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Text Cleaning

Once the text is extracted, it needs to be cleaned up and prepared. This is the bridge between raw text and structured input preparation and it ensures better chunking as it reduces the risk of splitting on irrelevant and malformed content. The process itself involves transforming raw, messy, or noisy text into a standardised, consistent format that can be interpreted effectively. This process often includes removing unwanted characters, stripping HTML tags, correcting encoding issues, normalising whitespace and punctuation, and eliminating boilerplate elements such as footers, headers, or repetitive disclaimers.

Key aspects of text cleaning include:

  • Removing Noise: Real-world documents include extra bits that aren’t useful for QA or search. This could be HTML tags, template text repeated on every page (headers, footers) and navigation menus from web pages. These should be removed so they don’t confuse the model. For example, cleaning might strip out boilerplate like page numbers or legal disclaimers unless those are needed.

  • Standardizing Text: This involves fixing encoding issues (think of all the weird characters), normalizing punctuation and case (lowercasing everything, unless case matters) and standardizing date or number formats if needed. Consistent text makes it easier to match queries to content.

  • Redacting Sensitive Information: If documents contain Personally Identifiable Information (PII) or other sensitive data that should not be exposed, cleaning will handle that. Techniques include identifying patterns like emails, phone numbers, social security numbers and replacing them with placeholders or tokens (or removing them). This step can overlap with policy enforcement, ensuring that the AI doesn’t train on or reveal private data inadvertently.

  • Filtering Out Irrelevant Parts: Sometimes only a portion of a document is relevant to the knowledge base. For instance, an email chain might include a long reply thread where only the latest message is important making all the older replies irrelevant and able to be truncated. Or in logs, maybe only error messages are needed and everything else can be dropped. Deciding these rules is part of preprocessing.

  • Spelling Correction: In some cases, especially if source text has OCR errors or typos, a spell-check or correction could be applied. However, this must be done carefully to not alter meanings. Modern embedding models handle some typos robustly, so this is optional.

  • Maintain Alignment with Source: Even while cleaning, it’s often useful to keep a reference to the original source location (like document ID and page number). Many pipelines add metadata for this. For example, after cleaning and chunking, each chunk might carry source: "Report_X.pdf", page: 5 so that if it’s retrieved later, the system can trace back to the exact origin for context or citation.

Text cleaning makes the data consistent and safe. It directly affects the quality of retrieval. For example, if every chunk from a document has a clear metadata tag like title or category added during enrichment (next step), you can later filter search results by those tags. But if text is dirty (say, contains a lot of URL fragments or random numbers), the embeddings could be less meaningful and the language model might produce irrelevant output or include the garbage text in its answer.

In short, cleaning transforms raw text into AI-ready text that is free of junk, standardized in form and scrubbed of disallowed content. It’s a crucial step to improve accuracy and ensure compliance with data handling policies.