Healthcare Assistance

Natural Language Processing (NLP) Text Preprocessing & Generative Preparation for GenAI Assistant

Healthcare Organization

Timeline: 4 months
Team: 3-5 specialists

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

Automated Text-Preprocessing Scripts
Named-Entity & Syntax Filtering
Manual QA Layer
Generative-Model Input Pipeline

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