def __init__(self, name, makeString=True, wordVec=True, senna=True):
if name.isdigit():
self.name = name
self.googleURL = name
else:
self.name = name
self.googleURL = data.getGoogleURL(self)
if makeString:
self.string = data.getGoogleCase(self)
tokenizer = linguistics.getTokenizer()
self.tokens = tokenizer.tokenize(self.string)
self.tokenSummary = {1:[], 2:[], 3:[], 4:[]}
learning.readLabels([self], 'tokens')
#if wordVec:
# self.representation = wordVector.getRepresentation(self)
if senna:
self.sennaMatrix = linguistics.getSennaMatrix(self)
self.sentences = linguistics.getSennaAlignedSentences(self)
self.srlSentences = linguistics.getSrlSentences(self)
self.indicators = linguistics.resolveAnaphora(self)
self.srlSummary = {1:[], 2:[], 3:[], 4:[]}
learning.readLabels([self], 'srl')
if 'V' in clause and clause['V'] == word:
polyfitInput[0] += 1
for clause in dm:
if 'A0' in clause and clause['A0'] == word:
polyfitInput[1] += 1
if 'V' in clause and clause['V'] == word:
polyfitInput[1] += 1
print polyfitInput
model[i] = np.polyfit([1, m], polyfitInput, 1)
print model
return findShortChains()
if __name__ == "__main__":
cases = data.getAllSavedCases()
labeledTraining = learning.findLabels(cases)
learning.readLabels(labeledTraining)
unlabeledCases = filter(lambda x:x not in labeledTraining, cases)
unlabeledTraining = unlabeledCases[:-1]
testing = [unlabeledCases[-1]]
learning.labelCases(labeledTraining, unlabeledTraining, testing, numIterations=20)
for cas in testing:
print 'case ' + str(cas.name)
d = {}
for person, summarySentences in cas.summary.iteritems():
personSentences = []
for sentence in summarySentences:
personSentences.append(cas.sentences[sentence])
d[person] = personSentences
for person, summarySentences in d.iteritems():
print coherentSummary(summarySentences, cas.sentences, cas.srlSentences)
for clause in dm:
if 'A0' in clause and clause['A0'] == word:
polyfitInput[1] += 1
if 'V' in clause and clause['V'] == word:
polyfitInput[1] += 1
print polyfitInput
model[i] = np.polyfit([1, m], polyfitInput, 1)
print model
return findShortChains()
if __name__ == "__main__":
cases = data.getAllSavedCases()
labeledTraining = learning.findLabels(cases)
learning.readLabels(labeledTraining)
unlabeledCases = filter(lambda x: x not in labeledTraining, cases)
unlabeledTraining = unlabeledCases[:-1]
testing = [unlabeledCases[-1]]
learning.labelCases(labeledTraining,
unlabeledTraining,
testing,
numIterations=20)
for cas in testing:
print 'case ' + str(cas.name)
d = {}
for person, summarySentences in cas.summary.iteritems():
personSentences = []
for sentence in summarySentences:
personSentences.append(cas.sentences[sentence])
d[person] = personSentences