Identify Person, Place and Organisation in content using Python

#Import all the required libraries.

import os

from nltk.tag import StanfordNERTagger

import pandas as pd

#Set environmental variables programmatically.

#Set the classpath to the path where the jar file is located

os.environ['CLASSPATH'] = "<path to the file>/stanford-ner-2015-04-20/stanford-ner.jar"

#Set the Stanford models to the path where the models are stored

os.environ['STANFORD_MODELS'] = '<path to the file>/stanford-corenlp-caseless-2015-04-20-models/edu/stanford/nlp/models/ner'

#Set the java jdk path

java_path = "C:/Program Files/Java/jdk1.8.0_161/bin/java.exe"

os.environ['JAVAHOME'] = java_path

#Set the path to the model that you would like to use

stanford_classifier  =  '<path to the file>/stanford-corenlp-caseless-2015-04-20-models/edu/stanford/nlp/models/ner/english.all.3class.caseless.distsim.crf.ser.gz'

#Build NER tagger object

st = StanfordNERTagger(stanford_classifier)

#A sample text for NER tagging

text = 'srinivas ramanujan went to the united kingdom. There he studied at cambridge university.'

#Tag the sentence and print output

tagged = st.tag(str(text).split())

print(tagged)

[(u'srinivas', u'PERSON'), 
(u'ramanujan', u'PERSON'), 
(u'went', u'O'), 
(u'to', u'O'), 
(u'the', u'O'), 
(u'united', u'LOCATION'), 
(u'kingdom.', u'LOCATION'), 
(u'There', u'O'), 
(u'he', u'O'), 
(u'studied', u'O'), 
(u'at', u'O'), 
(u'cambridge', u'ORGANIZATION'), 
(u'university', u'ORGANIZATION')]

1. It is a discriminative model unlike the HMM model and thus models the conditional probability
2. It does not assume independence of features unlike the HMM model. This means that the current word, previous word, next word are all considered for model as features
3. Relative to HMM or Max ent Markov Models, CRFs are the slowest