][0], testSents[index][2], testSents[index][3]) for index in range(len(testSents)) if testSents[index][1] == 'H']
hiTagger = tagger.Tagger(hiLearner)
hiTagger.loadTestSentences(map(lambda x:x[0], hiTestSents))
hiTagger.tag()
taggedSentneces = dd(list)
assert len(enTestSents) == len(enTagger.outputs)
for sentIndex in range(len(enTagger.outputs)):
output = enTagger.outputs[sentIndex]
taggedSentneces[enTestSents[sentIndex][1]].append((output, enTestSents[sentIndex][2]))
assert len(hiTestSents) == len(hiTagger.outputs)
for sentIndex in range(len(hiTagger.outputs)):
output = hiTagger.outputs[sentIndex]
taggedSentneces[hiTestSents[sentIndex][1]].append((output, hiTestSents[sentIndex][2]))
for sentId in taggedSentneces:
sent = []
taggedChunks = sorted(taggedSentneces[sentId], cmp=lambda x, y:int(x[1]) - int(y[1]))
for chunk in taggedChunks:
sent.extend(chunk[0])
taggedSentneces[sentId] = sent
taggedFB = 'C:\Users\\t-phgad\Documents\Project\Data\\cominedWordsLangsTagged.csv'
writer = UnicodeWriter(open(taggedFB, 'w'), lineterminator='\n')
for sent in taggedSentneces.itervalues():
for line in sent:
writer.writerow(line)
writer.writerow(["", ""])
def tagBigboss():
bigBossData = "C:\Users\\t-phgad\Documents\Project\Data\Bigboss\FromDocs\CSSentsSegWithIDs.csv"
uniTestData = "C:\Users\\t-phgad\Documents\Project\Data\Bigboss\FromDocs\\bigbossDev.uni.txt"
# prepareTestDataUni(bigBossData, uniTestData)
testSents = Reader.readTaggsedSentences(uniTestData)
taggerTags = getTaggerTags(bigBossData)
inferredTaggerTags = inferTaggerTags(bigBossData)
tagTaggedSentences(testSents, taggerTags)
print "Length of tags:", len(taggerTags)
print "Length of inferred tags:", len(inferredTaggerTags)
def tagWithManualTaggerTags(unsegSentences):
taggedSentences = tagPure(unsegSentences)
taggerTags = ["H", "H", "E", "H", "H", "H", "H", "H", "H", "H", "E", "H", "E", "E", "H", "E", "E", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H"]
assert len(taggedSentences) == len(taggerTags)
enLearner = learner.Learner()
enTrainData = 'C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\WSJ\\train.uni.txt'
enTrainSents = Reader.readTaggsedSentences(enTrainData)
enLearner.train(enTrainSents)
enLearner.laplaceSmoothTransitions()
hiLearner = learner.Learner()
hiTrainData = 'C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\Hindi\\train.uni.txt'
hiTrainSents = Reader.readTaggsedSentences(hiTrainData)
hiLearner.train(hiTrainSents)
hiLearner.laplaceSmoothTransitions()
hiVitDecoder = tagger.ViteriDecoder(hiLearner)
enVitDecoder = tagger.ViteriDecoder(enLearner)
outputs = []
index = 0
for _, topKOutput in taggedSentences.iteritems():
sentence = map(lambda x:x[0], topKOutput)
topKTags = map(lambda x:x[1], topKOutput)
if taggerTags[index] == "E":
tags = enVitDecoder.decodeTopK(topKTags, sentence)
elif taggerTags[index] == "H":
tags = hiVitDecoder.decodeTopK(topKTags, sentence)
output = zip(sentence, tags)
outputs.append(output)
index += 1
print "Context Decoding Accuracy:", accuracy(unsegSentences, outputs)
taggedFB = 'C:\Users\\t-phgad\Documents\Project\Data\\FB\\FBContextMLTagged.csv'
writer = UnicodeWriter(open(taggedFB, 'w'), lineterminator='\n')
# for sent in taggedSentneces.itervalues():
for sent in outputs:
for line in sent:
writer.writerow(line)
writer.writerow(["", ""])
assert len(enTestSents) == len(enTagger.outputs)
for sentIndex in range(len(enTagger.outputs)):
output = enTagger.outputs[sentIndex]
taggedSentneces[enTestSents[sentIndex][1]].append((output, enTestSents[sentIndex][2]))
assert len(hiTestSents) == len(hiTagger.outputs)
for sentIndex in range(len(hiTagger.outputs)):
output = hiTagger.outputs[sentIndex]
taggedSentneces[hiTestSents[sentIndex][1]].append((output, hiTestSents[sentIndex][2]))
for sentId in taggedSentneces:
sent = []
taggedChunks = sorted(taggedSentneces[sentId], cmp=lambda x, y:int(x[1]) - int(y[1]))
for chunk in taggedChunks:
sent.extend(chunk[0])
taggedSentneces[sentId] = sent
taggedFB = 'C:\Users\\t-phgad\Documents\Project\Data\\Bigboss\\longPosAutoTags.csv'
writer = UnicodeWriter(open(taggedFB, 'w'), lineterminator='\n')
for sent in taggedSentneces.itervalues():
for line in sent:
writer.writerow(line)
writer.writerow(["", ""])
if __name__ == '__main__':
unsegBigBoss = 'C:\Users\\t-phgad\Documents\Project\Data\Bigboss\FromDocs\CSSentsWithIDs.csv'
unsegSentences = Reader.readTaggsedSentencesCSV(unsegBigBoss, 2, 5)
unsegSentences = map2UniWithLtag(unsegSentences)
# prepareTestDataUni(bigBossData, uniTestData)
tagBigboss(unsegSentences)
def tagFB(): exractedFB = 'C:\Users\\t-phgad\Documents\Project\Data\\FB\\FBPOSAnnotated.csv' unsegSentences = Reader.readTaggsedSentencesCSV(exractedFB, 0, 3) #tagUntaggedSentences(unsegSentences) tagTaggedSentences(unsegSentences)
def tagFB(): exractedFB = 'C:\Users\\t-phgad\Documents\Project\Data\\FB\\FBPOSAnnotated.csv' unsegSentences = Reader.readTaggsedSentencesCSV(exractedFB, 0, 3) #tagWithBigbossTransitions(unsegSentences) tagWithManualTaggerTags(unsegSentences)
def loadDictionaries(self): enTaggedData = 'C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\Hindi\\train.uni.txt' hiTaggedData = 'C:\Users\\t-phgad\Documents\Project\Data\POSAnnotated\WSJ\\train.uni.txt' enWordTags = Reader.readTaggsedSentences(enTaggedData) hiWordTags = Reader.readTaggsedSentences(hiTaggedData) self.learner.guesser.loadDicts(enWordTags, hiWordTags)
def loadTestSentencesFromFile(self, inputFile): self.testSentences = Reader.readTaggsedSentences(inputFile)