def constructFeatures(discourseBank, treeBank):
featureSet = []
for relation in discourseBank:
if relation['Type'] == 'Explicit':
arg1Set = set()
arg2Set = set()
arg1Tokens = []
arg2Tokens = []
for i in relation['Arg1']['TokenList']:
arg1Set.add(i[3])
#arg1Tokens.append(i[4])
for i in relation['Arg2']['TokenList']:
arg2Set.add(i[3])
#arg2Tokens.append(i[4])
arg1 = list(arg1Set)
arg2 = list(arg2Set)
arg1.sort()
arg2.sort()
#print arg1Set, arg2Set
if arg1[-1] < arg2[0]:
label = 'PS'
if (len(arg1Set) == 1 and len(arg2Set) == 1):
if (arg1Set.pop() == arg2Set.pop()):
label = 'SS'
connective = relation['Connective']['TokenList']
connectiveRawText = relation['Connective']['RawText']
#print relation
#chm= conn_head_mapper.ConnHeadMapper()
#head,connectiveTokens = chm.map_raw_connective(relation['Connective']['RawText'])
#connectiveStartValue=relation['Connective']['TokenList'][0][4]
#connectiveTokens=[i+connectiveStartValue for i in connectiveTokens]
sentenceOffset = connective[0][3]
connectiveTokens = [token[4] for token in connective]
doc = relation['DocID']
ptree = treeBank[doc]['sentences'][sentenceOffset]['parsetree']
ptree = nltk.ParentedTree.fromstring(ptree)
if ptree.leaves() != []:
featureSet.append(
(argPositionFeat.getFeatures(ptree, connectiveRawText,
connectiveTokens), label))
#print featureSet
return featureSet
def constructFeatures(discourseBank,treeBank):
featureSet=[]
for relation in discourseBank:
if relation['Type']=='Explicit':
arg1Set=set()
arg2Set=set()
arg1Tokens=[]
arg2Tokens=[]
for i in relation['Arg1']['TokenList']:
arg1Set.add(i[3])
#arg1Tokens.append(i[4])
for i in relation['Arg2']['TokenList']:
arg2Set.add(i[3])
#arg2Tokens.append(i[4])
arg1=list(arg1Set)
arg2=list(arg2Set)
arg1.sort()
arg2.sort()
#print arg1Set, arg2Set
if arg1[-1] < arg2[0]:
label='PS'
if (len(arg1Set)==1 and len(arg2Set)==1):
if (arg1Set.pop()==arg2Set.pop()):
label='SS'
connective=relation['Connective']['TokenList']
connectiveRawText = relation['Connective']['RawText']
#print relation
#chm= conn_head_mapper.ConnHeadMapper()
#head,connectiveTokens = chm.map_raw_connective(relation['Connective']['RawText'])
#connectiveStartValue=relation['Connective']['TokenList'][0][4]
#connectiveTokens=[i+connectiveStartValue for i in connectiveTokens]
sentenceOffset=connective[0][3]
connectiveTokens=[token[4] for token in connective]
doc=relation['DocID']
ptree=treeBank[doc]['sentences'][sentenceOffset]['parsetree']
ptree = nltk.ParentedTree.fromstring(ptree)
if ptree.leaves() != []:
featureSet.append((argPositionFeat.getFeatures(ptree, connectiveRawText, connectiveTokens), label))
#print featureSet
return featureSet
def classifyOther(sentence, wordString, wordNum, skip, argPosClassifier,
senseClassifier, argClassifier):
wordList = sentence['words']
parsetree = nltk.ParentedTree.fromstring(sentence['parsetree'])
if parsetree.leaves() != []:
leaf_index = range(wordNum, wordNum + skip + 1)
c = wordString.strip()
"""
c = ""
for i in (leaf_index):
if i == 0:
c = c + parsetree[parsetree.leaf_treeposition(i)]
else:
c = c + " " + parsetree[parsetree.leaf_treeposition(i)]
c=c.strip()
leave_list = parsetree.leaves()
s=''
for i in leave_list:
if i in [',','.','!','?','%','(',')','$','#','@','*','^'] or i[0] in ["'",'"','`']:
s=s+i
else:
s=s+' '+i
s = s.encode('utf-8')
semantic_feat = semantic.semantic_features(s)
"""
fv1 = argPositionFeat.getFeatures(parsetree, c, leaf_index)
argPosLabel = argPosClassifier.classify(fv1)
argDict = finalArgsExtractor.argsExtract(argClassifier, parsetree,
leaf_index)
arg1string = argDict['arg1']
arg2string = argDict['arg2']
arg1string = arg1string.replace(' ,', ',')
arg2string = arg2string.replace(' ,', ',')
arg1string = arg1string.replace('`` ', '"')
arg2string = arg2string.replace('`` ', '"')
while u" n't" in arg1string:
arg1string = arg1string.replace(" n't", "n't")
while u" n't" in arg2string:
arg2string = arg2string.replace(" n't", "n't")
while u" 's" in arg1string:
arg1string = arg1string.replace(" 's", "'s")
while u" 's" in arg2string:
arg2string = arg2string.replace(" 's", "'s")
arg1 = []
arg2 = []
leaves = parsetree.leaves()
arg1words = argDict['arg1'].split()
arg2words = argDict['arg2'].split()
ind = -1
string = ' '.join(leaves)
if ' '.join(arg1words[:2]) in string:
ind = string.index(' '.join(arg1words[:2]))
ind = len(string[:ind].split()) - 1
if ind == 0:
ind = -1
for word in arg1words:
ind += leaves[ind + 1:].index(word) + 1
arg1.append(ind)
ind = -1
if ' '.join(arg2words[:2]) in string:
ind = string.index(' '.join(arg2words[:2]))
ind = len(string[:ind].split()) - 1
if ind == 0:
ind = -1
arg2words = argDict['arg2'].split()
for word in arg2words:
ind += leaves[ind + 1:].index(word) + 1
arg2.append(ind)
# leaves=parsetree.leaves()
# arg1words=argDict['arg1'].split()
# for word in arg1words:
# arg1.append(leaves.index(word))
#
# arg2words=argDict['arg2'].split()
# for word in arg2words:
# arg2.append(leaves.index(word))
fv2 = explicitSenseFeat.featureExtraction(parsetree, leaf_index,
sentence, argDict, c)
#senseFeatureVector.update(semantic_feat)
senseLabel = senseClassifier.classify(fv2)
return argPosLabel, senseLabel, arg1, arg2
def classifyOther(sentence,wordString,wordNum,skip,argPosClassifier,senseClassifier,argClassifier):
wordList=sentence['words']
parsetree = nltk.ParentedTree.fromstring(sentence['parsetree'])
if parsetree.leaves()!=[]:
leaf_index=range(wordNum,wordNum+skip+1)
c=wordString.strip()
"""
c = ""
for i in (leaf_index):
if i == 0:
c = c + parsetree[parsetree.leaf_treeposition(i)]
else:
c = c + " " + parsetree[parsetree.leaf_treeposition(i)]
c=c.strip()
leave_list = parsetree.leaves()
s=''
for i in leave_list:
if i in [',','.','!','?','%','(',')','$','#','@','*','^'] or i[0] in ["'",'"','`']:
s=s+i
else:
s=s+' '+i
s = s.encode('utf-8')
semantic_feat = semantic.semantic_features(s)
"""
fv1 = argPositionFeat.getFeatures(parsetree,c,leaf_index)
argPosLabel=argPosClassifier.classify(fv1)
argDict=finalArgsExtractor.argsExtract(argClassifier,parsetree,leaf_index)
arg1string=argDict['arg1']
arg2string=argDict['arg2']
arg1string=arg1string.replace(' ,',',')
arg2string=arg2string.replace(' ,',',')
arg1string=arg1string.replace('`` ' , '"')
arg2string=arg2string.replace('`` ' , '"')
while u" n't" in arg1string:
arg1string = arg1string.replace(" n't", "n't")
while u" n't" in arg2string:
arg2string = arg2string.replace(" n't", "n't")
while u" 's" in arg1string:
arg1string = arg1string.replace(" 's", "'s")
while u" 's" in arg2string:
arg2string = arg2string.replace(" 's", "'s")
arg1=[]
arg2=[]
leaves=parsetree.leaves()
arg1words=argDict['arg1'].split()
arg2words=argDict['arg2'].split()
ind = -1
string = ' '.join(leaves)
if ' '.join(arg1words[:2]) in string:
ind = string.index(' '.join(arg1words[:2]))
ind = len(string[:ind].split())-1
if ind == 0:
ind = -1
for word in arg1words:
ind += leaves[ind+1:].index(word)+1
arg1.append(ind)
ind = -1
if ' '.join(arg2words[:2]) in string:
ind = string.index(' '.join(arg2words[:2]))
ind = len(string[:ind].split())-1
if ind == 0:
ind = -1
arg2words=argDict['arg2'].split()
for word in arg2words:
ind += leaves[ind+1:].index(word)+1
arg2.append(ind)
# leaves=parsetree.leaves()
# arg1words=argDict['arg1'].split()
# for word in arg1words:
# arg1.append(leaves.index(word))
#
# arg2words=argDict['arg2'].split()
# for word in arg2words:
# arg2.append(leaves.index(word))
fv2=explicitSenseFeat.featureExtraction(parsetree,leaf_index,sentence,argDict,c)
#senseFeatureVector.update(semantic_feat)
senseLabel=senseClassifier.classify(fv2)
return argPosLabel,senseLabel,arg1,arg2
