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stanfordcorenlp

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.

Prerequisites

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

Stanford CoreNLP (Download Page)

Installation

pip install stanfordcorenlp

Example

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
 (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)]

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:
    print(nlp.word_tokenize(sentence))
    print(nlp.pos_tag(sentence))
    print(nlp.ner(sentence))
    print(nlp.parse(sentence))
    print(nlp.dependency_parse(sentence))

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)
nlp.close()

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)
nlp.close()

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)

Debug

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()

Build

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

$ python setup.py bdist_wheel --universal

You will see the .whl file under dist directory.