Named Entity Recognition (NER)
We’ve all been there: trying to sift through pages and pages of text, hoping to find that one key piece of information. It’s like searching for a needle in a very large haystack. So, I thought, "Why not let a machine do the heavy lifting?" That’s how the NER system was born—trained to recognize names, places, and even dollar amounts in a fraction of the time it would take a human. Whether it’s legal documents, articles, or any other text, this system makes sure nothing important slips through the cracks, giving you insights without the eye strain.
2 Months
NLP, Text Analysis
Legal Tech, Healthcare, Media & Publishing
Challenge
Processing unstructured text data was labor-intensive and inefficient, making it difficult to extract meaningful information like names, locations, and monetary values from large datasets. The challenge was to build an automated solution to extract these entities from text quickly and accurately.
Results
Built an NER model that significantly improved the extraction of key entities such as people, locations, and monetary values from large datasets. The system achieved 95% extraction accuracy, drastically improving data sorting and information retrieval for downstream analysis. With a processing speed of 2.3 seconds per document, the system scaled effectively for large datasets.
95%
Extraction Accuracy
50%
Improved Document Processing Speed
20%
Decrease in Misclassification errors after Fina-Tuning Model
Process
Data Preprocessing:
Cleaned and tokenized the unstructured text data for efficient input into the model.
Applied stop-word removal and text normalization to ensure model accuracy.
Model Development:
Built the NER model using PyTorch and Hugging Face Transformers.
Fine-tuned a pre-trained language model to improve accuracy for entity extraction.
Model Deployment:
Deployed the NER model using AWS Lambda for scalable, serverless processing.
Integrated the system with internal applications through a RESTful API for easy access to real-time entity extraction.
Visualization & Reporting:
Created a dashboard using Power BI to visualize entity distribution and highlight important insights.
Conclusion
This NER system dramatically improved the extraction of valuable information from unstructured text data, offering a precise and scalable approach to data sorting and retrieval. By building on state-of-the-art NLP models and cloud-based deployment, the system delivered quick, accurate results at scale. The project demonstrated the versatility of NLP in diverse industries, from healthcare to legal tech, unlocking key insights from massive text datasets.