sner
Python wrapper around the Stanford Named Entity Recognizer (NER) Server and the Part-Of-Speech (POS) Tagger Server.
Stanford Named Entity Recognizer Project
Stanford Part-Of-Speech Tagger Project
Installation
pip install sneror
python setup installStart
NER Client
Run the following commands to start the NER server
cd your_stanford_ner_dir
java -Djava.ext.dirs=./lib -cp stanford-ner.jar edu.stanford.nlp.ie.NERServer -port 9199 -loadClassifier ./classifiers/english.all.3class.distsim.crf.ser.gz -tokenizerFactory edu.stanford.nlp.process.WhitespaceTokenizer -tokenizerOptions tokenizeNLs=falseUse the following in Python to access the NER server
from sner import Ner
test_string = "Alice went to the Museum of Natural History."
tagger = Ner(host='localhost',port=9199)
print(tagger.get_entities(test_string))
The following results are expected
[('Alice', 'PERSON'),
('went', 'O'),
('to', 'O'),
('the', 'O'),
('Museum', 'ORGANIZATION'),
('of', 'ORGANIZATION'),
('Natural', 'ORGANIZATION'),
('History', 'ORGANIZATION'),
('.', 'O')]POS Client
Run the following commands to start the POS server
cd your_stanford_pos_dir
java -cp stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTaggerServer -port 9198 -model models/english-bidirectional-distsim.taggerUse the following in Python to access the POS server
from sner import POSClient
test_string = "Alice went to the Museum of Natural History."
tagger = POSClient(host='localhost', port=9198)
print(tagger.tag(test_string))The following results are expected
[('Alice', 'NNP'),
('went', 'VBD'),
('to', 'TO'),
('the', 'DT'),
('Museum', 'NNP'),
('of', 'IN'),
('Natural', 'NNP'),
('History', 'NN'),
('.', '.')]
