Help needed for Python NLP Named Entity Recognition

[Nipponho]

Help! I need to come out with python codes to extract name, designation, company, address, zip code, telephone number, country city, email address from any input corpus of text strings.

I managed to figure out the codes to extact phone number, e mail and names and will figure out the rest like country, city zip code etc. later. Unfortunately I am stuck now as my python skills is very elementary. Can anyone help me to amend my codes such that I can input any corpus and the codes will help me extract phone number, e mail and names as ouputs?

Thanks!

 

[Nipponho]

import re
import nltk
from nltk.corpus import stopwords
from nltk import sent_tokenize
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')


stop = stopwords.words('english')


document = """
Hey,
This week has been crazy. Attached is my report on IBM. Can you give it a quick read and provide some feedback.
Also, make sure you reach out to Claire (claire@xyz.com).
You're the best.
Cheers,
George W.
212-555-1234
"""



# Remove stop words
document = ' '.join([i for i in document.split() if i not in stop])

# Segment the corpus into sentences.
sentences = sent_tokenize(document)

# Segment the corpus into sentences.
#sentences = nltk.sent_tokenize(document)

# Tokenize each sentence into an array of words.
sentences = [nltk.word_tokenize(sent) for sent in sentences]

# POS Tag each word in each sentence
sentences = [nltk.pos_tag(sent) for sent in sentences]


def extract_phone_numbers(sentences):
r = re.compile(r'(\d{3}[-\.\s]??\d{3}[-\.\s]??\d{4}|\(\d{3}\)\s*\d{3}[-\.\s]??\d{4}|\d{3}[-\.\s]??\d{4})')
phone_numbers = r.findall(sentences)
return [re.sub(r'\D', '', number) for number in phone_numbers]

def extract_email_addresses(sentences):
r = re.compile(r'[\w\.-]+@[\w\.-]+')
return r.findall(sentences)

def extract_names(sentences):
names = []
for tagged_sentence in sentences:
for chunk in nltk.ne_chunk(tagged_sentence):
if type(chunk) == nltk.tree.Tree:
if chunk.label() == 'PERSON':
names.append(' '.join([c[0] for c in chunk]))
return names

if __name__ == '__main__':
numbers = extract_phone_numbers(sentences)
emails = extract_email_addresses(sentences)
names = extract_names(sentences)