Named Entity Recognition (NER) in NLP: Complete Guide

Quick answer: Learn Named Entity Recognition, how it identifies people, organisations, locations and dates in text, and how to implement it in Python using spaCy.

What is Named Entity Recognition?

Named Entity Recognition, or NER, identifies and classifies named entities in text, such as people, organisations, locations, dates and monetary values, into predefined categories.

Why NER Matters

NER converts unstructured text into structured information that can be searched, filtered and analysed, powering applications like resume parsing, news categorisation, and extracting key facts from legal or medical documents at scale.

NER Using spaCy

import spacy

nlp = spacy.load('en_core_web_sm')
text = "Aniruddha Kalbande founded Fireblaze AI School in Nagpur on 31 October 2017."

doc = nlp(text)

for ent in doc.ents:
    print(ent.text, ent.label_)

# Aniruddha Kalbande PERSON
# Fireblaze AI School ORG
# Nagpur GPE
# 31 October 2017 DATE

Common Entity Types

Visualising Entities

from spacy import displacy

displacy.render(doc, style='ent', jupyter=True)

How Modern NER Models Actually Work

Modern NER systems typically use neural sequence models trained on large annotated corpora, learning both the words themselves and their surrounding context to decide whether a span of text is an entity and, if so, which category it belongs to.

Training a Custom Entity Type

Pretrained models like spaCy's default one cover common categories, but many real business applications need custom entity types, such as recognising specific course names or product codes. This requires fine-tuning the model on your own labelled examples.

Practical Applications

• Automatically extracting company names and people from news articles

• Pulling structured fields like candidate name and skills from resumes

• Identifying medication names and dosages in clinical notes

• Redacting personal information from documents for privacy compliance

Common Interview Questions

What is the difference between NER and POS tagging?

POS tagging assigns a grammatical role, like noun or verb, to every single word. NER identifies specific spans of text, often multiple words, that refer to a named entity like a person or organisation, and classifies that span into an entity category.

Why might a pretrained NER model need fine-tuning for a specific business use case?

General-purpose models are trained on entity categories common in general text, but a specific domain, such as recognising specific product codes or course names, needs its own labelled training examples to recognise those specialised entities reliably.