How to perform tokenization using NLTK

Tokenization is an essential step in Natural Language Processing (NLP) that involves breaking down a text into smaller units called tokens. Depending on the specific task and requirements, these tokens can be words, sentences, or subwords. It is crucial in various tasks such as text classification, named entity recognition, and more.

In this Answer, we will explore how to perform tokenization using the Natural Language Toolkit (NLTK) library in Python.

Installing NLTK

Before we dive into the code, let's make sure that we have NLTK installed. Open the terminal or command prompt and run the following command to install NLTK:

Once NLTK is installed, we can start using it for tokenization.

Importing the NLTK library

To use the NLTK library in the Python code, we need to import it. Add the following line of code at the beginning of the Python script or notebook:

Tokenizing text into words

The most common form of tokenization is splitting a text into individual words.

Code example

NLTK provides several tokenizers for this purpose. Let's see an example using the word tokenizer:

Code explanation

Here's a line-by-line explanation for the above code:

• Line 1: We import the NLTK library.

• Line 2: We import the word_tokenize function from the nltk.tokenize module. This function is used to tokenize a sentence into individual words.

• Line 4: We define a variable input_text and assign it the string "Welcome to Educative". This is the sentence that we want to tokenize.

• Line 5: We call the word_tokenize function on the input_text variable and assign the result to the individual_words variable. This function splits the text into individual words and returns them as a list.

• Line 7: We use the print function to display the contents of the individual_words list.

Tokenizing text into sentences

Tokenizing text into sentences is another common form of tokenization.

Code example

NLTK provides a sentence tokenizer for this purpose. Here's an example:

Code explanation

Here's a line-by-line explanation for the above code:

• Line 1: We import the NLTK library.

• Line 2: We import the sent_tokenize function from the nltk.tokenize module. This function is used to tokenize a text into individual sentences.

• Line 4: We define a variable input_text and assign it the string. This is the text that we want to tokenize into sentences.

• Line 5: We call the sent_tokenize function on the input_text variable and assign the result to the sentences variable. This function splits the text into individual sentences and returns them as a list.

• Line 7: We use the print function to display the contents of the sentences list.

Conclusion

Tokenization is a fundamental step in NLP that allows us to break text down into smaller units for further analysis and processing. In this Answer, we explored how to perform tokenization using the NLTK library in Python. We also learned how to tokenize text into words and sentences. NLTK provides a wide range of tokenizers and options, making it a powerful tool for handling text data in NLP tasks.