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Table of contents
1.
Introduction 
2.
What is Tokenization in NLP?
3.
Types of Tokenisation
4.
Why do we need Tokenization? 
5.
How to perform Tokenization:
5.1.
Word tokenization using python split function: 
5.2.
Natural Language Toolkit(NLTK): 
6.
FAQS
7.
Key Takeaways
Last Updated: Mar 27, 2024
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Tokenization in NLP

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Ashwin Goyal
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Introduction 

Do you find the sheer amount of text data available on the internet fascinating? Are you trying to figure out how to work with this text data but don't know where to start? After all, machines recognize numbers, not characters in our language. And with machine learning, that can be a rugged terrain to negotiate.

A multi-stage method is required to solve an NLP challenge. Before we consider moving on to the modelling stage, we must first clean the unstructured text data. There are a few critical processes to cleaning data:

Tokenization of words

Predicting each token's bits of speech

Lemmatization of text

Stop words can be identified and removed, among other things.

What is Tokenization in NLP?

Tokenization is breaking down a phrase, sentence, paragraph, or even an entire text document into smaller components like individual words or phrases. Tokens are the names given to each of these smaller units.

Words, numerals, or punctuation marks could be used as tokens. By finding word boundaries, Tokenization creates smaller units. What are word limits, exactly?

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Types of Tokenisation

There can be different kinds of boundaries that can be chosen in NLP, for example, spaces, each character or subwords etc.

For example: Let's consider a sentence "Natural Language Processing"

Word Tokens: "Natural" - "Language" - "Processing"

SubWord Tokens: "Natu" - "ral" - "lan"- "guage"- "pro"- "cessing" 

Character Tokens: "N"-" a"-" t"-" u"-" r"-" a"-" l" and so on. 

There are many more types of Tokenization in NLP, in which Byte Pair Encoding(BPE) is one of the widely used techniques. 

Why do we need Tokenization? 

Consider the English language in this situation. Pick any sentence that comes to you and keep it in mind as you read this section. This will make it easier for you to grasp the significance of Tokenization.

We must first identify the words that make up a string of letters before we can process natural language. As a result, Tokenization is the most fundamental step in the NLP process (text data). This is significant because the text's meaning may be easily deduced by examining the words in the text.

So the way our mind needs to understand the meaning of individual words the same way our program needs to learn the meaning of a chunk of data, and that's where Tokenization comes to the rescue. 

How to perform Tokenization:

Word tokenization using python split function: 

If we want to split a sentence about the space or any other character in it, we can use the python split function and do it very quickly. 

sentence= "I am a coding ninja, and I am improving a lot."
tokens=sentence.split()
print(tokens)

Output: 

['I', 'am', 'a', 'coding', 'ninja,', 'and', 'I', 'am', 'improving', 'a', 'lot']

But in general, we should use libraries already optimized for the purpose of Tokenization; let's have an example of a popular library. 

Natural Language Toolkit(NLTK): 

We will use this library specially designed for the purpose of NLP for Tokenization. 

from nltk import tokenize
text = "coding is life and we are coding ninjas. "
print(tokenize.sent_tokenize(text))
print(tokenize.word_tokenize(text))

 

Output: 

['coding is life and we are coding ninjas.']

['coding', 'is', 'life', 'and', 'we', 'are', 'coding', 'ninjas', '.']

There are many other ways to tokenize the data with other libraries, like Keras or Gensim, but they are almost the same. 

FAQS

1. Why is Tokenization important in NLP?
The tokens help with context comprehension and the building of the NLP model. Tokenization aids in interpreting the meaning of the text by analyzing the sequence of words.

2. What are some of the other ways Tokenization can be achieved?
There are a lot more libraries like Keras, and SkLearn in python by which Tokenisation can be achieved, and we can also achieve Tokenization by Regular Expressions regex in python. 

3. What are various challenges in Tokenisation? 
Choosing the correct form of Tokenization is very important; each type has its pros and cons, but in general, word Tokenization is very common. 

4. Can I do NLP without Tokenization?
No, it's like you are trying to learn a language without knowing the meaning of some words; the meaning of words must be known to make more sentences and process other sentences in future. 

Key Takeaways

In a nutshell, Tokenization is nothing but breaking a large text into small parts, which makes the model learn the language easily and learn better parameters. It's like trying to make some words out of the given language.  

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