How to perform Named Entity Recognition NER in Python with NLP

Named Entity Recognition (or NER) uses the ever-growing power of Natural Language Processing (NLP) to extract a list of named entities from a string, such as people, places, and organizations. This has all kinds of great applications, but how can we get this functionality into your project without wrecking your weekend plans to do it?

Now our first step is to simply install our client library, which will require this pip install command.

pip install git+https://github.com/Cloudmersive/Cloudmersive.APIClient.Python.NLP.git

And now we are going to call the function titled extract_entities_string_post, which will process our input string and perform our NER.

from __future__ import print_function
import time
import cloudmersive_nlp_api_client
from cloudmersive_nlp_api_client.rest import ApiException
from pprint import pprint
# Configure API key authorization: Apikey
configuration = cloudmersive_nlp_api_client.Configuration()
configuration.api_key['Apikey'] = 'YOUR_API_KEY'
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
# configuration.api_key_prefix['Apikey'] = 'Bearer'
# create an instance of the API class
api_instance = cloudmersive_nlp_api_client.ExtractEntitiesStringApi(cloudmersive_nlp_api_client.ApiClient(configuration))
value = 'value_example' # str | Input string
try:
# Extract entities from string
api_response = api_instance.extract_entities_string_post(value)
pprint(api_response)
except ApiException as e:
print("Exception when calling ExtractEntitiesStringApi->extract_entities_string_post: %s\n" % e)

Already finished! Super easy.