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Discover how to efficiently compute cosine similarity between files in two directories using Python and SpaCy. Learn step-by-step solutions with practical examples.
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Computing Cosine Similarity Between Two Directories of Files in Python

When working with natural language processing (NLP), you may find yourself needing to compare text data to see how similar they are. One common approach is to compute what's known as cosine similarity. This technique is particularly helpful when you have two directories filled with text files that you wish to analyze. In this guide, we’ll explore how to compute cosine similarity between human-written transcripts and machine-generated transcripts using Python's SpaCy library.

The Problem Statement

Imagine you have two sets of files located in different directories:

Human Transcripts: Files that contain transcripts manually written by humans.

IBM Watson Transcripts: Automated transcripts generated by the IBM Watson speech-to-text service.

Both directories contain the same number of files, each transcribing matched telephony recordings. Your objective is to compute the cosine similarity between these corresponding files and print or save the results along with the filenames. Let's examine the code and understand the issue and the eventual solution.

Understanding the Initial Code

Here’s a simplified version of the initial code that attempts to achieve this:

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

Issues with the Approach

Iterating Through Two Directories: When attempting to compare two directories, the loop tries to process both lists simultaneously which could lead to unexpected errors like too many values to unpack.

Insufficient Looping: In the attempt to use an index-based approach, the loop only iterates through two indexes, making it ineffective for files more than two.

The Solution

To effectively compute cosine similarity between all corresponding files in the two directories, we can follow these steps:

Step 1: Load the SpaCy Model

First, ensure you have the SpaCy English model loaded properly:

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

Step 2: Define Directories

Define the directories where your text files are located:

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

Step 3: Using zip() for Concurrent Iteration

Using the zip() function allows you to iterate through both directories simultaneously. Here's how to implement it:

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

Step 4: Output the Results

Once you run this code, it will compare all files in both directories and print their cosine similarity scores along with the filenames.

Troubleshooting Common Errors

File Encoding Issues: If you encounter an encoding error (for example, UTF-16), ensure your text files are saved in a compatible format, such as UTF-8.

Wrap the Code in a Function: For reusability, consider wrapping the comparison logic in a function that allows you to choose between different models or directories.

Conclusion

Computing cosine similarity between the transcriptions in two directories can be straightforward with the right approach. By using Python’s SpaCy library and iterating through the directories using zip, we can efficiently compare all corresponding files. Now you can use this knowledge for other similar projects or expand this functionality for more extensive datasets.

By implementing the above steps, you are well on your way to mastering cosine similarity comparisons in NLP tasks. Happy coding!