def getDuffyScore(sent1, sent2):
tree_1 = tree()
tree_2 = tree()
out1 = getNLPToks(sent1)
out2 = getNLPToks(sent2)
generateTree(out1['parse'], tree_1)
generateTree(out2['parse'], tree_2)
flipTree(tree_1)
flipTree(tree_2)
(rscore_st, nscore_st) = CollinsDuffy(tree_1, tree_2, 0.8, 1, 1)
return rscore_st, nscore_st
def getMoschittiScore(sent1, sent2):
tree_1 = tree()
tree_2 = tree()
out1 = getNLPToks(sent1)
out2 = getNLPToks(sent2)
generateTree(out1['parse'], tree_1)
generateTree(out2['parse'], tree_2)
flipTree(tree_1)
flipTree(tree_2)
(rscore_st, nscore_st) = MoschittiPT(tree_1, tree_2, 0.8, 1, 1)
# return rscore_st,nscore_st
return nscore_st
def parseCandidateBooks(candidate):
# print('parsing')
pTrees=list()
pSents=list()
pWithoutTokenTrees=list()
for sent in candidate:
sentParse=getNLPToks(sent)
tempTree=tree()
tempTree2=tree()
generateTree(sentParse['parse'],tempTree)
generateTree(sentParse['parse'],tempTree2)
pSents.append(sentParse['parse'])
flipTree(tempTree)
flipTree(tempTree2)
pTrees.append(tempTree)
pWithoutTokenTrees.append(removeTokens(tempTree2,sent))
print('candidate')
return (pTrees,pSents,pWithoutTokenTrees)
def parseBook(candidate):
pTrees = list()
pSents = list()
for sent in candidate:
sentParse = getNLPToks(sent)
tempTree = tree()
generateTree(sentParse['parse'], tempTree)
pSents.append(sentParse['parse'])
flipTree(tempTree)
pTrees.append(tempTree)
return (pTrees, pSents)
def parseNewText(chunk):
#print('Parsing chunk')
# chunk=chunkTuple[0]
# location=chunkTuple[1]
# nlp=StanfordCoreNLP(location)
parseChunk=list()
parseSentenceChunk=list()
parseWithoutTokenChunk=list()
for sent in chunk:
sentParse=getNLPToks(sent)
tempTree=tree()
tempTree2=tree()
generateTree(sentParse['parse'],tempTree)
generateTree(sentParse['parse'],tempTree2)
parseSentenceChunk.append(sentParse['parse'])
flipTree(tempTree)
flipTree(tempTree2)
parseChunk.append(tempTree)
parseWithoutTokenChunk.append(removeTokens(tempTree2,sent))
print('over')
return (parseChunk,parseSentenceChunk,parseWithoutTokenChunk)
def parseCandidateBooks(candidate):
pTrees=list()
pWithoutTokenTrees=list()
for para in candidate:
para=sent_tokenize(para)
sentTrees=list()
sentWithoutTokenTrees=list()
for sent in para:
sentParse=getNLPToks(sent)
tempTree=tree()
tempTree2=tree()
generateTree(sentParse['parse'],tempTree)
generateTree(sentParse['parse'],tempTree2)
flipTree(tempTree)
flipTree(tempTree2)
sentTrees.append(tempTree)
sentWithoutTokenTrees.append(removeTokens(tempTree2,sent))
pTrees.append(sentTrees)
pWithoutTokenTrees.append(sentWithoutTokenTrees)
print('candidate')
return pTrees,pWithoutTokenTrees
def parseNewText(paraChunk):
# print('Parsing chunk')
parseChunk=list()
parseWithoutTokenChunk=list()
for para in paraChunk:
paraParse=list()
paraWithoutTokenParse=list()
para=sent_tokenize(para)
for sent in para:
sentParse=getNLPToks(sent)
tempTree=tree()
tempTree2=tree()
generateTree(sentParse['parse'],tempTree)
generateTree(sentParse['parse'],tempTree2)
# parseSentenceChunk.append(sentParse['parse'])
flipTree(tempTree)
flipTree(tempTree2)
paraParse.append(tempTree)
paraWithoutTokenParse.append(removeTokens(tempTree2,sent))
parseChunk.append(paraParse)
parseWithoutTokenChunk.append(paraWithoutTokenParse)
print('over')
return parseChunk,parseWithoutTokenChunk
def parseNewText(paraChunk):
print('Parsing chunk')
parseChunk = list()
for para in paraChunk:
paraParse = list()
para = sent_tokenize(para)
for sent in para:
sentParse = getNLPToks(sent)
tempTree = tree()
generateTree(sentParse['parse'], tempTree)
# parseSentenceChunk.append(sentParse['parse'])
flipTree(tempTree)
paraParse.append(tempTree)
parseChunk.append(paraParse)
return parseChunk
def parseBook(candidate):
pTrees = list()
# pSents=list()
for para in candidate:
para = sent_tokenize(para)
sentTrees = list()
for sent in para:
sentParse = getNLPToks(sent)
tempTree = tree()
generateTree(sentParse['parse'], tempTree)
# pSents.append(sentParse['parse'])
flipTree(tempTree)
sentTrees.append(tempTree)
pTrees.append(sentTrees)
# return (pTrees,pSents)
return pTrees
for book in booksList:
for sent in reducedSentences[book]:
reducedBooks[book].append(books[book][sent])
pickling_on = open("./bible/reducedBooks.pickle", "wb")
pickle.dump(reducedBooks, pickling_on)
i = 0
parseTrees = list()
parsedSentences = list()
for sent in text:
if i % 10 == 0:
print(i)
sentParse = getNLPToks(sent)
tempTree = tree()
generateTree(sentParse['parse'], tempTree)
parsedSentences.append(sentParse['parse'])
flipTree(tempTree)
parseTrees.append(tempTree)
i = i + 1
pickling_on = open("./bible/parseTrees.pickle", "wb")
pickle.dump(parseTrees, pickling_on)
potentialParseTrees = dict()
potentialParsedSentences = dict()
for book in booksList:
print(book)
candidate = reducedBooks[book]