How to Keep Special Characters Together in word_tokenize Using Python

Learn how to properly tokenize strings in Python, specifically how to keep special characters like the C assignment operator `- ` intact using NLTK's `word_tokenize`.
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How to Keep Special Characters Together in word_tokenize Using Python

In the realm of Natural Language Processing (NLP), encountering special characters is common. While working with text data, you may face challenges, such as separating or tokenizing words properly without disrupting the meaning. One such common issue arises when using Python's word_tokenize from the NLTK library.

In this guide, we will explore a situation where a string, containing special characters like ->, gets split during tokenization. We will then present a solution to handle this effectively while keeping those characters together.

The Problem with word_tokenize

When we attempt to tokenize a string containing specific special characters using the word_tokenize method, it often leads to undesirable splits. For example:

[[See Video to Reveal this Text or Code Snippet]]

The output of this code snippet is:

[[See Video to Reveal this Text or Code Snippet]]

Here, instead of keeping -> intact, the tokenizer has split it into two separate tokens: '-' and '>'. This can lead to confusion, particularly when the special character has significance, such as in programming languages.

The Solution: Using RegexpTokenizer

To resolve the issue of keeping special characters together, we can use NLTK's RegexpTokenizer. Unlike word_tokenize, this tokenizer allows us to define regular expressions that can better cater to our needs.

Here's How to Implement It:

Import the Required Library
First, ensure NLTK is imported, specifically the RegexpTokenizer.

Define Your Tokenizer with Regular Expressions
You can create an instance of RegexpTokenizer, defining a regex pattern that includes words, dots, digits, and the special character combination you're interested in.

Tokenize the Sentence
Apply your custom tokenizer to the sentence.

Example Code

[[See Video to Reveal this Text or Code Snippet]]

Expected Output

When you run the code above, you should receive a tokenized output as follows:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By utilizing the RegexpTokenizer, we can control how our strings are tokenized, allowing us to keep special characters together as coherent words. This method is particularly useful for NLP tasks that involve programming languages or technical strings where such characters have significant meanings.

In summary, if you're dealing with tokenization in Python and need to retain special sequences like ->, consider switching from the conventional word_tokenize to RegexpTokenizer. Happy coding!