Natural Language Processing (NLP) Text Preprocessing & Generative Preparation for GenAI Assistant
Healthcare Organization
KEY IMPACT
Provided a high-quality text corpus prepared for generative-model training, enabling downstream generative tasks (descriptive paragraph generation, topic modelling) with clean and consistent input, and formed a backbone for enterprise-grade NLP modelling that ensures data readiness, governance, and consistency.
The Challenge
The task involved preparing large corpora (books, articles, text contents) by cleaning and normalising the data so it could be used to train Generative Pretrained Transformers. The preprocessing needed to remove numbers, URLs, table of contents, symbols, convert first-person narratives to collective form, eliminate odd proper-nouns/product-names, and manually proofread where the automation couldn't resolve inconsistencies.
Our Solution
Developed a script-based preprocessing pipeline to: remove extraneous syntax (numbers, URLs, references), clean narrative voice, standardise text, eliminate unwanted named entities. Where automated cleaning could not resolve sentence inconsistencies, a manual proof-reading step was embedded to ensure high quality before training.
Results & Outcomes
Provided a high-quality text corpus prepared for generative-model training
Enabled downstream generative tasks (descriptive paragraph generation, topic modelling) with clean and consistent input
This preprocessing platform forms a backbone for enterprise-grade NLP modelling
Ensures data readiness, governance, and consistency
Technologies Used
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