Tokenization | Core Concepts

What is Tokenization?

Tokenization is the process of breaking down text into smaller units called tokens. These tokens can be words, phrases, or even characters, depending on the context of the analysis. Tokenization is a crucial step in natural language processing (NLP) as it enables further analysis and manipulation of text data.

Why is Tokenization Important?

Tokenization serves several important purposes in NLP:

It simplifies the analysis of text data by breaking it into manageable parts.
It allows for the identification of meaningful words and phrases.
It prepares the text for further processing, such as stemming, lemmatization, and sentiment analysis.

Types of Tokenization

There are mainly two types of tokenization:

1. Word Tokenization

In word tokenization, the text is split into individual words. This is commonly used in most NLP applications.

2. Sentence Tokenization

In sentence tokenization, the text is divided into sentences. This is useful for applications that require understanding the structure of the text.

Tokenization with NLTK

The Natural Language Toolkit (NLTK) is a popular Python library for working with human language data. It provides easy-to-use methods for tokenization.

Installing NLTK

To get started, you need to install the NLTK library. This can be done using pip:

pip install nltk

Word Tokenization Example

Below is an example of how to perform word tokenization using NLTK:

import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize
text = "Hello, world! Welcome to tokenization."
tokens = word_tokenize(text)
print(tokens)

Output:

['Hello', ',', 'world', '!', 'Welcome', 'to', 'tokenization', '.']

Sentence Tokenization Example

Here’s how to perform sentence tokenization:

from nltk.tokenize import sent_tokenize
text = "Hello, world! Welcome to tokenization. Let's learn more."
sentences = sent_tokenize(text)
print(sentences)