# Simple usage
from stanfordcorenlp import StanfordCoreNLP
nlp = StanfordCoreNLP(r'G:\JavaLibraries\stanford-corenlp-full-2018-02-27')
sentence = 'Guangdong University of Foreign Studies is located in Guangzhou.'
print 'Tokenize:', nlp.word_tokenize(sentence)
print 'Part of Speech:', nlp.pos_tag(sentence)
print 'Named Entities:', nlp.ner(sentence)
print 'Constituency Parsing:', nlp.parse(sentence)
print 'Dependency Parsing:', nlp.dependency_parse(sentence)
nlp.close() # Do not forget to close! The backend server will consume a lot memery.

# Tokenize
[u'Guangdong', u'University', u'of', u'Foreign', u'Studies', u'is', u'located', u'in', u'Guangzhou', u'.']
# Part of Speech
[(u'Guangdong', u'NNP'), (u'University', u'NNP'), (u'of', u'IN'), (u'Foreign', u'NNP'), (u'Studies', u'NNPS'), (u'is', u'VBZ'), (u'located', u'JJ'), (u'in', u'IN'), (u'Guangzhou', u'NNP'), (u'.', u'.')]
# Named Entities
 [(u'Guangdong', u'ORGANIZATION'), (u'University', u'ORGANIZATION'), (u'of', u'ORGANIZATION'), (u'Foreign', u'ORGANIZATION'), (u'Studies', u'ORGANIZATION'), (u'is', u'O'), (u'located', u'O'), (u'in', u'O'), (u'Guangzhou', u'LOCATION'), (u'.', u'O')]
# Constituency Parsing
 (ROOT
  (S
    (NP
      (NP (NNP Guangdong) (NNP University))
      (PP (IN of)
        (NP (NNP Foreign) (NNPS Studies))))
    (VP (VBZ is)
      (ADJP (JJ located)
        (PP (IN in)
          (NP (NNP Guangzhou)))))
    (. .)))
# Dependency Parsing
[(u'ROOT', 0, 7), (u'compound', 2, 1), (u'nsubjpass', 7, 2), (u'case', 5, 3), (u'compound', 5, 4), (u'nmod', 2, 5), (u'auxpass', 7, 6), (u'case', 9, 8), (u'nmod', 7, 9), (u'punct', 7, 10)]

# _*_coding:utf-8_*_
# Other human languages support, e.g. Chinese
sentence = '清华大学位于北京。'
with StanfordCoreNLP(r'G:\JavaLibraries\stanford-corenlp-full-2018-02-27', lang='zh') as nlp:
    print(nlp.word_tokenize(sentence))
    print(nlp.pos_tag(sentence))
    print(nlp.ner(sentence))
    print(nlp.parse(sentence))
    print(nlp.dependency_parse(sentence))

 # General json output
nlp = StanfordCoreNLP(r'path_to_corenlp', memory='8g')
print nlp.annotate(sentence)
nlp.close()

text = 'Guangdong University of Foreign Studies is located in Guangzhou. ' \
       'GDUFS is active in a full range of international cooperation and exchanges in education. '
props={'annotators': 'tokenize,ssplit,pos','pipelineLanguage':'en','outputFormat':'xml'}
print nlp.annotate(text, properties=props)
nlp.close()

# Use an existing server
nlp = StanfordCoreNLP('https://localhost', port=9000)

import logging
from stanfordcorenlp import StanfordCoreNLP
# Debug the wrapper
nlp = StanfordCoreNLP(r'path_or_host', logging_level=logging.DEBUG)
# Check more info from the CoreNLP Server 
nlp = StanfordCoreNLP(r'path_or_host', quiet=False, logging_level=logging.DEBUG)
nlp.close()