Named Entity Recognition (NER) is a crucial task in natural language processing (NLP) that involves identifying and classifying named entities in text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

Basics of NER

  • What is NER? Named Entity Recognition is the process of identifying entities in text and categorizing them into predefined entity types.

  • Why is NER important? It is essential for various applications such as information extraction, machine translation, sentiment analysis, and more.

Getting Started

To get started with NER, you can follow this tutorial:

  • Install necessary libraries: Python, NLTK, spaCy, or Stanford NER.

    pip install nltk spacy

  • Prepare your dataset: A labeled dataset with text and corresponding entity annotations.

  • Train a model: Use the dataset to train a model for NER.

Tools and Libraries

  • NLTK: A leading platform for building Python programs to work with human language data. NLTK NER

  • spaCy: An industrial-strength NLP library. spaCy NER

  • Stanford NER: A tool developed by Stanford University. Stanford NER

Example

Here's a simple example using spaCy to perform NER on a sentence:

import spacy

nlp = spacy.load("en_core_web_sm")
text = "Apple Inc. is an American multinational technology company headquartered in Cupertino, California."

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

Further Reading

For more detailed tutorials and resources, check out the following links:

NER Example