Tokenization in NLP – Beginner’s Guide to Text Preprocessing

Learn how to split text into smaller units using tokenization in NLP. This beginner-friendly tutorial covers word and sentence tokenization in Python, preparing text for machine learning and AI applications.

1. Introduction

Tokenization is a fundamental NLP technique that splits text into smaller units called tokens, such as words or sentences. These tokens are the building blocks for further text analysis, including text classification, sentiment analysis, and language modeling.

Types of Tokenization:

  1. Word Tokenization: Splits text into individual words.
  2. Sentence Tokenization: Splits text into sentences.

2. Python Example

Using the NLTK library for tokenization:


import nltk
from nltk.tokenize import word_tokenize, sent_tokenize

nltk.download('punkt')

text = "Natural Language Processing is amazing. Tokenization is the first step."

# Sentence Tokenization
sentences = sent_tokenize(text)
print("Sentences:", sentences)

# Word Tokenization
words = word_tokenize(text)
print("Words:", words)

Output:


Sentences: ['Natural Language Processing is amazing.', 'Tokenization is the first step.']
Words: ['Natural', 'Language', 'Processing', 'is', 'amazing', '.', 'Tokenization', 'is', 'the', 'first', 'step', '.']

3. Best Practices

  1. Remove punctuation and special characters if not needed.
  2. Use consistent case normalization (e.g., lowercase all words).
  3. Combine tokenization with stopword removal, stemming, or lemmatization for preprocessing.
  4. Choose word or sentence tokenization based on the NLP task.

4. Outcome

After learning tokenization, beginners will be able to:

  1. Split text into words and sentences for analysis.
  2. Prepare text for machine learning or deep learning models.
  3. Understand tokenization as a core step in NLP pipelines.