Named entity recognition with Python

Named entity recognition is a natural language processing task that involves identifying and classifying named entities in text. These entities can include names of people, organizations, locations, dates, and more. Step 1: Import the necessary libraries for named entity recognition. The popular library for this task is spaCy. 
import spacy

Step 2: Load a pre-trained spaCy model that includes named entity recognition capabilities. 
nlp = spacy.load("en_core_web_sm")

Step 3: Create a function that takes in a text input, processes it with the loaded spaCy model, and extracts named entities. 
def extract_entities(text):
    doc = nlp(text)
    entities = [(entity.text, entity.label_) for entity in doc.ents]
    return entities

Step 4: Test the function with a sample text and print out the identified named entities. 
text = "Apple is a technology company headquartered in Cupertino, California."
entities = extract_entities(text)
print(entities)

By following these steps, you can easily perform named entity recognition in Python using spaCy.

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