Tokenization 2021

Different Ways to Perform Tokenization 2021



Intermediate Data NLP Python Technique Unstructured Text

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Want to start with Natural Language Processing (NLP)? Here is a good first step

Learn how to use token – a key element in preparing your NLP model building data

We present different ways of making tokens in text data.



Introduction about Tokenization 2021

Are you fascinated by the amount of text information available online? Looking for ways to work with this text data but don’t know where to start? Machines, of course, recognize numbers, not the letters of our language. And that can be a tricky place to navigate in machine learning.


So how can we control and refine this text data to create a model? The answer lies in the wonderful world of Natural Language Processing (NLP).


Solving the NLP problem is a multistep process. We need to clean up random text data first before we even consider reaching the modeling stage. Cleaning the data has a few important steps:


The making of Word tokens

Predicting the speech parts of each token

Text of lemmatization

Identify and delete static words, and more


In this article, we will talk about the first step in making tokens. We will first see what tokens are made and why they are needed in NLP. We will then look at six different ways to make Python tokens.


This text is not required. Anyone with an interest in NLP or data science will be able to follow along. If you are looking for an end-to-end course for NLP, you should check out our comprehensive course:


Indigenous Language Processing using Python

Tokenization 2021



What is token production in NLP?

Why is token production required?

Different Ways to Perform Tokenization in Python

Making tokens using Python split function ()

Making tokens using Common Expressions

Making tokens using NLTK

Making tokens using Space

Making tokens using Kara

To make tokens using Gensim



What is token production in NLP?

Tokenization 2021Tokenization 2021

Making tokens is one of the most common tasks when it comes to working with text data. But what does the word ‘tokenization’ mean?


Making tokens actually breaks a phrase, sentence, paragraph, or whole text document into smaller parts, such as individual words or terms. Each of these subdivisions is called tokens.


The tokens could be words, numbers or punctuation marks. In tokenization, smaller units are created by locating word boundaries. Wait – what are word boundaries?

These are the ending point of a word and the beginning of the next word. These tokens are considered as a first step for stemming and lemmatization (the next stage in text preprocessing which we will cover in the next article).

Difficult? Do not worry! The 21st century has made learning and knowledge accessibility easy. Any Natural Language Processing Course can be used to learn them easily.


Why is Tokenization required in NLP?Tokenization 2021

I want you to think about the English language here. Pick up any sentence you can think of and hold that in your mind as you read this section. This will help you understand the importance of tokenization in a much easier manner.

Before processing a natural language, we need to identify the words that constitute a string of characters. That’s why tokenization is the most basic step to proceed with NLP (text data). This is important because the meaning of the text could easily be interpreted by analyzing the words present in the text.

Let’s take an example. Consider the below string:

“This is a cat.”

What do you think will happen after we perform tokenization on this string? We get [‘This’, ‘is’, ‘a’, cat’].

There are numerous uses of doing this. We can use this tokenized form to:

Count the number of words in the text
Count the frequency of the word, that is, the number of times a particular word is present
And so on. We can extract a lot more information which we’ll discuss in detail in future articles. For now, it’s time to dive into the meat of this article – the different methods of performing tokenization in NLP.


Methods to Perform Tokenization in Python

We are going to look at six unique ways we can perform tokenization on text data. I have provided the Python code for each method so you can follow along on your own machine.


1. Tokenization using Python’s split() function
Let’s start with the split() method as it is the most basic one. It returns a list of strings after breaking the given string by the specified separator. By default, split() breaks a string at each space. We can change the separator to anything.


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