Getting started with Spacy PhraseMatcher

import spacy
import common_module as cm
from spacy.matcher import PhraseMatcher
nlp = spacy.load('en_core_web_sm')
matcher = PhraseMatcher(nlp.vocab)
import string
import pickle
import requests
from bs4 import BeautifulSoup

persons = ['john','miky','mikle','mike']
orgs = ['apple','microsoft','nokia','samsung']
locations = ['india','japan','singapore','usa','america','dubai']

#Making patterns and adding them to matcher()
persons_patterns = [nlp.make_doc(term) for term in persons]
persons_patterns

# Converted into nlp documents nlp.make_doc used to simplyfy the process

orgs_patterns = [nlp.make_doc(term) for term in orgs]
orgs_patterns

locations_patterns = [nlp.make_doc(term) for term in locations]
locations_patterns

# Adding the patterns to the matcher
matcher.add("PER", None, *persons_patterns)
matcher.add("ORG", None, *orgs_patterns)
matcher.add("LOC", None, *locations_patterns)

TEST_CASES = ['Mike is from India and he is working on samsung office','John working on apple office in USA']

def update_output(string_id,text,output):
    if text not in output[string_id]:
        output[string_id].append(text)
        
def match_processor(text):
    document = nlp(text)
    matches = matcher(document)
    output = {'PER':[],"ORG":[],'LOC':[]}
    for match_id, start, end in matches:
        string_id = nlp.vocab.strings[match_id]
        span = document[start:end]
        update_output(string_id,span.text,output)
    return output

for sentence in TEST_CASES:
    print(match_processor(sentence))