Python Text Parsing: Split List into Chunks While Keeping Preceding Delimiters

Learn how to effectively parse and split text in Python to extract questions and answers while preserving their context with delimiters.
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Python Text Parsing: Split List into Chunks While Keeping Preceding Delimiters

When working with text derived from Optical Character Recognition (OCR) of Q&A deposition documents, you may encounter a need to extract structured information from raw output. The challenge is to split the textual content into distinct fragments—questions and answers—while retaining awareness of their context. Buckle up as we dive into a practical solution for parsing text in Python!

Understanding the Problem

Upon scanning through publicly available Q&A depositions, you might find that the resulting text lacks clear structures, making it difficult to separate questions from answers. The typical structure of the OCR output looks somewhat like this:

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

In the example above, questions start with "Q" and answers with "A", but not all text fits neatly into these categories. Some text may also appear before the first question.

The Desired Outcome

To create a more usable structure, you may want to achieve one of the following results:

A list with questions and answers followed by their respective delimiters.

A dictionary-like structure linking each text chunk with its corresponding prefix indicating if it is a question or an answer.

Here are potential formats you could be interested in:
1.

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

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The Solution

To solve this parsing problem, the following Python code uses regular expressions to iterate through the text, capturing the relevant segments while maintaining links to their respective delimiters.

Step-by-step Breakdown

Import the Required Library:
First, ensure you have the re (regular expressions) module available.

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Define Regular Expression Patterns:
Define the regex pattern that identifies the prompts for questions and answers.

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

Locate Delimiters:

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

Iterate Through Delimiters:
Loop through each pair of delimiters to slice the text into meaningful chunks.

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

Handle the Final Segment:
After the loop, remember to account for the final segment that may lack a subsequent delimiter:

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

Example Output

When you run this code, you will get a neatly structured list indicating whether each text fragment is a question or an answer:

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

Conclusion

Parsing text, especially from OCR sources, can often lead to messy results. However, with Python's powerful regex capabilities, you can easily extract meaningful structured data while preserving context. This method not only enhances readability but also facilitates further analysis on the Q&A data.

Now, you have a robust approach to tackle similar challenges where structured text extraction is critical!