Python wrapper for Stanford CoreNLP.
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PyPI GitHub release PyPI - Python Version

stanfordcorenlp is a Python wrapper for Stanford CoreNLP. It provides a simple API for text processing tasks such as Tokenization, Part of Speech Tagging, Named Entity Reconigtion, Constituency Parsing, Dependency Parsing, and more.


Java 1.8+ (Check with command: java -version) (Download Page)

Stanford CoreNLP (Download Page)

Py Version CoreNLP Version
v3.7.0.1 v3.7.0.2 CoreNLP 3.7.0
v3.8.0.1 CoreNLP 3.8.0
v3.9.1.1 CoreNLP 3.9.1


pip install stanfordcorenlp


Simple Usage

# 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.

Output format:

# 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
      (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)]

Other Human Languages Support

Note: you must download an additional model file and place it in the .../stanford-corenlp-full-2018-02-27 folder. For example, you should download the stanford-chinese-corenlp-2018-02-27-models.jar file if you want to process Chinese.

# _*_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:

General Stanford CoreNLP API

Since this will load all the models which require more memory, initialize the server with more memory. 8GB is recommended.

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

You can specify properties:

  • annotators: tokenize, ssplit, pos, lemma, ner, parse, depparse, dcoref (See Detail)

  • pipelineLanguage: en, zh, ar, fr, de, es (English, Chinese, Arabic, French, German, Spanish) (See Annotator Support Detail)

  • outputFormat: json, xml, text

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)

Use an Existing Server

Start a CoreNLP Server with command:

java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000

And then:

# Use an existing server
nlp = StanfordCoreNLP('http://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)


We use setuptools to package our project. You can build from the latest source code with the following command:

$ python bdist_wheel --universal

You will see the .whl file under dist directory.