In this tutorial, we are going to learn how to build a simple tokenizer in Python. Tokenization is a key step in Natural Language Processing (NLP). It is a way of separating or splitting a piece of text into smaller units, also known as tokens. Tokens are the building blocks of Natural Language. Let’s dive in!
Step 1: Install Required Packages
Let’s start off by installing the required Python libraries. You need to have the nltk library installed in order to accomplish tokenizing text. Open your command prompt and type the below command to install the NLTK library.
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pip install nltk |
Step 2: Import Libraries
Next, we need to import the installed libraries. In Python, we use the import keyword to do this.
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import nltk |
Step 3: NLTK Data
NLTK comes with a lot of collections of data and corpora. Let’s download the required dataset for tokenization.
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nltk.download('punkt') |
Step 4: Define Your Text
Define a sample text that we will be tokenizing. Let’s consider the following sentence for our exercise.
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input_text = "This is our sample text for tokenizing exercise in Python." |
Step 5: Tokenizing the Text
We will be using the word_tokenize function from the nltk library’s tokenize module. Let’s use it to split our sample text into separate words or tokens.
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from nltk.tokenize import word_tokenize tokens = word_tokenize(input_text) |
Step 6: Displaying Tokens
Finally, let’s print out the tokens or the list of words we have split our sentence into.
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print(tokens) |
When you run the code, it will provide an output similar to the below:
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['This', 'is', 'our', 'sample', 'text', 'for', 'tokenizing', 'exercise', 'in', 'Python.'] |
Full Code
In the context of this Python tutorial, your full Code should look like this:
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import nltk nltk.download('punkt') from nltk.tokenize import word_tokenize input_text = "This is our sample text for tokenizing exercise in Python." tokens = word_tokenize(input_text) print(tokens) |
Conclusion
That’s it! We have built a simple tokenizer in Python using the Natural Language Toolkit (NLTK). We took a sample piece of text, split it into the smallest units ‘tokens’ and then displayed them.
Tokenization is a basic but essential part of natural language processing tasks such as word count, semantic analysis, indexing, etc.
Becoming familiar with it will both broaden your Python horizons and deepen your understanding of NLP. Keep practicing and stay tuned for more Python tutorials!