Named Entity Recognition in python using StandfordNER and NLTK

Using StandfordNER and NLTK for Named Entity Recognition in Python

StanfordNER is a popular tool for a task of Named Entity Recognition. Named Entity Recognition (NER) labels sequences of words in a text which are the names of things, such as person and company names, or gene and protein names. However, the implementation of StanfordNLP is in Java. So there is a way to use this wonderful tool in python as well, since you know, Python is life for a programmer. NLTK, which is a python library for Natural Language Processing, provides an interface of Stanford NER. The steps to implement it are as follows:

Import StanfordNER Wrapper from nltk.tag

from nltk.tag import StanfordNERTagger

Note that the old class name was NERTagger instead of StanfordNERTagger.

Download the zip file of StanfordNER from its link and unzip its contents. It contains a file called stanford-ner.jar. We need to set path of the jar file and models

model = 'stanford-ner/classifiers/english.all.3class.distsim.crf.ser.gz'
jar = 'stanford-ner/stanford-ner.jar'

Note the path. You need to add the path where the jar file belongs in your directory.

Now we use those paths in the StanfordNERTagger Wrapper to make it work in python

st = StanfordNERTagger(model,jar)

Now add the input sentence as follows to get the output.

print st.tag(‘John is studying at SUNY Buffalo University in NY’.split())

Which results in the following output.

(u'PERSON      ', u'John')
(u'ORGANIZATION', u'SUNY Buffalo University')

Hope that was helpful. Comment below for hugs of bugs.