How to Calculate Bigram Frequencies and Next Words Using Python

Learn how to compute the frequency of next words in a bigram list with Python by following this easy step-by-step guide.
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Understanding Bigram Frequencies in Python

In natural language processing (NLP), bigrams play a vital role in understanding the relationship between words in a sentence. A bigram is a pair of consecutive words collected from a given text, and analyzing them can provide insights into how words are frequently followed by others.

In this post, we'll explore how to calculate the frequency of each next word for a given word in a bigram list using Python, accompanied by a hands-on example. We'll answer an essential question: How can we effectively structure a dictionary to hold these relationships?

The Problem

Let's consider the sentence:

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Our goal is to transform this sentence into a circular list and then extract bigrams that represent pairs of consecutive words. Once we have these bigrams, we want to record the frequency of each word followed by its next word in a structured dictionary format.

With our input, we eventually aim to generate the following output dictionary:

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

Setting Up Our Data

Step 1: Prepare the Sentence

We start by splitting the sentence into individual words and then turning it into a circular list:

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

At this point, our sentence list looks like this:

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

Step 2: Generate Bigrams

Next, we need to create bigrams from this list. This can be done using the following function:

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

After running this, our bigrams output will be:

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Building the Frequency Dictionary

Step 3: Compute Frequencies

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Expected Output

When we print our dictionary d, we will see the following:

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

Common Mistakes

One common issue when trying to create a similar dictionary is incorrectly using len(my_bigrams) in a loop, which can lead to an IndexError. Always make sure you check the right length and that your logic correctly accounts for how many times a word has been added.

Conclusion

Using the approach outlined above, you can effectively compute bigram frequencies and understand the relationships between words in your text data. This kind of analysis is crucial in various NLP tasks, including language modeling and predictive text generation.

Feel free to apply this code to your sentences and explore the relationships between words. Happy coding!