NLP NamedEntityRecognition

NLPNer.py

import nltk
from nltk.corpus import state_union
from nltk.tokenize import PunktSentenceTokenizer

train_text = state_union.raw("2005-GWBush.txt")
sample_text = state_union.raw("2006-GWBush.txt")

custom_sent_tokenizer = PunktSentenceTokenizer(train_text)

tokenized = custom_sent_tokenizer.tokenize(sample_text)

def process_content():
    try:
        for i in tokenized[5:]:
            words = nltk.word_tokenize(i)
            tagged = nltk.pos_tag(words)

##            NER is built in tool for chunking
##            binary = True, this means either something is a named entity, or not.
##            There will be no further detail
            namedEnt = nltk.ne_chunk(tagged, binary=True)
            namedEnt.draw()
    except Exception as e:
        print(str(e))


process_content()