import pprint
if __name__ == '__main__':
nlp = StanfordCoreNLP('http://localhost:9000')
# text = "Non tolerance was gandhijis weapon."
# text = ("We went to pitapit,it can be expensive but not hygienic.")
# text = ("The dishes at Alkareem are highly recommended.")
text = ("The sitting which is mostly outdoor is the prettiest you can come across in CP")
# text = ('I loved The Crispy Vegetables but found the Wontons to be devoid of any flavor')
# text = ("delicious veg manchurian.")
# text = ('London is good at studies but bad at sports.')
# text = ("The tiger prawns here,it doesn't get better.")
# text = ('Check out the pics to find out who greeted me on my first visit to Bercos CP branch, it can be expensive but not hygienic.')
output = nlp.annotate(text, properties={
'annotators': 'tokenize,ssplit,pos,depparse,parse,ner',
'outputFormat': 'json'
})
# pprint.pprint(output)
tree = output['sentences'][0]['parse']
print tree
x = output['sentences'][0]['collapsed-dependencies']
# pprint.pprint(x)
print '-------------------------------------------------'
for i in range(len(x)):
print x[i]['dep'] + '-->' + x[i]['governorGloss'] + '-' + str(x[i]['governor']) + ' ' + x[i]['dependentGloss'] + '-' + str(x[i]['dependent'])
# print(output['sentences'][0]['parse'])
# output = nlp.tokensregex(text, pattern='/Pusheen|Smitha/', filter=False)
# print(output)
# output = nlp.semgrex(text, pattern='{tag: VBD}', filter=False)
# print(output)
print '-------------------------------------------------'
nlp = StanfordCoreNLP('http://localhost:9000')
# text = "Non tolerance was gandhijis weapon."
# text = ("We went to pitapit,it can be expensive but not hygienic.")
# text = ("The dishes at Alkareem are highly recommended.")
text = (
"The sitting which is mostly outdoor is the prettiest you can come across in CP"
)
# text = ('I loved The Crispy Vegetables but found the Wontons to be devoid of any flavor')
# text = ("delicious veg manchurian.")
# text = ('London is good at studies but bad at sports.')
# text = ("The tiger prawns here,it doesn't get better.")
# text = ('Check out the pics to find out who greeted me on my first visit to Bercos CP branch, it can be expensive but not hygienic.')
output = nlp.annotate(text,
properties={
'annotators':
'tokenize,ssplit,pos,depparse,parse,ner',
'outputFormat': 'json'
})
# pprint.pprint(output)
tree = output['sentences'][0]['parse']
print tree
x = output['sentences'][0]['collapsed-dependencies']
# pprint.pprint(x)
print '-------------------------------------------------'
for i in range(len(x)):
print x[i]['dep'] + '-->' + x[i]['governorGloss'] + '-' + str(
x[i]['governor']) + ' ' + x[i]['dependentGloss'] + '-' + str(
x[i]['dependent'])
# print(output['sentences'][0]['parse'])
# output = nlp.tokensregex(text, pattern='/Pusheen|Smitha/', filter=False)
# print(output)
def typedependencies(sent_list, neg_words, compound_word_list):
pos_dict = {}
depend_dict = {}
depend_list = []
proper_names = []
# neg_words = []
compound_dic = {}
nlp = StanfordCoreNLP('http://localhost:9000')
for i in range(len(sent_list)):
compound_list = []
print sent_list[i]
output = nlp.annotate(sent_list[i],
properties={
'annotators':
'tokenize,ssplit,pos,depparse,parse,ner',
'outputFormat': 'json'
})
# pprint.pprint(output)
x = output['sentences'][0]['basic-dependencies']
# pprint.pprint(output['sentences'][0]['parse'])
# pprint.pprint(x)
# print '-------------------------------------------------'
for j in range(len(x)):
if 'compound' in x[j]['dep']:
# compound_word(x[j])
ll = [
x[j]['governorGloss'], x[j]['governor'],
x[j]['dependentGloss'], x[j]['dependent']
]
compound_dic[x[j]['governor']] = x[j]['governorGloss']
compound_dic[x[j]['dependent']] = x[j]['dependentGloss']
# compound_list.append(ll)
d = [
x[j]['dep'], x[j]['governorGloss'],
str(x[j]['governor']), x[j]['dependentGloss'],
str(x[j]['dependent'])
]
depend_list.append(d)
# getting the negative words..
if 'neg' in x[j]['dep']:
x1 = x[j]['governorGloss'].lower()
x2 = x[j]['dependentGloss'].lower()
if x1 not in stopwords:
neg_words.append([x1, x[j]['governor']])
else:
neg_words.append([x2, x[j]['dependent']])
if 'conj' in x[j]['dep']:
x1 = x[j]['governorGloss'].lower()
x2 = x[j]['dependentGloss'].lower()
if x1 in neg_prefix:
neg_words.append([x2, x[j]['dependent']])
# elif (x2 == 'not' or x2 == 'nor' or x2 == 'non'):
# neg_words.append(x1)
elif x2 in neg_prefix:
neg_words.append([x1, x[j]['governor']])
print(x[j]['dep'] + '-->' + x[j]['governorGloss'] + '-' +
str(x[j]['governor']) + ' ' + x[j]['dependentGloss'] + '-' +
str(x[j]['dependent']))
print '==================================='
for key, value in sorted(compound_dic.items()):
compound_list.append([key, value])
# print compound_word(compound_list)
compound_dic.clear()
y = output['sentences'][0]['tokens']
for k in range(len(y)):
pos_dict[y[k]['word']] = y[k]['pos']
if 'NNP' in y[k]['pos']:
proper_names.append(y[k]['word'])
depend_dict[i] = depend_list
depend_list = []
if len(compound_list) > 0:
w = compound_word(compound_list)
else:
w = []
for jj in range(len(w)):
if w[jj] != '':
print w[jj]
compound_word_list.append(w[jj])
print '--------NAMES------' + str(proper_names)
print '--------NEGATIVE----' + str(neg_words)
return depend_dict, pos_dict, proper_names
def typedependencies(sent_list,neg_words,compound_word_list):
pos_dict = {}
depend_dict = {}
depend_list = []
proper_names = []
# neg_words = []
compound_dic = {}
nlp = StanfordCoreNLP('http://localhost:9000')
for i in range(len(sent_list)):
compound_list = []
print sent_list[i]
output = nlp.annotate(sent_list[i], properties={
'annotators': 'tokenize,ssplit,pos,depparse,parse,ner',
'outputFormat': 'json'
})
# pprint.pprint(output)
x = output['sentences'][0]['basic-dependencies']
# pprint.pprint(output['sentences'][0]['parse'])
# pprint.pprint(x)
# print '-------------------------------------------------'
for j in range(len(x)):
if 'compound' in x[j]['dep']:
# compound_word(x[j])
ll = [x[j]['governorGloss'],x[j]['governor'],
x[j]['dependentGloss'],x[j]['dependent']]
compound_dic[x[j]['governor']] = x[j]['governorGloss']
compound_dic[x[j]['dependent']] = x[j]['dependentGloss']
# compound_list.append(ll)
d = [x[j]['dep'],x[j]['governorGloss'],str(x[j]['governor'])
,x[j]['dependentGloss'],str(x[j]['dependent'])]
depend_list.append(d)
# getting the negative words..
if 'neg' in x[j]['dep']:
x1 = x[j]['governorGloss'].lower()
x2 = x[j]['dependentGloss'].lower()
if x1 not in stopwords:
neg_words.append([x1,x[j]['governor']])
else:
neg_words.append([x2,x[j]['dependent']])
if 'conj' in x[j]['dep']:
x1 = x[j]['governorGloss'].lower()
x2 = x[j]['dependentGloss'].lower()
if x1 in neg_prefix:
neg_words.append([x2,x[j]['dependent']])
# elif (x2 == 'not' or x2 == 'nor' or x2 == 'non'):
# neg_words.append(x1)
elif x2 in neg_prefix:
neg_words.append([x1,x[j]['governor']])
print (x[j]['dep'] + '-->' + x[j]['governorGloss'] + '-'
+ str(x[j]['governor']) + ' ' + x[j]['dependentGloss'] +
'-' + str(x[j]['dependent']))
print '==================================='
for key,value in sorted(compound_dic.items()):
compound_list.append([key,value])
# print compound_word(compound_list)
compound_dic.clear()
y = output['sentences'][0]['tokens']
for k in range(len(y)):
pos_dict[y[k]['word']] = y[k]['pos']
if 'NNP' in y[k]['pos']:
proper_names.append(y[k]['word'])
depend_dict[i] = depend_list
depend_list = []
if len(compound_list) > 0:
w = compound_word(compound_list)
else:
w = []
for jj in range(len(w)):
if w[jj] != '':
print w[jj]
compound_word_list.append(w[jj])
print '--------NAMES------' + str(proper_names)
print '--------NEGATIVE----' + str(neg_words)
return depend_dict,pos_dict,proper_names
time.sleep(10)
# Other human languages support, e.g. Chinese
nlp = StanfordCoreNLP(local_corenlp_path, lang='zh', quiet=False)
sentence = '清华大学位于北京。'
print(nlp.word_tokenize(sentence))
print(nlp.pos_tag(sentence))
print(nlp.ner(sentence))
print(nlp.parse(sentence))
print(nlp.dependency_parse(sentence))
del nlp
time.sleep(10)
# General Stanford CoreNLP API
nlp = StanfordCoreNLP(local_corenlp_path, memory='8g', lang='zh')
print(nlp.annotate(sentence))
del nlp
time.sleep(10)
nlp = StanfordCoreNLP(local_corenlp_path)
text = 'Guangdong University of Foreign Studies is located in Guangzhou. ' \
'GDUFS is active in a full range of international cooperation and exchanges in education. '
print(
nlp.annotate(text,
properties={
'annotators': 'tokenize,ssplit,pos',
'pinelineLanguage': 'en',
'outputFormat': 'xml'
}))
