Automated Data Extraction Tool
A leading manufacturing firm specializing in high-volume production of automotive components. The company operates multiple production lines and faces frequent scheduling bottlenecks due to fluctuating customer demands.
Extracting key parameters and their values from computational materials science literature is a complex task, requiring automated NLP solutions for efficient data extraction and database generation. Manual extraction is time-consuming and prone to inconsistencies
Designed and developed a domain-specific information extraction system from scientific texts, focusing on critical material properties.
Fine-tuned a transformer-based encoder-decoder model, systematically optimizing performance through iterative experimentation.
Achieved high extraction accuracy, with the model demonstrating a strong balance between precision and recall.
The trained model effectively extracted parameters and values with high accuracy, demonstrating the potential of NLP-driven automation in computational materials science research. Further improvements could enhance extraction from diverse literature sources.
